CHAPTER-V 100

CHAPTER-V Novel organic-inorganic nanocomposite membranes of

chitosan for pervaporative dehydration of ethanol

ABSTRACT

Preyssler type heteropolyacid, H14[NaP5W30O110], was used as filler to modify

chitosan membranes to develop nanocomposite membranes by solution casting for

use in pervaporation dehydration of ethanol. The physico-chemical properties of the

membranes were investigated. Membrane permeability and selectivity was improved

by adding filler nanoparticles. With increasing filler content, amorphous regions of

the membranes increased due to homogeneous mixing of filler particles in chitosan

matrix. Compared to plain chitosan membrane, nanocomposite membranes exhibited

high thermal stability and improved separation performance. A dramatic

improvement in separation factor of 35,991 for 5 wt.%-loaded nanocomposite

membrane with 10 wt.% feed water mixture at 300C from a base value of 96 observed

for nascent chitosan membrane, is the highest achieved so far using chitosan-based

membrane for ethanol dehydration. Increase of feed water composition and

temperature on pervaporative dehydration of ethanol resulted in a drastic reduction of

membrane performance. Diffusion behavior of water and ethanol through the

membranes was analyzed by Fick’s equation. Pervaporation data were explained in

terms of fractional free volume and porosity, hydrophilicity, surface free energy and

roughness of the membranes. Pervaporation performance was analyzed using the

principles of sorption-diffusion model as well as Flory-Huggins theory.

Results of this chapter are communicated to Journal of Membrane Science

CHAPTER-V 101

V.1. INTRODUCTION

Development of pervaporation (PV) membranes exhibiting unique barrier

properties for ethanol dehydration would profoundly impact the existing

technologies as well as provide entirely new materials. This breakthrough

potential has led to considerable research efforts [1] in developing organic-

inorganic hybrid nanocomposites, which seemingly offer a relatively accessible

means of altering properties of the already existing materials, without recourse to

synthesizing entirely new molecules. Through hybridization, it is possible to

capture the desirable attributes of the organic and inorganic components in a single

composite that possibly offers new synergistic properties. In PV separation,

however, ongoing efforts [2-6] continue to explore the utility of incorporating

nanoparticles into dense membranes for a variety of reasons that mainly includes

improvement in mechanical strength and barrier performance. Incorporation of

nanosized nonporous inorganic fillers increases the diffusion path length for

penetrant molecules to transport across the membrane due to creation of tortuous

path around filler particles [7].

Recent interests in developing nanocomposite membranes (NCMs) have

widely explored the capability of existing nascent membranes used for PV

separation [8-11]. Among the polymers employed, chitosan (CS), which belongs

to a family of polysaccharides and exhibits poor polymer chain packing in part due

to its rigid backbone, low interchain cohesion having hydroxyl as well as amine

reactive groups, high free volume giving low selectivity to liquid penetrants, has

been widely explored in combination with other systems in PV separation [12-14].

Even though CS membrane was first used in ethanol dehydration by Masaru et al

[15], subsequently crosslinking [16,17], blending and polyelectrolyte complex

linkage [18,19] strategies have been used to enhance PV performance of CS

membrane. As of now, no reports are available on NCMs of CS containing

Preyssler type heteropolyacid, H14[NaP5W30O110] i.e., H14-P5 for PV dehydration

CHAPTER-V 102

of ethanol. The available data of flux and selectivity of chitosan or chitosan-based

membranes are not high enough to use them in large-scale operations for ethanol

dehydration. By adding nanofillers, such as H14-P5, low water-selective nature of

amorphous CS can be improved due to the creation of free volume and tortuous

pathways for molecular diffusion to occur [20].

Despite rich hydrophilic hydroxyl and amino groups, CS membrane alone

is not efficient for effective ethanol dehydration. Therefore, its membrane

performance can be improved by combining superior separation performance of

the rigid absorptive inorganic nanoparticles to derive an ideal NCM with desirable

membrane properties. The H14-P5 is chosen as filler because of its strong Bronsted

acidity function with high hydrolytic and thermal stability; having 14 acidic

protons, it is an efficient ‘super acid’. The Preyssler’s anion has a cyclic assembly

of five PW6O22 units, each derived from Keggin’s anion [PW12O40]3− by the

removal of two sets of three corner-shared WO6 octahedra [21]. The bridging and

terminal oxygen atoms are on the periphery of the structure and therefore, are

available to associate with water molecules to form hydrates that are thought to

enhance water selectivity.

Realizing the usefulness of this heteropolyacid to enhance barrier performance,

the NCMs of CS loaded with different amounts of H14-P5 are considered. Membranes

are characterized and used in PV dehydration of ethanol. Ethanol separation is

chosen, since it is the most common renewable biofuel derived from fermentation,

wherein ethanol dehydration has been found to be difficult due to the formation of

azeotropic mixture. The prevailing industrial technologies like azeotropic or

extractive distillation suffer from high energy consumption and the need for an

auxiliary agent. Comparatively, PV has distinct advantages such as a low-energy cost

and ecofriendly nature, which is considered to be a promising alternative [22].

Sorption and diffusion anomalies as well as PV separation data are analyzed based on

the principles of sorption-diffusion model [23] as well as Flory-Huggins theory [24].

Separation optimization of NCMs was investigated by studying the effect of

CHAPTER-V 103

temperature; feed water composition, thermodynamic interaction parameters as well

as surface roughness and free energy, hydrophilicity, proton conductivity, ion

exchange capacity, porosity and free volume effects.

V. 2. RESULTS AND DISCUSSION

V.2.1. X-RD

As displayed in XRD curves of Fig. V.1, crystalline peaks of CS

increasingly diminish by the addition of H14-P5 nanoparticles, giving more of

amorphous regions. Pure CS has peaks at 2θ of 14.6o and 20.3o, but for NCMs,

these have shifted to higher angles of 14.8o to 15.3o and 20.5o to 20.8o, indicating

increasing amorphous nature of CS, enabling high permeation flux. The

interaction between nanoparticles and CS facilitates good dispersion of

nanoparticles.

Fig. V.1. X-RD curves of CS, NCM-5, NCM-10 and NCM-15 membranes.

