characterisation of the effect of filler size on handling
TRANSCRIPT
Characterisation of the effect of filler size on
handling, mechanical and surface
properties of resin composites
A thesis submitted to the University of Manchester for the degree of
Doctor of Philosophy
in the Faculty of Medical and Human Sciences
2012
Haitham Idris Elbishari
School of Dentistry
2
List of Contents
List of Contents ....................................................................................... 2
List of Figures .......................................................................................... 7
List of Tables ......................................................................................... 10
List of Abbreviations ............................................................................ 11
Abstract .................................................................................................. 13
Declaration ............................................................................................. 14
Copyright Statement ............................................................................. 15
The Author ............................................................................................. 16
Dedication .............................................................................................. 17
Acknowledgement ................................................................................. 18
CHAPTER ONE .................................................................................... 19
General Introduction and Literature Review .................................... 19
1.1 Dental Caries and Restoration of Teeth ........................................................... 20
1.2 Direct Restorative Materials ............................................................................. 21
1.2.1 Dental Amalgam ........................................................................................... 22
1.2.2 Glass Ionomer Cement .................................................................................. 22
1.2.3 Resin-based Composite Material .................................................................. 23
1.3 Composition and Classification of Resin Composite ...................................... 24
1.3.1 Resin matrix: ................................................................................................. 24
1.3.2 Fillers ............................................................................................................ 27
1.3.2.1 Macro-filled resin composites ................................................................ 30
1.3.2.2 Micro-filled resin composites ................................................................. 31
1.3.2.3 Hybrid resin composites ......................................................................... 31
1.3.2.4 Nano-resin composites ........................................................................... 32
1.3.3 Coupling agent .............................................................................................. 33
1.3.4 Others ............................................................................................................ 34
3
1.3.4.1 Activator-initiator system ....................................................................... 34
1.3.4.2 Inhibitors ................................................................................................ 37
1.3.4.3 Optical modifiers .................................................................................... 37
1.4 New Development in Resin Composite Materials ........................................... 38
1.4.1 Organically Modified Ceramics (Ormocers) Restorative Materials ............. 38
1.4.2 Silorane Restorative Materials ...................................................................... 39
1.5 Polymerisation Shrinkage of Resin Composite ............................................... 41
1.5.1 Degree of conversion: ................................................................................... 42
1.5.2 Methods to control polymerisation shrinkage: .............................................. 43
1.6 Handling Properties of Pre-cured Resin Composites ..................................... 46
1.6.1 Factors affecting viscosity of resin composites ............................................ 46
1.6.1.1 Resin Matrix .......................................................................................... 46
1.6.1.2 Filler particles ........................................................................................ 47
1.6.1.3 Temperature ........................................................................................... 47
1.6.2 In-vitro measurement of handling properties ................................................ 48
1.7 Physical and Surface Properties ....................................................................... 49
1.7.1 Surface Roughness ........................................................................................ 49
1.7.2 Gloss .............................................................................................................. 49
1.8 Mechanical properties of resin composite materials....................................... 50
1.8.1 Fracture toughness ........................................................................................ 50
1.8.2 Wear .............................................................................................................. 50
1.8.2.1 Two body wear ....................................................................................... 51
1.8.2.2 Three body wear ..................................................................................... 51
1.9 Voids within Resin composite ........................................................................... 52
1.9.1 In-vitro measurements of voids: ................................................................... 53
1.9.1.1 Light microscopy: .................................................................................. 53
1.9.1.2 Electron microscopy: ............................................................................. 54
1.10 Micro Computed Tomography [µCT]: .......................................................... 55
1.11 Summary ........................................................................................................... 56
4
CHAPTER TWO .................................................................................. 57
General Aims and Objectives ............................................................... 57
2.1 Aims of the study ................................................................................................ 58
2.2 Objectives of the study ....................................................................................... 58
CHAPTER THREE .............................................................................. 60
Methodology .......................................................................................... 60
3.1 Introduction ........................................................................................................ 61
3.2 Packing stress measurement ............................................................................. 61
3.2.1 Calibration of load cell .................................................................................. 64
3.3 X-ray Computed Tomography ......................................................................... 66
3.3 X-ray Computed Tomography ......................................................................... 66
3.3.1 SkyScan-1072 System ................................................................................... 66
3.2.1.1 Object scanning ...................................................................................... 67
3.3.1.2 Reconstruction ....................................................................................... 69
3.2.1.3 Analysis .................................................................................................. 70
3.4 Optical Computed Topography ........................................................................ 72
3.4.1 Talysurf CLI 1000 ......................................................................................... 72
3.4.1.1 Sample scanning..................................................................................... 75
3.4.1.2 Data analysis .......................................................................................... 76
CHAPTER FOUR ................................................................................. 78
Effect of Filler Size and Temperature on Packing Stress and
Viscosity of Resin composites. .............................................................. 78
4.1 Abstract ............................................................................................................... 79
4.2 Introduction ........................................................................................................ 80
4.3 Materials and methods ...................................................................................... 81
4.4 Results and Discussions ..................................................................................... 84
4.5 Conclusions ......................................................................................................... 90
5
CHAPTER FIVE ................................................................................... 91
The Effect of Filler Size on the Presence of Voids within Resin
Composite ............................................................................................... 91
5.1 Abstract ............................................................................................................... 92
5.2 Introduction ........................................................................................................ 93
5.3 Materials and Methods ...................................................................................... 95
5.4 Results ................................................................................................................. 97
5.5 Discussion .......................................................................................................... 100
5.6 Conclusion ......................................................................................................... 101
CHAPTER SIX .................................................................................... 102
Filler Size of Resin Composites, Percentage of Voids and Fracture
Toughness: Is there a Correlation? ................................................... 102
6.1 Abstract ............................................................................................................. 103
6.2 Introduction ...................................................................................................... 104
6.3 Materials and Methods .................................................................................... 106
6.4 Results ............................................................................................................... 109
6.5 Discussion .......................................................................................................... 112
6.6 Conclusions ....................................................................................................... 113
CHAPTER SEVEN ............................................................................. 114
Is Deterioration of Surface Properties of Resin Composites Affected
by Filler Size? ...................................................................................... 114
7.1 Abstract ............................................................................................................. 115
7.2 Introduction ...................................................................................................... 116
7.3 Materials and Methods .................................................................................... 117
7.4 Results ............................................................................................................... 121
7.5 Discussion .......................................................................................................... 126
6
7.6 Conclusions ....................................................................................................... 127
CHAPTER EIGHT ............................................................................. 128
Effect of Filler Size on Gloss and Colour Stability of Resin
Composites ........................................................................................... 128
8.1 Abstract ............................................................................................................. 129
8.2 Introduction ...................................................................................................... 130
8.3 Materials and Methods .................................................................................... 132
8.4 Results ............................................................................................................... 135
8.5 Discussion .......................................................................................................... 142
8.6 Conclusion ......................................................................................................... 143
CHAPTER NINE ................................................................................ 144
Discussion, Conclusions and Future Work Recommendations ...... 144
9.1 General Discussion ........................................................................................... 145
9.2 Conclusions ....................................................................................................... 151
9.3 Recommendation for future work .................................................................. 152
References ............................................................................................................... 153
APPENDICES ........................................................................................................ 167
Appendix 1: Publication 1 ................................................................................... 167
Word Count: 34,129
7
List of Figures
Figure 1.1 Factors initiating caries 20
Figure 1 2 Structure of MMA 24
Figure 1 3 Structure of Bis-GMA 25
Figure 1 4 Structure of TEGDMA 26
Figure 1.5 Structure of UDMA 26
Figure 1.6 Classifications of resin composites based on filler size 29
Figure 1.7 Macro-filled Composite 30
Figure 1 8 Micro-filled Composite 31
Figure 1.9 Hybrid Composite 32
Figure 1.10 Nanocomposites 33
Figure 1.11 Structure of Silane Coupling agent 34
Figure 1.12 Polymerisation of soft material to hard dental composite 36
Figure 1.13 Chemical structure of Ormocers 38
Figure 1.14 Chemical structure of Silorane 39
Figure 1.15 Horizontal layering technique 45
Figure 1.16 Oblique layering technique 45
Figure 1.17 3D model of resin composite MOD restoration 55
Figure 2.1 Studies outline 59
Figure 3.1 Schematic diagram showing various parts of the packing stress
measurement apparatus
62
Figure 3.2 The packing stress measurement apparatus showing the
connected temperature controlled base
62
Figure 3.3 Time dependant packing stress profile curve 63
Figure 3.4 Calibration of the load cell graph 64
Figure 3.5 SkyScan-1072 micro-CT 67
Figure 3.6 SkyScan with the specimen chamber door open and specimen
placed on the holder
67
Figure 3.7 An initial x-ray image (X-ray shot) of a resin composite.
Parameters of scanning are present in the top right hand corner
68
Figure 3.8 View of a 2-D image of the specimen (A) with, the functional
window (B), and a 2-D slice at the selected green line level (C)
69
8
Figure 3.9 Representative 2D slices resulting from different reconstruction
threshold values
70
Figure 3.10 Raw image (A), in which the region of interest is defined (B)
resulting in a new 2-D slice of the selected area (C)
70
Figure 3.11 3-D model of resin composite sample (A) and (B) 3D image
with pseudo colour
71
Figure 3.12 Talysurf CLI 1000 profilometer 73
Figure 3.13 Illustrating diagram of working principle of CLA gauge 73
Figure 3.14 Data acquisition software window 74
Figure 3.15 Data acquisition software window shows difference between
before and after scanning
75
Figure 3.16 Diagram illustrating different steps in Talymap software 76
Figure 3.17 3D model of scanned sample 77
Figure 4.1 Schematic diagram showing various parts of the packing stress
measurement apparatus
83
Figure 4.2 Time dependant packing stress profile curve 85
Figure 4.3a Linear correlations between packing stress (MPa) and unimodal
composites at 23oC and 37oC
87
Figure 4.3b Bar Chart of packing stress (MPa) at 23oC and 37oC for
multimodal composites
87
Figure 4.4a Linear correlations between viscosity (MPa.s) and unimodal
composites at 23oC and 37oC
88
Figure 4.4b Bar Chart of viscosity (MPa.s) at 23oC and 37oC for multimodal
composites
88
Figure 5.1 2D reconstructed image of I3 sample 97
Figure 5.2 3D model of resin composite sample (A) and (B) 3D image with
pseudo colour
98
Figure 5.3 Bar Char of Mean (SD) of all materials with Correlation
between filler Size of unimodal composite and % of Voids
98
9
Figure 6.1 Schematic drawing of the SEN specimen 107
Figure 6.2 2D of sample used and 3D image of scanned part 109
Figure 6.3 Bar chart illustrating Mean and SD of Voids% for all materials
with linear correlation shown between unimodal composites and
percentage of voids.
111
Figure 6.4 Bar chart illustrating Mean and SD of Fracture toughness for all
materials
111
Figure 7.1 Toothbrush-simulating machine 119
Figure 7.2 Schematic diagram of tooth brushing abrasion apparatus 120
Figure 7.3 Linear correlation between filler size and gloss retention 122
Figure 7.4 3D model of scanned sample 125
Figure 7.5 Linear correlation between filler size and surface roughness
parameters
125
Figure 8.1 Mean (SD) of ∆E of all samples stored in distilled water 139
Figure 8.3 Mean (SD) of ∆E of all samples stored in red wine 139
Figure 8.3 Mean (SD) of ∆E of all samples stored in coca cola 140
Figure 8.4 Correlation between Filler size and surface gloss: a-Distilled
Water, b-Red Wine, c-Coca Cola
141
10
List of Tables
Table 1.1 Properties of the ideal restorative material 21
Table 4.1 Composition of resin composites used in the study 82
Table 4.2 Mean (SD) values of packing stress (MPa) of different resin
composites at 23 oC and 37 oC
86
Table 4.3 Mean (SD) values of viscosity (MPa.s) of different resin
composites at 23 oC and 37 oC
86
Table 5.1 Compositions of resin composite materials used in the study 95
Table 5.2 Parameters used with Micro-CT in the study 96
Table 5.3 Mean (SD) values of Voids % of all resin composites tested 99
Table 6.1 Compositions of resin composites materials used in the study 107
Table 6.2 Mean (SD) values of Voids % and KIC (MNm-1.5) of all resin
composites tested
110
Table 7.1 Composition of Materials used in the study 117
Table 7.2 Mean (SD) of gloss of all material tested before and after tooth
brushing abrasion of 20,000 cycles
122
Table 7.3 Mean (SD) of 2D roughness parameters 123
Table 7.4 Mean (SD) of 3D roughness parameters 124
Table 8.1 Composition of Resin composites used in the study 132
Table 8.2 Mean and SD of surface gloss and colour changes (∆E) of
materials stored in distilled water
136
Table 8.3 Mean and SD of surface gloss and colour changes (∆E) of
materials stored in red wine
137
Table 8.4 Mean and SD of surface gloss and colour changes (∆E) of
materials stored in coca cola
138
11
List of Abbreviations
A a proportional coefficient
BisGMA bisphenol-A glycidyl methacrylate
(2,2-bis[4-(2-hydroxy-3-methacryloyloxypropoxy)phenyl]propane)
C-factor configuration factor
DC degree of conversion
E Activation energy
GIC glass-ionomer (polyalkenoate) cement
LED light-emitting diodes
MMA methylmethacrylate
PMMA poly(methyl methacrylate)
QTH quartz–tungsten–halogen
SEM scanning electron microscopy
TC Tetric Ceram
TEGDMA triethylene glycol dimethacrylate
TTEMA tris[4-(2-hydroxy-3-methacryloxypropoxy)]methane
UDMA urethane dimethacrylate
(1,6-bis(methacryloyloxy-2-ethxycarbonylamino)-2,4,4-
trimethylhexan)
UV ultraviolet
VLC visible-light cured oC Centigrade
E modulus of elasticity (Young’s modulus)
MPa megapascal
nm nanometre
R universal gas constant/relaxation function
s & sec second(s)
t time
T absolute temperature
vol% percentage content by volume
wt% percentage content by weight
∆E Change in colour
12
ε strain
tp Packing stress
γ shear strain
η viscosity
µm micrometer
σ stress
§ section
13
Abstract
Resin composites have been in the dental field for over forty years. They are now
thought to be the most commonly used restorative material due to their aesthetic and
mechanical properties. Although resin composites have high success rates as
restorations, they do not offer all properties of an ideal restorative material. The aims
of this research were to characterise the effects of variation in resin composite
formulation on handling, mechanical; and physical properties. In particular the
influence of the size and distribution of the inorganic components was investigated
through the study of experimental formulations.
Packing stress and viscosity were assessed with pentrometer principle at two
different temperatures (23 and 37 ºC). It was found that filler size was strongly
correlated with both packing stress and viscosity. Additionally, temperature has a
dominant effect on packing stress and viscosity.
Micro computed tomography [µCT] was used to investigate percentage of voids
[% voids] in 3D dimensions. It was found that smaller filler size incorporated less %
voids. In contrast filler size and disruption had a little effect on fracture toughness of
resin composites.
3D surface topography was used to investigate the surface roughness before and after
tooth brush abrasion. It was found filler size had a significant influence in both gloss
retention and surface roughness (smaller filler size exhibited higher surface gloss).
Finally, the effect of different storage media (distilled water, Coca Cola and red
wine) on colour stability and gloss were investigated. It was found that dietary habits
effect discolouration of resin composite restorations with the acidic drinks caused
more staining.
14
Declaration
No portion of the work referred to in the thesis has been submitted in support of an
application for another degree or qualification of this or any other university or other
institute of learning.
Haitham Idris Elbishari
2012
15
Copyright Statement
i. The author of this thesis (including any appendices and/or schedules to this thesis)
owns any copyright in it (the “Copyright”) and s/he has given The University of
Manchester the right to use such Copyright for any administrative, promotional,
educational and/or teaching purposes.
ii. Copies of this thesis, either in full or in extracts, may be made only in accordance
with the regulations of the John Rylands University Library of Manchester. Details
of these regulations may be obtained from the Librarian. This page must form part of
any such copies made.
iii. The ownership of any patents, designs, trade marks and any and all other
intellectual property rights except for the Copyright (the “Intellectual Property
Right”) and any reproductions of copyright works, for example graphs and tables
(“Reproductions”), which may be described in this thesis, may not be owned by the
other and may be owned by third parties. Such Intellectual Properties Rights and
Reproductions cannot and must not be made available for use without the prior
written permission of the owner(s) of the relevant Intellectual Property Rights and/or
Reproductions.
iv. Further information on the conditions under which disclosure, publication and
exploitation of this thesis, the Copyright and any Intellectual Property Rights and/or
Reproductions described in it may take place is available from the Head of School of
Dentistry.
16
The Author
I graduated from the School of Dentistry at University of Benghazi-Libya in 2000. I
worked as a teaching assistant in the department of Fixed and Removable
Prosthodontics at the same School between 2002 and 2005. I joined the University of
Manchester in September 2006 to pursue my postgraduate study programme in Fixed
and Removable Prosthodontics. In December 2007, I completed my Master of
Science degree which was awarded with Merit. In January 2008 I enrolled for a four-
year clinical PhD (Doctor of Clinical Dental Science in Fixed & Removable
Prosthodontics). In 2009 I won the Colgate Prize for the best poster presentation in
the postgraduate presentation day at the School of Dentistry. I have also worked in
the University of Manchester as an Enquiry Based Learning [EBL] tutor from
September 2010 to March 2011. In November 2011 I succeeded in passing the
Specialty Membership examination of the Royal College of Surgeons of Edinburgh
(MPros RCSEd). During my programme I have published the following paper
Elbishari, H., Satterthwaite, J. & Silikas, N (2011). Effect of Filler Size and
Temperature on Packing Stress and Viscosity of Resin-composites.
International Journal of Molecular Sciences, 12(8), 5330-5338
I have also submitted other papers to a variety of scientific journals. I have also
attended different national and international dental conferences, at which the
following studies were presented:
Can filler-size affect the colour and gloss of resin-composites?
(Abstract number 58- the Academy of Dental Materials Italy 2010)
Effect of filler size on presence of voids within resin-composites
(Abstract number 1453 -IADR Barcelona 2009)
Effect of filler size on voids and fracture toughness of resin composites
(20th European Dental Materials Conference Manchester 2009)
In-vitro characterisation of voids within resin-composite restorations using
micro-CT
(Abstract number 199- Pan European IADR London 2008)
17
Dedication
“Say: My Lord, increase me in knowledge”
IN THE NAME OF ALLAH
And with His blessing
The All-Knowing, The Most-Wise
This work is dedicated to the memory of my late father Dr Idris Elbishari. I am
totally indebted to the tremendous inspiration he gave me throughout his life.
I also want to dedicate this thesis to the spring of love, my mother Nabila. I always
enjoyed her care and guidance. This thesis is also dedicated to my lovely wife Ola
and my children Idris and Mohammed and to my brothers Rafaa and Hani.
Finally I would also like to dedicate this work to my friends M Hatamleh,
A Alnazzawi, A El-Ma`aita and A Alrahlah.
18
Acknowledgement
All praises are due to ALLAH for his merciful guidance throughout my life and
during my stay in Manchester.
A PhD project always requires the attentive devotion of the researcher. This was
absolutely impossible without the encouragement, help and guidance of family,
mentors, colleagues and friends throughout my study.
Firstly, I would like to thank my supervisors Dr Julian D Satterthwaite and Dr Nick
Silikas for their kind help, tremendous effort and rightly guidance in their capacity as
supervisors and even beyond. Their insights through my study were very helpful.
The tremendous patience and efforts they showed during my study and especially
with guidance on the drafts of this thesis are really appreciated. I would also like to
thank Professor David Watts for his continuous help and advice as my academic
advisor.
My thanks are also extended to Mr. Brian Daber, Mrs. Shena Reynolds and
Mrs. Rose-Marie Parr for their help and assistance throughout my study.
My thanks are also due to Dr Craig Barclay, Professor Nick Grey, Dr Joanne
Cunliffe and other faculty members, nurses and supporting staff of the
Prosthodontics department. Their help and encouragement during my clinical
training in the unit are unforgettable. I proclaim my sincere thanks to my mother
Nabila, to my lovely wife Ola and my children Idris and Mohammed and to my
brothers Rafaa and Hani. Special thanks are due to Dr Muhanad Hatamleh for his
support and advice, and to my friends Ahmad El-Ma`aita and Ahmad Alnazzawi.
The list of friends and colleagues who shaped my wonderful experience in
Manchester is quite extensive. I would like this acknowledgment to be considered as
a personal thank you to each of them.
19
CHAPTER ONE
General Introduction and Literature Review
20
Bacteria
Tooth
Time
Sugar
1.1 Dental Caries and Restoration of Teeth
Dental caries is defined as a multifactorial disease process resulting in
demineralisation of dental hard tissues by microbial activity. Bacteria, fermentable
carbohydrates in dietary sugars, time and a susceptible tooth surface are the principle
fundamentals required to produce a carious lesion (Figure 1.1). The breakdown of
this cycle by eliminating one or more of previously mentioned elements can prevent
or arrest this process. Despite the simplicity of the procedure by which dental caries
can be prevented or at least arrested, dental caries still remains a major cause of pain,
and loss of function and form in much of the population. In 2002, the World Health
Organisation [WHO] approximated that about 80% of the world’s population
suffered from dental decay, making it the most common non-communicable disease
in the world (Guilbert, 2003).
