Bionanocomposites from Renewable Resources for ...
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Bionanocomposites from Renewable Resources for Applications in the Plastic
Industry
by
Michael Ryan Snowdon
A Thesis
Presented to
The University of Guelph
In partial fulfillment of requirements
for the degree of
Master of Science
in
Plant Agriculture
Guelph, Ontario, Canada
© Michael Snowdon, May, 2014
ABSTRACT
BIONANOCOMPOSITES FROM RENEWABLE RESOURCES FOR APPLICATIONS
IN THE PLASTIC INDUSTRY
Michael Ryan Snowdon Advisor: Dr. Manjusri Misra
University of Guelph, 2014 Co-Advisor: Dr.Amar Mohanty
This study is an investigation into the physical and mechanical properties of
carbonaceous filler based biocomposites. The effect of low loading at 1, 3 and 5 wt% was
investigated by using nano sized carbon filler in the form of carbon black in the initial part of the
study and a polymeric material of poly(butylene succinate) as the matrix. The addition of the
filler increased the mechanical, thermal and electrical properties of the composites as both
materials are relatively hydrophobic in nature. The 5 wt% carbon black loading showed the
greatest increase in overall properties. For the second section of the study a biobased lignin
alternative carbon powder was produced as a substitute to carbon black by temperature and ball
milling optimization. The optimized 900 °C carbonized 24 hour ball milled lignin, showing
improved surface area and thermal conductance was then tested with the same polymer. The
possible applications of the bionanocomposites produced include automotive interior parts,
appliances, packaging, and consumer goods.
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Acknowledgements
I would like to express my sincere gratitude to my advisor Dr. Manjusri Misra for
providing me with the opportunity to pursue graduate studies under her supervision along with
providing sound advice and guidance along the way. I would also like to thank my co-advisor
Dr. Amar Mohanty for his guidance and encouragement throughout my Masters. Their
collaborative mentoring has been beneficial and I am deeply appreciative for their support.
I would like to acknowledge all of the researchers and staff of ‘Bioproducts Discovery
and Development Centre’ (BDDC) for which their vast array of knowledge and experience has
been an invaluable tool in my academic endeavors. Their help should not go unnoticed and am
very grateful for it. I also need to thank Jay Leitch for his assistance and technical prowess in the
Nanoscience facility.
A special thanks to my committee members, Dr. Peter Pauls and Dr. Barry Micallef for
their valuable comments, advice and suggestions towards my thesis.
I would also like to give a big thank you to my family including my parents and brother
for their continuous support and belief in me throughout my degree program.
I would like to thank the Ontario Ministry of Economic Development and Innovation
(MEDI), Ontario Research Fund - Research Excellence Round 4 program and the Ontario
Ministry of Agriculture and Food (OMAF) and Ministry of Rural Affairs (MRA) New Directions
and Alternative Renewable Fuels research program for their funding of the research project.
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TABLE OF CONTENTS
Abstract…………………………………………………………………………………....ii
Acknowledgements……………………………………………………………...….……iii
Table of Contents………………………………………………………………………...iv
List of Tables…………………………………………………………………………..…ix
List of Figures………………………………………………………………….………...xi
List of Acronyms and Abbreviations………………………………………………........xiv
CHAPTER 1 INTRODUCTION………………………………………………………………..1
1.1 Overview of Plastics………………………………………………………………2
1.2 Composites………………………………………………………...………………3
1.3 Biopolymers……………………………………………………………………….6
1.4 Biocomposites……………………………………………………………………10
1.5 Bionanocomposites………………………………………………………………12
1.6 Importance of Biopolymer Research…………………………………………….15
CHAPTER 2 LITERATURE REVIEW……………………………………………………....17
2.1 Aliphatic polyesters……………………………………………………………...18
2.1.1 Poly(butylene succinate)…………………………………………………...20
2.2 Carbon-based Nanomaterials…………………………………………………….23
2.2.1 Carbon Black………………………………………………………………25
2.3 Lignin…………………………………………………………………………….29
2.4 Lignin-based Carbon……………………………………………………………..33
2.4.1 Lignin-based Activated Carbon……………………………………………33
2.5 Hypotheses...………………………………………………….………………….35
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2.6 Objectives………………………………………………......................................36
CHAPTER 3 MELT PROCESSING AND CHARACTERIZATION OF
BIONANOCOMPOSITES MADE FROM POLY(BUTYLENE SUCCINATE)
BIOPLASTIC AND CARBON BLACK…………………………………..…………………..37
3.1 Abstract…………………………………………………………………………..38
3.2 Introduction……………………………………………………………………....38
3.3 Materials and Methods……………………………………………………….…..40
3.3.1 Materials…………………………………………………………………....……40
3.3.2 Processing of Composites………………………………………………………..41
3.3.3 Characterization………………………………………………………………….41
3.3.3.1 Mechanical Testing………………………………………………………42
3.3.3.2 Dynamic Mechanical Analysis (DMA)………………………………….42
3.3.3.3 Differential Scanning Calorimetry (DSC)……………………………….43
3.3.3.4 Thermal Conductivity……………………………………………………44
3.3.3.5 Electrical Resistance and Conductivity………………………………….44
3.3.3.6 Density Measurement……………………………………………………47
3.3.3.7 Scanning Electron Microscopy (SEM)…………………………………..47
3.3.3.8 Optical Microscopy………………………………………………………47
3.3.3.8 Statistical Analysis……………………………………………………….48
3.4 Results and Discussion…………………………………………………………..49
3.4.1 Mechanical Properties……………………………………………………………49
3.4.1.1 Tensile Properties………………………………………………………...49