10 20 30 40 50

H14

-P5

2θ

CS

NCM-5

NCM-10

Cou

nts

NCM-15

CHAPTER-V 104

100 200 300

0.0

0.2

0.4

0.6

61.7 oC

60.2 oC55.2 oC

54.3 oC

294 oC291 oC

288 oC

287 oC

NCM-15

NCM-10

NCM-5

CS

Hea

t flo

w (m

W)

Temperature (oC)

V.2.2. DSC

DSC curves of CS and NCMs presented in Fig. V.2, CS shows the first

endothermic melting point due to the release of moisture at 54.3oC, whereas

NCM-5, NCM-10 and NCM-15 release moisture at 55.2, 60.2 and 61.7oC,

respectively, suggesting increased water retention capacity of NCMs compared to

CS. Exothermic melting point due to polymer degradation is observed at 287oC for

CS, while for NCM-5, NCM-10 and NCM-15 at 288, 291 and 294oC, respectively,

indicating improvement in thermal stability after incorporation of H14-P5

nanoparticles into CS matrix.

Fig. V.2. DSC thermograms of CS, NCM-5, NCM-10 and NCM-15 membranes.

CHAPTER-V 105

V.2.3. DMTA

DMTA is used to investigate microstructure of the membranes. The tan δ

plot as a function of temperature displayed in Fig. V.3 suggests that Tg is the peak

temperature, while relaxation strength corresponds to the height of tan δ peak.

Storage modulus, E′, cross-linking density, νe and number of moles of elastically

effective network chains/cm3, Mc (molar mass between crosslinks) of the

membranes are calculated as: RTE mI

e Φ= ρυ 3 and emcM υρ= where modulus

EI is determined from DMTA analysis at a frequency of 1 Hz at 30oC taking ρm for

membrane density, φ as front factor (φ=1) and RT the energy term.

The systematically increasing values (see Table V.1) of νe for increasing

loading of CS membrane as in NCMs indicate tighter networks compared to CS;

for the latter, Mc is highest, which decreased with increasing filler loading,

suggesting increasingly stronger membranes. Notice that E′ values of NCMs are

higher than CS, which increased with increasing H14-P5 content, while the

corresponding tan δ peaks are shifted to higher temperatures, showing broader

peaks. The peak values increased from 131.56 to 152.48, 152.6 and 154°C for CS,

NCM-5, NCM-10 and NCM-15, respectively, suggesting that the interaction

between hydroxyl groups of CS and PO4 and WO6 of H14-P5 are responsible for

increasing the crosslink density as well as Tg of NCMs with increasing loading.

The observed lower relaxation strength at higher H14-P5 content indicates that

more of CS chain segments are chemically bonded or entrapped within the H14-P5

cluster, restricting the mobility of CS chains.

CHAPTER-V 106

Table V.1. Calculated parameters of the membranes from DMTA data at 30oC and details

of PALS.

V.2.4. SEM

The distribution of nanoparticles in the CS matrix exerts influence on

transport characteristics. Typical SEM images of CS and NCMs displayed in Fig.

V.4(a) – V.4(e) did not clearly reveal the surface roughness, since smooth surfaces

are observed for both CS and NCM-5 with no phase separation or aggregation of

H14-P5 particles. In case of NCM-15, one can visualize a slight roughness of the

surface. A typical cross-section of NCM-15 suggests the homogenous distribution

of filler particles with no agglomeration and no cracks in the bulk of NCMs.

Tg EI

(Pa)

x 10-9

ρ

(g/cm3)

νe

(mol/cm3)

Mc

(g/mol) τ3

±0.02

ns

I3

±0.2

%

Vf

± 1.5

Å3

fv

± 0.12

%

CS 131.6 0.96 0.8079 0.41 1.95 1.78 8.74 77.51 1.22

NCM-5 152.5 1.58 0.8071 1.48 0.55 1.74 10.8 73.9 1.44

NCM-10 152.6 1.35 0.8085 1.68 0.48 1.73 11.2 73.11 1.47

NCM-15 154.0 1.40 0.8116 1.74 0.47 1.70 12.3 70.5 1.56

CHAPTER-V 107

Fig. V.3. DMTA curves for EI, EII and tan δ of CS, NCM-5, NCM-10 and NCM-15

membranes.

50 100 150 200

-3.0G

0.0

3.0G

6.0G

9.0G

12.0G

15.0GCS

Temperature (oC)

EI /

EII (d

ynes

/cm

2 )

0.06

0.09

0.12

0.15

0.18

0.21Tg = 131.56oC

tan

δ

50 100 150 200

0

10G

20G

30G

40G

50G

tan δ

EII

EI

Tg= 153.14oC

NCM-15

Temperature (oC)

EI / EII

0.10

0.12

0.14

0.16

0.18

0.20

0.22

tan

δ

50 100 150 2000.0

5.0G

10.0G

15.0G

20.0G

25.0G

30.0G

35.0G

40.0G

45.0G

50.0GNCM-10

Tg=152.6 oC

EI

EII

tan δ

Temperature (oC)

EI / EII

0.1

0.2

0.3

0.4

0.5

tan

δ

50 100 150 200

5.0G

10.0G

15.0G

20.0G

25.0G Tg = 152.48 oC

EII

EI

NCM-5

Temperature (oC)

EI / EII

0.100

0.125

0.150

0.175

0.200

tan

δ

CHAPTER-V 108

Fig. V.4. SEM images of (a) CS, (b) NCM-5, (c) NCM-10, (d) NCM-15 and

(e) cross sectional view of NCM-15 membrane.

(a) (b)

(c) (d)

(e)

CHAPTER-V 109

V.2.5. AFM

From the AFM images, surface roughness of the membrane was calculated

in terms of Rq, a classical amplitude parameter used to assess the surface texture.

The Rq actually measures the average length between peaks and valleys, and

deviation from the mean line on the entire surface within the sampling length.

Hence, it is a good general description of the height variation, but is insensitive to

wavelength and occasional high peaks and low valleys. The AFM pictures (see

Fig. V.5.)show increasing surface roughness with increasing H14-P5 content,

reflecting increased hydrophilicity. However, SEM images (Fig. V.4) did not

reveal clearly the surface roughness between different membranes.

The Rq values of the membranes show increasing trends with increased

loading, namely, CS (365 nm) < NCM-5 (513 nm) < NCM-10 (704 nm) < NCM-

15 (948 nm) and the highest Rq for NCM-15 indicates greater surface roughness.