Figure 1.1 Factors initiating caries
Despite the disease being preventable, caries often results in cavitation of the
susceptible tooth and this requires treatment in order to remove the diseased tooth
tissue and then fill the resultant cavity to restore function and form. Restoration of
unhealthy teeth due to caries (or trauma) and restore form of the teeth and also to
restore function by avoiding extraction and improving the mastication, has been
documented since the first century AD, Aulus Cornelius (Celsus), a Roman
physician, is the earliest to mention the filling of teeth in his ‘De Medicina’ (Celsus,
1935), where he recommended the use of lead or lint. However, this was to aid
extraction rather than restoration.
21
In the 10th century AD Razi, a Persian physician was the first to restore cavities in
teeth: he used compressed wool which had already been dipped in boiling oil to treat
carious teeth. Razi used a hand-held drill and filled the teeth with a material that
mainly composed of myrrh, a sticky brown substance with a strong smell which is
nowadays used in making perfume. Another material he used to fill the teeth was
camphor, a white sticky colourless substance (Khalifa, 1937; Almahdi, 2003).
1.2 Direct Restorative Materials
The use of directly placed restorative materials to fill cavities in teeth is the treatment
of choice for preserving function and form in the majority of carious teeth since their
first use in the 10th century. Many studies have been carried out to develop new
materials and to improve exiting materials in order to meet ideal requirements for
restorative materials (Table 1.1)
Biological Mechanical Other
Non-toxic (to patient &
clinician)
High strength
(compressive & flexural)
Bonds to enamel and
dentine
Non-irritant to oral/
dental tissues
Durable (fracture
toughness)/ low wear &
non-abrasive
Radiopaque
Bio-active (anti-bacterial,
promotes reparative
dentine)
Dimensionally stable
(during set & over time)
Aesthetic (tooth-
coloured, translucent,
opalescent, fluorescent)
Bio-mimetic (exhibiting
mechanical properties
similar to tooth structure)
Good handling
characteristics (command
set), technique (ease of
placement) & moisture
insensitive
Highly polishable Non-soluble & non-
absorbent
Table 1.2 Properties of the ideal restorative material
22
1.2.1 Dental Amalgam
Dental amalgams are produced by combining an alloy of silver, tin and copper with
mercury. The increase in copper content of newer generation amalgams has imparted
higher strength, higher corrosion resistance, less marginal breakdown, and lower
creep. Although having a proven track record and decades of service, the properties
of amalgam, when compared to the properties of an ideal dental restorative material
(Table 1.1), fail in several categories. Notably, amalgam does not bond to tooth
substance, it exhibits creep (gradual flow under applied stress), it is not tooth
coloured. Moreover, there has been growing doubt raised about the use of dental
amalgam: this is due to increasing concern regarding the hazards of mercury in
dental amalgams to patients and dental staff (Molin, 1992). Despite all of the
developments of dental amalgam, and well regarded publications supporting its
safety and continued clinical use (Eley, 1997a, 1997b) the use of amalgam has
become controversial. For example, in Germany some patients claim that the use of
amalgam is seriously threatening their health (Roulet, 1997). Also the use of
amalgam has decreased in several countries for environmental reasons, for example
some northern European countries, in particular Sweden and Norway and also in
other countries such as the USA, Australia and the UK (Burke, 2004).
Due to the above mentioned hazards and the problem of amalgam corrosion, the
demand for using tooth coloured restorations (mainly resin composite materials) has
increased by both patients and dentists (Ziskind et al., 1998), although the use of the
amalgam is still predominant (Burke, 2004).
1.2.2 Glass Ionomer Cement
Glass Ionomer Cement [GIC] or polyalkenoate was introduced in 1968, by Wilson &
Kent (Wilson & Kent, 1971 & 1973). This material is derived from silicate cement,
the first tooth coloured restorative material, and it is made by combining alumino-
silicate glass powder with a solution of polymers or copolymers of acrylic acid. This
material has a clinical significance of adhesion to tooth structure (McLean & Wilson,
1977).
23
GIC has significant clinical advantages including the ability to release fluoride over
time, ability to bond to tooth structure and a translucent tooth-coloured nature. The
clinical application of GIC is generally limited to restoration of primary teeth,
class V cervical caries cavities (but not erosion as it is not stable in acid
environments) and cementation of fixed prostheses. The limited use of GIC is
due to some major disadvantages; moisture sensitive-absorption and loss of
liquid cause surface disruption and contraction respectively (Earl et al., 1989); poor
wear resistance (van Duinen et al., 2005); and, relatively low compressive and
flexural strengths even in recent developments of GIC (Yap et al., 2003).
1.2.3 Resin-based Composite Material
An acrylic resin, polymethylmethacrylate [PMMA], was introduced in the early
1950’s to replace Silicate cement. Acrylic resin was used as an anterior restorative
material because of its tooth-like appearance, easy manipulation and its low cost. It
also has been used for the construction of denture bases, denture teeth and temporary
crowns and bridges. This material was used to restore anterior teeth for only a short
period of time because it suffered from poor colour stability and poor dimensional
stability, having significant polymerisation shrinkage and inadequate resistance to
wear. These materials were subsequently developed in two ways: alteration of the
monomer resins and incorporation of filler particles as a dispersed phase to form a
composite material.
According to the Oxford English dictionary composite is “a thing made up of several
elements or parts” (Hornby and Cowie, 2000). In the context of dental materials
science, a resin composite is a mixture produced from two or more different
components. According to the Glossary of Prosthodontic Terms (2005) resin
composite is defined as “a highly cross-linked polymeric material reinforced by a
dispersion of amorphous silica, glass, crystalline, or organic resin filler particles
and/or short fibres bonded to the matrix by a coupling agent” (Terms, 2005).
24
1.3 Composition and Classification of Resin Composite
Enamel and dentine are good examples of natural composite materials as both
consist of organic and inorganic materials (predominately hydroxyapatite crystals).
Enamel consists of 5% organic and 95% inorganic materials, while dentine consists
of 25% organic and water and 75% inorganic material. Like enamel and dentine,
resin composite is mainly composed of an organic phase (resin matrix) and an
inorganic or dispersed phase (filler particles). In addition, resin composite also
contains and another component which is a coupling agent (interfacial phase).
1.3.1 Resin matrix:
The resin matrix is a synthetic monomer that forms a cross-linked structure after
polymerisation. Monomers originally used in resin composites were
methylmethacrylate [MMA] (Figure 1.2). The MMA was replaced by an aromatic
dimethacrylate oligomer and the reaction product of Bisphenol-A and glycidyl
methacrylate [Bis-GMA] (2,2-bis[4-(2-hydroxy-3- methacryloxypropoxy) phenyl]
propane) was developed by Bowen in 1962 (Bowen, 1962) hence it is called
Bowen’s resin, and still the most often used resin in many resin composites (Figure
1.3).
Figure 1.2 Structure of MMA
25
Figure 1.3 Structure of Bis-GMA
BisGMA is a highly viscous monomer with a high molecular weight, the difference
in size of MMA and BisGMA monomers is significant. This difference gives the
later the benefit of producing a material that not only has better mechanical
properties but also has much less polymerisation shrinkage than materials in which
small monomers (such as MMA ) are used: 7.5 vol% compared to 22 vol% (Bowen,
1963).
The major problem with BisGMA is that the monomer is highly viscous and this
makes the material difficult to handle. This viscosity is due to the presence of two
hydroxyl groups. In order to achieve adequate handling properties and adequate
degree of conversion, BisGMA is diluted with monomers of lower molecular mass
such as triethyleneglycol dimethacrylate [TEGDMA] (Figure 1.4). However, this
dilution has the undesirable effect of increasing polymerisation shrinkage (Feilzer
and Dauvillier, 2003; Gonçalves et al., 2010; Gonçalves et al., 2011). Another way
to overcome the problem is by substituting the hydroxyl (-OH) groups with an
ethoxy species (-CH2-CH2-O) to give an ethoxylated BisGMA.
26
Figure 1.4 Structure of TEGDMA
Many resin composites now contain urethane dimethacrylate (1,6-
bis(methacryloyloxy-2-ethoxycarbonylamino)-2,4,4-trimethylhexan) [UDMA]
(Figure 1.5) either alone or in combination with other monomers. This UDMA has
the same molecular mass as BisGMA but it is less viscous, this property of UDMA
makes it superior to a BisGMA and TEGDMA mixture because of a higher degree of
conversion and lower polymerisation shrinkage (Peutzfeldt, 1997; Gonçalves et al.,
2010).
Figure 1.5 Structure of UDMA
27
There are also other monomers which are available for use commercially or still
being investigated. One of these monomers is tris[4-(2-hydroxy-3-
methacryloxypropoxy)]methane (TTEMA). A resin composite containing a mixture
of TTEMA and TEGDMA (3:2 respectively) shows less polymerisation shrinkage of
10% than the resin composite containing a mixture of BisGMA and TEGDMA (3:2
respectively) (Chung et al., 2002).
1.3.2 Fillers
Filler particles are the dispersed phase of resin composite materials and can be
defined as “the inorganic and/or organic resin particles that are designated to
strengthen a composite, decrease thermal expansion, minimise polymerisation
shrinkage, and reduce the amount of swelling caused by water sorption” (Anusavice
and Phillips, 2003).
Fillers were first added to resins in the early 1950`s to improve their properties
(Knock and Glenn, 1951). Although the incorporation of filler particles into resins
improves their properties, there is a limit on the maximum fraction of inorganic
fillers that can be added to the resin: as the fillers increase, the material becomes
more viscous, more than 80% of filler fraction results in a stiff material that is not
easy to manipulate (Lutz et al., 1983).
Fillers positively influence many properties, such as increasing radiopacity (Chan et
al., 1999), this radiopacity can be similar to that of enamel and thus facilitate
distinguish it from any marginal gap or voids (Schulz et al., 2008), minimising
polymerisation shrinkage (Alvarez-Gayosso et al., 2004; Gonçalves et al., 2011;
Skovgaard et al., 2011), reinforce and increase the strength (Ferracane et al., 1987)
and reducing dimensional changes (Soderholm, 1984).
Fillers are made of different materials; with glass or silica the most commonly used
filler particles. Glass particles have better optical properties. Previously most fillers
were made of quartz due to its superior mechanical properties, however they are
abrasive particles and can lead to increased enamel wear (O'Brien, 2008). Other
glass particles used are, for example, borosilicate glass, lithium, barium aluminium
28
silicate strontium or zinc glass. The other material widely used to make filler
particles is silica. Silica particles are polishable. Silica filler particles available are
colloidal silica, fused silica, zirconia silica and amorphous silica.
Filler particle size varies, from as large as 100µm to very small size between 0.1 to
100nm (Beun et al., 2007) . Broadly, the size of filler particles is used to classify
resin composite into four main types. These types are macrofilled composite (10µm),
microfilled (3µm), hybrid (range from 0.4 to 1.0 µm), and nanocomposite (typically
of about less than 250nm). Additionally, resin composite can be characterised by the
volume fraction of the fillers which could be midway-filled where the filler volume
load is <60 vol% or compact-filled which has a filler load of >60 vol% (Lutz and
Phillips, 1983; Willems et al., 1993).
29
Figure 1.6 Classifications of resin composites based on filler size
30
1.3.2.1 Macro-filled resin composites
The original resin composites, often termed traditional or conventional, consisted of
macro-filler particles (Figure 1.7). These were ground, crushed or milled, splinter-
shaped glass or quartz particles ranging from 1–100µm (later 5 – 30µm), typically
the size of a human hair thickness (about 50µm) (Ferracane and Greener, 1984).
Although adding large particles improved physical properties (such as compressive
strength) there were several disadvantages: a reduced surface area to volume ratio
meant there was a small interface between the two phases, reducing the bond
between filler and matrix; also the difference in hardness between the phases meant
that large particles (which tended to protrude from the surface of the finished
restoration) eventually broke away leaving relatively deep holes and leading to
inevitable failure of the filling (Venhoven et al., 1996) due to poor wear resistance of
the material. The consequent surface roughness would leave the filling prone to
staining and plaque accumulation over time. Also, these materials were difficult to
polish since the particles were larger than the wavelength of visible light. Later use
of smaller, rounder and softer filler improved polishability, but these materials
continued to exhibit poor wear resistance because particles were still dislodged under
masticatory force (Lutz and Phillips, 1983).
Figure 1.7 Macro-filled Composite
31
1.3.2.2 Micro-filled resin composites
This type of composite (Figure 1.8) has two subtypes, homogenous and
heterogeneous. In homogenous micro-filled resin composites all the filler particles
are smaller than the wavelength of visible light (less than 0.04 µm) so the material is
highly polishable and since there are no particles to be dislodged this lustre is
maintained over time. Using only micro-filler particles meant an increased surface
area to volume ratio. Although a previously desirable property, an unwanted
consequence is a significant increase in viscosity so that such materials is severely
limited as not to render the material clinically unusable (Lutz et al., 1983; Lutz &
Williams, 1983). Of course, decreasing the filler load has a deleterious effect on the
physical properties of these materials and to overcome this problem, a solution is
found by combining micro-filler particles with larger particles produced from pre-
polymerised pyrogenic silica in resin matrix to produce what is called heterogeneous
microfilled composites. Thus, allowing increased filler loading without increasing
viscosity and jeopardizing handling characteristics.
Figure 1.8 Micro-filled Composite
1.3.2.3 Hybrid resin composites
Most currently available commercial resin composites are hybrid composites (Figure
1.9). Hybrid composites contain more than one size of filler particle in order to
maximise filler loading and result in better mechanical properties. Macro-filler
particles are interspersed with smaller silica micro-filler particles. These glass
spheres are of the magnitude of 0.04µm (later 0.05 – 0.1µm). Due to the varying
32
size, the percentage of volume of filler could be increased which imparted increased
stiffness, hardness, compressive strength and wear resistance (presumably by
increasing the particle surface area to volume ratio and reducing the area of unfilled
resin exposed to food bolus fibres during mastication) (Jorgensen and Asmussen,
1978; Braem et al., 1989; Bayne et al., 1992; Venhoven et al., 1996). The addition
of micro-filler particles allows initial polishability and improves surface
morphology, but since macro-filler particles are still dislodged, roughness of the
surface will occur over time.
Figure 1.9 Hybrid Composite
1.3.2.4 Nano-resin composites
More recently, developments in nanotechnology have produced potentially clinically
superior resin composites for use in both aesthetic and load-bearing situations.
Nanotechnology permits the uses of nanoscale (1-100nm) level of filler size (Sharma
et al., 2010). Thus microfilled composite could have been called nanofilled
composite, but they were not due to lack of detection of nanotechnology at that
period (Ferracane, 2011). Nanometre-sized filler particles and larger groups of fused
nano-particles (nano-clusters) are dispersed in a resin matrix to produce a
nanocomposite (Figure 1.10). Combining individual particles and clusters allows for
increased filler loading without increased viscosity imparting improved physical
properties and good handling characteristics. The material is highly polishable and
since nano-clusters will breakdown under force as opposed to becoming dislodged,
33
this lustre is long lasting (Ernst, 2005). Nano-filled composite are thought to be
alternative favourably with current universal and micro-filled composites (Beun et
al., 2007), however recent studies have shown that either no difference or better
properties of micro-filled composite compared with nano-filled composites with the
exception of surface roughness (Garoushi et al., 2011; Palaniappan et al., 2011).
Figure 1.10 Nanocomposites
1.3.3 Coupling agent
A coupling agent is an agent applied to filler particles to ensure chemical bonding to
the resin matrix. This bond is essential to strengthen resin composite and distribute
the force generated under function between the two pastes. The most commonly used
coupling agents are organosilanes, which are bipolar molecules, in particular γ-
methacryloxypropyl trimethoxysilane (Figure 1.11). The basic structure of
organosilanes consists of X group in one side which is either alkoxy or chloro and
this side is bonded to filler particles, in the presence of water, by the formation of a
siloxane bond (-Si-O-Si-), whereas on the other side R group which is bonded to the
resin matrix by the formation of covalent bonds when the resin is polymerised (Kaas
and Kardos, 1971). The chemical bonding of fillers and resin matrix improves
mechanical properties of resin composite materials and makes them stronger than
materials with non silanated fillers (McCabe and Wassell, 1999; Chan et al., 2007).
34
Figure 1.11 Structure of Silane Coupling agent
1.3.4 Others
In addition, to the three main components resin composite there are some other
components which are:
Activator-initiator system
Inhibitors
Optical modifiers
1.3.4.1 Activator-initiator system
Activator-initiator system is essential to polymerise the soft uncured resin to a hard
restoration (Figure 1.12). Initially chemical or autopolymerising composite was
supplied in two pastes: one contained the activator (usually a tertiary amine) and the
second paste contained the initiator (usually benzyl peroxide). These two pastes were
mixed together prior to application, resulting in free radical production which is
essential for the polymerisation of the resin composite.
35
Development of resins activated by ultraviolet light [UV] in 1970 overcame the
disadvantages of chemical activated resin, a form of ‘command set’. The first such
resin to be available commercially was NuvaFil in 1972 (de Silva, 1973; Light and
Rakow, 1973). This type of resin contained benzoin methyl ether (instead of benzoyl
peroxide). This ether undergoes photolysis and produces free radicals when exposed
to UV light. Further improvement in the activation system led to the introduction of
visible light cured [VLC] resin, without the problems associated with a UV light
source. VLC was introduced in 24th February 1976 and first used in Turner Dental
School in Manchester (Rueggeberg, 2011). VLC resin composites contain two
materials, an α-diketone and a tertiary amine, which are light sensitive materials. The
α-diketone is camphoroquinone while the most commonly used tertiary amine is
dimethyl-amino-ethyl-methacrylate [DMAEMA] (Taira et al., 1988).
The main advantages of VLC resins are better handling properties and the
effectiveness of polymerisation. However, there are some possible drawbacks which
are:
Greater polymerisation shrinkage (Sakaguchi et al., 1992).
Polymerisation on exposure to dental unit’s light (Dionysopoulos and Watts,
1990).
Limited penetration depth of curing light (Rueggeberg et al., 2000).
36
Figure 1.12 Polymerisation of soft material to hard dental composite
37
1.3.4.2 Inhibitors
Inhibitors prevent unwanted polymerisation when the material is exposed to the
room light or during storage. Butylated hydroxy toluene [BHT] in concentration of
0.01wt% is the most common used inhibitor. BHT is also used as a food preservative
to prevent oxidation and malodour.
1.3.4.3 Optical modifiers
Optical modifiers pigments are added to resin composite to give the material a
natural appearance to resemble the tooth and also to achieve good colour stability.
Theses pigments are typically metal oxide particles added to the resin to adjust the
translucency and opacity to mimic shade of tooth structure (enamel and dentine).
The most commonly used opacifiers are titanium dioxide and aluminium oxide
(0.001 to 0.007 wt%). These optical modifiers can affect transmission of curing light
through layers of resin composite and thus the darker the shade the more curing time
is needed.
38
1.4 New Development in Resin Composite Materials
1.4.1 Organically Modified Ceramics (Ormocers) Restorative Materials
Organically Modified Ceramic [Ormocers] is a hybrid material which is made by
special processing based on nanoscale technology, mixing organic and inorganic
components at a nanoscopic scale rather than by conventional means of physical
mixing of different component of a matrix. Ormocers have been developed as an
alternative to the dimethacrylate based composites (Moszner and Salz, 2001). The
chemical structure of Ormocers is based on organically modified alkoxides and
functionalised organic oligomers/polymers (Figure1.13).
Figure 1.13 Chemical structure of Ormocers (Moszner and Salz, 2001)
The organic constituent of Ormocers is used for cross linking the network whilst the
inorganic component improves mechanical properties and other properties such as
thermal and chemical stability. Another advantage of these materials is that the large
size of the monomer molecule minimises polymerisation shrinkage (Rosin et al.,
2002; Kournetas et al., 2004) and wear (Lutz and Krejci, 2000; Manhart et al.,
2000). In a recent study, Ormocer-based material demonstrated the lowest decrease
in hardness following immersion in solvent for a period of time compared with
dimethacrylate-based composites and as a result it has been proved to be more
39
resistant to solvent degradation than any other material tested (Cavalcante et al.,
2011).
1.4.2 Silorane Restorative Materials
The name silorane is derived from the combination of its chemical building blocks
siloxanes and oxiranes (Figure 1.14) (Weinmann et al., 2005). The siloxane block
acts like a backbone for the silorane structure and also it improves the physical
properties of composite by providing hydrophobicity to the silorane thus reducing
the water sorption. Moreover this hydrophobic nature tends to absorb less stain from
a normal daily diet (Weinmann et al., 2005).
Figure 1.14 Chemical structure of Silorane
The network of siloranes is generated by the cationic ring opening polymerisation of
the cycloaliphatic oxirane groups, which results in low shrinkage and consequently
low polymerisation stress (Weinmann et al., 2005). Cationic cure begins when an
acidic cation unlocks the oxirane ring and produces a new acidic centre which is
called a carbocation. After the addition to an oxirane monomer, the epoxy ring is
opened to form a chain, or in the case of two- or multifunctional monomers a
network is formed (Weinmann et al., 2005). The major contrast between silorane and
methacrylate based composite curing is that methacrylates are cured by free radical
intermediates whereas siloranes are cured by polymerisation of oxiranes via cationic
intermediates.
40
Silorane based materials have lower polymerisation shrinkage, but an overall mixed
mechanical and higher flexural strength and fracture toughness than methacrylate-
based restorative materials (Lien and Vandewalle, 2010). However a recent study
has shown that silorane based materials exhibited higher colour change and surface
degradation (Pires-de-Souza et al., 2011).
