nanoparticle-polymer-composites the solution and spray drying … · 2018-04-17 · the solution...
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Nanoparticle-Polymer-Composites
the solution and spray drying process with an emphasis on
colloidal interactions
Der Fakultät für Maschinenbau, Verfahrens- und Energietechnik
der Technischen Universität Bergakademie Freiberg
eingereichte
DISSERTATION
zur Erlangung des akademischen Grades
Doktor-Ingenieur
(Dr.-Ing.)
vorgelegt
von Dipl.-Ing. Martin Rudolph
geboren am 10.03.1983 in Frankenberg/Sa.
Freiberg, den 11.09.2012
I
In Liebe und tiefer Dankbarkeit
Mei e F au Julia u d ei e „Apfel äu he Malte
II
Abstract
When it comes to preparing nanoparticle-polymer-composites for the synthesis of functional
materials, the homogeneous distribution of the individual filler particles can be a tough
challenge due to strong attractive interactions. In this work a process chain for the
preparation of highly filled nanoparticle-polymer-composites is presented and the colloidal
interactions are investigated. The central element of this process chain is an organic solvent
based mixture of stabilized nanoparticles and the dissolved polymer to be spray dried. The
unit processes comprise: nanoparticles synthesis in water, phase transfer of the particles to
the organic solvent by a liquid-liquid phase transfer with an amphiphilic substance, mixing
the dissolved polymer with the transferred particles and spray drying the prepared solution
to withdraw the solvent rapidly. The bottleneck to obtain well dispersed composites are the
type of adsorbing stabilizing amphiphile as well as the complex particle interactions within
the organic solvent including the dissolved polymer. The nanoparticles investigated in the
experiments are super-paramagnetic magnetite nanoparticles with a primary particle size of
approximately 15 nm. They are transferred to dichloromethane with the amphiphiles being
different fatty acids (C8 to C18, saturated and unsaturated). A physical model describing the
deagglomeration phenomenon upon chemical adsorption of certain fatty acids onto
agglomerated particles is presented and numerically assessed. Ricinoleic acid is found to
produce the most stable nanoparticle suspension. Four different thermoplastic unbranched
chain polymers, namely poly(methyl methacrylate) PMMA, poly(bisphenol A carbonate) PC,
poly(vinyl butyral) PVB and poly(styrene) PS are chosen as the continuous phase of the
composites due to their mechanical properties, common use and solubility. Adding PMMA,
PC and PS lead to reduction of the primary particle concentration in the stabilized
nanoparticle dispersion independent from the fatty acid used. The spray dried and injection
molded composites exhibit large agglomerates of the nanoparticles fillers with sizes in the
lower micron range. In the dispersions PVB leads to adsorption on the nanoparticles and
further stabilization of the suspension. Composites with the polymer PVB show well
dispersed particles which are almost entirely deagglomerated and homogeneously
distributed. It is most probably due to the hydroxyl groups in the PVB structure that
adsorption and stabilization can occur. The other polymers are non-adsorbing and cause
flocculation possibly by depletion mechanisms. Prove of concept shows that the process
chain results in well dispersed composites when compared to the classic melt compounding
method.
III
Kurzfassung (German)
Die Herstellung von Nanonpartikel-Polymer-Kompositen für die Synthese von funktionalen
Materialien stellt auf Grund von starken attraktiven Wechselwirkungen der Nanopartikel
eine große technologische Herausforderung. In der vorliegenden Arbeit wird eine
Prozesskette für die Darstellung hochgefüllter Nanopartikel-Polymer-Komposite vorgestellt,
wobei näher auf die kolloidalen Wechselwirkungen eingegangen wird. Das zentrale Element
der Prozesskette ist ein, in einem Sprühtrocknungsprozess zu verarbeitendes organisches
Lösungsmittel, welches stabilisierte Nanopartikel und gelöste Polymere beinhaltet. Die
Einzelprozesse der Kette sind: Nanopartikelsynthese in der wässrigen Phase, Transfer der
Nanopartikel in das organische Lösungsmittel in einem Flüssig-Flüssig-Phasentransferprozess
durch amphiphile Substanzen, Herstellung einer Mischung von gelösten Polymeren mit den
transferierten und stabilisierten Partikeln und Sprühtrocknung des Lösungssystems für den
schnellen Entzug des Lösungsmittels. Die Schwerpunkte, um gut dispergierte Komposite zu
erhalten, sind sowohl die Art des adsorbierenden und stabilisierenden Amphiphils, als auch
die komplexen Partikelwechselwirkungen im organischen Lösungsmittel mit gelösten
Polymeren. Die Nanopartikel des Experimentalteils sind super-paramagnetische Magnetit
Nanopartikel mit einer Primärpartikelgröße von etwa 15 nm. Diese werden mit
unterschiedlichen Fettsäuren als Amphiphile (C8 bis C18, gesättigt und ungesättigt) in das
Lösungsmittel Dichlormethan transferiert. Ein physikalisches Modell wird vorgestellt und
numerisch gewertet, welches das Phänomen der Deagglomeration beschreibt, wenn
bestimmte Fettsäuren beim Phasentransfer auf den agglomerierten Nanopartikeln
chemisorbieren. Die Rizinolsäure führt hier zu den stabilsten Nanopartikelsuspensionen. Vier
unterschiedliche, geradlinige, unverzweigte, thermoplastische Polymere, namentlich
Poly(Methyl Methacrylat) PMMA, Poly(Bisphenol A Karbonat) PC, Poly(Vinyl Butyral) PVB
und Poly(Styrol) PS werden, auf Grund ihrer mechanischen Eigenschaften, ihrer weiten
Verbreitung und guten Löslichkeit, als kontinuierliche Phase der Komposite verwendet. Die
Zugabe von PMMA, PC und PS führt zur Abnahme der Primärpartikelkonzentration in den
stabilisierten Nanopartikeldispersionen, unabhängig von der Art der Fettsäure. Die
sprühgetrockneten und spritzvergossenen Komposite weisen große Agglomerate im Bereich
von wenigen Mikrometern auf. Das Polymer PVB führt in den Dispersionen zur Adsorption
auf der Nanopartikeloberfläche und weiterer Stabilisierung der Suspension. Komposite mit
dem Polymer PVB zeigen gut dispergierte Füllstoffpartikel, welche fast komplett
agglomeriert und homogen verteilt vorliegen. Wahrscheinlich sorgen die Hydroxylgruppen in
der Struktur des PVBs dazu, dass Adsorption und Stabilisierung auftritt. Die anderen
Polymere absorbieren nicht und führen möglicherweise auf Grund von Verarmungsflockung
zur Destabilisierung. Es wird gezeigt, dass die entwickelte Prozesskette zu besser
dispergierten Kompositen, im Vergleich zur klassischen Schmelze Einarbeitung führt.
IV
Acknowledgements / Danksagung
Diese Arbeit entstand während meiner Tätigkeit als wissenschaftlicher Mitarbeiter am
Institut für Mechanische Verfahrenstechnik und Aufbereitungstechnik der TU Bergakademie
Freiberg von Oktober 2008 bis März 2012. Das von mir bearbeitete, DFG finanzierte Projekt
„E t i klu g ei e P ozesskette fü die “ these u d Ve a eitu g ho hgefüllte Pol e -
Na opa tikelko posite in Kooperation mit dem Institut für Polymerwerkstoffe und
Ku ststoffte h ik de TU Clausthal liefe te de „“toff und den wissenschaftlichen Spielplatz
für meine Untersuchungen und wissenschaftlichen Erkenntnissen.
Ich bedanke mich bei meinem Betreuer und Doktorvater Herrn Prof. Dr.-Ing. Urs A. Peuker
für das mir übertragene, hoch interessante Projekt. Er hatte in meiner Promotionszeit stets
ein offenes Ohr für Ideen, gab mir weitreichende Freiheiten und übte die in der
Wissenschaft so nötige Kritik. Dankbar bin ich ihm auch sehr für die vielen Möglichkeiten
meine Arbeit in Journalen und auf internationalen Tagungen vorgestellt haben zu dürfen.
Hochachtungsvoll bedanke ich mich bei Herrn Prof. Dr.-Ing. Wolfgang Peukert der
Universität Erlangen-Nürnberg für die Bereitschaft zum Korreferat.
Da die Arbeit schon umfangreich genug ist und auch um niemandem durch Vergessen
ungerecht zu werden, möchte ich es vermeiden nun folgend namentlich Dank zu verstreuen.
Mein besonderer Dank geht an sämtliche Mitarbeiter des Instituts für Mechanische
Verfahrenstechnik und Aufbereitungstechnik auch denen die es mal waren. Vielen Dank
auch an viele weitere Wissenschaftler und unzählige Studenten (insbesondere den Hiwis,
Studienarbeitlern und Diplomanden) der ehrwürdigen TU Bergakademie Freiberg. Für die
spannende Kollaboration danke ich den Mitarbeitern vom PUK der TU Clausthal. Des
Weiteren bedanke ich mich bei vielen Wissenschaftlern und Studenten aus aller Welt für
tolle Arbeiten sowie Diskussionen und Anregungen bei Treffen und Tagungen.
„Eine wissenschaftliche Arbeit kann nur so gut werden wie die Kritik die man an ihr ausübt.“
Der letzte Absatz der Danksagung gehört wie gewohnt den Mitmenschen, die das Leben so
lebenswert machen. Zuallererst bedanke ich mich daher bei meiner Frau Julia für eine
wunderschöne Beziehung und dem faszinierendstem „P ojekt meines Lebens, unseren
wunderbaren Sohn Malte. Meinen Eltern danke ich für viel Freiheit und währende
Unterstützung. Vielen Dank für die Wegbegleitung auch meinem Bruder Thomas und seiner
Frau Franziska, sowie meinen lieben Schwiegereltern. Ebenso wertschätze ich all die vielen
lieben und aufrichtigen Menschen um mich herum.
V
Contents
1 INTRODUCTION 1
2 NANOPARTICLE-POLYMER-COMPOSITES – A STATE OF THE ART 4
2.1 Terminology 5
2.1.1 Nanomaterials 5
2.1.2 Polymers 7
2.1.3 State of Dispersion 8
2.2 Composite Synthesis/Preparation 9
2.2.1 Separate Synthesis of Particles and Polymers 9
2.2.2 Synthesis of Nanoparticles within the Polymer 12
2.2.3 Synthesis of the Polymer with the Presence of Nanoparticles 12
2.3 Industrial Products 13
3 THESIS MOTIVATION – DEVELOPMENT OF A MODULAR PROCESS CHAIN FOR
COMPOSITE PREPARATION 15
3.1 Process Steps 15
3.1.1 Nanoparticle Synthesis 16
3.1.2 Liquid-Liquid Phase-Transfer 16
3.1.3 Polymer Addition 17
3.1.4 Spray Drying 18
3.1.5 Powder Agglomeration 19
3.2 Composition 20
3.2.1 Composite Composition 20
3.2.2 Colloid Composition 23
4 FOCUS ON THE PHASE TRANSFER OF NANOPARTICLES 25
4.1 Theory 25
4.1.1 Phase Transfer 25
4.1.2 Adsorption of Surfactants 26
4.1.3 Steric Stabilization 29
4.2 Physical Model of Deagglomeration at the Interface 32
VI
4.2.1 Gedankenexperiment 32
4.2.2 Geometrical Model 33
4.2.3 Numerical Results 35
4.3 Experimental Results 37
4.3.1 Visualization of the Phase Transfer 37
4.3.2 Particle (Agglomerate) Size Distribution 38
4.3.3 Primary Particle Concentration 39
4.3.4 Inert Decomposition of Chemisorbed Ricinoleic Acid on Magnetite Nanoparticles 42
5 NANOPARTICLES AND POLYMERS IN AN ORGANIC SOLVENT 49
5.1 Theory 50
5.1.1 Polymers in Solution 50
5.1.2 Solubility 55
5.1.3 Phenomena in Nanoparticle Polymer Mixtures 57
5.2 Pair Interactions – DLVO-like Consideration 58
5.3 Experimental Results 62
5.3.1 Influence of the Polymer 63
5.3.2 Influence of the Surfactant 71
5.3.3 Stabilization by Adsorption of PVB 75
5.3.4 Influence of Mechanical Dispersing Methods on the Stability 81
5.3.5 Kinetics of Flocculation at Low Nanoparticle Concentration and high PMMA concentrations 83
5.3.6 Influence of the Solvent 86
6 HIGHLY FILLED COMPOSITES 91
6.1 Theory 91
6.1.1 Spray Drying 91
6.1.2 (Micro) Injection Molding 97
6.1.3 Image Processing and the Mathematical Description of the State of Dispersion 98
6.1.4 Filler Concentration – Agglomerate Concentration – Stability Relation 101
6.2 Experimental Results – Spray-Dried Microparticle Composites 104
6.2.1 Compositional Separation 104
6.2.2 Yield of Product 108
6.2.3 Influence of the Polymer 109
6.2.4 Influence of the Surfactant 110
6.2.5 Increasing Filler Concentration F 113
VII
6.3 Experimental Results – Injection Molded Composites 116
6.3.1 PMMA vs. PC vs. PVB 117
6.3.2 Increasing Filler Concentration – PMMA composite 123
6.3.3 Identity of Agglomerates 125
6.3.4 Solution and Spray Drying Process vs. Melt Mixing 126
7 GENERAL CONCLUSIONS AND OUTLOOK 129
APPENDIX A1
REFERENCES R1
VIII
List of Symbols
A parameter, area
B constant
c concentration, speed of light
C scattering cross section
CH HAMAKER constant
d differential, thickness, diameter
D surfactant ratio, distance, solubility distance
E extinction, energy
f frequency
F filler concentration, force, group contributor
G GIBBS free energy
h specific enthalpy, PLANCK constant
H enthalpy
i imaginary unit
ID interparticle distance
k BOLTZMANN constant, constant,
imaginary part of the complex refractive index K constant
l length
L length
m mass
M molar mass
n number, refractive index
N number, degree of polymerization
NA LOCHSCHMIDT constant (AVOGADRO constant)
p pressure
PI polydispersity index
q size ratio
Q cumulative distribution, heat
R radius, gas constant
R2 coefficient of determination
s distance
S surface area, specific surface area, entropy
IX
SP structural parameter
t time
T absolute temperature
u error
UE electrophoretic mobility
v volume, velocity
V volume
w mass concentration
W energy of interaction
x variable, particle size
X ratio
z thickness
Z atomic number (number of protons)
α angle, constant, heat transfer coefficient
β constant, mass transfer coefficient
surface coverage
length, thickness, solubility distance
difference, concentration of surfactants, group contributor
dielectric constant
zeta potential
dynamic viscosity
θ angle, state
θ temperature
wavelength
mass fraction, ratio
circumference to diameter ratio for a circle, FLORY-HUGGINS parameter
density, specific weight
σ constant, specific surface energy
τ correlation time
φ relative volume concentration
Φ grafting density
φ volume concentration
χ FLORY interaction parameter
X
List of Abbreviations
Abs absorption
AdG ALEXANDER-DE-GENNES theory
AFM atomic force microscopy
ALR air to liquid ratio
AO ASAKURA and OOSAWA theory
ATR attenuated total reflection
BET BRUNAUER-EMMETT-TELLER theory
BSE back scattering electron detection
CA caprylic acid
cov coefficient of variance
DCM dichloromethane, methylene chloride
DFG Deutsche Forschungsgemeinschaft
DLS dynamic light scattering (cf. PCS, QELS)
DLVO DERJAGUIN-LANDAU-VERWEY-OVERBEEK theory
DMT DERJAGUIN-MULLER-TOPOROV model
DTG differential thermal gravimetry
EA ethyl acetate
FA fatty acid
FTIR FOURIER transform infrared spectroscopy
FWHM full width half maximum
HSP HANSEN solubility parameters
IR infrared (spectroscopy)
JKR JOHNSON-KENDALL-ROBERTS model
LA linoleic acid
MA myristic acid
MMA methyl methacrylate
NNLS non-negative least squares
Nu NUSSELT number
OA oleic acid
Oh OHNESORG number
PC poly(bisphenol A carbonate)
pc-AFM phase contrast atomic force microscopy
XI
PCS photon cross correlation (cf. DLS, QELS)
Pe PECLÉT number
pH negative decimal logarithm of the hydrogen ion activity
PM planetary ball mill
PMMA poly(methyl methacrylate)
Pr PRANDTL number
PS poly(styrene)
PSD particle size distribution
PT phase transfer experiment
PTFE poly(tetrafluoro ethylene)
PVA poly(vinyl alcohol)
PVAc poly(vinyl acetate)
PVB poly(vinyl butyral)
QELS quasi elastic light scattering (cf. DLS, PCS)
RA ricinoleic acid
Re REYNOLDS number
Sc SCHMIDT number
SE secondary electron detection
SEM scanning electron microscopy
Sh SHERWOOD number
ST spray drying experiment
TEM transmission electron microscopy
TEOS tetraethyl orthosilicate
TGA thermogravimetric analysis
US ultrasound, ultrasonication
UT Ultra-Turrax®
UV/VIS ultraviolet and visual light spectrometry
vdW VAN DER WAALS
We WEBER number
XRD x-ray diffraction
YSZ yttrium stabilized zirconium
XII
List of Indices
0 original, pristine, contact, unit, number weighted
3 volume weighted
50 median value of a distribution
A adsorption, area
abs absolute
c contour
C=C carbon-carbon double bond
C-C carbon-carbon single bond
d dispersive
ext extinction
g glass transition
G gyration
h hydrogen bonding
H enthalpic
i parameter, fraction
int intensity weighted
m molar, average
M mixing, MARK-HOUWINK-SAKURADA
max maximum
min minimum
n by number, number specific
p polar
rel relative
s segment
S entropic
scat scattering
sp specific
t total, HILDEBRANDT
w by weight, weight specific
wb wet bulb
Θ theta state
XIII
List of figures
figure 1: Schemes of the three main types of nanomaterials (from left to right) nanotubes with diameters smaller 100 nm, nanoplatelets with thickness smaller 100 nm and nanoparticles with diameters smaller 100 nm ....................................................... 5
figure 2: (left) Model geometry for the calculation of the relative number of atoms/clusters at the surface Nsurface/Ntotal, cf. eq. (72), (right) evaluation of eq. (72) for gold Au and magnetite Fe3O4 with given atom/cluster sizes Rcluster ..................................... 6
figure 3: Definition of the state of dispersion (poorly dispersed in the upper left, well dispersed in the lower right) by both a good distribution (more homogeneous from left to right) as well as a thorough deagglomeration (more successful from top to bottom) ......................................................................................................... 8
figure 4: Scheme of composite formation by mixing a dry nanoparticle powder with a polymer melt, the mixer represents any type of high shear mixing device such as an extruder ............................................................................................................ 10
figure 5: Scheme of composite formation by mixing a nanoparticle solvent based colloid with a polymer solution, the nanoparticles can be well dispersed e.g. by adsorption of end-grafted molecules .......................................................................................... 10
figure 6: Scheme of composite formation by compounding a nanoparticle and a polymer powder with high forces in a mill, typically a high energy ball mill ...................... 11
figure 7: Scheme of composite formation by attraction of oppositely charged particles and polymers in water and withdrawing the water for composite synthesis ............. 11
figure 8: Scheme of composite formation by mixing a pre-courser (symbolized as small dots in the left frame) with a polymer in solution and initiation of nanoparticle synthesis ................................................................................................................ 12
figure 9: Scheme of composite formation by dispersing nanoparticles in a monomer solution (on the left represented as an emulsion droplet) and subsequent polymerization and withdrawing of all solvents ............................................................................ 13
figure 10: Synthesis of the nanoparticles by a precipitation reaction in an aqueous environment .......................................................................................................... 16
figure 11: Transfer of the nanoparticles from the aqueous to an organic phase (immiscible solvent) by adsorption of amphiphilic molecules ................................................. 16
figure 12: Mixing the organic solvent based hydrophobic nanoparticles with a polymer solution in the same solvent ................................................................................. 17
figure 13: Spray Dryer with a two-fluid nozzle in the co-current regime. A small fraction of large particles falls through the drying cylinder by gravity separation, the largest fraction of particles by mass is the product collected at the coarse output of a cyclone by centrifugal force separation. ............................................................... 18
figure 14: Press agglomeration of the spray dried fine composite micropowder and granulation in a rotor-mill for an improved powder handling .............................. 19
figure 15: Surfactant ratio D, cf. eq. (4) with Msurfactant = 282.46 g/mol, Ssurfactant = 2.4·10-19 m2
[95] and nanoparticle = 5.2·106 g/m3 for magnetite Fe3O4 ....................................... 21
figure 16: Interconnection of the composition values: filler concentration F, surfactant ratio D a d olu e o e t atio of the pol e φpolymer for the given specific weights
XIV
of the three components using eq. (7), volume concentrations have to be larger than 0.26 which is the limit for closest packing of spheres .................................. 22
figure 17: Interparticle distance as a function of the filler concentration for monodisperse and homogeneously distributed fillers of different size (D = 0.2 and specific weights as mentioned in figure 16). The inset shows the geometry for the body-centered cubic space. ............................................................................................ 22
figure 18: Interconnection of the polymer concentration in the organic solvent based mixture cpolymer, the solids concentration csolid and the filler concentration F in the solvent free composite material, cf. eq. (11). ....................................................... 24
figure 19: Principle structure of a fatty acid molecule with a hydrophilic carboxyl head group and a hydrophobic alkyl chain with certain length and functionalities (double bonds, functional groups). .................................................................................... 26
figure 20: Principle of Adsorption of Fatty Acids on Magnetite: (a) monolayer covered particle, (b) before adsorption in aqueous solution (mediating water layer in blue) with high pH resulting in negatively charged magnetite surface and disassociated carboxyl group [99], (c) chelating bidentate bond between fatty acid and magnetite [95] and (d) monodentate mononuclear configuration [116] ............................................................................................................................... 29
figure 21: Geometries of two types of end-grafted molecule covered interacting particle surfaces at distance D (left) brush type as expressed in eq. (17), (right) mushroom type in eq. (18) .................................................................................... 30
figure 22: Schematics representing the gedankenexperiment of an agglomerated nanoparticle doublet passing the liquid-liquid interface where fatty acid molecules adsorb and push the particles apart by a disjoining force when tails of opposing end-grafted molecules overlap [23] ...................................................... 32
figure 23: Representation of the geometrical model of particles with radii R in contact, covered by a layer of molecules with the thickness . The defined region of interest, which attributes to the repulsive force is highlighted. Calculations are based on the distance of overlap of the layers D which are a function of the angle α. At αmin D = and at αmax D = 0. [23] .................................................................. 33
figure 24: Angle dependent pressure between the spheres with radii of 7.5 nm and the pa a ete la e thi k ess i steps of . nm, from 0.4 nm to 2.4 nm, for a rather high degree of adsorption of s = 0.5 nm according to eq. (23), [23] ......... 35
figure 25: Repulsive forces according to eq. (24) (left) as a function of the adsorption dista e s a d ith the pa a ete of la e thi k ess , ight as a function of the la e thi k ess ith the pa a ete adso ptio dista e s i o pa iso to the constant absolute VAN DER WAALS force (horizontal line, cf. eq. (26)), [23] ........... 36
figure 26: relative repulsive force (cf. eq. (27)) as a function of adsorption distance s and la e thi k ess , [ ] ............................................................................................ 36
figure 27: Vials of phase transfers of magnetite nanoparticles from an aqueous phase (upper half) to a DCM phase (below liquid interface) with the grafting molecules/surfactants as defined in the image above the vials all at pH 9 in the aqueous phase except for myristic acid with a second phase transfer at pH 8 [23] ............................................................................................................................... 38
figure 28: Intensity weighted particle size distributions of the samples in figure 27 applying analytical centrifugation with a cut-off size of 30 nm, additionally the primary particles distribution is determined from the stable colloid using DLS [23] ......... 39
XV
figure 29: Mass concentration of primary particles after phase transfer derived from colloidal interactions studied in 6.3.2, values presented in table 8, [23]............................ 40
figure 30: Solubility plot of the fatty acids pristine (FA - filled blue symbols) and grafted to the magnetite surface (FA-Fe3O4 - open blue symbols) calculated using the group contribution method in A.11, compared to the solvent DCM (red circle), values from [140] .............................................................................................................. 41
figure 31: Correlation of primary particle concentration given in figure 29 and the solubility distance between the fatty acid capped magnetite and DCM calculated using eq. (90) and values found in A.11.......................................................................... 42
figure 32: ATR-FTIR results of pristine ricinoleic acid, pristine magnetite as well as ricinoleic acid adsorbed on magnetite [21] .......................................................................... 43
figure 33: (left) first derivative of the TGA results (DTG) of pure ricinoleic acid (RA), ricinoleic acid coated magnetite (RA-Fe3O4) and Aerosil® 200 (RA-SiO2) in inert atmosphere [21], (right) TGA of RA-Fe3O4 with the mass losses at the three distinct steps, the error bars show the 95 % quantile of three measurements ................................. 44
figure 34: FTIR of the evolving gases for the major steps of decomposition of the three samples in figure 33, (left) RA including the ATR-FTIR spectrum at room temperature in the top graph, (middle) RA-Fe3O4 and (right) RA-SiO2 [21] ......... 45
figure 35: Powder diffractograms of pristine precipitated and fatty acid grafted magnetite RA-Fe3O4 and of the mixed iron oxide residue FeOx after inert gas TGA with ide tified ajo diff a tio a gles Θ of ag etite Fe3O4, wüstite FeO, Ferrite α-Fe and Hematite Fe2O3. [21] .............................................................................. 47
figure 36: Polymer coils in solution for different polymer solvent interactions (left) real chain in a good solvent, (middle) ideal chain with equal interaction of polymer segments and solvent molecules, (right) real chain in a bad solvent. The symbols accou t fo : χ – the FLORY interaction parameter, RG – the radius of gyration, ls – the segment length, N – the number of monomer units and TΘ – the theta temperature .......................................................................................................... 52
figure 37: Graphical visualization of the radius of gyration RG normalized with the segment length ls, depending on the FLORY i te a tio pa a ete χ a d the u e of segments NS, cf. eq. (40) ....................................................................................... 53
figure 38: Concentration regimes of polymers in solution (left) dilute solution, (middle) solution at the overlap concentration c*polymer and (right) semi dilute regime in accordance with [156] ........................................................................................... 54
figure 39: Principle types of colloidal regimes between neutral nanoparticles and neutral polymers in an organic solvent (a) depletion flocculation, (b) depletion stabilization, (c) bridging flocculation, (d) steric stabilization, cf. [118, 126, 127, 134, 155, 160] ........................................................................................................ 57
figure 40: Scheme of two interacting particles of diameter 15 nm surrounded by grafted molecules with a length of 2.0 nm in a solution of polymer coils with a diameter of 7.5 nm which do not adsorb at the particle surface resulting in a depletion layer surrounding the particles (dashed circles) [23] ............................................ 59
figure 41: DLVO-like addition of colloidal interactions with regard to VAN DER WAALS and depletion attraction (by ASAKURA and OOSAWA AO) as well as BORN and steric-osmotic repulsion with R = 7.5 nm, RG = 4.0 nm, s = 0.6 nm, = 1.5 and φ = 1.0, [23] ........................................................................................................................ 61
figure 42: Distance dependent total interaction, following eq. , left fo a i g [ ] (right) for varying φ and constant parameters mentioned in figure 41. .............. 61
XVI
figure 43: Light extinction at 600 nm of diluted samples E600 nm and gravimetrically determined primary particle concentration wPrimary as a function of the PMMA concentration at constant nanoparticle concentrations; the inset is a photograph displaying the samples (b) only holding the primary particles after centrifugation with increasing polymer concentration from left to right .................................... 63
figure 44: Light extinction at 600 nm of diluted samples E600 nm and gravimetrically determined primary particle concentration wPrimary as a function of the PC concentration at constant nanoparticle concentration; the inset is a photograph displaying the samples (b) only holding the primary particles after centrifugation with increasing polymer concentration from left to right .................................... 64
figure 45: Representation of the data in figure 43 and figure 44 as a function of: (left) the elati e pol e o e t atio φ usi g the o e lap o e t atio s dete i ed
with the intrinsic viscosity in A.9.1 and (right) the number concentration of polymer coils cpolymer/Mn. ...................................................................................... 65
figure 46: Light extinction at 600 nm of diluted samples E600 nm and gravimetrically determined primary particle concentration wPrimary as a function of the PVB concentration at constant nanoparticle concentration; the inset is a photograph displaying the samples (b) only holding the primary particles after centrifugation with increasing polymer concentration from left to right .................................... 66
figure 47: Correlation of the photometric extinction E600 nm and the primary particle concentration wPrimary as well as the total particle volume concentration for the extinctio easu e e t φ, t o diffe e t slopes fo the desta ilizatio ith PMMA and PC and the stabilization with PVB, w0
Primary = 91.7% located at the intersection of the linear models (dashed line) [20] ............................................. 67
figure 48: Photometric primary particle investigation as a function of the polymer concentration for both destabilizing polymers PMMA and PC as well as for the stabilizing PVB and three different phase transfer batches: PT101104, PT100902II and PT100305 ........................................................................................................ 69
figure 49: Normalized extinction curves for the three phase transfer batches as a function of (left) PMMA as well as (right) PC, the fitted lines follows the mathematical model in eq. (61) ............................................................................................................... 69
figure 50: Primary particle concentrations of the five investigated samples in nanoparticle polymer mixtures with different concentrations of the polymer PMMA in DCM. The solid lines present mathematical fits of eq. (61), published in [23] ............... 72
figure 51: Primary particle maps by combining results of the investigations in figure 50 and table 8 with eq. (62) for ricinoleic (RA), linoleic (LA) and oleic acid (OA) with the same scale in the three dimensions ...................................................................... 73
figure 52: Primary particle concentrations of the five investigated samples in nanoparticle polymer mixtures with different concentrations of the polymer PVB in DCM; presented in context (scaling) with figure 50 above, published in [23]................ 74
figure 53: The median diameter of the number weighted particle size distribution x50,0 of diluted samples (b) measured with DLS as a function of the pristine concentrations cpolymer of PMMA, PC and PVB in the samples (a) ........................ 76
figure 54: Numerically obtained volume weighted particle size distribution from the DLS measurements of the (b) samples with the polymers (left) PMMA and (right) PVB; the polymer concentrations cPMMA and cPVB refer to the undiluted samples (a) ........................................................................................................................... 76
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figure 55: (left) PVB adsorption on the RA-Fe3O4 ag etite a opa ti les su fa e as a function of the PVB concentration in the mixture cPVB, (right) layer thickness of the adsorbed PVB layer, calculated using the data in figure 53 ........................... 77
figure 56: Density of the adsorbed PVB layer as a function of the PVB solution concentration cPVB with the fitted data from figure 55 using eq. (65) and a nanoparticle diameter xnanoparticle of 15 nm and a nanoparticle specific weight of 5200 g/l ...... 78
figure 57: Relative mass loss in the third degradation step for inert gas TGA between 600°C and 900 °C for PMMA and PVB and Ra-Fe3O4 as a function of the polymer concentration ........................................................................................................ 79
figure 58: Colloidal stability of PVB mixed with RA-Fe3O4 nanoparticles at four different mass ratios in solutions of PMMA with the concentration cPMMA .................................. 80
figure 59: Scheme of the hypothetical model of adsorption of PVB on the surface of magnetite nanoparticles carrying chemically grafted fatty acid surfactant molecules as well; (left) train adsorption of PVB at vacant magnetite surface sites, (right) surface hydroxide groups shall interact with the hydroxyl groups of the PVB backbone. ................................................................................................ 80
figure 60: Primary Particle Concentration as determined gravimetrically (TGA) and by light extinction at 600 nm (UV/VIS) with eq. (59) for a DCM based mixture of RA-Fe3O4 and PC with D = 0.2 and cpolymer = 51.2 g/l (F = 0.3) for four different mixing procedures (US – sonotrode ultrasonication, UT – Ultra Turrax) ......................... 81
figure 61: Destabilization curve of RA-Fe3O4 (PT091227II) in DCM with PMMA, without and with 1 min sonotrode ultrasonication (US) ........................................................... 82
figure 62: Dispersing a phase transfer batch without polymer in DCM using a planetary ball mill, (left) intensity weighted particle ................................................................... 83
figure 63: Time development of the intensity weighted frequency particle size distribution (in logarithmic scaling) determined with DLS with respect to the viscosity of the polymer solution for a magnetite dispersion without pristine agglomerates at cnanoparticles = 1.2 g/l and cpolymer = 58 g/l; (left) destabilizing PMMA (right) stabilizing PVB ....................................................................................................... 84
figure 64: Time dependent light extinction to monitor agglomeration of a RA-Fe3O4 dispersion in DCM with dissolved PMMA, the polymer is mixed with the stable nanoparticle dispersion at t = 0 min ..................................................................... 85
figure 65: Correlation of extinction rates (set-in and maximum slopes of the lines in figure 64) with the PMMA concentration ....................................................................... 85
figure 66: Primary particle concentration wPrimary (left) and normalized photometric extinction E600nm/E0,600nm (right) as function of the PMMA concentration for the solvents: DCM and EA ........................................................................................... 87
figure 67: 100 % completed phase transfers of magnetite nanoparticles originating from the same precipitation batch (a) to dichloromethane DCM (PTDCM120227) by gravity driven transport and stirred emulsification in a beaker, (b) to MMA (PTMMA120227) and (c) to styrene ST (PTST120227) by mixer-settler extraction in separation funnels, (left) strong emulsion formation for MMA and ST, (right) after breaking the emulsion by reducing the pH to 6.0 with 10 ml 1N HCl .......... 88
figure 68: Extinction based determination of the primary particle concentration of RA-Fe3O4 in DCM (PTDCM120227) and MMA (PTMMA120227) under the presence of PMMA with the polymer concentration on the abscissa; (left) absolute (right) normalized values; the extinction ratio for the polymer free point of MMA to DCM is 8.6 % .......................................................................................................... 89
XVIII
figure 69: Extinction based determination of the primary particle concentration of RA-Fe3O4 in DCM (PTDCM120227) and ST (PTST120227) under the presence of PS with the polymer concentration on the abscissa; (left) absolute (right) normalized values; the extinction ratio for the polymer free point of ST to DCM is 80.5 % ............... 90
figure 70: Principle scheme of an external mixing two fluid nozzle with turbulent atomization, adopted from [214] .......................................................................... 92
figure 71: Time progression of mass and temperature of a drying droplet with constant surface area due to film formation, taken from [225] with the steps A through D described in the text .............................................................................................. 95
figure 72: Drying progression of a droplet with suspended small particles and shell formation, taken from [230] ................................................................................. 96
figure 73: (left) principal scheme of an injection molding device (right) top view of an optical micrograph of a PMMA-RA-Fe3O4 composite with F = 0.3 and D = 0.2 of a test structure ................................................................................................................ 98
figure 74: Image processing steps (from left to right) of a phase contrast AFM image on the left showing dark magnetite and light polymer phases, binarized after thresholding and with a watershed, automatic detection and measurement of >800 individual particles (including aggregates and agglomerates) and finally the VORONOI diagram of the binarized image .............................................................. 99
figure 75: Coefficients of variance normalized with the first value covmax for the VORNOI and the line method and a set of simulated images with (from left to right) improving state of dispersion taken from fig. 7 in [55], covmax is 1.08 for the VORONOI method and 1.42 for the line method ................................................................. 101
figure 76: Visualization of the volume (area) fractions of the primary and agglomerated particles as well as the total volume fraction of nanoparticles applying eqs. (77) - (80), parameters w0
Primary and A are chosen in connection with colloidal stability investigations of PMMA and PC in 6.3.1 ............................................................. 103
figure 77: Relative agglomerate concentration as a function of the total filler concentration F and the polymer concentration cpolymer with the parameters of colloidal stability of eq. (80) applying eqs. (77) - (80) ..................................................................... 103
figure 78: Schematic set-up of the co-current spray dryer used in this thesis with three particle fractions (cylinder, cyclone and filter) where the cyclone fraction is the product of the process and should be the largest fraction in mass, the solvent is recovered in a condenser and the dry air recycled passing a heating device .... 104
figure 79: (left) representative particle size distributions of the microparticles of the three fractions of the spray dryer measured with laser diffraction and (right) TGA curves of the corresponding samples with an initial composition of PMMA RA-Fe3O4 with F = 0.3 and DRA = 0.2; the yields are 11 %, 74 % and 15 % for cylinder, cyclone and filter, respectively ............................................................................ 105
figure 80: (left) TGA measured mass residue at 600 °C of various spray drying experiments with RA-Fe3O4 and DRA = 0.2 for the three fractions of the spray dryer at different initial filler concentrations of magnetite F; (right) relative magnetite concentration of the cylinder or the filter fractions compared to the corresponding cyclone fraction with the cyclone fraction residual mass on the abscissa ................................................................................................................ 106
figure 81: High temperature mass loss (residual mass at 600 °C compared to residual mass after magnetite reduction at 900 °C, compare with investigations in 5.3.4) of the TGA analyzed particles of various spray drying experiments with RA-Fe3O4 based
XIX
on 38, 51 and 23 samples for cylinder, cyclone and filter materials, respectively ............................................................................................................................. 107
figure 82: BSE-SEM images of the particles of the cyclone fraction of a PMMA-based composite with RA-Fe3O4 DRA = 0.2 and F = 0.3, the image on the right is a close-up of a region in the approximate center of the image on the left, magnification is 3,000x ............................................................................................................... 108
figure 83: Yield of product at the coarse exit of the cyclone as a function of the filler concentration F for PMMA-based composites with RA-Fe3O4 and DRA = 0.2 .... 109
figure 84: Inverted BSE-SEM images of composites with RA-Fe3O4 at F = 0.3 and DRA = 0.2 for the destabilizing polymers PMMA and PC as well as the stabilizing polymer PVB [27] ...................................................................................................................... 110
figure 85: BSE-SEM images of spray-dried composites of (from left to right) RA, LA, OA, MA and CA coated magnetite (appearing light for high back scatter electron densities of iron atoms) in PMMA, magnification of 2000x and 10000x in the upper and lower row, respectively [23] ................................................................................ 111
figure 86: BSE-SEM images of spray-dried composites of (from left to right) RA, LA, OA, MA and CA coated magnetite (appearing light for high back scatter electron densities of iron) in PVB, magnification of 2000x and 10000x in the upper and lower row, respectively; to be compared to the images in figure 85 [23] ............................ 112
figure 87: Inverted BSE-SEM images of individual spray-dried microparticles with similar size from the cyclone fraction with RA-Fe3O4 at DRA = 0.2 at (from left to right) F = (0.3, 0.5, 0.8) with comparable contrast to visualize the impact of the increasing magnetite concentration, magnification 20,000x ............................. 114
figure 88: Volume weighted PSD of the three spray dryer fractions at the cylinder, cyclone and filter for the PMMA-based composites of RA-Fe3O4 with DRA = 0.2 and the filler concentrations F = (0.3, 0.5, 0.8) ................................................................ 114
figure 89: Bright field optical microscopy images, (from left to right) PMMA, PC and PVB for (top row) F = 0.3 and (bottom row) F = 0.5, lens magnification: 20x ................. 117
figure 90: Inverted BSE-SEM images (from left to right) PMMA, PC and PVB for (top row) F = 0.3 and (bottom row) F = 0.5, magnification: 5,000x .................................... 120
figure 91: Inverted BSE-SEM images (from left to right) PMMA, PC and PVB for (top row) F = 0.3 and (bottom row) F = 0.5, magnification: 50,000x .................................. 122
figure 92: (top row) inverted BSE-SEM images of PMMA-based RA-Fe3O4 composites with DRA = 0.2 and (from left to right) F = (0.3, 0.4, 0.5, 0.6), (bottom row) binary image of the SEM images above for particle detection, magnification: 2,000x . 123
figure 93: Inverted BSE-SEM images of PMMA-RA-Fe3O4 composites with DRA = 0.2 and (from left to right) F = (0.3, 0.4, 0.5, 0.6), magnification: 24,000x................................ 124
figure 94: Visualization of agglomerates of flocculated magnetite nanoparticles (a) under optical microscope of a flocculated suspension, (b) as dark spots in composite microparticles and (c) in a cross-section of an injection molded composite material with inverted BSE-SEM; al images with the same scaling; the diagram depicts the size distribution of the agglomerates in the suspension compared to the cross-section showing very similar sizes [27] ............................................... 126
figure 95: Comparison of Inverted BSE-SEM of cross-sections of (left) a melt compounded sample of RA-Fe3O4 compounded with PMMA and (right) one that was spray-dried and injection molded with PMMA and RA-Fe3O4; for both samples F = 0.3 and DRA = 0.2 ........................................................................................................ 127
XX
figure 96: Chemical structures of the fatty acids: (a) caprylic acid CA, (b) myristic acid MA, (c) oleic acid OA, (d) linoleic acid LA, (e) ricinoleic acid RA ........................................ A4
figure 97: (left) Thermal decomposition in N2 atmosphere with a heating rate of 20 K/min (from left to right, red to blue) PMMA, PVB, PS, PC; (right) contact angle with water on a thin film using sessile drop analysis .................................................... A6
figure 98: Chemical structure of the repeating unit of poly(methyl methacrylate) ................ A6
figure 99: Chemical structure of the repeating unit of poly(bisphenol A carbonate) [247] .... A7
figure 100: Chemical structure of the three repeating units of poly(vinyl butyral) with n butyral, m alcohol and p acetate subunits [247] .................................................. A8
figure 101: Chemical structure of the repeating unit of poly(styrene) .................................... A9
figure 102: Chemical structure of dichloromethane .............................................................. A11
figure 103: Chemical structure of ethyl acetate .................................................................... A11
figure 104: Chemical structure of methyl methacrylate ........................................................ A11
figure 105: Chemical structure of styrene ............................................................................. A12
figure 106: Comparison of a micrograph of a fractured composite surface of RA-Fe3O4 in PMMA with F = 0.3 and DRA = 0.2 (left) with detection of the secondary electrons and (right) when detecting the back scattered electrons with much better phase contrast (the lighter sections are due to strong back scattering of the heavy iron atoms in the magnetite nanoparticles) ............................................................... A15
Figure 107: pc-AFM image of a PMMA-RA-Fe3O4 composite with F = 0.3 ............................ A17
figure 108: (left) powder diffractogram (XRD) of the washed and dried co-precipitated magnetite, all peaks correspond to the magnetite crystal system, the four major peaks are used for calculation of the crystallite size (right) using the Williamson-hall plot [260] ...................................................................................................... A20
figure 109: (left) transmission electron micrograph of the precipitated magnetite nanoparticles in a PMMA matrix with F = 0.3, (right) particle size distribution (number frequency) as obtained from image analysis of the TEM image .......... A20
figure 110: (top) Intensity weighted cumulative particle size distribution of phase transferred magnetite particles as determined with the cuvette centrifuge and (bottom) volume weighted particle size distribution of the supernatant after centrifugation without polymer as measured with DLS compared to the TEM investigation of encapsulated magnetite nanoparticles (inset) [20] ............................................ A21
figure 111: Particle size distributions of precipitated magnetite in water with full ion strength after the reaction with given zeta-potential (blue line measured with analytic centrifugation) and after washing reducing the ion concentration increasing the absolute zeta-potential (green line measured with DLS) .................................... A22
figure 112: Steps of a gravity driven phase transfer using a organic solvent which is heavier than water, e.g. DCM .......................................................................................... A23
figure 113: Three representative sets of TGA analyses of the composition of the solids in the pristine dispersion - samples (a) and in the supernatant - samples (b) to determine the primary particle concentration wPrimary (numbers explained in table 25) [20] ....................................................................................................... A27
figure 114: Configuration of the lab-scale spray dryer with inert gas flow (left) photograph after spray drying a PMMA-based composite with RA-Fe3O4 and F = 0.3, (right) schematic drawing with: a) external mixing two fluid nozzle, b) ventilator for drying gas circulation, c) heater, d) condenser; c)-d) cannot be seen in the photograph on the left ........................................................................................ A33
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figure 115: (left) dielectric properties of magnetite, data extracted from [271] as a function of the e e g E, hi h is ight al ulated fo a ele gths ........................... A35
figure 116: real and imaginary part of the refractive index of magnetite as calculated from the data in figure 115 using eq. (90) ................................................................... A36
figure 117: Extinction, scattering and absorption cross-sections Cext, Cscat and Cabs of magnetite nanoparticles with a diameter of 15 nm and optical properties defined in figure 116 in dichloromethane with a refractive index of 1.4242 as a function of the wavelength, notice that scattering and absorption values are 100- and 0.5-fold, i.e. extinction is mainly due to absorption.................................................. A37
figure 118: dynamic viscosities of the polymers of this thesis in DCM as well as PMMA in EA as a function of the polymer concentration, determined with UBBELOHDE viscosimetry ......................................................................................................... A38
figure 119: Reduced viscosity over polymer concentration for various polymers in DCM and PMMA in EA. The lines show the linear fit to evaluate the intrinsic viscosity following eq. (46) ................................................................................................. A38
figure 120: (left) correlation coefficient and (right) frequency distribution intensity weighted of one single DLS experiment for ricinoleic acid transferred particles containing agglomerates at time steps t1 through t4 which are 2 minutes apart each; additionally the result for the sample after centrifugation containing only primary particles.................................................................................................. A41
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List of tables
table 1: A selection of industrial polymer-nanoparticle composites which are already on the market ................................................................................................................... 14
table 3: median particle size x50,int, fraction of primary particles from the intensity weighted distribution Qint, primary particle concentration w0
Primary, calculated solubility distance between the fatty acid coated magnetite (FA-Fe3O4) and the solvent (DCM) DFA-Fe3O4 – DCM using eq. (28) and A.11 as well as the calculated FLORY-HUGGINS parameter χ using eq. (52) ....................................................................... 41
table 4: Factors given in equation (29) with errors representing 1.96 times standard deviation, the resulting oxygen to iron ratio in the iron oxide after reduction is calculated to be 0.76 ± 0.09, compared to the value in pristine magnetite which is 1.33. ....................................................................................................................... 46
table 5: Compositional minerals analysis of the FeOx residue, which is made up of four identified mineral structures. The mass concentrations are calculated from the diffractogram using the XRD device software and the molar concentrations follo f o φ= i/Mi ⁄∑ i/Mi), the resulting iron to oxygen ratio is 0.99 ......... 48
table 6: Impact of increasing parameters (the other parameters shall be constant) of the total DLVO-like interaction on the colloidal stability as a consequence of a changing absolute attraction energy |Wtotal,min| and distance Dtotal,min ↑↑↑/↓↓↓ p og essi el , ↑↑/↓↓ li ea l , ↑/↓ deg essi el
increasing/decreasing), starting values for the parameters as good approximates [23] ........................................................................................................................ 62
table 7: Results for the mathematical fit parameters using eq. (61) of each batch for both polymers as well as for the three batches combined; estimation of the initial primary particle concentrations w0
Primary of batches PT101104 and PT100921 using the initial extinction values E0
600 nm and the given w0Primary of batch
PT100305 (see figure 47) ....................................................................................... 70
table 8: Results of the first order exponential decay fit parameters A and w0Primary for all five
samples investigated ............................................................................................. 72
table 9: Granulometric data: median microparticle size, specific surface area calculated from the particle size distribution, BET surface and structural parameter SP of the spray-dried samples with PMMA as the matrix polymer [23] ............................ 111
table 10: Granulometric data: median microparticle size, specific surface area calculated from the particle size distribution, BET surface and structural parameter SP of the spray-dried samples with PVB as the matrix polymer [23] ................................. 113
table 11: Specific Weight and dynamic viscosity (cf. A.9) of the spray-dried dispersion and granulometric data of the cyclone particle fraction for different filler concentrations of PMMA-based RA-Fe3O4 composites with DRA = 0.2, calculated spe ifi eight , edia pa ti le size , , spe ifi su fa e a eas al ulated with the PSD SPSD (cf. eq. (76)) and measured with BET SBET, SAUTER diameter x3,2 calculated with PSD and structural parameter SP (cf. eq. (76)) .......................... 115
table 12: Summary of data obtained from the binarized images of figure 89, coefficient of variance of the VORONOI polygons covVORONOI (cf. 6.1.3), median size of the number weighted distribution of FERET diameters xFERET 50,0, area fraction of the detected
XXIII
agglo e ates φAgglomerates (related to the overall volume / area fraction of the filler nanoparticles in parentheses), agglomerated particle concentration from the primary particle concentration reported in 5.3.1 with wAgglomerates = 1 - wPrimary and number of agglomerates detected .............................................................. 118
table 13: Summary of data obtained from the binarized images of figure 90, coefficient of variance of the VORONOI polygons covVORONOI (cf. 6.1.3), median size of the number weighted distribution of FERET diameters xFERET 50,0, area fraction of the detected agglo e ates φAgglomerates (related to the overall volume / area fraction of the filler nanoparticles in parentheses), agglomerated particle concentration from the primary particle concentration reported in 5.3.1 with wAgglomerates = 1 - wPrimary and number of agglomerates detected .............................................................. 121
table 14: Summary of data obtained from the binarized images in figure 92, coefficient of variance of the VORONOI polygons covVORONOI (cf. 6.1.3), median size of the number weighted distribution of FERET diameters xFERET 50,0, area fraction of the detected agglo e ates φAgglomerates (related to the overall volume / area fraction of the filler nanoparticles in parentheses), agglomerated particle concentration from the primary particle concentration reported in 5.3.1 with wAgglomerates = 1 - wPrimary and number of agglomerates detected .............................................................. 124
table 15: Summary of data obtained from the binarized images in figure 95, coefficient of variance of the VORONOI polygons covVORONOI (cf. 6.1.3), median size of the number weighted distribution of FERET diameters xFERET 50,0, area fraction of the detected agglo e ates φAgglomerates (related to the overall volume / area fraction of the filler nanoparticles), and number of agglomerates detected ............................. 127
table 16: Chemicals used for magnetite nanoparticle synthesis ............................................. A3
table 17: List of fatty acids used for the experiments in this thesis ........................................ A3
table 18: General physical properties of the polymers used in this thesis[154, 247] ............. A5
table 19: HSP values of the polymers used in this thesis ......................................................... A6
table 20: Polymer chain properties of the PMMA batch Diakon CLG 902............................... A7
table 21: Polymer chain properties of the PC batch Makrolon 2407 ...................................... A8
table 22: Polymer chain properties of the PVB batch used, including the composition of the three functional units displayed in figure 100 ...................................................... A9
table 23: List of solvents used in the experiments of this thesis ........................................... A10
table 24: General physical properties of the solvents ........................................................... A10
table 25: Concentrations cpolymer and cnanoparticles of the investigated dispersions of polymers, nanoparticles in DCM as an organic solvent with a surfactant (fatty acid) to nanoparticle mass ratio D of 0.2, as well as the resulting filler concentration F of the particles in a composite synthesized with the dispersion withdrawing the solvent assuming specific weights of 5.2 g/cm3 and 1.2 g/cm3 for the magnetite nanoparticles and the polymer as well as the surfactant layer, respectively. .... A25
table 26: Important setting parameters of the lab scale spray dryer .................................... A33
ta le : E aluatio of the i t i si is osities [η], the o e lap o e t atio *polymer using
eq. (42), the radius of gyration RG using eq. (46), the hydrodynamic radius determined with DLS and the ratio of hydrodynamic radius to radius of gyration X using eq. (41) ....................................................................................................... A39
table 28: Polymer solubility in DCM for PMMA, PC and PVB with HSP values from table 19 and table 24, solubility distance D1,2 using eq. (50), FLORY interaction parameter χ using eq. (52) ....................................................................................................... A39
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table 29: Solubility of PMMA in the solvents dichloromethane, ethyl acetate and methyl methacrylate with HSP values from table 19 and table 24, solubility distance D1,2 using eq. (50), FLORY interaction parameter χ using eq. (52) .............................. A40
table 30: Solubility of PS in dichloromethane and styrene with HSP values from table 19 and table 24, solubility distance D1,2 using eq. (50), FLORY interaction parameter χ using eq. (52) ....................................................................................................... A40
table 31: Molar group parameters of the group contribution method of the groups relevant for the structure of fatty acids ............................................................................ A44
table 32: Number of specific groups in the fatty acids used ................................................. A44
table 33: Number of specific groups in the grafted fatty acids, neglecting influence from the chemically bound complex .................................................................................. A45
table 34: HSP of FA calculated with the group contribution method and the number of groups from table 32 and solubility distance in DCM DFA-Fe3O4-DCM with eq. (50) and HSP for DCM in A.1.4 .................................................................................... A45
table 36: solubility distances of fatty acid caped Fe3O4 in different solvents using eq. (50) as well as FLORY interaction parameter using eq. (52) and the HSP of the solvents reported in A.1.4 and of the FA-Fe3O4 listed above ............................................ A46
1
1 Introduction
For the stability and growth of modern economies it is a crucial fact that the research and
development in new materials plays a very important role [1]. Especially the scientific and
technological advances in the field of nanoparticles, which manifest manifold physical
properties of various types offer new perspectives for material applications in merely any
field of industry. Classic polymer products can be turned into high performance functional
nanoparticle-polymer-composites by incorporation of nanoparticles [2-13]. When preparing
these composites one has to take care of the special interfacial phenomena and resulting
particle interactions to prevent undesired nanoparticle clustering to agglomerates and
aggregates [14, 15]. To achieve a high state of dispersion is the main task when wanting to
prepare nanoparticle-polymer-composites. This means a homogeneous distribution of
primary nanoparticles within the continuous polymer matrix is desired. There are numerous
techniques and strong research efforts to meet this demand.