Therefore, NCM-15 selectively sorbed more water from the feed than others, but

due to its high fractional free volume (fv=1.56 %), along with water molecules,

some ethanol may also be permeated, thereby reducing membrane selectivity to

water, but increasing the flux and permeance. The 3-D optical images of Fig. V.6

show similar trends to AFM pictures.

CHAPTER-V 110

Fig. V.5. AFM images of CS, NCM-5, NCM-10 and NCM-15 membranes.

CHAPTER-V 111

Fig. V.6. 3-D optical images of CS, NCM-5, NCM-10 and NCM-15 membranes.

CHAPTER-V 112

V.2.6. Surface free energy from contact angle

Membrane surface property has an impact on its separation performance. A

smooth surface with lower surface energy has higher contact angle, θ for water

and higher hydrophobicity. Our measurements show decreasing contact angle with

increasing filler loading, namely, CS (88) > NCM-5 (83) > NCM-10 (80) > NCM-

15 (76) (see Fig. V.7), which indicates increasing hydrophilicity. From the contact

angle data, surface free energy was calculated using Young’s equation. [25]:

slsvlv cos γγθγ −= (V.1)

where γlv is surface tension of water in equilibrium with its saturated vapor, γsv is

surface tension of membrane in equilibrium with saturated vapor of water and γsl is

interfacial tension between membrane and water. As per Neumann’s theory of

equation of state, surface tension is expressed as [26]:

)( 2000124702 svlv.svlvsvlvls e γγγγγγγ −−×−+= (V.2)

upon solving and simplifying using Taylor series, we get,

22

4421

2sec

41

2sec

41

lvlv

lvlv

lvsv γω

θωγ

γθωγ

γγ −−

+±

+= (V.3)

where ω = 0.0001247. Thus, we have calculated using the known values of

γlv, ω and θ . Now rewriting eq. (V.3) for the best fit γsv, we get,

( ) ( ) ( )ω

γωγ

θγωγ

θγγ21

42sec

42sec 2

244−−

+−+= lv

lvlv

lvlvsv

(V.4)

Contact angle data and the calculated γSV values for different membranes are listed

in Table V.2. The γsv values of the NCMs increased with increasing filler loading,

but contact angle decreased.

CHAPTER-V 113

Table 2. Some relevant data on membrane performance and their PV performances in 10 wt. % feed water mixture at 30oC.

σ = Proton conductivity; θ = Contact angle in degree; γSV = Surface energy; IEC= Ion exchange capacity; a† = not

determined.

Membranes IEC

(meq/g)

σ

(S/cm) x 103

θ (o) γSV

(mJ/m2)

Porosity

(%)

Vf

(%)

DS

(%)

J

(kg/m2h)

Permeance

(g/m2hkPa)

βij αij

Jw Je

CS 1.35 a† 88 28.4 30.2 1.22 15.2 0.09 6.8 3.5 96 8

NCM-5 1.64 1.04 83 31.8 43.8 1.47 22.4 0.11 10.2 0.01 35991 2725

NCM-10 1.80 2.52 80 33.8 45.0 1.50 29.9 0.13 11.6 0.07 8173 613

NCM-15 2.10 2.56 76 36.5 48.0 1.56 44.4 0.18 17.3 0.11 6914 549

CHAPTER-V 114

Fig. V.7. Contact angle and degree of swelling vs. amount of H14-P5 loading at

300C.

V.2.7. Membrane performance

V.2.7.1. Influence of filler nanoparticles on extent of swelling, ion exchange

capacity and proton conductivity

Close proximity in solubility parameter, δ of 43.04 J1/2/cm3/2 for CS to that

of water (δ = 47.8 J1/2/cm3/2) [27] contributes to its high water selectivity and flux

during ethanol dehydration. Unfortunately, high hydrophilicity of CS encounters

high degree of swelling, DS, at high water concentration, thereby reducing its

0 5 10 15

76

78

80

82

84

86

88

H14-P5 loaded (wt. %)

Con

tact

ang

le (d

eg)

15

20

25

30

35

40

45

Deg

ree

of sw

ellin

g (%

)

CHAPTER-V 115

membrane performance. Some studies in the literature attempted to improve the

PV performance of chitosan-based membranes for ethanol dehydration [28-30],

but the results are best of academic interests and little of practical significance due

to the observed low separation factors and fluxes. The present study aims to

improve the PV performance of chitosan-based membranes by combining its

favorable properties, namely, flexibility and processability with those of H14-P5

nanoparticles as assessed by proton conductivity and ion exchange capacity data

of NCMs. Increase of filler concentration intensifies the ionic interaction with CS

polymer to boost its performance. However, only the appropriate amount (5 wt.%)

of filler loading out of the two other combinations (10 and 15 wt.% of filler)

investigated have produced NCMs with optimum efficiency for ethanol

dehydration.

It is visualized that H14-P5 nanoparticles used as inorganic fillers into the

dense CS matrix will help to adjust the chain packing density by creating tortuous

pathways and free volume space for easy transport of liquids. By incorporating the

nanoparticles, it is shown that the crystalline domains of CS would diminish with

the appearance of amorphous phase regions (see XRD profiles in Fig. V.1), due to

the disruption of intermolecular hydrogen-bonding of the original CS crystallites,

as a result of favorable interaction between H14-P5 nanoparticles and the CS

chains. From a close examination of degree of swelling, DS data of Table V.1, we

find an increase from 15.2 % for 10 wt. % feed water mixture at 30oC for the

unfilled CS to 22.4 for NCM-5, reaching a maximum value of 44.4 % for NCM-

15, which indicates increasing water sorption capacity of NCMs than the CS. Such

an increase in swelling is due to favorable interaction of the nanoparticles with the

CS matrix. This dependence is supported by increased membrane hydrophilicity

(due to decreasing contact angle) and increase in DS of NCMs with increasing

filler loading for 10 wt.% water-ethanol feed mixture at 300C as displayed in Fig.

V.7.

CHAPTER-V 116

From the results of Table V.2, one can also observe close interdependencies

between extent of swelling, ion exchange capacity, IEC and proton conductivity.