41
1.5 Polymerisation Shrinkage of Resin Composite
Polymerisation shrinkage occurs due to molecular densification during
polymerisation. This bulk shrinkage leads to a change in dimension, for linear
shrinkage this can be described by the term ‘strain’ [ε], the strain arising due to
polymerisation shrinkage is often referred to as shrinkage-strain [Sε]. When
polymerisation shrinkage does not occur freely, but is constrained or the matrix is
rigid enough, tensile forces (i.e. stress) may result. This may occur within the
material at the filler-matrix interface, or at the interface of the composite and cavity
wall and is often referred to as shrinkage-stress [Sσ].
Although shrinkage-strain is the cause, it is the shrinkage-stress effects that may be
seen as being responsible for the problems with adhesive resin-based restorations
that are encountered clinically (Bowen et al., 1983; Davidson and Feilzer, 1997).
Polymerisation shrinkage stress between resin composite and tooth structure can be
as high as 13 MPa. The stress can severely affect the bond between resin composite
and tooth-structure; this will lead to bond failure and a marginal gap and subsequent
leakage of saliva and secondary caries. The stress caused by the polymerisation
shrinkage depends on several factors such as: the modulus of elasticity of resin
composite (also called Young’s modulus), the flowability of the composite in the
pre-gelation phase and the extent of the reaction i.e. the degree of conversion [DC]
(Condon and Ferracane, 1998).
There have been many attempts to overcome the problem of polymerisation
shrinkage either by altering the composition of resin composite or by improving the
curing unit or curing techniques, also by altering the placement or handling
techniques. For example, it has been found that the size of the filler particles has a
significant effect on the polymerisation shrinkage, the smaller the filler size the
lower the polymerisation shrinkage, for example, adding 10% of nanofiller particle
to a hybrid composite results in 32% reduction in polymerisation shrinkage due to
the high viscosity of the resin which prevents its flow before gelation and causing the
polymerisation shrinkage to occur after the material becomes rigid (Condon and
Ferracane, 1998). This may however, in turn lead to increased stress (Watts and
Satterthwaite, 2008). Although nanocomposite has the least polymerisation
42
shrinkage, this can be reduced further by replacing the functional silanated nanofiller
with either non functional silane treated or untreated nanofiller particles (Condon
and Ferracane, 1998).
Another important fact regarding polymerisation shrinkage is the direction of the
shrinkage i.e. the shrinkage vector. The direction of shrinkage vectors depends on a
variety of factors, such as the shape of the cavity and the boundary conditions i.e.
bonding to cavity walls. For a well-bonded composite restoration, the shrinkage
vectors are directed down and towards the bonded margins and if the bonding to the
walls is poor the vector orientation is away from the light direction and again
towards the remaining bonded areas i.e. the direction is usually towards the bonded
walls and away from the free surface (Versluis et al., 1998).
A light curing unit with high intensity of light has been recommended for a better
curing depth and physical properties of the resin composite; however, it has a
negative effect as it increases the contraction stress, as the DC is increased and the
pregelation flow is decreased (Lim et al., 2002).Thus, the use of a low intensity
curing light is desirable to reduce the polymerisation stress contraction. This is
probably due to the decrease in the rate of polymerisation that allows more time for
the molecules to be rearranged (Lim et al., 2002) i.e. increase in pregelation flow. It
is preferable to use this light in a multi-phase curing method with stepped or ramped
intensity as this may reduce the polymerisation shrinkage stress by up to 12% (Lim
et al., 2002).
1.5.1 Degree of conversion:
The DC is “the percentage of carbon-carbon bonds converted to single bonds to form
a polymeric resin, also the percentage of polymerised methacrylate groups”
(Anusavice and Phillips, 2003). The higher the DC the better the mechanical
properties of the resin composite material. Polymerisation shrinkage is dependent on
the DC, as the DC is directly proportional to the shrinkage strain, i.e. the higher the
DC the higher the shrinkage strain (Sakaguchi et al., 1992).
43
Given that shrinkage strain is largely inescapable, any decrease in DC will result in
less favourable mechanical properties, methods for reducing the effect of shrinking
resin composite are intended at controlling the shrinkage stress during
polymerisation. Two main approaches attempt to either increase the pre gelation
flow or to alter the constraint (‘direct’ the shrinkage).
1.5.2 Methods to control polymerisation shrinkage:
As mentioned previously, resin composite materials undergo polymerisation
shrinkage, this shrinkage causes deformation changes in the material and if the
deformation is linear, then this linear change is described as polymerisation strain
(strain is change in length per unit initial length).
As mentioned previously (§1.4), shrinkage strain may not occur due to the efficacy
of the bond between the resin material and the walls of the cavity i.e. a boundary is
constrained, and the cross-linking density inhibits the accommodation of the
shrinkage strain by viscoelasticity of the polymer (Charton et al., 2007). Instead of
polymerisation strain, polymerisation stress can happen due to the generation of
tensile force (stress is force per unit area), this leads to clinical problems such as gap
formation and marginal leakage and cusp deflection (Davidson and de Gee, 1984;
Davidson-Kaban et al., 1997).
As polymerisation shrinkage is a big disadvantage of resin composites many studies
have been carried out to reduce this phenomena either by developing a low shrinking
monomer or by introducing a new curing light to reduce the polymerisation
shrinkage (Atai and Watts, 2006). Unfortunately the optimal light curing intensity is
still controversial; however, the use of ‘Soft Start’ polymerisation technique has
some advantages in delaying the shrinkage strain (Silikas et al., 2000).
As mentioned previously (§ 1.3.1), one way to reduce the polymerisation shrinkage
is by altering a monomer which can be achieved by changing the ratio of the resin
monomer, and it was found by increasing the percentage of BisGMA in a mixture of
BisGMA and TEGDMA the shrinkage reduced due to its high molecular weight.
80% or more of BisGMA in the mixture of BisGMA and TEGDMA was found to be
44
effective in reducing the shrinkage strain as well as the rate of the degree of
conversion (Atai et al., 2005). Also composite formulation with high filler content
and less monomer will have low polymerisation stress and polymerisation strain
(Condon and Ferracane, 2000).
Clinically shrinkage of resin composite restoration, whether it is shrinkage stress or
strain, can be controlled by several clinical methods, these include:
Application of bonding resin.
The application of 80 µm thickness of unfilled resin would help to reduce the stress
in class V composite restoration (Rees et al., 1999).
The placement technique.
Polymerisation stress can be affected by placement techniques (bulk or incremental
layers), however, polymerisation strain is not i.e. shrinkage stress can be controlled
to an extent due to controlling the constraint of the shrinkage. There is a direct
relationship between the stress force generated by the polymerisation shrinkage and
the ratio between the bonded and free surfaces, which is called configuration factor
[C-factor], i.e. the higher C-factor the more stress force will be generated.
This C-factor can be minimised by increasing the free surface and decreasing the
bonded surface and this can be done by using an incremental layering technique
which can be through either a horizontal layering technique (Figure 1.15) or an
oblique layering technique (Figure 1.16). This ensures adequate depth of cure and
reduces the stress. However, some studies recommended the use of a bulk technique
because it reduces the stress at the cavosurface margin (Deliperi and Bardwell, 2002)
and also distributes the stress generated by polymerisation shrinkage, reducing the
coronal deformity and inhibiting the initiation of cracks within enamel (Versluis et
al., 1996).
45
Figure 1.15 Horizontal layering technique
Figure 1.16 Oblique layering technique
Controlling the curing light intensity.
The proper selection of the curing light could effectively reduce the polymerisation
shrinkage for example using 25% for 10 seconds then 50% intensity for 10 seconds
then 100% intensity for 20 seconds can significantly decrease polymerisation
shrinkage (Dennison et al., 2000) and also the use of a low intensity curing light is
desirable to reduce the polymerisation stress contraction.
The use of dentine bonding agent.
The competition between the stress and the bond to the walls of the cavity is a major
cause of the marginal gap and subsequent restoration failure (Davidson et al., 1984).
Previously only enamel bonding agents were used, in which situation as the
polymerisation shrinkage occurs the composite will be bonded only to the enamel,
and there will be a marginal gap along the dentinal walls of the cavity (Davidson and
de Gee, 1984).
46
1.6 Handling Properties of Pre-cured Resin Composites
Resin composites are viscoelastic materials in nature. The viscoelasticity and the
ratio of viscosity to elasticity are important factors in determining the handling
properties of resin composites at their pre cure state. Handling characteristics of resin
composites such as ease of placement, malleability (being easy shaped and formed)
and stickiness to tooth not to instruments are directly related to their viscosity
(Ferracane et al., 1981; Leinfelder and Roberson, 1983; Bayne et al., 1992; Opdam
et al., 1996b; Al-Sharaa and Watts, 2003). Hence, composite viscosity has a great
influence in affecting restorative treatment in terms of clinical time and the quality of
a restoration (Tyas et al., 1998; Lee et al., 2003).
In addition to the viscosity, the handling characteristics are highly affected by the
flowability and elasticity of resin composites (Lee et al., 2006). Although the well
known classification of resin composites is the classification according to filler size,
it is also appropriate to classify them according to their viscosity to flowable,
medium viscosity and packable composites (Lee et al., 2006).
1.6.1 Factors affecting viscosity of resin composites
The flowability of resin composites is regulated by various factors,
which are:
Resin matrix
Filler particles
Temperature
1.6.1.1 Resin Matrix
As mentioned previously (§1.3.1) Bis-GMA monomer is commonly used due to
several advantages which are: low shrinkage, low volatility, rapid hardening by free
radical polymerisation and good mechanical properties post curing (Moszner and
Salz, 2001). Bis-GMA monomer is highly viscous at room temperature; hence there
is a need to dilute it with other low viscosity monomers. Previous results have
47
shown that the viscosity of a resin composites can be reduced by increasing the
proportion of low molecular weight diluting such as TEGDMA (Ellakwa et al.,
2007; Beun et al., 2008), however there is a limit to which these monomers can be
added to dilute the resin material as it has been shown to increase polymerisation
shrinkage (Ellakwa et al., 2007).
1.6.1.2 Filler particles
As mentioned before (§1.3.2) the size and shape of the inorganic filler particles has a
big influence on the viscosity of resin composites; particularly, interlocking between
filler particles, and inter-facial interactions between filler particles and the resin
matrix, both play an important role in manifesting viscoelastic properties (Leinfelder
and Prasad, 1998). The viscosity of resin composites increases with increased filler
content (Lee et al., 2003). To express the effect of percentage of filler particles
content on the viscosity of resin composites, filler Vol % instead of Wt % is used
mostly. The reason behind using Vol% of the filler particles is that the rheology
depends on the hydrodynamic force which acts on the surface of particles or
aggregates of particles, which is not related to density of filler particles (Lee et al.,
2003).
Regarding filler particles shape, in the case of similar filler volumes the viscosity
increases in the order of spheres, grains, plates, and rods. Additionally, for rod-
shaped fillers such as glass fibre, as the ratio of length to diameter increases, so does
the viscosity.
1.6.1.3 Temperature
As temperature increases, the viscosity of resin composites decreases (Silikas and
Watts, 1999; Lee et al., 2006).
This can be expressed by the following equation:
48
/ Equation 1
Where µ is viscosity A is a proportional coefficient; E is activation energy for flow;
R is the Gas constant (8.31451 kJ mol-1) and T is the temperature in degree Kelvin.
1.6.2 In-vitro measurement of handling properties
There is a lack of literature regarding the measurement of stickiness of resin
composites. In 2003 a new method to characterise stickiness of resin composites was
described (Al-Sharaa and Watts, 2003). The method is based on the use of a flat
ended steel probe to assess the stickiness of resin composites measured as the height
of the peak obtained by curing the material shortly after it separated from the probe.
Samples were placed in a mould at two different temperatures (23 and 37 °C). A
stainless-steel instrument was placed on the surface of the uncured sample and was
lifted vertically after 2s. Subsequently the sample elevated to a maximum height, until
detachment from the instrument occurred. The elevated material was cured by light
curing unit at 600 mW/cm2 for 40s. These elevated profiles were measured for both
height and projected area of elevation (Al-Sharaa and Watts, 2003).
49
1.7 Physical and Surface Properties
1.7.1 Surface Roughness
The desired form, smoothness and glossy appearance of resin composite restorations
requires finishing and polishing of the restorations. Roughness of a restoration
surface leads to accumulation of plaque, gingival irritation and can cause staining
and subsequently poor aesthetics of the restoration (Jefferies, 2007). Additionally,
rough occlusal composite restoration surfaces can cause excessive wear of the
opposing enamel (Watanabe et al., 2005; Jefferies, 2007).
Polishing of resin composite restorations produces restorations with lower surface
roughness and high gloss values; although other research suggests that the smoothest
composite surface is obtained by setting under polyester matrix film (Stanford et al.,
1985; Lee et al., 2005).
1.7.2 Gloss
Gloss describes the capacity of any surface to reflect directed light, hence a perfect
mirror surface is believed to have maximum gloss (Kakaboura et al., 2007a). A
material surface shows high gloss when all light beams are reflected at nearly the
same angle as they hit the surface. Surface roughness is directly influence the surface
gloss (Lee et al., 2005; Lu et al., 2005).
Clinically, the more aesthetic materials are those which exhibit better glossy surface.
It has been recommended to apply unfilled resin over restorations to give a glaze
appearance however this does not minimise surface roughness after tooth brushing
abrasion (Sarac et al., 2006), which affects surface properties of resin composites
(Lee et al., 2002).
50
1.8 Mechanical properties of resin composite materials
1.8.1 Fracture toughness
Fracture of restorations is one of the main factors in failure of resin composite
restorations placed in large cavities (van Dijken, 2000; Van Nieuwenhuysen et al.,
2003). One of the most used means to investigate fracture resistance of resin
composite materials is the measurement of fracture toughness (Ilie et al., 2011).
Fracture toughness can be defined as the critical stress intensity factor at the
beginning of rapid crack propagation in a solid containing a crack of known shape
and size (Anusavice and Phillips, 2003). Fracture toughness measurement is based
on the assumption that fracture happens due to the presence of microscopic defects
in the material and that crack propagation requires the creation of two surfaces which
increases surface energy (Griffith, 1921). The released energy comes from stored
elastic energy of the loading system, this assumption was made initially for brittle
materials.
Later on, this assumption was altered to explain fracture toughness not only in brittle
materials but also in elastic materials (Irwin, 1957). It was found that the stress field
around a sharp crack in a linear elastic material could be exceptionally defined by a
parameter is called the stress intensity factor [K], and that fracture occurs when the
value of K goes beyond a critical value [KC] (Irwin, 1957). KC then was changed to
KIC with I defining the different ways of loading materials to enable a crack to
propagate i.e. I is the crack opening under normal force applied in a perpendicular
direction to the crack (Ilie et al., 2011).
1.8.2 Wear
When a material comes into contact with another material abrasive wear occurs.
Hence wear can defined as the progressive loss of material from the contacting
surfaces of a body, caused by relative motion at the surface (Mair et al., 1996;
Ramalho and Miranda, 2006). This abrasive wear can occur as two body wear where
the abrasive material abrades the softer material or three body wear in which loose
abrasive particles remove the softer material. The severity and time of material wear
51
depends on the size, shape and hardness of abrasive particles (Rabinowicz and
Mutis, 1965; Ohtani et al., 2003).
1.8.2.1 Two body wear
This type of wear is mainly due to fatigue from cyclic loading (Wu et al., 1984; de
Gee and Pallav, 1994). Loss of occlusal composite restoration is caused by two body
wear at occlusal contact points. The wear process at the contact point is occurred
when the hard surface sliding on smooth surface of a restorative material, which
contributes to the antagonistic enamel wear, this is can also be seen on occlusal
surfaces (Sarrett et al., 2000).
1.8.2.2 Three body wear
This is caused during mastication as a result of the presence of abrasive food
particles between teeth or restorations and also due to toothpaste and toothbrush
during brushing. Three body wear involves the loss of resin matrix and further
dislodgement of filler particles. Several factors regulate or minimise three body
wear, such as filler particles, size and space between filler particles (Jorgensen et al.,
1979; Bayne et al., 1992). A previous study has shown different resin matrices have
different rates of wear, with resin composites containing UEDMA/TEGDMA
matrices more resistant to wear than those using Bis-GMA/TEGDMA matrices
(Söderholm et al., 2001). Wear of resin composite materials could be a result from
different mechanisms, (O'Brien and Yee, 1980):
Wear of the resin matrix.
Loss of filler by failure of its bond to the matrix.
Loss of filler through shearing of exposed particles.
Loss of filler through cracking and failure of the matrix.
Exposure of entrapped air bubbles.
The presence of voids within materials weakens them and reduces their resistance to
wear. Additionally the presence of voids within resin composites increases the
abrasion rate and it also facilitates the resin matrix to absorb chemicals from the food
and saliva and becomes weakened. Therefore, the matrix becomes more easily
abraded or fractured (Jorgensen, 1980; Ogden, 1985).
52
1.9 Voids within Resin composite:
Another significant disadvantage of resin composite materials and resin composite
restorations is the presence of voids or porosities, due to air entrapment during
manufacturing or handling of the material (Chadwick et al., 1989). The presence of
voids within resin composite restorations may cause several drawbacks (Opdam et
al., 1996b):
They can lead to leakage and subsequent discolouration if they are present at
the margin of the restoration.
Decreased flexural strength of the restoration if located between the
increments of the restoration.
Wear of the restoration is increased due to stress concentration areas around
the voids.
May be misdiagnosed as secondary caries in the radiograph.
Also it causes incomplete adhesion between the resin composite and the
dentine (Purk et al., 2007).
Despite all of the above mentioned drawbacks the presence of voids may decrease
shrinkage stress development due to the inhibiting effect of oxygen (present in the
voids) during the setting reaction, and they can also result in an increased free
surface area within the restoration, to where the polymerisation vector is directed as
discussed previously (Alster et al., 1992).
Incorporation of voids is significant during spatulation of chemically cured resin
composite (Valcke and Duggan, 1981) but the introduction of single paste light-
activated resin composite has resulted in fewer voids within the resin-material
(compared with chemical cured material), partly due to its syringing application
(Watts et al., 1983). Although this application of resin composite can reduce voids, it
does not eliminate them due to air introduced through the nozzle of the syringe
during insertion of the material (Ogden, 1985). Although this introduction of air can
be minimised through the use of a small diameter syringe (Fano et al., 1995).
There is also a relation between water uptake by resin composite and the presence of
voids, and both are directly proportional to each other. It has been found that water
53
uptake by resin composite produces microscopic holes which allows the penetration
of the water and metal ions in the saliva (Wadgaonkar et al., 2006), but also the
presence of voids within the resin composite restoration increases the water uptake.
Due to the problems caused by voids within resin composite restorations and their
effect on the life span of the restoration, and because these voids are not only caused
during manufacturing but also during handling procedures, some attempts have been
carried out to reduce the formation of voids during application of the material. These
include for example:
using of a small diameter syringe of about 0.5mm (Fano et al., 1995).
the use of low and medium consistency resin composites (Opdam et al.,
1996b).
the use of less viscous materials such as flowable and injectable composites
(Chuang et al., 2001; Opdam et al., 2002).
1.9.1 In-vitro measurements of voids:
As mentioned previously the presence of voids within resin composite restorations
can lead to failure of the restoration as they increase the propagation of cracks,
increase the wear rate and reduce the strength of the restoration (McCabe and Ogden,
1987). Many studies have been carried out to assess and quantify these voids in order
to eliminate or at least minimise them to achieve good restorations. These mainly
employ high magnification tools including light microscopy and electron
microscopy.
1.9.1.1 Light microscopy:
Light microscopy has been used to measure voids within resin composite
restorations and to compare voids between restorations with different types of
composite: for example, packable and injectable (Opdam et al., 2002). In their study
Opdam et al. sectioned the restoration into two slices and voids were examined in
each slice using the following rating scale:
0 no porosities visible.
1 small porosities visible < 1mm.
2 large porosities visible > 1mm.
54
Similarly, Alster et al. (1992) measured voids with the help of a camera mounted on
a light microscope. The amount of total voids was calculated on digitised images of
these blocks of the material by the use of a PC-based image-analysis system.
Another way is by examining the degree of the dye penetration(Chuang et al., 2001).
In this study the teeth were soaked in a dye for 24 hours. The restored teeth were
afterwards sectioned and then observed with a 50× stereo-microscope in order to
record and score the degree of dye penetration and internal voids were recorded and
scored. The measurement of voids was carried out in three sites which were the
gingival wall, the cervical half of the restoration and in the occlusal half of the
restorations. A numerical score was given to voids as follows:
0 = no void and 1 = void exists.
1.9.1.2 Electron microscopy:
Electronic microscopy has higher magnification than light microscopy and it has
been used to measure voids within resin composite restoration in a variety of ways.
In previous studies, electron microscopy has been used in addition to a digital
camera in order to analyse voids. In these studies (Ogden, 1985; Chadwick et al.,
1989) resin composite specimens were sliced after being stained and photographed
using low-angle incident lighting (via a fibre-optic light source), and then examined
under electron microscopy in order to quantify the voids.
The above mentioned methods both have some disadvantages in relation to the study
of voids within resin composite: a light microscope is able to identify voids which
are 1mm or larger and an electronic microscope is able to detect voids which are
about or greater than 100µm (Ogden, 1985; Chadwick et al., 1989). Another
disadvantage is that both of these methods are destructive which means the resin
composite restoration cannot examined as a whole for the presence of voids.
Moreover these methods are technically demanding as the specimens need some
preparation before voids can be examined such as slicing, staining and the use of
additional device, for instance digital camera.