In this doctoral thesis one technique of nanoparticle-polymer-composite preparation is
focused on in detail. It is the process chain which is based on solution blending of stabilized
hydrophobic nanoparticles with dissolved polymers and spray drying of this complex mixture
[4, 7, 16-18] to obtain highly filled composites. These composites are based on thermoplastic
polymers where the nanoparticle filler concentration by volume exceeds 10 %. With the
motivation to develop a process chain for composite preparation, this thesis critically
investigates and analyzes the process units with a focus on colloidal interactions. It is meant
to explain the physical-chemical limitations of preparing well disperse composites using the
solution and spray drying method.
The work is structured in seven chapters (including the introduction and general conclusions
and an outlook in chapters 1 and 7, respectively) and an appendix including the materials
and methods.
The various pathways of preparing nanoparticle-polymer-composites are introduced in
chapter 2 where the main terms: nanomaterials, polymers and state of dispersion are
introduced and a list of commercial composites is presented as well.
In chapter 3 the unit processes of the developed process chain are specified and the limiting
compositional parameters of the solution based mixture and the final composite are
explained.
Chapter 4 theo eti all a d e pe i e tall fo uses o the u it p o ess li uid-liquid phase
t a sfe . It ill p ese t a e ph si al odel des i i g the effe t of ph si -chemical
2
particle deagglomeration upon surfactant adsorption at a liquid interface in 4.2.
Experimental results on fatty acid adsorption on and deagglomeration of magnetite
nanoparticles are given in 4.3.
The following chapter 5 is dedicated to the phenomena in non-aqueous organic solvent
based nanoparticle-polymer-mixtures. In paragraph 5.2 a short discussion on a DLVO-like
treatment of the governing particle interactions is presented. Experimental results on the
colloidal stability investigations based on two newly developed methods of determining
quantitatively the primary particle concentration are presented in paragraph 5.3.
Finally solvent-free highly filled nanoparticle-polymer-composites are investigated and
discussed in chapter 6. Here it is differentiated between composite microparticles from the
spray dryer in paragraph 6.2 and cross-sections of injection molded composites in paragraph
6.3.
Most results have been published in the following peer reviewed papers (1-5) and
conference proceedings (6-9):
1) Phase-contrast atomic force microscopy for the characterization of the
distribution of nanoparticles in composite materials, In: Chemie Ingenieur Technik
82 (2010), p. 2189-2195 [19]
2) Coagulation and stabilization of sterically functionalized magnetite nanoparticles
in an organic solvent with different technical polymers, In: Journal of Colloid and
Interface Science 357 (2011), p. 292-299 [20]
3) A TGA/FTIR perspective of fatty acid adsorbed on magnetite nanoparticles -
decomposition steps and magnetite reduction, In: Colloids and Surfaces A 397
(2012), p. 16-23 [21]
4) Nanocomposites based on technical polymers and sterically functionalized soft
magnetic magnetite nanoparticles: synthesis, processing, and characterization, In:
Journal of Nanomaterials 2012 (2012), Article ID 670531 [22]
5) Phase transfer of agglomerated nanoparticles - deagglomeration by adsorbing
grafted molecules and colloidal stability in polymer solutions, In: Journal of
Nanoparticle Research 14 (2012), p. 990 [23]
6) Synthesis of highly filled nanomagnetite polymeric composites via sterically
stabilized organosols and the spray drying process, WCPT6, Nuremberg, april 26th
– 29th 2010 [24]
3
7) Processing and characterization of highly filled polymer nanoparticle composites
for micro injection molding applications, ECCM14, Budapest, june 7th – 10th 2010
[25]
8) On the significance of nanoparticle interactions for the synthesis of highly filled
polymer-nanoparticle-composites with the solution and spray-drying process, In:
V.S. Litvinenko (Ed.), International Forum-Competition of Young Researchers -
Topical Issues of Subsoil Usage, Ministry of Education and Science of the Russian
Federation, St. Petersburg, april 20th – 22nd 2011, ISBN: 978-5-94211-506-7, pp.
278-281 [26]
9) Nanoparticles in organic solvents with polymers - stability and consequences upon
material synthesis through spray drying and melt moulding, In: G. Tiddy, R.B.H.
Tan (Eds.), NanoFormulation, The Royal Society of Chemistry, 2012, ISBN: 978-1-
84973-378-6, p. 177-187 [27]
4
2 Nanoparticle-Polymer-Composites – A State of the
Art
The 20th century is often referred to as the century of plastics (macromolecular/polymeric
materials)1, because synthetic polymers have been developed and widely applied improving
materials technologies in nearly any sector of industry ever since. In the 19th century already
first signs of polymerization reactions have been reported. However, in 1907 the first
synthetic polymeric material, a thermoset, has been presented by Joe Baekeland and new
polymeric materials have been developed and synthesized continuously. In order to improve
and influence their properties other classes of materials are incorporated, such as soot in
rubber for tires and wood chips in Bakelite. One such class of materials being incorporated
are the ever emerging nanomaterials.
The 21st century is sometimes called the century of nanotechnology [28] which has been
proposed by RICHARD FEYNMAN in 1959 already with his famous talk The e’s Plenty of Room
at the Botto .
It is a logical consequence to combine nanomaterials and polymers in so called polymer-
nanoparticle-composites. And while more and more nanomaterials are being discovered and
developed, substantial research is focused on synthesizing nanocomposites making use of
the special properties of the nanomaterials. Therefore one can find quite a lot of review
articles on nanomaterial-polymer-composites. However, they are somewhat limited to only
focusing on special classes of nanomaterials, their properties, applications and/or syntheses
routes. As an example the Polymer Nanocomposites Handbook [29] is mainly focused on
clays being nanoplatelets. Other review articles on platelet type nanomaterials in a polymer
matrix are found in [5, 30, 31]. A thorough review on nanoparticles, their preparation and
composite formation with a focus on properties is given by HANEMANN and SZABÓ in [32].
CAMENZIND et al. deliver a thorough review on nanocomposites with flame-synthesized
nanoparticles as the disperse phase [9]. Polymer nanoparticle composites for optical and
magnetic applications with a focus on properties and ways of syntheses are found in [8].
Special focus on transparent nanocomposites regarding the nanoparticle surface design for a
successful incorporation is found in a review by ALTHUEs et al. [33]. Finally an interesting and
recommendable review on modeling the properties of polymer nanoparticle composites is
found in [34].
The purpose for incorporating nanomaterials is to change and advance the properties of the
polymer, as mentioned above. These changes can be manifold, e.g.:
1 http://www.plasticsnews.com/century/bakelite.html, march 26th 2012
5
- I p o i g Me ha i al st e gth a d stiff ess, heali g cracks and increase scratch
resistance,
- Improving the thermal stability, flame retardancy,
- Influencing the chemical stability,
- Improving the barrier properties e.g. for gases,
- Influencing the electric conductivity,
- Introducing catalytic properties,
- Improving and Influencing the optical properties, e.g. refractive index, color,
luminescence or
- Incorporating special magnetic properties, such as super-paramagnetism.
In this thesis the nanomaterial focused on are super-paramagnetic magnetite nanoparticles,
which are to be dispersed in a thermoplastic polymer. Super-paramagnetism means that the
particles are magnetic in a magnetic field but show no remanence and therefore no
magnetization loss on hysteresis. A selected set of papers focused on nanocomposites with
magnetic properties can be found in the references [11, 35-44].
This chapter gives an overview on the state of the art in the science of synthesizing
composite materials. They are consisting of nanomaterials dispersed in a polymeric matrix,
which are in this study limited to thermoplastics. Before summarizing the methods for
incorporation nanomaterials in 2.2, the basic terminology, which has already been used
above is introduced next.
2.1 Terminology
2.1.1 Nanomaterials
Nanomaterials are clearly defined in ISO/TS 27687:2008 as objects with at least one
dimension smaller than 100 nm. Furthermore one has to distinguish between objects with
different aspect ratios where one, two or three dimensions can meet the definition of a
nanomaterial. The images in figure 1 schematically display the three main types of
nanomaterials with distinguishable aspect ratios.
figure 1: Schemes of the three main types of nanomaterials (from left to right) nanotubes with diameters
smaller 100 nm, nanoplatelets with thickness smaller 100 nm and nanoparticles with diameters
smaller 100 nm
6
Nanotubes exhibit one very long dimension (length) which can go up to several micrometers,
e.g. carbon nanotubes [45]. Nanoplatelets are planar nanomaterials which are thinner than
100 nm, e.g. clays [46] or graphene [47]. Very often nanomaterials in general are referred to
as nanoparticles. However, the term nanoparticle clearly defines an object with a low aspect
ratio, i.e. nearly spherical and a size smaller than 100 nm.
One reason why nanoparticles and nanomaterials, in general, show special physical and
chemical properties, is their large surface area. A simple estimate to calculate the relative
number of atoms/clusters at the surface of a nanoparticle is:
3
Abulk
clustercluster
3
cluster
total
surface
6
2
1
11
N
MR
R
R
N
N
.
(1)
Here the number of atoms at the surface Nsurface related to all atoms in the particle Ntotal is a
function of the particle radius R and the characteristic length of a unit, which the particle is
made of, i.e. atoms or clusters Rcluster. The cluster radius Rcluster can be estimated with the
molar mass of a cluster Mcluster, the bulk density of the particle bulk and AVOGADRO’s number
NA. In figure 2 a graphical scheme of the estimation as well as a graph with the results of the
calculation for magnetite and gold are presented.
figure 2: (left) Model geometry for the calculation of the relative number of atoms/clusters at the surface
Nsurface/Ntotal, cf. eq. (72), (right) evaluation of eq. (72) for gold Au and magnetite Fe3O4 with given
atom/cluster sizes Rcluster
Certainly the surface atoms/clusters will not be identical in chemical and physical properties
to the bulk atoms/clusters for they are not entirely surrounded by atoms/clusters of the
same type and must be saturated (no dangling bonds or radicals) towards the surrounding
medium. As a consequence the particles exhibit a high surface energy which leads to strong
interactions such as aggregation (due to high reactivity and sintering) or agglomeration
which has to be dealt with when processing these materials. Magnetite is chosen in figure 2
7
since it is the model nanoparticle system applied in the experiments of this thesis and the
particle radius is R ≈ 7.5 nm, which leads to 10 % of all Fe3O4 clusters located at the surface.
2.1.2 Polymers
The term polymer stands for a wide range of materials consisting of large molecules. These
are made up of small repeating units, which are called monomers. A polymer is
characterized by a great variety of chemical composition, confirmations, properties and
applications which are only very briefly summarized here. If the repeating units are of the
same type, one is referring to homopolymers. The macromolecule can be chainlike or
branched. Individual macromolecules can fruthermore be cross linked with each other.
Thermoplastics are polymers which are not crosslinked, they can undergo a shaping process
under high temperatures. They are meltable and soluble in certain solvents. Crosslinked
polymers are categorized as gels, elastomers and duroplastics depending on the flexibility of
the network, in solvents they do not dissolve but swell.
If the polymer is made up of two types of monomers, they are referred to as co-polymers.
Three and more monomers in a macro molecule are possible as well. Polymers with a net
charge in aqueous solution are called polyelectrolytes. The individual macromolecules in a
polymer can be ordered and semi-crystalline in solid state. Typical transparent polymers are
amorphous. The impact of plastics and their wide range of materials applications are for
example described in [48].
In the present work the focus is on amorphous thermoplastics, which are soluble in organic
solvents and widely used in industrial applications. Examples for such amorphous
thermoplastics are:
- Poly(styrene) (PS),
- Poly(methyl methacrylate) (PMMA),
- Poly(bisphenol A carbonate) (PC),
- Poly(vinyl acetate) (PVAc),
- Poly(vinyl butyral) (PVB) and
- Poly(vinyl alcohol) (PVA).
Important characteristic dimensional properties of a polymer are [49]:
- molecular mass of the monomer M0,
- molecular mass of the polymer M,
- degree of polymerization N = M/M0,
- weight and number average molar mass Mw and Mn with
8
- index of polydispersity PI = Mw/Mn ≥ 1.
2.1.3 State of Dispersion
An important term related to the science of nanoparticle-polymer-composites is the state of
dispersion. In the literature there is however sometimes a confusing usage of this term. Here
it shall be clarified that the state of dispersion is a combination of the deagglomeration of
the disperse phase, i.e. the particles and their distribution within the continuous phase, i.e.
the polymer. The homogeneous distribution of the particles is achieved by mixing processes,
e.g. enforced turbulences and diffusion. The task of a deagglomeration process is to
overcome the attractive forces between the particles which are mainly adhesive forces (VAN
DER WAALS interactions)2. The images of figure 3 depict the interplay of distribution and
deagglomeration to describe the state of dispersion. Certainly most mechanical processes to
disperse particles achieve to both deagglomerate and distribute at once, for they are based
on high shear turbulent systems [50-53]. It will be shown in this work however, that
deagglomeration mechanisms, which will not influence the distribution, can exist as well.
figure 3: Definition of the state of dispersion (poorly dispersed in the upper left, well dispersed in the lower
right) by both a good distribution (more homogeneous from left to right) as well as a thorough
deagglomeration (more successful from top to bottom)
Another term related to the state of dispersion is the stability, which includes the factor
time. One can consider a dispersion to be sta le if a good state of dispe sio does ot decrease with time which can happen in both directions in figure 3 starting at the bottom
right. The distribution is disproved by spatial concentration which can be induced by a force
field, such as gravity. Agglomeration is a consequence of particle collisions which exhibit a
higher energy than the repulsive barrier between the particles.
The mathematical description of the state of dispersion of particles in a composite material
can be difficult, because both distribution and deagglomeration have to be considered.
2 If there are strong cohesive forces to be overcome then one should speak of deaggregation instead, which is a comminution process and consumes more energy.
9
There is an ongoing discussion of adequate measures to quantify the state of dispersion, e.g.
using tessellation (VORONOI polygons) [54, 55] or other methods [56-58], cf. 6.1.3.
2.2 Composite Synthesis/Preparation
This chapter offers an overview on the various possible process principles to
synthesize/prepare nanoparticle-polymer-composites. The focus is on composites for
advanced materials excluding specialized composites like functional core-shell particle
systems. The reviewed articles are characterized in three main groups:
- composites where the disperse (nanoparticles) and continuous phase (polymer) are
synthesized prior to the composite synthesis process,
- composites where the disperse phase is synthesized in situ of an as-synthesized
polymer and
- composites where the continuous phase is synthesized around the as-synthesized
disperse one.
It shall be kept in mind that all experiments in this doctoral thesis are related to the method
Mi i g i a pol e solutio , which is introduced in chapter 2.2.1.
2.2.1 Separate Synthesis of Particles and Polymers
When there is a desire to combine both existing nanoparticles as well as readily synthesized
polymeric materials there are quite different techniques to achieve a well-dispersed
composite. Four routes of synthesis are presented here in this paragraph.
Mixing in a polymer melt
Thermoplastic polymers which are emphasized in this study melt at elevated temperatures,
typically around 100 °C to 300 °C. In extrusion based processes, e.g. compounding or
injection molding, high shear is introduced in a highly viscous polymer melt to distribute and
deagglomerate solid particles, which are typically introduced in a side-stream. This
compounding is already well established in polymer technologies when it comes to disperse
microparticle polymer composites [59, 60]. Nanoparticle-polymer composites have been
processed using this simple technique as well [5, 30]. In the following figure 4 this method is
schematically depicted.
10
figure 4: Scheme of composite formation by mixing a dry nanoparticle powder with a polymer melt, the mixer
represents any type of high shear mixing device such as an extruder
Obviously the state of dispersion depends on the shear rate [61] and other parameters such
as residence time, where an optimum of the parameters is investigated in [62]. The problem
in succeeding with a good state of dispersion when distributing nanoparticle agglomerates is
that the shear forces are too low to act at scales below 100 nm where the deagglomeration
should occur. Hence, usually the homogneization is good but the deagglomeration is poor.
Mixing in a polymer solution
To overcome the problem of a poor deagglomeration another technique is simply to mix a
stable nanoparticle dispersion with a solution of a polymer. As will be shown in 5.1.1, the
soluble polymers will occur in the shape of coils dispersed in the solvent. The nanoparticles,
moreover the nanomaterials are well deagglomerated within the same solvent. A schematic
description of this method is presented in figure 5.
figure 5: Scheme of composite formation by mixing a nanoparticle solvent based colloid with a polymer solution,
the nanoparticles can be well dispersed e.g. by adsorption of end-grafted molecules
When it comes to dispersing nanoplatelets, namely clays, this is a common process in order
to exfoliate (deagglomerate) the clay layers [30, 63-66]. This method has been introduced by
BANERT and PEUKER for spherical particles such as magnetite dispersed in an organic solvent,
namely dichloromethane and mixed with polymer solutions of PMMA or PVB [4, 7, 67].
Furthermore TiO2 and ZrO2 have been well dispersed by solvent based mixtures with pristine
and modified PC [68]. Besides synthesizing bulk composites the technique of mixing
nanoparticles in a polymer solution is important in film casting methods, e.g. when
dispersing fullerenes in PMMA [69]. In order to achieve a good result one has to prevent
agglomeration induced by the polymers and consequently concentrate on the solubility of
the compounds [70].
11
This method seems straight forward, however the interactions between well dispersed
nanoparticles and dissolved polymers can be very critical as will be emphasized in this
present thesis, which focuses on this solution based process.
Milling
A common method for synthesizing metal alloys with nanocrystalline phases is high energy
ball milling [71]. Briefly, dry powders are mixed and well dispersed in the chamber of a dry
media mill, such as a planetary ball mill. Certainly it seems worthwhile adopting this process
to synthesize well dispersed nanoparticle-polymer-composites out of a dry nanoparticle
powder with a polymer powder by high energy mixing. This has been reported in [72, 73],
where fumed silica is being dispersed in a PMMA matrix. However, the main problem here is
the undesired change of the bulk polymer properties, mainly reduction of the chain length of
the polymer [72-75] [76]. In figure 6 a scheme of this method is depicted.
figure 6: Scheme of composite formation by compounding a nanoparticle and a polymer powder with high
forces in a mill, typically a high energy ball mill
Nanotubes have also been reported to distribute and deagglomerate well in PE using the
principle of ball mill mixing [77]. Besides the aforementioned problem of influencing the bulk
properties of the polymer, certainly this method is difficult to scale-up for a higher material
throughput and is therefore not of interest for technical processes.
Hetero-coagulation
A fourth type of process for this kind of composite synthesis is hetero-coagulation [78]. A
principle scheme is presented in figure 7.
figure 7: Scheme of composite formation by attraction of oppositely charged particles and polymers in water
and withdrawing the water for composite synthesis
Certainly this is a very exclusive method for composite synthesis which is limited for water
based systems and opposite surface charges for the nanoparticles and the particulate
12
polymers within the same aqueous environment (pH, salt types and ionic strengths).
However, it was reported to be a successful way to synthesize fire retarded composites [78].
2.2.2 Synthesis of Nanoparticles within the Polymer
Aside from the four techniques for composite synthesis described in chapter 2.2.1 it is also
considerable to synthesize the nanoparticle fillers individually within a solvent swollen
polymer network. Such a network can be crosslinked polymers or porous polymeric
structures. For this, the building blocks to form nanoparticles need to be well dispersed
within the polymer, e.g. salts for a co-precipitation or pre-coursers for a sol-gel-synthesis
based formation, respectively. A scheme for this principle of composite synthesis is
presented in figure 8.
figure 8: Scheme of composite formation by mixing a pre-courser (symbolized as small dots in the left frame)
with a polymer in solution and initiation of nanoparticle synthesis
Good results regarding the state of dispersion are reported for sol-gel based systems such as
the formation of silica from TEOS pre-coursers [79] in a nylon polymer.
The nanoparticles used in this thesis are magnetite nanoparticles synthesized in a co-
precipitation reaction, as explained in detail later. ALI-ZADE has reported on the composite
synthesis of co-precipitated magnetite nanoparticles in porous polystyrene or collagen
structures with excellent results concerning nanoparticle distribution [80, 81].
In the reviewed literature the obstacles of this method have not been reported. However, it
is necessary for a successful synthesis to be given a polymer three dimensional structure
with pores that are accessible for the reagents. Therefore, the method obviously is not
suitable for thermoplastic polymers which are not cross-linked but of interest in this work,
cf. 2.1.2. Furthermore the nanoparticle synthesis in the presence of the polymer must be
undesirably different from a controlled synthesis without the polymer.
2.2.3 Synthesis of the Polymer with the Presence of Nanoparticles
Contrary to 2.2.2 one can also synthesize the matrix polymer around the already existing
nanoparticles. A straight forward way to achieve this is by dispersing and stabilizing
nanoparticles in a monomer or monomer solution and synthesizing the polymer by a
polymerization reaction. In figure 9 the scheme for this method is presented.
13
figure 9: Scheme of composite formation by dispersing nanoparticles in a monomer solution (on the left
represented as an emulsion droplet) and subsequent polymerization and withdrawing of all solvents
In [82] fatty acid stabilized magnetite nanoparticles are dispersed in methyl methacrylate
droplets of an emulsion. Magnetite PMMA composites are achieved by radical modified
suspension polymerization with a filler content of up to 11 % by weight. GYERGYEK et al.
report on the synthesis of magnetite-PMMA composites as well, applying in-situ dispersion
polymerization of MMA in n-decane [35]. The composites prepared there reach a filler
concentration of up to 48 % by weight with a high saturation magnetization due to the
super-paramagnetic properties of the oleic acid coated nanoparticles. Quantum dot filled
composites of zinc sulfide ZnS in acrylate polymers have been prepared in [83] using
dispersion polymerization. The transparency of the resulting composite reveals the high
state of dispersion. It is stressed that, in order to obtain a good incorporation and a well
dispersion, the nanoparticle surface needs to be designed to have a good interaction with
the polymer and the polymer precursors preventing particle agglomeration. Another way to
synthesize composites by polymerization can be based on two component systems, e.g. for
thermosetting polymers [84]. For this the particles are dispersed in the epoxy resin and
subsequently the hardener phase is added.
In [85] and as well in [36] highly filled magnetite thermoplastic polymer composites are
synthesized by miniemulsion polymerization. Both studies reveal agglomeration of the
sterically stabilized nanoparticles induced by the polymerization. This may conclude that
attractive interactions are introduced by the polymers that have not been present in the
nanoparticle monomer dispersion. When the goal is to create a highly disperse composite,
cf. 2.1.3, these attractions need to be dealt with and prevented. Furthermore the presence
of the nanoparticles might influence the polymer synthesis, so that one may be losing
control over desired polymer bulk properties.
2.3 Industrial Products
This final paragraph presents selected nanoparticle-polymer composites which are already
products on the market. They have been researched in [31] and on the world wide web 1,3,4.
14
table 1: A selection of industrial polymer-nanoparticle composites which are already on the market345
company product application properties
Südchemie Nanofil® polymer additive flame retardant
Bayer Durethan® coatings strength, barrier
properties, gloss
Honeywell Aegis® packaging barrier properties against
water and air
Noble Polymers Forte®,
Nubrid® automotive
strength, temperature
resistancy
Polykemi Scancomp® automotive, home care strength, low density,
gloss, scratch resistance
Mitsubishi Gas
Chemical Company M9 beverage packaging high barrier properties
Grado Zero Espace Absolut Black polymer additive homogeneous surfaces,
intense black coloring
RAS Materials AgPure polymer additive antimicrobial
Silanotex Nano-Silber polymer additive antimicrobial
ApNano Materials NanoG® military, packaging low density, strength,
energy adsorption
Evonik Hanse
Chemie Nanocone®
electronics, dental care,
medical technology
strength, transparent,
flexibility
Evonik nanoresins
AG Nanocryl® coatings
scratch resistance,
transparency, strength
NanoSky Nanoterra
soil® road construction
non-toxic,
environmentally friendly,
longer lasting roads
Polyone Nanoblend® polymer additive improved mechanical
and thermal properties
Chemtura Polybond® packaging barrier properties
TurboBeads® (several) biochemical, medical,
chemistry magnetic
3 www.nanoproucts.de, January 27th 2011 4 www.nanocompositech.com/commercial-nanocomposites-nanoclay.htm, January 27th 2011 5 http://www.turbobeads.com, June 10th 2012
15
3 Thesis Motivation – Development of a Modular
Process Chain for Composite Preparation
This chapter presents and discusses the background and motivation of this thesis. It explains
the topics of the following chapters and defines certain limits, e.g. material, composition and
processes.
From october 2008 to march 2012 the German research foundation (DFG – Deutsche
Forschungsgemeinschaft) supported the project PE1160/7-1 devoted to The Development
of a Process Chain for the Synthesis and Processing of Highly Filled Polymer-Nanoparticle-
Co posites [86]. The contribution of this thesis is to answer the urging questions related to
this project. Concluding, the project report shall present and discuss the How to and the
thesis addresses the Why so. So before the Whys can even be addressed the reader of this
thesis must be given the Hows in this chapter. Therefore the paragraph 3.1 presents each
process step pointing out the relation to the subsequent chapters. In paragraph 3.2
important compositional parameters are introduced since in some cases of this research
there are up to four components to deal with.
The starting point for the project are papers by ZHOU, PEUKER, BANERT, HICKSTEIN and
MACHUNSKY [7, 18, 67, 87-90]. These introduce the solvent based process for preparing
nanoparticle-polymer-composites (cf. 2.2.1) as well as the liquid-liquid phase transfer of
nanoparticles are introduced. It shall be emphasized in this context, that the scientific
contributions of this thesis, based on the various publications, cf. chapter 1, are mainly
concerned with special colloidal interactions.
3.1 Process Steps
The solvent based process for the synthesis of nanoparticle-polymer composites developed
in the research project comprises several individual process steps, namely:
- particle synthesis,
- particle functionalization and particle transport to an organic solvent,
- addition of the dissolved polymer,
- quick evaporation of the solvent in a spray dryer and finally
- composite powder agglomeration.
These steps are briefly introduced in the following paragraphs.
16
3.1.1 Nanoparticle Synthesis
figure 10: Synthesis of the nanoparticles by a precipitation reaction in an aqueous environment
The first step is to synthesize the nanoparticles in an aqueous milieu by a precipitation
process. The model system of nanoparticles is the super-paramagnetic iron oxide magnetite
Fe3O4. Super-paramagnetism is a special property of iron oxides in the nanoscale. These iron
oxides are well magnetizable in a magnetic field (high saturation magnetization), yet the
magnetic character is lost when the magnetic field is zero (no remanence). In the appendix
A.3 the procedure of the co-precipitation as well as the nanoparticle characterization by
TEM, XRD, Zeta Potential and DLS are presented there. Due to the high ionic strength in the
aqueous phase the particles agglomerate and settle rapidly in a force field, e.g. gravity. The
surface characteristic of the particles is hydrophilic and they carry a net charge which is
negative. However, the particles both need to be hydrophobic in order to incorporate in the
hydrophobic polymer matrix of a composite and should be deagglomerated to obtain a high
state of dispersion. These demands are fulfilled using the process step of liquid-liquid phase-
transfer.
3.1.2 Liquid-Liquid Phase-Transfer
figure 11: Transfer of the nanoparticles from the aqueous to an organic phase (immiscible solvent) by
adsorption of amphiphilic molecules
The liquid-liquid phase-transfer process of particles is in principle similar to the widely
applied liquid extraction of ionic or molecular species [91]. One could therefore also refer to
it as particle extraction. Details of this method and new scientific findings offered by this
thesis are to be found in chapter 4.
17
Just briefly, in this second step of the process chain the surface of the nanoparticles is
hydrophobized and the agglomerates are partially deagglomerated by entering an organic
solvent, which is immiscible with water. This is achieved by adsorption of amphiphilic fatty
acid molecules at the liquid interface. The organic solvent mainly used in this research is
dichloromethane with a higher specific weight compared to water. Thus it is located below
the water phase underlying gravity. The transport of the particles to the interface is achieved
by settling. Without the amphiphilic substance at the interface of the liquids, the particles
rest on top of the organic phase due to their hydrophilicity.
The profit of this liquid-liquid phase-transfer is that there is no need for drying the
nanoparticles and subsequently resuspending them in the organic phase. Therefore
undesired agglomeration or even aggregation of the particles is prevented.
The result of phase transfer is, depending on the fatty acid used, a deep black long term
stable colloid. The stabilization mechanism of the nanoparticles is the steric repulsion
between the chains of the adsorbed fatty acid molecule layers. It is introduced in chapter 4,
as well. When using dichloromethane as a carrier solvent there are a wide range of soluble
thermoplastic polymers, such as PMMA, PC and PVB, to mix with the colloid, which is
accounted for in the next unit process.
3.1.3 Polymer Addition
Adding a polymer solution to a stable colloidal dispersion of phase transferred particles
seems to be a rather simple procedure. However, it turns out to be the bottleneck of the
entire process chain, because the state of dispersion is mostly affected in this step, given
that the previous phase transfer process delivers a stable dispersion. Chapter 5 covers this
issue in greater detail.
figure 12: Mixing the organic solvent based hydrophobic nanoparticles with a polymer solution in the same
solvent
Just briefly noted here, a soluble polymer is dissolved in the same type of solvent which the
particles are transferred to. When dissolved, the individual polymer chains will coil-up with a
coil dimension that is in the same order of magnitude compared to the size of the stabilized
18
particles. Depending on whether the polymer can adsorb on the surface of the particles the
colloid can be destabilized and undesired flocculation may occur. However, if this is the case
then not all of the particles will flocculate and settle. There is still a fraction of long term
stable primary particles left. Visually the destabilized nanoparticle polymer dispersion may
be as deep black as before and seem stable. Hence, one would not necessarily be aware of
the problem of flocculation and carry on with the next step. This next step is the quick
evaporation of the solvent in a spray dryer in order to preserve the high state of dispersion
in a nanoparticle colloid polymer mixture.
3.1.4 Spray Drying
An important step in the solvent based processing of nanoparticle-polymer-composites is
the rapid removal of the solvent, because the high state of dispersion of the nanoparticles in
the polymer solution has to be preserved. This can be achieved by quick evaporation in a
spray dryer. In addition with this process one can design composite microspheres which are
already a desired final product, e.g. functional particles such as magnetic beads for
bioseparation purposes [4, 92].
figure 13: Spray Dryer with a two-fluid nozzle in the co-current regime. A small fraction of large particles falls
through the drying cylinder by gravity separation, the largest fraction of particles by mass is the
product collected at the coarse output of a cyclone by centrifugal force separation.
The spray drying unit process itself is based on the following individual steps:
At first, a feed dispersion, namely the nanoparticles in the polymer solution, is
atomized into small droplets with several micrometers in size using a nozzle or more
generally an atomizer. This atomization characterized by a droplet size distribution is
influenced by the atomizer design (e.g. pressurized gas two fluid nozzle or rotary
atomizer and the dimensions of these devices), the dispersion properties (e.g.
viscosity, surface tension and specific weight) and the process parameters (e.g. mass
flow of the dispersion, gas pressure and temperature).
Secondly, the droplets are contacted with a hot gas at temperatures above or close
to the evaporation temperature of the solvent. This is drying step, is based on heat
19
and mass transport between the gas and the drying droplets and also depends on
various parameters. The flow regime of the droplet and gas can be co-current or
counter-current and is defining the temperature gradient and thus the kinetics of
drying. Several properties of the droplet components in terms of thermal
conductivity, heat of evaporation and heat of crystallization as well as interactions
between the components influence the kinetics of drying and the final morphology of
the dry particles and with it the bulk powder properties of the product.
The third individual process of a spray dryer is the separation of the dry particles
from the solvent carrying hot gas flow. Very large partiocles are gathered at the spray
drying vessel. Usually the majority of the material is to be found at the coarse outlet
of an aerocyclone. Small particles will be trapped in a filter. The solvent carrying gas
flow is either released or recycled by a condenser. The condenser captures the
solvent and then heats the gas up again to act as the drying gas.
Pressurized gas two fluid nozzles produce a very fine powder which is hard to process
further, if desired. In order to improve the bulk powder properties, such as flowability and
specific weight of the bulk powder, an agglomeration process is needed. Such a process has
also been developed within the research project and is briefly explained in the next
paragraph.
3.1.5 Powder Agglomeration
The task of powder agglomeration is to improve flowability and increase the powder bulk
density by increasing the grain size. This can be achieved by press agglomeration and
subsequent granulation in a rotor sieve mill [93].
figure 14: Press agglomeration of the spray dried fine composite micropowder and granulation in a rotor-mill
for an improved powder handling
The process has been developed as part of a diploma thesis by ANNE HORSCHIG [94]. Grain
sizes within the powder are increased from 10 µm to 1 mm. This leads to an increase of the
bulk density by a factor of 10. Pressures up to 1.6 MPa are applied in a tabletting press with
20
a diameter of 50 mm and a final tablet thickness of 10 mm. Using a JENIKE shear tester it is
shown that the flowability and thus process ability improves significantly.
3.2 Composition
This paragraph introduces the compositional parameters and limitations of the solvent
based process described above. There are two considerations regarding the composition
within the solvent free composite in paragraph 3.2.1 as well as the composition of the
solvent based dispersion in paragraph 3.2.2.
3.2.1 Composite Composition
An important concentration characterizing the final composite is the filler concentration F,
defined in eq. (2). It is taking into account the masses of the nanoparticles, the surfactant
and the polymer mnanoparticles, msurfactant and mpolymer, respectively.
polymerlesnanopartic
lesnanopartic
polymersurfactantlesnanopartic
lesnanopartic
composite
lesnanopartic
1 mmD
m
mmm
m
m
m F
(2)
The surfactant ratio D describes a fixed ratio of the surfactants (fatty acids in this study) to
the nanoparticles in eq. (3), with the mass of the surfactants and nanoparticles msurfactant and
mnanoparticles, respectively.
lesnanopartic
surfactant
m
mD (3)
This parameter D is limited by the amount of fatty acids adsorbed onto the nanoparticles
and can be transformed into a function of the size of the nanoparticles xnanoparticle in eq. (4)
and figure 15, with Snanoparticle and Vnanoparticle being the surface and volume of the particle,
respectively.
lenanoparticlenanoparticAsurfactant
surfactant
lenanoparticlenanoparticAsurfactant
surfactantlenanopartic
lenanopartic
surfactant
6
xNS
M
VNS
MS
m
mD
(4)
Eq. (4) is illustrated in figure 15. The surfactant parameters necessary are the surface of the
adsorption site Ssurfactant, hi h is i the o de of … ·10-19 m2 as well as the molecular mass
of the surfactant Msurfactant, which is 282.46 g/mol for oleic acid.
21
figure 15: Surfactant ratio D, cf. eq. (4) with Msurfactant = 282.46 g/mol, Ssurfactant = 2.4·10
-19 m
2 [95] and
nanoparticle = 5.2·106 g/m
3 for magnetite Fe3O4
The specific weight of the composite composite can be calculated with the parameters F and D
as well as the components individual specific gravities as shown in eq. (5).
polymersurfactantlesnanoparticcomposite
1
DFFDFF (5)
Another important parameter regarding the composite is the volume fraction of the
nanoparticles φnanoparticles in eq. (6).
1
polymersurfactantlesnanoparticlesnanopartic 1
111
D
F
D
(6)
Accordingly the volume fraction of the polymer is defined in eq. (7).
11
surfactant
polymer1
nano
polymerpolymer 1
111
11
DDFD
F
(7)
Assuming the particles are spherical and monodisperse, the volume fraction of the polymer
can in no case be smaller than a threshold value of 0.26, which is the volume fraction of
voids in the closest packing of spheres. The relation of eq. (7) is depicted in figure 16 for
given specific gravities of the three components. With less surfactants per nanoparticles D
and higher filler concentrations F, the polymer volume fraction decreases. In other words,
higher theoretical filler concentrations can be achieved for lower amounts of surfactants. If
the polymer volume fraction approaches the threshold value of 0.26 percolation would
occur, which is the unavoidable contact of well dispersed individual nanoparticles.
22
figure 16: Interconnection of the composition values: filler concentration F, surfactant ratio D and volume
o e tratio of the pol er φpolymer for the given specific weights of the three components using
eq. (7), volume concentrations have to be larger than 0.26 which is the limit for closest packing of
spheres
The distance between the particles would approximate zero. This interparticle distance ID
can easily be calculated with a given packing structure of the spherical particles. For a body-
centered cubic packing ID (red arrow in the inset drawing in the graph of figure 17) is a
function of the particle diameter x and the volume fraction of the nanoparticles φnanoparticles
as expressed in eq. (8).