3.4.1.2 Flexural Properties……………………………………………………….54
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3.4.1.3 Impact Strength……………………………………………………….…56
3.4.2 Dynamic Mechanical Analysis (DMA)………………………………………….59
3.4.3 Thermal Analysis………………………………………………………………...61
3.4.3.1 Differential Scanning Calorimetry (DSC)……………………………….61
3.4.3.2 Thermal Conductance……………………………………………………63
3.4.4 Electrical Resistance and Conductivity………………………………………….65
3.4.5 Surface Morphology and Particle Dispersion……………………………………69
3.4.5.1 Scanning Electron Microscopy (SEM)…………………………………..69
3.4.5.2 Optical Microscopy………………………………………………………71
3.5 Conclusion……………………………………………………………………….73
CHAPTER 4 A STUDY OF CARBONIZED LIGNIN AS AN ALTERNATIVE TO
CARBON BLACK……………………………………………………………………………..74
4.1 Abstract…………………………………………………………………………..75
4.2 Introduction………………………………………………………………………75
4.3 Materials and methods…………………………………………………………...77
4.3.1 Materials…………………………………………………………………………77
4.3.2 Lignin Carbonization…………………………………………………………….77
4.3.3 Ball Milling of Carbonized Lignin…….……………………………………...…78
4.3.4 Characterization………………………………………………………………….79
4.3.4.1 Raman Spectroscopy……………………………………………………..79
4.3.4.2 BET Surface Area Analysis……………………………………………...80
4.3.4.3 Fourier Transform Infrared Spectroscopy (FTIR)……………………….80
4.3.4.4 Particle Size Measurement……………………………………………….81
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4.3.4.5 Electrical Conductivity…………………………………………………..81
4.3.4.6 Thermal Conductivity………………………………………………...….82
4.3.4.7 Scanning Electron Microscopy with Energy Dispersive X-ray
Spectroscopy (SEM-EDS)……………………………………………………….82
4.3.4.8 Statistical Analysis……………………………………………………….83
4.4 Results and Discussion………………………………………………………..…84
4.4.1 Raman Spectroscopy……………………………….……………………84
4.4.2 BET Surface Area………………………………………………………..87
4.4.3 Fourier Transform Infrared Spectroscopy (FTIR)……………………….90
4.4.4 Particle Size Analysis……………………………………………………92
4.4.5 Electrical Conductivity………………………………..…………………94
4.4.6 Thermal Conductivity……………………………………………………96
4.4.7 Elemental Analysis………………………………………………………99
4.5 Conclusion……………………………………………………………………...101
CHAPTER 5 PHYSICAL AND MECHANICAL PROPERTIES OF LIGNIN BASED
CARBON BLACK AS FILLER IN POLY(BUTYLENE SUCCINATE)…………………102
5.1 Introduction: A Link between Chapters………………………………………...103
5.2 Materials and Methods………………………………………………………….103
5.2.1 Materials………………………………………………………………..............103
5.2.2 Processing and Characterization………………………………………………..104
5.3 Results and Discussion………………………………………………………....104
5.3.1 Mechanical Properties……………………………………………………….....104
5.3.1.1 Tensile Properties…………………………………………………...….104
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5.3.1.2 Flexural Properties……………………………………………………...107
5.3.1.3 Impact Strength…………………………………………………………111
5.3.2 Dynamic Mechanical Analysis (DMA)……………………………...…………111
5.3.3 Thermal Analysis……………………………………………………………….114
5.3.3.1 Differential Scanning Calorimetry (DSC)……………………………...114
5.3.3.2 Thermal Conductance…………………………………………………..116
5.3.4 Electrical Resistance and Conductivity………………………………………...118
5.3.5 Surface Morphology and Particle Dispersion…………………………………..121
5.3.5.1 Scanning Electron Microscopy (SEM)…………………………………121
5.4 Conclusion……………………………………………………………………...123
CHAPTER 6 GENERAL DISCUSSION AND CONCLUSIONS…………………………124
REFERENCES………………………………………………………………………………..128
APPENDIX……………………………………………………………………………………146
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List of Tables
Table 2.1: Various Types of Aliphatic Polyesters with R being a side group, x being the number
of monomer units, and n and m being the number of repetitive CH2 groups within the monomer
unit………………………………………...………………………..……………………………19
Table 2.2: Manufacturing processes & feedstocks of carbon black…………………………......27
Table 2.3: Estimated carbon black sales by application field……………………………………28
Table 3.1: Means, standard deviation (SD), and the results of means contrasts for tensile stress
(TS), tensile modulus (TM), % elongation at break, flexural stress (FS), flexural modulus (FM),
and impact strength of bionanocomposites with different carbon black content………………...57
Table 3.2: Means, standard deviation (SD), and the results of means contrasts for the heat
deflection temperature and the storage modulus at 25 °C of PBS carbon black
bionanocomposites……………………………………………………………………………….58
Table 3.3: The glass transition temperature, Tg, melting temperature, Tm, enthalpy of fusion,
∆Hm, crystallization temperature, Tc, enthalpy of solidification, ∆Hc, and crystallinity, χ, based
on the DSC curves and heat deflection temperature (HDT) of PBS carbon black
bionanocomposites……………………………………………………………………………….62
Table 3.4: Means, standard deviation (SD), and the results of means contrasts for thermal
conductivity, thermal diffusivity and specific heat of PBS carbon black bionanocomposites…..64
Table 3.5: Means, standard deviation (SD), and the results of means contrasts for the resistance
due to the aggregates, Ra, the contact resistance from the gaps between adjacent aggregates, Rg,
the capacitance of the gaps, C, and the electrical conductivity of PBS carbon black
bionanocomposites……………………………………………………………...………………..67
Table 4.1: Surface area, pore radius and pore volume of carbon black, lignin treated to various
x
carbonization temperatures, and 900 °C carbonized lignin treated to different ball milling
times…………………………………………………………………………………………..….89
Table 4.2: Means, standard deviation (SD), and the results of means contrasts for thermal
conductivity, thermal diffusivity and specific heat of carbon black and 900 °C carbonized 24