Increased swelling resulted in increased IEC as well as proton conductivity with

increasing filler loading; this is due to increased mobility of ions in the water

phase. A high membrane swelling leads to high mobility of water molecules, both

of which enhance flux or permeance to water. Due to the formation of higher

number of nano-trapping levels in NCMs, interaction of solvent molecules with

the CS matrix will be facilitated, thus contributing to higher proton conduction. In

other words, protons available in H14-P5 take maximum advantage of the

polymeric voids in the NCM matrix to exhibit high conductivity.

As shown in Fig. V.8, higher proton conductivity of NCM-10 and NCM-15

membranes than NCM-5 is a clear evidence of the increase in DS with proton

conductivity. The presence of H14-P5 ameliorates membrane hydrophilicity by

forming hydrogen-bonding between CS chains and the Keggin anion, thus

increasing proton conductivity with increasing temperature, since the process is

thermally activated. The IEC also varies similar to proton conductivity, since both

are influenced by the extent of swelling and that water influences the cluster and

channel size, plasticizes and modifies the membrane properties such that the

observed increase in DS is the result of water retention capacity of filler

nanoparticles due to H-bonding interaction of H2O with PO4 or WO6 groups of

H14-P5. These effects contribute to the improved membrane performance with

filler loading, as will be explained further in section V.2.7.2.

Conductivity increases with increasing temperature due to increased

mobility of water as well as structural reorientation of the matrix. Proton transport

in these systems occurs by the Grotthuss mechanism in which proton forms

hydrogen-bond with water molecules to exit as H3O+, hopping from one active

component to other through a tunneling mechanism. The fact that both proton

conductivity and IEC increase with increasing H14-P5 loading as well as with

temperature supports the observations of swelling results. Notice that IEC is only

CHAPTER-V 117

an indirect, but reliable approximation of proton conductivity. The IEC results

shown in Table V.2 increase with filler loading, and also, proton conductivity

increases to double compared to NCM-15 (6.3 x 10-3 S/cm at 110oC) becomes

twice higher than NCM-5 (3.1 x 10-3 S/cm), suggesting increased interactions of

nanoparticles at higher loading. Almost identical optimum proton conductivities of

NCM-10 and NCM-15 suggest the threshold loading capacity of the membranes.

Arrhenius activation energies calculated from proton conductivity vs temperature

plots shown in Fig. 8 are higher for NCM-5 (32.7 kJ/mol) than for NCM-10 (22.6

kJ/mol) and NCM-15 (25.9 kJ/mol), and these values are in the range observed for

Grotthuss mechanism. High activation energy of NCM-5 suggests its high water

content and hence, high separation ability, since some excess water molecules

present in the membrane are likely to be involved in hydrating H14-P5 and that

water molecules available for hopping mechanism are lower, thereby increasing

the permeation flux and selectivity to water.

V.2.7.2. Influence of filler nanoparticles on separation factor, flux, selectivity

and permeance

As per DMTA data given in Table V.1, introduction of H14-P5 nanoparticles

restricts the chain segmental mobility and increases the membrane strength.

Crosslink densities increase with increasing wt.% loading of H14-P5, while molar

mass between crosslinks decreases, due to chain packing, but storage modulus

increases. Separation factor and flux along with selectivity and permeance results

for 10 wt.% feed water composition at 300C are displayed in Fig. V.9 as a function

of filler loading. Notice that a separation factor of 96 and selectivity of 8 for CS

increased dramatically to its highest separation factor of 35,991 and a selectivity

of 2725 with only a moderate increase in flux from 0.090 kg/m2h to 0.113 kg/m2h

for NCM-5, while permeance of 1.05 x 108 kg/m2s for CS increased to 1.05 x 108

kg/m2s for NCM-5 compared to the much lower separation factors of 8,173 and

6,914 and selectivities of 613 and 549 for NCM-10 and NCM-15 membranes;

however, water permeances of 1.83 x 108 and 2.56 x 108 kg/m2s for NCM-10 and

CHAPTER-V 118

NCM-15 are observed as well as a slight increase in fluxes of 0.130 and 0.182

kg/m2h.

Notice the sudden increases in separation factor and selectivity values for

NCM-5, which declined very fast with higher loadings, namely, 10 and 15 wt.%,

probably due to particle agglomeration, leading to a more heterogeneous

structure. Also, the large amount of Keggin anion disrupts the diffusion path

length by blocking the voids of NCM, but at lower loading, the polymer chain will

readjust to allow more water molecules to transport across the membrane, thus

increasing selectivity to water. In general, all the NCMs showed increasing

selectivity and separation factor as well as flux and permeance compared to

unfilled CS membrane. From a molecular viewpoint also, ethanol is a bigger and

lesser polar molecule than water, will have restricted permeability, thus favoring

more water permeation.

In a previous study by Liu et al [31], silica nanoparticles modified with

sulfonic groups provide extra free volume to the chitosan chains, consequently

altering the spaces for water molecules to permeate through. With the addition of 5

parts per hundred of functionalized silica into CS, the resulting membrane

exhibited a separation factor of 919 (a much lower value than ours) and a flux of

0.410 kg/m2.h, which is quite larger than what we observed for PV dehydration in

10 wt.% water feed mixture with ethanol at 700C. A similar effect was observed

by Chen et al., [29], wherein separation factor of water increased to a maximum

value of 597 at 10 wt.% loading of 3-aminopropyl-triethoxysilane for 15 wt.% of

feed water mixture with ethanol at 500C, and later decreased with increasing filler

loading, but permeation flux kept increasing to reach a value of 0.887 kg/m2.h.

Wang et al., [32] investigated the effect of clay content on pervaporation

performance of 90 wt.% aqueous ethanol solution through polyamide/clay

nanocomposite membrane, wherein they observed a sharp decrease of separation

factor upon increasing the clay content. However, compared with the typical

literature work [33], our results represented by 33,991 for separation factor with a

CHAPTER-V 119

somewhat low flux of 0.113 kg/m2.h for 10 wt.% aqueous ethanol solution in feed

at 300C, which that compared to many other membranes, the present NCMs

showed relatively much higher separation factors with comparable fluxes to water.

Efforts are underway to increase the fluxes. Both high separation factor and

permeation flux are expected for a permeation dehydration operation, and most

case, a trade-off exists between these two factors.