1433
3/1
lesnanopartic
xID (8)
Combining eqs. (8) and (6) the interparticle distance ID is plotted as a function of F for three particle diameters x in figure 17 on a log-lin-scale.
figure 17: Interparticle distance as a function of the filler concentration for monodisperse and homogeneously
distributed fillers of different size (D = 0.2 and specific weights as mentioned in figure 16). The inset
shows the geometry for the body-centered cubic space.
The interparticle distance approaches zero for filler concentrations F slightly larger than 0.8,
because the packing of spheres is not the closest in eq. (8).
23
3.2.2 Colloid Composition
For the solvent based process of composite synthesis there is an additional solvent
component to be considered when looking at the composition of the complex dispersion of
surfactant stabilized nanoparticles in a polymer solution. Crucial parameters regarding the
processing of the complex colloid are presented in this paragraph.
An economically as well as ecologically important parameter is the concentration of solids
within the solvent csolid. It is defined in eq. (9) using the mass of the solvent msolvent and the
mass of the composite mcomposite as a sum of the masses of nanoparticles, surfactants and
polymer.
solventcomposite
compositesolid mm
mc (9)
The lower this value is, the more solvent has to be processed which increases the economic
cost for processing and the ecologic impact due to higher energy consumptions and
potential solvent release to the environment.
As will be shown in the experimental part of this thesis the concentration of the polymer in
the colloid cpolymer, given in eq. (10), influences the process ability as well as the stability of
the colloid. Generally, higher polymer concentrations will tremendously increase the
viscosity of the colloid, which is an important factor for the spray drying process, cf. 3.1.4
and chapter 6. The impact of cpolymer on the colloidal stability is the focus of chapter 5. For
destabilizing polymers the stability increases with lower cpolymer.
solid
solidsolidsolventpolymer 1
1
c
DFccc
(10)
The main parameter for synthesizing a certain composite is the filler concentration, defined
in eq. (2), which can be expressed as a function of csolid and cpolymer, presented in eq. (11).
D
cc F
1
11
solvent
solidpolymersolvent
(11)
In figure 18 the relation in eq. (11) for D = 0.2 and the specific weight of the solvent DCM
solvent = 1330 g/l is visualized. Due to suppressed droplet formation, a polymer
concentration higher than 60 g/l for PMMA dissolved in DCM cannot be processed in a spray
drying step, cf. chapter 6.
24
figure 18: Interconnection of the polymer concentration in the organic solvent based mixture cpolymer, the solids
concentration csolid and the filler concentration F in the solvent free composite material, cf. eq. (11).
After having introduced the process chain developed as a practical background of this thesis
as well as the most important compositional parameters, in the following chapter theoretical
and experimental investigations and advances concerning the liquid-liquid phase transfer of
magnetite nanoparticles are presented.
25
4 Focus on the Phase Transfer of Nanoparticles
This chapter is dedicated to the liquid-liquid phase transfer of hydrophilic nanoparticles, as
part of the process chain in 3.1.2. After introducing the theoretical background and the
present literature in paragraph 4.1, a new physical model for a deagglomeration
phenomenon of this process is presented in paragraph 4.2. Subsequently in 4.3 experimental
results are discussed. These cover the study on the state of dispersion after phase transfer
for different types of fatty acids adsorbed on magnetite nanoparticles as well as the
spectroscopic coupled thermo gravimetric study on the chemisorption of ricinoleic acid.
4.1 Theory
After having synthesized nanoparticles in an aqueous co-precipitation reaction, as is the case
in this work (cf. appendix A.3), it is necessary to modify the surface to become hydrophobic
and to transfer the particles to an organic solvent phase (cf. appendix A.4).
Hydrophobization is achieved by grafting amphiphilic substances, such as carboxylic acids /
fatty acids, onto the particle surface. Transfer of the particles to the organic solvent phase
can e.g. be realized by washing and subsequent removal of the water and redispersing the
dried nanoparticles in the solvent. However, besides high energy consumption due to the
additional drying and mechanical redispersion steps and loss of material within the washing
step, the particles are endangered by oxidation and strong aggregation. A convenient one-
step process is the direct transfer of the nanoparticles to the water immiscible organic
solvent across the liquid-liquid interface, cf. 4.1.1. The amphiphilic molecules are dissolved
within the solvent phase and will be located at the interface for they have a surface active
character and can hence be referred to as surfactant molecules. The hydrophilic head group
points towards the aqueous phase where the adsorption onto the nanoparticle surface
occurs, cf. 4.1.2. After adsorption and hydrophobization has occurred the transferred
nanoparticles are furthermore stabilized by the adsorbed species through steric stabilization,
cf. 4.1.3.
4.1.1 Phase Transfer
The liquid-liquid phase transfer of nanoparticles is in principle an extraction of particles as
compared to the well-established extraction of ion-complexes or molecules [91]. The
mechanism of nanoparticle transfer has been described by MACHUNSKY and PEUKER [88].
Magnetite nanoparticles with a diameter of about 15 nm are transferred from water to
dichloromethane with the surfactant material oleic acid, which is known to adsorb
26
chemically on the magnetite surface [21, 95-97]. The transport of the particles to the
interface is achieved by settling of agglomerated nanoparticles. Without surfactants at the
interface the magnetite particles will not enter the hydrophobic solvent phase for the
surface properties of magnetite in water are of hydrophilic nature [98]. Fatty acids other
than oleic acid have proven to result in a successful transfer of the particles as well, however
qualitatively different states of dispersions are observed [99]. MÉRIGUET et al. report on the
direct diffusion driven phase transfer of water stable maghemite ( -Fe2O3) nanoparticles into
cyclohexane and nonane using didodecylammonium bromide as an ionic surfactant [100].
Both in [99, 100] the problem of emulsion formation is mentioned which is due to the fact
that the surfactant molecules also act as good emulsifiers. Diffusion driven liquid-liquid
phase transfer has also been reported for gold nanoparticles [101-104]. PRAKASH et al. show
that it is also possible to reverse the phase transfer from an organic solvent phase into water
for magnetite nanoparticles and quantum dots [105] using a surfactant in the aqueous phase
which will cause bilayer formation with the already existing first layer on the nanoparticle
formation and thus stabilization.
4.1.2 Adsorption of Surfactants
Surfactants in general are surface active substances, i.e. they change the surface (or
interfacial) energy by concentrating at a surface or an interface. In the sense of this study
surfactants are amphiphilic molecules which comprise both of a hydrophilic and a
hydrophobic (or lipophilic) section. One class of such amphiphilic molecules are the fatty
acids, which consist of a hydrophilic polar carboxylic head group and a hydrophobic aliphatic
tail.
Fatty Acids
Fatty acid is a more widely used term for the carboxylic acids extracted from fats
(triglycerides), after chemical removal of the glycerin. The structure can be well explained
with the head-tail model, as depicted in figure 19, where the dotted line stands for a linear
hydrocarbon rest R of different length.
figure 19: Principle structure of a fatty acid molecule with a hydrophilic carboxyl head group and a hydrophobic
alkyl chain with certain length and functionalities (double bonds, functional groups).
27
The phase behavior of fatty acids in aqueous solutions are described in [106]. At a pH above
6, depending on the length and saturation of the hydrocarbon rest, the carboxylic group
disassociates leaving behind a negatively charged carboxylate molecule.
This will also be the case if the molecules are located at an interface of water and an
immiscible organic solvent [107]. Disassociation is given by the following equilibrium formula
in eq. (12), where the negative charge of the carboxylate is delocalized between the two
oxygen atoms.
OHCOOROHCOOHR 32 (12)
The carboxyl group is the functionality, which will adsorb at the inorganic nanoparticle
surface [108]. This adsorption of fatty acid molecules will be introduced and discussed in the
next paragraph.
An important feature of the fatty acids is their maximum length when fully stretched, which
will be important for the phase transfer and the stability of functionalized nanoparticles. The
maximum length can be calculated with equation (13).
CCCCCC
CCCC lnln
2
sinmax
(13)
In this equation nC-C and lC-C are the number and length of C-C-bonds, αC-C is the angle
between three neighboring sp3-hyridized C-atoms and nC=C and lC=C are the number and
length of C=C-bonds. Values for the bond lengths and angles are lC-C = 0.154 nm,
lC=C = 0.150 nm and αC-C = 109.47° [109]. The ratio of C-C and C=C bonds, characteristic for a
fatty acids, is given by the lipid number. The fatty acids used in this study and their molar
mass M, maximum length max, lipid number and special functional groups beside the
carboxyl group are presented in table 2.
table 2: List of the fatty acids used in this study with maximum chain lengths as defined in eq. (13)
Name Abbreviation M in g/mol Length
max in nm
Lipid Number Special
Groups
Ricinoleic Acid RA 298.5 2.16 C18:1 -OH (C12)
Linoleic Acid LA 280.5 2.19 C18:2
Oleic Acid OA 282.5 2.16 C18:1
Myristic Acid MA 228.4 1.63 C14:0
Caprylic Acid CA 144.2 0.88 C8:0
28
Adsorption
Adsorption, in the present context, is an interaction of a substance (molecule, ion, complex,
etc.) with a solid surface [110-112]. Depending on the strength and reversibility of the
interaction physical and chemical adsorption are distinguished. For physical adsorption the
heat of adsorption is Hphysisorption < 40 kJ/mol. Heats of adsorption for chemisorptions are in
the order of chemical reactions Hchemisorption > 80 kJ/mol [112].
Typically adsorption is characterized by the amount of adsorbed molecules , which is the
amount (mass or number) of adsorbts related to the mass or surface area of the adsorbent,
so that [ ] = (g/g, mol/g, g/m2, mol/m2). It is usually investigated as a function of the
concentration c of the adsorbtive in the solution. Plotting over c results in adsorption
isotherms which can reveal the type of adsorption, whether it is physical or chemical and
furthermore giving clues on the arrangement of the substance in mono- or multilayers.
Chemical adsorption is present if by reducing the concentration of the adsorbent the
adsorbed amount does not follow the adsorption isotherm for increasing concentrations.
Analytical descriptions of surfactant adsorption isotherms are given in [112]. A very
prominent analytical formula is the Langmuir isotherm in eq. (14).
cK
cK
A
Amax 1
(14)
Here max is the maximum amount of adsorbt, when the entire surface of the adsorbent is
covered with a monolayer of adsorbt molecules. The constant KA is a function of the free
enthalpy of adsorption.
Fatty Acids Adsorption on Magnetite Surfaces
It is a well observed fact that fatty acids chemically adsorb on the surface of magnetite [4, 7,
20, 21, 24, 88, 95-97, 99, 108, 113-117]. Once the fatty acids are adsorbed, they can well be
described by a brush-like structure, where the hydrophobic tails point toward the solvent
[118, 119], as presented in figure 20a. Before the fatty acid adsorbs, typically at high pH the
negatively charged magnetite surface [98] approaches the negatively charged carboxylate,
as depicted in figure 20b. The charges are screened by the intermediate water layer, which
enables the approach of adsorbent and adsorpt [116]. However, the mechanism of
adsorption is presently discussed and no one well accepted theory exists. Three different
possible ways are presented in general. The most cited is the occurrence of chelating
bidentate binding by ZHANG et al. [95], which is presented in figure 20c. ROONASI et al. assume
a bidentate mononuclear bond [96]. Monodentate mononuclear configurations are
presented by MACHUNSKY et al. and most recently by CHERNYCHOVA et al. [99, 116]. However,
in MACHUNSKY et al. the carbonyl group is preserved, which is not expected for disassociated
29
specimens, which are represented by carboxylate groups, as approved by several different
analytical investigations [116].
figure 20: Principle of Adsorption of Fatty Acids on Magnetite: (a) monolayer covered particle, (b) before
adsorption in aqueous solution (mediating water layer in blue) with high pH resulting in negatively
charged magnetite surface and disassociated carboxyl group [99], (c) chelating bidentate bond
between fatty acid and magnetite [95] and (d) monodentate mononuclear configuration [116]
Adsorption isotherms with the adsorbent magnetite are published by KOROLEV et al. in [120]
with the fatty acids oleic (C18:1), linoleic (C18:2) and linolenic acid (C18:3) in cyclohexane
and heptane. It reports on Langmuir type adsorption with similar constants KA, cf. eq. (14).
The parameter max depends on the solvent. It increases or decreases with more double
bonds for heptane or cyclohexane, respectively.
The maximum amount of adsorpt max is an important value, when it comes to determining
the area of adsorption of a single molecule Smolecule, which furthermore is necessary to know
for assessing the grafting density of monolayer adsorption φ. For Smolecule, values ranging
from 20 Å2 to 38 Å2 are reported in the literature [21, 95, 96, 108]. The following relation in
eq. (15) can be applied for determining the surface of an adsorbed molecule head group
Smolecule, grafting density φ or maximum amount of adsorpt in a dense monolayer max, with
the specific surface of the particle Sparticle [21]. In this relation the following experimentally
accessible units are used: [ max] = mol/g and [Sparticle] = m2/g.
Amax
particlemolecule N
SS (15)
Once adsorption of the amphiphilic molecule occurred, the particles are hydrophobic and
the end-grafted molecule tails point toward the solvent causing a steric repulsion upon
contact of two approaching particles. This steric stabilization is introduced in the next
paragraph.
4.1.3 Steric Stabilization
One aim of the phase transfer of nanoparticles is the stabilization in the organic solvent
phase. Due to a typically low dielectric constant , double layer repulsion can be neglected
and is therefore not the source of stabilization against VAN DER WAALS attraction [121].
However, due to overlap of surfactant or polymer layers on interacting nanoparticles
30
surfaces another, so called steric repulsion defines stability [99, 122-125]. In COSGROVE [126],
BREZESINSKI and MÖGEL [127] and DÖRFLER [112] there is an enthalpic and an entropic reason
presented for describing stabilization. An expression based on the Gibbs free energy of
overlapping polymer layers GM is given in equation (16), using the entropic and enthalpic
FLORY-HUGGINS pa a ete s S a d H, respectively [112, 128]. Steric stabilization occurs for
GM > 0.
Vsolvent
S dNV
VG A2
2
SHM 2
kT (16)
The molar segment volumes of polymer and the solvent are expressed by 2SV and 2
solventV ,
respectively. Furthermore, the density of the polymer surface layers is given by the integral
in eq. (16). The following three cases are distinguished for stabilization:
- pure entropic stabilization for ( S < 0)^( H < 0 ^ H/ S < 1),
- pure enthalpic stabilization for S > ^ H > ^ H/ S > 1) and
- combined entropic / e thalpi sta ilizatio fo S < 0)^ H > 0).
The first theoretical explanation for steric stabilization with small end-grafted molecules,
taki g i to a ou t the le gth of elati el sho t od-like ole ules is gi e MACKOR in
the 1950s [129, 130] and discussed by OVERBEEK in an introductory paper of 1966 [131].
MACKOR showed theoretically how carbon black nanoparticles are stabilized in hydrocarbons
by 2 nm long molecules, yet not by 1 nm long molecules [129].
For the analytical expression of interaction energies two structural regimes of the end-
grafted adsorbed molecules are generally distinguished, the brush and the mushroom type,
as depicted in figure 21 [118].
figure 21: Geometries of two types of end-grafted molecule covered interacting particle surfaces at distance D
(left) brush type as expressed in eq. (17), (right) mushroom type in eq. (18)
31
Most recently published by BUTT in [118] is a good summary of analytic expressions for the
interaction energies due to steric repulsion of polymer covered surfaces and the brush and
mushroom regime, referred to as the ALEXANDER-DE-GENNES [119, 132, 133] and DOLAN-
EDWARDS models [123], respectively.
The following eqs. (17) and (18) define the repulsive interaction energy W between two
spherical particles as a function of the geometrical parameters in figure 21, particle distance
D, brush length and radius of gyration of the mushroom RG. Please note that the DERJAGUIN
approximation [134] has been applied, yet the particle radius does not appear in the
relations.
122
52
71
35
8)(
4
7
4
5
3brush D
DsTkDW (17)
GR
D
eTks
DW
2mushroom
136 (18)
The distance between two adsorbed molecules s is related to the surface coverage in a
monolayer as given in eq. (15), with [ ] = molecules/m2.
2
1
s (19)
The well-established ALEXANDER-DE-GENNES theory (AdG) [119, 132, 133], which leads to
eq. (17), offers an analytical expression for the pressure between two planar surfaces in
distance D covered by end-grafted polymers with chain length and a separation of s
between the molecules on the surface in eq. (20) [133].
4
3
4
9
3 2
2)(
D
Ds
TkDP (20)
The length of the grafting layer is a function of the maximum length of the molecules (cf.
table 2) as well as the solubility of the molecules which influences their conformation as
stated in the experimental part in paragraph 4.3. Furthermore is a function of s as well
[107, 133].
It is interesting to note that in the parenthesis there is a strong positive term which causes
the planes to push apart and a smaller negative pressure. The first term is due to
unfavorable structural ordering upon compaction of the layers (note that the theory
assumes that the brushes of the two particles do not penetrate). The negative term accounts
for the favorable elastic deformation of the prior stretched molecules.
32
In the next paragraph this model for the pressure is applied for a physical model which
describes a phenomenon occurring when agglomerated nanoparticles are phase transferred,
namely the deagglomeration upon surfactant adsorption.
4.2 Physical Model of Deagglomeration at the Interface
For the phase transfer of magnetite nanoparticles from an unstable aqueous phase to an
organic solvent with the amphiphiles oleic or ricinoleic acid it has been observed that the
agglomerates break-up and are deagglomerated, when crossing the interface [7, 20, 88, 99,
114]. This phenomenon is accounted for in this chapter. At first a gedankenexperiment is
presented of what is thought to occur at the liquid interface. Based on the hypothetical ideas
of this experiment a physical model, applying a simplified sphere geometry, is developed and
numerical results are presented.
Experimental investigations with different fatty acids and the assessment of the primary
particle concentration are presented in paragraph 4.3.
4.2.1 Gedankenexperiment
In figure 22 a set of schematics depict the basic ideas and hypothetical events occurring at
the liquid-liquid interface.
figure 22: Schematics representing the gedankenexperiment of an agglomerated nanoparticle doublet passing
the liquid-liquid interface where fatty acid molecules adsorb and push the particles apart by a
disjoining force when tails of opposing end-grafted molecules overlap [23]
At first a strong attractive force (LONDON VAN DER WAALS type) is holding the particles together,
which means they are agglomerated. Electric double layer repulsion is suppressed due to a
high electrolyte concentration. At the liquid interface the amphiphiles adsorb on the surface
of the particles as a brush type. Close to the contact of the particles the molecule tails will
start overlapping with a resulting repulsive pressure described best by the ALEXANDER-DE-
GENNES theory (AdG). Under certain circumstances, which are discussed below, the repulsive
33
forces will overcome the attractive force and deagglomeration occurs. In the next section a
geometrical model based on this gedankenexperiment is introduced leading to the
calculation of a disjoining force based on overlapping spheres and the AdG.
4.2.2 Geometrical Model
To make use of the AdG in eq. (20), for calculation of a repulsive force, the model geometry
of particles with equal radii R in contact with a layer of molecules is simplified and shown
in figure 23.
figure 23: Representation of the geometrical model of particles with radii R in contact, covered by a layer of
molecules with the thickness . The defined region of interest, which attributes to the repulsive force
is highlighted. Calculations are based on the distance of overlap of the layers D which are a function
of the angle α. At αmin D = and at αmax D = 0. [23]
As a logic approach for calculating the integral force in the region of interest in figure 23,
angles α are introduced. The integration limits αmin and αmax are defined in eq. (21).
R
a
RRRa
Ra
minmax,1minmax,
222
min
2max
sin2
42422
2
(21)
The distances amin and amax are horizontal lines reaching from the point of contact of the
hard spheres with radius R to the point where the vertical distance between the hard
spheres equals and where the circumferences of the (R + ) spheres intersect, respectively.
The distance D between the true particle surfaces (without the adsorpt layer) can be
formulated as a function of the introduced angle α in eq. (22).
2
cos12RD (22)
34
Combining the AdG in eq. (20) with eq. (22) leads to eq. (23).
4
3
4
9
3 2
2cos12
2cos12
2)(
R
Rs
TkP (23)
This is an expression for the local disjoining pressures at a given angle α. For α ≥ αmax the
pressure is zero and at the angle αmin the pressure should be the strongest.
Consequently, the repulsive disjoining force Frepulsion is calculated, integrating the local
pressure P(α) times the differential angle dependent area dA(α), which the local pressure is
acting on from αmin to αmax, in eq. (24).
max
min
)(d)(repulsion
APF (24)
Again by applying sphere geometrics, the differential angle dependent area dA(α) is assessed
with eq. (25). This area describes an infinitesimal small ring segment on the hard sphere
surface with radius R.
2cos
2cos
2cos)(22
sin2
22)(d
2
2
2
2
dαRR
dxxD
RA
d
(25)
Due to the fact that the differential dα is part of the cos-function, an analytical result of the
integral in eq. (24) is not presented.
To account for the attractive force between the particles, a simple analytical expression of
VAN DER WAALS interactions is used, which disregards contact deformations (HERTZ, JKR, DMT)
as well as repulsion from electrostatic interactions or hydration forces [110], given in
eq. (26).
20
Hder Waalsvan 12 a
RCF
(26)
For the HAMAKER constant CH a value of 2.2 E-20 J is assumed which has recently been
reported for magnetite in organic solvents [135]. The radius of the particle R is taken to be
7.5 nm in accordance to the actual size of the particles in this work (cf. A.3.3). The contact
distance a0 shall be 0.2 nm in agreement with [110]. This yields a force of
Fvan der Waals = 3.4 10-10 N.
35
The repulsive force in eq. (24) will only be successful in deagglomerating the particles, if it is
larger than the absolute attractive force in eq. (26). It is therefore convenient to introduce a
relative repulsive force x(Frepulsion) in eq. (27).
der Waalsvan
der Waalsvan repulsion
repulsion F
FFFx
(27)
If this relative force of repulsion is higher than zero, the particles will deagglomerate. On the
other hand the particles will remain agglomerated in the organic phase after
hydrophobization occurred for values below zero.
4.2.3 Numerical Results
The numerical results of the expressions in 4.2.2, presented in this paragraph, are obtained
using Matlab® version R2011b. At first, the local pressure as a function of the angle α and
the parameter layer thickness is visualized in figure 24 for s = 0.5 nm and R = 7.5 nm. This
adsorption distance of s = 0.5 nm corresponds to a surface coverage of = 4 molecules/nm2
and with this a molecule surface head area of 25 Å2, a typical value found in the literature for
fatty acids [21, 95, 96, 110]. The range of the layer thickness corresponds to the dimensions
of stretched fatty acids, as presented in table 2.
figure 24: Angle dependent pressure between the spheres with radii of 7.5 nm and the parameter layer
thi k ess i steps of 0. nm, from 0.4 nm to 2.4 nm, for a rather high degree of adsorption of
s = 0.5 nm according to eq. (23), [23]
The left and right end of each curve in figure 24 is located at αmin and αmax, respectively. The
highest pressure is occurring at αmin with an increase for larger .
In figure 25 the results of the integration of eq. (24) using eqs. (23)and (25) are presented as
a function of the adsorption distance s and the layer thickness . For comparison the
absolute value of the VAN DER WAALS attraction force is included in the graphs as a horizontal
line. The repulsive force increases with increasing and thus longer molecules and with
higher surface coverage of the molecules, i.e. smaller s. It is important to note that both
36
values are not only a function of the end-grafted molecule geometry but also on the
solubility dependent conformation, whether they are fully stretched.
figure 25: Repulsive forces according to eq. (24) (left) as a function of the adsorption distance s and with the
para eter of la er thi k ess , right as a fu tio of the la er thi k ess ith the parameter
adsorption distance s in comparison to the constant absolute VAN DER WAALS force (horizontal line, cf.
eq. (26)), [23]
The following figure 26 shows a three dimensional graph of the relative repulsion in eq. (27)
with the variables s and . In the black triangular on the bottom left, the particles are not
disjoined, for the repulsive force is lower than the attractive force.
figure 26: relative repulsive force (cf. eq. (27) as a fu tio of adsorptio dista e s a d la er thi k ess , [23]
Certainly eq. (26) is a very simple expression for the attractive force as a level, where it is
decided, whether the particles are disjoined or not. The actual attractive force could deviate
in both directions. Yet it is fascinating that the repulsive disjoining force of the introduced
model has a similar order of magnitude and could be reliable. In future research studies it
could hence be tried to evaluate such a disjoining force by a smart experimental set-up, e.g.
using colloidal probe microscopy [136].
37
In the next paragraph the phase transfer of magnetite nanoparticles from water to the
solvent dichloromethane (DCM) is investigated for the fatty acids (FA) presented in table 2.
4.3 Experimental Results
Following the experimental procedures for nanoparticle synthesis and phase transfer in the
appendices A.3 and A.4, dichloromethane based magnetite colloids have been prepared with
the fatty acids in table 2. In 4.3.1 the phase transfer results are visualized to draw first
conclusions on the colloidal stability and the success of the deagglomeration, described
above. The agglomerate size distribution of the samples is investigated in 4.3.2. Derived
from complex colloidal investigations in 5.3.2, the primary particle concentration is
presented in 4.3.3 with a discussion on the solubility of the molecules. Finally, in paragraph
4.3.4 the adsorption of ricinoleic acid is investigated with a thermal gravimetric and
spectroscopic method.
4.3.1 Visualization of the Phase Transfer
In figure 27 six test-tubes with completed phase transfers of magnetite from the lighter
water phase to the heavier immiscible DCM phase using the fatty acids in table 2, are
presented. Due to reproducibility difficulties with the precipitation process (cf. 5.3.1), only
one magnetite precipitation batch is being used for all samples of this study. The first test-
tube on the far left displays the case when no fatty acid is dissolved in DCM and the particles
will settle to and remain on the interface because of their hydrophilic surface characteristics.
Consequently the liquid-liquid interface is found in the middle part of each test tube,
pointed out on the right test tube. All fatty acids adsorb at the particle interface and lead to
hydrophobization and thus ability to enter the non-aqueous dichloromethane phase.
One can notice sediments in the solvent phase for the four tubes to the right with OA, MA
and CA. Furthermore there are stable organic solvent soles with no visible sediment
formation for the second and third tube with RA and LA as well as a stable aqueous sole for
the third tube to the right with MA and an aqueous pH of 9.0. For RA and LA this concludes
that the agglomerates of the aqueous phase are completely disjoined when the fatty acid
molecules adsorb at the interface. For the other systems it seems like the majority of the
agglomerates of the aqueous phase are preserved and settle within the organic phase. The
special feature for MA is that a stable nanoparticle dispersion is formed in the aqueous
phase at high pH of 9.0.
38
figure 27: Vials of phase transfers of magnetite nanoparticles from an aqueous phase (upper half) to a DCM
phase (below liquid interface) with the grafting molecules/surfactants as defined in the image above
the vials all at pH 9 in the aqueous phase except for myristic acid with a second phase transfer at
pH 8 [23]
Gravimetric analyses show that only a small amount of roughly 10 % of particles is not found
in the solvent phase in this case. When the pH is reduced to 8.0 using 1M HCl all particles
enter the DCM phase and no stable aqueous sole is formed. An explanation can be, that
adsorption takes place at the interface and dissociated not adsorbed molecules enter the
upper aqueous phase forming a bilayer around already hydrophobic nanoparticles and
sterically stabilizing those in the high ionic aqueous phase. The bilayer formation should also
be possible for CA since it is even smaller and should thus have a similar chance to enter the
aqueous phase entirely as it is mentioned in [99]. Yet this might be the case but due to the
short length of the molecule stabilization in water may not be possible by bilayer formation
and settling and reentering the organic solvent phase will dominate.
Due to the intense light absorbing properties of magnetite nanoparticles, cf. appendix A.8, it
is not clear whether there are agglomerated nanoparticles also for the RA and LA samples
and if those two samples have a different content of deagglomerated supernatant
nanoparticles.
4.3.2 Particle (Agglomerate) Size Distribution
In addition to the photographs and observations above the intensity weighted particle size
distribution of the agglomerates in the water phase (solid line without symbols on the right)
as well as the transferred nanoparticle assemblies (lines with symbols) using the analytical
centrifugation method are presented in figure 28. The line on the left represents a dynamic
light scattering result of the stable supernatant after centrifugation with only primary
particles present. Dynamic light scattering must not be applied for the agglomerate
containing samples for it does not lead to reliable and reproducible information, cf. A.10.
39
figure 28: Intensity weighted particle size distributions of the samples in figure 27 applying analytical
centrifugation with a cut-off size of 30 nm, additionally the primary particles distribution is
determined from the stable colloid using DLS [23]
The median size of aqueous agglomerates is 1.3 µm and in any case larger than the particles
found after phase transfer with median size values of 0.3 µm, 0.5 µm, 0.6 µm, 0.7 µm and
1.1 µm for RA, LA, OA, MA and CA, respectively. There is an unknown fraction of particles
smaller than the resolution limit (at about 30 nm) of the analytical centrifuge which stands
for primary particles. This fraction is especially noticeable for RA and LA. The size of these
particles is obtained by analyzing the particles in the supernatant after centrifugation with
DLS, resulting in a median intensity weighted size of 24 nm.
The quantitative assessment of the weight fraction of these primary particles wPrimary is
presented in the next paragraph, when assessing the colloidal stability in mixtures with
PMMA, derived from results in 5.3.2.
4.3.3 Primary Particle Concentration
The primary particle mass concentration wPrimary is a quantitative measure, which can be
experimentally assessed in nanoparticle polymer mixtures, following the procedures in
appendix A.5. The colloidal stability investigations presented in 5.3.2 can, by means of
extrapolation using eq. (61), deliver the primary particle mass concentration under the
absence of a dissolved polymer.
For the phase transfer samples introduced above, the initial primary particle concentration
w0
Primary, without dissolved polymer, is presented in figure 29.
40
figure 29: Mass concentration of primary particles after phase transfer derived from colloidal interactions
studied in 5.3.2, values presented in table 8, [23]
The values correlate well with the agglomerate sizes x and primary particle fractions
Qint(xPrimary) in figure 28. What the images in figure 27 cannot visualize though, is the
significant difference of stable primary particles of the RA and LA samples. Ricinoleic acid
achieves to deagglomerate almost all of the aqueous agglomerates, whereas the other C18
acids linoleic and oleic acid with similar maximum chain length max (cf. table 2) lead to less
primary particles, thus are less efficient in deagglomerating. Following the physical model of
deagglomeration in 4.2, either the grafting density is lower for LA and OA (and with this the
distance between adsorpts s would be higher), or the actual layer thickness is lower, due
to reduced solubility and thus less stretching, cf. 5.1.2. Obviously both could be the case as
well. MA and CA are smaller molecules and thus lower disjoining forces, due to smaller are
expected in any case.
In order to judge on the solubility of the end-grafted fatty acids on the magnetite
nanoparticles (FA-Fe3O4) in the solvent dichloromethane (DCM), the HANSEN solubility
distance DFA-Fe3O4-DCM given by eq. (28) is evaluated [70, 137-139].
2DCMh,OFe-FAh,
2DCMp,OFe-FAp,
2DCMd,OFe-FAd,
DCMOFe-FA
43
43
43
43
4
D (28)
For this the HANSEN solubility parameters (HSP) relating to disperse, polar and hydrogen
bonding interactions d, p and h, respectively, are determined by calculations presented in
A.11. Since the carboxyl group is chemically attached to the magnetite surface it is more
convenient to calculate the HSPs without this group (FA-Fe3O4). Disperse parameters d are
similar for the components presented and therefore not depicted in the p - h solubility plot
in figure 30.
41
figure 30: Solubility plot of the fatty acids pristine (FA - filled blue symbols) and grafted to the magnetite surface
(FA-Fe3O4 - open blue symbols) calculated using the group contribution method in A.11, compared to
the solvent DCM (red circle), values from [140]
On the solubility plot, a better solubility is represented by a shorter distance between the
fatty acid species and the solvent DCM. A better solubility results in the fatty acids to be
more stretched towards the solvent, because fatty acid – solvent interactions are more
profitable from a thermodynamic perspective. This is the case especially for ricinoleic acid,
which is due to the OH-group and the double bond. Calculated solubility distances are listed
in table 3, together with the median size x50,int and primary particle fraction Qint(40 nm) from
the size distribution in figure 28 and the initial primary particle mass fraction w0
Primary in
figure 29. Furthermore, using eq. (52), the FLORY interaction parameter is calculated and
listed in table 3, as well. More details on this parameter are given in 5.1.2. Generally, it is to
note here, that the smaller this value, the better the solubility, thus the more favorable the
fatty acid – solvent interaction. A value of χ = 0.5 is found for equal interactions of fatty acid
– fatty acid and fatty acid – solvent.
table 3: median particle size x50,int, fraction of primary particles from the intensity weighted distribution Qint,
primary particle concentration w0
Primary, calculated solubility distance between the fatty acid coated magnetite
(FA-Fe3O4) and the solvent (DCM) DFA-Fe3O4 – DCM using eq. (28) and A.11 as well as the calculated FLORY-HUGGINS
parameter χ using eq. (52)
fatty acid x50,int
in µm
Qint(40 nm)
in %
w0
Primary
in %
DFA-Fe3O4 – DCM
in MPa-1/2
χ
in -
RA 0.3 30.8 96.3 ± 2.3 2.98 0.03
LA 0.5 7.1 35.1 ± 4.4 7.74 0.23
OA 0.6 1.9 8.0 ± 1.8 8.16 0.29
MA 0.7 1.6 2.1 ± 0.7 10.08 0.39
CA 1.1 1.3 2.8 ± 0.7 10.16 0.40
42
All end-grafted fatty acids have a FLORY interaction parameter smaller than 0.5. However, the
best solubility is found for magnetite with grafted ricinoleic acid. The correlation of initial
primary particle concentration w0
Primary and HANSEN solubility distances DFA-Fe3O4 – DCM is
presented in figure 31.
figure 31: Correlation of primary particle concentration given in figure 29 and the solubility distance between
the fatty acid capped magnetite and DCM calculated using eq. (90) and values found in A.11
As mentioned above, the solubility determines the actual layer thickness , used for the
model in 4.2. For same max, as is the case for RA, LA and OA a lower solubility would reduce
stretching behavior and thus the actual layer thickness . The impact of grafting density and
therefore adsorpt distance s would in the future need to be evaluated by adsorption
isotherms of the different fatty acids, especially for the C18 specimens RA, LA and OA, which
has not been done for the studies presented here.
4.3.4 Inert Decomposition of Chemisorbed Ricinoleic Acid on
Magnetite Nanoparticles
In this section, the previously published investigation on the thermal degradation of
ricinoleic acid capped magnetite nanoparticles using ATR-FTIR, FTIR coupled TGA and XRD is
reviewed [21]. The study is part of this thesis’ research program, evaluating the composition
of composites of fatty acid capped magnetite nanoparticles with polymers using thermo
gravimetry. It is found that chemically grafted ricinoleic acid causes the reduction of
magnetite at temperatures between 600 °C and 900 °C in inert atmosphere and the best
measure for the magnetite content is the residual mass at 600 °C, as opposed to higher
temperatures, which is often found in the literature.
Starting Point Hypotheses from the Literature
In several references, fatty acid capped magnetite nanoparticles are analyzed with TGA and
different conclusions are drawn on residual masses at temperatures of 600 °C or 900 °C after
heating in inert atmosphere [39, 85, 95-97, 105, 115, 141-148]. In most cases the different
43
mass losses, usually three, are attributed to physic- and chemisorbed bilayers of the fatty
acid [95, 105, 142-148]. The residual mass is in the majority of the investigations attributed
to magnetite. However there are investigations that prove that there is a very dominant
reduction of magnetite in inert atmosphere and vacuum for temperatures higher than about
600 °C depending on the size of the magnetite crystals and with this on the surface area of
the nanoparticles [97, 115]. Along with the reduction of magnetite a severe evolution of CO2
is observed. The reduction must therefore be achieved by residual carbonaceous
compounds at this temperature which were reported by ROONASI and HOLMGREN [96] when
looking at the IR spectrum of residues derived after heating to 550 °C. This concludes that
neither the residual mass at 600 °C nor at 900 °C will represent the magnetite concentration.
Furthermore it is evidence that the decomposition mechanism proposed by ZHANG et al. [95]
which is very often cited is not consistent.
Molecular Vibration Spectroscopy of RA, Fe3O4 and RA-Fe3O4
Before looking at the thermal degradation of ricinoleic acid capped magnetite (RA-Fe3O4),
ATR-FTIR is applied to investigate the type of binding of RA to magnetite, cf. 4.1.2. For this,
the IR spectra of pristine ricinoleic acid, pristine magnetite and ricinoleic acid capped
magnetite samples are presented in figure 32 for the wavenumber range of 1900 cm-1 to
1000 cm-1.
figure 32: ATR-FTIR results of pristine ricinoleic acid, pristine magnetite as well as ricinoleic acid adsorbed on
magnetite [21]
For pristine magnetite (low absorption of IR) there are weak bands at 1630 cm-1 and roughly
1120 cm-1, which can be attributed to surface bound H2O, because the sample is measured
in ambient environment and can be compared to the findings of CAI et al. [149]. The distinct
band at 1700 cm-1 for ricinoleic acid is due to the vibration of the carbonyl C=O as part of the
carboxyl-group. This band is significantly reduced for the RA-Fe3O4 sample. The new bands
for ricinoleic acid bound magnetite at about 1530 cm-1 and 1425 cm-1 reveal the chelating or
monodentate mononuclear chemical binding of the RA from its carboxylate on the
44
magnetite surface [116]. Consequently, it is affirmed that ricinoleic acid chemically adsorbs
on the magnetite surface.
TGA of Ricinoleic Acid, Pristine and Physically or Chemically Bound
Pristine ricinoleic acid (RA), ricinoleic acid coated magnetite by the phase transfer procedure
(RA-Fe3O4) with mRA/mFe3O4 = 0.2 and ricinoleic acid mixed with fumed silica Aerosil® 200
with 12 nm primary particle size (RA-SiO2) with mRA/mSiO2 = 0.2 are heated in a pure nitrogen
atmosphere from room temperature to 900 °C at 20 K/min. The differential mass losses are
presented in the left graph in figure 33. In the right hand graph, the thermogram of RA-Fe3O4
is depicted. Fumed silica is used as a material with a similar particle size like the magnetite of
this study, yet chemical adsorption of fatty acids has not been reported and it is assumed to
not occur.
figure 33: (left) first derivative of the TGA results (DTG) of pure ricinoleic acid (RA), ricinoleic acid coated
magnetite (RA-Fe3O4) and Aerosil® 200 (RA-SiO2) in inert atmosphere [21], (right) TGA of RA-Fe3O4
with the mass losses at the three distinct steps, the error bars show the 95 % quantile of three
measurements
The decomposition (evaporation) of the pure fatty acid takes place in a single step at a peak
temperature of about 350 °C. The decomposition however is slightly broader towards
smaller temperatures. Gases leaving the gas FTIR analysis at this temperature have a distinct
scent of ricinoleic acid.
For both particulate samples there are three distinct steps of decomposition, and the first
two match in temperature range. The first decomposition is located at lower temperatures
than the decomposition of pristine RA. The second step coincides with the pure fatty acid.
RA is entirely decomposed up to 400 °C, whereas a third decomposition is occurring for the
RA at SiO2 at around 460 °C and the majority of the RA-Fe3O4 degradation is happening at a
temperature of about 800 °C. The residual concentration at 600 °C for RA-SiO2 is 79% and
thus only slightly lower than the specified value of 83.3% which is most probably due to
surface bound water, since fumed silica very hygroscopic. The residual content of RA-Fe3O4
45
at 900 °C is 71.19 % and much lower than the magnetite content, concluding that some are
even all of the magnetite must be reduced.
The investigation of the FTIR of the evolving gases in the individual steps in the next
paragraph allows for a better judgment on the mechanism of decomposition.
FTIR of Evolving Decomposition Gases
The gas phase FTIR spectra of the major decomposition steps for the samples of figure 33
are presented in figure 34 in the wavenumber range of 4000 cm-1 to 1500 cm-1, including the
ATR-FTIR spectrum of pristine RA.
figure 34: FTIR of the evolving gases for the major steps of decomposition of the three samples in figure 33,
(left) RA including the ATR-FTIR spectrum at room temperature in the top graph, (middle) RA-Fe3O4
and (right) RA-SiO2 [21]
Due to the before mentioned broader decomposition towards temperatures below 350 °C
two IR spectra obtained from the decomposition gases at 300 °C and 350 °C are evaluated
for pristine RA. The common features of both gas phase spectra are the dominant symmetric
and asymmetric C-H stretching modes for sp3-hybridized carbon at 2934 cm−1 and 2865 cm−1
and for sp2 hybridized carbon (double bond) at 3016 cm-1, the C=O carbonyl stretching
bands at 1778 cm−1 and 1741 cm−1 as well as the OH gas phase stretching (from the hydroxyl
group) at 3577 cm−1. These modes clearly point towards the ricinoleic acid molecules or
fractions of these molecules in the gas phase. However at the lower temperature of 300 °C
there also appears a mode at 2705 cm−1 which indicates C-H stretching at aldehyde
functionalities. An explanation for this is the autoxidation of the fatty acid resulting in
aldehyde and alkane volatile compounds with hydroperoxides as intermediates [150, 151].
For the hydroxyl group containing unsaturated ricinoleic acid there might also be alcohols
that are formed as volatiles of the autoxidation. The typical alkane as well as alcohol bands
are already resolved in the before mentioned gas phase ricinoleic acid bands. The effect of
46
autoxidation is well known from food chemistry and leads to degradation of fatty acids, they
go ad . Most of the gas phase at °C however is already made up of ricinoleic acid.
For RA-Fe3O4 at the lower temperature step with a peak value of about 255 °C in figure 34
(middle), it is to recognize that the spectrum shows a very strong evolution of aldehydes
identified with the C-H stretching bands at 2710 cm−1 and 2813 cm−1. In discrepancy to the
spectra obtained for the ricinoleic acid presented above the carbonyl stretching shows a
singular band at 1741 cm−1 assuming that the aldehyde content is much higher. This would
conclude that autoxidation is either catalyzed by the iron compound [150] or more effective
because of the high surface area exposed to atmosphere before TGA. A possible hydroxyl
band of alcohols and the ricinoleic acid at 3577 cm−1 is superimposed by stretching bands of
gaseous water. Furthermore there is a high signal of bands related to CO2 at roughly
2300 cm−1 originating from C=O stretching vibrations (which means a relatively low content
for CO2 is a highly absorbing molecule). In the second decomposition step with a peak
temperature of about 380 °C no more bands pointing to aldehyde are found. However the
carbonyl band at 1732 cm−1 is lower than the ones shown for RA. Furthermore one cannot
clearly identify strong C-H stretching for sp2 hybridized carbon from the unsaturated fatty
acid. It has been reported in [96, 97, 152] that dehydrogenation also starts to take place in
this temperature range which obviously cannot be detected in the IR spectra. It is assumed,
that in the second step physically bound fatty acid is detached and evaporates but the
unsaturated bonds disappear in the gas phase because of reactions with the hydrogen which
results from dehydrogenation of chemically grafted acids. As a consequence of
dehydrogenation, carbonaceous residues must be left on the nanoparticle surface, which are
able to reduce magnetite at a peak temperature of about 760 °C. The gases evolved at this
reaction are COx. This is also what is found looking at the evolved gas spectrum. In the range
of 2360 cm−1 and 2309 cm−1 CO2 stretching bands and for 2183 cm−1 as well as 2108 cm−1 CO
stretching bands dominate. For the last step of degradation the following reaction pathway
is proposed in eq. (29).
FeOFeCOCOCOFe edc2bcb43a nnnnnnn (29)
In [21] the molar numbers na through ne have been determined and presented in table 4.
table 4: Factors given in equation (29) with errors representing 1.96 times standard deviation, the resulting
oxygen to iron ratio in the iron oxide after reduction is calculated to be 0.76 ± 0.09, compared to the value in
pristine magnetite which is 1.33.
na 1.89 ± 0.29 nd 1.36 ± 0.27
nb 1.05 ± 0.17 ne 4.32 ± 1.01
nc 1.15 ± 0.32
47
For RA-SiO2, in figure 34 (right), one special feature is the strong absorption bands of
desorbing water which has been bound to SiO2. For the first two decomposition steps the C-
H stretching bands between 3000 cm−1 and 2700 cm−1 are similar to the ones presented for
RA-Fe3O4. This concludes that again autoxidation of RA plays a role and must be due to the
high surface area of the fumed silica which is accessible for oxidation of the fatty acid. For
RA-Fe3O4 however, the aldehyde related stretching bands are more pronounced which must
be due to the possible catalyzing effect of iron. Contrary to the last step of decomposition
for RA-Fe3O4 a band at 3087 cm−1 appears, which is due to stretching vibrations of C-H at sp2
hybridized carbon. This points towards the missing dehydrogenation of chemically grafted
RA molecules and rather the strong physically adsorbed RA is detached from the SiO2
surface. The occurrence of CO2 and CO vibrations is only weak when compared to the last
step of RA-Fe3O4. In summary the decomposition of RA-SiO2 is following the order: first
release of volatiles of RA autoxidation at 290 °C, then decomposition and detachment of
loosely bound RA at 370 °C and finally at 460 °C desorption of tightly, yet physically bound
ricinoleic acid.