55
1.10 Micro Computed Tomography [µCT]:
µCT has many advantages for studying dental tissues and materials as it is an
accurate non-destructive method for examining both teeth and materials. Micro
computed tomography [µCT] data appear to present significant advances in the
ability to virtually reconstruct the tissues of the tooth as well as dental materials with
optimum details in in-vitro environment (Nielsen et al., 1995). µCT has improved
resolution over conventional techniques such as light and electron microscopy. µCT
allows the specimen to be assessed by producing various sections with high pixel
resolution, each section or slice can then be analysed in three dimensions (3D)
(Figure 1.17).
Figure 1.17 3D model of resin composite MOD restoration
Previously, the application of µCT was limited by a resolution ability of 1 to 2mm,
however this resolution has been improved to achieve 25 to 15µm and possibly less
than 10µm with the recent introduction of spiral scanning (Jung et al., 2005). µCT
has been used in a variety of dental studies. For example, it has been used as a
research tool in three dimensional tomography of composite fracture surfaces
(Drummond et al., 2005), x-ray tomographic imaging of Ti/SiC composite
(McDonald et al., 2003) as well as imaging of root canal obturation (Jung et al.,
2005). The advantages of µCT can be summarised as:
Non-destructive technique for examining dental tissues and dental materials.
High resolution of up to 10µm (or less).
Three dimensional images can be re-constructed.
56
1.11 Summary
Dental amalgam has been used extensively to restore teeth, and has been the standard
material for more than a century although with developments in the range and
properties of the materials available for the restoration of teeth, it may not be
considered that the preparation of teeth to receive amalgam restorations is causing
unnecessary damage to tooth structure, moreover there is increasing concerns
regarding health and environmental issues regarding the mercury content of
amalgam. The most promising alternatives to amalgam restorations today are resin-
based composites, which are based on an organic methacrylate matrix and inorganic
glass filler. In fact, the placement of resin-based composites has been developed into
an important mode of treatment in contemporary dental practice.
Resin composite has become the material of choice for restoring anterior and
posterior teeth. However, this material still has some disadvantages despite the
development of new resin materials. Disadvantages such as polymerisation
shrinkage, wear, water sorption and porosity within the resin material make it
difficult to achieve a good restoration with long survival time.
Handling properties of resin composites is very important and can affect the long
term survival of restorations. During condensation, stickiness of material to hand
instrument may result in voids within final restoration. Voids in restorations are
probably due to air entrapment in the resin composite during the manufacturing
process or during handling of the material by the clinician (Ogden, 1985; Chadwick
et al., 1989). Voids which are located within the layers of the resin restorative
material can cause microleakage, and also increased stress which subsequently will
cause fracture of the restoration (Opdam et al., 1996b). Additionally, these voids
may lead to rough surface of restoration which subsequently can cause
discolouration and increased wear.
57
CHAPTER TWO
General Aims and Objectives
58
2.1 Aims of the study
The aims of this research were to characterise the effects of filler size on the
handling, mechanical and surface properties of resin composites.
The specific objectives were to investigate a series of experimental, model resin
composites in order to investigate the effect of filler size on:
Packing stress and viscosity
Presence of voids within resin composites and fracture toughness
Surface roughness, gloss and colour stability of resin composites
2.2 Objectives of the study
The specific objectives were to investigate a series of experimental model resin
composites and one commercial resin composite in order to:
Investigate the effect of filler size and temperature on handling properties of
pre-cured composite
Characterise the effect of filler size on the presence of voids in 3D
Investigate the effect of filler size and voids percentage on fracture
toughness
Investigate the effect of filler size on gloss and colour stability
Characterise the effect of filler size on surface properties after toothbrush
abrasion
The outline of the studies is shown diagrammatically in Figure 2.1:
59
Figure 2.1 Studies outline
60
CHAPTER THREE
Methodology
61
3.1 Introduction
A range of both standard and novel research techniques were employed to meet the
objectives of the current research. Standard techniques such as colour stability and
gloss of resin composite and mechanical properties e.g. fracture toughness. All these
methodologies are thoroughly explained in their relevant chapters. The novel
techniques used in the current research were:
a) The use of novel apparatus to measure the packing stress of uncured resin
composites (Chapter 4)
b) The use of x-ray micro-computed tomography to investigate pores within
resin composites (Chapter 5 and 6)
c) The use of 3D topography scanning to investigate surface roughness of resin
composites (Chapter 7)
3.2 Packing stress measurement
A precision instrument was designed and fabricated upon the penetrometer principle.
The apparatus (Figures 3.1 and 3.2) consisted of a lever with an arm pivoting via a
load-bearing pin, on a vertical steel pillar (B) bolted to a steel base (A). The lever,
pillar and steel base formed a horizontal U shape with the lever extending beyond the
base. A thin cylindrical rod (diameter = 3.18mm) was pushed via the lever arm into
each unset material to a controlled depth (2.5 mm) under a constant load.
The control of penetration depth was achieved by a stop plate mounted on an
additional pillar. A reduced friction bearing (C) was also vertically positioned to
limit any angular motion of the lever produced a linear displacement of the rod. The
test samples were placed in a movable cavity within a temperature controlled base
(D). A calibrated thermocouple tip inserted into a hole drilled into the rim of
temperature controlled base monitored the temperature of the cavity. The free end of
the lever was weighted by a 500 g mass (M).
62
Figure 3.1 Schematic diagram showing various parts of the packing stress
measurement apparatus: A-steel base; B-steel pillar; C-friction bearing;
D-temperature controlled base; M-weight
Figure 3.2 The packing stress measurement apparatus showing the connected
temperature controlled base
A movable open ended cylindrical brass small cavity (6.35 mm diameter and 4.5 mm
depth) with two different controlled temperatures was used in this study. 142.4 mm3
of composite material was placed in the cavity using a flat end plastic hand
instrument; a glass slab was used to level the material with the mould’s surface. The
plunger flat end was placed lightly on the surface of the composite material to be
63
investigated, following the first test, composite material repacked into the mould,
and material was added as required, plunger head cleaned and test repeated six times
(n = 6) for each material. A representative recording of stress is shown in Figure 3.3:
upon application of the plunger, an initial ‘spike’ in stress is recorded; the
‘persistence time of peak stress’ [tp] was taken as the time after the initial spike [t1]
to the time of dissipation of recorded stress [t2]. The ‘mean packing stress’ [σ] was
taken as the average of the stress recorded at t1 [σi] and t2 [σf]. The viscosity [µ]
was calculated (Equation 2) as the mean packing stress multiplied by the persistence
time of peak stress, thus:
Equation 2
Figure 3.3 Time dependant packing stress profile curve
64
3.2.1 Calibration of load cell
During the investigation, force was transferred to a load cell in transitory manner.
The size of load cell was 3mm height and 13mm diameter. The load cell was
connected to signal conditioning device (ENTRAN. model PS-30A). The load
transfer was a consequence of the stiffness of the pastes which transferred some of
the force from the penetrometer plunger. The force signal obtained with the signal
conditioning amplifier was recorded continuously on data acquisition software
(DASYLab 8 software) as a signal in volts (V). The load cell was calibrated by the
use of different masses (0.1kg, 0.2kg, 0.5kg, 1kg, 2kg and 5kg) placed in sequence
directly on the load cell (Figure 3.4).
Force (N)
0 10 20 30 40 50
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
Load
Sig
nal (
V) r = 0.9997
Figure 3.4 Calibration of the load cell graph
65
Hence the slope can be calculated from the calibration graph which was (0.03);
packing stress (MPa) and viscosity related packing stress (MPa.Sec) can be
calculated as follows:
Force (N) = [9.8 × Signal (V)] / Slope
Area = π r²
Where r = 1.59mm (half of diameter of the tip of plunger which is = 3.18 mm)
Area = π × (1.59)² = 7.94 mm²
Stress (MPa) = Force (N) / Area
Stress (MPa) = [9.8 × Signal (V) / Slope] / π r²
The calibration for the load cell has been applied using force by Newton so:
Stress (MPa) = [Signal (V) / Slope] / π r²
Slope = 0.03
Stress (MPa) = [Signal (V) / 0.03] / 7.94
Viscosity (MPa.Sec) = Stress (MPa) × Time (sec)
66
3.3 X-ray Computed Tomography
The use of optical microscopy provides two-dimensional visualisation of objects.
Three-dimensional information is important and conventionally achieved by cutting
the object into thin slices, and then visualising them under a microscope. However,
it is an unreliable method as the object structure itself can be altered by the
preparation technique and the distance between the slices is usually too coarse to
avoid loss of three-dimensional information (Skyscan, 2009). With the advent of
digital image capture, reconstructing a virtually sliceable three-dimensional model of
an object from two-dimensional images became a reality. This has revolutionized
almost every area of science (Philip, 2007). This process is called tomography,
which is derived from the Greek word; tomos (section, slice, cutting) (Wikipedia,
2011 ). X-ray tomography enables visualization and measurement of the three
dimensional object structure non-destructively. Computed X-ray tomography [CT]
was first introduced by Hounsfield in 1968 (Philip, 2007). Whereas conventional CT
systems are of low resolution (800 μm) (Paulus et al., 2000), Micro Computed
Tomography [µCT] has a spatial resolution of up to 5 μm (Skyscan, 2009). Recently
nano-CT was introduced, which has improved the detectability to the submicron
level (Philip, 2007).
µCT has been used in different fields in dental research (Kakaboura et al., 2007b;
Magne, 2007; Hammad et al., 2009; Mokeem Saleh et al., 2009; Strebel et al., 2009;
Zou et al., 2009; Hatamleh and Watts). In the current research, two Chapters (5 and
6) used µCT to investigate the percentage of voids within resin composites and its
correlation with filler size and with fracture toughness.
3.3.1 SkyScan-1072 System
The SkyScan-1072 system (SkyScan, Kontich, Belgium) is a compact desktop
system for x-ray microscopy and micro tomography (Figures 3.5). It is a
combination of an x-ray shadow microscopic system and a computer with
tomographic reconstruction software. For a 3-D investigation of any object, three
sequential processes are carried out; object scanning, reconstruction and 2-D or 3-D
analysis. These are carried out with the help of software provided with the SkyScan
package.
67
The SkyScan µCT machine is shielded with lead, to prevent x-ray leakage. The
equipment mainly comprises:
An x-ray micro-focus tube in which both the high-voltage and current are
preset.
A specimen stage with precision manipulator.
An x-ray CCD camera: 1024 ×1024 pixels.
Figure 3.5 SkyScan-1072 micro-CT
3.2.1.1 Object scanning
After specimen preparation, the SkyScan unit is activated and the specimen placed
into the specimen chamber (Figure 3.6). Scanning parameters are then adjusted. The
voltage and current are usually preset (100kV and 98uA, respectively) but can be
modified.
Figure 3.6 SkyScan with the specimen chamber door open and specimen placed on
the holder
68
The vertical position, angle and magnification of the specimen can be adjusted. An
initial x-ray image (X-ray shot) is obtained to confirm the suitability of the chosen
parameters. In this research, composite specimens were scanned using the following
parameters: image 19µm pixel size at 14.39×14.39 resolution; 45° rotation step, 180°
sample rotation; 4 seconds exposure time and averaging by one frame with no filter
(Figure 3.7)
Figure 3.7 An initial x-ray image (X-ray shot) of a resin composites. Parameters of
scanning are present in the top right hand corner
The scanning parameters mean that every time the specimen rotates 45°, one x-ray
image is taken within 4 seconds. This continues until the object (specimen)
completes the 180° rotation. Once the parameters are chosen, the file name is set and
the scanning begins. The acquired projections are in the form of 16-bit .tiff files that
will be used for reconstruction.
69
3.3.1.2 Reconstruction
The NRecon software is used to reconstruct the specimen and generate 2D cross-
sectional slices across the length of the specimen. The scanned .tiff files are imported
into the software and an image of the specimen is shown with the reconstruction
command window. At a desired cross-sectional level and with the preview
command, a 2D slice is generated at a representative area (defined by the green line)
of the specimen (Figure 3.8).
A
B C
Figure 3.8 View of a 2-D image of the specimen (A) with, the functional window
(B), and a 2-D slice at the selected green line level (C)
If the generated slice is to the operator’s satisfaction, these threshold values are then
applied to the entire specimen. The grey scale can be modified, and the fine tuning
command allows trying different threshold values to get the best one that suites the
specimen (Figure 3.9).
70
Figure 3.9 Representative 2D slices resulting from different reconstruction threshold
values. The most suitable one is in the middle. The graphs above represent the grey
scale of the threshold values
When parameters are chosen, the whole specimen reconstruction is carried out. A
series of 2D images in the form of bmp files are generated.
3.2.1.3 Analysis
The CTAn software allows 2D or 3D analysis of the specimen structure and
performance of various measurements. In addition, a 3D model of the specimen can
be generated. The bmp files are first imported into the software and the 2D slices of
the specimen are visualised. Then the area of interest to be studied is defined by the
Region of interest command (Figure 3.10).
Figure 3.10 Raw image (A), in which the region of interest is defined (B) resulting
in a new 2-D slice of the selected area (C)
71
The bmp files then imported into the CTAn software. The raw images are first
transformed to binary ones with two-brightness grades only: black and white. A
threshold value is chosen that produces a binary image which represents, to the
greatest extent, the raw image. When the binary threshold values are chosen, 2D
slices undergo custom processing which produces 2D slice images that are the same
as the raw ones. Within the custom processing window, Thresholding is carried out
so the black dots within the specimens turn into white and white turns into black,
then a Despeckle command is performed to remove unwanted small speckles, which
might affect measurement of pores. Finally the 2D or 3D analysis commands allow
calculation of the desired measurements. In this study 3D calculation was carried out
to measure the voids. Also a 3D model (Figure 3.11) is produced and visualized
using the CTVol software.
Figure 3.11 3-D model of resin composite sample (A) and (B) 3D image with
pseudo colour (red represents the voids and blue represents resin composite).
72
3.4 Optical Computed Topography
Topography derived from Greek word; topos which means place and can be defined
as the study of Earth's surface shape. It is also the description of surface shapes and
features (Wikipedia, 2011). One tool used to measure surfaces is the profilometer,
which is either 2D or 3D. 2D profilometers can be either contact (stylus) or non-
contact. The disadvantages of a 2D profilometry machine include: tracing only one
line and inability to record defects outside this line (Bourauel et al., 1998),
additionally the stylus tip of a contact profilometer is very sharp and can be
destructive and may cause scratches to the surface under excessive force. For the
above mentioned drawbacks, the use of non-destructive 3D measurements has
increased. 3D profilometry has been used in the dental research field (Kakaboura et
al., 2007a; Ranjitkar et al., 2009; Hahnel et al., 2011; Munhoz et al., 2011). In the
current research, optical computed topography (Talysurf CLI 1000) was used in
Chapter 7 to investigate the effect of filler size on the surface roughness of resin
composites before and after tooth brush abrasion.
3.4.1 Talysurf CLI 1000
A 3D profilometer Talysurf CLI 1000 (Taylor Hobson Precision, Leicester, UK)
(Figure 3.12) can be supplied with one or a combination of different gauges which
are: laser gauge (non-contact large range measurement); inductive gauge (contact
measurement); or confocal point gauge based on the chromatic length aberration
(CLA) principle (non-contact high resolution measurement). Each of these gauges
has a different working principle. The gauge used in the current research (Chapter 7)
was the chromatic length aberration (CLA) confocal point gauge. The working
principle of this gauge is based on CLA confocal deduces the surface height of a
feature using an aberration technique which focuses the different elements of white
lights (Taylor Hobson Precision, 2009). A white light beam is focused onto the
surface through a lens with chromatic length aberration. Due to this aberration, the
focus point is at different Z-position for the different wavelengths. The reflected
light is sent to a spectrometer through a pin hole. The spectrometer provides an
intensity curve depending on the wavelength. The focused wavelength is the one
corresponding to the maximum intensity (Figure 3.13).
73
Figure 3.12 Talysurf CLI 1000 profilometer
Figure 3.13 Illustrating diagram of working principle of CLA gauge (Taylor Hobson
Precision, 2009)
74
The Talysurf CLI 1000 is composed of different soft and hardware which are:
1. The measurement instrument consisting of :
Two cross-slides on the x- and y-axes
A unit which houses the gauges and camera and permits manual
adjustment between them
Z-column/z-gantry with a motorised part which allows the gauges to
move in the vertical direction
A granite base
A keypad which enables the operator to define the required
measurements
2. The control unit
3. Data acquisition software (Figure 3.14)
4. TalyMap analysis software
Figure 3.14 Data acquisition software window
75
3.4.1.1 Sample scanning
The surface roughness of each sample was assessed by placing each one over a flat
surface above the cross-slides and scanning by a confocal optical single point sensor
(CLA 3 mm) with 0.25 mm cut-off length. The sampling rate of the gauge was 500
Hz. The mode of measurement was East-West gauge measurement direction, i.e.
from right to left rather than from top to bottom. For each sample, the start and end
of scan points were adjusted with a maximum spacing of 10µm. The measurement
speeds were 5mm/s and 5mm/s on return. The scanned image is indicated by grey
shade (black is used for the lowest areas and white is used for the highest areas)
(Figure 3.15).
Figure 3.15 Data acquisition software window shows difference between before and
after scanning
76
3.4.1.2 Data analysis
The data obtained as a result of surface scanning were analysed by TalyMap analysis
software (Taylor Hobson Precision, Leicester, UK). The first step in TalyMap
software is levelling the scanned image. From the levelled image 2D and 3D surface
profiles can be provided and additionally, surface roughness parameters
(Figure 3.16) can be calculated and a 3D view created (Figure 3.17). The 2D
roughness parameters measured were: Ra (the arithmetic mean of the absolute
departures of the roughness profile from the mean line); Rt (the maximum peak to
valley height of the profile in the assessment length), and 3D roughness parameters
were: Sa (the arithmetic mean deviation of the surface); St (the total height of the
surface, the height between the highest peak and the deepest valley).
Figure 3.16 Diagram illustrating different steps in Talymap software
77
Figure 3.17 3D model of scanned sample
78
CHAPTER FOUR
Effect of Filler Size and Temperature on Packing Stress and
Viscosity of Resin composites.
Elbishari H, Satterthwaite JD, Silikas N.
Int J Mol Sci. 2011;12(8):5330-8
(Appendix 1: Paper copy)
79
4.1 Abstract:
The objective of this study was to investigate the effect of filler size on the packing
stress and viscosity of uncured resin composite at 23 °C and 37 °C. A precision
instrument used was designed upon the penetrometer principle. Eight resin
composite materials were tested. Packing-stress ranged from 2.60 to 0.43 MPa and
viscosity ranged from 2.88 to 0.02 MPa.s at 23 °C. Values for both properties were
reduced significantly at 37 °C. Statistical analysis, by ANOVA and post hoc
methods, were carried out to check any significant differences between materials
tested (P < 0.05). Conclusions: Filler size and distribution will affect the viscosity
and packing of resin composites during cavity placement.
Keywords: packing; resin composites; nanofillers; viscosity
80
4.2 Introduction
The demand for dental aesthetic restorations has led to the development of resin
composite material. Typically dental composites consist of a matrix and fillers bound
together. Early resin composite gave rise to concerns regarding toughness, durability
and strength (Stangel and Barolet, 1990). Several improvements of these two
components over the last 20 years have increased the use of dental composites and in
many cases have replaced amalgam as the restorative material (Kohler et al., 2000;
Knobloch et al., 2002). Despite all improvements in dental composites, fracture of
restorations, particularly in large cavities in the posterior region, is one of the most
common causes of resin composite restoration failure for the first five years of
placement and the second most common cause of failure between five and ten years
of placement. (Collins et al., 1998; Gaengler et al., 2001; Brunthaler et al., 2003;
Opdam et al., 2004). In order to address this, efforts have been focussing on either
altering the monomer system or improving filler technology and the use of fibres to
reinforce the matrix (Moszner and Salz, 2001; Bae et al., 2004).
Recently, polymer nanofibres and titanium nanoparticles have been added to resin
composite to improve its properties (Chen, 2010). Current composite materials are
almost as strong and tough as amalgam, but not as strong as ceramic and casting
alloys (Ferracane, 2011). However, these improvements in their mechanical
properties have affected the viscosity of resin composite (Taylor et al., 1998; Silikas
and Watts, 1999). Their viscosity is directly related to ease of resin placement,
malleability and stickiness to tooth and instruments in so called handling
characteristics (Ferracane et al., 1981; Opdam et al., 1996a; Bayne et al., 1998;
Leinfelder and Prasad, 1998; Al-Sharaa and Watts, 2003).
While the effect of resin composite filler size and shape on the mechanical properties
(Kim et al., 2002; Masouras et al., 2008) and shrinkage (Satterthwaite et al., 2009)
have been documented in the literature, the effect of filler size and morphology on
the rheological behaviour of uncured resin composite is minimal (Lee et al., 2006;
Beun et al., 2009).
81
The aim of this study was to investigate the effect of different filler size and
distribution on the packing stress and viscosity of uncured resin composites at two
different temperatures (23 °C versus 37 °C). The null hypotheses were that different
filler size, distribution and temperature have no effect on i) the packing stress and ii)
viscosity of uncured resin composite.
4.3 Materials and methods
The resin composites used in the study were all visible light cured, and included 7
model formulations (Ivoclar Vivadent, Schaan, Liechtenstein) together with an
established commercially available formulation (Tetric Ceram [TC]- Ivoclar
Vivadent, Schaan, Liechtenstein) used as a control.
The resin matrix was the same for all materials and was a combination of BisGMA,
UDMA and TEGDMA with 0.33% camphoroquinone. All of the model composites
had a particulate dispersed phase of the same volume fraction (56.7%), which was
treated with a silane coupling agent (methacryloxypropyltrimethoxysilane). The
filler particles were systematically graded in size, and were either spherical or
irregular in shape. The spherical particles were silica, and the irregular particles were
ground glass (Ba–Al–B–silicate glass).