Fig. V.8. Plots of (a) proton conductivity vs temperature and (b) lnσ vs. 1000/T.

CHAPTER-V 120

Fig. V.9. Separation factor, flux, selectivity and permeance of water as a function

of filler loading at 300C for 10 wt.% water-containing feed mixture.

0 5 10 150

5000

10000

15000

20000

25000

30000

35000

40000(a)

H14-P5 loading

Sepa

ratio

n fa

ctor

,βij

0.08

0.10

0.12

0.14

0.16

0.18

Flux

(kg/

m2 h)

0 5 10 15

0

1000

2000

3000

4000

5000

6000

H14-P5 loading

Sele

ctiv

ity,α

ij

0.8

1.2

1.6

2.0

2.4

2.8(b)

J i x 1

08 (kg/

m2 s)

CHAPTER-V 121

V.2.7.3.Influence of filler nanoparticles on free volume and surface morphology

Free volume in NCMs plays a major role in determining the overall

performance of the membranes. In this work, positron annihilation lifetime

spectroscopic analysis (PALS) was used for the analysis of free volume in

polymers. As per free volume theory, diffusion is not a thermally activated process

as in the molecular model, but is assumed to be the result of random redistribution

of free volume voids within a polymer matrix [34]. Voids are formed during the

statistical redistribution of free volume within the matrix. The effect of filler

loading on free volume, fractional free volume and porosity of the membranes, is

shown in Fig. V.10, while numerical data are presented in Table V.2. It is

observed that fractional free volume and porosity increase in a similar manner

with filler loading.

The decrease in free volume is due to the restricted mobility of chain

segments in the presence of filler particles, resulting in reduced free volume

concentrations of the matrix, since the contact surface area between the filler and

the matrix is higher in NCMs, thus reducing free volume concentration. It is also

observed that % fractional free volume increases with filler loading, which can be

attributed to the aggregation of filler nanoparticles with a consequent additional

void formation. The impact of nanoparticles on free volume and the barrier

performance has been discussed before [35], wherein it was concluded that

permeability of NCMs is mainly influenced by the fractional free volume effects.

Thus, at H14-P5 content > 5 wt. %, higher fractional free volume or lower free

volume originated from the phase separation of organic phase and inorganic phase

becomes non-selective, resulting in a reduced water selectivity and separation

factor. Such effects were also observed earlier on other membrane systems [4,5].

Overall, the data from PALS indicate that addition of filler increases the accessible

free volume in NCM and that the nanoparticles are able to disrupt the packing of

rigid, bulky CS chains, thereby subtly increasing the free volume available for

molecular transport.

CHAPTER-V 122

As displayed in Fig. V.10, increase of filler loading has a systematic

increase in % porosity and % fractional free volume, due to thermodynamic or

kinetic considerations. This trend is also reflected in AFM and SEM pictures as

well as optical images. Surface free energies calculated from contact angle data

and presented in Table V.2 also show increasing trend with increasing filler

loading. For instance, a surface energy of 28.4 mJ/m2 found for CS increased to

36.5 mJ/m2 for NCM-15. The surface energy of CS calculated by atomistic

simulations [36] using the minimized amorphous CPK model of CS (Fig. V.11)

also gave at higher loadings. The height profiles of optical images are based on

automatically captured images; height, width, and height difference on the surface

are plotted in Fig. V.6 in which color bars indicate the heights of 3D images. The

highest position is displayed in red, while the lowest is in blue. NCM-5 shows the

lowest height profile compared to NCM-10 and NCM-15, suggesting its favorable

surface properties and interaction of filler nanoparticles with the CS matrix to

enhance the barrier performance. However, due to poor compatibility of CS with

H14-P5 nanoparticles at higher loading, the membrane performance also becomes

lower. Moreover, due to a decreased chain-packing relaxation ability of NCMs at

higher filler loading, lower values of αs and αd are observed for NCM-10 and

NCM-15 compared to NCM-5.

V.2.7.4. Influence of filler nanoparticles on sorption and diffusion selectivity

Figure V.12 displays the dependence of sorption selectivity and diffusion

selectivity on filler loading for 10 wt.% feed water composition mixture at 300C.

Notice that sorption selectivity curve is higher than diffusion selectivity, which

suggests the dominant effect of sorption process than the diffusion. At increased

filler loading, both these parameters increase, reaching maxima at 5 wt.% of H14-

P5, followed by a steep decline of diffusion selectivity and a somewhat steady

decline of sorption selectivity with increasing filler content of the matrix. At lower

filler loading, however, the observed high selectivity is due to increased favorable

interaction of water molecules with nanoparticles in the NCM-5 matrix.

CHAPTER-V 123

0 5 10 15

30

40

50

60

70

80

Free volume size in Å3 (Vf ) Porosity (%) Fractional free volume (%) (fv)

H14-P5 loading (wt. %)

Poro

sity

(%) /

Vf

1.2

1.3

1.4

1.5

1.6

f v (%)

Fig. V.10. Plots of porosity, free volume and fractional free volume vs amount of

H14-P5 loading at 30oC.

Fig. V.11. Amorphous (CPK) model of chitosan (colors: carbon atoms - grey,

hydrogen – white, oxygen – red and nitrogen – blue).

CHAPTER-V 124

On comparing the theoretically calculated sorption selectivity curve (dotted

line) with the experimental sorption selectivity curve (solid line) as in Fig. V.12,

we find that the theoretical curve falls lower than the experimental curve

throughout the filler composition. However, the changes observed in experimental

results of NCMs are due to surface adsorption of H14-P5 particles. Sorption

selectivity is always higher for NCMs than the unfilled CS due to higher water

selectivity of NCMs, meaning large amount of water molecules get adsorbed by

the hydrophilic H14-P5 particles, making it more hydrophilic, thereby extracting

higher amount of water on permeate side, consequently enhancing the water

selectivity.