Powder X-Ray Diffraction of Pristine Fe3O4 and the TGA-residue FeOx
In this paragraph the results of the powder diffraction analysis of pristine magnetite after
the precipitation and of the iron oxide residue after TGA in inert atmosphere to 900°C and
cooling to room temperature, are presented. This is important to support the findings above
and especially for verification of the reaction mechanism in eq. (29). Both diffractograms are
depicted in figure 35.
figure 35: Powder diffractograms of pristine precipitated and fatty acid grafted magnetite RA-Fe3O4 and of the
mixed iron oxide residue FeOx after inert gas TGA with identified major diffraction angles (2Θ) of
magnetite Fe3O4, wüstite FeO, Ferrite α-Fe and Hematite Fe2O3. [21]
Before the thermal analysis, the only crystal structure identified is magnetite Fe3O4 with
broad diffraction peaks due to the small crystallite size. This size is calculated to be 14.8 nm
using the Williamson-Hall method, cf. A.3.1. As expected from the argumentations above in
the TGA residue wüstite and ferrite are identified amongst magnetite and hematite. The
48
occurrence of magnetite and hematite can have different reasons. Magnetite found could
either be unreduced specimens, a product of maghemite reduction or a product of oxidized
residue which might happen between TGA and XRD analysis. Hematite can be obtained by
the following solid state reaction in eq. (30) [97].
32OFeFeFeO3 (30)
In order to check the before discussed mass balance to quantify the reduction reaction in
eq. (29) the mineral composition in terms of mass and molar concentrations of the four
identified minerals are presented in table 5. The two major components (molar
concentration) are wüstite and ferrite, as expected. The occurrence of magnetite and
hematite has been addressed above, however it is found that the oxygen to iron ratio is 0.99
and thus higher than in the mass balances for the TGA results in table 4. The deviations
might be caused by oxygenation reactions before XRD and after TGA. If one would neglect
the occurrence of magnetite, the oxygen to iron ratio would be 0.78 and within the margin
of error of the result in table 4.
table 5: Compositional minerals analysis of the FeOx residue, which is made up of four identified mineral
structures. The mass concentrations are calculated from the diffractogram using the XRD device software and
the olar o e tratio s follo fro φ= i/Mi ⁄∑ wi/Mi), the resulting iron to oxygen ratio is 0.99
Mineral i Formula Molar weight
Mi in g/mol
Mass concentration
wi in -
Molar concentration
φi in -
Magnetite Fe3O4 231.53 0.41 0.18
Wüstite FeO 71.84 0.32 0.46
Ferrite Fe 55.85 0.15 0.28
Hematite Fe2O3 159.66 0.12 0.08
It has to be mentioned, that there is also the possibility to obtain maghemite -Fe2O3 even in
the RA coated sample before TGA, however it cannot clearly be distinguished from
magnetite using XRD analyses only. However oxidation of the precipitated magnetite is
expected not to play a significant role, for fresh samples have been investigated and
oxidation has been proven to be slow [153]. From [153] it can also be deducted, that the
mixed iron / iron oxide, formed by reduction, might be performed, resulting in a core-shell
structure with the magnetite in the center of the residual compound.
49
5 Nanoparticles and Polymers in an Organic
Solvent
In chapter 4 it was shown, that by a phase transfer process it is possible to graft the
amphiphilic fatty acids chemically on the surface of magnetite nanoparticles. This leads to
hydrophobization and steric stabilization of the particles in a water immiscible organic
solvent. The right choice of molecule (high grafting density with a small adsorpt distance s
and thick adsorption layer with the length ) furthermore achieves to physic-chemically
deagglomerate the particles into primary particles with a few nanometers in diameter.
Ricinoleic acid has proven to be the best choice when preparing a stable dichloromethane
based organosol with a primary particle concentration of more than 95 % by weight. Such a
colloid can be chosen as the source to prepare nanoparticle-polymer-composites by mixing
with a dissolved polymer and subsequent drying.
In this chapter it will be shown, that it is by far not a simple task to preserve the state of
dispersion when mixing a stable colloid with a polymer solution, as introduced as an
important step of the process chain to prepare nanoparticle-polymer composites in 3.1.3.
The resulting complex colloid underlies special types of interaction, which are being
introduced theoretically and assessed experimentally.
Classic colloid stabilization by electric double layer interactions is not of importance in such
systems, however, in 5.2 a DLVO-like treatment of the governing interactions is presented.
Beforehand it will be introduced how polymers behave in solution in 5.1.1. The solubility of
polymers and particles in solvents are defined in 5.1.2 and the phenomena occurring in
solvent based nanoparticle-polymer mixtures are pointed out in 5.1.3.
The state of dispersion is experimentally assessed in the experimental section in 5.3. At first,
the influence of the polymer is investigated for the well dispersed ricinoleic acid coated
magnetite nanoparticles with the polymers poly(methyl methacrylate) PMMA,
poly(bisphenol A carbonate) PC and poly(vinyl butyral) PVB in 5.3.1. The influence of
different fatty acids and different solvents in presented in 5.3.2 and 5.3.6, respectively.
Section 5.3.3 is addressing the mechanisms of stabilization when using PVB. Finally, a
preliminary study on the kinetics of flocculation is presented in 5.3.5.
50
5.1 Theory
5.1.1 Polymers in Solution
This section introduces the behavior of long chain molecules in solution, namely
thermoplastic homopolymers, which are not cross-linked. These macromolecules will
typically form coils, which can be described as soft colloids, with dimensions depending on
the total chain length, as presented by the molar mass of a polymer, the segment length,
and the interaction between polymer segments and the solvent molecules as well as the
polymer concentration [109, 154-157].
Thermodynamics
The dissolution of polymers in solvents is a classic thermodynamic problem of mixing, with
the Gibbs free energy of mixing Gmix given in eq. (31) [109, 111, 156, 158, 159].
mixmixmix STHG (31)
From statistical thermodynamics, as part of the FLORY-HUGGINS theory, the entropy and
enthalpy of mixing are defined in eqs. (32) and (33), respectively [109].
TkNH polymersolventmix (32)
polymerpolymersolventsolventmix lnln NNkS (33)
In these equations Nsolvent and Npolymer are the number of solvent and polymer molecules,
φsolvent and φpolymer are the volume concentrations of the solvent and the polymer and χ is
the FLORY HUGGINS interactions parameter. This is the only parameter which is component
specific, depending on the material combination of polymer and solvent.
Combining eqs. (32) and (33) with eq. (31) leads to eq. (34).
polymersolventpolymerpolymersolventsolventmix lnln NNNTkG (34)
The last term is enthalpic and the first two contribute to the entropic effect of mixing.
Multiplying eq. (34) with AVOGADRO’s u e NA and regarding φsolvent + φpolymer = 1 leads to
the partial molar free energy of mixing in eq. (35).
2polymerpolymer
solventpolymermmix
111ln
NTRG (35)
51
The osmotic pressure in a polymer solution can be defined by eq. (36) [109].
solvent
mix G (36)
After some expansion and with a series expansion eqs. (35) and (36) can be combined to
eq. (37) [158].
...2
11 2polymer2
spolymerpolymerm c
M
Nvc
MTR (37)
The second term of this virial equation of the osmotic pressure stands for the intermolecular
interactions. This term equals zero for χ = 0.5, is positive when χ = (0, 0.5) and negative for
χ > 0.5. Concluding, the following can be said about the FLORY interaction parameter.
- For χ = 0.5 the polymer-polymer interactions are equal to the polymer-solvent
interactions, which is often referred to as the Θ-state. The solvent temperature
where this relation is found is called Θ-temperature TΘ. Dissolved polymers will in
this case be deforming into random coils behaving as ideal chains, cf. figure 36.
- Typically when the temperature of a solvent is larger than the Θ-temperature, the
solubility of a polymer increases, which means that the polymer-solvent interactions
are in favor. This is the case for χ = (0, 0.5) and the solvent is then called a good
solvent. The polymer coils will eventually expand.
- The opposite is true for a bad solvent, where χ > 0.5 and T < TΘ. In this case polymer-
polymer interactions are in favor and the coil would contract, or not even dissolve in
the first place.
Below in 5.1.2 it will become obvious that the FLORY-HUGGINS parameter is related to the
solubility of a polymer in a solvent. But first it will be shown how the coil parameters can be
described using the FLORY theory.
Coil Dimensions
In figure 36 a polymer coil in solution is schematically depicted for different polymer-solvent
to polymer-polymer interactions.
52
figure 36: Polymer coils in solution for different polymer solvent interactions (left) real chain in a good solvent,
(middle) ideal chain with equal interaction of polymer segments and solvent molecules, (right) real
chain in a bad solvent. The s ols a ou t for: χ – the FLORY interaction parameter, RG – the radius
of gyration, ls – the segment length, N – the number of monomer units and TΘ – the theta
temperature
The i age i the iddle ep ese ts the so alled ideal hai , hi h is a a do oil, he e there are equal polymer-polymer and polymer-solvent interactions, the so called Θ-state.
Fu the o e the seg e ts do ot feel ea h othe along the polymer backbone [160].The
FLORY interaction parameter χ, describing the interaction, equals 0.5 in this case, cf. eq. (37).
A measure for the size of the coil is the radius of gyration RG, which for an ideal chain is given
by eq. (38) [161].
21sG 6
1sNlR (38)
Here ls is the length of a segment or sometimes also referred to as KUHN length or
persistence length. Consequently Ns is the number of segments in the polymer chain. The
entire length of a polymer, walking along its backbone, is given by the contour length Lc in
eq. (39).
00
0ssc lM
MlNlNL (39)
Usually the molar masses M and M0 of the polymer and its monomer units, respectively, are
given. Derived from this is the actual number of monomer units N, the degree of
polymerization. The length of one unit is l0 and equals 0.25 nm for vinylic monomers, such as
present in PMMA. Depending on the flexibility of the polymer, due to the monomer-
monomer interactions, the segment length is a multiplicity of the unit length. For PMMA,
which is one of the polymers used in this study ls/l0 = 8.7 [118].
Real Chains can be described with the excluded volume v, the volume around a chain
segment which is not accessible for another segment of the same chain. It is positive for
good solvents when the segments repel each other and can often be approximated with
v = ls3. For bad solvents the segments attract and the excluded volume is defined as negative.
53
As a consequence of the real segment interactions, in a good solvent the coil will be swollen.
In a bad solvent the coils shrink, in a sense the polymer segments approach each other
e ause the sol e t is too old .
The radius of gyration obviously also depends on the degree of polymerization and thus N,
as well as M [118]. For ideal chains at the Θ-state the radius of gyration increases with the
square root of the molar mass and RG ~ N1/2, cf. eq. (38). The swollen coils in a good solvent
increase less degressively with the molar mass and a good approximation is RG ~ N3/5 [109,
157, 161]. Finally the coils in bad solvents increase in size more degressively, with RG ~ N1/3
[162]. A general expression for the radius of gyration in theta and better solvents using the
FLORY-HUGGINS interaction parameter χ is given in eq. (40) [161].
5221
s21
ssG 215.611309.0 NNlR (40)
The relation normalized with the segment length ls is depicted in figure 37. It shows that the
radius of gyration RG is a multiple of the segment length ls strongly increasing with the
number of segments Ns.
figure 37: Graphical visualization of the radius of gyration RG normalized with the segment length ls, depending
on the FLORY i tera tio para eter χ a d the u er of seg e ts NS, cf. eq. (40)
The radius of gyration is directly experimentally accessible with sophisticated neutron or
electron scattering techniques only [157]. However the size of polymer coils can also be
determined by means of dynamic light scattering, cf. A.2.1. With this method however the
hydrodynamic radius RH is measured instead. A relation of hydrodynamic radius and radius
of gyration is discussed in [163], represented in eq. (41). Notably, the hydrodynamic radius is
in any case smaller than the radius of gyration.
1,G
H XR
RX (41)
54
The ratio X is smaller in good solvents compared to theta solvents, with an average value of
X = 0.77.
Overlap Concentration
The behavior of polymers in solution very much depends on the concentration of the
polymer coils. Three regimes are typically distinguished and depicted in figure 38. In the
dilute regime the individual polymer coils, when homogeneously distributed are not in
contact. This occurs right at the overlap concentration c*polymer. Polymer solutions with
concentrations higher than the overlap concentration are referred to as semi-dilute and the
individual coiled chains will interpenetrate.
figure 38: Concentration regimes of polymers in solution (left) dilute solution, (middle) solution at the overlap
concentration c*polymer and (right) semi dilute regime in accordance with [156]
The overlap concentration, with [c*polymer] = g/l, can be estimated in terms of the so-called
intrinsic viscosity [ ] in eq. (42) [157].
1*polymer c (42)
General remarks on the viscosity of polymer solutions as well as the intrinsic viscosity are
introduced in the following section.
Viscosity
The increase of the dynamic viscosity of a dilute colloidal system with increasing volume
concentration of the dispersant φ is a famous EINSTEIN relation and presented in eq. (43). The
dispersant free dynamic viscosity of the pure solvent is given by 0.
5.210 (43)
This holds also true for dilute polymer solutions, for the polymer coils are nothing but soft
colloid particles [109, 158]. For polymer solutions, typically a specific viscosity sp is defined
with the relation in eq. (44).
55
polymer
3A
0
0sp
345.25.2 c
M
RN G (44)
To characterize a polymer solution, the specific viscosity is determined as a function of the
polymer concentration cpolymer. With this it is possible to assess the intrinsic viscosity [ ],
which is a function of the molar mass, as given by the MARK-HOUWINK-SAKURADA relation in
eq. (45) [156].
MK
cc M
polymer
sp
0polymer
l im (45)
Here the factor KM is a polymer-solvent pair specific parameter, which is e.g. listed in [154].
The MARK-HOUWINK-SAKURADA exponent α is a function of χ and equals 0.5 for theta solvents
or 0.7 – 0.8 for good solvents. For rigid polymers it is larger than unity [156]. Practically the
intrinsic viscosity can be determined as the y-intercept plotting sp/cpolymer against cpolymer.
Combining eqs. (44) and (45) leads to an expression to estimate the radius of gyration with
the intrinsic viscosity in eq. (46).
3
AG 10
3
N
MR
(46)
5.1.2 Solubility
In the previous paragraph the FLORY-HUGGINS theory has been introduced as a way to
describe the solubility of polymers in solvents. In general, the solubility describes how a
certain species (a number of atoms, molecules or even particles) tend to mix with another
species. This is typically achieved if the two species are similar, or in othe o ds alike , as i the fa ous sa i g like dissol es like . The ua tifi atio of the si ila it is ealized at first by the HILDEBRANDT approach [137, 154], introducing the HILDEBRANDT solubility
parameters t, a material constant which is the square root of the volume specific energy of
cohesion, as shown in eq. (47).
21
m
cohesivet
V
E (47)
This parameters, with [ t] = MPa1/2, is material specific and a function of the temperature.
The HILDEBRANDT approach now claims, that the likelihood of mixing of two substances is
higher, if the difference of their solubility parameters is small. The enthalpy of mixing in
terms of solubility parameters is then given by eq. (48) [137].
56
2polymert,t,solventpolymersolventmmix VH (48)
Even though widely used for polymer solubility in solvents, the HILDEBRANDT parameters
prediction of solubility are limited, for the main problem is that the cohesive energies and
thus the parameters are a sum of different types of interactions, namely dispersive Ed, polar
Ep and hydrogen bonding Eh. By regarding these contributions HANSEN introduces the so-
called HANSEN solubility parameters (HSP) d, p and h [137, 138, 164].
2222
hpdcohesive
hpdt
V
E
V
E
V
E
V
E
(49)
The improvement of the HANSEN approach is, that not only solubility of solubles but also
dispersibility of insolubles can be predicted [137]. Two substances are alike (e.g. a solvent
dissolves a polymer, a particle mixes well in a polymer matrix) if the distance of their
solubility D12 is small, with eq. (50).
2h,2h,12
p,2p,12
d,2d,112 4 D (50)
In A.11 the HSP of surfactants on the surface of particles are calculated using the group
contribution method. HSPs of solvents are well known and listed e.g. in [140]
Combining eqs. (32) and (48) it becomes obvious, that the HILDEBRANDT and the FLORY-HUGGINS
parameters are related which can be seen in eq. (51) [109].
2polymert,t,solventm
m,solvent
T
R
v (51)
With β typically taken as 0.35 [109]. In [139] a similar relation is given for the HSP, as shown
in eq. (52).
4T
212
m
solventm, D
R
v (52)
The e pi i al o e tio fa to α is determined to be approximately 0.6 [139]. The molar
volume of the solvent vm,solvent can be calculated dividing the molar mass by the specific
weight Msolvent/ solvent, which results in 63.86 cm3/mol for dichloromethane, used in this
study.
57
5.1.3 Phenomena in Nanoparticle Polymer Mixtures
The colloidal stability of dispersions of nanoparticles will dramatically be affected by addition
of dissolved polymers [124, 125, 161, 165-175]. Four general effects have to be distinguished
as shown in figure 39.
figure 39: Principle types of colloidal regimes between neutral nanoparticles and neutral polymers in an organic
solvent (a) depletion flocculation, (b) depletion stabilization, (c) bridging flocculation, (d) steric
stabilization, cf. [118, 126, 127, 134, 155, 160]
The polymer-nanoparticle interactions can lead to polymer adsorption or the polymer can be
non-adsorbing [155]. For both cases the state of dispersion can be improved by stabilization
or will decrease due to destabilizing effects. Non-adsorbing polymers typically destabilize the
nanoparticle dispersion by depletion [134, 160, 161, 165, 166, 176-178], which will be
emphasized in the following paragraph 5.2, schematized in figure 39a. Typically at low
concentrations of non-adsorbing polymers, depletion stabilization can occur as well [155,
170], shown in figure 39b. The polymers may also be able to adsorb on the nanoparticle
surface. In this case again stabilization as well as destabilization is possible. Stabilization can
be explained by introduction of steric repulsive interactions due to the adsorbed polymer, cf.
4.1.3 and figure 39d. This is the case if a polymer molecule only adsorbs on the surface of a
single particle. Bridging flocculation is a result of multi-particle adsorption of the polymer
molecules, due to many anchoring points in the polymer backbone [111, 118], depicted in
figure 39c.
The destabilizing depletion effects can furthermore take place when the polymer
concentration of adsorbing polymers (situation in figure 39d) is much higher than the
amount necessary for adsorption [125, 179]. In case the solvency changes, the effect of
58
stabilization or destabilization can also change within the four options presented [118, 180,
181]
Phase Separation due to Depletion
If a colloidal system is destabilized through depletion, case (a) in figure 39, not all particles
need to be destabilized but a phase of stable primary particles and a phase of flocculated
particles can co-exist [134, 161, 168, 169, 180, 182, 183]. This can occur certainly as well,
while polymerization takes place [85, 184]. In general, polymer blends exhibit phase
separation as well [157, 185]. The more polydisperse the polymer is, the higher the amount
of flocculated particles becomes, as discussed in [186] showing that the depletion effect is
increased with polydispersity. The structure of agglomerates as a result of depletion
flocculation is presented in [15, 187-190]. A quantitative description of the depletion
interaction is presented in the next paragraph in eq. (56) and figure 40.
5.2 Pair Interactions – DLVO-like Consideration
The well established DLVO theory, namely the addition of VAN DER WAALS and electrostatic
double-layer interactions, enjoys great popularity when it comes to explaining and predicting
colloidal stability in aqueous dispersions [110]. However, in non-aqueous dispersions with
low dielectric constant , where double-layer interactions are negligible, classic DLVO does
not represent the governing forces. Nevertheless, a DLVO-like addition of governing
interaction mechanisms will reveal minima and maxima in the interaction potentials, which
can be used to explain colloidal stability. This is based on the assumption, that the
interactions are independent from each other and additive.
For the experimental system in 5.3 with non-adsorbing polymers such as PMMA in a
dispersion of sterically stabilized magnetite nanoparticles, one has to consider the attractive
pair potentials of LONDON type VAN DER WAALS [191] and due to osmotic depletion of polymer
coils in the vicinity of the functionalized nanoparticle surface described by ASAKURA and
OOSAWA [134, 165]. The opposing repulsive interactions are due to steric-osmotic pressures
in either brush-type [110, 133] or mushroom-type [123] adsorbed polymer layers, cf. 4.1.3,
as well as the BORN mechanism of overlapping electron shells of surface atoms [192].
The following analytical definitions for the separate interactions are all multiples of the
energy kT. In figure 40 a schematic drawing of the mixed system of sterically stabilized
nanoparticles in a solution of polymer coils is presented.
59
figure 40: Scheme of two interacting particles of diameter 15 nm surrounded by grafted molecules with a length
of 2.0 nm in a solution of polymer coils with a diameter of 7.5 nm which do not adsorb at the particle
surface resulting in a depletion layer surrounding the particles (dashed circles) [23]
The dashed circle around the particles represents the depletion layer and is approximately
the size of the functionalized particle plus twice the polymer coil size. When these layers
overlap, the particles will be pushed closer to each other. The driving force for this attractive
interaction is the osmotic pressure in the zone of overlap, where the polymer coil
concentration is lower than in the particle surrounding volume, which is due to the depletion
of the coils.
The LONDON type VAN DER WAALS interaction WvdW between two spheres with radii R at a
contact distance D is presented in eq. (53), depending on the HAMAKER constant CH of the
particles in the solvent [191].
2
2
22H
vdW
2
4ln
2
2
4
2
6)(
R
D
R
D
R
D
R
D
R
D
R
DTk
CDW (53)
The radius of the nanoparticles investigated in this work is about 7.5 nm and CH is taken to
e . ∙ -20 J, as recently published by FAURE et al. [135].
An approximation for BORN repulsion is given in [192] for a sphere-plate interaction and
presented in eq. (54) with the parameter σ = 0.5.
77
6
Born
6
2
8
7560)(
D
DR
DR
DR
Tk
CDW H
(54)
The interaction of steric repulsion between brush polymer covered surfaces with the layer
thickness and adsorption distance s is defined in eq. (55) [110].
60
D
es
DW
2brush-steric 100)( (55)
This relation is valid for D/(2 ) = [0.2, 0.9] [110] and a simplified version of eq. (17). A good
approximation for the layer thickness is the length of the molecules, which are presented in
chapter 4 for the fatty acids used in this study. A commonly found literature value for s is
about 0.6 nm, which is equal to an adsorption area per molecule of 36 Å2.
The pair attraction potential between two particles in a polymer solution is presented with
eq. (56) [134]. This simple expression assumes the depletion layer thickness to be equal to
the radius of gyration of the polymer coil.
R
Dq
R
DqqDW
42
3
2)(
2
3depletion (56)
In this case the nanoparticle radius or hard sphere size is the nanoparticle crystal plus its
surface molecule layer. The variable q stands for the ratio of the radius of gyration of the
polymer coils RG and the nanoparticle radius (R + ). The dimensionless concentration φ,
given in eq. (57), is the relative volume concentration of the polymer coils in a nanoparticle
free system and can be defined with the polymer concentration cpolymer and the critical
concentration c*, where the coils start overlapping, defined in eq. (42).
*Polymer
c
c (57)
The critical concentration of overlap in return is roughly the inverse of the intrinsic viscosity
[ ], cf. eq. (42), which can be obtained with low concentration rheological experiments
[193]. As can be seen from the determination of the viscosity in A.9, the concentration for
overlap for the PMMA used in this study, dissolved in DCM, is 32.2 g/l.
In figure 41 all four interactions are presented together with the additive total interaction
Wtotal, combining eqs. (53) - (56) to eq. (58). The variables are defined in the figure caption
and base on magnitudes to be faced with in the present study.
)()()()()( depletionbrush-stericBornder Waalsvan total DWDWDWDWDW (58)
61
figure 41: DLVO-like addition of colloidal interactions with regard to VAN DER WAALS and depletion attraction (by
ASAKURA and OOSAWA AO) as well as BORN and steric-osmotic repulsion with R = 7.5 nm, RG = 4.0 nm,
s = 0.6 nm, = 1.5 and φ = 1.0, [23]
Furthermore in figure 42 the impact of grafting layer thickness (left diagram) as well as
elati e pol e o e t atio φ ight diag a o the total i te a tio are visualized.
figure 42: Distance dependent total interaction, following eq. (58), (left) for varying [23] (right) for varying φ
and constant parameters mentioned in figure 41.
Contrary to classic DLVO interactions, in the left graph in figure 42 there is no repulsive
interaction maximum which manifests stability. However, the steric osmotic repulsion leads
to a steep increase of interaction with the point of zero interaction increasing with the layer
thickness. An important feature of the colloidal behavior in this DLVO-like total interaction, is
the magnitude of the always present interaction minimum located at roughly twice the layer
thickness . With an increasing polymer concentration the absloute interaction minimum
increases almost linearly, remaining at roughly the same interactions distance.
Generally, the tendency to agglomerate, upon particle impact, will increase with a higher
absolute attraction minimum |Wtotal,min|. Furthermore, the resulting agglomerate structure
will be more compact, the closer this attraction is to the surface of the particles Dtotal,min. The
following table 6 summarizes the effects of the influencing parameters in eqs. (53)-(56) on
|Wtotal,min| and Dtotal,min and with this on the expected colloidal stability.
62
table 6: Impact of increasing parameters (the other parameters shall be constant) of the total DLVO-like
interaction on the colloidal stability as a consequence of a changing absolute attraction energy |Wtotal,min| and
distance Dtotal,min ↑↑↑/↓↓↓ progressi el , ↑↑/↓↓ li earl , ↑/↓ degressi el i reasi g/de reasi g , starting values for the parameters as good approximates [23]
parameter
increasing
(constant value)
unit |Wtotal,min| Dtotal,min colloidal
stability
R (7.5) nm ↑↑↑ ↓↓ ↓
CH ( . ∙ -20) J ↑ ↓↓ ↓
(1.0) nm ↓↓↓ ↑↑ ↑
s (0.5) nm ↑ ↓↓ ↓
RG (4.0) nm ↓ ↑ ↑
φ (0.5) - ↑↑ ↓ ↓
Consequently the most effective way to increase stability is realized using longer end-grafted
brush type molecules and thus thicker layers . This can also be achieved by improving the
solubility (solvent molecule interactions) so that the end-grafted layers stretch further,
which has also been discussed in 4.3.3. Besides, solvent polymer interactions are an
important factor influencing the polymer coil size, which increases with increasing solubility,
cf. [181] and eq. (40). However, this will also effect the polymer volume concentration,
which for a fixed cPolymer will increase due to an increase of the intrinsic viscosity [ ].
Furthermore, the solubility of adsorbed surfactants is interconnected with the maximum
length of the surfactant max and thus the layer thickness . Another important factor related
to the solubility of the polymers as well as of the tails of the end-grafted fatty acid molecules
is the temperature. Typically the higher the temperature, the better the solubility. Yet, on
the other hand, higher temperatures increase particle impact probability and strength of
impact, which could reduce the colloidal stability.
5.3 Experimental Results
This section is presenting experimental results assessing the colloidal stability of mixtures of
stable magnetite nanoparticles from the phase transfer in chapter 4 interacting with
dissolved polymers. The main parameter, which is focused on below, is the primary particle
concentration wPrimary, which is a quantitative measure and determined following the
procedures described in the appendix A.5. At first the influence on colloidal stability using
different polymers with technical quality is discussed in 5.3.1. Using different fatty acid
surfactants, corresponding to the study in 4.3, the impact on colloidal stability is investigated
in 5.3.2. Paragraph 5.3.3 presents an in-depth investigation on the mechanism of
63
stabilization using the polymer PVB. The following paragraph 5.3.4 addresses the question,
how the mechanical mixing procedure influences the destabilization using PMMA or PC.
Finally, 5.3.5 and 5.3.6 discuss preliminary studies on the coagulation kinetics for
destabilizing polymers, and the influence of different solvents on colloidal stability,
respectively.
5.3.1 Influence of the Polymer
In this section and the included subsections, the fatty acid surfactant used for magnetite
nanoparticle transfer to DCM and functionalization, is ricinoleic acid (RA). The reason for this
choice is, that in 4.3.3 it was shown, that RA-Fe3O4 has the highest initial primary particle
concentration and thus most stable system.
Poly(methyl methacrylate) and Poly(bisphenol A carbonate)
In figure 43 the impact of different PMMA concentrations on the spectroscopically and
gravimetrically determined primary particle concentration E600 nm and wPrimary are displayed,
respectively.
figure 43: Light extinction at 600 nm of diluted samples E600 nm and gravimetrically determined primary particle
concentration wPrimary as a function of the PMMA concentration at constant nanoparticle
concentrations; the inset is a photograph displaying the samples (b) only holding the primary
particles after centrifugation with increasing polymer concentration from left to right
Both lines decrease with increasing polymer concentration. This means that the PMMA coils
are destabilizing the nanoparticle dispersion and lead to coagulation of a fraction of the
nanoparticles, presumably by depletion. Yet the residual primary particle phase remains long
term stable. All (b) samples of this thesis (see explanation in A.5) have been stored for over
two years without any sediment or turbidity recognizable. As presented in A.1.3 and A.9 the
PMMA coil size is about half the size of the ricinoleic acid grafted nanoparticles, so the
parameter q in this system is smaller than one. At the highest polymer concentration of
52 g/l there are only about 35 % of primary particles in the mixture (lighter curve in figure
64
43), whereas without polymer addition the initial primary particle concentration is
approximately 90 %, approving the results in 4.3.3. Since both curves show a similar
decreasing trend, the expected linearity of E600 nm and wPrimary, as stated in appendix A.5.3 is
satisfied, which will be discussed below in connection with figure 47.
The stability results for PC are plotted in figure 44 with the same scaling like figure 43.
Similar to PMMA the primary particle concentration is decreasing with increasing polymer
concentration, yet this destabilizing effect is stronger. At a concentration of 52 g/l only about
10 % of the particles are stable primary particles and the (b) sample is much more diluted
compared to PMMA. The PC coil size, however, is similar to PMMA, cf. A.1.3 and A.9.
Therefore, if depletion is claimed as the mechanism of destabilization, which is assumed and
introduced in 5.2, the parameter q would not explain this difference. This concludes that the
elati e pol e o e t atio φ ust e the dete i i g pa a ete , for the nanoparticle
concentration is constant.
figure 44: Light extinction at 600 nm of diluted samples E600 nm and gravimetrically determined primary particle
concentration wPrimary as a function of the PC concentration at constant nanoparticle concentration;
the inset is a photograph displaying the samples (b) only holding the primary particles after
centrifugation with increasing polymer concentration from left to right
There are two reasonable possibilities to normalize the abscissae in figure 43 and figure 44
for a better comparison of differences in the destabilization for PMMA and PC. On the one
hand the rough estimations of overlap concentrations for both PMMA and PC in table 27
(see appendix A.9.1), with c*PMMA = 32.2 g/l and c*
PC = 52.6 g/l, can be used to calculate the
relative polymer concentration, yet the uncertainty of the overlap concentrations obtained
is very high. Nevertheless, this graph is presented in figure 45 (left). The second option is, to
compare PMMA and PC, by plotting the coil concentration defined as cpolymer/Mn, with
[cpolymer/Mn] = mmol/l. The corresponding graph is show in figure 45 (right). This strategy is
similar to the comparison based on the relative polymer concentration, since the coil
concentration defines the relative polymer concentration for polymers with the same coil
size.
65
figure 45: Representation of the data in figure 43 and figure 44 as a function of: (left) the relative polymer
concentration φ using the overlap concentrations determined with the intrinsic viscosity in A.9.1 and
(right) the number concentration of polymer coils cpolymer/Mn.
The obtained results for normalization are contradictory at first. By taking into account the
confidence range of the data points it is possible that at least the normalization towards the
coil concentration on the right leads to comparable results. Still remaining differences can be
due to different polydispersities of the polymers, which are reported to be PIPMMA = 2.14 and
PIPC = 2.56, cf. A.1.3. Another differing factor can be, that indeed the coil size for PMMA is
slightly larger compared to PC, which would increase the depletion effect at the same coil
number concentration, explaining the slight shift of the curves to the left for figure 45 (right).
The relative polymer concentration plot on the left in figure 45 is non-conclusive for the
mean values. At a similar coil size and a lower molecular weight for PC the overlap
concentration should be smaller for PC compared to PMMA. However, the experimentally
obtained overlap concentrations are not significantly different by statistics.
Regarding composite preparation, the six measurement points for the wPrimary curves above
(figure 43 thorugh figure 45)correspond to the filler concentrations F = (0.8, 0.7, 0.6, 0.5, 0.4,
0.3) from left to right for csolid = 0.05, cf. eq. (11). Consequently, when aiming at synthesizing
composites with a higher filler concentration F, the primary particle concentration wPrimary is
increasing, this is a positive effect for highly filled composites. However, when withdrawing
the solvent in the drying step of composite synthesis, the polymer concentration increases,
due to an increasing csolid. This would lead to depletion flocculation again, depending on the
time scale of drying and flocculation. In case drying is quicker than agglomeration, then the
state of dispersion and thus wPrimary should be preserved.
Poly(vinyl butyral)
The third investigated polymer is PVB. Its corresponding colloidal stability results are
presented in figure 45.
66
figure 46: Light extinction at 600 nm of diluted samples E600 nm and gravimetrically determined primary particle
concentration wPrimary as a function of the PVB concentration at constant nanoparticle concentration;
the inset is a photograph displaying the samples (b) only holding the primary particles after
centrifugation with increasing polymer concentration from left to right
Contrary to PMMA and PC, destabilization is not encountered but stabilization seems to
occur since there is an increase in the fraction of particles in the supernatant. Furthermore
the increasing trend between the two curves differs, so the E600 nm to wPrimary relation for the
PVB stabilization must be different compared to PMMA and PC. The extinction of the diluted
(b) samples increases strongly, which is supported by the visual impression of the
photograph in figure 46.
Only judging on this plot, it is not quite sure whether agglomerates of the pristine dispersion
are deagglomerated physic-chemically by adsorption of the PVB on the particle surface,
similar to the mechanism introduced in 4.2 or whether these agglomerates are captured in
larger PVB structures prevented from settling by a similar specific weight as compared to the
carrying solvent. The backbone of the PVB used holds about 25% of vinyl-alcohol monomer
units and naturally a few vinyl-acetate groups, cf. A.1.3. These groups may lead to strong
attractive hydrogen bond interactions with the nanoparticles either directly with the plain
magnetite surface [194, 195] or with the hydroxyl groups of adsorbed ricinoleic acid both
resulting in additional stabilization by steric hindrance, cf. 4.1.3. The following sections 5.3.2
and 5.3.3 will shed more light on the possible mechanism of stabilization by PVB resulting in
a hypothetical binding model in figure 59.
Next it is to be discussed which information can be drawn from the relation between the
two ways to obtain the primary particle concentration, the UV/VIS extinction determination
and the TGA study.
Extinction vs. Gravimetric Analysis
In figure 47 the 600 nm extinction values of all three polymer samples are plotted against
the mass primary particle concentration wPrimary, which relates to the volume concentration
67
of nanoparticles in the diluted dispersion of the (b) samples φnanoparticles, as expressed by
eq. (93) in A.5.3.
figure 47: Correlation of the photometric extinction E600 nm and the primary particle concentration wPrimary as well
as the total particle volume concentration for the extinction measurement φ, two different slopes for
the destabilization with PMMA and PC and the stabilization with PVB, w0
Primary = 91.7% located at the
intersection of the linear models (dashed line) [20]
A good linear relation of light extinction over particle concentration is shown for both
destabilizing polymers (PMMA and PC) with deviations caused by erroneous measurement
results from the TGA investigations. This finding suggests a constant extinction cross section
Cext (cf. A.8) and thus equal particle scattering sizes with no influence of scattering by the
polymer. It is argued, that this furthermore shows, that there is no formation of larger
structures of particles interconnected by strong adsorbing polymers. As a conclusion
depletion flocculation must be the dominant factor of destabilization.
The expected linear relations obtained from the destabilizing polymers are given in eqs. (59)
and (60).
Primary600nm 0084.06830.0 wE (59)
lesnanopartic600nm 7.893.7291 E (60)
The factor 7291.3 in eq. (60) results in a scattering cross-section Cext of 2.97·10-18 m-2, using
eq. (92) with x = 15 nm and d = 1 cm. This is about 10-times smaller than the value calculated
in A.8, possibly due to the unknown influence of the grafted fatty acid layer and limited
reliable optical data for magnetite nanoparticles.
If the stabilization by PVB was only due to deaggregation of agglomerates without
adsorption of the PVB, the same linearity compared to PMMA and PC would be assumed, i.e.
the lighter line in figure 47 should simply extend the darker one. This is obviously not the
case but instead a very steep increase of extinction over particle concentration is observed
68
and both lines intersect at the primary particle concentration of the pristine sample without
polymers in solution which is calculated to be 91.7%. As a deduction from the steep slope of
the PVB s ste ’s e ti tio values, the scattering cross-section deviates from the constant
value of the destabilizing situation which can be attributed to structure formation
influencing light scattering behavior. Dynamic light scattering experiments would be
expected to support this assumption because of changing hydrodynamic radii, which is
discussed in 5.3.3.
Varying Phase Transfer Batches
All the investigations above have been realized with one magnetite precipitation batch,
following the procedure in A.3. This batch again was used for one phase transfer batch, with
the sample name PT100305 (phase transfer on march 5th 2010), following the procedure in
A.4. The reason for sticking to one batch for investigations of the colloidal stability and its
influences above is that the subjective quality of magnetite precipitation batches is not
reproducible. Some of the subjective markers are:
- the speed of sedimentation of magnetite (should be rapid but can be very slowly),
- the height of sediment (should be compact but can be very loose),
- the color of the supernatant (should be clear but can be slightly bluish) and
- the suspension pH (should be in the range 8.9 … 9.1).
The reasons for this problem of reproducibility are not clarified and have not been assessed
as part of the research of this thesis. However, the sources of fluctuation can probably be
limited to: a) the unstable (not quantified) quality of deionized water from the purification
device, b) the alterations in and aging of the manually fabricated high speed stirrer of the
reaction vessel as well as c) possible aging of the technical quality ricinoleic acid. Therefore,
by now the stirrer has been replaced and the purification vessel reactivated every time the
p e ipitatio got too fa off . If the precipitation behaves differently it is assumed, that the
iron salt conversion to magnetite is not complete, which the bluish appearance of the
supernatant and the settling behavior due to different particle interactions can be hints for.
Not reacted iron ions will furthermore influence the adsorption of the fatty acids [108, 196,
197] at the liquid-liquid interface and thus influence the state of dispersion in the organic
phase. When using precipitations which are too far off of the specifications mentioned
above it can occur that the phase transfer is incomplete even after a long standing time.
Even though the precipitations used in this work met the visual inspections and proper
suspension pH values and the phase transfer was only used when completed within less than
6 hours standing time, it is not impossible for the conditions of adsorption to differ, with yet
not described consequences on the state of dispersion of the particles in the organic solvent.
69
In figure 48 the colloidal stability of three different phase transfer batches (PT101104,
PT100902II and PT100305) by addition of PMMA, PC or PVB by means of extinction analysis
of diluted supernatants are compared. The different phase transfer batches originate from
different precipitation batches as well, but are prepared with the same type of RA and DCM
at DRA = 0.2 and RA = 0.02.
figure 48: Photometric primary particle investigation as a function of the polymer concentration for both
destabilizing polymers PMMA and PC as well as for the stabilizing PVB and three different phase
transfer batches: PT101104, PT100902II and PT100305
Looking at the differences between the polymers for each phase transfer batch, the results
are consistent with the findings above. Both PMMA and PC are destabilizing the colloid, with
a stronger impact by PC, when comparing on the basis of [cpolymer] = g/l. PVB stabilizes all of
the dispersions with a similar endpoint. The difference between the batches is the initial
extinction E0
600 nm without any polymer. This value is proportional to the initial primary
particle concentration and decreases in the order PT100305, PT101104 and PT100902II.
Comparing the batches for the same polymer type (same color of lines in figure 48) it can be
noticed that the curves show the same progression. Therefore the data is normalized with
the initial extinction for PMMA and PC in figure 49.
figure 49: Normalized extinction curves for the three phase transfer batches as a function of (left) PMMA as well
as (right) PC, the fitted lines follows the mathematical model in eq. (61)
70
Normalization for PVB is not satisfactory, for it is stabilizing and the state of dispersion
improves independently from the initial particle concentration, yet with a similar endpoint at
which no sediment or turbidity is noticeable after centrifugation. The normalization of the
ordinate axis enables to visualize, that all three batches have a comparable progression for
both destabilizing polymers. A mathematical model to fit the results is inferred from the PC-
curves, with a first order exponential decay function, represented by eq. (61).
Polymer
0Primary
Primary0600nm
600nm cAe
w
w
E
E (61)
The parameter A is the decaying rate. In table 7 the fitted results for the three batches
separately and combined are presented for both polymers. Additionally w0Primary is estimated
for PT101104 and PT100902II from the given initial extinction and the known ratio of E0600 nm
and w0Primary for PT100305. The high margin of error comes from the uncertainty of the
normalized data points of the extinction curve and is not a measure for the quality of the fit.
For judgment of the fit quality, the coefficient of determination is given as well, recognizing
the uncertainty of the data points.
table 7: Results for the mathematical fit parameters using eq. (61) of each batch for both polymers as well as
for the three batches combined; estimation of the initial primary particle concentrations w0
Primary of batches
PT101104 and PT100921 using the initial extinction values E0
600 nm and the given w0
Primary of batch PT100305
(see figure 47)
sample PMMA PC E
0600 nm
in -
w0
Primary
in % A in l/g R2 A in l/g R
2
PT101104 0.029
± 0.027 0.993
0.061
± 0.009 0.993
0.366
± 0.093
51.1
± 16.3
PT100902II 0.023
± 0.042 0.970
0.073
± 0.026 0.993
0.216
± 0.008
30.1
± 3.1
PT100305 0.028
± 0.021 0.956
0.047
± 0.045 0.996
0.658
± 0.043 91.7
combined 0.028
± 0.015 0.969
0.062
± 0.008 0.989 - -
For all samples R2 is sufficiently high to argue on a reasonable mathematical model. In any
case the decaying rate is higher for PC as compared to PMMA. As suspected in figure 48 the
initial primary particle concentration is the highest for PT100305. Sample PT100902II only
contains 30 % of initial primary particle content as a result of a less successful phase transfer.
The destabilization mechanism proves to be not a function of the primary particle
concentration but only on the type of polymer.
71
Even though the initial primary particle concentration is not reproducible, due to the
implications at the beginning of this subsection, the colloidal stability investigation of this
paragraph is very well reproducible for all three polymers.
Future studies will need to investigate this reproducibility problem to guarantee composite
synthesis with a reliable prediction of the primary particle content.
5.3.2 Influence of the Surfactant
In 4.3.3 the initial primary particle concentrations w0Primary for phase transfers with different
fatty acids but the same precipitation batch have been presented. Those values originate
from the study presented in this section, dedicated to the influence of the type of fatty acid
surfactants on the colloidal stability of sterically stabilized magnetite nanoparticles in
polymer solutions both destabilizing (PMMA) and stabilizing (PVB).
Above and in [20, 26, 27] it is shown, that for ricinoleic acid (RA) coated magnetite
nanoparticles in DCM, PMMA leads to flocculation and phase separation because of non-
adsorbing interactions contrary to PVB, which adsorbs and stabilizes the colloid by additional
steric repulsive interactions. Ricinoleic acid is a C18 unsaturated hydroxilized fatty acid,
double bond at C(9) and with a hydroxyl group at C(12). The task of this section is to assess,
whether PMMA and PVB behave similarly for the other fatty acids used for the phase
transfer process in 4.3, namely the C18 acids linoleic acid (LA) with two double bonds at C(9)
and C(12) and oleic acid (OA) with one double bond at C(9) as well as the saturated C14
myristic acid (MA) and C8 caprylic acid (CA).
All the colloidal stability investigations in this section are only based on the gravimetric
method of determination of the primary particle concentrations and are published in [23].
Poly(methyl methacrylate)
In figure 50 the results of colloidal stability of the DCM based magnetite dispersions with
different fatty acids and varying polymer concentration of PMMA are presented. The
ordinate axis depicting the primary particle concentration wPrimary is in logarithmic scale and
an approximately linear decrease over the PMMA concentration cPMMA can be noticed for all
samples. Deviations from this finding for small primary particle concentrations of samples
MA and CA may result from a higher margin of error for the TGA method applied.
Nevertheless, a decrease means that independent from the type of fatty acid PMMA leads to
depletion phase separation where the magnetite concentration of the phase with primary
particles decreases, as it is the case in 5.3.1 as well.