hour ball milled lignin……………………………………………………………………………95
Table 5.1: Means, standard deviation (SD), and the results of means contrasts for tensile stress
(TS), tensile modulus (TM), % elongation at break, flexural stress (FS), flexural modulus (FM),
and impact strength of bionanocomposites with different carbonized lignin content……….....109
Table 5.2: Means, standard deviation (SD), and the results of means contrasts for the heat
deflection temperature and the storage modulus at 25 °C of PBS carbonized lignin
bionanocomposite………………………………………………………………………………110
Table 5.3: The glass transition temperature, Tg, melting temperature, Tm, enthalpy of fusion,
∆Hm, crystallization temperature, Tc, enthalpy of solidification, ∆Hc, and crystallinity, χ, based
on the DSC curves and heat deflection temperature (HDT) of PBS carbonized lignin
bionanocomposites……………………………………………………………………………..115
Table 5.4: Means, standard deviation (SD), and the results of means contrasts for thermal
conductivity, thermal diffusivity and specific heat of PBS carbonized lignin
bionanocomposites………………………………………………………………….…………..117
Table 5.5: Means, standard deviation (SD), and the results of means contrasts for the resistance
due to the aggregates, Ra, the contact resistance from the gaps between adjacent aggregates, Rg,
the capacitance of the gaps, C, and the electrical conductivity of PBS carbonized lignin
bionanocomposites……………………………………………………………………………...119
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List of Figures
Figure 1.1: Composite constituents classification………………………………...………………5
Figure 1.2: Biopolymers classification……………………………………………………….…...8
Figure 1.3: Carbon cycle of petroleum based and biobased polymers…………...……………….9
Figure 1.4: Classification of biocomposites………………………………………………….….11
Figure 1.5: Bionanocomposite publications by year………………………………………….…14
Figure 1.6: The global production capacity of bioplastics………………………………….…...16
Figure 2.1: 2-Stage poly(butylene succinate) production process…………….…………………22
Figure 2.2: The three monomers of lignin……………………………………………………….31
Figure 2.3: Portrayal of a lignin polymer from poplar hardwood…………………………….....32
Figure 3.1: Circuit model for the electrical resistance of carbon black within a polymer matrix in
the percolation region………………………………………………………………….…….…..46
Figure 3.2: Tensile stress at yield and the tensile moduli of the PBS carbon black
bionanocomposites (mean±SD)……………………………………………………………….…51
Figure 3.3: The % elongation at break and impact strength of the PBS carbon black
bionanocomposites (mean±SD)………………….……………………………………………....52
Figure 3.4: Change in tensile yield strength ratio of bionanocomposites as CB wt% is
increased…………………………………………………………………………………………53
Figure 3.5: Flexural stress and flexural moduli of the PBS carbon black bionanocomposites
(mean±SD)…………………………………………………………………….…………………55
Figure 3.6: Tan δ (lines) and storage moduli (symbols) at 25 °C of the PBS carbon black
bionanocomposites (mean±SD)………………………………………………………………….60
Figure 3.7: Electrical conductivity of the PBS carbon black bionanocomposites (mean±SD)….68
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Figure 3.8: SEM images of the cryo-fractured surfaces for the PBS carbon black
bionanocomposites samples of 1, 3, and 5 wt% at 5000x magnification demonstrating the filler
dispersion, with arrows pointing to carbon black particles within the polymer matrix…..……...70
Figure 3.9: Optical microscopy images of the PBS carbon black bionanocomposites at 20x
magnification demonstrating the carbon black dispersion within the polymer matrix at loadings
of 1, 3, and 5 wt%.……………………………………………………………………………….72
Figure 4.1: Deconvoluted Raman spectra of carbonized lignin at 600, 750, and 900 °C and
carbon black normalized to the same height.…………………………………………………....86
Figure 4.2: FTIR spectra of A) lignin carbonized at different temperatures and B) the 900 °C
carbonized lignin sample after various ball milling times relative to carbon black…….....…….91
Figure 4.3: Distribution of particle diameters of precarbonized lignin and the 900 °C carbonized
lignin after ball milling at different time intervals……..……………………...…………………93
Figure 4.4: Electrical conductivity versus compression pressure of carbon black and 900 °C
carbonized 24 h ball milled lignin…………………….…………………………………………82
Figure 4.5: SEM images and elemental composition of powder samples of lignin, carbonized
ball milled lignin, and carbon black measured by EDS………………………………………...100
Figure 5.1: Tensile stress at yield and the tensile moduli of the PBS carbonized lignin
bionanocomposites (mean±SD)..............................................................................................….105
Figure 5.2: The % elongation at break and impact strength of the PBS carbonized lignin
bionanocomposites (mean±SD)………………………………………………………………...106
Figure 5.3: Flexural stress and flexural moduli of the PBS carbonized lignin bionanocomposites
(mean±SD)………………………………………………………………………….…………..108
Figure 5.4: Tan δ (lines) and storage moduli (symbols) at 25 °C of the PBS carbonized lignin
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bionanocomposites (mean±SD)………………………………………………………………...113
Figure 5.5: Electrical conductivity of the PBS carbonized lignin bionanocomposites
(mean±SD)……………………………………………………………………………………...120
Figure 5.6: SEM images of the cryo-fractured surfaces for the PBS carbonized lignin
bionanocomposites samples of 1, 3, and 5 wt% at 5000x magnification demonstrating the filler
dispersion, with arrows pointing to carbon black particles within the polymer matrix.………..122
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List of Acronyms and Abbreviations