The above-mentioned effects can also be explained on the basis of free

volume effects in polymers, since higher fractional volume or lower free volume

are originated from the phase separation of organic polymer phase and inorganic

phase, which becomes non-selective at higher filler loading, resulting in a

reduction of sorption selectivity as well as diffusion selectivity with increasing

filler loading; similar trends were also observed in the literature [4,5]. The two

main factors that influence sorption selectivity of the films are the availability of

free volume in the polymer matrix and chemical compatibility between the

polymer chain and the solvent mixture. A higher material volume accessible for

liquid sorption due to higher flexibility of NCM-5 than NCM-10 and NCM-15

(Table V.1), would increase the water permeation in NCM-5 compared to NCM-

10 and NCM-15. However, the decline in sorption and diffusion selectivity at high

filler loadings is due to the tortuous pathways for permeating molecules to travel

across the nanocomposite matrix.

The reduced sorption and diffusion selectivity at higher loadings is,

therefore, influenced by the geometry of the filler as well as molecular level

interaction of the matrix with the filler, resulting in a high volume. Similarly,

decrease in diffusion selectivity with extent of filler loading is because of the

increasing aggregation of nanoparticles at higher concentrations, resulting in the

CHAPTER-V 125

0 5 10 150

100

200

300

400

500

600

αs exptal αs calc αd

Amounts of H14-P5 loaded (wt. %)

Sorp

tion

sele

ctiv

ity (α

s )

0

3

6

9

12

15

Diff

usio

n se

lect

ivity

(αd )

weakening of polymer chains, thus facilitating slower diffusion selectivity.

However, the penetrant molecules can easily pass through the inter-phase between

the polymer matrix and the filler particles at lower loadings, but at higher

loadings, they seem to experience a difficult pathway because of the molecular

level dispersion of nanoparticles in the matrix as also observed by PALS data.

Fig. V.12. Sorption selectivity and diffusion selectivity vs. H14-P5 loading (dotted

curve for sorption selectivity represents theoretical calculations)

V.2.7.5. Influence of feed water composition on pervaporation performance and

diffusion coefficient

Feed water composition exerts a considerable effect on membrane

performance as seen from the plots of separation factor, flux, selectivity and

permeance vs feed water composition at 300C displayed in Fig. V.13. Feed water

composition was varied from 10 to 40 wt.% by maintaining the feed temperature

300C. Both separation factor and selectivity show a drastic decline by increasing

CHAPTER-V 126

water composition of the feed mixture. The selective separation of water through

NCMs at higher feed water composition is attributed to increased hydrophilic-

hydrophilic interactions between H14-P5 nanoparticles and the CS matrix.

In a general case of increasing water content, permeation flux also

increases, while separation factor decreases because of the enhanced swelling of

NCMs and such increased membrane swelling is likely to exert a negative effect

on separation factor because the swollen and plasticized upstream membrane layer

permeates a large amount of water molecules by rejecting most of ethanol. On the

other hand, ethanol permeance is much smaller by 2-3 orders of magnitude than

water, but not displayed graphically, to reduce the number of presentations.

Swelling characteristics of NCMs are determined in terms of equilibrium swelling

ratio. Fig. V.14 illustrates the swelling behaviors of all the membranes in varying

amounts of feed water composition at 300C. A smaller degree of swelling of CS

suggests that its permeation flux in pervaporation separation of water is small and

that swelling increases in the order: CS > NCM-5 > NCM-10> NCM-15.

Diffusion coefficient plays an important role in understanding the transport

across the membrane during pervaporation and hence, diffusion coefficients were

estimated from the Ficks theory. These data plotted in Fig. V.15 as a function of

feed water composition, suggest decreasing trends for water diffusion in case of

CS, NCM-5 and NCM-10 up to 30 wt.% of water, beyond which they slightly

increase with increasing feed water concentration. On the other hand, for ethanol,

diffusion coefficients increased continuously with increasing feed water

composition. Diffusion of water is much higher than ethanol by two orders of

magnitude throughout the entire range of feed composition, but increase with

increasing feed water content, thus contributing to increased water flux and

permeance. For an ideal permeation where penetrant molecules do not plasticize

the membrane, but independently permeate through the membrane, then

permeation of each liquid component would be independent of feed water

composition, thus leading to constant flux over the entire feed water composition.

CHAPTER-V 127

On the other hand, increasing water sorption capacity of NCMs is the result of

increased diffusion path lengths for the penetrant molecules as a result of

increased free volume or porosity as well amorphous regions of the matrix as

discussed before.

Fig. V.13. Separation factor, flux, selectivity and permeance of water as a function

of feed water composition at 30oC for 10 wt. % water-containing feed mixture.

0 5 10 150

5000

10000

15000

20000

25000

30000

35000

40000(a)

H14-P5 loading

Sepa

ratio

n fa

ctor

,βij

0.08

0.10

0.12

0.14

0.16

0.18

Flux

(kg/

m2 h)

0 5 10 15

0

1000

2000

3000

4000

5000

6000

H14-P5 loading

Sele

ctiv

ity,α

ij

0.8

1.2

1.6

2.0

2.4

2.8(b)

J i x 1

08 (kg/

m2 s)

CHAPTER-V 128

10 20 30 40

20

40

60

80

100

DS

(%)

Feed water composition (wt. %)

CS NCM-5 NCM-10 NCM-15

10 20 30 400.0

0.1

0.2

0.3

0.4 CS

10 20 30 400

5

10

15

De x

1012

(m2 /s)

Feed water composition (wt. %)

NCM-10

NCM-15

NCM-5

Dex

1012

(m2 /s)

Feed water composition (wt. %)10 20 30 40

0.75

1.00

1.25

1.50

1.75

2.00 NCM-15

NCM-10

NCM-5

CS

Dw x

1010

(m2 /s)

Feed water composition (wt. %)

Fig. V.14. Swelling vs. feed water composition at 30oC

Fig. V.15. Effects of feed water composition on diffusion coefficients.

CHAPTER-V 129

V.2.7.6. Influence of temperature on pervaporation performance and diffusion Temperature has a significant effect on membrane performance. The

influence of temperature over 30o-60oC on separation factor, selectivity, flux and

permeance is shown in Fig. V.16 for 10 wt. % feed water mixture. Increase of

temperature increases the chain segmental mobility and thereby the flux and

permeance will increase. On the other hand, separation factor and selectivity

curves show a decline with increase of temperature as was also studied before

[30,31]. At higher temperatures, diffusion of water is high, which restricts

membrane separation ability. In case of NCM-5, a drastic reduction in separation

factor of 1,087 is observed at 60oC compared to its original value of 33,651 at

300C. Similar observations can be seen with NCM-10 and NCM-15 membranes.