72
figure 50: Primary particle concentrations of the five investigated samples in nanoparticle polymer mixtures
with different concentrations of the polymer PMMA in DCM. The solid lines present mathematical fits
of eq. (61), published in [23]
The data is fitted with the mathematical relation given in eq. (61). Both A and w0Primary are
fitted, for the initial primary particle concentration without polymer cannot be directly
measured with the gravimetric method, where the degradation of the polymer is crucial but
results from linear fitting log(wPrimary) versus cpolymer. The fitted results are summarized in
table 8.
table 8: Results of the first order exponential decay fit parameters A and w0
Primary for all five samples
investigated
sample fatty acid A in l/g R2 w
0Primary in %
PT110802_III Ricinoleic Acid (RA) 0.037 ± 0.001 0.999 98.5 ± 2.5
PT110802_II Linoleic Acid (LA) 0.035 ± 0.005 0.891 32.2 ± 5.0
PT110802_I Oleic Acid (OA) 0.034 ± 0.009 0.718 8.4 ± 1.6
PT110802_IV Myristic Acid (MA) 0.065 ± 0.015 0.784 2.0 ± 0.7
PT110802_V Caprylic Acid (CA) 0.002 ± 0.009 -0.238 2.0 ± 0.3
The colloidal stability decreases in the order RA, LA, OA, CA and MA. The slope of decrease is
independent from the fatty acids and nearly constant for RA, LA and OA with A ≈ 0.036 l/g
and statistically comparable to the results in table 7 (deviations for MA and CA are assumed
to be due to the uncertainty of the measurement). However, the initial primary particle
content varies significantly, even though the same precipitation batch has been used and is
not the influencing factor as it was the case in table 7. An explanation for the different
primary particle concentrations is given in chapter 4 (discussed in paragraph 4.3.3) using the
model of deagglomeration presented in paragraph 4.2.
In summary, it can be stated that the mathematical model (eq. (61)) is a well description of
the colloidal destabilization and the course of stability does neither depend on the initial
primary particle concentration nor the type of fatty acid.
73
With a given mathematical relation of wPrimary versus cpolymer, the map in figure 18 – cpolymer as
a function of the composite solids concentration in the solvent based mixture csolid and the
filler concentration F in the solvent free composite – can be formulated as a primary particle
map for destabilizing polymers with the parameters A and w0
Primary. The combination of
eqs. (11) and (61) leads to eq. (62)
solid
solventsolid
-1
11
0PrimaryPrimary
solvent
solid0Primary
Primarysolvent
1
11
ln
c
DFAc
eww
DA
cw
wA
F
(62)
The primary particle maps for the C18 fatty acids RA, LA and OA in a DCM based PMMA
solution with eq. (62) and the parameters A and w0Primary from table 8 are presented in figure
51.
figure 51: Primary particle maps by combining results of the investigations in figure 50 and table 8 with eq. (62)
for ricinoleic (RA), linoleic (LA) and oleic acid (OA) with the same scale in the three dimensions
With these maps the primary particle concentration can be predicted, showing that the best
results regarding the state of dispersion in the composite are to be expected for high filler
concentrations in the composite and low solid concentrations in the solvent based mixture
to be processed. Filler concentrations are defined by the specifications of the desired
composite product. The second is a question of the economic and ecological process design,
since low csolid means an increased usage and processing of solvents which can be costly and
environmentally unfriendly.
In the next subsection the influence of fatty acid surfactants on the interactions in PVB
solutions is investigated.
Poly(vinyl butyral)
An urging question to be answered is, whether the type of the surfactant fatty acid will
influence the colloidal interactions of stabilized magnetite nanoparticles with PVB, which has
been described as a stabilizing polymer for RA covered magnetite nanoparticles above in
74
5.3.1. In figure 52 the primary particle concentration as a function of three different PVB
concentrations in a DCM based system is presented for all five surfactants. For all samples an
increase in the concentration is noticeable, which for the C18-acids concludes, that
independent from the degree of saturation as well as an additional hydroxyl group, the
stability is increased by the addition of PVB. It is supposes that its adsorption is the
mechanism of stabilization. This leads to the absence of attractive depletion interactions and
further stabilization by the polymer. At the highest PVB concentration the apparent primary
particle concentration in the supernatant is almost 100 % for all samples. Consequently the
stabilization is not limited to the hydroxyl group bearing ricinoleic acid for which adsorption
will be confirmed in the following paragraph. When only looking at ricinoleic acid it could
have been concluded, that the hydroxyl groups of the fatty acid interact with the once
present in the PVB structure. In general, the polarity of the hydroxyl groups in the PVB
structure is seen as the cause of adsorption. Independent from the structure of the fatty
acid, it could be unoccupied magnetite nanoparticle surface which offers adsorption sites for
the OH-bearing PVB, as it has been reported by HSIANG et al. and JEAN et al. [194, 195].
figure 52: Primary particle concentrations of the five investigated samples in nanoparticle polymer mixtures
with different concentrations of the polymer PVB in DCM; presented in context (scaling) with figure
50 above, published in [23]
Myristic acid shows the most dramatic increase in apparent primary particle concentration.
Only small amounts of PVB already stabilize the system, whereas for the other fatty acids a
quasi linear increase in stability is noticed for the logarithmic plot. For a DLVO like discussion
of this type of polymer colloid interaction, the effect of depletion flocculation is absent, so
that: Wtotal = WvdW + WBorn + Wsteric and Wdepletion = 0 (cf. 5.2). The PVB molecules may not
adsorb as brushes, hence the additional steric component could only be described by DOLAN
and EDWARDS theory instead of the AdG, which is valid for the brush regime only, cf. 4.1.3.
75
5.3.3 Stabilization by Adsorption of PVB
In the previous two paragraphs it is shown that in DCM based solutions with the polymer
PVB the colloidal stability of fatty acid stabilized magnetite nanoparticles improves
independently from both the initial concentration of primary particles and the type of fatty
acid. It is argued that the stabilization mechanism is due to adsorption which results in an
additional steric stabilization, case (d) in figure 39. In literature, the ability of PVB to stabilize
non-aqueous ferritic particle dispersions has been described already and adsorption
occurred [194, 195]. Therefore, in this paragraph the mechanism of stabilization of the fatty
acid coated magnetite nanoparticles is assessed. At first an assumption linked to the findings
in figure 47 is checked, which is that the primary particle size would increase when PVB
adsorbs on the particles.
Primary Particle Size
Primarily, in order to check whether the nanoparticles in the supernatant meet the
definition of primary particles (cf. A.5) the further diluted samples (b) from the extinction
measurement are characterized using dynamic light scattering. In the graph in figure 53 the
results of this set of measurements are presented plotting the number weighted median
particle sizes x50,0 over the polymer concentration cpolymer in the corresponding samples (a).
At zero polymer concentration a particle size of about 20 nm is measured for all systems as
expected. There is a slight drop in median particle size for the destabilizing polymer
dispersions especially for PC. This effect might be caused in the following possible ways. As
mentioned in 5.2, depletion interactions are stronger for larger particles and thus leaving
behind the smaller ones in the supernatant. Furthermore, the polymer coils might influence
the structure of the fatty acid layer around the nanoparticles compressing it and with this
lowering its hydrodynamic radius. Additionally, the measurement could simply be influenced
by the less scattering but faster fluctuating smaller coils with respect to the magnetite
nanoparticles. Nevertheless, for the destabilizing polymers the assumption of primary
particles in the supernatant is met. A very interesting development of particle size over
polymer concentration is noticed for the PVB and functionalized magnetite nanoparticle
dispersion. The particle size increases with higher initial polymer concentration, which can
well be described with a Langmuir type adsorption model and a fitted maximum particle size
xmax of 52.5 nm as shown in figure 53.
76
figure 53: The median diameter of the number weighted particle size distribution x50,0 of diluted samples (b)
measured with DLS as a function of the pristine concentrations cpolymer of PMMA, PC and PVB in the
samples (a)
The increase in primary particle size for PVB is attributed to the occurrence of the coverage
of the nanoparticles with PVB via adsorption. However this effect may not account for the
large increase of extinction over primary particle concentration in figure 47, which is
assumed to be caused by larger structures of nanoparticles and PVB that are not covered by
the number weighted particle size distribution investigated in figure 53, but by plotting the
numerically obtained (NNLS method applied in the Zetasize software [198, 199]) volume
weighted particle size distribution (frequency type µ3(x)) data of the DLS measurements in
figure 54. For PVB there are fractions larger 100 nm noticed with an increased amount for
increasing PVB concentrations. The increasing viscosity effect by the polymer must be
neglected, though, because the samples investigated are the diluted (b) samples with a
polymer concentration too low to induce such a strong viscosity effect.
figure 54: Numerically obtained volume weighted particle size distribution from the DLS measurements of the
(b) samples with the polymers (left) PMMA and (right) PVB; the polymer concentrations cPMMA and
cPVB refer to the undiluted samples (a)
Since the nanoparticle in PVB solutions are long term stable, the structures must be loosely
bound with a resulting low overall specific weight because they do not settle under the
experimental centrifugation conditions of this study (A.5) and no supernatant is noticed after
77
more than one year sample storage. Yet by definition they should not be accounted for as
primary particles and might be as problematic for material synthesis as the agglomerates in
the sediment. Nevertheless, this explains the extreme increase in extinction values in figure
47. In order to support the assumption of PVB adsorption and thus its stabilizing behavior, in
the next subsection, the polymer adsorption is quantified.
Adsorption Study
The results of the adsorption of PVB on the nanoparticle surface, as described in A.5.4, are
plotted in the left diagram in figure 55. The straight line is the case for 100 % adsorption and
the fitted line follows the assumption of a Langmuir Type adsorption, as discussed below.
Furthermore, only considering the number weighted median particle sizes of figure 53, the
PVB layer thickness z(cPVB) = (x50,0(cPVB) – x050,0) is calculated and plotted in the right diagram
in figure 55, also fitted with a Langmuir type curve, which is discussed below, as well.
figure 55: (left) PVB adsorption on the RA-Fe3O4 magnetite a oparti les surfa e as a fu tio of the PVB
concentration in the mixture cPVB, (right) layer thickness of the adsorbed PVB layer, calculated using
the data in figure 53
For the destabilizing polymers PMMA and PC, adsorption is been detected (as checked for
samples with cpolymer = 25 g/l, not presented in figure 55), supporting the assumption of
destabilization through depletion interactions (case (a) in figure 39), rather than bridging
flocculation (case (c) in figure 39). The lack of adsorption for PMMA and PC furthermore
verifies the experimental procedure applied showing that the washing with DCM is efficient
in removing non-adsorbed polymers. The adsorption of PVB, which has been assumed in the
light scattering and particle size analyses above as well as the layer thickness z can be
described with a Langmuir type isotherm with the relations and fitted parameters (constants
k and kz as well as maximum adsorption max,PVB and maximum layer thickness zmax,PVB) in
eqs. (63) and (64).
04.0 and g/g497.0,1 ΓPVBmax,
PolyΓ
PolyΓPVBmax,
kΓck
ckΓΓ (63)
78
12.0 and nm72.16,1 zPVBmax,
Polyz
PolyzPVBmax,
kzck
ckzz (64)
Knowing the adsorbed amount of PVB and the layer thickness z one can calculate the PVB
layer density PVB_adsorbed as a function of the polymer concentration cpolymer, with eq. (65).
This corresponds to the PVB concentration at the nanoparticle surface [160].
3lenanopartic
3lenanopartic
lenanopartic3
lenanopartic
shell
edPVB_adsorbedPVB_adsorb
2 xzx
x
V
m
(65)
In figure 56 the adsorbed PVB layer density is plotted as a function of the polymer
concentration in the solution cPVB, considering eqs. (63) and (64).
figure 56: Density of the adsorbed PVB layer as a function of the PVB solution concentration cPVB with the fitted
data from figure 55 using eq. (65) and a nanoparticle diameter xnanoparticle of 15 nm and a nanoparticle
specific weight of 5200 g/l
The progression of the curve in figure 56 shows that the density of the adsorbed layer
decreases with increasing polymer concentration and thus increasing layer thickness. This is
a conclusive result, when compared to the review of FLEER for polymers at an interface [160].
The steep decrease of the curve let’s assume that the first PVB polymers adsorb with several
anchoring points along its backbone (the train configuration). The density of free, non-
adsorbed PVB coils in solution is defined by the coil dimensions, due to solubility (cf. 5.1.1)
and can be calculated with eq. (66).
A3
coil
PVBPVB_coil
26
NR
M
(66)
With a molar weight of the PVB of MPVB = 32,000 g/mol (see A.1.3) and a coil dimension
represented by the measured hydrodynamic radius of RPVB-coil = 8.5 nm (see A.9) the coil
density yields 20.7 g/l, which is lower than the density of the adsorbed PVB, as expected.
79
Thermo gravimetric Study
In 4.3.4 and [21] it was shown, that the mass loss between 600 °C and 900 °C for inert gas
TGA analyses can be attributed to residues of adsorbed ricinoleic acid reducing the
magnetite nanoparticles to FeOx. However the influence of a polymer on that high
temperature degradation step has not been reported in that context. In this subsection the
relative mass loss at the high temperature step of degradation is presented as a function of
the polymer concentration in the dispersion cpolymer for PMMA and PVB, depicted in figure
57.
figure 57: Relative mass loss in the third degradation step for inert gas TGA between 600°C and 900 °C for
PMMA and PVB and Ra-Fe3O4 as a function of the polymer concentration
There is no statistically significant difference for the data points of the PMMA curve.
However, a clear increasing trend for the adsorbing PVB can be noticed with a similar
degressive behavior as mentioned above in figure 55. The results for PC are not shown, due
to an unclear influence of the high temperature residues and complex thermal
decomposition of pristine poly(bisphenol A carbonate) [200].
The results in figure 57 allow for the assumption that the high temperature mass loss could
at least be used qualitatively to judge on the adsorption behavior of organic matter on
magnetite. This needs to be evaluated in a future research.
Destabilization in PVB-PMMA mixtures
So far it is shown in various experiments and on different samples, that the polymer PVB
stabilizes a dispersion of fatty acid grafted magnetite nanoparticles with xnanoparticles ≈ 15 nm
in the solvent dichloromethane by adsorption. PMMA tends to destabilize such particle
dispersions. It is now interesting to know, whether the presence of PVB in a dispersion with
dissolved PMMA (assuming it is the desired matrix material) could reduce or even prevent
destabilization. In figure 58 there are four stability curves for RA-Fe3O4 (PT100902II) in a
DCM solution of PMMA with different amounts of PVB added, before adding the PMMA. A
80
PVB content of mPVB/mFe3O4 = 0.3 equals a PVB concentration of cPVB = 7.3 g/l for which the
stabilizing effect already is apparent in figure 48.
figure 58: Colloidal stability of PVB mixed with RA-Fe3O4 nanoparticles at four different mass ratios in solutions
of PMMA with the concentration cPMMA
The destabilizing effect of PMMA is not reduced by the addition of PVB, rather it is
enhanced. The decrease of the relative extinction is even more pronounced with increasing
PVB content. This is e e logi , he a k o ledgi g the PMMA’s e ha is of destabilization as a depletion induced effect. The effective particle diameter increases with
increasing PVB content (cf. figure 53) and with this the attractive energy between the
particles is increased, supporting the results in 5.2.
Hypothetical Model of Adsorption of PVB
Based the various knowledge of adsorption of PVB gathered and argued above, a
hypothetical model of adsorption is proposed and visualized in figure 59.
figure 59: Scheme of the hypothetical model of adsorption of PVB on the surface of magnetite nanoparticles
carrying chemically grafted fatty acid surfactant molecules as well; (left) train adsorption of PVB at
vacant magnetite surface sites, (right) surface hydroxide groups shall interact with the hydroxyl
groups of the PVB backbone.
PVB adsorbs directly on vacant surface sites which are not occupied by fatty acid surfactant
molecules. The interaction may occur between surface hydroxide groups of the magnetite
81
nanoparticles and the hydroxyl groups of the PVB structure. In this case physical adsorption
by hydrogen bonding would be probable. Since PVB bears several OH-groups the adsorption
of the polymer coil takes place as a train adsorption. Certainly for OH-bearing ricinoleic acid
the PVB adsorption may also take place between bound RA and PVB.
5.3.4 Influence of Mechanical Dispersing Methods on the Stability
When faced with agglomeration of nanoparticles it is often convenient to apply dispersive
stress by mechanical agitation, in order to overcome the attractive forces between the
particles [50-53, 201-203]. Devices which are typically used produce a flow field with high
shear to disperse particles, such as the rotor-stator-mixer Ultra-Turrax (UT) or a cavitation
inducing sonotrode (US) [50, 204-206]. Another common method especially for dispersing
nanoparticles smaller than 100 nm is agitated ball milling [52, 202, 207-210]. However, shear
or ball milling does not only lead to deagglomeration but can induce agglomeration as well
[211]. Furthermore it is shown in [52] that the agglomerate limit down to which dispersing
acts is rather large with approximately 100 nm compared to the 20 nm primary particle size
of the present study. In this paragraph it will be discussed, whether the state of dispersion
can be improved by applying high-shear devices (UT and US) or planetary ball milling (PM).
In the first experiment, a mixture of RA-Fe3O4 (PT110211) with a solution of PC in DCM at
cPC = 52 g/l (F = 0.3) is treated with simple stirring, sonotrode ultrasound (200 W input
power), Ultra-Turrax T25 (level 1) each for 15 min and compared to a quickly mixed sample
with a retention time close to 0 min. After the mixing procedure, the primary particle
concentration is assessed as described in A.5. For the extinction measurement, the relation
in eq. (59) is used to calculate wPrimary. The results are presented in figure 60. Due to the
pessimistically assumed uncertainty for the gravimetric determination (cf. A.5.2), the
differences between the four types of dispersing are not statistically significant.
figure 60: Primary Particle Concentration as determined gravimetrically (TGA) and by light extinction at 600 nm
(UV/VIS) with eq. (59) for a DCM based mixture of RA-Fe3O4 and PC with D = 0.2 and cpolymer = 51.2 g/l
(F = 0.3) for four different mixing procedures (US – sonotrode ultrasonication, UT – Ultra Turrax)
82
For the determination of the primary particle concentration using repetitive extinction
measurements at 600 nm (green columns in figure 60), the primary particle concentration
for 0 min mixing is significantly higher compared to the others. This means that none of the
shear inducing mixing procedures applied can improve the state of dispersion of this
depletion flocculated sample. The erroneous of the composition of the mixture is not
accounted for i the data’s u e tai ties, but would have concluded, that all four samples do
not vary statistically.
Another experiment on the influence of cavitation by ultrasonication is related to the
colloidal stability of RA-Fe3O4 in DCM (PT091227II) with the destabilizing polymer PMMA.
The same experiment has conducted twice, one without mechanical agitation (no US) and
one appl i g so ot ode ult asou d fo ’ ’ U“ prior to the procedure in A.5.2. Both
experimental results are plotted in figure 61.
figure 61: Destabilization curve of RA-Fe3O4 (PT091227II) in DCM with PMMA, without and with 1 min
sonotrode ultrasonication (US)
The trend is the same for both curves due to the destabilizing effect of PMMA, presumably
by depletion flocculation. However, the primary particle concentrations for the cavitation
agitated mixtures are all lower than the simple mixtures. This again shows that the
dispersing device is in this case not dispersing but rather decreasing the colloidal stability
slightly.
The final experimental set-up includes a planetary ball mill, cf. A.6. Without addition of a
polymer it is tested, whether it is possible to reduce the fraction of agglomerates after the
phase transfer. The dispersion is conducted up to 25 min and every 5 min a sample is
analyzed with dynamic light scattering. The results of intensity weighted particle size
distributions (frequency distribution of µint(x)) as well as the development of the second
cumulant median particle size x2nd cumulant over dispersing time tPM are depicted in the two
graphs in figure 62.
83
figure 62: Dispersing a phase transfer batch without polymer in DCM using a planetary ball mill, (left) intensity
weighted particle
Even the planetary ball milling dispersing method, which has been successfully applied to
disperse diamond nanoparticles with primary sizes much lower than 20 nm [209, 210], does
not improve the state of dispersion of the transferred magnetite nanoparticles. Based on the
increase of the second cumulant particle size, agglomeration is induced by the planetary ball
mill dispersion. Such favored agglomeration has been accounted for by excessive dispersing
devices, such as planetary ball milling, as mentioned in [203].
In summary, all three experimental setups in this paragraph showed, that the dispersing
devices applied, cannot improve the state of dispersion. For all samples a disapprovement is
noticeable instead. This concludes that the destabilizing mechanisms are not overcome by
mechanical dispersing. Due to the compositional limitations of the solution and spray drying
process it is not feasible to introduce another stabilizing agent to stabilize the mechanically
dispersed particles, as it is often done [212].
5.3.5 Kinetics of Flocculation at Low Nanoparticle Concentration and
high PMMA concentrations
In the previous paragraphs, information is gathered on the colloidal stability of variably fatty
acid stabilized magnetite nanoparticles in dichloromethane based dispersions and different
kinds of dissolved polymers. It was found that PMMA as well as PC destabilize the stable
fatty acid grafted magnetite nanoparticle dispersions, presumably by depletion flocculation.
PVB is most probably stabilizing the dispersions further by adsorption and additional steric
interactions.
In this section the kinetics of (depletion) flocculation are assessed by time dependent
particle size measurements as well as light extinction over time. Both methods rely on a low
particle concentration, due to the strong absorption of magnetite in the visible
electromagnetic spectrum. The magnetite nanoparticle sample is RA-Fe3O4 obtained from
84
PT100305 (as used in 5.3.1) after withdrawing the agglomerates with a magnet and
centrifugation and determining the particle and fatty acid concentration gravimetrically,
resulting in wnanoparticles = 0.0113 g/g and DRA = 0.276. In each experiment the nanoparticle
concentration is reduced to cNanoparticles = 1.2 g/l, compared to the 24 g/l in the processed
systems, which have been considered in the discussions above. So the particle mass
concentration is reduced by a factor of 20, which means that the particle number is reduced
by a factor of 8000, which is 203. The polymer concentration is chosen so that flocculation
can be monitored with the methods applied, with cPMMA > 50 g/l.
It is to show, that indeed the primary particles flocculate to large agglomerates and that the
speed of flocculation is a function of the polymer concentration, which defines the attractive
strength of interaction. It furthermore gives rise to the hope that flocculation could be
suppressed by quickly mixing and evaporation which has not been achieved in experiment
yet.
In figure 63 the intensity weighted frequency particle size distributions over time of the
stable RA-Fe3O4 dispersion immediately after mixing with both the destabilizing polymer
PMMA and the stabilizing polymer PVB to cpolymer = 58 g/l are displayed. The measured
polymer viscosities from A.9 are used to obtain DLS results.
figure 63: Time development of the intensity weighted frequency particle size distribution (in logarithmic
scaling) determined with DLS with respect to the viscosity of the polymer solution for a magnetite
dispersion without pristine agglomerates at cnanoparticles = 1.2 g/l and cpolymer = 58 g/l; (left)
destabilizing PMMA (right) stabilizing PVB
For PMMA after about 10 min larger agglomerates with sizes >100 nm appear and the
fraction of smaller particles is reduced. Due to the limitations of DLS mentioned in A.10, the
information obtained is not to be taken quantitatively. Yet it can for sure be stated, that
agglomeration sets in. Contrary, as expected, PVB stabilizes the dispersion. The particles are
slightly larger than the primary particles at t = 0 for PMMA, which is due to the adsorption of
the PVB onto the nanoparticles, cf. figure 53.
85
A more straightforward way to assess the kinetics of flocculation is by extinction
measurements, for the initial slope dE/dt is proportional to the agglomeration rate [183,
213]. Here the kinetics is investigated using time dependent extinction at 600 nm with a
Perkin Elmer UV/VIS photo spectrometer. In figure 64 four agglomeration curves with E600 nm
on the ordinate and the time up to 50 min on the abscissa are displayed. The PMMA
concentration is varied between 59 g/l and 72 g/l. Lower concentrations did not show a
significant increase of extinction over more than one hour.
figure 64: Time dependent light extinction to monitor agglomeration of a RA-Fe3O4 dispersion in DCM with
dissolved PMMA, the polymer is mixed with the stable nanoparticle dispersion at t = 0 min
For the initial slopes are zero for all samples, the set-in and maximum slopes of the
extinction curves of figure 64 are presented in figure 65. The set-in slope is defined here as
dE/dt, where d2E/dt
2 has a first order maximum.
figure 65: Correlation of extinction rates (set-in and maximum slopes of the lines in figure 64) with the PMMA
concentration
Summarizing the results, the higher the PMMA concentration is, the faster the coagulation
becomes. Experiments on the coagulation by adding PC failed, since it was too rapid and in
addition the extinction values went into saturation (at around E = 4).
86
It is very difficult to draw conclusions from the kinetics of flocculation for the higher
concentrated systems, which are applied to synthesize nanoparticle-polymer-composites,
since there is no information on the appearance of the entire phase diagram. Experiments of
directly mixing the nanoparticle dispersions and the polymer solutions prior to atomizing
and solvent evaporation in the spray drying step with residence times lower than 2 s let
suggest that in systems with high nanoparticles concentrations, flocculation is much more
rapid. This would be expected when acknowledging that the coagulation rate is a function of
the particle concentration. Kinetics investigation in the way presented, however, are not
applicable for these systems because of the high absorption of light of magnetite
nanoparticles, cf. A.8. A feasible way to investigate this in the future would be using X-Ray
investigations instead of visible light extinctions.
5.3.6 Influence of the Solvent
This paragraph discusses the influence of the solvent on the colloidal stability with and
without polymers dissolved. All samples so far were based on the organic solvent
dichloromethane. For a technical scale process it would be profitable to substitute this
health and environmentally hazardous solvent. Furthermore it is intriguing to find out how a
monomer based particle dispersion is influenced by addition of the polymerized monomer,
for instance methyl methacrylate (MMA) and PMMA or styrene (ST) and PS. This would show
that the destabilization occurring with the polymerization method of composite synthesis
(cf. 2.2.3) as e.g. reported in [85] is caused by the same destabilizing effect studied above for
PMMA and PC in DCM.
The follo i g t o su se tio s i t odu e to the esults o tai ed i t o sepa ate stude t’s theses which are both based on techniques developed in this doctoral thesis. They are both
related to using different solvents for the process of composite synthesis. The first one
shows how the usually applied solvent dichloromethane can be substituted by the less
hazardous yet good solvent ethyl acetate (EA). The second work is on the phase transfer of
magnetite nanoparticles to DCM, MMA and ST as well as the colloidal stability under the
presence of PMMA for DCM and MMA samples and PS for DCM and ST samples.
Dichloromethane Substitution with Ethyl Acetate
In a diploma thesis of TINA BREMERSTEIN [214] it is evaluated how EA, a good solvent for
PMMA, could substitute DCM in the process chain of nanoparticle-polymer-composite
preparation by the solution and spray drying method. The results on the colloidal stability
and the consequences on the spray drying results are discussed in a manuscript to be
submitted [215]. The substitution occurs after the phase transfer step by solvent mixing of
RA-Fe3O4 in DCM with EA and low pressure rotary evaporation of the DCM.
87
The results on the colloidal stability for both solvents by determining the primary particle
concentration with the TGA as well as the UV/VIS method are presented in figure 66. Due to
a difference in the magnetite concentration in both solvents, the extinction is normalized
with the initial extinction value. By comparison on the basis of the gravimetrically
determined primary particle concentration (figure 66 (left)) one can notice smaller yet not
significantly smaller values for EA and a good agreement on the progression of both curves.
The same analogy can be noticed for the normalized extinction curves in the graph in figure
66 (right). In A.9.1 it is reported that the solubility of PMMA in both solvents is similar with
no significant difference. Nevertheless, taking the calculated solubility by literature reported
HANSEN solubility parameters (in table 29) for granted, PMMA is better soluble in DCM with a
Flory interaction parameter χ of 0.10 as compared to EA with χ = 0.31. As a consequence the
polymer coils in DCM are expected to be more extended and at the same cPMMA and thus
same coil number concentration, depletion will be stronger in the DCM solution. However,
there is also a rather large uncertainty in the HANSEN solubility parameters of polymers.
figure 66: Primary particle concentration wPrimary (left) and normalized photometric extinction E600nm/E0,600nm
(right) as function of the PMMA concentration for the solvents: DCM and EA
In summary it is shown that for similar solubilities the destabilization of a given nanoparticle
species is similar for the same dissolved polymer. Furthermore it is shown, that DCM can be
replaced by EA, yet DCM is still necessary for the phase transfer step since the water
solubility of EA is too high.
In the next subsection DCM is replaced in the phase transfer step by MMA and ST for the
PMMA and PS composite preparation purpose, respectively.
Phase Transfer of Magnetite Nanoparticles to Methyl Methacrylate and Styrene
and the Colloidal Stability with PMMA and PS
The basis of this subsection is the experimental student research of ROBERT HARTMANN [216],
which has been presented on a conference in St. Petersburg in 2012 [217]. The working
hypothesis of this work is, that the polymer PMMA should also destabilize a MMA based RA-
Fe3O4 dispersion, following that the problems of destabilization discovered in this doctoral
88
thesis are as well of importance for the composite synthesis based on dispersing particles in
a monomer with subsequent polymerization, cf. 2.2.3. To extent the investigations, another
common monomer system is tested as well, which is styrene (ST) with poly(styrene) (PS).
Contrary to the previous subsection, the solvents MMA and ST, both lighter than water, are
to replace DCM, heavier than water, entirely, meaning beginning with the particle phase
transfer. To achieve comparable results, all experiments are based on a single homogeneous
mixture of three precipitation batches, which is used for phase transfers to DCM, MMA and
ST, with DRA = 0.2. The batch phase transfer experiments for the lighter solvents MMA and
ST are conducted in typical separation funnels, which are used for batch mixer-settler
extractions as well. All phase transfers are carried out by emulsification, which is stirring for
DCM and shaking for MMA and ST. In any case stable emulsions are formed, most
dominantly for styrene. An obvious mechanism of stabilization of the droplets is
electrostatic, due to the disassociated RA at the solvent-water interface. This means, that
reducing the pH would reduce disassociation behavior and thus stability. Therefore, to break
the stable emulsion, 1N HCl is added to obtain pH 6.0. Photographic images of the three
completed phase transfers before and after breaking the emulsion by adding HCl are
displayed in figure 67.
figure 67: 100 % completed phase transfers of magnetite nanoparticles originating from the same precipitation
batch (a) to dichloromethane DCM (PTDCM120227) by gravity driven transport and stirred
emulsification in a beaker, (b) to MMA (PTMMA120227) and (c) to styrene ST (PTST120227) by mixer-
settler extraction in separation funnels, (left) strong emulsion formation for MMA and ST, (right)
after breaking the emulsion by reducing the pH to 6.0 with 10 ml 1N HCl
After completed phase transfer, the particle free water phase is located at the top for DCM
and at the bottom for both MMA and ST. Settling droplets of RA-Fe3O4 in MMA are noticed
in the image b on the right of figure 67. It is assumed, that transferred agglomerates (due to
ineffective physicochemical deagglomeration, cf. 4.2) lead to local specific gravities, which
are higher than water. These zones of high specific weight will eventually drip off the liquid-
liquid interface. For the purpose of testing the colloidal stability when mixing in a polymer
89
solution all transferred particles are extracted together with DCM, MMA or ST and only the
water is removed after three washing steps.
For the MMA based system, the effect of dissolved PMMA is investigated with the UV/VIS
extinction method (see A.5.3) using the newer photo spectrometer Cary 60 from Agilent
Technologies. The comparative results for DCM and PMMA as well as MMA and PMMA are
depicted in the graphs in figure 68 both for absolute extinction at 600 nm (left) and for the
normalized extinction (right).
figure 68: Extinction based determination of the primary particle concentration of RA-Fe3O4 in DCM
(PTDCM120227) and MMA (PTMMA120227) under the presence of PMMA with the polymer
concentration on the abscissa; (left) absolute (right) normalized values; the extinction ratio for the
polymer free point of MMA to DCM is 8.6 %
At first it is to notice, that both lines are decreasing, meaning the polymer leads to
destabilization, which is expected for DCM, based on all findings so far. This means that
PMMA remains a non-adsorbing polymer in MMA as well. For MMA the initial extinction is
only 8.6 % of the initial value for DCM, which corresponds to the relation of the initial
primary particle concentrations after phase transfer of MMA and DCM, cf. eq. (61).
Consequently, the deagglomeration is less effective for RA in MMA, compared to RA in DCM.
Following the discussion of the new model of deagglomeration in 4.2 this could be due to a
lower coverage of RA on the nanoparticle surface, leading to a lower adsorption distance s,
or due to a smaller adsorption thickness because of the poorer solubility of the RA tails of
RA-Fe3O4 in MMA with χ = 0.06, compared to DCM with χ = 0.03, in table 36. Aside this
different behavior of DCM and MMA for phase transfer of magnetite nanoparticles using the
fatty acid ricinoleic acid, the polymer PMMA leads to a similar deagglomeration, when
looking at the normalized extinction in figure 68 (right). Similar to the observations in 5.3.1,
regarding eq. (61), this means that the mechanism of destabilization is similar in both cases.
Supposing depletion is the destabilizing mechanism (see 5.2), the relative coil volume
o e t atio φ a d the oil size RG should be similar in MMA and DCM. The coil number
concentration certainly is the same, for the same polymer, with this the same number of
segments, is used in both experiments. The only clue on the coil dimension is based on the
90
calculated FLORY interaction parameters in table 29 together with eq. (40). Since the
solubility of PMMA is better in DCM than in MMA with the calculated FLORY interaction
parameters χ of 0.10 to 0.24, respectively, the polymer coils in DCM should be bigger and
with this the relative volume concentration would be bigger, as well. With this, the depletion
destabilization should be stronger for DCM, yet in figure 68 the insignificant difference
points in the other direction.
In figure 69 the results of 600 nm extinction and normalized extinction versus concentration
of PS in a DCM and a ST dispersion of RA-Fe3O4 are displayed. In table 36 it is shown that
the solubility of RA-Fe3O4 is much better in DCM compared to ST with χ of 0.03 and 0.29,
respectively. In table 30 it is stated, that the PS solubility is similar in DCM and ST.
figure 69: Extinction based determination of the primary particle concentration of RA-Fe3O4 in DCM
(PTDCM120227) and ST (PTST120227) under the presence of PS with the polymer concentration on
the abscissa; (left) absolute (right) normalized values; the extinction ratio for the polymer free point
of ST to DCM is 80.5 %
Again both curves have a decreasing trend, which means PS is a destabilizing polymer, just
like PMMA. The destabilizing trend, found in figure 69 (right) is similar for both solvents and
even similar to PMMA in DCM and MMA in figure 68 (right). This is plausible, looking at the
almost equal FLORY interaction parameters of PS in DCM and ST, following the similar coil
dimensions. As it was the case comparing MMA and DCM, the difference between DCM and
ST is the phase transfer and the deagglomeration efficiency. The initial concentration of
primary particles in ST after phase transfer is only 80.5 % of the concentration in DCM, so
the deagglomeration is less effective with the solvent styrene and the fatty acid ricinoleic
acid. Even though the RA-Fe3O4 solubility is poorer in ST compared to MMA, the primary
particle concentration after phase transfer is much higher. In this case the correlation of
w0
Primary versus the solubility distance D1,2 for different fatty acids, reported in 4.3.3 and
figure 31 does not seem to be reliable in this case. It would have predicted a higher initial
primary particle concentration for MMA compared to ST. Therefore the discussion based on
HANSEN solubility parameters is not helpful in this case.
91
6 Highly Filled Composites
In chapter 2 various methods of composite syntheses and preparations are introduced of
which only one is applied in this thesis, namely the method of solution blending of
separately synthesized nanoparticles and polymers of paragraph 2.2.1 as depicted in figure
5.
This chapter summarizes important findings on the solvent free nanoparticle-polymer-
composites prepared from the complex colloidal dispersion discussed in chapter 5 above,
using the methods of spray drying and subsequent (micro) injection molding. The volume
concentration of the nanoparticles is in any case higher than 10 %, therefore they are
referred to as highly filled composites. Parameters describing the composition are
introduced above in 3.2. Paragraph 6.1 offers a brief introduction to the important
theoretical backgrounds necessary to understand the discussions on the experimental
results in 6.2 and 6.3. It will cover both methods of composite preparation by spray drying
(cf. 6.1.1) as well as injection molding (cf. 6.1.2) and introduce ways to evaluate the
dispersion of particles in a cross-section of a composite by image processing and geometrical
mathematical methods (cf. 6.1.3). The results of spray drying of organic solvent based
nanoparticle polymer mixtures are presented in paragraph 6.2. In the last paragraph of this
chapter, cross-sections of injection molded composites are investigated and evaluated
varying the matrix polymer and filler concentration in paragraph 6.3.
6.1 Theory
6.1.1 Spray Drying
Spray Drying is a well-established industrial process to produce particulate solids from
solutions, suspensions and emulsions [218]. The industrial applications are diverse, so that
spray dryers are found e.g. in the pharmaceutical sector or the food industry (especially for
dairy products) and ceramics industry as well [219-222].
The principle mechanism of spray drying is best divided into the following three individual
process steps:
a) atomization of the liquid (solution, suspension or emulsion),
b) particle formation by drying of the individual droplets and
c) solids separation from the drying gas, using e.g. aero-cyclones and/or filters.
92
Below, the first two steps are discussed theoretically, for they are defining the properties of
the final product of spray drying. Those properties are determined by the powder bulk
product which is characterized by a particle size distribution and a particle morphology
distribution. In case there are several solid components in the feed liquid, their distribution
within a dry particle is of interest as well. Hence, particle morphology and components’ distribution are discussed below as well.
Atomization of a Liquid
Atomization of a bulk liquid into small droplets (spherical liquid particles) is a
thermodynamically unfavorable process which acquires input of energy. This energy is
necessary to dynamically deform the liquid opposing the viscous friction characterized by
the viscosity and to create new surface area (interface of liquid and surrounding gas) which
is characterized by a specific surface energy σ. There are different techniques available to
atomize a liquid categorized by the energy input [223, 224]. Most often nozzles and rotary
atomizers are applied.
In this section only the atomization using an external mixing two fluid nozzle, applied in the
experiments of this thesis, is considered. A scheme of such a nozzle is depicted in figure 70.
figure 70: Principle scheme of an external mixing two fluid nozzle with turbulent atomization, adopted from
[214]
The liquid is flowing through the inner cylinder with the diameter dnozzle under a relatively
low pressure. At the exit a pressurized gas flow expands resulting in a high gas velocity and a
turbulent break-up of the liquid film. A few micrometers below the exit point the liquid film
is already atomized into very small droplets with SAUTER diameters x3,2 on the micrometer
scale. Since it is a turbulent process, the modeling of the droplet size, characterized with x3,2,
is typically achieved empirically applying dimensional analysis. This results in the
dimensionless REYNOLDS number Re, WEBER number We, gas WEBER number Wegas, OHNESORG
number Oh and liquid mass loading of the gas µ [220]. The REYNOLDS number Re is the ratio of
inertia to viscous forces acting on the liquid and defined in eq. (67).
93
liquid
liquidnozzlerel dv
Re (67)
In the numerator the characteristic velocity and length are the relative velocity between gas
and fluid vrel and the inner diameter of the liquid part of the nozzle dnozzle, respectively.
Furthermore, the specific weight of the liquid liquid is characteristic for inertia and the
dynamic viscosity liquid for the viscous forces.
The dimensionless WEBER number We is the ratio of inertia forces of the liquid (of the gas for
the gas WEBER number Wegas) to the surface forces at the interface gas fluid and presented in
eq. (68).
gasnozzle2rel
gas
liquidnozzle2rel
dv
We
dvWe
(68)
The denominator is made up of the specific surface energy σ with [σ] = J/m2 = N/m, which is
depending on the interactions between the gaseous and the liquid material specious. It can
thus be described as the energy necessary to increase the surface area by 1 m2.
Another result of the dimensional analysis is the OHNESORG number Oh which is a
combination of Re and We and defined in eq. (69).
liquidnozzle
liquid
ρdσRe
WeOh
(69)
Low OHNESORG numbers are typical for high surface and inertia forces compared to lower
viscous forces. The final dimensionless parameter is the ratio of liquid mass flow to gas mass
flow in eq. (70), which is also often found as the inverse value, termed ALR (air to liquid
ratio).
ALRm
m 1
gas
liquid
(70)
Based on many experimental results, MULHEM et al. present an empirical relation for the
SAUTER diameter of droplets generated by an external mixing two fluid nozzle in eq. (71)
[220].
4.0
gas
0622.0nozzle3,2 21.0
We
Ohdx
(71)
94
Combining eq. (71) with eqs. (68), (69) and (70) one can better judge on the strength of the
individual influencing parameters in eq. (72). Please note that the units are defined to be
following SI conventions with a negative exponent of the empirical exponent presented.
0311.0liquid
0.0622liquid
3689.04.0gas
4.0
gas
liquid5689.08.0rel2,3 21.0
m
mdvx nozzle
(72)
The influencing parameters are ordered with increasing absolute exponent values.
Conclusion can be drawn, that a change in the first parameters has the stronger effect on
the droplet size. The relative velocity vrel is typically increased by raising the gas pressure and
thus the gas velocity, which will reduce the droplet size. Smaller nozzle diameters will result
in smaller droplets as well. The last four parameters are material specific and can
technologically be influenced by the temperature (pressure for the specific weight of the
gas) only. When working with liquids that are more viscous with higher liquid, the droplet
size is expected to be higher, the other values being constant.
Drying of a Droplet
In a spray dryer the atomized droplets come in contact with a hot gas in different possible
regimes. The spray dryer used in this study is based on a co-current flow of spray and hot
drying gas. Other possibilities are counter-current and cross-flow. For the co-current regime
the characteristic is that drying is very rapid in the beginning and at the end of the drying
step the peak temperature is relatively low, which is important for heat sensitive materials,
such as the organics used in this study. In the co-current regime, the final particle
temperature is as high as the inlet temperature of the hot gas.
In general the drying of a droplet is a phenomenon of combined heat and mass transport.
Additionally solids formation has to be accounted for. The mass transported is the solvent
which is transported across the droplet surface Sdroplet out of the particle to the drying gas. A
simple model for the convective mass transport at the droplet surface including a driving
force and a transport coefficient is presented in eq. (73).
gas solvent,surface solvent,droplet
solvent ppTRS
m (73)
The pressures in the parenthesis are the partial pressures of the solvent at the surface and in
the drying gas. The higher their difference is, the more solvent is transported in time. R and T
are the gas constant and total temperature, respectively. Finally, the mass transfer
coefficient β needs to be evaluated with the help of dimensional analysis using empirical
relations of SHERWOOD number Sh, SCHMIDT number Sc and REYNOLDS number Re. The
SHERWOOD number Sh represents the ratio of mass transport by convection to mass transport
95
by diffusion. Whereas, the SCHMIDT number Sc relates the kinematic viscosity to the diffusion
coefficient and REYNOLDS number is described above.
Similar to mass transport, the transport of heat flowing from the drying gas into the droplet
is characterized by a transport coefficient and a driving force, which is the temperature
difference. The droplet area specific heat flow due to convection is given in eq. (74).
gasparticledroplet
TTS
Q (74)
Again the transport coefficient, which in this case is the heat transfer coefficient α, is
assessed with the help of the empirical relation of three different dimensionless numbers.
The REYNOLDS number is the same like above and instead of SHERWOOD and SCHMIDT number
for mass transport, the NUSSELT number Nu and PRANDTL number Pr are introduced for
transport of heat. Similar to the SHERWOOD number, the NUSSELT number relates heat
transport by convection to heat transport by conduction (diffusion). A heat transport
analogy to the SCHMIDT number is the PRANDTL number, defined as the ratio of kinematic
viscosity and heat conductivity.
A typical time progression of mass and temperature of a drying droplet, with hypothetical
constant surface area, is depicted in figure 71.
figure 71: Time progression of mass and temperature of a drying droplet with constant surface area due to film
formation, taken from [225] with the steps A through D described in the text
The first step A is characterized by the heating of the droplet up to a constant wet-bulb
temperature θwb with an acceleration of the mass flow of the solvent. In step B the mass
flow is constant and high at the constant wet-bulb temperature, which is defined in eq. (75),
assuming a sufficiently high conductivity of heat inside of the drying droplet. For the case
that the surface area of the drying droplet is changing the mass flow is expected to decrease
instead of being constant.
96
gasdroplet
solventsolventwb
S
hm (75)
So far only one parameter is not described, which is the specific heat of evaporation of the
solvent hsolvent. Once the solvent mass transport is hindered by a solids layer (shell
formation) on the dried droplet surface, the temperature increases in step C and the mass
flow decelerates. At step D the temperature of gas and particle are similar and the solvent
content in the particle is approaching equilibrium, no more drying occurring. Certainly the
progression of mass and temperature very much depend on the solids formation while the
droplet is drying influencing heat and mass transport. Altogether the particle morphology is
defined by these progressions as well.
Particle Morphology
The particle design by spray drying is extremely variable especially due to the manifold
possible particle morphologies, depending on various process and material properties.