Tetric Ceram contained heterogeneous, multimodal filler particles, comprising
Barium glass 1 μm, Ba–Al–FB–silicate 1 μm, SiO2 40 nm, spherical mixed oxide
0.2 μm, and ytterbium trifluoride. The composition of the resin composites is
summarized in Table 4.1.
A precision instrument was designed and fabricated upon the penetrometer principle.
The apparatus used (Figure 4.1) consisted of a lever with an arm pivoting via a load-
bearing pin, on a vertical steel pillar B bolted to a steel base A. The lever, pillar and
steel base formed a horizontal U shape with the lever extending beyond the base. A
thin cylindrical rod (diameter= 3mm) was pushed via the lever arm into each unset
material to a controlled depth (2.5mm) under a constant load.
82
Resin
compo
site
Filler Particles( Ground Glass [Ba-Al-B-silicate glass]) Matrix
Shape Size (nm) Wt% Vol%
I1 Irregular 450 76.4 56.7
BisGMA,
UDMA,
TEGDMA
I2 Irregular 700 76.4 56.7
I3 Irregular 1000 76.4 56.7
I4 Irregular 1500 76.4 56.7
I5 Irregular 450, 1000
(1:3) 76.4 56.7
I6 Irregular 450, 700 &1500 (1:1:3) 76.4 56.7
SP Spherical 100 72.4 56.7
TC
Lot :
C4949
0
Irregular&
Spherical 40, 200 &1000 79 60
Table 4.1 Composition of resin composites used in the study
The control of penetration depth was achieved by a stop plate mounted on an
additional pillar. A reduced friction bearing C was also vertically positioned to limit
any angular motion of the lever produced a linear displacement of the rod. The test
samples were placed in a movable cavity within the temperature controlled base D.
A calibrated thermocouple tip inserted into a hole drilled into the rim of temperature
controlled base monitoring the temperature of the cavity, when connected to
electrical supply. The free end of the lever was weighted by a 500 g mass M.
83
Figure 4.1 Schematic diagram showing various parts of the packing stress
measurement apparatus: A-steel base; B-steel pillar; C-friction bearing;
D-temperature controlled base; M-weight
A movable open ended cylindrical brass small cavity (6.35mm diameter and 4.5mm
depth) with two different controlled temperatures was used in this study; materials
were investigated at two temperatures: 23oC and 37oC.
142.44mm3 of Composite material was placed in the cavity using a flat end plastic
hand instrument, a glass slab used to level the material with the mould’s surface. The
plunger flat end was placed lightly on the surface of the composite material to be
investigated, following the first test, composite material repacked into the mould,
adding material also done as required, plunger head cleaned and test repeated six
times (n=6) for each material. A representative recording of stress is shown in Figure
1: upon application of the plunger, an initial ‘spike’ in stress is recorded: the
‘persistence time of peak stress’ [tp] was taken as the time after the initial spike [t1]
to the time of dissipation of recorded stress [t2]. The ‘mean packing stress’ [σ] was
taken as the average of the stress recorded at t1 [σi] and t2 [σf]. The viscosity [µ]
was calculated (Equation 2) as the mean packing stress multiplied by the persistence
time of peak stress, thus:
Equation 2
84
Packing stress and viscosity data among the eight groups were analysed using One-
Way ANOVA (v. 16, SPSS, Il, USA) (P<0.05) Prior to post-hoc tests, data were
analysed for equal variances using homogeneity test (P<0.05). For data of packing
stress at 37 °C, and viscosity measurements at both temperatures equal variances can
be assumed thus Bonferroni test was applied, however Dunnett’s T3 was applied for
data of packing stress at 23 °C as equal variances cannot be assumed). Effect of the
temperature on each materials was analysed using t-test for paired data (P<0.05).
Linear correlation was checked between filler size and packing stress at both
temperatures and between filler size and viscosity at both temperatures.
4.4 Results and Discussions
Advanced developments in filler technology of resin composites have steered the
improvement process of optimizing resin composite properties. This study aimed to
investigate the effect of different filler sizes and distributions on the handling
properties of resin composites at both, clinic temperature (23 °C) and patient body
temperature (37 °C). Packing stress and viscosity were investigated for different
resin composites that range in fillers size from 100-1500 nm; and vary in filler
distribution (i.e. uni-modal, bi-modal and tri-modal). The packing stress was
measured by the load cell as illustrated by the stress-time curve shown in Figure 4.2.
Means and standard deviations of both packing stress and viscosity are presented in
Tables 4.2 and 4.3. Statistically significant differences were present among each
property tested (P<0.05) at both temperatures as shown in the tables. Accordingly,
both null hypotheses were rejected.
85
Figure 4.2 Time dependant packing stress profile curve
86
Group
Packing Stress at 23°C
Mean (SD)
Packing Stress at 37 °C
Mean (SD)
I1 2.10 (0.10)a* 1.56 (0.15)a, b
I2 2.07 (0.09)a* 1.71 (0.12) a, c
I3 2.09 (0.15)a* 1.65 (0.11)a, d
I4 2.60 (0.62)a* 1.58 (0.09)a, d
I5 0.43 (0.08)b* 0.50 (0.06) e
I6 1.45 (0.09)c* 0.82 (0.12)f
TC 2.12 (0.10)a 1.93 (0.19)c
SP 2.09 (0.11)a* 1.44 (0.06)b, d
Within each column; different superscript letters indicate significant differences between the
groups (P<0.05).
Within each row asterisk indicate significant differences between the paired groups
(P<0.05).
Table 4.2 Mean (SD) values of packing stress (MPa) of different resin composites at
23 oC and 37 oC.
Group Viscosity at 23°C
Mean (SD)
Viscosity at 37 °C
Mean (SD)
I1 0.55 (0.27)a, d, e* 0.04 (0.01)a
I2 1.52 (0.20)b* 0.27 (0.05)b
I3 1.42 (0.34)b* 0.52 (0.14)b, c
I4 2.60 (0.62)b,c* 0.48 (0.13)b, c
I5 0.02 (0.01)d* 0.003 (0.001)d
I6 0.09 (0.02)e* 0.01 (0.002)d
TC 2.88 (0.61)c* 0.63 (0.10)c
SP 0.27 (0.06)a* 0.04 (0.01)a
Within each column; different superscript letters indicate significant differences between
the groups (P<0.05).
Within each row asterisk indicate significant differences between the paired groups
(P<0.05).
Table 4.3 Mean (SD) values of viscosity (MPa.s) of different resin composites at
23 oC and 37 oC.
87
Generally, as the fillers increased in size, packing stress and viscosity increased at
both temperatures. Positive correlation was evident as shown in Figures 4.3a and
4.4a. However, this increase was not statistically significant among some filler sizes
(P>0.05).
Filler Size(nm)
SP (100)
I1(450nm)
I2(700nm)
I3(1000nm)
I4(1500nm)
Pac
kin
g S
tres
s (M
Pa)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
23oC (r=0.70)37oC (r= 0.60)
Figure 4.3a Linear correlations between packing stress (MPa) and unimodal
composites at 23oC and 37oC.
Filler Size (nm)
Bi-modal(450:1000)
Tri-modal(450:700:1500)
TC(40:200:1000 )
Pac
king
Str
ess
(MPa)
0
1
2
3
4
23oC37oC
Figure 4.3b Bar Chart of packing stress (MPa) at 23oC and 37oC for multimodal
composites
88
Figure 4.4a Linear correlations between viscosity (MPa.s) and unimodal composites at
23oC and 37oC.
Figure 4.4b Bar Chart of viscosity (MPa.s) at 23oC and 37oC for multimodal
composites
Filler Size (nm)
SP (100)
I1(450nm)
I2(700nm)
I3(1000nm)
I4(1500nm)
Vis
cosi
ty (
MP
a.s)
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
23oC (r=0.95) 37oC (r=0.93)
Filler Size (nm)
Bi-modal(450:1000)
Tri-modal(450:700:1500)
TC(40:200:1000 )
Vis
cosi
ty (
MP
a.s)
0
1
2
3
4
23oC37oC
89
Uni-modal composites showed the same trend with packing stress and viscosity at
both temperatures. Filler size of 1500 nm exhibited the highest packing stress at 23
°C (2.60 MPa). Despite the positive correlation between filler size and packing stress
at 23 °C and 37 °C (r=0.70 and 0.60 respectively) and between filler size and
viscosity 23 °C and 37 °C (r=0.95 and 0.93 respectively), the increase in both
packing stress and viscosity was not statistically significant amongst most of
unimodal composites. This can clearly be seen between SP (100nm) and I1 (450nm)
which could be due to the difference in filler shape.
The trend of multimodal composites was the same with packing stress and viscosity
at both temperatures. Tetric Ceram which has three different filler sizes
(40:200:1000 nm) in so called tri-modal fillers exhibiting not only the highest values
among multimodal composites, but also was the highest among all materials in
packing stress at 37 °C and viscosity at both temperatures. This material was also the
second highest in packing stress at 23 °C (P<0.05). This is probably due to the fact
that resin composite material achieves its thicker consistency by increasing filler
size, modifying filler distribution and adding other types of fillers such as glass
fibres (Choi et al., 2000). Moreover, as the temperature increases the flow of the
resin composite increases since resin matrix becomes diluted (Knight et al., 2006).
Tetric Ceram was the most viscous material at both temperatures among all the
materials tested (P<0.05) which could be due to the higher volume and weight
percentage of filler content. However, there was a significant reduction in its
viscosity when tested at 37 °C, and this is likely due to the fact that an increase in
temperature will decrease the viscosity (Silikas and Watts, 1999).
On the other hand, the I5 a bimodal (450:1000) exhibited the lowest viscosity among
all materials. Its viscosity was remarkably low, despite the fact that the two different
fillers sizes were identical to the unimodal I1 and I3 respectively that exhibited
higher viscosity values. Furthermore, its packing stress was also the lowest. The tri-
modal I6 (450:700:1500, 1:1:3) also presented lower viscosity values compared to
the unimodal formulations. It appears that combination of filler sizes result in more
flowable and less stiff composites.
90
4.5 Conclusions:
Within the limitations of this study, it can be concluded that:
Filler size and distribution have an effect on the packing stress and viscosity.
Temperature has a prominent effect on the handling properties of resin
composite, i.e. as temperature increases the packing stress and viscosity
decreases.
Filler sizes and their combinations (bimodal and trimodal distributions) can
have a fine-tuning effect on the handling properties and clinical performance.
91
CHAPTER FIVE
The Effect of Filler Size on the Presence of Voids within
Resin Composite
Elbishari H, Silikas N, Satterthwaite JD.
Submitted to the European Journal of Prosthodontics
and Restorative Dentistry
92
5.1 Abstract
Objectives: To assess the effect of filler size on the presence of voids within resin
composites. Eight light-cure resin composites (seven model and one commercial)
were used in this study. Discs (6mm x 2mm) were used to prepare samples (n=8).
Each sample was then scanned, reconstructed and analysed using Micro-Computing
Tomography. Data were analysed using ANOVA and post hoc methods to detect any
significant differences between materials tested (p<0.05).Conclusion: The filler size
and distribution had an effect on the percentage of voids in resin composites and
increased with larger fillers.
Keywords: resin composite, voids, handling properties, porosity, micro-CT
93
5.2 Introduction
Resin composite is the most widely used tooth-coloured restorative material. It
comprises of: organic resin matrix, inorganic filler, coupling agent, colouring
pigments, and polymerisation initiator and inhibitor. The filler component of resin
composite is added to resins in order to improve their properties (Knock and Glenn,
1951) and it is still the key for improving the mechanical properties including
flexural strength, modulus of elasticity (Braem et al., 1989), compressive strength
and hardness (St Germain et al., 1985), as long as it is well bound to the resin
matrix(Bowen, 1964). Also the increase of filler content has an influence in
minimising polymerisation shrinkage. There is a limit on the maximum possible
fraction of filler that can be incorporated into a resin. As the filler fraction increases,
so does the packability and the viscosity of the material (Elbishari et al., 2011), a
weight fraction of above 80% results in a material that is stiff and not easy to
manipulate (Lutz et al., 1983).
These filler particles have been altered in terms of their size to improve the material
properties, and most of resin composites are now incorporating nanofillers ranging
from 5nm to 100nm. The nanofillers aim to improve surface smoothness and gloss,
polymerisation shrinkage and biocompatibility without altering mechanical
properties (Mitra et al., 2003; Watanabe et al., 2008; Chen, 2010).
The effect of filler particle volume fraction has been extensively studied particularly
with respect to mechanical properties (Braem et al., 1989), but also shrinkage. Filler
particles size has attracted less attention with some reports characterising its effect
on mechanical properties(Li et al., 1985) and shrinkage (Satterthwaite et al., 2009).
Others have assessed the effect of filler size and contents on the elastic properties of
resin composite material (Elbishari et al., 2011). No studies have assessed the effect
of fillers on presence of voids.
Given the problems outlined above, most commercial composites currently
available, do not contain one type of filler, but have a combination of sizes, i.e. they
are multimodal. This aims to achieve maximum filler load with superior mechanical
properties whilst maintaining good surface finish. Typically two or three sizes of
94
filler are employed (i.e. bimodal or trimodal blends), with the ‘spaces’ between large
filler particles being occupied by filler of smaller size: these are classed as hybrid
composites.
Voids within resin composite restorations and their negative effect has been
previously investigated (Leinfelder and Roberson, 1983; McCabe and Ogden, 1987),
these voids are present in the restoration either due to manufacturing process or
during handling and packing technique. The relation of the viscosity of resin
composites (flowable-composites) and voids has also been studied and found that
flowable-composites reduce voids within a class II restoration (Chuang et al., 2001).
There is no doubt that the improved filler technology and their size has enhanced the
properties of resin composite material, however the relation of filler size and the
voids within the material is still not understood.
The aim of this study was to study the effect of filler size on the percentage of voids
with resin composite in 3D using a micro computed tomography (Micro-CT).
The null hypotheses tested were (i) there was no effect of filler size on the presence
of voids within resin composites and (ii) there was no correlation between filler size
and presence of voids.
95
5.3 Materials and Methods
The resin composites used in this study were all visible-light-cured [VLC] and
comprised of 7 model composites (Ivoclar Vivadent, Schaan, Liechtenstein) and one
commercial composite (Tetric Ceram, Ivoclar Vivadent, Schaan, Liechtenstein). All
resin composites used were composed of the same matrix which was a combination
of Bis-GMA, UDMA and TEGDMA. All of the model composites had a particulate
dispersed phase of the same volume fraction (56.7%), which was treated with a
silane coupling agent. The filler particles were either irregular particles of ground
glass melts or silica spherical particles. The composition of resin composites is
illustrated in Table 5.1.
Resin
composite
Filler Particles(Ground Glass [Ba-Al-B-silicate glass]) Matrix
Shape Size (nm) Wt% Vol%
I1 Irregular 450 76.4 56.7
BisGMA,
UDMA,
TEGDMA
I2 Irregular 700 76.4 56.7
I3 Irregular 1000 76.4 56.7
I4 Irregular 1500 76.4 56.7
I5 Irregular 450, 1000
(1:3) 76.4 56.7
I6 Irregular 450, 700 &1500
(1:1:3) 76.4 56.7
SP Spherical 100 72.4 56.7
TC
Lot :
F53738
Irregular&
Spherical 40, 200 &1000 79 60
Table 5.1 Compositions of resin composite materials used in the study
96
A Teflon mould was used to prepare eight disc samples (6mm in diameter and 2mm
thickness) of each material. Each was cured for 20 s from each side to ensure curing
depth using a halogen light curing unit (Optilux 501, Kerr SDS, Peterborough, UK)
with an irradiance of 550 mW/cm2 and then smoothed using silicone carbide water
proof abrasive paper (150 CW) and then (600 CW). Samples were placed into the
chamber of a micro-CT (SkyScan1072, SkyScan, Kontich, Belgium) with a fixed
current of 98mA and voltage of 104kv. Pilot images were taken to allow adjustment
of scanning parameters for good quality scanned images which were similar for all
samples. Parameters used with micro-CT are summarised in Table 5.2.
Each sample was then scanned and resultant coronal and sagittal views were then
stored in 16 bit TIFF files (using N-recon software, SkyScan, Kontich, Belgium).
Images for each sample were then converted into a 3D image (using CTAn software,
SkyScan, Kontich, Belgium), in which binary images were obtained. A 3D model
was created using CTvol software (SkyScan, Kontich, Belgium). The percentage of
voids within each sample was calculated. Data were imported into statistical
software package (SPSS ver 16.0, Il, USA) and analysed using One-Way ANOVA
(p<0.05). Prior to post-hoc tests, data were analysed for equal variances using
Levene’s test for homogeneity (p<0.05), and as equal variances could not be
assumed Dunnett’s T3 was applied.
Parameters
Magnification 24.3
Pixel 11.24µm
Y-position 6mm
Rotation 180º
Rotation Step 45º
Exposure time 4 Sec
Gain 1
Averaging 1 frames
Filter No
Scanning time 32 minutes
Table 5.2 Parameters used with Micro-CT in the study
97
5.4 Results
Voids were seen in the 2-D reconstructed images (Figure 1) as well as in the 3-D
model (Figure 2a and 2b) constructed and viewed by the aid of CTVol software.
The voids % varied from 0.28 % for TC (40:200:1000nm) to 3.48% for I4 composite
(1500nm). Sp (100nm) exhibited the lowest % voids (0.44%) amongst the unimodal
composites.
Generally the percentage of voids increased with increasing filler size (r=0.97)
(Figure 3). The difference of the %voids between the materials was statistically
significant (p<0.05). The mean and standard deviation (SD) of all materials tested is
summarised in Table 5.3.
Figure 5.1 2D reconstructed image of I3 sample
98
Figure 5.2 3D model of resin composite sample (A) and (B) 3D image with pseudo
colour (red represents the voids and blue represents resin composite).
Figure 5.3 Bar Char of Mean (SD) of all materials with Correlation between Filler
Size of unimodal composite and % of Voids
99
Table 5.3 Mean (SD) values of Voids % of all resin composites tested.
Material Voids%
Sp 0.44 (0.03) a
I1 0.57 (0.03) b
I2 1.47 (0.16) c
I3 3.0 (0.04) d
I4 3.48 (0.05) d
I5 0.69 (0.02) e
I6 0.36 (0.03) f
TC 0.28 (0.05) g
Different small letter superscript indicate significant
difference between the groups (p <0.05)
100
5.5 Discussion
The use of model resin composites with controlled formulations to evaluate a
property (e.g. voids percentage), allows for more meaningful comparisons to be
drawn. The model resin composites used in this study had irregular or spherical
shaped filler particles in nanometre range and filler volume fraction was < 60% thus
can be classified as a densified midway filled resin composite (Willems et al., 1992).
As it is evident from Table 5.3, the percentage of voids within multimodal TC
material (40:200:1000nm) was the lowest with mean value of (0.28%), followed by
unimodal Sp material (100nm) with mean value (0.44%).This low percentage of
voids could be due to agglomeration and formation of large aggregates of smaller
fillers (Anusavice and Phillips, 2003). From these results it is clear that both size and
distribution have an influence in the presence of voids within resin composites;
hence the first null hypothesis was rejected.
The highest percentage of voids was recorded within I4 unimodal model composite
(1500nm) with mean value of (3.48%).These figures illustrated the effect of filler
size and distribution on the presence of voids. The correlation between the
percentage of voids and filler size was a strong correlation (r=0.97), thus the second
null hypothesis was rejected.
These voids can arise during manufacturing processes or during handling and mixing
procedures (Chadwick et al., 1989). Voids may cause drawbacks when they are
present within a restoration depending on where they are located (Opdam et al.,
1996b) such as: marginal leakage and discolouration when present at the margins,
increased wear due to the stress concentration around voids, decreased flexural
strength if located between the layers of the restoration, may be misdiagnosed as
secondary caries in the radiograph and also can cause incomplete adhesion between
the resin composite and dentine (Purk et al., 2007).
Despite the negative aspects that voids may cause when present within the resin
restoration, it has been suggested that their presence may have a potential benefit as
they may decrease shrinkage stress development due to the inhibiting effect of
101
oxygen within the voids during the setting reaction and also as increased free surface
within the restoration (Alster et al., 1992).
Voids within resin composite have been measured in previous studies by different
methods (Medlock et al., 1985; Ogden, 1985; McCabe and Ogden, 1987; Chadwick
et al., 1989; Wilson and Norman, 1991; Mentink et al., 1995; Opdam et al., 2002;
Samet et al., 2006), all of which were destructive and use low magnification tools. In
the current study the use of micro-CT allowing measurement of voids in 3-D and
enhanced characterisation in a non-destructive manner. This 3D method has been
used in other studies to measure voids within root canal filling materials (Hammad et
al., 2009) and within maxillofacial silicone materials (Hatamleh and Watts, 2011).
5.6 Conclusion:
With the limitation of this study it was concluded the percentage of voids was i)
significantly higher with larger filler compared to smaller filler size, and ii) higher
with composites of unimodal distribution than tri-modal distribution.
102
CHAPTER SIX
Filler Size of Resin Composites, Percentage of Voids and
Fracture Toughness: Is there a Correlation?
Elbishari H, Silikas N, Satterthwaite JD.
Accepted for publication in Dental Materials Journal
103
6.1 Abstract
Abstract: The objective of this study was to investigate the correlation between filler
size, fracture toughness and voids.
Seven model resin composites and one commercial have been used in the study.