Here, higher molecular mobility and vapor pressure of ethanol under a temperature

above its boiling point should be taken into consideration. Further, the change of

free volume impacts ethanol diffusivity more remarkably than water diffusivity

resulting in lower selectivity at higher operation temperature.

As per free volume theory, increase in temperature will increase the thermal

mobility of polymer chains generating extra void space with increased sorption

and diffusion. The driving force for permeation is concentration gradient, which

results from a difference in partial vapor pressure of permeant molecules between

feed and permeate mixtures. As the feed temperature increases, vapor pressure in

the feed compartment also increases, but vapor pressure at permeate side will not

be affected, resulting in increasing driving force, since the latter is closely related

to phase transition in PV process, but is dependent of temperature. These

phenomena are caused by the fact that the flux and separation factor depend on

both intrinsic membrane properties and influence of the experimental operating

conditions, while permeance and selectivity exclude the effect of experimental

operating conditions.

CHAPTER-V 130

In the present work, one can observe a similar changing trend for both

permeances and fluxes with increasing temperature. It is reasonable because the

temperature is an important parameter affecting the intrinsic properties of the

membranes [37]. As per sorption-diffusion principles [23], permeance and

selectivity reflect the true membrane performance; while selectivity is the

selectivity of the membrane and the separation factor is the separation factor of the

pervaporation process [38]. Thus, using permeance and selectivity instead of flux

and separation factor can significantly decouple the effect of operating conditions

on performance evaluation. As a result, normalizing the flux with respect to the

driving force would clarify and quantify the contribution by the nature of the

membrane to separation performance.

Fig. V.17 displays increasing diffusion coefficients of water as well as

ethanol with increasing the temperature from 30oC to 60oC. Notice smaller values

of diffusion coefficients for ethanol than water. Arrhenius equation was used to fit

diffusivity and permeation flux data to calculate activation energies. From the

estimated results presented in Table V.3, we find positive values of activation

energy, which suggests increase of permeation flux and diffusion coefficient with

increasing temperature. Activation energies of permeation and diffusion for water

(Epw) are much lower than those of ethanol (Epe), suggesting easy permeation of

water molecules through the membranes as well as water selective nature of the

NCMs compared to CS. In case of NCMs, the activation energy values are higher

than unfilled CS, but for NCM-5, the activation energies of flux and diffusion are

lower than those of NCM-10 and NCM-15, suggesting efficient separation by the

NCM-5 membrane. Activation energies thus increase with increasing wt%

loading, which reveals that the rate at which water molecules transport through

nanocomposite membranes increases much faster than ethanol and that water is

separated first.

CHAPTER-V 131

30 40 50 600

5000

10000

15000

20000

25000

30000

35000

40000

NCM-10

NCM-15

NCM-5

CS

Sepa

ratio

n fa

ctor

, αij

30 40 50 6040

50

60

70

80

90

100

Sepa

ratio

n fa

ctor

, αij

Temperature (oC)30 40 50 60

0

1000

2000

3000

4000

5000

6000

Sele

ctiv

ity, α

ij

CS

30 40 50 606

8

10

12

14

16

NCM-10

NCM-15

NCM-5

Sele

ctiv

ity, α

ij

Temperature (oC)

30 40 50 600.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

CS

Flux

(kg/

m2 h)

30 40 50 600.08

0.10

0.12

0.14

0.16

0.18

0.20

NCM-10

NCM-15

NCM-5

Flux

(kg/

m2 h)

Temperature (oC)

30 40 50 600

3

6

9

12

15

18

21

J i x 1

08 (kg/

m2 s)

30 40 50 601.0

1.5

2.0

2.5

3.0

Ji x

108 (k

g/m

2 s)

NCM-10

NCM-5

NCM-15

Temperature (oC)

Fig. V.16. Separation factor, flux, selectivity and permeance of water as a function

temperature.

CHAPTER-V 132

30 40 50 60

2

4

6

8

10

Dw x

1010

(m2 /s

)

NCM-10

NCM-15

NCM-5

30 40 50 600.8

1.0

1.2

1.4

1.6

1.8

2.0

CS

Dw x

1010

(m2 /s)

Temperature (oC)30 40 50 60

0.0

0.5

1.0

1.5

2.0

2.5

De x

1012

(m2 /s)

30 40 50 601

2

3

4

5

De x

1012

(m2 /s)

NCM-15

NCM-10

NCM-5

CS

Temperature (oC)

Fig. V.17. Diffusion coefficient vs. température.

Table V.3. Activation energies of all the membranes calculated from Arrhenius

plots.

Activation energies in kJ/mol

CS NCM-5 NCM-10 NCM-15

Ep 21.01 40.24 47.69 46.09

Epw 19.41 40.08 47.49 45.82

Epe 38.19 133.6 109.8 111.6

Ed 19.72 40.11 47.53 45.87

Edw 19.34 40.08 47.49 45.82

Ede 39.48 133.7 109.9 111.8

CHAPTER-V 133

V.4. CONCLUSIONS

Developments of novel membrane materials with improved performances

would reduce the cost of membrane systems, making them a more attractive

option in difficult separation problems than the conventional technologies.

Compared to unfilled chitosan membrane, the nanocomposite membrane prepared

by incorporating 5 wt. % of H14-P5 nanoparticles has shown significantly

improved pervaporation performance as its separation factor increased remarkably

with a moderate increase of flux for 10 wt.% water in the feed at 30oC. These

effects are attributed to increasing surface energy, fractional free volume and

hydrophilicity due to favorable interactions between nanoparticles and the

polymer matrix. Sorption experiments show that sorption selectivity and diffusion

selectivity as well as diffusion coefficients of water and ethanol were greatly

influenced by feed concentration and temperature. At higher feed water

compositions and temperatures, the membrane performance declined. Sorption

selectivity, being the dominant effect over diffusion, suggests water-selective

nature of the membranes; these observations also fit into the explanations

advanced by sorption-diffusion model. Flory-Huggins theory enabled accurate

assessment of binary interaction parameters used to understand the pervaporation

process. Feed composition has played a significant role in permeation flux,

whereas it has only a slight influence on ethanol. The sorption, diffusion, and

permeation parameters were assessed on the basis of sorption-diffusion model.