Consequently, there are many publications dealing with particle morphology in connection
with spray drying [226-229] yet only a few offer models of particle formation. HANDSCOMB
et al. introduce a model of a drying droplet with suspended small particles and shell
formation discussing the resulting possible morphologies [230]. Taken from this publication
is the graphic in figure 72.
figure 72: Drying progression of a droplet with suspended small particles and shell formation, taken from [230]
At very low solids concentration of less than 1 % by weight no particles are formed from the
drying d oplet. Ve high d i g te pe atu es ill lead to puffed pa ti les ith dia ete s considerably larger than the initial droplet diameter. At moderate drying temperatures the
flexibility of the forming shell distinguishes solid particle formation for rigid dry shells and
hollow particles. Depending on the elastic behavior of the flexible shell the hollow particles
can be spherical or shriveled. In case the shell is very dense the hollow spheres can burst and
leave blistered particles.
97
An important dimensionless number determining the particle morphology is the PECLET
number Pe. It is the ratio of evaporation rate of the solvent solvent and diffusion rate of the
solutes Dsolutes [226]. At high PECLET numbers the evaporation rate is much higher than the
diffusion of the solutes resulting in surface enrichment of the solutes and shell formation.
In need for a descriptive morphology parameter of the composites prepared in 6.2, the
structural parameter SP is introduced in [23] and presented in eq. (76).
i
3,i1
m,icomposite
PSDBET
PSD 6with,100 xS
S
SSP (76)
The structural parameter SP is defined with the BET determined mass specific surface SBET
and the calculated mass specific surface from the particle size distribution SPSD. It ranges
from zero to 100. Smaller SP values account for higher structuring, which can be due to
nanoparticle agglomerates, increased micro porosities or general shape deviations from a
sphere. Solid spheres with smooth surfaces will lead to SP = 100. The specific weight of the
composite microparticle composite with F = 0.3 and D = 0.2 amounts to 1.51 g/cm3, with
magnetite = 5.20 g/cm3, fatty acid = 0.90 g/cm3 and polymer = 1.20 g/cm3. Taken from the particle
size distributions determined with laser diffraction the average particle size xm,i and the
weight fraction i of fractions i are used for calculating the specific surface, in this case
assuming spherical particles. It can be stated that, the smaller the structural parameter SP,
the more irregular the shape/morphology of the particles. In addition to the microparticle
morphology, liberated, not encapsulated nanoparticles will result in a higher BET surface and
thus reduce the SP value as well.
Compositional Separation
In case there are several solid components in the spray-dried liquid, a separation in
composition can occur [220, 225, 226]. If one component is bigger than the droplets formed
in the spray drying process, than there obviously will be a depletion of this component in the
smaller particles formed and thus a compositional segregation depending on the particle size
[220]. Separation within one drying droplet is due to different mobilities (diffusivity) of the
individual components and thus different PECLET numbers, as defined above [226].
Components can specifically concentrate on the particle surface because of this. If there are
slightly asymmetric drying conditions for a single droplet than indentations in the particles
can occur with higher concentrations of components with lower diffusivity [225].
6.1.2 (Micro) Injection Molding
One application of spray-dried composite microparticles, composed of nanoparticles and
thermoplastic polymers, is the preparation of microstructured components for
98
microsystems. This is achieved by micro injection molding, a polymer processing technique
[231-233]. Simple injection molding is a widely used shaping method in the plastics industry.
In general, thermoplastic polymer granules are fed in an extruder, which is heating up the
granules to obtain a polymer melt. This melt is intensively mixed by the screw extruder
device and at the end injected through a needle into a structured molding tool, which is a
negative form of the final product [231]. The injection occurs at very high pressures of
several tens of bar. In figure 73 (left) there is a schematic drawing of an extruder fed on the
right with the composite particles prepared by spray drying, transported and molten from
right to left and finally injected into a microstructured tool on the left. In figure 73 (right)
there is an optical micrograph in top-view of a micro injection molded test structure with a
nanoparticle-polymer-composite PMMA-RA-Fe3O4 with F = 0.3 and D = 0.2. There are well
reproduced edge structures proving a good result of micro injection molding using a rather
high filled composite.
figure 73: (left) principal scheme of an injection molding device (right) top view of an optical micrograph of a
PMMA-RA-Fe3O4 composite with F = 0.3 and D = 0.2 of a test structure
In micro injection molding the molding tool is microstructured with elements that have
thicknesses or lengths of a few micrometer only [233]. Therefore, when processing
composites, the size of the filler component of the thermoplastic polymer needs to be
sufficiently smaller than the smallest structures to guarantee the same composition within
the microstructured elements. Well dispersed nanoparticles satisfy this condition.
6.1.3 Image Processing and the Mathematical Description of the State
of Dispersion
In paragraph 2.1.3 the te state of dispe sio is introduced and descriptively discussed. It
is shown that the dispersion is characterized by homogeneity and a deagglomeration
component. When judging the actual dispersion of a particulate component in a matrix,
typically a cross-section is investigated but in many studies only comparatively and
descriptively evaluated. Often only the size of the disperse component (particles, aggregates
and agglomerates) is the sole quantity [72, 79, 81, 82, 234, 235], even though it is only
judging the deagglomeration success and not the homogenization. Researching the
literature for mathematical measures describing objectively and quantitatively the state of
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dispersion leads to fe fi di gs ithout a o e a d o l universal measure [54-56, 58, 236,
237]
The premise to evaluate the dispersion, based on cross-section imaging, is an objective
reproducible image processing procedure for extracting and detecting the disperse
component. Certainly this very much also depends on the quality of the primary image of
the cross-section and the contrast of the phases, which however is not part of this
paragraph, thus assuming image quality is sufficient. In this paragraph the image processing
procedure of binarization and particle detection is introduced and different objective
quantifications of the state of dispersion are proposed. Image acquisition for a good contrast
and subsequent image processing is discussed in A.2.2 and [19], respectively.
There are three steps of processing (binarization, particle recognition, VORONOI tessellation)
presented in figure 74 which will be discussed below.
figure 74: Image processing steps (from left to right) of a phase contrast AFM image on the left showing dark
magnetite and light polymer phases, binarized after thresholding and with a watershed, automatic
detection and measurement of >800 individual particles (including aggregates and agglomerates)
and finally the VORONOI diagram of the binarized image
Binarization
The very first image processing step is generating a grey value image with either 256 or
65536 grey values, corresponding to 8-bit and 16-bit images, respectively. The highest grey
value is white and the zero value typically corresponds to black. It is assumed that the darker
pixels to correspond to the particulate phase, as is the case in figure 74 on the left showing
an 8-bit grey value image. The next step is the binarization, which sets all pixels below a
certain value to zero (particles) and above to one (matrix). This is achieved by thresholding.
The second image in figure 74 is the result of setting the threshold to a grey value of 200.
What is also done in the second image is a binary image tool, called watershed, which
segments very structured elements [238]. This should not have been done, when wanting to
preserve the ability to recognize connected particles as agglomerates.
With the as-prepared image an automated particle recognition and size determination can
be achieved using the standard freeware program in scientific image processing ImageJ
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[238]. Otherwise one can also define the area filling ratio φA by dividing the number of pixels
with value zero (particles) by the total number of image pixels.
Particle Recognition and Size Determination
The third image in figure 74 depicts the result of particle recognition with 836 individual
particles and a median number weighted FERET size of 100 nm. The FERET size determined by
the program ImageJ is the longest distance between any two points of the individual
particle. Another possible diameter is the area equivalent sphere diameter.
VORONOI Polygons
A next processing step is the generation of so-called VORONOI polygons around the
segmented particles. These polygons have the property that every point within a polygon is
closest to the finite element surrounded by the polygon [54, 239]. Such tessellations can be
helpful in determining quantitative measures for the state of dispersion and have been
promising tools for this purpose [19, 55, 240].
State of Dispersion Measures
Ce tai l it is diffi ult t i g to put the easu e of state of dispe sio i o e u e , maybe as impossible as trying to put the particle size distribution in a single value. Yet it is
necessary to start defining objective ways to quantify the state of dispersion.
One proposal is based on the horizontal distance between particles, which will be referred to
as the li e ethod [56, 241, 242]. Depending on the state of dispersion the coefficient of
variance cov of all separation distances li will be low for a good dispersion and high for a
poor dispersion. The VORONOI polygons introduced above can be used similarly [55], defined
he e as the VORONOI ethod . The cov of the polygon areas Ai will be low for a good
dispersion and high for a poor one.
Both methods are compared in figure 75 evaluating a set of simulated dispersion with
improving homogeneity from left to right. One can see that the VORONOI method is more
sensible when compared to the line method.
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figure 75: Coefficients of variance normalized with the first value covmax for the VORNOI and the line method and
a set of simulated images with (from left to right) improving state of dispersion taken from fig. 7 in
[55], covmax is 1.08 for the VORONOI method and 1.42 for the line method
A better quantitative resolution is noticeable for the cov of the areas of the VORONOI
polygons (darker bars in figure 75) as compared to the cov of the distances of the line
method. Thus the VORONOI method is expected to offer a better measure for the state of
dispersion as it is based on a 2D analysis instead of the 1D and directional based line
method. Applying the VORONOI method to the four images in figure 3, where the state of
dispersion was introduced, results in covVORONOI = (0.581, 0.254, 0.538, 0.129) from top left
(agglomerated and demixed) to bottom right (deagglomerated and mixed).
6.1.4 Filler Concentration – Agglomerate Concentration – Stability
Relation
In paragraph 5.3.1 it is shown that for destabilizing polymers PMMA and PC, the primary
particle concentration wPrimary decreases with increasing polymer concentration in the
dispersion cpolymer. For a constant solids concentration in the dispersion csolid the filler
concentration of a dry composite material F prepared from such a dispersion increases with
decreasing polymer concentration cPolymer, cf. eq. (11) on page 23. This concludes that with
increasing filler concentration the relative agglomerate concentration φAgglomerates / φtotal,
agglomerates related to all nanoparticles, is reduced. It shall be assumed that the area
concentration of particles in a planar cross-section is equal to the volume concentration of
particles within the composite part. Since a higher filler concentration also leads to more
nanoparticles in total, the absolute agglomerates concentration in a composite cross-section
φAgglomerates does not necessarily need to decrease.
This paragraph explains theoretically how to predict the concentration of agglomerated
nanoparticles and primary nanoparticles within a solid composite cross-section from stability
experiments in dispersion. It is presumed in these calculations, that the state of dispersion is
not affected by the composite preparation through spray drying and injection molding,
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which certainly is questionable and can be reconsidered when discussing experimental
results.
First of all, the agglomerate volume or area fraction (concentration) is defined in eq. (77)
with the total volume fraction and the fraction of primary particles.
PrimarytotalesAgglomerat (77)
Both the primary and the total particle fractions can be defined with the filler concentration
F, the surfactant ratio D and the specific weights of the three solid components,
nanoparticles, surfactants and the polymer, in eq. (78).
1
arytotal/Primpolymersurfactantlesnanoparticarytotal/Prim 1
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D
F
D
(78)
As introduced in 3.2.2, the total filler concentration is a function of the polymer cpolymer and
the solids concentration csolid in the dispersion as well as D and solvent. Furthermore, by
definition of the primary particle concentration wPrimary in A.5, the filler concentration of the
primary particles is the product of total filler concentration F = Ftotal and wPrimary. Both
ingredients of eq. (78) are presented in eq. (79).
PrimarytotalPrimary
solvent
solidpolymertotal 1
11
1
1
wFF
D
cc
DF
(79)
Finally, as demonstrated in paragraph 5.3, the primary particle concentration wPrimary in
dispersions with destabilizing polymers can be formulated as a function of the polymer
concentration in the dispersion cpolymer with two parameters: the initial primary particle
concentration w0Primary and an exponential decay rate A.
polymer0PrimaryPrimary
cAeww
(80)
In figure 76 there is a graphic evaluation of eqs. (77) through (80) plotting the volume
fractions φtotal, φPrimary and φAgglomerates as functions of F and cpolymer for parameters of the
destabilizing polymers PMMA and PC reported in 5.3.1 with csolid = 0.05 and w0Primary = 0.9 as
well as APMMA = 0.025 and APC = 0.050.
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figure 76: Visualization of the volume (area) fractions of the primary and agglomerated particles as well as the
total volume fraction of nanoparticles applying eqs. (77) - (80), parameters w0
Primary and A are chosen
in connection with colloidal stability investigations of PMMA and PC in 5.3.1
It shall be recognized that the agglomerate fraction φAgglomerates increases up to a filler
concentration of 0.65 for APMMA and 0.70 for APC and dropping for higher filled composites.
Nevertheless, the relative agglomerate concentration is decreasing with increasing filler
concentration and thus decreasing polymer concentration in the dispersion for all
destabilizing colloidal parameter conditions, as plotted in figure 77 for selected values of
initial primary particle concentration and decay rate.
figure 77: Relative agglomerate concentration as a function of the total filler concentration F and the polymer
concentration cpolymer with the parameters of colloidal stability of eq. (80) applying eqs. (77) - (80)
It is shown that there is a reproducibility problem for magnetite nanoparticle precipitation
batches in 5.3.1 which will affect the initial primary particle concentration. This effect as well
as the aforementioned impact of different polymers on the decay rate can be acknowledged
when wanting to predict the fraction of agglomerates in a composite supposing one has
knowledge on the parameters and, furthermore, the state of dispersion is not changed by
spray drying and/or injection molding.
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6.2 Experimental Results – Spray-Dried Microparticle
Composites
This paragraph is dedicated to discuss the results of the spray drying of dichloromethane
based dispersions of fatty acid grafted magnetite nanoparticles with different polymers as
well as fatty acids and for variable filler concentrations. Spray drying of non-aqueous
formulations can be found in the literature for example in [66, 226, 243-246].
The following investigations are based on BSE-SEM imaging (cf. A.2.2), granulometry with
laser diffraction and BET analysis (cf. A.2.1) as well as compositional analysis with TGA (cf.
A.2.4).
6.2.1 Compositional Separation
At first an interesting phenomenon concerning the spray drying of the destabilized PMMA
sytems is discussed, which is the compositional segregation. The spray-dried particles can be
collected in three fractions in the spray dryer. In figure 78 one can see a schematic drawing
of the spray dryer set-up used for this study with the location of the three particle fractions:
cylinder, cyclone and filter.
figure 78: Schematic set-up of the co-current spray dryer used in this thesis with three particle fractions
(cylinder, cyclone and filter) where the cyclone fraction is the product of the process and should be
the largest fraction in mass, the solvent is recovered in a condenser and the dry air recycled passing a
heating device
The majority of the particles are found in the cyclone with a yield of about 80 % by weight.
The other particles are located in the spray cylinder in front of or in the bag filter behind the
cyclone. When collecting the particles, which appear brown to black depending on the filler
concentration, one can see the darkest appearance for the cylinder and the lightest for the
filter fraction. Since the color black is induced by the strong light absorption of the magnetite
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particles (cf. A.8), this subjective finding suggests that the magnetite concentration varies
within those fractions.
PMMA-based Composites
In order to investigate the previous assumption the particle size distributions of these
fractions are determined with laser diffraction and the composition is analyzed with TGA,
assuming that the magnetite concentration is approximately equal to the residual mass at
600 °C, corresponding to the findings in 4.3.4 and [21]. A representative set of these two
analyses for the three fractions of a PMMA composite with RA-Fe3O4 at F = 0.3, DRA = 0.2 and
csolid = 0.07 is given in the two graphs of figure 79.
figure 79: (left) representative particle size distributions of the microparticles of the three fractions of the spray
dryer measured with laser diffraction and (right) TGA curves of the corresponding samples with an
initial composition of PMMA RA-Fe3O4 with F = 0.3 and DRA = 0.2; the yields are 11 %, 74 % and 15 %
for cylinder, cyclone and filter, respectively
The particles of the filter fraction are the finest, which is expected. Based on the median
values, the particles in the cylinder of this volume weighted sum distribution Q3(x) are the
largest with about 8 µm compared to 3 µm and 5 µm for the filter and cyclone material,
respectively, which can be confirmed by SEM imaging. Looking at the residual masses at a
temperature of 600 C, before the last step of decomposition (due to magnetite reduction, cf.
4.3.4) in the right graph of figure 79, the magnetite concentration is the highest for the
cylinder and the lowest for the filter sample, as it had been assumed. The values are 43 %,
32 % and 23 % for cylinder, cyclone and filter, respectively. Combining the information of
particle size and magnetite concentration in figure 79 one finds out that larger particles have
a higher magnetite concentration. Furthermore the composition of the cyclone fraction
correlates well with the set value of F = 0.3 and thus meets the specifications. Therefore the
filler concentration in the small filter material is too low and in the larger cylinder fraction
too high. For the deviations in magnetite concentration are similar and the yield of both
fractions is similar, the total material composition meets the specification again. In summary
there is a compositional separation depending on the spray-dried microparticle size. This
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must be occurring already during the atomization step. It is supposed, that large depletion
induced floccules are situated within the larger droplets. In other words, the atomized
smaller droplets which will eventually be the filter material after drying are depleted of
magnetite for they are smaller than the large filler agglomerates, following the results of
MULHEM et al. in [220].
This separation phenomenon is studied for a number of spray drying experiments with
PMMA, the fatty acid ricinoleic acid (RA-Fe3O4) at DRA = 0.2 and varying filler concentrations
F. The individual results are presented in two graphs in figure 80.
figure 80: (left) TGA measured mass residue at 600 °C of various spray drying experiments with RA-Fe3O4 and
DRA = 0.2 for the three fractions of the spray dryer at different initial filler concentrations of
magnetite F; (right) relative magnetite concentration of the cylinder or the filter fractions compared
to the corresponding cyclone fraction with the cyclone fraction residual mass on the abscissa
The data is scattering rather strongly, which must be due to different precipitation and
phase transfer batches used and the varying resulting state of dispersions in the polymer
free dichloromethane suspensions, cf. 5.3.1. Looking at the graph on the left in figure 80, it is
to notice, that the cyclone magnetite concentration (green upward triangle), measured with
the 600 °C TGA residue, correspond well to the calculated expected filler concentration F.
They approximately lie on the line through the origin. All samples from the cylinder and the
filter are above and below this line, respectively. This confirms the argumentation from
above, showing that the cylinder material is enriched in magnetite due to large agglomerates
and consequently there is a lower content of magnetite in the filter material. The graph in
figure 80 (right) show the measured magnetite concentrations of the cylinder and filter
particles related to the concentration of the corresponding cyclone material as a function of
the magnetite concentration of the cyclone material. The deviations of the cylinder material
are increasing with decreasing magnetite concentration and thus increasing polymer
concentration in the spray-dried dispersion. This trend goes along well with the findings in
chapter 5, for a higher polymer concentration in the solvent based dispersion and thus lower
magnetite concentration in the dried composite leads to increased magnetite agglomerates
which will gather in the larger particles of the spray dryer. Again the uncertainty in the data
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presented is most probably due to the deviations in the quality of the different precipitation
and phase transfer batches, cf. paragraph 5.3.1.
PC- and PVB-based Composites
The amount of data for the destabilizing polymer PC and the stabilizing polymer PVB is too
little to be presented conclusively. Yet, for PC a strong compositional separation is
noticeable. For PVB neither the visual inspection (difference in light absorption) nor
compositional analyses reveal a statistically significant compositional separation within the
differently sized particle fractions.
High Temperature Degradation
Looking again at the PMMA based composites studied in this paragraph, there is another not
yet described difference between the three fractions related to the inert atmosphere
decomposition between 600 °C and 900 °C, which has been attributed to the reduction of
magnetite to lower oxygen containing iron compounds by residual carbon from adsorbed
species, cf. paragraph 4.3.4 and [21]. For all samples presented in figure 80 the relative mass
loss between 600 °C and 900 °C is determined as (w600 °C-w900 °C)/w600 °C. The average values
and the 95 % confidence level are presented in figure 81.
figure 81: High temperature mass loss (residual mass at 600 °C compared to residual mass after magnetite
reduction at 900 °C, compare with investigations in 4.3.4) of the TGA analyzed particles of various
spray drying experiments with RA-Fe3O4 based on 38, 51 and 23 samples for cylinder, cyclone and
filter materials, respectively
The lowest values is found for the cylinder material, which is yet not statistically significantly
lower than the average relative mass loss of the cyclone particles. A significant difference
occurs for the filter material, with the highest relative mass loss. In [21] it is argued, that the
residual carbon responsible for the magnetite reduction in this temperature window is from
the chemically bound fatty acid molecules. This would mean that the magnetite
nanoparticles captured in the fine filter fraction bear more chemically bound fatty acids. This
is logical assuming the magnetite of the filter fraction are mostly made up of primary
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nanoparticles and not the agglomerates from the phase transfer or the polymer addition
with a high specific surface area covered by chemically adsorbed fatty acid molecules. Thus it
is assumed that the grafting density of organic matter on agglomerated magnetite
nanoparticles is lower and hence the high temperature mass loss is lower as well.
Separation of Components in a Single Microparticle
With the help of BSE-SEM imaging another separation phenomenon can often be observed
as shown in figure 82 for a PMMA-based composite. This micrograph reveals asymmetric
truncated particles with high magnetite content on one side of the composite microparticle.
figure 82: BSE-SEM images of the particles of the cyclone fraction of a PMMA-based composite with RA-Fe3O4
DRA = 0.2 and F = 0.3, the image on the right is a close-up of a region in the approximate center of the
image on the left, magnification is 3,000x
This type of separation could occur at the moment when the droplet is formed and/or after
the atomization step while drying of the particle, cf. last subsection of paragraph 6.1.1. It is
supposed that the dimension of the magnetite agglomerate visible corresponds to the
agglomerate dimension in the destabilized dispersion. This will be considered again below in
paragraph 6.3.3.
6.2.2 Yield of Product
It is shown in the previous paragraph that only the cyclone material should be considered as
the product of the composite preparation process using the method of spray drying an
organic solvent based mixture of stabilized nanoparticles and a dissolved polymer, especially
for destabilizing polymers. This is because the compositional specifications of the material in
the cylinder and in the filter are too far off of the specified values for filler concentrations F.
Therefore in this paragraph the yield of cyclone material is investigated depending on the
filler concentration F for RA-Fe3O4 and DRA = 0.2. The results are presented in figure 83
plotting the yield against the filler concentration, where the yield is the mass of material in
the cyclone coarse exit related to the entire solid content spray-dried.
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figure 83: Yield of product at the coarse exit of the cyclone as a function of the filler concentration F for PMMA-
based composites with RA-Fe3O4 and DRA = 0.2
It can be seen that the yield of the product at the cyclone increases with increasing filler
content from 70 % up to 90 %. This is due to a decreasing yield in the cylinder. There are two
explanations for this. On the one hand, the stickiness of the microparticles is reduced with
increasing filler concentration so the particles will not get stuck in the cylinder. However, this
would mean that the coarse particles would be found in the cyclone, which is not the case
when looking at the fraction size distributions as presented below in 6.2.5. On the other
hand, chapter 5 teaches that with increasing filler concentration, thus decreasing polymer
concentration in the spray-dried dispersion, the stability of the nanoparticles improves and
the content of agglomerates is reduced. With this, the reduction of amount of coarse
particles in the cylinder could be due to a reduced amount of magnetite nanoparticle
agglomerates, which could influence formation of large particles as reported in [220].
Besides the better yield with increasing filler concentration the powder bulk properties are
affected in a way that the bulk powder is denser and shows a better flowability. Furthermore
the electrostatic charging of the particles whilst collecting in the cyclone is reduced, which is
due to the reduction of the dielectric polymer content. This improves the discharge of
material out of the collection vessel.
6.2.3 Influence of the Polymer
The investigations on the colloidal stability of dichloromethane dispersions of RA-Fe3O4 with
the polymers PMMA, PC and PVB are discussed in paragraph 5.3.1. In this chapter it is to
show that the colloidal stability impact can be correspondingly visualized in the dispersion of
magnetite nanoparticles within the spray-dried composite microparticles. Therefore, the
distribution of magnetite nanoparticles in the polymer matrix after quick solvent
evaporation in the spray dryer with a polymer concentration of cpolymer = 52 g/l is presented.
Composite microparticles with the composition by weight of: 30 % Fe3O4, 6 % ricinoleic acid,
64 % polymer (PMMA, PC or PVB), are shown in figure 84. In the center area of each image a
110
single spherical spray-dried microparticle is depicted with a diameter of about 2 . The
images are inverted back-scattering scanning electron micrographs (BSE-SEM). The heavy
iron atoms in the magnetite nanoparticles scatter electron back more efficiently than the
other atoms in the composite (carbon, oxygen and hydrogen) resulting in higher electron
densities which appear black when inverting the intensities of the BSE-SEM image. The
microparticles rest on a sticky carbon patch which appears lightly grey.
figure 84: Inverted BSE-SEM images of composites with RA-Fe3O4 at F = 0.3 and DRA = 0.2 for the destabilizing
polymers PMMA and PC as well as the stabilizing polymer PVB [27]
Phase separation leads to large agglomerates of magnetite apparent in the PMMA and even
more so in the PC composite microparticles. For PC, much less smaller magnetite spots can
be detected, taking into account that the resolution with PC is poorer compared to PMMA
because of the enhanced electrostatic charging of PC. For PVB, regarding the low resolution
of the electron microscope, no agglomerates are to be found and the microparticle appears
much more homogeneous. Using a higher resolution system, the distribution of magnetite in
the cross-section of injection molded samples is presented in paragraph 6.3.1, below.
6.2.4 Influence of the Surfactant
It is shown in paragraph 4.3 how different fatty acids, namely ricinoleic acid (RA), linoleic
acid (LA), oleic acid (OA), myristic acid (MA) and caprylic acid (CA) influence the state of
dispersion after the phase transfer process step. Continuing in paragraph 5.3.2, the impact
of these different fatty acids on the colloidal stability in solutions of PMMA and PVB is
discussed. In this paragraph the influence of the fatty acid surfactant type on the composite
preparation with the spray drying method is presented. The results are published as well in
[23].
PMMA-based Composites
In figure 85 BSE-SEM images of microparticle composites are presented with F = 0.3 in the
destabilizing polymer PMMA at two magnifications. When processing composites with this
composition, the polymer concentration in the solvent based mixture being atomized is
52 g/l. In figure 50 of paragraph 5.3.2 investigating the colloidal stability, this composition is
found in the far right points and the primary particle concentrations are 14.9 %, 6.0 %, 2.4 %,
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~0 % and 1.7 % for RA, LA, OA, MA and CA, respectively. The other nanoparticles are
agglomerated in yet unspecified sizes.
figure 85: BSE-SEM images of spray-dried composites of (from left to right) RA, LA, OA, MA and CA coated
magnetite (appearing light for high back scatter electron densities of iron atoms) in PMMA,
magnification of 2000x and 10000x in the upper and lower row, respectively [23]
For all samples one can notice a phase separation as well as heterogeneous distribution of
the nanoparticles, which form rather large agglomerates (white spots). The largest of these
agglomerates are found for MA and CA samples. A dramatic phase separation is observed for
the MA sample, which also shows the lowest primary particle concentration for the solvent
based polymer concentration. In any case the findings coincide with the destabilization of
PMMA with a high polymer concentration in figure 50.
In table 9 the granulometric parameters median size x50,3 obtained with laser diffraction and
the calculated specific surface area SPSD as well as the measured specific surface area
obtained with the BET method SBET are presented for the different fatty acid coated
magnetite PMMA composite microparticles together with the structural parameter SP,
accounting for morphology as introduced in paragraph 6.1.1.
table 9: Granulometric data: median microparticle size, specific surface area calculated from the particle size
distribution, BET surface and structural parameter SP of the spray-dried samples with PMMA as the matrix
polymer [23]
fatty acid x50,3 in µm SPSD in m2/g SBET in m2/g SP
RA 3.37 1.51 2.77 54.5
LA 3.08 1.74 3.90 44.6
OA 3.17 1.72 4.00 43.0
MA 3.95 1.35 12.44 10.6
CA 3.08 1.69 8.00 21.1
The particle sizes do not vary significantly, only MA stands out with larger particles, which
also leads to a smaller calculated SPSD. It is supposed that the larger agglomerates for MA as
compared to CA are due to the reduction of the pH from 9 to 8 in order to guarantee a
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complete phase transfer, as shown in figure 27 and described in paragraph 4.3.1. The
structural parameter SP, however, correlates well with the order of colloidal stability,
presented in table 8. Thus it is supposed, that the presence of non encapsulated liberated
nanoparticle agglomerates dominates this value. The more nanoparticles are not
encapsulated in the matrix polymer, the lower the value is, especially since the morphology
of the microparticles is similar, cf. figure 85.
PVB-based Composites
In figure 86 the BSE-SEM images of microparticle composites with F = 0.3 in the stabilizing
polymer PVB for two magnifications are presented in a comparable way to the PMMA based
composites above, cf. figure 85.
figure 86: BSE-SEM images of spray-dried composites of (from left to right) RA, LA, OA, MA and CA coated
magnetite (appearing light for high back scatter electron densities of iron) in PVB, magnification of
2000x and 10000x in the upper and lower row, respectively; to be compared to the images in figure
85 [23]
Two main differences are observed comparing PMMA composites with PVB composites. The
particles are larger and the phase separation is reduced. Only for OA (third images from the
left in figure 86) bright spots depicting dense agglomerates are visible. For MA,
microparticles with different filler contents are observed. However, no dense agglomerates
occur, which means that the distribution is still poor but the deagglomeration due to PVB
adsorption is successful. Both RA and LA do not show any signs of phase separation.
Certainly the size of the particles is determined by the viscosity of the solvent based mixture
which influences droplet formation in the external mixing two fluid pressurized nozzle. As
the viscosity of PVB in DCM is indeed larger than for PMMA, larger droplets are resulting
independently from the fatty acid. The reason why the nanoparticle fillers are distributed
more homogeneously with less agglomeration is the stabilization which is found in figure 52.
In comparison to the discussion for the PMMA composites, above, the granulometric data of
the PVB composite microparticles are presented in table 10.
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table 10: Granulometric data: median microparticle size, specific surface area calculated from the particle size
distribution, BET surface and structural parameter SP of the spray-dried samples with PVB as the matrix
polymer [23]
fatty acid x50,3 in µm SPSD in m2/g SBET in m2/g SP
RA 5.18 1.24 1.57 78.9
LA 5.81 1.13 1.78 63.5
OA 6.56 1.02 4.60 22.2
MA 6.71 1.00 5.81 17.2
CA 7.18 0.98 3.54 27.7
Just as mentioned before, the particles are larger than the PMMA composite microparticles,
which goes along with smaller calculated specific surfaces SPSD. The structural parameter SP,
however, is larger than for the PMMA composites, except for OA which will be discussed
below. If the SP was independent from the morphology and the polymer, the results would
show an improved encapsulation of the nanoparticles and less liberated agglomerates. For
OA grafted magnetite in PVB solution the following could occur. In figure 52 it can be seen,
that for the highest PVB concentration OA has the lowest content of primary particles, so
PVB is less efficient in stabilizing OA. As for the hypothesis of adsorption of PVB at
unoccupied magnetite surface sites, discussed in paragraph 5.3.3, it would mean that OA
might be less efficient in this mechanism as compared to the other fatty acids. In other
words, the surface of magnetite with OA is less accessible for the PVB to adsorb. Since the
BET surfaces are similar for PMMA and PVB samples the difference in SP for OA must in this
case be due to the low calculated SPSD (larger particle size) and dominated by morphological
differences of the polymers.
6.2.5 Increasing Filler Concentration F
In the final paragraph dedicated to the microparticles prepared by spray drying, the
influence on the particle morphology by increasing filler concentration F is discussed. Three
individual particles of the cyclone fraction of PMMA-based composites with filler
concentrations of 30 %, 50 % and 80% are depicted in the BSE-SEM micrographs of figure 87.
The tremendous backscattering of electrons by the iron atoms in the magnetite
a opa ti les e o es o ious, looki g at the i easi g da k ess of the i opa ti les with higher magnetite concentration, making it hard to judge on the nanoparticle
distribution within the composite microparticles for very high filler concentrations. This is
the reason, why the previously discussed investigations are based on composites with
F = 0.3.
114
figure 87: Inverted BSE-SEM images of individual spray-dried microparticles with similar size from the cyclone
fraction with RA-Fe3O4 at DRA = 0.2 at (from left to right) F = (0.3, 0.5, 0.8) with comparable contrast
to visualize the impact of the increasing magnetite concentration, magnification 20,000x
Looking at the morphology it can be noted, that generally the particles with filler
concentrations above 30 % are more compact and less truncated. However, broken particles
still reveal the shell formation whilst drying. The particle size distributions of the three spray
dryer fractions of the samples with F = (0.3, 0.5, 0.8) determined with laser diffraction are
presented in figure 88.
figure 88: Volume weighted PSD of the three spray dryer fractions at the cylinder, cyclone and filter for the
PMMA-based composites of RA-Fe3O4 with DRA = 0.2 and the filler concentrations F = (0.3, 0.5, 0.8)
For all filler concentrations the largest particle sizes are occurring for the cylinder fraction,
followed by the cyclone fraction and the filter fraction, which is expected when compared
with the results in figure 79. There is a clear trend for the PSDs of the product fraction at the
cyclone and the filler concentration. For an increasing filler concentration the particles are
smaller. No such trend is occurring at the other two fractions. The cylinder material shows
larger particles for the highest filler concentration and comparable PSDs for the other two.
The filter fraction is smallest for F = 0.5 and for F = 0.8 not much different from the cyclone
fraction. A reason for a missing trend at the filter fraction is the deviation of the cyclone
performance and cut-off size which also depends on the specific weight of the particles.
In table 11 the granulometric data of the cyclone particle fractions for the different filler
concentrations presented in figure 88 is summarized. The composite specific weight is
calculated and all the other values are based on measurements.
115
table 11: Specific Weight and dynamic viscosity (cf. A.9) of the spray-dried dispersion and granulometric data of
the cyclone particle fraction for different filler concentrations of PMMA-based RA-Fe3O4 composites with
DRA = 0.2, calculated specifi eight , edia parti le size 0, , spe ifi surfa e areas al ulated ith the PSD SPSD (cf. eq. (76)) and measured with BET SBET, SAUTER diameter x3,2 calculated with PSD and structural parameter
SP (cf. eq. (76))
F
in -
dispersion
in g/cm3
dispersion
in mPa·s
composite
in g/cm3
x50,3
in µm
SPSD
in m2/g
x3,2
in µm
SBET
in m2/g
SP
in -
0.3 1.34 2.6 1.51 4.1 (2.3)† 1.41 2.8 (1.9)† 2.11 66.8
0.5 1.35 1.6 1.87 3.0 (1.7)† 1.39 2.3 (1.5)† 2.26 61.5
0.8 1.37 0.5 2.74 1.8 (1.0)† 1.46 1.5 (1.0)† 1.86 78.5
†multiplicity of the particle size at F = 0.8
The SP value results in the lowest structuring for the highest filler concentration of F = 0.8
which can be explained both by a less truncated spherical shape and a denser less structured
shell. However, the differences must be critically observed, since the composites are based
on different precipitation and phase transfer batches with different initial primary particle
concentrations which have not been assessed. This could also be a reason, why the SP value
of the F = 0.3 sample is higher than the one reported for the same composition in table 9.
A more profound comparison must be considered looking at the resulting particle sizes x50,3
and x3,2. One can see that the particles prepared with a filler concentration of 30 % are 2.3
and 1.9 times larger than the for a higher filler concentration of 80 %. The particle sizes of
the compound with 50 % magnetite nanoparticle concentration are 1.7 and 1.5 times larger
than the particles with 80 % magnetite for x50,3 and x3,2, respectively. There are different
causalities for different particle sizes.
If, hypothetically, the droplet size was not affected by the filler concentration and the
particles were solid, then smaller particles are expected for higher filler concentrations and
constant solids concentration csolid in the dispersion, simply due to the higher specific gravity.
Equation (81) shows the relation of droplet diameter xdroplet and composite particle diameter
xcomposite of a solid particle as a function of the components specific gravities , the
compositional parameter F and D and the concentration of solids in the dispersion csolid.
3
1
polymer
lesnanopartic
surfactant
lesnanoparticsolvent
solidlesnanopartic
composite
droplet
1
11
1
DFFDFF
c
x
x
(81)
With DRA = 0.2, csolid = 0.07 and specific gravities of 5.2 g/cm3, 0.9 g/cm3 and 1.2 g/cm3 for
magnetite, surfactant and polymer, respectively, the previous equation results in
116
xdroplet/xcomposite = (2.53, 2.69, 3.05) for F = (0.3, 0.5, 0.8). Assuming equal droplet sizes the
composite diameters of the samples with 30 % and 50 % magnetite would be 1.2 and 1.1
times larger than the particles with F = 0.8, respectively.
The droplet size however is not expected to be the same, since the viscosity as well as the
fluid density are influenced by the polymer content in the dispersion with cpolymer = (64, 40,
4) g/l for F = (0.3, 0.5, 0.8) and csolid = 0.07 resulting in the dispersion specific weight and
dynamic viscosity as presented in table 11. Applying eq. (72) and assuming that only the
liquid viscosity and specific weights are different the droplet sizes of F = 0.3 and F = 0.5
would be 1.11 and 1.08 times larger than for F = 0.8. With respect to eq. (81) this would lead
to particle ratios of 1.34 for F = 0.3 and 1.22 for F = 0.5 to F = 0.8, respectively. These values
are still smaller than the ones reported in table 11, which means that the shell forming
aspect whilst drying of the droplet is influenced by the solids composition. A higher polymer
content could therefore lead to thinner shells and thus more expanded hollow spheres,
which again would have a higher tendency to collapse and lead to the observed truncated
shapes.
6.3 Experimental Results – Injection Molded Composites
One application for the spray-dried microparticle composites discussed above are micro
injection molding processes. For this the fine cohesive powder is agglomerated by a press
and granulation procedure developed as part of a diploma thesis by HORSCHIG, cf. 3.1.5 and
[94]. This previous step is necessary to guarantee sufficient powder flowability for
processing in the injection molding device, where the granules are fed through a hopper, cf.
6.1.2. The final aim is to produce parts with this shaping method. In order to investigate the
ate ial’s e ha i al p ope ties the elt is i je ted i a tool to ge e ate te sile test
specimens. These dog-bone shaped components are used in the present thesis for cross-
section imaging to investigate the distribution of the magnetite nanoparticles within the
polymer matrix. However, the material properties are not discussed as part of this thesis but
to be found e.g. in [22].
The final section of this thesis, dedicated to reveal the effects of mixing a stable nanoparticle
dispersion with a polymer solution in order to prepare nanoparticle-polymer-composites,
shall visualize and quantitatively investigate the impact of the complex particle interactions
in the solvent based dispersion on the final material processing step of injection molding.
Due to the introduced problem of reproducibility of different precipitation batches (cf. 5.3.1)
it proves to be difficult to compare different injection molding batches, which contain these
uncertainties as well. Nevertheless and keeping the reproducibility problem in mind, in the
following four subsections:
117
- composites of PMMA, PC and PVB are compared at two filler concentrations in 6.3.1,
- varying filler concentrations are compared for PMMA based composites in 6.3.2,
- the identity and origin of the agglomerates are briefly discussed in 6.3.3 and finally
- the solution and spray drying method for composite preparation is compared to the
conventional method of melt compounding in 6.3.4.
6.3.1 PMMA vs. PC vs. PVB
In this paragraph injection molded composites of the three polymers PMMA, PC and PVB
with filler concentrations of F = 0.3 and F = 0.5 of ricinoleic acid capped magnetite
nanoparticles with DRA = 0.2 are compared. In paragraph 5.3.1 it is shown that within PMMA
as well as within PC (depletion) flocculation occurred and this leads to the reduction of the
stable primary particle concentration wPrimary with increasing polymer concentration cpolymer.
This destabilizing effect is even stronger for PC. Contrarily, the polymer PVB stabilizes the
dispersion and increases the concentration of stabilized particles. Below, there are three
different sets of cross-sectional images with different magnifications and resolutions. This is
to ensure to include both the agglomerated magnetite nanoparticles as well as the primary
ones for investigation.
The first set of six images (three polymers with two filler concentrations) is presented in
figure 89 and obtained with bright field optical microscopy imaging after image tilt focus
corrections. Magnetite appears in black. The field of view is 76,800 µm2. Bright lines are
attributed to scratches by the polishing method applied, described in [19]. The resolution is
approximately 0.8 µm.
figure 89: Bright field optical microscopy images, (from left to right) PMMA, PC and PVB for (top row) F = 0.3
and (bottom row) F = 0.5, lens magnification: 20x
All images in figure 89 reveal more agglomerates for the filler concentration of 50 % as
compared to the 30 % in the upper row. The background gray scale is getting darker with
higher filler concentration because of more primary particles and smaller agglomerates not
118
resolved by the images. However, the gray scale values should not be compared between
different polymers because of different optical parameters of the pristine polymers. So there
does not have to be a higher content of primary particles for the 50 % PC sample as
compared to PMMA. Only very few agglomerates are visible for PVB. Furthermore the size of
agglomerates is bigger for PMMA and PC as compared to PVB. Continuing discussions are
based on quantitative particle detection and distribution analyses presented in table 12. For
each polymer and filler concentration two images of different spots on the sample are
investigated and processed to obtain information on size and concentration of agglomerates
and calculate the dispersion parameter covVORONOI , cf. 6.1.3.
table 12: Summary of data obtained from the binarized images of figure 89, coefficient of variance of the
VORONOI polygons covVORONOI (cf. 6.1.3), median size of the number weighted distribution of FERET diameters
xFERET 50,0, area fraction of the dete ted agglo erates φAgglomerates (related to the overall volume / area fraction of
the filler nanoparticles in parentheses), agglomerated particle concentration from the primary particle
concentration reported in 5.3.1 with wAgglomerates = 1 - wPrimary and number of agglomerates detected
polymer F
in %
covVORONOI
in -
xFERET 50,0
in µm
φAgglomerates
in %
φAgglomerates
/ φtotal
in %
wAgglomerates
in %
number of
agglomerates
detected
PMMA 30 0.881 1.15 7.1 82 64.8 4,900
PMMA 50 0.880 2.17 12.5 71 32.4 2,837
PC 30* 0.693 1.57 4.1 47 90.9 2,570
PC 50* 0.805 3.30 7.7 44 46.9 992
PVB 30 0.591 0.83 0.3 3 0.8 120
PVB 50 0.595 0.93 0.3 2 3.9 163
* questionable values for there is a high mass loss of larger magnetite agglomerates
The data in table 12 points out, the largest particles at equal filler concentrations are to be
found in the PC samples and the smallest ones in both PVB samples. This approves the
tendencies of colloidal stability, where PC is heavily destabilizing and PVB stabilizes well. The
distribution analysis with VORONOI polygons concludes that the poorest dispersion exists for
both PMMA samples and the best for both PVB samples. This must be due to the mixing of
the agglomerates within the polymer melt in the injection molding device. The fourth
column in table 12 shows the area fraction of particles detected and this is compared to the
area fraction of particles in the composite (relative agglomerate concentration value in the
fifth column). The total agglomerate concentration increases with increasing filler
concentration and is higher for PMMA as compared to PC and PVB. However, the relative
agglomerate concentration decreases with increasing filler concentration. From what is
concluded in 5.3.1 the agglomerate concentration should decrease with decreasing polymer
concentration in the dispersion and thus increasing filler concentration in the prepared
119
composite for equal csolid. So the decrease of relative agglomerate concentration in column
six is conclusive and should follow the data given in the seventh column of table 12. This
phenomenon of increasing agglomerate concentration with increasing filler concentration
and decreasing relative agglomerate concentration is discussed in 6.1.4. Since PVB is
stabilizing, the relative agglomerate concentration is expected to increase with increasing
filler concentration.
One would expect a higher relative agglomerate concentration for the PC samples when
compared to PMMA, for the destabilization with PC is stronger, cf. figure 76. The reasons for
this discrepancy may be:
- a higher number of undetected unresolved agglomerates for PC, which also would
explain the darker grey scale of the background or
- a loss of magnetite during the processing of the dispersion and thus an
overestimation of the actual calculated filler concentration F.
For the PC sample with a set filler concentration of F = 0.5 a magnetite concentration of
wTGA,600 °C = 0.38 is determined for the molded samples and wTGA,600 °C = 0.28 for the cyclone
fraction of one of the two spray-dried samples as part of the injection molded granules. The
same spray-dried sample showed a measured magnetite concentration of 0.69 for the
cylinder fraction of the spray dryer.
Furthermore the relative agglomerate concentration for the PMMA sample is higher than
the determined values from 5.3.1 for both filler concentrations with a high initial primary
particle concentration of about 90 %. There are also several reasons for this happening. On
the one hand it could be that the precipitation batches used for preparing the injection
molded PMMA-based samples led to lower initial primary particle concentrations and thus
higher relative agglomerate concentrations, cf. 6.1.4. Another possible explanation is, that
the large agglomerates are taken as a compact area of magnetite phase, assuming the
porosity of these agglomerates to be zero, which certainly cannot be the case. Furthermore
the optical method is not limited to the uppermost part of the samples and picks up
agglomerate information from below the actual surface. This will increase the determined
filler concentration as well.