Single edge notch mould was used to prepare samples (n=8). A selected area of 1mm
below and above the notch was scanned with micro CT and then the percentage of
voids calculated. A Universal testing machine was used to measure fracture
toughness. Data of both percentage of voids and fracture toughness were analysed
using ANOVA and post hoc methods were carried to check any significant
differences between the materials tested (p<0.05). Conclusion: Filler size is strongly
correlated to % voids but has no effect on fracture toughness.
Key words: resin composite, voids, fracture toughness, porosity, micro-CT
104
6.2 Introduction
High demands of aesthetic dentistry and new developments in resin composites have
led many dentists to use resin composite instead of amalgam for restoration of teeth
including posterior teeth. The success rate of direct resin composite restorations in
posterior teeth has been found to be 90% over 5 years(Wilson et al., 1988; Wassell et
al., 2000), dropping to 77% at 8 years(Barnes et al., 1991). Despite the good clinical
success, direct composite restorations have been associated with undesirable
characteristics such as excessive wear, marginal leakage caused by polymerization
shrinkage, voids, sensitivity after placement, and insufficient proximal contact and
contour (Bryant, 1987; Burke et al., 1991). The two main reasons for replacement of
resin composite restorations are secondary caries and fracture (Sarrett, 2005): these
are related to resin composite being a brittle material and its clinical longevity is
affected by surface flaws, which may propagate through the material matrix leading
to fracture and subsequent caries (Ferracane et al., 1987). Resin composites with
higher fracture toughness will better withstand high stress level(Goldman, 1985) are
thus have improved clinical service.
Another major disadvantage of direct composite is the presence of voids within the
final restoration which may arise due to manufacturing procedures or handling
techniques (Chadwick et al., 1989). When present, these voids may cause drawbacks
within a restoration such as (Opdam et al., 1996b): marginal leakage and
discolouration, increased wear due to the stress concentration around voids,
decreased flexural strength and also incomplete adhesion between the resin
composite and dentine (Purk et al., 2007).
There have been several attempts to solve the problems associated with direct
composite restorations, including increasing the percentage of filler content in the
composite matrix and reducing the size of filler particles (Lloyd and Iannetta, 1982;
Lloyd and Mitchell, 1984; Johnson et al., 1993). Several studies showed that heavier
filler loading would result in increase in fracture toughness of the material
(Watanabe et al., 2008), while others concluded that filler content has no role in
fracture behaviour (Rodrigues Junior et al., 2008). Generally the filler contents have
a significant influence in the mechanical properties, with the highly filled composites
105
being the strongest (Ferracane, 2011). The aim of this study is to investigate the
effect of filler size on fracture toughness and presence of voids. The objectives were
to measure fracture toughness KIC on a series of model resin composites and one
commercial using the Single Edge Notch technique and to use microtomography to
quantify the voids in the same materials. The model composites used in the study
have different filler distributions (unimodal, bimodal and trimodal) while the
commercial composite used is a multimodal. The filler shape of all composites used
was irregular, spherical or a combination as in the commercial composite. The
hypotheses of this study were that different filler size has no effect on i) the fracture
toughness of resin composites; and, ii) the presence of voids within resin composites.
106
6.3 Materials and Methods
Sample preparations
Single-edged notch (SEN) specimens (n = 8) for each group, conforming to British
Standard 5447 (1977), were prepared in a PTFE-lined brass mould which could be
split so that no force was required to remove the set specimen from the mould. The
specimen size and geometry are shown in Figure 6.1. The overall external
dimensions were 3 mm×6 mm×25 mm, and a sharp notch (a) to half the beam height
(w) was 3 mm. A central sharp notch of specific length was produced by inserting a
straight-edged scalpel blade into an accurately fabricated slot at mid-height in the
plastic mould which extended down half the height to give a/w =0.5. The blade had a
straight cutting edge, honed on both sides with a blade edge radius less than 0.3 μm.
The crack plane was perpendicular to the specimen length. Samples consisted of
seven light cured model composites (Ivoclar Vivadent, Schaan, Liechtenstein)
together with an established commercially available formulation (Tetric Ceram-
Ivoclar Vivadent, Schaan, Liechtenstein) used as a control. Table 6.1 illustrates the
composition of materials used. They were all polymerised with a QTH light curing
unit (Optilux 501, Kerr SDS, Peterborough, UK) with a 10mm diameter curing tip
and an irradiance of 550mW/cm2 as measured with the radiometer incorporated into
the appliance. To ensure optimal curing depth each sample was cured from the top
surface for 60s and then from each side for further 60s after disassembling of the
mould.
Microtomograpy 3-D Scanning
All samples were scanned with a high resolution micro-CT (Model 1072, SkyScan,
Kontich, Belgium) operated under the following conditions: 98µA fixed current;
voltage of 100kV voltage; 19 µm pixel size at 14.39×14.39 resolution; 180° rotation;
4s exposure time and averaging by one frame. Data obtained were then input into a
software package (N-Recon, SkyScan) and reconstructed resulting in 100 slices of
2D images. Each 2D image, using CTAn software (SkyScan), was then converted
into a 3D image. The percentage of volume occupied by voids within each sample
was calculated in an area 1.0 mm below and 1.0 mm above the notch.
107
Resin
composite
Filler Particles(Ground Glass [Ba-Al-B-silicate glass]) Matrix
Shape Size (nm) Wt% Vol%
I1 Irregular 450 76.4 56.7
BisGMA,
UDMA,
TEGDMA
I2 Irregular 700 76.4 56.7
I3 Irregular 1000 76.4 56.7
I4 Irregular 1500 76.4 56.7
I5 Irregular 450, 1000
(1:3) 76.4 56.7
I6 Irregular 450, 700 &1500
(1:1:3) 76.4 56.7
SP Spherical 100 72.4 56.7
TC
Lot :
F53738
Irregular&
Spherical 40, 200 &1000 79 60
Table 6.1 Compositions of resin composites materials used in the study
Figure 6.1 Schematic drawing of the SEN specimen
a (height of the notch) = 3mm, B = 3.0 mm, W = 6.0 mm, X = 25 mm, L = 10 mm
108
Fracture toughness
Fracture toughness of each sample was tested using a universal testing machine
(Zwick/Roell Z020, Leominster, UK) at cross head speed of (0.127mm/min). The
maximum force at fracture (P) was recorded and fracture toughness (KIC) was
calculated using the following equation:
KICPL
BW Y Equation 3
Where P=the peak load at fracture; L = length; B = width; W = height;
Y=calibration functions for given geometry
Y=[1.93(a/w)1/2 - 3.07(a/w)3/2 + 14.53(a/w)5/2 - 25.11(a/w)7/2 + 25.80(a/w)9/2]
Data analysis
Voids and KIC data for the eight groups were analysed with a statistical software
package (SPSS ver 16.0, Il, USA) using One-Way ANOVA (p<0.05). Prior to post-
hoc tests, data were analysed for equal variances using Levene’s test for
homogeneity (p<0.05): for KIC data, equal variances could be assumed thus
Bonferroni test was applied, however Dunnett’s T3 was applied for data of voids as
equal variances could not be assumed.
109
6.4 Results
The voids could be seen in both 2D and 3D images (Figure 6.2). The percentages of
voids for each group are shown in Table 6.2. TC (40:200:1000nm) exhibited the
lowest percentage of voids amongst all the eight groups (0.27%). For the unimodal
composites the percentage of voids ranged from 0.44% for SP (100nm) to 3.53% for
I4 (1500nm). Generally the percentage of voids increased with the increase in filler
size (r=0.97) (Figure 6.3). The difference of the %voids between the materials was
statistically significant (p<0.05).
For unimodal composites the KIC values ranged between 1.50 MNm-1.5 to
1.10 MNm-1.5, with the highest KIC value seen with I1 (450nm). Overall, the highest
KIC value was seen with TC (40:200:1000nm). Generally the filler size has no effect
on the fracture toughness (r=0.02). Statistically significant differences were present
between some of the materials tested (p<0.05). All KIC values are shown in Table 6.2
and illustrated in Figure 6.4.
Figure 6.2 2D of sample used and 3D image of scanned part
A-The single edge notch sample with red circle around the notch and surrounded
area which was scanned, B- zoomed 3D image illustrates the notch and voids
110
Groups
Voids %
Mean(SD)
KIC MNm-1.5
Mean(SD)
I1
(450nm)
0.59 (0.05) a 1.50 (0.11) a
I2
(700nm)
1.49 (0.23) b 1.33 (0.04) b
I3
(1000nm)
2.96 (0.12) c 1.20 (0.08) c,e
I4
(1500nm)
3.53 (0.24) d 1.26 (0.07) b,c
I5
(450:1000nm)
1.18 (0.28) b 1.27 (0.06) b,c
I6
(450:700:1000nm)
0.56 (0.09) a,e 1.45 (0.06) a
TC
(40:200:1000nm)
0.27 (0.07) f 2.00 (0.09) d
SP
(100nm)
0.44 (0.04) e 1.10 (0.06) e
Within each column; different superscript letters indicate significant differences between the
groups (P<0.05).
Table 6.2 Mean (SD) values of Voids % and KIC (MNm-1.5) of all resin composites
tested.
111
Figure 6.3 Bar chart illustrating Mean and SD of Voids% for all materials with
linear correlation shown between unimodal composites and Voids% (r=0.97).
Figure 6.4 Bar chart illustrating Mean and SD of Fracture toughness for all
materials.
112
6.5 Discussion
The presence of voids within resin composite material could be due to manufacturer
error or poor handling technique (Chadwick et al., 1989). As mentioned above, these
voids have a negative effect on the physical and mechanical properties of resin
composite material such as: leakage around the margin and subsequent
discolouration, increased wear and reduced flexural strength (Opdam et al., 1996b).
Voids have been investigated and measured with different techniques, such as light
and electron microscopes both of which are destructive methods (Ogden, 1985;
McCabe and Ogden, 1987; Chadwick et al., 1989; Opdam et al., 1996a), and the
novel non-destructive method using micro CT (Hammad et al., 2009; Hatamleh and
Watts).
In this study, filler size was found to have an effect on the presence of voids. As
filler size increases so does the percentage of voids with the highest percentage of
3.53 % found in a resin composite material which contained the largest filler
particles of 1500 nm. It is clear that increasing the filler size increases voids and this
is obvious from the strong correlation between %Voids and filler size (r=0.97).
Hence the first null hypothesis was rejected.
Fracture is one mode of clinical failure of resin composite restoration; it could be
bulk or marginal fracture (Manhart et al., 2004). The resistance of a material to
fracture is measured by fracture toughness (KIC) which is an intrinsic property of a
resin composite material and its resistance to crack propagation, and a material
which has a higher KIC value has the ability to resist the initiation and propagation of
the crack(Kim and Okuno, 2002).
Unlike %Void data, KIC values are not affected by variations in filler size or
distribution (r=0.02). Hence the second null hypothesis was confirmed. Furthermore,
there was no correlation between KIC and voids (r=0.20). Tetric Ceram
(40:200:1000) had the highest value which probably could be to higher filler loading.
The lowest value was obtained by SP that has the smallest fillers (100 nm) and is the
only one with solely spherical shape fillers. Additionally, it has the lowest filler
113
content, the combination of these factors seem to result to inferior mechanical
properties.
Filler size has previously been investigated in relation to fracture toughness: one
study suggests that resin composite material with 80% wt of fillers, 10% of which is
microfiller, would have an optimum fracture toughness (Johnson et al., 1993),
whereas another study showed KIC values of hybrid and nanofilled composites are
significantly higher than those of microfilled composites (Watanabe et al., 2008).
Generally there is a lack of published data regarding the relationship between
fracture toughness and presence of voids. Studies relating to polymers have shown
that fracture toughness decreases markedly with the increase of voids (El-hadek and
Tippur, 2002; Kearney et al., 2008). Voids also significantly reduced the fracture
toughness of Bis-GMA based composite with hydroxyapatite whiskers (Zhang and
Zhang, 2010).
6.6 Conclusions
The use of model composited has been proved to be very useful in elucidating trends
between properties. In this paper it was shown that the size of filler particles within
resin composites is directly related to percentage of voids but does not influence
fracture toughness.
114
CHAPTER SEVEN
Is Deterioration of Surface Properties of Resin Composites
Affected by Filler Size?
Elbishari H, Silikas N, Satterthwaite JD.
Submitted to Journal of Clinical Oral Investigations
115
7.1 Abstract
Statement of the problem: Resin composite restorations can lose their aesthetic
properties in clinical service.
Purpose: To investigate the effect of filler size on surface gloss and roughness of
resin composites before and after tooth brushing abrasion using 3D non-contact
surface topography.
Materials and Methods: Seven model resin composites and one commercial were
tested in the study. All materials were first polished and then the surface gloss and
2D and 3D roughness parameters were recorded. Materials then subjected to
abrasion in tooth brushing simulator. Roughness parameters were recorded after
10.000 cycles and after 20.000 cycles both roughness and gloss were recorded. One
way ANOVA and Bonferroni post hoc test (p<0.05) was used to analyse data.
Results:
Conclusion: Filler size is strongly correlated to gloss and surface roughness
retention.
Key words: resin composite, wear, surface topography, gloss
116
7.2 Introduction
Resin composites have been increasingly used in restorative dentistry for more than
four decades (Watts et al., 2008) and are used routinely for restorations in the
anterior and posterior regions. They feature a wide range of aesthetic and mechanical
properties making them the most widely used material for restoration of teeth
(Dietschi et al., 1994). However, they still exhibit drawbacks in terms of
polymerization shrinkage, wear and loss of aesthetics upon use. Filler particle
technology is an important factor influencing both physical and mechanical
properties. Improvements of surface smoothness and gloss retention can be achieved
by reducing the filler size (Turssi et al., 2005; Cavalcante et al., 2009).
Resin composites containing nano-sized fillers can offer better aesthetics and wear
resistance (Mitra et al., 2003; Paravina et al., 2004). Finishing and polishing are
important not only for aesthetic reasons but also for the longevity of restoration
(Goldstein, 1989) and the gingival and periodontal health. This is because the surface
texture of resin composites has an influence on plaque accumulation, which may
lead to gingival and periodontal inflammation and also discoloration of restorations
(Heintze et al., 2006). There is a direct correlation between surface roughness and
plaque accumulation, as surface roughness increases, so does the deposition of
plaque (Bollen et al., 1997). Previous in vitro studies showed that a mean roughness
(Ra) above the 0.2μm threshold was related to a substantial increase in bacteria
retention on the surface of the restoration (Quirynen and Bollen, 1995).There are
several studies measuring surface roughness of resin composites. However, there are
limited studies exploring the retention of the initial surface properties.
The objective of the present study was to assess the effect of different filler size on
the surface roughness (2D and 3D measurements) and gloss of resin composites
before and after tooth brushing abrasion. A series of model composites with varying
filler size and distribution were examined. Also, a non-contact 3D method to
evaluate surface roughness was employed. The following null hypotheses were
formulated:
i) Filler size has no effect on the gloss retention of resin composite materials
ii) Filler size has no effect on surface roughness of resin composite materials
117
7.3 Materials and Methods
Seven model resin composites (Ivoclar Vivadent, Schaan, Liechtenstein) and one
commercial resin composite (Tetric Ceram, Ivoclar Vivadent, Schaan, Liechtenstein)
were investigated in this study. All resin composites (model and commercial) were
visible light cured composites containing the same resin matrix which was a
combination of Bis-GMA, UDMA and TEGDMA, with camphoroquinone. All
model composites had a dispersed phase with the same volume fraction (56.7%),
which was treated with a silane coupling agent (methacryloxypropyltrimethoxy
silane). The filler particles were graded in size, and were either spherical or irregular.
The spherical particles were silica and made from solution (SiO2), the irregular
particles were ground glass melts (Ba–Al–B-silicate glass). The composition of the
resin composites is summarized in Table 7.1.
Resin
composite
Filler Particles(Ground Glass [Ba-Al-B-silicate glass]) Matrix
Shape Size (nm) Wt% Vol%
I1 Irregular 450 76.4 56.7
BisGMA,
UDMA,
TEGDMA
I2 Irregular 700 76.4 56.7
I3 Irregular 1000 76.4 56.7
I4 Irregular 1500 76.4 56.7
I5 Irregular 450, 1000
(1:3) 76.4 56.7
I6 Irregular 450, 700 &1500
(1:1:3) 76.4 56.7
SP Spherical 100 72.4 56.7
TC
Lot :
C49490
Irregular&
Spherical 40, 200 &1000 79 60
Table 7.1 Composition of materials used in the study
118
Specimen preparation - Four disc specimens (10 mm x 2 mm) were prepared for
each material used. Teflon moulds were used to prepare these specimens. The
samples were irradiated for 40 s from each surface with a light curing unit (Optilux
501, Demetron, Danbury, USA) emitting 550 mW/cm2 irradiance, as measured with
the radiometer incorporated into the appliance. After polymerization all specimens
were polished.
Polishing procedures -The samples were initially finished with a sequence of 400-,
600-, 800- and 1200- grit SiC papers under continuous water cooling. To obtain a
glossy surface, the specimens were further polished with Sof-Lex contouring and
polishing discs (3M Dental Products, St. Paul, MN, USA). Finally, the specimens
were placed in an ultrasonic water bath (Transonic T 310, Camlab Limited,
Cambridge, England) for 2 min to remove any residual debris. The specimens were
then stored in distilled water at 37°C for 24 hr.
Surface gloss – The surface gloss of each sample was measured with a glossmeter
(Novo Curve, Rhopoint, Instrumentation LTD, East Sussex, England) which was
calibrated against a black glass standard provided by the manufacturer. Five
measurements per specimen were performed at 60º light incidence and a mean value
for each measured specimen was chosen. These measurements were taken at baseline
and after brushing.
Surface roughness - Surface roughness for all the samples was measured with a non-
contact single point sensor: Talysurf CLI 1000 (Ametek Taylor Hobson Precision,
Leicester, UK). Each sample was placed over a flat surface above the cross-slides
and scanned by a confocal optical single point sensor (CLA 3 mm) with 0.25 mm
cut-off length. The sampling rate of the gauge was 500 Hz. The mode of
measurement was East-West gauge measurement direction, i.e. from right to left
rather than from top to bottom. For each sample, the start and end of scan points
were adjusted with maximum spacing of 10µm.The measurement speeds were
5mm/s and 5mm/s on return. The data obtained as a result of surface scanning were
then analysed by TalyMap (Ametek Taylor Hobson Precision, Leicester, UK)
analysis software to provide 2D and 3D surface profiles and calculate surface
roughness parameters and create a top 3D view. The following 2D roughness
119
parameters were measured: (1) Ra (the arithmetic mean of the absolute departures of
the roughness profile from the mean line), (2) Rt (the maximum peak to valley
height of the profile in the assessment length), and 3D roughness parameters were
(3) Sa (the arithmetic mean deviation of the surface), (4) St (the total height of the
surface, the height between the highest peak and the deepest valley).
All samples were then subjected to simulated wear in a custom-built ‘tooth-brushing
machine’ which has been described previously (Cavalcante et al., 2009). The
toothbrush machine had four separate stations and four separate toothbrush holders
which were driven by a motor (Figure 7.1). Therefore, four specimens were
simultaneously but individually subjected to an equal amount of
toothbrush/toothpaste abrasion during each testing period. Each toothbrush (Oral-B
40 Indicator, Regular), was fixed in the toothbrush holder so that all the bristles were
in contact with the specimen (Figure 7.2). The testing machine was adjusted to apply
2.5 N vertical load on the specimen during horizontal movement of the toothbrush
throughout the test. A commercial tooth paste (Colgate Total, Colgate-Palmolive,
Guildford, UK) was used to form a slurry according to ISO/TS 1469-1 (2:1, water:
toothpaste). All specimens were brushed for 20,000 cycles. This corresponds to
approximately 4 years of tooth brushing (Tanoue et al., 2000). 2D and 3D roughness
parameters were measured after 10,000 cycles and after 20,000 cycles of tooth
brushing.
Figure 7.1 Toothbrush-simulating machine
120
Figure 7.2 Schematic diagram of tooth brushing abrasion apparatus. A-2.5 N metal
load, B-Toothbrush holder, C-Toothbrush head, D-Composite sample, E- Glass
container, F-Silicon mould.
All data were entered in a statistical software package (SPSS ver.16.0, Chicago, Il,
USA) and evaluated using one-way analysis of variance (ANOVA) and Bonferroni
post hoc test (p<0.05) for the difference between surface gloss (at baseline and after
20,000 cycles of tooth brushing abrasion) and for the difference between surface
roughness (at baseline, after 10,000 cycles and after 20,000 cycles). Linear
correlation was checked between filler size and each roughness parameter (at
baseline, after 10,000 cycles and after 20,000 cycles).
121
7.4 Results
Gloss retention - Gloss values ranged between 72.3 and 84.3 GU before abrasion and
between 5.9 and 61.3 GU after toothbrush abrasion (Table 7.2). For all materials a
statistically significant reduction in gloss was observed after toothbrush abrasion
(p<0.05). I4 (1500 nm) exhibited the lowest gloss retention (8.1 %) before and after
tooth brushing abrasion. Nonetheless, at base line, it was not significantly different
from I6 (multimodal distribution material 450, 700 &1500nm). The highest gloss
retention (72.8%) was shown by TC (40, 200 &1000nm) and it was significantly
different from the other composites. Surface gloss values were strongly correlated
with filler size before (r=0.96) and after tooth brushing abrasion (r=0.90) (Figure
7.3).