The membranes exhibited significantly lower Arrhenius activation energies for

water than ethanol, suggesting higher separation ability and water selective nature

of the membranes. The hybrid membranes of this study are very promising in

pervaporation applications and possibly in gas separation as well.

CHAPTER-V 134

V.5. REFERENCES

[1] P. Shao, R.Y.M. Huang, J. Membr. Sci. 287 (2007) 162-179.

[2] M. Sairam, M.B. Patil, R.S. Veerapur, S.A. Patil, T.M. Aminabhavi, J.

Membr. Sci. 281 (2006) 95-102.

[3] S.G. Adoor, M. Sairam, L.S. Manjeshwar, K.V.S.N. Raju, T.M.

Aminabhavi, J. Membr. Sci. 285 (2006) 182–195.

[4] V.T. Magalad, G.S. Gokavi, K.V.S.N. Raju, T.M. Aminabhavi, J. Membr.

Sci. 354 (2010) 150-161.

[5] V.T. Magalad, A.R. Supale, S.P. Maradur, G.S. Gokavi, T.M.

Aminabhavi, Chem. Eng. J. 159 (2010) 75-83.

[6] S.G. Adoor, B. Prathab, L.S. Manjeshwar, T.M. Aminabhavi, Polymer 48

(2007) 5417.

[7] R.M. Barrer, in Diffusion in polymers, J. Crank. G.S. Park, Eds.

Academic press, London, 1968, p 165-217.

[8] B.V.K. Naidu, M. Sairam, K.V.S.N. Raju, T.M. Aminabhavi, J. Membr.

Sci. 260 (2005) 142-155.

[9] L.Y. Jiang, T.S. Chung, J. Membr. Sci. 327 (2009) 216-225.

[10] S.W. Kang, J. Hong, J.H. Park, S.H. Mun, J.H. Kim, J.C.K. Char, Y.S.

Kang, J. Membr. Sci. 321 (2008) 90-93.

[11] S.J. Lue, D.T. Lee, J.Y. Chen, C.H. Chiu, C.C. Hu, Y.C. Jean, J.Y. Lai, J.

Membr. Sci. 325 (2008) 831-839.

[12] R.S. Veerapur, K.B. Gudasi, T.M. Aminabhavi, J. Membr. Sci. 304

(2007)102-111.

[13] K.S.V. Krishna Rao, M.C.S. Subha, M. Sairam, N.N. Mallikarjuna, T.M.

Aminabhavi, J. Appl. Polym. Sci. 103 (2007) 1918-1926.

[14] D. Yang, J. Li, Z. Jiang, L. Lu, X. Chen, Chem. Eng. Sci. 64 (2009) 3130-

3137.

[15] M. Masaru, I. Reikichi, M. Seiich, Y. Shuzo, M. Akira, T. Yoshinobu,

Kobunshi Ronbunshu 42 (1985) 139–142.

CHAPTER-V 135

[16] D. Anjali Devi, B. Smitha, S. Sridhar, T.M. Aminabhavi, J. Membr. Sci.

280 (2006) 45-53.

[17] D. Anjali Devi, B. Smitha, S. Sridhar, T.M. Aminabhavi, J. Membr. Sci.

262 (2005) 91-99.

[18] X.H. Zhang, Q.L. Liu, Y. Xiong, A.M. Zhu, Y. Chen, Q.G. Zhang, J.

Membr. Sci. 327 (2009) 274-280.

[19] J.J. Shieh, R.Y.M. Huang, J. Membr. Sci. 127 (1997) 185–202.

[20] K. Ghosal, B.D. Freeman, Polym. Adv. Technol. 5 (1994) 673-697.

[21] M.H. Alizadeh, T. Keramani, R. Tayebee, Monatshefte fur Chemie. 138

(2007) 165-170.

[22] T.M. Aminabhavi, R.S. Khinnavar, S.B. Harogoppad, U.S. Aithal, Q.T.

Nguyen, K.C. Hansen, J. Macromol. Sci. Rev. Macromol. Chem. Phy. C.

34 (1994) 139-204.

[23] J.G. Wijmans, R.W. Baker, J. Membr. Sci. 107 (1995) 1-21.

[24] P.J. Flory, Principles of Polymer Chemistry, Cornell University Press,

Ithaca, New York, 1953.

[25] M. Khayet, M.M. Nasef, J.I. Mengual, J. Membr. Sci. 263 (2005) 77-95.

[26] C.M. Chan, Polymer surface modification and characterization, Hanser:

Cincinnati (1994) 35- 62

[27] R. Ravindra, K.R. Krovvidi, A.A. Khan, Carbohydr. Polym. 36 (1998)

121-127.

[28] T. Uragami, Y. Tanaka, S. Nishida, Desalination 147 (2002) 449-454.

[29] J.H. Chen, Q.L. Liu, X.H. Zhang, Q.G. Zhang, J. Membr. Sci. 292 (2007)

125-132.

[30] X. Chen, H. Yang, Z. Gu, Z. Shao, J. Appl. Polym. Sci. 79 (2001) 1144-

1149.

[31] Y. L. Liu, C.Y. Hsu, Y.H. Su, and JY. Lai, Biomacromolecules, 6 (2005)

368–373.

CHAPTER-V 136

[32] Y.C. Wang, S.C. Fan, K.R. Lee, C.L. Li, S.H. Huang, H.A. Tsai, J.Y. Lai,

J. Membr. Sci., 239 (2004) 219-226.

[33] D. Xu, S. Hein, K. Wang, Mater. Sci.Techn, 24 (2008) 1076-1087.

[34] D. Turnbull, M.H. Cohen, J Chem. Phys., 34 (1961)120-125.

[35] S. Anilkumar, M.G. Kumaran, S. Thomas, J. Phys. Chem. B. 112 (2008)

4009-4015.

[36] B. Prathab, R Parthasarathi, P. Manikandan, V. Subramanian, T.M.

Aminabhavi, Polymer. 47 (2006) 6914-6924.

[37] J. G. Wijmans, J. Membr. Sci., 220 (2003) 1-3.

[38] H.M. Guan, T.S. Chung, Z. Huang, M. L. Chng, S. Kulprathipanja, J.

Membr. Sci., 239 (2004) 219-226.