Unresolved agglomerates, not detectable by optical microscopy based on relatively high
wavelength electro-magnetic radiation, should become visible by higher resolution BSE-SEM
imaging. Hence, the six samples of figure 89 are reinvestigated at different spots on the
sample. This time the cross-section is generated by cracking the tensile specimen from the
injection molding procedure, no polishing is applied and hence the sample surface is not as
smooth as before and shows cracks.
120
In figure 90 images with a magnification of 5,000x are displayed with a field of view of
400 µm2 and an approximate resolution of 100 nm.
figure 90: Inverted BSE-SEM images (from left to right) PMMA, PC and PVB for (top row) F = 0.3 and (bottom
row) F = 0.5, magnification: 5,000x
With this resolution the individual primary nanoparticles cannot be resolved either so that
all particles detected are taken to be agglomerates. These images allow to visualize smaller
agglomerated structures especially for the PC sample with F = 0.5. However, the individual
primary particles are still not visible, especially for the PVB samples not more individual
particles than before can be seen on the images. Image processing and particle detection for
the 5,000x magnification BSE-SEM samples is applied just like for the optical micrographs
above and the data is presented in table 13.
The median FERET diameters are all smaller than the once presented in table 12. The reason
for this is the higher resolution of SEM compared to optical microscopy. Contrary to the
lower resolution images the number of agglomerates detected is increasing for the higher
filler concentration. This together with decreasing size means, that more small agglomerates
are detected for higher filler concentrations. Again, for the higher filler concentrations the
lower particle sizes are detected for the destabilizing polymer PMMA and PC. All quantified
states of dispersions are poorer than for the optical microscopy investigations with a higher
field of view with cov values above 1. This could mean that the microscopic mixing is not as
thorough as the macroscopic mixing.
121
table 13: Summary of data obtained from the binarized images of figure 90, coefficient of variance of the
VORONOI polygons covVORONOI (cf. 6.1.3), median size of the number weighted distribution of FERET diameters
xFERET 50,0, area fra tio of the dete ted agglo erates φAgglomerates (related to the overall volume / area fraction of
the filler nanoparticles in parentheses), agglomerated particle concentration from the primary particle
concentration reported in 5.3.1 with wAgglomerates = 1 - wPrimary and number of agglomerates detected
polymer F
in %
covVORONOI
in -
xFERET 50,0
in µm
φAgglomerates
in %
φAgglomerates
/ φtotal
in %
wAgglomerates
in %
number of
agglomerates
detected
PMMA 30 2.865 0.28 20.7 238 64.8 689
PMMA 50 1.556 0.14 14.8 84 32.4 2,739
PC 30* 1.159 0.30 24.4 280 90.9 972
PC 50* 0.893 0.24 16.7 94 46.9 1,980
PVB 30 1.012 0.12 2.4 28 0.8 1,339
PVB 50 1.289 0.24 4.4 25 3.9 348
* questionable values for there is a high mass loss of larger magnetite agglomerates
Another distinguishing difference between table 12 and table 13 is the relative agglomerate
concentration in the sixth column. Some of these values are higher in the latter and exceed
100 % which is impossible. This could be due to the following three reasons.
1) The porosity Agglomerates of the agglomerates is not considered and with this taken to
be zero, whereas for closest packing of the nanoparticles within the agglomerate, the
porosity would be 0.26 resulting in a correction factor of 0.74 (1 – ) for the
determined area fractions. This correction factor would be even lower for more
loosely packed agglomerates.
2) The back scatter electron information reaches down below the investigated surface
area several 100 nm in depth, cf. A.2.2. As a consequence of the penetration of the
electron beam in the submicron scale agglomerates not located right at the surface
are detected as well and increase the agglomerate concentration.
3) The agglomerate concentration could be higher because the magnetite precipitation
batches used to produce the spray-dried dispersions led to a lower initial primary
particle concentration, lower than the one present for the samples the data in
column seven of table 13 is extracted from.
Finally, a closer look has to be taken on the bottom row of figure 90 for the composites with
F = 0.5. There is a grayish background for PVB, a homogeneously dotted background for
PMMA and a clustered dotted background for PC. For PVB and PMMA this concludes that
the primary particles must be well dispersed. For the PC sample it seems like there are
fractal loosely agglomerated structures of the nanoparticles. A higher magnification will shed
light on this.
122
In figure 91 the six composite samples with the matrix polymer PMMA, PC and PVB and the
filler concentrations of 30 % and 50 % are presented in inverted BSE-SEM images with a
magnification of 50,000x. The field of view and approximate resolution are 4 µm2 and 10 nm,
respectively. These images are not processed for particle detection, for the resolution
difference caused by the different charging behaviors of the matrix polymers does not allow
for a thorough image processing as achieved before.
figure 91: Inverted BSE-SEM images (from left to right) PMMA, PC and PVB for (top row) F = 0.3 and (bottom
row) F = 0.5, magnification: 50,000x
All six images allow for a good investigation of the state of dispersion of the primary particles
as well as smallest agglomerates. The differences between the three matrix polymers are
outstanding and as expected from the colloidal stability investigations of chapter 5. For both
PVB samples, on the right of figure 91, the very well dispersed primary particle state, as
expected from the investigations of the stability in solvent based dispersion in chapter 5, is
presented. For the PMMA samples on the left of figure 91 it seems like there is a very loose
structure between the primary particles and as if they were not as dispersed as for PVB.
With the higher filler concentration of F = 0.5 the image resolution is poor, which could be
due to charging effects similar to this filler concentration and PC. In the PC sample with 30 %
magnetite only very few primary particles are recognizable and located close to the ten
visible agglomerates. At the higher filler concentration of F = 0.5 the state of dispersion
seems to have improved over the lower magnetite concentration, yet the poor resolution
does not allow for judging whether primary particles or tightly bound agglomerates occur.
123
6.3.2 Increasing Filler Concentration – PMMA composite
In this paragraph PMMA-based composites with RA-Fe3O4 and DRA = 0.2 are compared with
increasing filler concentrations of 30 %, 40 %, 50 % and 60 %. The polished sample cross-
sections are investigated with the lower resolution FEI Phenom BSE-SEM device and
magnifications of 2,000x in figure 92 and 24,000x in figure 93. The fields of view are
14,400 µm2 and 100 µm2 with resolutions of 0.5 µm and about 40 nm, respectively. The
PMMA samples with F = 0.3 and F = 0.5 in this section are not the same like the ones
investigated in paragraph 6.3.2, above.
figure 92: (top row) inverted BSE-SEM images of PMMA-based RA-Fe3O4 composites with DRA = 0.2 and (from
left to right) F = (0.3, 0.4, 0.5, 0.6), (bottom row) binary image of the SEM images above for particle
detection, magnification: 2,000x
The binarized images in the bottom row of figure 92 are evaluated by image processing
methods described in 6.1.3, as it was done before in table 12 and table 13 and the data is
presented in table 14. The state of dispersion is about constant for the samples with 30 % up
to 50 % magnetite with cov values between 0.715 and 0.759. Only the highest filled sample
investigated stands out with a value of 1.371 characterizing a poorer dispersion. The median
FERET diameters are smallest for the lowest filled sample. Supposing the agglomerates are
formed in the dispersion before spray drying this infers that with higher stability for lower
polymer concentrations the agglomerates are bigger. It is more probable that additional
agglomeration occurs for the melt mixing in the injection molding device. This could also
explain the high number of agglomerates larger than about 10 µm in the F = 0.6 sample,
which is larger than most microparticles produced in the spray dryer.
The absolute agglomerate fraction is smallest for 30 % and has a maximum at 40 %. It seems
like the 40 % value is a bit off, which can also be concluded from the relative agglomerate
concentration which should decrease with increasing filler concentration, cf. 6.1.4. All
relative agglomerate concentrations are too high and must be questioned, especially for
values higher than 100 %. There are three reasons for this presented in 6.3.1, above.
124
table 14: Summary of data obtained from the binarized images in figure 92, coefficient of variance of the
VORONOI polygons covVORONOI (cf. 6.1.3), median size of the number weighted distribution of FERET diameters
xFERET 50,0, area fra tio of the dete ted agglo erates φAgglomerates (related to the overall volume / area fraction of
the filler nanoparticles in parentheses), agglomerated particle concentration from the primary particle
concentration reported in 5.3.1 with wAgglomerates = 1 - wPrimary and number of agglomerates detected
F
in %
covVORONOI
in -
xFERET 50,0
in µm
φAgglomerates
in %
φAgglomerates
/ φtotal
in %
wAgglomerates
in %
number of
agglomerates
detected
30 0.741 4.96 11.4 131 64.8 4,932
40 0.715 6.16 23.0 180 51.4 4,539
50 0.759 6.96 19.6 111 32.4 2,653
60 1.371 6.59 20.5 86 21.4 3,473
Assuming only the underestimation of the porosity of the agglomerates was the reason the
values in column five of table 14 do not comply with the relative agglomerate concentrations
presented in column five of table 14. Additionally inferring, the colloidal stability was the
same with equal initial primary particle concentrations. Then the porosity may be calculated
by 1 – φAgglomerates/φtotal/wAgglomerates. This results in porosities of 0.50, 0.71, 0.71 and 0.75 for
the samples in increasing order of filler concentration. Concluding, the packing of the
agglomerates is densest for the lowest filler concentration, which is the least stable system
with the highest polymer concentration in the processed dispersion. However, the
presumptions made are too numerous and the uncertainties too high to overemphasize
these calculations. One problem not mentioned in this regard is that there might still be
smaller agglomerates not resolved by the 2,000x magnification in figure 92. Therefore figure
93 displays the samples with 24,000x magnification but poorer image resolution quality
when compared to the BSE-SEM investigations in 6.3.1, which is due to the different
microscopes used, cf. A.2.2.
figure 93: Inverted BSE-SEM images of PMMA-RA-Fe3O4 composites with DRA = 0.2 and (from left to right)
F = (0.3, 0.4, 0.5, 0.6), magnification: 24,000x
The difference between the images is marginal. However, besides the obvious agglomerates
with sizes around 1 µm, the background where the primary nanoparticles are assumed is
increasing in filler concentration (dark spots) FPrimary from left to right in figure 93 with
125
increasing F and thus expected increasing relative concentration of primary particles
wPrimary = 1 – wAgglomerates = f(1 - φAgglomerates/φtotal).
6.3.3 Identity of Agglomerates
So far it is shown that destabilizing polymers lead to agglomeration in the organic solvent
based dispersion (cf. chapter 5). Furthermore agglomerates are identified in the spray-dried
microparticles (cf. 6.2) and the injection molded composites (cf. 6.3). These three occurring
agglomerates have however not yet been put in context and compared, in order to show the
identity of the agglomerates and whether agglomerates found in the final injection molded
material possibly originate from the destabilized dispersion.
This section is to present the sizes of the agglomerates at different unit operations of the
process for the preparation of PMMA-based composite materials with RA-Fe3O4 and 30 %
magnetite concentration. This investigation is attributed to answer the question whether big
agglomerates found in the molded materials are only because of (depletion) flocculation
before spray drying or mainly due to the melt processing of the composite microparticles. In
figure 94 (a), (b) and (c) there are micrographic visualizations of the agglomerates at the
three process steps: DCM-based PMMA and RA-Fe3O4 mixture, spray-dried microparticle
composite and cross-section of an injection molded part, respectively. Images similar to
figure 94 (b) and (c) are presented in 6.2 and 6.3.2, respectively. For generating the optical
micrograph in figure 94 (a) a stable dispersion of RA-Fe3O4 in DCM carrying only primary
nanoparticles with cnanoparticles = 1.5 g/l is placed under an optical microscope between a
sample carrier and a cover slip. Only a homogenous grey liquid is visual in this case.
Subsequently a mixture with PMMA with cpolymer = 58 g/l is put under the microscope and
within a few minutes larger agglomerates appear, just like it is the case for the investigations
presented in figure 63 of paragraph 5.3.5. By image analysis 314 of these agglomerates are
evaluated and the number weighted sum frequency of the FERET diameters are presented in
the graph in figure 94 with a median size of 1.8 µm.
Based on the findings presented in figure 94, the agglomerates after depletion flocculation
visible under the light optical microscope (Carl Zeiss) in figure 94 (a) inspecting a thin film of
the destabilized dispersion, are of the same order like the agglomerates in the inverted BSE-
SEM of an injection molded sample cross-section in figure 94 (c). The reason that in the
diagram of the size distributions in figure 94, the agglomerates in the molded sample cross-
section seem to be smaller, is that agglomerates with sizes below 1 a ot e esol ed with the light optical microscope.
126
figure 94: Visualization of agglomerates of flocculated magnetite nanoparticles (a) under optical microscope of
a flocculated suspension, (b) as dark spots in composite microparticles and (c) in a cross-section of an
injection molded composite material with inverted BSE-SEM; al images with the same scaling; the
diagram depicts the size distribution of the agglomerates in the suspension compared to the cross-
section showing very similar sizes [27]
The agglomerate sizes in the spray-dried microparticle composites are difficult to assess due
to the morphology influence of the microparticles on the image. However, in the center of
the image in figure 94 (b), one can notice two microparticles with a diameter of about 5 and larger dark spots which are identified as magnetite agglomerates that appear on one
side of the microparticle in a ring shape resulting in Janus type particles. This is often to be
found looking at the spray-dried particles and could be related to the flow dynamics in the
atomized singular droplet pushing the high density agglomerates (specific weight of
magnetite is 5.2 g/cm3 compared to the polymer which is about 1.2 g/cm3) to one side of
the microparticle, cf. 6.2.1.
6.3.4 Solution and Spray Drying Process vs. Melt Mixing
In polymer composites processing, the conventional method of preparation is mixing the dry
disperse pa ti le ediu i a pol e elt usi g a so alled o pou de hi h is a extruder device with counter-rotating twin-screws that cause a high shear in the melt to
sufficiently mix the particles in the melt, cf. 2.2.1. Yet, it is argued that the shear is not strong
enough to disperse nanoparticle agglomerates, which is one reason for the solution and
spray drying method investigated in this thesis.
In this final paragraph a comparison of the dispersion of ricinoleic acid coated magnetite
nanoparticles in PMMA using both methods, is presented. In figure 95, the inverted BSE-SEM
images of sample cross-sections for parts prepared with either method are depicted. The
127
d pa ti les o pou ded ith the t aditio al ethod a e p e ipitated a d i i oleic
grafted magnetite nanoparticles using the phase transfer procedure and spray drying
without polymer addition.
figure 95: Comparison of Inverted BSE-SEM of cross-sections of (left) a melt compounded sample of RA-Fe3O4
compounded with PMMA and (right) one that was spray-dried and injection molded with PMMA and
RA-Fe3O4; for both samples F = 0.3 and DRA = 0.2
The images show very clearly, that both the homogeneity is better for the solution and spray
drying method and the sizes of the agglomerates are much smaller as well. Attention has to
be paid that the scaling on the left image is even bigger than on the right hand side to show
the bigger agglomerates. In order to follow the descriptive procedures of paragraphs 6.3.1
and 6.3.2 the images are processed and evaluated to judge on the state of dispersion and
the agglomerate size. Even though in such a case a visual description is sufficient for the
differences are very strong. However, the data is presented in table 15.
table 15: Summary of data obtained from the binarized images in figure 95, coefficient of variance of the
VORONOI polygons covVORONOI (cf. 6.1.3), median size of the number weighted distribution of FERET diameters
xFERET 50,0, area fra tio of the dete ted agglo erates φAgglomerates (related to the overall volume / area fraction of
the filler nanoparticles), and number of agglomerates detected
method covVORONOI
in -
xFERET 50,0
in µm
φAgglomerates
in %
φAgglomerates /
φtotal
in %
number of
agglomerates
detected
classic
compounding 4.207 1.33 14.4 166 4,735
solution and spray
drying 0.836 0.72 15.6 179 3,810
The state of dispersion for the melt compounded composite is very poor with a high
coefficient of variance of the VORNOI polygon areas of 4.2. The FERET diameter is larger for the
melt compounded system however very low, when comparing with the micrograph in figure
95. This is due to the number weighted distribution parameter x50,0. Since the material
128
composition is the same for both methods it is not surprising that the absolute as well as
relative agglomerate concentrations are similar.
This comparison clearly shows, that the classic melt mixing with high shear forces introduced
by the compounder screw geometries is not capable of dispersing nanoparticle
agglomerates as well as the solution and spray drying method both due to a poor
deagglomeration and furthermore a poorer homogenization. The deagglomeration for
PMMA-based solution and spray drying prepared composites is poor due to the colloidal
destabilization by the non-adsorbing polymer. However, the homogeneity proofs to be
rather well, which is probably due to the small size of the composite microparticles fed to
the extruder.
In conclusion, even if colloidal destabilization causes nanoparticle agglomeration the
solution and spray drying method may be the better choice, when wanting to prepare
nanoparticle-polymer composites which are highly filled and where the nanoparticles and
the polymers are synthesized separately.
129
7 General Conclusions and Outlook
As part of this thesis a process chain has been developed to prepare highly filled composites
of fatty acid stabilized magnetite nanoparticles dispersed in different thermoplastic
polymers. This process chain consists of the following individual process units: nanoparticle
synthesis with a co-precipitation reaction, liquid-liquid phase transfer from water to an
immiscible organic solvent phase with the help of amphiphilic molecules, mixing the
stabilized colloidal dispersion with a polymer solution and spray drying the solution to
withdraw the solvent and prepare microparticle composites. The following individual
process units have been investigated in detail: liquid-liquid phase transfer for the
hydrophobization and stabilization of the nanoparticles, mixing the stabilized organic solvent
based dispersion with a dissolved polymer and spray drying of the solution to prepare
composite microparticles to be used for injection molding applications. The phenomenon of
physical-chemical deagglomeration upon liquid-liquid phase transfer of nanoparticle
agglomerates by chemically adsorbing fatty acid molecules on co-precipitated magnetite is
described with a simple physical model based on the ALEXANDER-DE-GENNES theory and
experiments using five different types of fatty acids: ricinoleic, linoleic, oleic, myristic and
caprylic acid. The primary particle concentration of the transferred particles correlates with
the solubility of adsorbed fatty acid chains in the solvent dichloromethane. Repulsive forces
are introduced as a consequence of the physical model depending on the length of the
adsorbed fatty acids and the distance between adsorbed molecules on the nanoparticle
surface. The numerically determined repulsive forces are in the order of magnitude
compared to the attractive VAN DER WAALS forces. Once the repulsive forces overcome the
attractive VAN DER WAALS interactions deagglomeration occurs. In experiments ricinoleic acid
is found to be most effective in deagglomerating and it is capable of leading to
dichloromethane based dispersions with primary particle concentrations (crystallite size of
about 15 nm) of more than 90 % by weight. The decomposition of chemically adsorbed
ricinoleic acid on magnetite nanoparticles is described and a significant step of magnetite
reduction is found when heating the sample in inert atmosphere between 600 °C and 900 °C.
This is an important finding when discussing thermo gravimetric analyses for investigation of
the stabilized particles and the composites as well. Magnetite reduction is most probably
caused by residual carbonaceous species after detachment and decomposition of physically
adsorbed fatty acid and dehydrogenation of adsorbed fatty acids. Dichloromethane proves
to be a good solvent for the technical polymers poly(methyl methacrylate) PMMA,
poly(bisphenol A carbonate) PC, poly(styrene) PS and poly(vinyl butyral) PVB for preparing
composites based on a solution of the polymers in dichloromethane with fatty acid stabilized
magnetite nanoparticles. PMMA, PC and PS in technical quality cause drastic and rapid
130
flocculation of primary fatty acid stabilized magnetite nanoparticles in the solvents
dichloromethane, ethyl acetate, methyl methacrylate and styrene. The primary particle
concentration decreases exponentially with increasing polymer concentrations. This
tendency is not influenced by the type of fatty acids. It is found that PMMA and PC do not
adsorb on the fatty acid coated nanoparticle surface and the destabilization can therefore
best be described with depletion effects. The governing attractive and repulsive forces in a
dispersion of nanoparticles with non-adsorbing polymers and under the absence of
electrostatic double layer forces are added in a DLVO-like treatment. The analysis of this
superposition of the interactions shows that the polymer concentration and coil size as
compared to the nanoparticles size are the most important values determining stability.
Experiments on colloidal stability are based on gravimetric determination of the primary
particle concentration. This can furthermore be evaluated with light extinction
measurements of diluted samples, for the light extinction follows LAMBERT-BEER’s la fo the investigated particle concentrations. MIE theory calculations demonstrate the strong light
absorption of magnetite nanoparticles explaining the deep black color of stable dispersions.
As part of the stability experiments it is found that the polymer PVB leads to stabilization of
all dichloromethane based fatty acid coated magnetite nanoparticle dispersions
independent from the fatty acid used. PVB is observed to adsorb on the nanoparticle
surface. In addition the hydrodynamic size of the primary particles increases with increasing
PVB concentration and the light extinction versus primary particle concentration is different
from the destabilizing polymers PMMA and PC. The apparent p i a pa ti le concentration increases with increasing PVB concentration. Spray Drying is possible for any
composition of the organic solvent based mixture of fatty acid stabilized nanoparticles and
dissolved polymers. A maximum of 10 % by weight of solids in the solution is possible to
process, for higher viscosities lead to fine web-interconnected composite microparticles.
Due to flocculation the smaller spray dried particles found in the filter of the spray drying
device have a lower content of nanoparticle fillers. The largest particles exhibit a higher
content of nanoparticle fillers. More than 80 % of the microparticles make up the product
and are situated in the coarse outlet of an aero cyclone. Their composition is approximately
equal to the composition of disperse and continuous phase in the solution. Depending on
the primary particle concentration the morphology and homogeneity of the spray dried
microparticles is affected. A structural parameter is introduced to account for the
morphology of the spray dried composite microparticles with higher values for better
dispersed composite microparticles. For characterization of the composites both spray dried
microparticles and cross-sections of injection molded composites, the detection of back
scattered electrons is best to evaluate the distribution of the magnetite filers in the
continuous polymeric matrix. The state of dispersion in the organic solvent based
nanoparticle polymer mixtures correlates to the distribution of the nanoparticle fillers in the
final injection molded composites. PMMA and PC based composite exhibit a high content of
131
agglomerates whereas for PVB the nanoparticulate fillers are well dispersed in correlation to
the colloidal stability in organic solvent suspensions. PC shows a stronger effect in
destabilizing the suspensions and has a worse dispersion in the final composite. Finally it is
proven, that even for flocculated systems, the solution and spray drying method leads to a
better dispersed injection molded composite when compared to a classically melt-
compounded composite of the same composition for ricinoleic acid coated magnetite
nanoparticles in PMMA with a filler concentration of 30 % by weight.
There are many possible continuations of the findings of this thesis. As part of the liquid-
liquid phase transfer various influencing parameters are to be investigated with the aim to
increase the initial primary particle concentration. This is in part investigated most recently
in other PhD thesis at the institute of mechanical process engineering and minerals
processing at the TU Bergakademie Freiberg supported by a DFG priority program SPP 1273
Kolloid e fah e ste h ik . I o e tio to these a ti ities colloidal probe AFM
measurements for the phase transfer of hydrophilic particles under the presence of
adsorbing amphiphilic molecules have to be conducted. Furthermore it is essential to
develop a reproducible synthesis of magnetite nanoparticles. In order to understand and
support the stabilization mechanism of PVB it would be interesting to study the effect of
hydroxyl group content in the PVB structure and its impact on the stabilization of fatty acid
coated magnetite nanoparticle dispersions. Studies on the kinetics of flocculation for
destabilizing polymers will be necessary to understand the destabilization mechanism. The
injection molded composites need to be tested for their mechanical and magnetic properties
especially depending on the filler concentration of highly filled composites. For
characterization of the composites the novel pc-AFM (phase contrast atomic force
microscopy) technique for investigation of the state of dispersion needs to be developed to
guarantee reproducible results. The state of dispersion should be evaluated for different
injection molding parameters. Finally the process chain ought to be tested and further
developed for other nanoparticle and polymer systems.
A1
Appendix
A.1 Materials A3
A.1.1 Materials for Nanoparticle Synthesis A3
A.1.2 Fatty Acids A3
A.1.3 Polymers A4
A.1.4 Organic Solvents A10
A.2 Lab Analytics A13
A.2.1 Particle Size Analysis A13
A.2.2 Microscopy A14
A.2.3 Spectroscopy A17
A.2.4 Thermal Gravimetric Analysis – TGA A17
A.2.5 Powder X-Ray Diffraction – XRD A18
A.2.6 Viscosimetry A18
A.3 Iron-Oxide (Magnetite) Nanoparticle Synthesis A19
A.3.1 XRD Analysis A19
A.3.2 TEM Analysis A20
A.3.3 Particle Size and Charge Analyses A21
A.4 Nanoparticle Phase Transfer Procedure A23
A.5 Investigation of the Colloidal Stability – Primary Particle Concentration A25
A.5.1 Colloid-Polymer-Mixtures A25
A.5.2 Gravimetric Determination of the Primary Particle Concentration A25
A.5.3 UV/VIS Extinction Based Determination of the Primary Particle Concentration A28
A.5.4 Determination of Polymer Adsorption A29
A.6 Mechanical Dispersing Methods A31
A.6.1 Planetary Ball Mill – PM A31
A.6.2 Sonotrode Ultrasound – US A31
A.6.3 Rotor-Stator Mixing A32
A.7 Spray Drying A33
A2
A.8 Optical Properties of Magnetite A35
A.9 Viscosity of Polymer Solutions and Solubility A38
A.9.1 Intrinsic Viscosity A38
A.9.2 Polymer Solubility A39
A.10 Limitations of Dynamic Light Scattering for Dispersions with Primary Particles
and Agglomerates A41
A.11 Calculation of HANSEN Solubility Parameters with the Group Contribution Method
A43
A3
A.1 Materials
A.1.1 Materials for Nanoparticle Synthesis
The following table 16 lists all the chemicals used for synthesis of the magnetite
nanoparticles, which is described in A.3. All material is used as received. The de-ionized
water may not be reproducible in specifications, cf. 5.3.1.
table 16: Chemicals used for magnetite nanoparticle synthesis
substance formula supplier specification
water H2O (lab production from tabbed water with ion-
exchanger Seradest SD 2800)
ammonium
hydroxide NH4OH Sigma Aldrich
ACS reagent
26 %
ferric chloride
hexahydrate FeCl3·6H2O Carl Roth GmbH
Iron(III) chloride hexahydrate
≥ %, p.a., AC“
ferrous
sulphate
heptahydrate
FeSO4·7H2O Carl Roth GmbH Iron(II) sulphate heptahydrate
≥ . %, Ph.Eu ., U“P
hydrochloric
acid HCl Carl Roth GmbH
hydrochloric acid 1 mol/l - 1 N
volumetric standard solution
A.1.2 Fatty Acids
Fatty acids are surfactant molecules used in this thesis for phase transfer and
hydrophobization of magnetite nanoparticles. The following list in table 17 presents the
supply of the materials, which are all used as received.
table 17: List of fatty acids used for the experiments in this thesis
fatty acid abbreviation formula supplier specifications
caprylic acid CA C7H16O2 Sigma Aldrich ≥ %
myristic acid MA C14H28O2 Sigma Aldrich “ig a G ade, ≥ %
oleic acid OA C18H34O2 Alfa Aesar technical, ~90% (GC)
linoleic acid LA C18H32O2 Sigma Aldrich ≥ % GC
ricinoleic acid RA C18H34O3 Sigma Aldrich technical, ~80% (GC)
A4
The chemical structures of the organic acids are displayed in figure 96. It clearly shows the
difference in length depending on the number of C18 atoms. One can furthermore locate
the double bonds for OA and RA (at C9) as well as LA (at C9 and C12). The special feature of
ricinoleic acid is the hydroxyl group at the C12 position. There is another list of chain
properties for the five fatty acids in table 2 of paragraph 4.1.2.
figure 96: Chemical structures of the fatty acids: (a) caprylic acid CA, (b) myristic acid MA, (c) oleic acid OA, (d)
linoleic acid LA, (e) ricinoleic acid RA
All fatty acids used are derived from natural mixtures of different fatty acids. This is
especially notable for RA, which only exhibits a GC determined purity of about 80 % at
technical quality. The other 20 % is made up of other fatty acids such as OA and LA. This has
to be kept in mind when interpreting the experimental findings.
A.1.3 Polymers
The polymers used in this thesis need to be soluble in certain organic solvents, which must
also be suitable carrier liquids for the fatty acid stabilized iron oxide nanoparticles. There is a
choice of four widely applied polymers, namely poly(methyl methacrylate), poly(bisphenol A
carbonate), poly(vinyl butyral) and poly(styrene). All of them exhibit a high transmission for
visible light which in the case of PMMA, PC and PS is even better than for general window
glass material. Especially PMMA and PC are therefore used in the optics industry and
generally as a substitute for glass. PVB has many applications from binders in paints and
ceramics to lamination material for safety glass.
The specific weights are similar with the lightest for PS and the densest for PC and PMMA.
The thermal properties differ very much. Since all polymers listed are amorphous there is
not a distinct melting point but a glass transition θg temperature, which is the lowest for PVB
A5
and the highest for PC. This temperature marks a point where many physical properties such
as strength change, even though it does not stand for a phase transition point. The close
order changes from a glassy to a rubbery state.
table 18: General physical properties of the polymers used in this thesis[154, 247]
polymer acro-
nym
in g/cm3
θg
in °C
nD
in -
light
trans-
mittance
tensile
strength
in MPa
izod
impact
strength
in kJ/m2
poly(methyl
methacrylate) PMMA . … . 106 1.49 92 % … …
poly(bisphenol
A carbonate) PC 1.20 150 1.59 85 % 65.5 850
poly(vinyl
butyral) PVB 1.10 … 1.49 - … 58.7
poly(styrene) PS . … . 100 1.58 90 % … 19.7
In figure 97 (left) the thermal decomposition graphs are displayed showing another thermal
characterization to differentiate between the polymers. The most thermal stable material is
PC. This polymer also shows an incomplete decomposition, which is e.g. described in [200].
Optical properties are displayed in column five and six in table 18 with the refractive index
nD (at 20 °C) and the visible light transmittance (through a 3 mm sample for 600 nm),
respectively. The differences are interesting when optical applications demanding special
properties are of interest. Even more important certainly are the mechanical properties, for
most of the material is chosen because of these values. Both the maximum tensile strength
from tensile tests as well as the izod impact strength are reported in table 18. Again,
especially PC stands out with very high impact strength. This high value means, the material
can absorb a lot of energy contrary to the brittle PS. The high value for PVB justifies the
application in safety glass. Since PC might be more expensive and there are health concerns
about not reacted harmful bisphenol A, PMMA often is an alternative. However, the
strength of PC is usually a justification of using this type of polymer and one reason why it is
tested as a matrix polymer for highly filled composites in this thesis.
Certainly also interesting for certain applications is the wettability with water represented by
the contact angle presented in figure 97 on the right. PS is the most hydrophobic material
with an angle around 90° and PMMA similarly hydrophilic like PVB. Those values have been
experimentally determined on thin layers deposited on glass slides with the sessile drop
method.
A6
figure 97: (left) Thermal decomposition in N2 atmosphere with a heating rate of 20 K/min (from left to right, red
to blue) PMMA, PVB, PS, PC; (right) contact angle with water on a thin film using sessile drop analysis
Connected to the wettability are the HSP values, cf. 5.1.2, presented in table 19.
table 19: HSP values of the polymers used in this thesis
polymer d
in MPa1/2
p
in MPa1/2
h
in MPa1/2 source
PMMA 18.1 10.5 5.1 [248]
PC 18.1 5.9 6.9 [248]
PVB 18.6 4.4 13.0 [154]
PS 18.5 4.5 2.9 [248]
The high hydrogen bonding HSP for PVB is most probably due to the hydroxyl groups in its
structure, as presented below when presenting individual chain properties and supplier
information for all four polymers.
Poly(methyl methacrylate) – PMMA
trade name: Diakon CLG 902
supplier: Lucite
The PMMA used in this thesis is a granular batch for injection molding applications. The basic
structure of the repeating unit of PMMA is presented in figure 98. It shows a vinylic polymer
with a functional methyl opposed by a methacrylate group. The polymer is synthesized by
radical polymerization of the monomer methyl methacrylate.
figure 98: Chemical structure of the repeating unit of poly(methyl methacrylate)
A7
The following table 20 summarizes the chain properties of the PMMA sample. The left part
of the table lists the weight and number averaged molar weights Mw and Mn, the repeating
units’ molar weight M0, the number of repeating units N and the polydispersity index PI. On
the right hand side there are: the length of a repeating unit l0, the segment (or KUHN) length
ls, the segment to repeating unit ratio ls/l0, the number of segments Ns and the radius of
g atio i the θ-state RG.
table 20: Polymer chain properties of the PMMA batch Diakon CLG 902
physical
quantity
unit value source physical
quantity
unit value source
Mw g/mol 88,500 [60] l0 nm 0.25 [118]
Mn g/mol 41,305 [60] ls nm 2.175 [118]
M0 g/mol 100.117 - ls/l0 - 8.7 [118]
N g/mol 413 Mn/M0 Ns - 47 N·l0/ls
PI - 2.14 Mw/Mn RG nm 6.09 eq. (38)
Using eq. (40) the swollen radius of gyration for FLORY interaction parameters of χ = 0.10 and
χ = 0.31 (cf. A.9.2) can be calculated resulting in RG χ = 0.10) = 10.1 nm and
RG χ = 0.31) = 8.9 nm.
Poly(bisphenol A carbonate) – PC
trade name: Makrolon 2407
supplier: Bayer Material Science
The poly(bisphenol A carbonate) batch used in this thesis is a granular substance which is
used for injection molding applications as well as for sheet plastics. In figure 99 the structure
of the repeating unit is presented. Compared to the simple structure of PMMA the repeating
unit of PC is not vinylic and more complex. This is due to the fact that this polymer is
synthesized by the polycondensation reaction of bisphenol A and phosgene (COCl2) resulting
in ester bonds. Hence, PC is a typical representative of synthetic polyesters.
figure 99: Chemical structure of the repeating unit of poly(bisphenol A carbonate) [247]
According to table 20 for PMMA the most important chain properties of the PC batch used
are presented in table 21. The physical quantities are the same like in table 20.
A8
table 21: Polymer chain properties of the PC batch Makrolon 2407
physical
quantity
unit value source physical
quantity
unit value source
Mw g/mol 25,900 [249] l0 nm 1.065 [250]
Mn g/mol 10,100 [249] ls nm 3.000 [251]
M0 g/mol 254.285 - ls/l0 - 2.8 -
N g/mol 40 Mn/M0 Ns - 14 N·l0/ls
PI - 2.56 Mw/Mn RG nm 4.58 eq. (38)
Using eq. (40) the swollen radius of gyration for a FLORY interaction parameter of χ = 0.00 (cf.
A.9.2) can be calculated resulting in RG χ = 0.00) = 7.9 nm.
Poly(vinyl butyral) – PVB
trade name: Mowital B 30 T
supplier: Kuraray Europe
The polymer PVB is applied in this thesis, since it is used as a matrix polymer for composite
synthesis of magnetic beads for bioseparation purposes [92]. It is not a typical polymer for
injection molding of parts like PMMA and PC but can certainly be used therefore. Its rather
complex structure is made up of three different repeating units as presented in figure 100.
Yet it is not considered a co- or terpolymer.
figure 100: Chemical structure of the three repeating units of poly(vinyl butyral) with n butyral, m alcohol and p
acetate subunits [247]
The reason for the structure of commercial PVB is its synthesis pathway. Poly(vinyl acetate)
is the starting material which is synthesized to poly(vinyl alcohol) by hydrolysis [247]. Due to
statistical reasons there are few acetate groups as well as several alcohol subunits left in the
PVB structure. This composition is important for the use of PVB. The polymer is only soluble
in organic solvents and not in water like PVA and PVAc. However, the hydroxyl functional
groups are responsible for desired adhesive interactions necessary for the superior binder
properties and for glass lamination. These functionalities are also most probably the reason
for stabilization of fatty acid coated magnetite nanoparticles, cf. paragraph 5.3.3. Unlike the
two previously described polymers the polymer chain is not characterized by repeating unit
A9
and segment lengths, for which no information is found. Instead in table 22 besides the
chain lengths there is the approximate composition with weight fractions wPVB, wPVA and
wPVAc of butyral, alcohol and acetate subunits.
table 22: Polymer chain properties of the PVB batch used, including the composition of the three functional
units displayed in figure 100
physical
quantity
unit value physical
quantity
unit value
Mw g/mol 32,0007 wPVB - . … . 6
Mn g/mol 30,700 wPVA - . … . 6
M0 g/mol 61.43* wPVAc - 0. … . 6
N g/mol ≈ 5007
PI - 1.04
* average value for a hypothetic averaged repeating unit calculated with the average composition by weight of 59.5 % vinyl butyral, 25.5 % vinyl alcohol and 2.5 % vinyl acetate
Due to the lack of information of the segment lengths it is not possible to calculate
theoretical radii of gyration.
Poly(styrene) – PS
trade name: unspecified
supplier: unspecified
The fourth polymer used only for the preliminary experimental investigations in paragraph
5.3.6 is poly(styrene). Similar to PMMA it is a vinylic polymer typically derived from radical
polymerization of the monomer styrene. Its structure is displayed in figure 101. The special
functionality in the structure is the phenolic side group. It causes the small hydrogen
bonding HSP in table 19 as well as the high contact angle with water in figure 97.
figure 101: Chemical structure of the repeating unit of poly(styrene)
ATR-FTIR and TGA/FTIR investigations show that the material is PS with no additives found.
6 http://www.kuraray-am.com/pvoh-pvb/downloads/Mowital_Technical_Data_Sheet.pdf, march 29th 2012 7 http://www.kuraray-am.com/pvoh-pvb/downloads/Mowital_brochure.pdf, march 29th 2012
A10
A.1.4 Organic Solvents
Organic solvents are liquid carbon containing molecular substances which can dissolve gases,
other liquids or solids without a chemical reaction involved [140]. The organic solvents in this
thesis shall both act as good carrier liquids for fatty acid grafted magnetite nanoparticles as
well as be able to dissolve the polymers introduced above. In the following table 23 the four
solvents used in this thesis are presented.
table 23: List of solvents used in the experiments of this thesis
solvent acronym formula supplier specifications
dichloro-
methane DCM CH2Cl2 Carl Roth GmbH
ROTIPURAN® ≥ 99.5 %,
p.a., ACS, ISO
ethyl
acetate EA C4H8O2 Carl Roth GmbH
ROTIPURAN® ≥ 99.5 %,
p.a., ACS, ISO
methyl
methacrylate MMA C5H8O2 Carl Roth GmbH ≥ 99 %, extra pure
styrene ST C8H8 VWR International Merck KGaA, synthesis
quality
A summary of the general physical properties, specific weight and dynamic viscosity ,
refractive index nD, the HSP and the molar volume v, which is M/ , are to be found in table
24.
table 24: General physical properties of the solvents
solvent (20°C)
[252]
/g/cm3
(20°C)[252]
/mPa·s
nD
/-
d[140]
/MPa1/2
p[140]
/MPa1/2
h[140]
/MPa1/2
v
/cm3/mol
dichloro-
methane 1.331 0.436 1.42 18.2 6.3 7.8 63.86
ethyl
acetate 0.900 0.450 1.37 15.8 5.3 7.2 98.56
methyl
methacrylate 0.940 0.600 1.41 15.8 6.5 5.4 106.51
styrene 0.904 0.754 1.55 17.8 1.0 3.1 114.45
Dichloromethane – DCM
The chemical structure of the simple molecule dichloromethane is presented in figure 102. It
is the ostl used sol e t i this thesis’ e pe i e ts.
A11
figure 102: Chemical structure of dichloromethane
DCM is a polar aprotic solvent which is immiscible with water having a water solubility of
13 g/l at 20 °C (2 g/l at 30 °C). It is a very commonly used solvent e.g. as paint stripper.
Furthermore it can be found in spray painting operations, for decreasing of automotives and
leather products as well as in household products [140]. Since it may be carcinogenic its use
is limited and strongly regulated by EU REACH8. This is also one reason why as part of the
present research it is tested to substitute DCM with the following solvents.
Ethyl Acetate – EA
The chemical structure of the organic solvent ethyl acetate is depicted in figure 103. In this
thesis it is applied in paragraph 5.3.6.
figure 103: Chemical structure of ethyl acetate
Just like DCM it is a polar aprotic solvent. This ester is very common in use, e.g. in glues and
strippers but has the disadvantage over DCM that it can result in explosible atmospheres. Its
solubility in water is higher than for DCM with 83 g/l at 20 °C.
Methyl Methacrylate – MMA
The chemical structure of the solvent and vinylic monomer methyl methacrylate is shown in
figure 104. It is applied in the investigations of paragraph 5.3.6.
figure 104: Chemical structure of methyl methacrylate
8 Official Journal of the European Union L 86/7, Commission Regulation (EU) No 276/2010
A12
The structure is similar to ethyl acetate and it is aprotic. It is the base material for the
polymer PMMA. The reason for application in this thesis is to test its ability to transfer and
stabilize magnetite nanoparticles with the surfactant ricinoleic acid as well as check the
colloidal stability of the particles with dissolved PMMA. The solubility in water is 15 g/l at
20 °C similar to DCM.
Styrene – ST
The chemical structure of the solvent and vinylic monomer styrene is shown in figure 105. It
is applied in the investigations of paragraph 5.3.6 together with MMA.
figure 105: Chemical structure of styrene
The structure shows that styrene is the only non-polar solvent of the present thesis. Styrene
is the base material for the polymer PS and a number of common co-polymers such as ABS.
The reason for application in this thesis is to test its ability to transfer and stabilize magnetite
nanoparticles with the surfactant ricinoleic acid as well as check the colloidal stability of the
particles with dissolved PS. In water it is only soluble with 0.3 g/l at 20 °C.
A13
A.2 Lab Analytics
A.2.1 Particle Size Analysis
For details on the methods of particle size analysis, the following literature can be adviced
[198, 199, 253, 254].
Laser Diffraction
device: HELOS/KR with QUIXEL
company: Sympatec
Laser diffraction is a static particle sizing method of a bulk of particles which relies on the
evaluation of FRAUNHOFER diffraction. The measurement range is 0.1 µm to 8750 µm,
therefore it is applied for the spray dried particles in the present thesis. It is important to
disperse the particles, which is achieved by mixing of the powder in low viscosity silicon oil
M3 as the carrier liquid. Then ultrasonic treatment is applied in steps of 1 min until the PSD
is constant. The silicon oil guarantees good wettability without swelling of the composite
microparticles.
Analytical Centrifugation with Photo extinction
device: SA-CP 3
company: Shimadzu
Analytical centrifugation is a cumulative size method for fine particles in suspension. The
particle concentration in a certain plane is monitored with light extinction. The
measurement range is depending on the differences in specific weight and the liquid
viscosity and reaches down to 20 nm. The speed of rotation is accelerating with 120 min-2 up
to 5000 min-1 which corresponds to 2,040·g with respect to the radius of measurement
[255]. Dilution of the samples occurs until a certain extinction of the mixed suspension has a
distinct value (between 70 % and 120 % of an arbitrary unit). In the present work the
method is preferred for agglomerated nanoparticles with a broad PSD. Due to the
disregarded extinction intensity over size for particles smaller the illumination wavelength
(MIE scattering) the PSD is presented as intensity weighted.
Dynamic Light Scattering
device: Zetasizer ZS Nano
company: Malvern Instruments
The method of dynamic light scattering – DLS (synonyms: photon cross correlation – PCS,
quasi elastic lights scattering – QELS) is based on the free diffusion of particles due to
A14
BROWNian motion of the carrier liquid molecules [112]. Autocorrelation of time dependent
intensity fluctuations of scattered laser comprises the diffusion coefficient D. The well
established STOKES-EINSTEIN relation brings together this diffusion coefficient D and the
hydrodynamic particle size x. By definition of the ISO13321 the so called cumulant method
extracts a single mean size (z-average or xDLS) and a polydispersity index PDI as a result of
DLS. Since for a particle sizing method one is expecting to get a PSD there are mathematical
tools, such as the so called NNLS method (non-negative least square) to derive intensity
weighted PSD from the autocorrelation function. One questionable feature is the
assumption of GAUSSian profiles of the size fractions and the limitation to a certain limited
number of size fractions often creating size ranges with no particle content, which may not
meet reality. Therefore and by experience, the results of a PSD from a DLS method must be
carefully interpreted, cf. A.10. This is even more problematic when wanting to extract
volume or number weighted distributions since good knowledge of the complex optical
parameters must be given, especially when there are particles with sizes roughly as big as
the wavelength of the laser at about 600 nm (MIE scattering). Certainly when measuring
monomodal distributions of particles smaller 60 nm (RAYLEIGH scatterers) DLS is a perfectly
suitable method. Care must be taken for preventing dust or bigger particles in the sample.
Therefore the diluting solvent is typically filtered with a 200 nm PTFE membrane filter. The
Quartz glass cuvettes are also rinsed several times prior to measurements.