Surface roughness - All materials exhibited very smooth surfaces before toothbrush
abrasion. Initial values ranged from 0.01-0.03μm (Ra), 0.27-0.35μm (Rt) and 0.11-
0.57μm (Sa) and 31.94-80.63μm (St). After 10,000 cycles of abrasion values ranged
from 0.08-2.04μm (Ra), 1.14 - 2.60μm (Rt) and 0.61-2.03μm (Sa) and 40.62-
91.92μm (St). After 20,000 cycles 0.71-3.35μm (Ra), 1.90-3.11μm (Rt), 1.17-
2.93μm (Sa), 50.64-99.82μm (St). All 2D and 3D surface roughness measurements
are summarised in Table 7.3 and Table 7.4 respectively.
Bonferroni post hoc comparisons revealed significant mean differences in Ra, Rt, Sa
and St values before and after toothbrush abrasion. These differences were more
prominent for the unimodal larger filler size materials (750, 1000, 1500 nm)
compared to smaller filler size materials (100-450 nm) regardless the filler shape.
Among the multimodal composite resins, TC exhibited the lowest values of both 2D
and 3D data measurements before and after brushing; moreover, this material,
exhibited the lowest value among all materials retested in this study. Additionally 3D
model was created (Figure 7.4). Possible correlations between roughness parameters
and filler size were investigated at baseline (after polishing), after toothbrush
abrasion (10,000 cycles) and after toothbrush abrasion (20,000 cycles). These are
shown in Figure 7.5. Correlation values ranged from (r=0.99) for St after toothbrush
abrasion (20,000 cycles) to (r=0.38) for Rt at baseline.
122
Material Gloss (Initial) Gloss (after tooth brushing
abrasion)
Sp 80.90 (0.70) a* 51.10 (0.44)a*
I1 76.50 (0.52) b* 23.40 (0.39)b*
I2 75.95 (0.52) b,e* 16.90 (0.48)c*
I3 74.03 (0.22) c* 8.68 (0.63)d*
I4 72.30 (0.29) d* 5.85 (0.33)e*
I5 75.18 (0.34) e* 14.70 (0.56)f*
I6 72.73 (0.46) d* 11.98 (0.59)g*
TC 84.30 (0.47) f* 61.33 (1.10)h*
Within each column Similar superscripts indicate no significant difference (p<0.05).
Within each group Asterisks represent significant differences before and after tooth
brushing abrasion
Table 7.2 Mean (SD) of gloss of all material tested before and after tooth brushing
abrasion of 20,000 cycles.
Filler Size (nm)
100 450 750 1000 1500
Glo
ss (
GU
)
0
20
40
60
80
100 Gloss at StartGloss after brushing abrasion
r=0.96
r=0.90
Figure 7.3 Linear correlation between filler size and gloss retention
Group
Tooth brushing abrasion
at start
(0 cycle)
after 2years brushing
(after 10,000 cycles)
after 4years brushing
(after 20,000 cycles)
Ra Rt Ra Rt Ra Rt
Sp 0.02 (0.01)a* 0.30 (0.04)a* 0.10 (0.01) a,b* 1.30 (0.02) a* 0.90 (0.05) a* 1.90 (0.05) a*
I1 0.03 (0.01)a* 0.31 (0.02)a* 0.30 (0.06) a* 2.27 (0.24) b* 1.30 (0.07) b* 2.94 (0.06) b*
I2 0.03 (0.09)a* 0.35 (0.05)a* 0.70 (0.03) c* 2.28 (0.12) b,c* 1.64 (0.05) c,f* 3.02 (0.14) b*
I3 0.03 (0.01)a* 0.33 (0.02)a* 1.10 (0.08) d* 2.59 (0.07) c* 2.01 (0.07) d* 3.06 (0.06) b*
I4 0.03 (0.01)a* 0.31 (0.04)a* 2.04 (0.15) e* 2.60 (0.21) c* 3.35 (0.31) e* 3.10 (0.12) b*
I5 0.03 (0.01)a* 0.32 (0.03)a* 0.48 (0.12) a,b,c* 2.03 (0.04) b* 1.38 (0.11) b,c* 2.69 (0.06) c*
I6 0.03 (0.01)a* 0.32 (0.03)a* 0.64 (0.04) c* 2.09 (0.02) b* 1.74 (0.08) f* 3.11 (0.06) b*
TC 0.01 (0.01)a* 0.27 (0.02)a* 0.08 (0.02) a* 1.14 (0.04) a* 0.71 (0.04) g* 2.07 (0.08) a*
Within each column Similar superscripts indicate no significant difference (p<0.05).
Within each group Asterisks represent significant differences in Ra and Rt among tooth brushing abrasion cycles
Table 7.3 Mean (SD) of 2D roughness parameters
123
Group
Tooth brushing abrasion
at start
(0 cycle)
after 2years brushing
(after 10,000 cycles)
after 4years brushing
(after 20,000 cycles)
Sa St Sa St Sa St
Sp 0.14 (0.04) a* 33.35 (2.10) a* 0.66 (0.05) a* 40.62 (0.70) a* 1.26 (0.09) a* 55.87 (0.63) a*
I1 0.22 (0.02) a,b* 35.02 (2.33) a* 1.55 (0.07) b* 43.16 (1.90) a,b* 2.33 (0.21) b* 65.40 (0.42) b*
I2 0.17 (0.03) b,c* 50.52 (1.89) b* 1.93 (0.08) c* 69.19 (0.79) c* 2.93 (0.14) c* 79.90 (0.48) c*
I3 0.27 (0.03) c,d* 69.00 (2.51) c* 1.91 (0.04) c* 88.45 (1.56) d* 2.88 (0.12) c* 96.52 (0.65) d*
I4 0.57 (0.04) e* 80.63 (2.15) d* 2.03 (0.09) c* 90.92 (1.18) d* 2.89 (0.08) c* 102.82 (0.23) e*
I5 0.30 (0.02) d* 42.08 (1.82) e* 1.60 (0.08) b* 55.76 (0.43) e* 1.55 (0.05) d* 86.60 (0.63) f*
I6 0.30 (0.03) d* 51.97 (3.02) b* 1.53 (0.04) b* 46.25 (0.63) b* 1.63 (0.09) d* 82.70 (0.47) g*
TC 0.11 (0.02) a* 31.94 (2.38) a* 0.61 (0.04) a* 40.94 (0.98) a* 1.17 (0.04) a* 50.64 (0.83) h*
Within each column Similar superscripts indicate no significant difference (p<0.05).
Within each group Asterisks represent significant differences in Sa and St among tooth brushing abrasion cycles
Table 7.4 Mean (SD) of 3D roughness parameters
124
125
Figure 7.4 3D model of scanned sample
Figure 7.5 Linear correlation between filler size and surface roughness parameter
Filler Size (nm)
100 450 750 1000 1500
Ra
-1
0
1
2
3
4
r= 0.95
r= 0.96
r= 0.76
Filler Size (nm)
100 450 750 1000 1500
Rt
-1
0
1
2
3
4
r=0.38
r=0.44
r=0.88
Filler size (nm)
100 450 750 1000 1500
r=0.88
-1
0
1
2
3
4
Sa
r=0.87
r=0.84
r= 0.99
100 450 750 1000 1500
St
0
20
40
60
80
100
120
Filler Size (nm)
r= 0.96
r= 0.97
● 0 Cycle; ∆After 10,000 Cycles; ■After 20,000 Cycles
126
7.5 Discussion
The quality of a resin composite restoration surface depends on two main factors
which are the material composition and the polishing system used. Previous studies
have shown that the polishing system not only influences surface roughness, gloss
and colour stability but may also have a role in other properties such as micro
hardness and micro leakage (Paravina et al., 2004; Heintze et al., 2006; Venturini et
al., 2006).
The wear of resin composite material starts with gradual removal of the organic
component which lead to projection of unsupported filler particles and subsequent
exfoliation (Condon and Ferracane, 1997). Thus, the inter particle space has been
shown to play an important role in the wear resistance of resin composites, as the
inter particle space reduces the wear resistance of composite material improves. This
can be explained since in fillers that are closer together the organic resin is more
protected from abrasives and thus the wear is reduced (Jorgensen et al., 1979).
Gloss and surface roughness are usually linked together and the relationship between
the two has been illustrated in previous studies (O'Brien et al., 1984; Paravina et al.,
2004). One can affect the other and it is beneficial to study them simultaneously to
obtain a more representative view of the behaviour of material in terms of surface
properties. The method used in this study to evaluate surface roughness is relatively
novel and differs from the conventional methods used in the majority of previous
studies (Cavalcante et al., 2009). It has several advantages which are; non invasive
as it is scanned by a confocal optical single point sensor rather than using a stylus
that touches the sample and can obtain parameters not only in 2D but also in 3D. 3D
mapping is more representative of the surface and thus leads to more reliable results.
Additionally it can generate a 3D model. Despite all these advantages, this method is
relying on an experienced operator and can be more time consuming.
For all materials tested the surface became statistically less glossy after toothbrush
abrasion and this was statistically correlated to filler size. A clear trend could be seen
where an increase in filler size led to reduction in gloss before and after brushing
abrasion (Figure 7.3). Thus the first null hypothesis was rejected. This is in
127
agreement with previous studies (Heintze and Forjanic, 2005; Cavalcante et al.,
2009). However the correlation between filler size and surface gloss is stronger in
the current study. Among the multimodal resin composites, TC revealed higher gloss
than any other material used in the study, whether multimodal or unimodal.
Toothbrush abrasion increased all roughness parameters tested both in 2D and 3D
measurements. The difference between materials were statistically significant
(p<0.05), thus the second hypothesis was rejected. There were a strong correlation
between filler size and Ra, Sa and St (Figure 7.4). However for Rt the correlation
became more pronounced after 20,000 cycles of toothbrush abrasion which
corresponds to 4 years of tooth brushing.
The unimodal resin composites which have larger filler sizes I4 (1500nm) and I3
(1000nm) exhibited the highest values of all roughness parameters before and after
tooth abrasion. This was more prominent in 3D roughness parameters. This result is
in conflict with other published results (Heintze and Forjanic, 2005; Cavalcante et
al., 2009). This could be due to the difference in the technique used to evaluate
surface roughness (2D and 3D) and also could be due to larger variations in filler
sizes used in this study that might illustrate differences more clearly.
7.6 Conclusions
In this study, filler size was shown to have a significant influence on both surface
properties examined. The effect was illustrated more clearly in terms of retention.
After toothbrush abrasion that simulated long term clinical service, the resin
composites with the smaller filler size demonstrated the highest retention values.
This also highlights the importance of simulation experiments that will discriminate
between materials more accurately. Despite few differences being observed for gloss
and roughness after polishing, more could be seen after the abrasion process. That
sets a limitation in reporting only those initial values since it can lead to misleading
information for practitioners expecting that two materials will perform the same.
128
CHAPTER EIGHT
Effect of Filler Size on Gloss and Colour Stability of Resin
Composites
Elbishari H, Silikas N, Satterthwaite JD.
Submitted to Journal of Applied Oral Science
129
8.1 Abstract
Objective: To study the effect of filler size and storage media on the colour and
surface gloss stability of resin-composite materials.
Materials and Methods: Seven model composites and one commercial composite
were used in the study. After all samples were prepared and polished, surface gloss
and colour were recorded initially. Samples were then allocated to different groups
according to storage media (Distilled Water, Coca Cola, Red Wine). For surface
gloss two values were obtained (initially and after 3 months). Colour was recorded
after 24 hours, 2 weeks and 3 months. Data were analysed using One Way ANOVA
(p<0.05).
Results: The surface gloss values ranged from 84.9 GU for TC (40:200:1000nm) to
72.3 GU for I4 (1500nm) at baseline. The surface gloss decreased significantly for
all materials after 3 months of storage regardless the storage media. Colour changes
of materials stored in distilled water ranged from ∆E = 0.44 after 24 hours to
∆E = 3.10 after 3 months. Storage in red wine and coca cola resulted in greater
∆E (>3.3) for all materials, which were considered clinically perceptible.
Conclusions: Filler size has a significant influence in the surface gloss of resin
composite. In terms of colour change it had no effect initially but had a strong
correlation after 3 months of storage in all media.
Key words:
Resin composite, colour stability, gloss, filler size, dietary habits
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8.2 Introduction
Resin composites have become the most commonly used restorative materials in
dentistry. Their use in restorative dentistry varies from a simple pit and fissure
sealant to inlays and onlays. Their application may also include cavity liners, cores
and root canal posts, provisional restorations, cements for tooth prostheses and
orthodontic appliances. The composition of resin-based dental composites has
evolved significantly since the materials were first introduced to dentistry more than
50 years ago. Several attempts have been made to improve their performance. One of
them is the modification of fillers in relation to their size. A reduction in filler size
has seen the introduction of nanofillers. The resulting nanocomposite materials can
be polished to the highest surface gloss and the smoothest surface comparing to other
composites (Attar, 2007; Da Costa et al., 2007).
In addition to the aesthetic a smooth surface can prevent the plaque retention due to
the absence of micro-roughness. Moreover, surface smoothness reduces the
coefficient of friction and subsequently this may reduce wear rate (Kakaboura et al.,
2007a). For aesthetics, colour parameters may provide important information on the
serviceability of restorative materials. Changes in the colour of restorative materials
caused by staining agents have an influence in the gloss of the materials (Keyf and
Etikan, 2004; Lee et al., 2005).
While intrinsic staining is related to the composition of the material, staining by
external causes can also alter the colour of resin composite restorations. Extrinsic
factors such as adsorption or absorption of stains may also cause discoloration
(Satou et al., 1989; Abu-Bakr et al., 2000). These stains may vary according to
plaque and calculus, dietary habits and smoking and drinking habits. For example
red wine and coffee can cause severe discolouration, with total colour differences of
∆E >10 (Stober et al., 2001).
Surface gloss of resin composite restorations expresses the ability to reflect directed
light (Keyf and Etikan, 2004), and thus can be defined as a feature of visual
appearance that originates from geometrical distribution of light reflection by the
surface (Lee et al., 2005). In other words, its degree approach to a mirror surface, i.e.
131
a perfect mirror surface is believed to have maximum gloss (Kakaboura et al.,
2007a).
Gloss can be directly influenced by surface roughness (Lee et al., 2005; Lu et al.,
2005). Polished composite surfaces that have low surface roughness demonstrate
high gloss values (Stanford et al., 1985; Lee et al., 2005). Variation in gloss between
a restoration and the surrounding tooth enamel is important for two reasons. First,
the eye will detect differences in gloss between the resin composite and the
surrounding enamel, even if their colours are matched. The second reason is that
high gloss reduces the effect of a colour variation, since the colour of reflected light
is predominant rather than the colour of the underlying resin composite material
(O'Brien et al., 1984).
As shown previously the discolouration of resin composite restoration is either due
to intrinsic or extrinsic factors (Um and Ruyter, 1991; Yannikakis et al., 1998).
Intrinsic factors can alter the resin matrix or the interface of the matrix and the
fillers, which can result in discolouration of the resin material. Chemical
discolouration through the oxidation of the amine accelerator, the structure of the
polymer matrix and the unreacted pendant methacrylate groups can also take place
(Asmussen, 1983; Ruyter, 1988). The intrinsic colour of aesthetic materials may
change when materials are aged under various physico-chemical conditions such as
thermal changes and humidity (Iazzetti et al., 2000).
The aim of this study was to investigate the effect of filler size on gloss and colour
stability of resin composites. The objectives were to measure gloss and colour
stability on a series of model resin composites with systematically varied filler sizes
and one commercial resin composites at baseline and after storage in different media
(distilled water, red wine and coca cola). The model composites used in the study
have different filler distributions (unimodal, bimodal and trimodal) while the
commercial composite used was a multimodal. The filler shape was irregular,
spherical or a combination (as in the commercial composite). The null hypotheses of
this study were that i) filler size has no effect on the gloss and colour stability of
resin composites; and, ii) different storage media has no effect on the gloss and
colour stability of resin composites.
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8.3 Materials and Methods
Seven model light cure resin composites (Ivoclar Vivadent, Schaan, Liechtenstein)
and one commercial light cure resin composite (Tetric Ceram, Ivoclar Vivadent,
Schaan, Liechtenstein), were tested. The resin matrix consisted of a combination of
Bis-GMA, UDMA and TEGDMA. The fillers of all the model composites had the
same volume fraction (56.7%). The filler particles were either spherical or irregular.
The spherical particles were silica, and the irregular particles were ground glass
melts (Ba–Al–B-silicate glass). The composition of the resin composites is
summarized in Table 8.1
Resin
composite
Filler Particles( Ground Glass [Ba-Al-B-silicate
glass]) Matrix
Shape Size (nm) Wt% Vol%
I1 Irregular 450 76.4 56.7
BisGMA,
UDMA,
TEGDMA
I2 Irregular 700 76.4 56.7
I3 Irregular 1000 76.4 56.7
I4 Irregular 1500 76.4 56.7
I5 Irregular 450, 1000
(1:3) 76.4 56.7
I6 Irregular 450, 700 &1500
(1:1:3) 76.4 56.7
SP Spherical 100 72.4 56.7
TC
Lot :
C49490
Irregular&
Spherical 40, 200 &1000 79 60
Table 8.1 Composition of Resin composites used in the study including filler size,
shape, content and distribution. The resin matrix was the same in all materials.
133
Teflon moulds were used to prepare twelve disc specimens (10mm x 2mm) for each
material. The samples were irradiated for 40s from each surface with a light curing
unit (Optilux 501, Demetron, Danbury, USA) emitting 550mW/cm2 irradiance, as
measured with the radiometer incorporated into the appliance. After polymerization
all specimens were initially finished with a sequence of 400-, 600-, 800- and 1200-
grit SiC papers under continuous water cooling. To obtain a glossy surface, the
specimens were further polished with 0.5 inch Sof-Lex contouring and polishing
discs (3M Dental Products, St. Paul, MN, USA). Finally, the specimens were placed
in an ultrasonic water bath (Transonic T 310, Camlab Limited, Cambridge, England)
for 2 min to remove any residual debris.
Specimens were randomly allocated into 3 different groups according to storage
media; distilled water, red wine (Vina Maipo, Carmenere, Chile) and coca cola
(Coca Cola Enterprises Ltd, Uxbridge).
Surface gloss and colour measurements were recorded for all specimens. Surface
gloss was measured with a glossmeter (Novo Curve, Rhopoint, instrumentation
LTD, East Sussex, England) which was calibrated against a black glass standard
provided by the manufacturer. Five measurements per specimen were performed at
60º light incidence. The colour was determined with a colorimeter (Minolta Chroma-
meter CR-221, Osaka, Japan), according to the CIE-L*a*b* system with a D65
standard light source. L* parameter corresponds to the degree of lightness and
darkness; a* and b* coordinates correspond to red or green chroma (+a* = red, −a* =
green), and yellow or blue chroma (+b* = yellow, −b* = blue), respectively. The
readings were taken for each specimen while positioned against a white ceramic
plate which served as a background and also used to calibrate the colorimeter before
measurements were taken. Three readings were taken of each specimen and the
average was automatically calculated by the machine.
Surface gloss and colour measurements were recorded at baseline, after 24 hours, 2
weeks and 3 months. The colour change (∆E) was calculated for each sample
according to the following formula:
134
∆E ∆L ∆ ∆ Equation 4
Where ∆L*, ∆a*, and ∆b* are the differences in the respective values before and
after time storage
Data were entered into a statistical software package for analysis (SPSS ver.16.0,
Chicago, Il, USA).One-way ANOVA was applied to test for significant differences
between the materials (P<0.05) for each storage media at each specific time interval.
Prior to post-hoc tests, all data were tested for normal distribution using Levene’s
test of homogeneity of variance (α=0.05), following the assumption of equal
variances. Accordingly, the equal variance assumption was rejected (p<0.05), and
Dunnett’s T3 multiple comparison test was used to compare the groups.
Furthermore, within each material, a t test for paired data was performed to
investigate the effect of storage media on the surface gloss between baseline and 3
months time (p < 0.05).
135
8.4 Results
Mean values and standard deviations for gloss and colour change (∆E) for all
materials are presented in Tables 8.2, 8.3, and 4. Figures 8.1, 8.2 and 8.3 present
results of colour change. The gloss was found to be strongly correlated with filler
size at baseline (r=0.95) for all materials. The correlation between gloss and filler
size remained strong regardless of the time and storage media. Linear correlations
are presented in Figure 8.4.
For the samples stored in distilled water, colour change (∆E) was greatest after
3 months (p<0.05). However, there were statistical differences between the materials
after 24 hours and 2 weeks (P<0.05), but after 3 months the difference between the
materials was not statistically different (P>0.05). For the samples stored in both red
wine and coca, colour change (∆E) had almost the same trends within the same
material with more change exhibited in red wine. The difference in colour change
(∆E) was statistical different after 24 hours, 2weeks and 3 months (p<0.05). There
was weak correlation between filler size and colour change (∆E) after 24 hours in
distilled water, red wine and coca cola (r=0.38, r=0.31 and 0.13 respectively) and
also after 2 weeks (r=0.30, r=0.58 and r=0.53 respectively). However after 3 months,
the correlation between filler size and distilled water, red wine and coca cola became
stronger (r=0.93, r=0.92 and r=0.91 respectively).