PBS – poly(butylene succinate)
CB – carbon black
CL – carbonized lignin
Wt% - weight percentage
SD – standard deviation
DMA – dynamic mechanical analysis
DSC – differential scanning calorimetry
HDT – heat deflection temperature
SEM – scanning electron microscopy
SEM-EDS – scanning electron microscopy with energy dispersive X-ray spectroscopy
BET – Brunauer-Emmet-Teller analysis method for surface area measurements of solid materials
FTIR – Fourier transform infrared spectroscopy
ATR-IR – attenuated total reflectance infrared spectroscopy
χ – degree of crystallinity (%)
Tg – glass transition temperature
Tc – crystallization temperature
Tm – melting temperature
ΔHm – enthalpy of fusion
ΔHc – enthalpy of solidification
ID – peak intensity of the Raman spectrum D peak (~1355cm-1
)
IG – peak intensity of the Raman spectrum G peak (~1575cm-1
)
La – crystallite size of graphite
1
Chapter 1
Introduction
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1.1 Overview of Plastics
Plastics are one of the most widely used materials in the world with applications in broad
areas such as packaging, construction, automotive parts and electronics. They are polymeric
materials consisting of many long polymer chains made by either addition or condensation
polymerization of hydrocarbon or hydrocarbon like monomers (Flory, 1953). With most plastics
currently produced from petroleum sources they are non-renewable and large quantities are non-
compostable. Due to these properties plastics are not easily disposed of and end up causing
environmental harm.
A key method of lowering the impact of plastic waste is by recycling, but only a small
portion of plastics make it to this stage. One reason for this is the low recycling rate of plastics,
which is based on three factors including the percent of populations with programs, the percent
of population participation and the efficiency of participant (Cornell, 2007). Not only is the
return rate important in regards to plastic recycling, but the type of plastics capable of being
recycled are primarily the thermoplastics as they can be re-melted and reformed into new
materials while thermosets are unable to be reprocessed in this manner due to the large amount
of crosslinking these plastics contain. The other disposal methods for plastics consist of
incineration or landfill where both are seen as undesirable choices. Incineration creates increased
greenhouse gas emissions that elevate the carbon dioxide in our atmosphere, and landfill disposal
can lead to soil and water pollution.
Biobased and biodegradable plastics made from renewable feedstocks like crop residues have
now started to increase in production due to governmental policies and societal views on
environmental impacts like global warming and landfill expenses. These biobased plastics negate
the necessity for non-renewable fossil fuels, while biodegradable plastics including those that are
3
biobased can easily be compostable. The biodegradability of these biopolymers allow for
complete breakdown of the plastic into indistinguishable pieces through specific environmental
conditions. These plastics will need to be implemented over time to reduce the dependence on
non-renewable plastics in the long term. To make this happen the properties and costs of the
biobased materials must be improved to meet consumer needs.
1.2 Composites
A composite is defined as a material made of a combination of two distinct materials that
vary in composition or form at the macroscale. The main constituent that forms the continuous
phase of the composite is known as the matrix, while the second component within the
composite is termed the reinforcement or filler. The secondary component is called a
reinforcement only when the mechanical properties of the composite improves upon the initial
matrix material. The secondary element of the composite is termed a filler when other properties
such as thermal and electrical conductivity are modified or when there is a cost reduction by its
addition. It is possible to have a material that acts as both a reinforcement and a filler.
There are three categories by which composites may be sorted. The classifications
include fiber-reinforced composites, particle-reinforced composites and laminar composites
(sandwich structures) as seen in Figure 1.1 (Callistor, 2007). One of the most common matrix
materials used in the production of composites are polymers. By the addition of reinforcements
into the polymer matrix, the poor mechanical properties in comparison to those of metals and
ceramics can be enhanced allowing for use in applications where their strength and stiffness
would not be adequate otherwise. The manipulation of plastics is also much easier as handling,
processing and manufacturing is fairly simple. The composites made from plastics are also very
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easily shaped which enables them to be formed to meet specific applications while maintaining a
light weight advantage over other commercial products. Polymeric composites have been around
for the past century with composites reaching commodity status by the 1940s (Mohanty et al.,
2005). These plastic composites have been and still are made with non-renewable reinforcing
fillers such as glass fibers, carbonaceous particles and synthetic fibers that prevent or lower the
recyclability of the composite and alter the end life of the product. With both the matrix and filler
traditionally non-renewable in origin the biodegradability and environmental concerns still
remain causing manufacturers to look for partial or fully biobased alternatives.