Typical measurement settings are:
- wavelength 632.8 nm
- 173° backscattering
- 15 times 30 second measurement duration and averaging
- three measurement repetitions per sample
Furthermore the -potential or more precisely the electrophoretic mobility UE is determined
with this device using special electrophoretic cells and determining the velocity not by DLS
but by a laser Doppler method.
A.2.2 Microscopy
Optical Microscopy
device: Axiolab.A1
company: Carl Zeiss
The optical microscope works with three contrast modes: bright field, dark field and
differential interference contrast. Four objective lenses are installed: 5x, 10x, 20x and 50x. A
A15
digital camera AxioCam with five megapixels is attached. The images are digitally processed
using the firmware AxioVision.
Back Scattering Scanning Electron Microscopy – BSE-SEM
devices: Phenom Nanolab 600
company: FEI FEI
In electron microscopy there are several detection modes all based on different emissions
from the sample. Most widely used in imaging is detecting the so called secondary electrons
(SE-SEM) emitted from the surface with only few nm depth [256]. Their detection leads to
the common plastic images with good depth of field, ideal for imaging morphologies or e.g.
particles. Another, for composite analysis more interesting, imaging mode is based on the
electrons backscattered by the atoms they hit (BSE-SEM). Their detection leads to phase
sensitive images since heavier atoms are more efficient in back scattering electrons so they
appear lighter in the images. However, depending on the energy of the primary beam
electrons and the atomic number the depth information of backscattered electrons is in the
order of several 100 nm [257, 258]. Higher beam energies and lighter atoms cause a deeper
electron penetration.
The difference between SE- and BSE-SEM is visualized in figure 106 for the same sample area
of a fractured cross-section of a PMMA based RA-Fe3O4 composite with 30 % by weight
magnetite.
figure 106: Comparison of a micrograph of a fractured composite surface of RA-Fe3O4 in PMMA with F = 0.3 and
DRA = 0.2 (left) with detection of the secondary electrons and (right) when detecting the back
scattered electrons with much better phase contrast (the lighter sections are due to strong back
scattering of the heavy iron atoms in the magnetite nanoparticles)
The left image (secondary electron detection) only shows the morphology of the fractured
surface. The bright spots in the right image (back scattered electrons) show the location of
magnetite phases because the iron atoms are the heaviest atoms (Z = 26) in the composite
where only hydrogen, carbon and oxygen is found with atom numbers of 1, 6 and 8
respectively. Even sputter coating with an ultrathin layer of AgPd for suppressing sample
charging preserves the phase contrast.
A16
The two applied microscopes have the following specifications:
Phenom FEI: constant 5kV acceleration voltage, only back scattering information
available, low resolution and maximum 24,000x magnification, desktop
device
Nanolab 600 FEI: variable acceleration voltage, secondary as well as back scattering
detection, high resolution even at low kV, up to 50,000x magnifications
in BSE-mode, high-end device
The reason working with these two instruments is the limited availability of the superior
Nanolab 600.
Transmission Electron Microscopy – TEM
device: CM 30
company: Philips
The TEM images are acquired with a CM30 from Philips with an acceleration voltage of
300kV. The samples are prepared by embedding in an epoxy resin and sectioning in an
ultramicrotome with a layer thickness of approximately 80 nm. The sample slices are put on
a carbon grid.
phase contrast Atomic Force Microscopy – pc-AFM
device: XE 100, true non contact mode
company: Park Systems
As part of the efforts to characterize nanoparticle-polymer composites a method based on
non-contact atomic force microscopy (AFM) has been developed, namely phase contrast
atomic force microscopy (pc-AFM) [19]. However, the results of this method are not yet
reproducible and too many influencing parameters are disturbing the evaluation. Hence, it is
not used for the investigation of sample cross-sections in this thesis. Nevertheless it has
been giving the first hint of agglomerates in cross-sections because it enabled the resolution
of individual nanoparticles within the large agglomerate phases, presented in Figure 107.
A17
Figure 107: pc-AFM image of a PMMA-RA-Fe3O4 composite with F = 0.3
A.2.3 Spectroscopy
Photo spectrometry – UV/VIS
devices: Lambda 3B, dual beam Cary 60, single beam
company: Perkin Elmer Agilent
UV/VIS measurements are carried out in a Lambda 3B device from Perkin Elmer with dual
beam set-up using Quartz cuvettes and the carrier liquid in reference position. Only for the
investigations in 5.3.6 the new device Cary 60 from Agilent is used. Both devices work with
CZERNY-TURNER spectrometers. There are two light sources for the Lambda 3B (mercury for
UV and Halogen for VIS) and a single Xenon pulse lamp source for the Cary 60.
Fourier Transform Infrared Spectroscopy – FTIR
device: Tensor 27, equipped with ATR
company: Bruker Optics
For vibrational spectroscopic purposes FOURIER transform infrared spectroscopy (FTIR) is
applied. The IR source is in the MIR range. Solid and liquid samples are investigated using the
attenuated total reflection (ATR) sitting on a ZnSe crystal. The detector for these samples is
PELTIER cooled. An additional gas analysis chamber with nitrogen cooled detector and high
sensitivity is attached to the Tensor 27 for analyzing evolving gas from the TGA, cf. A.2.4.
A.2.4 Thermal Gravimetric Analysis – TGA
device: STA 449 F3 Jupiter
company: Netzsch
Thermal gravimetric analysis is realized with a simultaneous thermal analyzer STA 449 F3
Jupiter from Netzsch which is capable of simultaneously conducting differential scanning
calorimetry (DSC) as well as thermal gravimetric analysis (TGA). In this thesis only the TGA
A18
signals are used. The oven is made of SiC and has a temperature range from room
temperature to 1550 °C. Al2O3 crucibles are used. The purge and protection gas of the scale
is nitrogen N2 with 99.999 % purity. If not mentioned otherwise the samples are heated from
room temperature to 900 °C at a heating rate of 20 K/min with a purge gas flow of
20 ml/min. This leads to sufficiently dense evolving gases for a thorough evaluation with
coupled FTIR.
A.2.5 Powder X-Ray Diffraction – XRD
device: X’Pe t PRO MPD
company: PANalytical
Powder X-Ray diffraction is conducted with a X’Pe t PRO MPD f o PANal ti al usi g CuKα
radiation at a wavelength of 1.5406 Å. This method is applied for phase and crystallite size
analysis [259].
A.2.6 Viscosimetry
The kinetic viscosities of solvents and polymer solutions are determined with UBBELOHDE
viscosimetry [49]. The principle of this method is measuring the time a tested fluid needs to
free fall flow through a defined capillary. With respect to HAGEN-POISEUILLE’s la the ti e it takes to flow through this capillary is proportional to the kinetic viscosity. The capillaries
used are geometry Oc (0.5...3 mm2/s) a d geo et I . … mm2/s). The time is measured
automatically while the capillary filled with the tested liquid rests in a thermostatic bath.
A19
A.3 Iron-Oxide (Magnetite) Nanoparticle Synthesis
The standard procedure for co-precipitation of magnetite nanoparticles is presented here.
The following recipe is for the synthesis of 1 l suspension with 20 g Fe3O4.
- 47.03 g FeCl3∙6H2O (0.174 mol) and 24.19 g FeSO4∙7H2O (0.087 mol) are dissolved in
1 l deionized water using a 1 l round bottom flask
- The solution is stirred at 4000 min-1 for 10 min
- While stirring the solution is heated to 70 °C
- The stirring speed in raised to 9000 min-1 and quickly 60 ml of a 26 % NH4OH solution
are added at the tip of the rotor using a syringe
- After 2 min the stirrer is reduced to 4000 min-1 again and at 70 °C the dispersion is
stirred for another 20 min
- At the end of the process step the dispersion is spilled in a storage container and
cooled down to room temperature
The reaction steps are as follows:
OHOFeOHFeOHFeOOH
ClNHOHFeOOHOHNHFeCl
SONHOHFeOHNHFeSO
24322
4243
424244
42
6262
2
(82)
A.3.1 XRD Analysis
Using powder x-ray diffraction one can determine the phase and size of the crystallites by
investigating the peak positions and line broadening of the angle dependent x-ray
interferences, which can be defined as the primary particle size [260]. Using a GAUSSian
profile for the diffraction peaks one can find the peak location Θ and the width which is the
full width at half of the maximum FWHM. By plotting FWHM∙cos(Θ o e ∙si Θ) the
crystallite size is found in the y-intercept.
interceptecrystallit
y
Kx CuK
(83)
The SCHERRER correction K is often defined as unity, the wavelength of a CuKα radiation
source is CuKα = 1.5406 Å.
A20
figure 108: (left) powder diffractogram (XRD) of the washed and dried co-precipitated magnetite, all peaks
correspond to the magnetite crystal system, the four major peaks are used for calculation of the
crystallite size (right) using the Williamson-hall plot [260]
Using eq. (92) and the WILLIAMSON-HALL plot in figure 108 one obtains a crystallite size of the
magnetite crystals of xcrystallite = 14.8 nm. The peak positions correspond to either magnetite
or maghemite. Another study based on MÖSSBAUER spectroscopy identifies the magnetite
phase of freshly synthesized nanocrystals using the same co-precipitation procedure [153].
A.3.2 TEM Analysis
The TEM in figure 109 shows embedded magnetite nanoparticles. All individual particles are
evaluated by image processing with the FERET diameter (maximum FERET diameter at various
angles).
figure 109: (left) transmission electron micrograph of the precipitated magnetite nanoparticles in a PMMA
matrix with F = 0.3, (right) particle size distribution (number frequency) as obtained from image
analysis of the TEM image
In figure 109 on the right the graph shows the size distribution of the primary particles with
a maximum mode between 12.5 nm and 15 nm which corresponds to the crystallite size
determined with XRD in A.3.1. The largest detected particles are 35 nm and the smallest
roughly 5 nm.
A21
A.3.3 Particle Size and Charge Analyses
The phase transferred functionalized magnetite nanoparticles in DCM present a colloidal
system of a large fraction of stable primary particles of a mean size of about 20 nm and a
smaller fraction of sedimented agglomerates in the size range of about 100 nm up to 4 µm.
This pristine dispersion is characterized with analytical centrifugation and the intensity
weighted cumulative size distribution is depicted in figure 110 together with the volume
weighted frequency distribution of the primary particle fraction measured with DLS. The size
of the primary particles coincides well with the TEM and XRD results presented above. A
small deviation to larger sizes for the DLS measurement can be attributed to the higher
hydrodynamic diameter caused by the adsorbed layer of ricinoleic acid forming a brush-like
structure. Because of the lack of light scattering correction for the sedimentation analysis (cf.
figure 110 top) the broad distribution, spanning from about 20 nm to 4 µm, cannot be
evaluated with a mass weighted particle size distribution.
figure 110: (top) Intensity weighted cumulative particle size distribution of phase transferred magnetite
particles as determined with the cuvette centrifuge and (bottom) volume weighted particle size
distribution of the supernatant after centrifugation without polymer as measured with DLS compared
to the TEM investigation of encapsulated magnetite nanoparticles (inset) [20]
Not functionalized and phase transferred particles co-precipitated are agglomerated due to
the low absolute -potential of -5.0 mV. Their PSD is presented in figure 111. The median size
is approximately 1 µm. When washing the suspension after the precipitation reaction with
de-ionized water keeping the pH at 9.0, the absolute -potential rises to -29.7 mV and the
particles deagglomerate with median sizes of approximately 50 nm.
A22
figure 111: Particle size distributions of precipitated magnetite in water with full ion strength after the reaction
with given zeta-potential (blue line measured with analytic centrifugation) and after washing
reducing the ion concentration increasing the absolute zeta-potential (green line measured with DLS)
The net charge of the magnetite nanoparticles in water at pH of 9.0 is negative.
A23
A.4 Nanoparticle Phase Transfer Procedure
The experimental procedure to transfer aqueous magnetite nanoparticles to the heavier
fatty acid carrying solvent phase of dichloromethane is schematically visualized in four steps
in figure 112.
figure 112: Steps of a gravity driven phase transfer using a organic solvent which is heavier than water, e.g.
DCM
The steps for the gravity driven phase transfer in figure 112 are as follows:
(1) Mixing fatty acid with dichloromethane (under stirring for 5 minutes) and
placing the mixture in a sufficiently large beaker.
The composition of the mixture is defined by the surfactant ratio on magnetite nanoparticles
D and surfactant concentration in the solvent as shown in eq. (84). Furthermore the
nanoparticle concentration cnanoparticles in the solvent after completed phase transfer is given.
DDV
mc
mm
m
m
mD
solvent
solvent
lesnanoparticlesnanopartic
solventsurfactant
surfactant
lesnanopartic
surfactant
(84)
Typical values for D and are 0.2 and 0.02, respectively, cf. 3.2 and [88, 99]. These will
guarantee the complete transfer of the particles and a minimum amount of fatty acid
surfactants.
(2) The synthesized magnetite nanoparticle suspension is carefully added with
the properties: pH 9.0, 20 g/l Fe3O4 concentration and = -5 mV.
(3) The aqueous agglomerated nanoparticles quickly settle to the liquid-liquid
interface and by adsorption of the fatty acids enter the organic solvent phase.
Depending on the fatty acid and the quality of the precipitation batch after
less than 6 hours the entire particle mass is phase transferred.
A24
(4) A clear reflecting interface with absence of black particles in the upper water
phase marks the completed phase transfer. In order to reduce the amount of
ammonia salts at the interface and reduce the pH the magnetite free water is
replaced three times with an equal amount of deionized water. After the last
washing step the entire water is carefully removed.
In paragraph 5.3.6 the phase transfer of magnetite to a lighter organic solvent phase (methyl
methacrylate or styrene) is realized in cylindrical separation funnels. There the steps
(highlighted with *) are as follows.
(1)* Placing the precipitated nanoparticle suspension in the funnel.
(2)* Mixing the fatty acids with the solvent, with respect to eq. (84).
(3)* Adding the fatty acid solvent mixture to the separation funnel and emulsifying
by shaking. Next a repeated procedure of shaking for about 10 seconds and
letting the emulsion separate for 10 minutes six times. With D = 0.2 and
= 0.02 this guarantees a completed phase transfer for ricinoleic acid.
(4)* Addition of 1N HCl to reach a pH of 6.0 in the water phase and thus breaking
the emulsion leading to the clear and reflective interface.
(5)* Washing step of the particle free water phase three times with an equal
amount of deionized water and removal of the lighter water phase at the
bottom of the separation funnel.
The use of separation funnels for the gravity driven phase transfer to a heavier solvent phase
fails due to the choking of the lower outlet by not deagglomerated particle sediments, cf.
chapter 4.
A25
A.5 Investigation of the Colloidal Stability – Primary
Particle Concentration
A.5.1 Colloid-Polymer-Mixtures
Organic solvent based nanoparticle dispersions and polymer solutions are thoroughly mixed
using a vortexer (VORTEX 3 from IKA) for 15 min. The compositions and concentrations of
the experimentally investigated dispersions are presented in table 25.
table 25: Concentrations cpolymer and cnanoparticles of the investigated dispersions of polymers, nanoparticles in
DCM as an organic solvent with a surfactant (fatty acid) to nanoparticle mass ratio D of 0.2, as well as the
resulting filler concentration F of the particles in a composite synthesized with the dispersion withdrawing the
solvent assuming specific weights of 5.2 g/cm3 and 1.2 g/cm
3 for the magnetite nanoparticles and the polymer
as well as the surfactant layer, respectively.
# cpolymer
in g/l
cnanoparticles
in g/l
F
in % by weight
F
in % by volume
1 52.0 24.4 30.0 8.7
2 31.7 24.4 40.0 12.8
3 19.5 24.4 50.0 17.7
4 11.4 24.4 60.0 23.8
5 5.6 24.4 70.0 31.7
6 1.2 24.4 80.0 42.1
7 0.0 24.4 83.3 46.3
The values are chosen so that they meet economic and ecologic specifications for a relevant
technical process of material preparation, cf. paragraph 3.2. The solids mass concentration
csolid in the solvent is set constant at 0.07 which results in a well processable feed for the
spray dryer. The filling degrees F are chosen to meet the specifications for a highly filled
composite material exceeding volume filler concentrations of 10 %.
A.5.2 Gravimetric Determination of the Primary Particle
Concentration
In order to evaluate coagulation and colloidal stability, the weight fraction of primary
particles is quantified as this is a suitable criterion for the desired composite material. For a
broad distributed system with particles and agglomerates spanning a size range of about
10 nm to 10 µm classic particle sizing methods do not give reliable information on volume
weighted distributions, cf. A.2.1 and A.10. The new approach developed here is to
gravimetrically compare the samples of both a mixed system with all particle sizes and the
A26
supernatant of a centrifuged system with only primary particles. For this the nanoparticle
dispersion is mixed with the dissolved polymer at the desired compositions in a 10 ml
centrifugal cuvette. After intensive mixing using a vortexer (VORTEX 3 from IKA) for 15 min, a
sample of 2 ml is taken. Subsequently, the cuvettes are introduced in a lab centrifuge
(Hettich Universal 30F) at 2,800·g for 20 min. After centrifugation a sample b is taken of the
supernatant. The size of the particles x in the supernatant taken from a distance R1 = 0.01 m
below the surface of the dispersion which rotates at a radius of R2 = 0.08 m apart form the
rotation axis is calculated following STOKES law of settling particles in a centrifugal field
presented in eq. (85).
2
1
2
21fluid
2fluidlenanopartic
ln18
2
R
RR
ntx
(85)
The specific weights are 1.33 g/cm3 and 5.2 g/cm3 for the fluid DCM and the particles Fe3O4,
respectively. This results in particles smaller than 26 nm in the supernatant with a fluid
viscosity of 0.41 mPas without addition of a polymer. The sample 7 of table 25 has a the
highest fluid viscosity of 2.2 mPas, 2.8 mPas and 8.4 mPas for PMMA, PC and PVB,
respectively. This leads to particles smaller than 60 nm, 68 nm and 117 nm in the
supernatant for PMMA, PC and PVB, respectively.
Both samples a and b are oven dried for 3 h at 50 °C to evaporate the solvent. The
compositions are then characterized with thermo gravimetric analysis (TGA) using a Netzsch
STA 449 from room temperature to 900 °C with a heating rate of 20 K/min under a nitrogen
atmosphere. Figure 2 shows exemplarily the decomposition curves for the PMMA system
and the samples # 1, 3 and 5 with polymer concentrations cpolymer of 52.0 g/l, 19.5 g/l and
5.6 g/l, respectively. The pristine nanoparticle dispersion (sample # 7) is characterized as well
revealing the amount of fatty acid and a nanoparticle residual mass of about 83 %, as
expected.
The weight concentration of the primary particles wPrimary is simply the ratio of the mass
residues (at 600 °C, cf. 4.3.4 and [21]) of sample b wb to sample a wa, as presented in
eq. (86).
a
bPrimary w
ww (86)
The quantity w generally stands for mass concentrations. Indices a and b are the samples
before and after centrifugation, respectively, as explained in the graphical insets in figure
113.
A27
For polymers which do not decompose but have residual ash masses without any fillers like
for PC, the residual mass of the functionalized nanoparticles as described in eq. (86) has to
be corrected defined by the following eq. (87).
polymerr,nanor,
polymerr,*a/bnanor,
a/b ww
wwww
(87)
With wa/b* being the corrected mass concentrations using the residual mass of the fatty acid
coated nanoparticles wr,nano which are attributed to the decomposition of organic material
like surfactants used for stabilization (wr,nano = 0.833 for D = 0.2) and wr,polymer which accounts
for the residual mass due to ash when decomposing the pure polymer (wr,polymer ≈ 0.2 for
PC).
figure 113: Three representative sets of TGA analyses of the composition of the solids in the pristine dispersion -
samples (a) and in the supernatant - samples (b) to determine the primary particle concentration
wPrimary (numbers explained in table 25) [20]
The error for the primary particle concentration u(wPrimary) is given in eq. (88) with respect to
the errors of the TGA determined residual mass fractions u(wa) and u(wb).
2a
b
aba,Primary
1
w
w
wwuwu (88)
The errors from TGA measurements are taken to be u(wa) = u(wb) = u(wa,b) = 0.02, by
experience regarding sample preparation and few repetitive measurements of selected
samples.
The uncertainties and consequent errors of the polymer concentration cpolymer are caused by
the several gravimetric mixing steps of polymer solutions, nanoparticle dispersions and the
pure solvent. The following calculations in eq. (89) obtain the indices 1 for the polymer
solution, 2 for the nanoparticle dispersion and 3 for the solvent.
A28
3311
31122
23112
23112
1312131
1312131
2polymer
311
32211
311polymer
1
111
111
muwm
Duwmwm
wuwmDm
muwmwD
wuwmBm
mumwBw
Bcu
B
wm
mwDmwm
wmc
(89)
The following values shall be given: w1 = 0.15, u(w1) = 0.0015, w2 = 0.08, u(w1) = 0.0063,
D = 0.2, u(D) = 0.0006 and u(m1) = u(m2) = u(m3) = 0.01.
A.5.3 UV/VIS Extinction Based Determination of the Primary Particle
Concentration
Besides gravimetric determination of the primary particle concentration, it is of interest to
correlate wPrimary to the concentration dependent photo metrically determined extinction E
of the diluted supernatant at a fixed wavelength . This method is quicker than the time-
consuming TGA and could be used instead, if a linear dependency of E versus wPrimary is
noticed. This linear relation follows the assumption of a monodisperse distribution of
particles in the diluted supernatant and thus elimination of the size contribution in the
extinction E as described by MALYNYCH et al. [261] combining MIE’s scattering theory and
LAMBERT-BEER’s la in eq. (90):
edV
NxCxE log,, ext
, (90)
where x is the assumed constant particle diameter, NP is the particle number in the sample
Volume V and d is a fixed inner thickness of the cuvette (pathway of light through the
sample). Using the definition of the volume concentration of the particles in eq. (91):
V
xN
V
VN
6
3lenanopartic
, (91)
with the volume of a spherical particle VP with particle diameter x, eq. (90) can be
transformed into eq. (92).
edx
xCxE log6
,,3ext
(92)
For each sample b 20 µl are diluted with 1 ml of solvent resulting in a maximum volume
concentration of 9.3·10-5 if all nanoparticles are in the supernatant. This is the case for
wPrimary = 1 because the particle concentration of the samples a (before centrifugation) are
A29
constant and should decrease with wPrimary in the diluted samples b, so that the particle
volume concentration φ after dilution is (eq. (93)):
µl
µlcw
1000
20
lesnanopartic
lesnanoparticPrimarylesnanopartic
,
(93)
where nanoparticles is the specific weight of the nanoparticles, which is presumed to be
5,200 g/l for magnetite without considering the adsorbed fatty acid layer that also is not
apparent in wPrimary.
The dilutions are analyzed using a photospectrometer (Perkin Elmer Lambda 3B) with the
pure solvent as the reference sample in quartz cuvettes with d = 10 mm.
The primary particle concentration can also be defined with eq. (94).
0Primary
w, (94)
with the volume fraction of particles φ in the sample b and φ0 in the sample a.
Under the assumption of Rayleigh scattering (small particles with x < 0.1· which have a
refractive index close to 1) the scattering part of extinction is defined by [33] in eq. (95).
1
32
3,
solvent
lesnanopartic
4
3
n
nd
xxE
(95)
Here nnanoparticles and nsolvent are the real terms of the refractive indices of the nanoparticles
and the solvent, respectively. It shows that scattering is hyperbolic proportional to
wavelength with -4. Appendix A.8 presents optical properties and theoretical extinction
behavior of magnetite nanoparticles.
A.5.4 Determination of Polymer Adsorption
The adsorption of polymers onto the nanoparticle surface, here defined as the mass of
polymer mpolymer per mass of nanoparticles mnanoparticles, is determined by solvent evaporation
of samples b above a strong neodymium magnet and subsequent thorough washing with
DCM, holding back the super-paramagnetic nanoparticles and adsorbed matter. The washed
and dried residue is then analyzed with TGA to ascertain with the following relation:
1polymenor,
1polymerr,
lesnanopartic
polymer rwwm
m,
(96)
where wr,polymer and wr, no polymer are the residual mass fractions derived from TGA with and
without polymer, respectively. For PVB several polymer concentrations from 0 g/l to 52 g/l
A30
are considered, whereas for PC and PMMA only the adsorption at one polymer
concentration of 24.9 g/l is investigated. This procedure is necessary for the investigations in
paragraph 5.3.3.
A31
A.6 Mechanical Dispersing Methods
A.6.1 Planetary Ball Mill – PM
device: Pulverisette 5/4 type 05.102
company: Fritsch
Media mills, such as agitated ball mills are widely used for dispersing agglomerated
submicron particles and comminution down to nanoparticles sizes [52, 202, 262-264].
Planetary ball mills are non-agitated media mills which are also used for nanoscale
production of particles [265]. The special motion of the balls has recently been investigated
numerically [266].
For the experiments, balls with 0.1 mm diameter made of yttrium stabilized zirconium oxide
YSZ are applied (Alpine Power Beads). The batch grinding vessel has a volume of 80 ml and is
also made of YSZ. It is filled up to 30 % by volume with the balls including voids which equals
a mass of 20 g of balls. The suspension volume is 20 ml which corresponds to 1.5-times the
volume of the voids of the ball packing. The sun wheel is rotating at 37.7 s-1 with faster
counter-rotating planets with -82.6 s-1. The sun wheel and planet vessel diameters are
200 mm and 65 mm, respectively. This results in an acceleration of the dispersing vessel of
188.3 m/s2 (19·g).
A.6.2 Sonotrode Ultrasound – US
device: SONOPLUS HD 2200
company: Bandelin
Sonotrode ultrasonic treatment is very common for dispersing submicron particle
agglomerates [51]. The mechanical dispersing stress is caused by cavitation events which is
an instable grow of gas bubbles until collapse with subsequent high local shear stress [267].
Adviceable literature on effects of ultrasound cavitation, so called sonochemistry in general
is offered by SUSLICK [268, 269].
The sonotrode used is a 200 W SONOPLUS HD 2200 from Bandelin with the ultrasound
transducer UW 2200 and controller GM 2200. The sonotrode type is VS 70 T with a diameter
of 13 mm made of a titanium alloy. The maximum amplitude is reported as 153 µm9 at a
frequency of 20 kHz. For the experiments a suspension volume of 20 ml is dispersed and
cooled on the mantle of the batch vessel at 20 °C. To release larger not cavitating but
9 http://www.bandelin.com/produkte/p4/p41.htm, June 14th 2012
A32
ultrasound damping gas bubbles the US is pulsed with 50 %, which means ½ a second
ultrasound emission every second.
A.6.3 Rotor-Stator Mixing
device: Ultra-Turrax T18 basic
company: IKA
Dispersing with rotor stator systems is also a common method for submicron particles [50,
204]. The mechanism of deagglomeration is a very high shear in a small gap which is
constantly flushed with a circulating suspension. The device used in this study is an Ultra-
Turrax T18 basic from IKA equipped with the dispersing element S 10 N 10 G. Just like when
dispersing with US a suspension volume of 20 ml is stressed and cooled on the mantle of the
batch vessel at 20 °C. The rotor diameter is 7.6 mm10 with a 200 µm gap. The rotational
speed is set at 200 s-1 resulting in a mantle speed of the rotor of 9.6 m/s which is equal to
the speed of liquid at the rotor resulting in a shear rate of approximately 47,750 s-1 within
the gap.
10 http://www.ika.de/Products-Lab-Eq/Dispersers-Homogenizer-csp-177/, June 14th 2012
A33
A.7 Spray Drying
device: Mini Spray Dryer B-290
company: Büchi
Microparticle composite formation is achieved by quick solvent evaporation using a
commercial lab-scale co-current spray dryer Büchi B-290 with an inert nitrogen atmosphere
equipped with an external mixing two fluid nozzle (cf. figure 70) and a condenser Büchi B-
295. A photograph and a principle scheme of the system are presented in figure 114.
figure 114: Configuration of the lab-scale spray dryer with inert gas flow (left) photograph after spray drying a
PMMA-based composite with RA-Fe3O4 and F = 0.3, (right) schematic drawing with: a) external
mixing two fluid nozzle, b) ventilator for drying gas circulation, c) heater, d) condenser; c)-d) cannot
be seen in the photograph on the left
The important setting parameters are presented in table 26.
table 26: Important setting parameters of the lab scale spray dryer
inlet temperature (control parameter) 60 °C
temperature at the condenser -20 °C
flow rate of drying gas 3.50·101 m3/h
nozzle atomizing gas flow rate 1.05·100 m3/h
feed flow rate (squeeze pump) 7.20·10-4 m3/h
nozzle diameter (feed) 0.7 mm
nozzle diameter (atomizing gas) 1.4 mm
The cyclone cut-off size where 50 % of the feed is transferred to the coarse product is
approximately 1 µm, as determined experimentally.
A34
The experimental protocol is as follows:
(1) turning on the system and setting the condenser at a temperature of 10 °C
before flushing with dry nitrogen
(2) start the ventilator at maximum power (3.50·101 m3/h), the overpressure
behind the filter should read +30 mbar, a lower value may be due to a clocked
filter or a problem with a valve; overpressure is necessary for maintaining an
explosive safe state within the dryer
(3) 10 min purging the entire system with dry technical quality nitrogen at 283 l/h
through the nozzle, the oxygen sensor drops to a value of 0.0 %.
(4) turning on the heating element and setting the condenser to -20 °C
(5) waiting 10 min for a static process with constant temperatures at inlet and
outlet of the cylinder
(6) setting the purge and atomizing gas to 1.05·100 m3/h and pumping pure
solvent with a flow rate of 7.20·10-4 m3/h for five minutes until the system is
static again
(7) feeding the entire dispersion at a flow rate of 7.20·10-4 m3/h
(8) finishing with another five minutes atomizing pure solvent through the feed
(9) turning off the heating device and continuing drying gas circulation through
the system for 10 minutes, then turning off the ventilator and opening the
device to take out the material
A35
A.8 Optical Properties of Magnetite
This excursus is to introduce to the optical properties of the material magnetite and with this
explain the appearance of colloidal dispersions of magnetite nanoparticles. The strong
absorption behavior of magnetite is the bottleneck of characterizing the colloid. This is one
reason why e.g. the gravimetric method in A.5 had to be introduced instead of an optical
method.
A very important optical parameter is the wavelength dependent complex refractive index
which is given in eq. (97).
iknn ~ (97)
This physical quantity depends on the electromagnetic wavelength and is for example
important for MIE evaluation of particle sizes with the methods of dynamic light scattering or
laser diffraction. However, it turns out that it is hard to obtain this data in the literature.
One way to extract the complex indices is by measuring the dielectric properties, because of
the relation in eq. (98) [270].
2,
2
22
211
22
211 kn (98)
Dielectric constants 1 and 2 can be obtained e.g. by reflectance measurements [271]. The
data for magnetite is extracted from SCHLEGEL et al. [271] and presented as a function of the
energy E in figure 115 on the left. This energy can be transformed into wavelengths by
PLANCK’s fa ous elatio E = h·f with the PLANCK constant h and the frequency f with f = c / ,
where c is the speed of light and the wavelength.
figure 115: (left) dielectric properties of magnetite, data extracted from [271] as a function of the energy E,
which is (right) calculated for wavelengths
A36
With the data in figure 115 on the right and eq. (98) the complex refractive index can be
determined as a function of wavelength, which is graphically presented in figure 116 for the
visible electromagnetic spectrum in the center.
figure 116: real and imaginary part of the refractive index of magnetite as calculated from the data in figure
115 using eq. (90)
The imaginary part of the refractive index is responsible for the light absorption and the high
values in figure 116 justify why magnetite appears black. Magnetite is a highly absorbing
medium, which also corresponds to the deep black color of magnetite dispersion already at
low particle concentrations.
The light extinction in particulate dispersionsis introduced in (90) showing to be proportional
to the extinction cross section Cext which is determined by MIE theory. With the help of the
complex refractive index over a wide wavelength range in figure 116 and the free MIE
calculation program MiePlot11 one can calculate the extinction cross-section Cext as well as
the absorption and scattering cross-sections which make up Cext of magnetite nanoparticles
in any solvent. This value is necessary for calculating expected extinction values in UV/VIS
analyses.
The results of the Mie calculation are presented in figure 117. Since the extinction is
dominated by absorption by 99.50 % for 300 nm and 99.98 % for 900 nm the scattering cross
section in the graph is multiplied by 100 and the absorption is divided by two for
visualization purposes.
11 Version 4.2, http://www.philiplaven.com/mieplot.htm (march 22nd 2012)
A37
figure 117: Extinction, scattering and absorption cross-sections Cext, Cscat and Cabs of magnetite nanoparticles
with a diameter of 15 nm and optical properties defined in figure 116 in dichloromethane with a
refractive index of 1.4242 as a function of the wavelength, notice that scattering and absorption
values are 100- and 0.5-fold, i.e. extinction is mainly due to absorption
A38
A.9 Viscosity of Polymer Solutions and Solubility
Using capillary viscosimetry by UBBELOHDE (cf. A.2.6) the dynamic viscosities of polymer
solutions are determined and the results for PMMA, PC, PVB and PS in DCM as well as
PMMA in EA are plotted in figure 118.
figure 118: dynamic viscosities of the polymers of this thesis in DCM as well as PMMA in EA as a function of the
polymer concentration, determined with UBBELOHDE viscosimetry
In a linear plot of ordinate and abscissa the viscosity increases progressively for all solutions.
The least increase is noticed for PMMA in EA as well as in DCM. Most progressive of all is the
solution of PVB in DCM. Following the theory introduced in 5.1.1 it is more useful plotting
the reduced specific viscosity to evaluate the intrinsic viscosity [ ] in A.9.1.
A.9.1 Intrinsic Viscosity
Plotting the reduced viscosity sp/cpolymer (cf. eq. (44)) over the polymer concentration
enables to estimate the intrinsic viscosity [ ] which is related to the molar mass in eq. (45).
This is done for all combinations in figure 118 but PVB, for which a linear fit seems
unjustified. The results are plotted in figure 119 and quantitatively evaluated in table 27.
figure 119: Reduced viscosity over polymer concentration for various polymers in DCM and PMMA in EA. The
lines show the linear fit to evaluate the intrinsic viscosity following eq. (46)
A39
In table 27 the intrinsic viscosities are presented with the error evaluated by linear fitting the
data in figure 119 considering the data uncertainties. The fit quality is presented with R2.
table 27: Evaluation of the intrinsic viscosities [ ], the overlap concentration c*
polymer using eq. (42), the radius of
gyration RG using eq. (46), the hydrodynamic radius determined with DLS and the ratio of hydrodynamic radius
to radius of gyration X using eq. (41)
[ ] in l/g R2 in - c*
polymer in g/l RG in nm RH in nm X = RH/RG
PMMA
in DCM 0.031 ± 0.035 0.900 32.2 ± 36.4 5.9 ± 2.2 4.0 ± 0.7 0.68 ± 0.37
PC
in DCM 0.019 ± 0.038 0.980 52.6 ± 105.3 3.1 ± 2.1 3.0 ± 0.9 0.97 ± 0.95
PS
in DCM 0.036 ± 0.010 0.995 27.8 ± 7.7 - - -
PMMA
in EA 0.023 ± 0.003 0.962 43.5 ± 5.7 5.3 ± 0.2 - -
A.9.2 Polymer Solubility
The polymer solubilities in this paragraph are all based on HSP and evaluation of solubility
distance D1,2 using eq. (50) as well as the FLORY interaction parameter with eq. (52). The
theory behind this approach is introduced in 5.1.2.
The solubilities of PMMA, PC and PVB in DCM are presented in table 28. These information
are used for investigations and discussions in 5.3.1. A comparison of the solubility of the
polymer PMMA in the three solvents DCM, EA and MMA is needed for the discussions in
5.3.6. The results are presented in table 29. A comparison of the solubility of the polymer PS
in the two solvents DCM and ST is needed for the discussions in 5.3.6 as well. The results are
presented in table 30.
table 28: Polymer solubility in DCM for PMMA, PC and PVB with HSP values from table 19 and table 24,
solubility distance D1,2 using eq. (50), FLORY interaction parameter χ using eq. (52)
RH
in nm
D1,2
in MPa1/2
χ
in -
PMMA 4.0 ± 0.7 5.00 0.10
PC 3.0 ± 0.9 1.00 0.00
PVB 8.5 ± 0.4 5.59 0.12
A40
table 29: Solubility of PMMA in the solvents dichloromethane, ethyl acetate and methyl methacrylate with HSP
values from table 19 and table 24, solubility distance D1,2 using eq. (50), FLORY interaction parameter χ using
eq. (52)
D1,2
in MPa1/2
χ
in -
DCM 5.00 0.10
EA 7.25 0.31
MMA 6.10 0.24
table 30: Solubility of PS in dichloromethane and styrene with HSP values from table 19 and table 24, solubility
distance D1,2 using eq. (50), FLORY interaction parameter χ using eq. (52)
D1,2
in MPa1/2
χ
in -
DCM 5.25 0.11
ST 3.77 0.10
A41
A.10 Limitations of Dynamic Light Scattering for Dispersions
with Primary Particles and Agglomerates
Certainly dynamic light scattering is a straight forward method when it comes to
determining the size of nanoparticles. Yet, it shall be emphasized here, that, based on
numerous experiences with different types of nanoparticles, this method is not reliable for
dispersions with both small primary particles and large agglomerates tending to settle
rapidly. Generally, for a broad particle size distribution the volume or number weighted
particle size distribution cannot be assessed quantitatively, i.e. the fractions of the sizes are
difficult to be determined. A certain fraction of agglomerated nanoparticles can even make it
impossible to identify the primary particles.
In the following graphs in figure 120 correlation coefficients of a single dynamic light
scattering experiment are presented at different time steps of ricinoleic acid transferred
particles containing agglomerates. Furthermore the intensity weighted frequency
distributions are depicted.
figure 120: (left) correlation coefficient and (right) frequency distribution intensity weighted of one single DLS
experiment for ricinoleic acid transferred particles containing agglomerates at time steps t1 through
t4 which are 2 minutes apart each; additionally the result for the sample after centrifugation
containing only primary particles
There are three fractions found where the smallest clearly represents the primary particles.
Two larger fractions appear at about 100 nm and between 400 nm and 1 µm. The quantity of
the fractions changes with measurement time. This is even more intriguing for the
correlation coefficient which can be regarded as the data of DLS which has the least impact
by mathematical artifacts. The correlation coefficient for longer correlation times (plotted on
the abscissa) which correspond to the larger particles (agglomerates) decreases with
increasing measuring time. This concludes that the sample is losi g la ge pa ti les due to sedimentation and thus withdrawal from the measurement zone. For the purpose of a
statistically reliable sample the measurement time should even be longer when larger
particles occur, due to longer correlation times.
A42
Finally there are at least two reasons why DLS should not be used for such samples. First of
all the size range is too broad and secondly and most importantly the sample is not stable
due to sedimentation of the agglomerates.
A43
A.11 Calculation of HANSEN Solubility Parameters with the
Group Contribution Method
The concept of HSP is introduced in 5.1.2. Details on the theory and usage of HANSEN
Solubility Parameters (HSP) can furthermore be found in [137, 154]. For the fatty acids
capped onto the magnetite nanoparticle surface HOY’s ethod as des i ed i [154] is
applied with the input parameters Ft,i, Fp,i, Vi, T,i(P), B = 277 of the groups i which are defined
below in table 32 and table 33. The parameter ni stands for the number of each group i
within the substance.
The first parameter is the molar attraction function with contributes to the total solubility
parameter (HILDEBRANDT parameter) Ft in eq. (99).
i
FnF it,it (99)
Next is the polar component of the molar attraction function corresponding to the polar HSP
Fp in eq. (100).
i
FnF ip,ip (100)
The molar volume V is defined in eq. (101).
i
VnV ii (101)
The parameter T(P) in eq. (102) is the LYDERSON correction for polymer non-ideality.
i
PiT
PT n )(
,i)( (102)
Eqs. (99) - (102) are so called additive molar functions. The next two eqs. (103) and (104) are
named auxiliary equations necessary to finally evaluate the HSP.
)(
5.0~P
T
n (103)
V
Δ pTP
)()( 777 (104)
The HILDEBRANDT parameter t is defined in eq. (105).
A44
V
nBFthpdt
~222 (105)
The definition of the polar HSP p is given in eq. (106).
2
1
)( ~1
nBF
F
t
p
Ptp (106)
The HSP due to hydrogen bonding h is defined in eq. (107).
2
1
)(
)( 1
P
P
th (107)
With eq. (108) the dispersive HSP is to calculate using eqs. (105) - (107).
2h
2p
2td (108)
In table 31 the group contribution parameters are listed for the groups that make up the
structures of the fatty acids.
table 31: Molar group parameters of the group contribution method of the groups relevant for the structure of
fatty acids
group Ft,i in
J∙ 3)1/2/mol
Fp,i in
J∙ 3)1/2/mol
Vi
cm3/mol T,i
(P)
-CH3 303.5 0 21.55 0.0220
-CH2- 269.0 0 15.55 0.0200
=CH- 249.0 59.5 13.18 0.0185
-CHOH- 591.0 591 12.45 0.0490
-COOH 565.0 415 17.30 0.0400
The numbers of the groups for the fatty acids RA, LA, OA, MA and CA are presented in table
32.
table 32: Number of specific groups in the fatty acids used
Fatty Acid -CH3 -CH2- =CH- -CHOH- -COOH
Ricinoleic Acid (RA) 1 13 2 1 1
Linoleic Acid (LA) 1 12 4 0 1
Oleic Acid (OA) 1 14 2 0 1
Myristic Acid (MA) 1 12 0 0 1
Caprylic Acid (CA) 1 6 0 0 1
In case the fatty acids are grafted chemically to the magnetite surface the carboxyl group is
left out and the numbers of the individual groups are given in table 33.
A45
table 33: Number of specific groups in the grafted fatty acids, neglecting influence from the chemically bound
complex
Fatty Acid
capped Fe3O4 -CH3 -CH2- =CH- -CHOH- -COOH
RA-Fe3O4 1 13 2 1 0
LA-Fe3O4 1 12 4 0 0
OA-Fe3O4 1 14 2 0 0
MA-Fe3O4 1 12 0 0 0
CA-Fe3O4 1 6 0 0 0
Using eqs. (99) - (108) as well as the information in table 32, the HILDEBRANDT and HSP for the
fatty acids are calculated and presented together with the solubility distance to DCM in table
34.
table 34: HSP of FA calculated with the group contribution method and the number of groups from table 32 and
solubility distance in DCM DFA-Fe3O4-DCM with eq. (50) and HSP for DCM in A.1.4
Fatty Acid t in
MPa1/2 d in
MPa1/2 p in
MPa1/2 h in
MPa1/2
DFA-DCM
in MPa1/2
RA 19.68 16.84 8.32 5.89 3.89
LA 18.47 17.16 6.38 2.46 5.73
OA 18.31 17.30 5.73 1.78 6.31
MA 18.20 17.30 5.60 0.76 7.30
CA 18.33 16.87 7.10 1.05 7.30
Using eqs. (99) - (108) as well as the information in table 33, the HILDEBRANDT and HSP for the
fatty acids chemically bound to the Fe3O4 surface are calculated and presented together with
the solubility distance to DCM in table 35.
table 35: HSP of FA-Fe3O4 calculated with the group contribution method and the number of
groups from table 33 and solubility distance in DCM DFA-Fe3O4-DCM with eq. (50) and HSP for
DCM in A.1.4
Fatty Acid
capped Fe3O4 t in
MPa1/2 d in
MPa1/2 p in
MPa1/2 h in
MPa1/2
DFA-Fe3O4-DCM
in MPa1/2
RA-Fe3O4 19.40 17.27 6.89 5.55 2.98
LA-Fe3O4 18.07 17.61 4.02 0.50 7.74
OA-Fe3O4 17.90 17.68 2.81 0 8.61
MA-Fe3O4 17.66 17.66 0 0 10.08
CA-Fe3O4 17.38 17.38 0 0 10.16
The final table 36 compares the solubility distances and FLORY interaction parameters of end-
grafted fatty acids in the solvents dichloromethane, ethyl acetate, styrene and methyl
methacrylate.
A46
table 36: solubility distances of fatty acid caped Fe3O4 in different solvents using eq. (50) as well as FLORY
interaction parameter using eq. (52) and the HSP of the solvents reported in A.1.4 and of the FA-Fe3O4 listed
above
dichloromethane ethyl acetate styrene methyl methacrylate
D12
in MPa1/2 χ
in - D12
in MPa1/2 χ
in - D12
in MPa1/2 χ
in - D12
in MPa1/2 χ
in -
RA-Fe3O4 2.98 0.03 3.72 0.08 6.47 0.29 2.96 0.06
LA-Fe3O4 7.74 0.23 7.72 0.36 4.00 0.11 6.57 0.28
OA-Fe3O4 8.61 0.29 8.50 0.43 3.60 0.09 7.55 0.37
MA-Fe3O4 10.08 0.39 9.69 0.56 3.27 0.07 9.24 0.55
CA-Fe3O4 10.16 0.40 9.48 0.54 3.36 0.08 9.02 0.52
R1
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