Material
Distilled Water
Gloss (GU) Colour Change (∆E)
Baseline 3 months 24 hours 2 weeks 3 months
I1 76.4 (0.6) a* 66.1(0.3) a* 0.48(0.10)a,e,A 0.48(0.03)a,A 2.83(0.33)a,B
I2 76.0 (0.5) a,b* 63.5(0.3) b* 0.31(0.04)b,A 0.52 (0.46)a,A 3.0(0.47)a,B
I3 74.0 (0.3) c* 61.6(0.3) c* 0.40(0.05)a,b,A 0.60 (0.22)a,b,A 3.09(0.28)a,B
I4 72.4 (0.3) d* 59.5(0.4) d* 0.65(0.04)c,d,A 0.76 (0.03)b,d,A 3.10(0.09)a,B
I5 75.3 (0.3) b* 64.3(0.4) a,b* 0.72(0.06)c,A 1.07(0.24)e,A 2.84(0.30)a,B
I6 72.7 (0.4) d* 62.5(0.3) c,d* 0.62(0.02)c,d,e,A 0.91(0.09)b,e,A 2.88(0.23)a,B
Sp 80.7(0.7) e* 72.6(0.3) e* 0.49(0.09)a,d,A 0.72(0.19)a,b,A 2.69(0.42)a,B
TC 84.7(0.4) f* 78.3(0.1) f* 0.44(0.06)a,b,A 0.52(0.06)a,d,A 2.69(0.05)a,B
Surface Gloss- With each column different small letters superscript indicates significant difference (p<0.05) between materials
With each row asterisk superscript indicates significant difference (p<0.05) within the same material
Colour Change With each column different small letters superscript indicates significant difference (p<0.05) between materials
With each row different capital letters indicates significant difference (p<0.05) within the same material
Table 8.2 Mean and SD of surface gloss and colour changes (∆E) of materials stored in distilled water
136
Material
Red Wine
Gloss (GU) Colour Change (∆E)
Baseline 3 months 24 hours 2 weeks 3 months
I1 76.8 (0.2)a* 25.7 (0.4)a 4.50(0.11)a,A 9.40 (0.37)a,B 17.90(0.10)a,C
I2 75.9 (0.2)a,b* 21.2 (0.5)b 5.15(0.27)a,A 9.05(0.34)a,B 18.65(0.45)a,b,C
I3 74.0 (0.4)c* 17.6 (0.3)b 4.52(0.35)a,A 9.0(0.68)a,B 24.60(0.22)c,C
I4 72.4 (0.3)d* 12.1 (0.2)c 8.96(0.49)b,A 17.68(0.51)b,B 25.12(0.89)c,C
I5 75.0 (0.3)b* 29.5 (0.3)a,d 6.70(0.14)c,A 11.17(0.43)c,B 19.71 (0.59)b,C
I6 72.8 (0.5)d* 24.8 (0.2)a,b 8.85(0.61)b,A 12.70(0.59)d,B 25.20(0.69)c,C
Sp 80.9 (0.7)e* 33.1 (0.4)d 7.48(0.20)c,A 11.60(0.20)c,d,B 16.49(0.22)d,C
TC 84.9 (0.5)f* 56.7 (0.2)e 4.41(0.27)a,A 8.29(0.44)a,B 12.30(0.16)e,C
Surface Gloss- With each column different small letters superscript indicates significant difference (p<0.05) between materials
With each row asterisk superscript indicates significant difference (p<0.05) within the same material
Colour Change With each column different small letters superscript indicates significant difference (p<0.05) between materials
With each row different capital letters indicates significant difference (p<0.05) within the same material
Table 8.3 Mean and SD of surface gloss and colour changes (∆E) of materials stored in red wine
137
Material
Coca Cola
Gloss (GU) Colour Change (∆E)
Baseline 3 months 24 hours 2 weeks 3 months
I1 76.5 (0.4)a* 50.7 (0.4)a 3.88(0.12)a,b,A 7.90 (0.46)a,b,B 11.7(0.47)a,C
I2 75.8 (0.6)a,b* 47.7 (0.3)b 4.40(0.20)a,b,A 7.52 (0.56)a,B 12.60(0.21)a,C
I3 74.2 (0.5)c* 42.9 (0.1)c 3.44(0.20)b,A 7.01 (0.91)a,c,B 17.57(0.64)b,C
I4 72.3 (0.3)d* 35.6 (0.3)d 3.24(0.07)b,A 11.90 (0.16)d,B 19.64(0.61)c,C
I5 75.1 (0.3)b* 35.1 (0.7)d 4.35(0.10)a,A 8.82 (0.26)b.B 17.93(0.33)b,C
I6 72.7 (0.3)d* 33.3 (0.3)e 4.35(0.39)a,A 10.40 (0.09)e,B 18.85(0.55)b,c,C
Sp 80.9 (0.6)e* 40.7 (0.3)f 3.21(0.72)b,A 8.75 (0.10)b,B 11.98(0.20)a,C
TC 84.9 (0.3)f* 59.6 (0.3)g 3.32(0.13)b,A 6.34 (0.28)c,B 9.13(0.30)d,C
Surface Gloss- With each column different small letters superscript indicates significant difference (p<0.05) between materials
With each row asterisk superscript indicates significant difference (p<0.05) within the same material
Colour Change With each column different small letters superscript indicates significant difference (p<0.05) between materials
With each row different capital letters indicates significant difference (p<0.05) within the same material
Table 8.4 Mean and SD of surface gloss and colour changes (∆E) of materials stored in coca cola
138
139
Figure 8.1 Mean (SD) of ∆E of all samples stored in distilled water
Figure 8.2 Mean (SD) of ∆E of all samples stored in red wine
140
Figure 8.3 Mean (SD) of ∆E of all samples stored in coca cola
141
Figure 8.4 Correlation between Filler size and surface gloss
(a- Distilled Water, b- Red Wine, c- Coca Cola)
142
8.5 Discussion
The results show a strong correlation between filler size and gloss and also a
correlation between filler size and colour change which became stronger after 3
months of storage, thus the first null hypothesis was rejected. Both gloss and colour
changes were affected by the storage media with different degrees. These changes
increased as storage time increased; hence the second null hypothesis was rejected.
The surface gloss of all materials was in a range of 84.9 GU in TC (40:200:1000nm)
to 72.3 GU in I4 (1500nm) at baseline. The gloss measurements were strongly
correlated to filler size of unimodal resin composite materials (r=0.95), generally
higher gloss was exhibited by the smaller filler size material. The surface gloss
decreased as storage time increased, and reached the lowest measurements after 3
months of storage regardless the storage media, this reduction was statistically
significant (p<0.05) from the measurement at baseline irrespective to filler size, with
the greatest reduction exhibited by the material stored in the red wine. The reduction
in the surface gloss of all materials was always strongly related to filler size at each
time interval (Figure 8.4). The result of this study was in accordance with previous
results (Cavalcante et al., 2009), with the material that had the smallest filler size
amongst unimodal materials (Sp 100nm) showing the highest surface gloss despite
the difference in polishing systems used; however, the correlation between filler size
and gloss was stronger in the current study, which could be due to the variations
between studies in filler size used as well as different filler shapes.
Human eyes cannot identify a colour change (∆E) of less than one unit (Seghi et al.,
1989). According to Ryge criteria for clinical investigation of restorative material,
an Alpha rating corresponds to a colour change (∆E) ≤ 1, ∆E values between 1.1 and
3.3 units correspond to the Bravo rating, and when over 3.3 units, to Charlie rating
(Ryge, 1980). Hence in the current study, colour changes < 1.1 were considered
clinically unnoticeable (Seghi et al., 1989) and colour changes between 1.1 to 3.3
were considered noticeable but clinically acceptable, and colour changes higher than
3.3 were considered as clinically not acceptable (Ruyter, 1988).
Colour changes were also noticed when all materials were stored in distilled water.
These changes were in the range of ∆E = 0.44 after 24 hours and ∆E = -3.10 after
143
3 months, hence they are clinically not perceptible (Ruyter, 1988; Kakaboura et al.,
2007a).
Colour changes seen with materials stored in red wine and coca cola were considered
clinically perceptible as all of them were greater than 3.3 (Seghi et al., 1989), with
the red wine causing the greatest colour changes (∆E) which ranged from ∆E= 8.96
after 24 hours to ∆E= 25.20 after 3 months this was in accordance with other studies
(Topcu et al., 2009). The red wine used in this study contained 13.5% alcohol by
volume, previous studies have shown that alcohol facilitates discolouration of resin
composites by softening the resin matrix of the composites (Ferracane et al., 1998;
Deepa and Krishnan, 2000).
The colour changes were weakly related to filler size after 24 hours and 2 weeks;
however the storage media had greater influence than filler size. After 3 months the
effect of storage media was still clearly noticeable, however the effect of filler size
became stronger (r=0.85, r=0.92 and r=0.91) in distilled water, red wine and coca
cola respectively. Previous studies have showed that filler size was not correlated to
colour changes (∆E) < 3.3 which in harmony to the result of this study especially to
the specimens stored in distilled water (Cavalcante et al., 2009).
8.6 Conclusion
Dietary habits effect discolouration of resin composite restorations. The filler size of
resin composite materials has a great influence in the surface gloss of resin
composite restorations, with the higher gloss exhibited by materials with the smaller
filler size composing materials. Acidic drink causes more discolouration for resin
composites which was not clinically acceptable. The change in colour was not
strongly correlated till after 3 months.
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CHAPTER NINE
Discussion, Conclusions and Future Work
Recommendations
145
9.1 General Discussion
Resin composite materials have been used in restorative dentistry for over four
decades. Their use varies from simple restorative treatment such as fissure sealant to
construction of crowns and bridges. Other uses include cavity filling, building up
fractured teeth, post and cores and as cements. Despite the wide range of uses the
composition of resin composite materials is fairly consisting, being mainly
composed of a polymeric matrix (typically a dimethacrylate), filler particles
(classically made from radiopaque glass), a silane coupling agent required to bind
the filler particles to the resin matrix, and chemicals (initiators and inhibitors) that
promote or modulate the polymerisation reaction (Klapdohr and Moszner, 2005).
Previously the applications of resin composites were limited due to several
problems, for example polymerisation shrinkage, and wear and fracture under
masticatory function which limited their use to anterior teeth. Due to these problems,
there have been many attempts in order to improve the mechanical and physical
properties of resin composites.
Defining a target for mechanical and physical properties of resin composites is
difficult because there is little correlation between the properties of resin composites
and their clinical performance (Ferracane, 2011). Due to the fact that secondary
caries and restoration fracture are the main causes for replacement of resin composite
restorations (Sarrett, 2005), improvements on fracture toughness and strength as well
as minimising the shrinkage of resin composites are required.
Most current materials are composed of nanofillers and nano-hybrid fillers, and thus
many studies have focused on nanotechnology in order to reinforce filler particles
(Klapdohr and Moszner, 2005). Other developments include the silorane based resin
composites which provide lower polymerisation shrinkage than other dimethacrylate
based composites, additionally this material has been shown to have good
mechanical properties (Ilie and Hickel, 2009).
146
The aims of this research were to characterise handling, mechanical and physical
properties of resin composite materials. The resin composites used in this research
were seven model composites and one commercial. All of them have the same
organic matrix but different filler size and distributions.
The investigation of these materials using different methodologies was focussed onto
whether filler size affects properties of resin composites, namely handling,
mechanical and physical properties. The research aimed to see the effect of filler
size/shape and distribution throughout the lifetime of composites. Initially we
examined materials at pre-cured or during placement (handling etc), then after curing
in short and long term properties. The characterisation started by testing resin
composites at their pre-cure state. Both packing stress and viscosity were
investigated at both room temperature (23ºC) and body temperature (37ºC).
Packing stress can be defined as the force per unit area required to pack resin
composite materials into a cavity. Generally, so called ‘packable composites’ are
achieved by the inclusion of 100 µm long fibrous filler particles and/or textured
surfaces which tend to interlock and resist flow (Anusavice and Phillips, 2003). All
materials were tested using the pentrometer principle and results varied as shown in
Chapter 4.
Amongst the unimodal distributed materials tested, the resin composite with the
largest filler size (1500nm) exhibited the highest packing stress (2.60 MPa) at 23ºC
and (1.58 MPa) at 37ºC. Regarding the other materials tested, the packing stress
decreases as temperature rises from room temperature to body temperature.
Additionally viscosity related packing stress had the same trend for all materials
tested. Despite the linear correlation between filler size and packing stress at 23ºC
and 37ºC (r=070 and 0.60 respectively) (Figure 4.3a and Figure 4.4a) and between
filler size and viscosity at 23ºC and 37ºC (r=95 and 0.93 respectively), the increase
in packing stress and viscosity was not statistically different between most unimodal
composites (p<0.05). Furthermore, the trend of both packing stress and viscosity was
also the same within multimodal distributed composites. The commercial material
(Tetric Ceram) showed not only the highest values among multimodal composites
but also among all materials tested. This could be due to the fact that Tetric Ceram
147
has three different filler sizes (40:200:1000nm) and that thicker consistency resin
composites can be achieved by increasing filler size, filler volume and adding
different types of fillers such as glass fibres (Choi et al., 2000). The reduction in
both packing stress and viscosity with rise in temperature, could be due to the fact
that increasing temperature lead to increase the flow of resin matrix making the
resin composite more flowable (Knight et al., 2006) and decreasing thus its viscosity
(Silikas and Watts, 1999). Hence, Chapter 4 concluded that both filler size and
temperature have an effect on handling properties.
The handling of resin composites in terms of packing the materials has a big
influence in introducing voids within resin composite restorations. Generally, voids
are present in the restoration either due to the manufacturing process or during
handling and packing techniques. It was thought that packing stiffer materials i.e.
packable composite into a cavity could have an advantage of producing a void free
well adapted restoration, however previous study showed that packable composites
led to increased voids within restorations (Opdam et al., 1996b). Additionally, the
low viscosity of resin composites (flowable composites) was found to reduce voids
within a class II restoration (Chuang et al., 2001). Introducing voids into restorations
will have a negative impact and affect long survival of the restorations. Hence the
effect of filler size on the presence of voids in resin composites was investigated in
Chapter 5 utilising novel and non-invasive methodology involving high resolution
micro computed tomography [µCT]. Conventional methodologies are destructive
and use low magnification tools, while µCT allows 3-D measurement of voids in
very thin sections (up to 19 µm).
The material with the largest filler size (1500 nm) showed the highest percentage of
voids (3.48%) and the material which had the smallest filler size (100 nm) exhibited
the lowest percentage of voids (0.44%) amongst unimodal distributed resin
composites. Tetric Ceram showed the lowest percentage of voids (0.28%) of all
materials tested. This could be due to the fact that Tetric Ceram has three different
filler sizes (40:200:1000nm) one of which was the smallest filler size compared to all
other materials.
148
The percentage of voids was strongly related to filler size (r=0.97) (Figure 5.4) and
the difference of the percentage of voids between the materials was statistically
significant (p<0.05). It was concluded that filler size has a significant effect on the
presence of voids within resin composites.
As mentioned above the presence of voids within restorations has a big impact on
long term survival due to their adverse affects such as marginal leakage and
discolouration when present at the margins, increased wear due to the stress
concentration around voids, and decreased flexural strength (Opdam et al., 1996b).
As voids can affect the flexural strength of resin composites, mechanical properties
could also be affected. For this reason the influence of filler size on fracture
toughness and correlation between filler size, voids and fracture toughness have been
studied in Chapter 6.
A single edge notch method was used in this study, which is one of the two common
methods used to determine the fracture toughness of restorative materials
(Soderholm, 2010). In the methodology, percentage of voids was investigated around
the notch (1 mm above and 1 mm below). The data of percentage of voids was in
accordance with the study in Chapter 5. Regarding fracture toughness, the material
which had 450 nm filler size showed the highest fracture toughness value
(1.50 KIC MNm-1.5) of the unimodal distributed materials, while Tetric Ceram
exhibited the highest fracture toughness value (2.00 KIC MNm-1.5) of all materials
tested which is as high as amalgam and better than porcelain (Ferracane, 2011): this
could be due to higher filler volume (60%). In spite of differences in filler size and
distribution, all materials had the same matrix resin, thus there was not statistically
significant difference in fracture toughness between most of them. This could be due
to the fact that fracture toughness is dependent on the adhesion of filler particles to
resin matrices rather than the filler size (Lloyd and Mitchell, 1984; Ferracane et al.,
1987; Ferracane et al., 1998). Unlike voids%, the fracture toughness was not
correlated to filler size (r=0.2). Additionally there was no correlation between
fracture toughness and voids% (r=0.02). It could be concluded that filler size was
directly related to percentage of voids but that has no effect on fracture toughness.
149
Despite improvements in the mechanical properties of resin composite materials and
the expectations for further development to enhance strength and reduce
polymerisation shrinkage, resin composites in the oral cavity still face other physical
problems such as increase surface roughness, staining and decreased surface gloss.
For instance, wear of resin composites is still a problem which limits the use of resin
composites (for example in bruxist patients). This wear could be due to masticatory
function, two body wear or due to tooth brushing abrasion (three-body wear). For
this reason the effect of filler size on the surface roughness and gloss has been tested
in Chapter 7.
In this chapter, the surface gloss and the surface roughness of different materials
before and after tooth brushing abrasion were investigated. A different method from
the conventional 2D methods which have been extensively used in previous studies
was used to characterise surface roughness. The surface roughness was investigated
using non-contact 3D surface topography machine. At baseline (after polishing)
results showed that, gloss values ranged between 72.3 and 84.3 GU with the highest
value exhibited by Tetric Ceram followed by the material with 100nm filler size
(which showed the highest gloss amongst unimodal distributed materials). The
material which had the largest filler size (1500nm) exhibited the lowest value of all
materials tested. Gloss of all materials was reduced after tooth brush abrasion with
the highest gloss retention of 72.8% demonstrated by Tetric Ceram and the lowest
gloss retention of 8.1% was exhibited by the material with the largest filler size
(1500nm). The difference between materials was statistically significant (p<0.05)
before and after tooth brush abrasion. In addition within each material there was a
statistically significant difference (p<0.05). Surface gloss values were strongly
correlated with filler size before (r=0.96) and after tooth brushing abrasion (r=0.90)
(Figure 7.3). Regarding surface roughness, all materials tested showed a very smooth
surface after polishing and before tooth brush abrasion. After tooth brush abrasion,
surface roughness increased significantly and reached its maximum after 20,000
cycles of brushing which equivalent to 4 years of tooth brushing of teeth and
restorations (Kanter et al., 1982). The correlation between filler size and surface
roughness parameters ranged from (r=0.99) for St after toothbrush abrasion (20,000
cycles) to (r=0.38) for Rt at baseline (Figure 7.5).
150
It was concluded that filler size was shown to have a significant influence on surface
gloss and roughness and smaller filler size demonstrated better surface properties
after toothbrush abrasion.
Surface properties of resin composites are not only affected by mechanical abrasion
as shown in Chapter 7, but also by dietary habits. Hence the effect of filler size on
colour stability and gloss of resin composites stored in three different media
(distilled water, coca cola and red wine) was examined. Results from this chapter
showed that all materials tested exhibited some colour change (∆E) with different
degrees depending on materials, time and media. Regarding materials stored in
distilled water ∆E reached its maximum of 3.10 after 3 months of storage. As
according to Ryge criteria, changes between 1.1 to 3.3 were considered noticeable
but clinically acceptable, and colour changes higher than 3.3 were considered as
clinically not acceptable (Ryge, 1980; Ruyter, 1988). Hence the ∆E of all materials
stored in distilled water was not clinically noticeable. For materials stored in coca
cola and red wine the ∆E had almost the same trend, with more changes in materials
stored in red wine. Unlike materials which stored in distilled water, the ∆E of all
materials stored in coca cola and red wine was greater than 3.3, thus it was clinically
not acceptable. This could be due to the acidic nature of these media which
facilitates staining of resin composites by softening of the resin matrix (Cavalcante et
al., 2009). The correlation between filler size and ∆E was weak after 24 hours and
after 2 weeks and it was clear that the storage media had more affect than filler size.
However the correlation between filler size and ∆E became stronger after 3 months
(r=0.85, r=0.92 and r=0.91) in distilled water, red wine and coca cola respectively.
This chapter concluded that filler size had an influence on colour stability of resin
composites which became more obvious after 3 months. It was also concluded that
dietary habits had a significant influence on colour stability of resin composite
materials.
151
9.2 Conclusions
Within the limitations of this study it was concluded that:
Filler size, filler distributions and temperature have a significant effect on the
handling properties of resin composites in terms of packing stress and
viscosity. An increase in filler size resulted in more viscous materials, while
an increase in temperature resulted in less viscous materials.
µCT is a reliable non-destructive 3D method to characterise voids within
resin composites.
Filler size is strongly correlated to voids (%) within resin composites, and
this effect is significant.
Filler size and distribution do not affect fracture toughness of resin
composites
Voids(%) within resin composites have a negligible effect on fracture
toughness
Non-contact 3D surface topography is a reliable non-destructive 3D
characterisation of surface roughness of resin composites.
Filler size has a significant effect on the surface roughness of resin
composites after toothbrush abrasion
Filler size has a weak influence on the colour stability of resin composites but
its effect becomes more significant over time
Dietary habits have a significant influence on the discolouration of resin
composite materials.
Filler size, surface roughness and staining of resin composite materials all
have a significant effect on gloss retention.
152
9.3 Recommendation for future work
In order to complement the studies and future development of knowledge, the
following areas of future work are suggested:
Effect of filler volume (vol %) on handling properties
Have a series of model composites and compare with commercial ones. These
should include a nano-hybrid, a flowable and a bulk-fill.
Effect of filler size on stability of restoration in oral environment.
Suggested procedures to simulate that:
i) Use other techniques to evaluate surface topography e.g. Atomic Force Microscopy
[AFM].
ii) Use non-contact 3D surface topography to measure wear volume.
iii) Use chewing simulator to investigate mechanical abrasion.
153
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APPENDICES
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