5
Figure 1.1: Composite constituents classification (partly redrawn after reference Callistor, 2007)
6
1.3 Biopolymers
Biopolymers, also known as bioplastics are now entering the marketplace for consumer
use due to the shift in societal views to a more sustainable lifestyle. These materials are able to
reduce the environmental impact by lowering the carbon footprint. They may be separated into
three categories to distinguish between the raw source material and the biodegradability. The
types include petroleum-based biodegradable plastics, renewable resource-based bioplastics and
plastics from mixed sources (petroleum and renewable) (Mohanty et al., 2005). The various
classifications of biopolymers are depicted in Figure 1.2 where certain polymers may fall under
multiple categories.
Not all biopolymers are both biodegradable and biobased as some of these bioplastics are
either one or the other. In the case of the petroleum-based biodegradable polymers, the materials
are synthesized rather then found in nature through a chemical reaction known as polymerization
(Billmeyer Jr, 1962). The monomers used in the production of synthetic polymers are obtained
from fossil fuels. These polymers make up a large majority of the commercial marketplace
because of the range of plastic types and their properties. For renewable resource-based polymers
the monomers are derived from plant or other biological sources. With advancements in
technology the production of these polymers is starting to increase as the biobased monomers are
becoming readily available throughout the world (Mohanty et al., 2000). Polymers from mixed
sources can be produced by the combination of petroleum based and biobased polymers mixed
together or by using monomers from both petrol and bio resources to produce a polymer.
There are several polymers that do have the advantage of being both biobased and
biodegradable (PLA, PBS). These materials reduce the impact on the environment by preventing
waste as compositing becomes a viable option that allows for complete breakdown of the plastic
7
into indistinguishable pieces by microorganisms. Also entering into the marketplace are
polymers that were previously made only through synthetic means, but now have been produced
from biobased resources such as bio-polypropylene and bio-polyethylene that diminish the
reliance on petroleum-based monomers. By using these biobased polymers the ‘carbon cycle’
can be replenished on a similar time scale as seen in Figure 1.3 (Kijchavengkul & Auras, 2008).
The monomers for these biopolymers may be derived from a wide range of feedstocks, enabling
them to be prepared in several ways unlike petroleum-based polymers. These bioplastics can
help alleviate the fossil fuel dependence for the 16.7 million tons of landfill waste and 21 million
tons of plastic consumed annually (Hottle et al., 2013).
8
Figure 1.2: Biopolymers classification (redrawn after reference Mohanty et al., 2005)
9
Figure 1.3: Carbon cycle of petroleum based and biobased polymers (redrawn after reference
Kijchavengkul & Auras, 2008, with permission)
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1.4 Biocomposites
A biocomposite is a composite that is made from either the combination of both a
biopolymer and a synthetic reinforcement or a petroleum based non-biodegradable plastic and a
natural filler. It is also possible to have a biocomposite made entirely from biobased resources by
using a biopolymer and a natural filler. Therefore, for a composite material to be considered a
biocomposite it must be fully or partially biobased in origin with one or more of the constituents
being from a natural resource. When both the matrix and filler are from a renewable resource
they are referred to as ‘all green composites’. Though these biocomposites have a portion of
biobased material the composite itself may not be biodegradable depending on the biopolymer
used or the reinforcing filler added. Of the various types of biocomposites those that are
recyclable, compostable, eco-friendly and commercially adequate are considered to be a
sustainable bio-based product (Mohanty et al., 2002). A complete description of the types of
biocomposites is illustrated in Figure 1.4.
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Figure 1.4: Classification of biocomposites (modified after reference Mohanty et al., 2002)
Biofiber-Renewable Resource based Polymer (biodegradable)
(Kenaf/PLA)
Biofiber-Petroleum based Polymer (biodegradable and
non-biodegradable)
(Miscanthus/PBAT, Miscanthus/PP)
Biofiber-Renewable Resource based Polymer (non-
biodegradable)
(Wheat Straw/Bio-PP)
Synthetic fiber-Renewable Resource based Polymer (non-
biodegradable)
(Glass fiber/PLA)
BIOCOMPOSITES
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1.5 Bionanocomposites
Bionanocomposites are similar to biocomposites such that the reinforcement or filler is a
material that is either nano sized or is nanostructured. The combination of a biopolymer and an
inorganic compound that has at least one dimension at the nanoscale level is considered to be a
bionanocomposite material, where it may be referred to as an organic-inorganic biohybrid
(Darder et al., 2007). It is possible for the filler to be produced from a natural resource, allowing
it to be considered a ‘green bionanocomposite’. Bionanocomposites tend to contain relatively
low additions of nanoparticle fillers at less than 10% wt in most cases (Lagaron & Lopez-Rubio,
2011). These bionanocomposites usually have improved structural and functional properties that
can be applied to different applications. Bionanocomposites have only recently been studied
because of techniques that allow scientists to analyze the surface structures of materials with
atomic resolution such as scanning tunneling microscopy and atomic force microscopy. The ease
of characterization using such techniques has promoted an increased study in the field of
bionanocomposites. These nanofillers are now becoming more recognized as evidenced by a rise
in publications on bionanocomposites (Figure 1.5).
It is expected that these new polymeric nanocomposites will enter the sectors of inkjet
markets, automotive body molds (engine covers), batteries, computer chips and catalytic
converters within the next 5 years (Hussain et al., 2006). Within 10 years lighting improvements,
biosensors and memory devices are possible followed by aerospace, bionanotechnology and
further automotive advancements in a 15 year timespan (Hussain et al., 2006).
Bionanocomposites can also have specialized areas of applications that are distinct from
other biocomposites because of the unique characteristics of the nanoparticles used. These
nanofillers can vary in properties from being magnetic, electrically conducting, thermally stable,
13
fire retardant and mechanically enhancing (Alexandre & Dubois, 2000). The applications in
which bionanocomposites are currently being integrated are structural, food packaging,
biomedical, electrochemical, optical and sensory devices. Recently most of the bionanohybrid
materials being produced are made by assembling a biopolymer and a silicate clay that remain
biocompatible and that shows little to no toxic effects and chemical inertness (Ruiz-Hitzky et al.,
2009). There is still a lack of research into the development of new bionanocomposites that are
made from alternative nanofillers and biopolymers. This hesitation is occurring in part since
there is little knowledge of the possible side effects that the nanoparticles cause to human health
and the environment (Sozer & Kokini, 2009). Another reason for the slow start in mainstreaming
bionanocomposites is the compatibility problem between the organic and inorganic moieties and
the complete dispersion of the filler throughout the biopolymer matrix. Therefore, advancements
in the synthesis and methodologies used for the formation of bionanocomposites are necessary so
new bionanocomposites may be developed.
14
Figure 1.5: Bionanocomposite publications by year (redrawn after reference Thomson Reuters,
2014 ISI web of knowledge [v5.12] – web of science, citation report for the topic
‘bionanocomposite’ as of March 2014.
0
5
10
15
20
25
30
35
40
45N
um
ber
of
Pu
bli
cati
on
s
Year of Publication
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1.6 Importance of Biopolymer Research
Growth in the bioplastic industry has started to emerge as evidenced by the large amount
of plastics being produced annually worldwide. However, concerns of cost, raw material
availability, competitiveness towards synthetic polymers and end-of-life uses are still challenges
that require further research. New uses for bioplastics will encourage further development and
improvements in the sustainability of these environmentally-friendly materials. The
incorporation of certain reinforcements and fillers into biopolymers will improve their physical
and mechanical properties, enabling these biocomposites to enter new applications that were
previously occupied by the fossil fuel-based plastics.
These biobased polymers are increasingly being used in a broad array of applications
from aerospace, automotive, electronics, solar cells, packaging and household goods. As the
demand continues to rise in the industrial marketplace for bioplastics the production is also
expected to increase substantially. European Bioplastics estimates indicate that 6.2 million tons
of bioplastics will be produced globally by 2017 (Figure 1.6) (European Bioplastics, 2013). The
primary reasons for the increased focus on using biopolymers and biocomposites is a reduction
of CO2 emissions, reducing the use of petroleum-based plastics, reduced waste problems and
establishing a publicly-acceptable green alternative. Biocomposites and bionanocomposites will
significantly improve the next generation of bioplastics.
16
Figure 1.6: The global production capacity of bioplastics (redrawn after reference European
Bioplastics, 2013 bulletin issue 6/2013, Bioplastics Market Grows Above Average Between
2012 and 2017)
17
Chapter 2
Literature Review
18
2.1 Aliphatic polyesters
Aliphatic polyesters are semi-crystalline polymers that are formed primarily through the
polycondensation reaction of a glycol, also known as a diol such as ethylene glycol, 1,3-
propanediol and 1,4-butanediol, and an aliphatic dicarboxylic acid like adipic, sebacic or
succinic acid (Fujimaki, 1998). A secondary method of synthesis of aliphatic polyesters is done
through ring-opening polymerization of lactones, cyclic diesters and cyclic ketene acetals
(Albertsson & Varma, 2002). These aliphatic polyesters may be synthesized from natural or
synthetic precursors, such that the monomers differ in their structure from linear to branched
forms. Aliphatic polyesters are the most studied biodegradable polymers because they can be
degraded by the enzymes in microorganisms (Ikada & Tsuji, 2000). The biodegradable property
of these polymers is a significant advantage over other plastics, which would lead the way for
more eco conscience materials. An overview of the aliphatic polyesters is given in Table 2.1.
19
Table 2.1: Various Types of Aliphatic Polyesters with R being a side group, x being the number of monomer units, and n and m being
the number of repetitive CH2 groups within the monomer unit. (modified after reference Mochizuki & Hirami, 1997)
Polymer Chemical Structure Biodegradability Examples
Poly(α-hydroxy acid) -(O-CHR-CO)x- Chemical hydrolysis R=H Poly(glycolide) (PGA)
R=CH3 Poly(L-lactide) (PLLA)
Poly(3-hydroxyalkanoate) -(O-CHR-CH2-CO)x- Enzymatic hydrolysis
R=CH3 Poly(3-hydroxybutyrate) (PHB)
R=CH3,C2H5 Poly(3-hydroxybutyrate-co-
3-hydroxyvalerate) (PHBV)
Poly(ω-hydroxyalkanoate) -(O-(CH2)n-CO)x- Enzymatic hydrolysis n=3 Poly(β-propiolactone) (PPL)
n=5 Poly(ε-caprolactone) (PCL)
Poly(alkylene dicarboxylate) -(O-(CH2)m-O-CO-(CH2)n-CO)x- Enzymatic hydrolysis
m=2, n=2 Poly(ethylene succinate) (PES)
m=4, n=2 Poly(butylene succinate) (PBS)
m=4, n=2,4 Poly(butylene succinate-co-
butylene adipate) (PBSA)
20
2.1.1 Poly (butylene succinate)
Poly(butylene succinate) is a member of the polymer group known as aliphatic
polyesters. From Table 2.1 it can be seen that PBS is a poly(alkylene dicarboxylate) polymer
with m=2 and n=4. Poly(butylene succinate) is a thermoplastic polymer that is white, a density of
1.25 g m-3
, a glass transition temperature between -45 °C and -10 °C, a melting temperature in
the range of 90 °C to 120 °C and a processing window of 160-200 °C under normal conditions
(Fujimaki, 1998). The mechanical properties of poly(butylene succinate) are similar to
polyolefins such that the a tensile strength falls between polyethylene and polypropylene and the
stiffness is between a high and low density polyethylene (Fujimaki, 1998). The molecular weight
of poly(butylene succinate) is in the range of 3x104 to 20x10
4 g mol
-1 with a large polydispersity
from 2.0 to 6.3 (Chen, 2010). The one advantage of this polymer over the polyolefins is its good
biodegradability compared to other commodity plastics, making poly(butylene succinate) an
attractive replacement for nonrenewable-based resins. This polymer also has the highest melting
temperature of all polysuccinate derivatives, inferring a relatively high heat deflection
temperature (Yashiro et al., 2009). Another important characteristic of this biopolymer is that the
rates of hydrolysis are higher for polysuccinates than that of polyesters formed from higher
aliphatic dicarboxylic acids (Lahcini et al., 2010).
Poly(butylene succinate) is currently made by the condensation polymerization of
succinic acid and 1,4-butanediol. The succinic acid is derived from the petrochemical butadiene
via maleic anhydride which can then act as a precursor in the formation of 1,4-butanediol via γ-
butyrolactone (Bechthold et al., 2008). An example of the esterification and polycondensation
reaction to synthesize poly(butylene succinate) is shown in Figure 2.1. The synthesis of
poly(butylene succinate) requires a catalyst and elevated temperatures that are run for a specific
21
period. Presently it is possible with the biotechnology available to produce both succinic acid
and 1,4-butanediol from renewable resources. These chemicals are called bio-succinic acid and
bio-1,4-butanediol and allow for a completely biobased production of poly(butylene succinate).
Companies such as BioAmber, Myriant Technologies, and collaborations between Royal
DSM N.V. and Roquette Frères have started to produce biobased succinic acid at a commercial
scale at lower cost than fossil-based alternatives (Beauprez et al., 2010; Lyko et al., 2009). Most
of these facilities use microbial fermentation of glucose combined with CO2 fixation during the
process. Both Genomatica and BioAmber produce biobased 1,4-butanediol commercially using
Dupont’s patented hydrogenation technology (Marr, 2012). Companies that produce
poly(butylene succinate) are found worldwide from Asia, Europe to North America, and they
produce 1-5 kilotons per year (Babu et al., 2013). This biopolymer is then easily processed under
standard melt processing methods such as extrusion, injection molding and blow molding
(Smith, 2005). The applications include packaging materials (films and foams), dishware, fibers,
agricultural films, industrial materials and disposable medical materials. The only shortcomings
of poly(butylene succinate) are its low impact strength, poor gas barrier and high liquid
permeability. Researchers are now searching for reinforcements and fillers like nanoparticles that
can alleviate these drawbacks so a greater number of applications are possible.
22
Figure 2.1: 2-stage poly(butylene succinate) production process (redrawn after reference
Bioplastics Magazine, 2012)
23
2.2 Carbon-based Nanomaterials
Carbon-based nanoparticles are becoming one of the more dominant fields of study in
nanoscience. These carbonaceous materials all consist of elemental carbon with a wide range of
structures known as allotropes (Hirsch, 2010). These allotropes result from the multiple bonding
possible for carbon, including sp, sp2 and sp
3 orbital hybridization (Meyyappan, 2004). The
resulting nanoparticles include graphene, graphite, single and multi-walled carbon nanotubes,
fullerenes, and carbon black (Dresselhaus et al., 1996). The sp hybridized bond occurs when
carbon is bonded to two other atoms, resulting in a linear molecule. Whereas, in the case of
graphite, carbon atoms are in their sp2 hybridization state having in-plane bonding between the
three nearest carbon atoms creating a triagonal planar shape, with the remaining electrons
forming an interplanar π-bond (Dresselhaus et al., 1996). The sp3 hybrid of carbon is an example
of a diamond structure, with the bonding occurring between the four nearest carbon atoms to
create a tetrahedral conformation (Dresselhaus et al., 1996). However, carbon in its sp2
hybridized form can arrange into multiple shapes starting from its base structure of graphene
(Popov, 2004). Graphene is the main building block for the formation of other graphitic
nanoparticles. It consists of a single two-dimensional monolayer of carbon atoms in a
honeycomb lattice that when folded can form spherical-shaped fullerenes, if rolled it can form
tubular shaped nanotubes, and upon stacking of the flat sheets it can produce graphite (Geim &
Novoselov, 2007). Carbon black is also composed of small concentric graphene layers on the
surface of the carbon nanoparticle. These materials have varying degrees of size, shape, surface
area, sorption properties, molecular interactions, electronic, optical and thermal properties that
enable them to be used in many applications (Mauter & Elimelech, 2008). Commercial
applications of these graphitic nanostructures consist of material-processing (furnaces and
24
crucibles), electrical devices (electric brushes), membrane switches, resistors, electrochemical
uses (electrodes in primary and secondary cells), fuel cell separators, nuclear fission reactors,
mechanical bearings and seals and dispersions (inks) (Endo et al., 2008).
These nanoparticles are primarily synthesized from nonrenewable resources such as
gaseous hydrocarbons and petroleum-based oils. Synthesis of the carbon nanotubes, fullerenes
and graphene structures occurs mainly through chemical vapour deposition, arc discharge or
laser ablation (Khare & Bose, 2005). All of these methods require expensive equipment with
very sophisticated controls, intricate setups, high energy inputs, low yields and fossil fuel
feedstocks. Therefore, other simpler alternatives are being considered by using renewable
resource-based precursors.
Biobased carbon nanomaterials are becoming more appealing for new types of
nanoparticles due to a reduction in the carbon footprint and the growing amount of waste
biomass. The structural and compositional differences between biomass materials such as
cellulose, hemicellulose and lignin allow for the formation of specific nanoparticles. Carbon
nanospheres from cellulose are one example of this new innovation (Herring et al., 2003).
Carbon black has been synthesized from materials such as bamboo, empty fruit bunches
and coconut shells (Abdul Khalil et al., 2010). Most research has revolved around the
development of lignin based carbon fibers (Kadla et al., 2002). The preparation of these biobased
carbon nanomaterials is typically done using carbonization or pyrolysis methods. Carbonization
takes place when carbon containing material such as woody biomass is converted to carbon rich
materials such as charcoal by partial burning. The carbonization can be done under low oxygen
levels or inert atmospheres (pyrolysis) (Xie et al., 2009). Carbonization removes the oxygenated
functional groups, sulfur groups and nitrogen groups within the bulk material followed by the
25
release of aliphatic CH groups (Oberlin, 1984). Cokes are produced at either low temperature
carbonization below 750 °C or high temperature carbonization above 750 °C (Parr, 1929).
Pyrolysis is a specific type of carbonization that consists of heating organic matter in the absence
of oxygen by using a nitrogen atmosphere to convert biomass into solids (char), liquids (tar,
water) and gases (carbon dioxide, carbon monoxide) (Mohan et al., 2006). Pyrolysis can be
categorized into three temperature treatments: (1) high temperature above 500 °C where
decarboxylation, dehydration and decarboxylation occur; (2) mid-range temperatures between
300 and 500 °C that allow de-polymerization; and (3) low temperature heating below 300 °C that
causes reduction in molecular weight, water, CO2 and char formation (Shafizadeh, 1982). These
methods show promise in lowering the energy requirements for the production of various
nanoparticles.
2.2.1 Carbon Black
There is a vast supply of carbon blacks commercially available for use as fillers to
improve mechanical, electrical and optical properties of materials. Carbon blacks vary widely in
their physical and chemical properties. Carbon black particles consist of nearly spherical primary
particles that usually fall within a size distribution of 10 to 300 nm that fuse together to obtain a
surface area ranging from 6 to 1500 meters squared per gram (Katz & Mileski, 1987). The
degree of agglomeration of the aggregates is quite different between the carbon blacks, which
also cause diverse differences in the chain-like structure formed and the porosity of the material
(Katz & Mileski, 1987). Carbon blacks differ chemically in the oxygenated surface structures
and the residual elements that remain from its preparation (Schaeffer et al., 1953). All carbon
blacks consist of approximately 95 to 99 percent elemental carbon arranged in a combination of
26
amorphous and graphitic bonding (Schaeffer et al., 1953).
There are several types of industrial carbon blacks that are manufactured at a large scale.
The top five carbon blacks include furnace black, lampblack, channel black, thermal black and
acetylene black (Huang, 2002). Furnace blacks are obtained from the partial combustion of oil
droplets and a similar but older method used for lampblacks, channel blacks are made by partial
combustion of natural gas, and thermal black is formed through thermal decomposition of natural
gas and acetylene black manufactured using exothermic decomposition of acetylene
(Dresselhaus et al., 1996). Table 2.2 lists the carbon blacks and the feedstocks used to produce
the various carbon nanoparticles. The feedstocks for the production of all carbon blacks presently
consist of a fossil fuel resource.
Most carbon black is used as an additive to rubber materials used primarily for tires. Tires
and other rubbers use carbon black as a reinforcement since it increases the strength of the
material, including abrasion resistance and tear resistance (Rigbi, 1980). Table 2.3 shows the
present use of carbon blacks based on estimated sales; plastics make up the greatest portion of
sales for non-rubber materials. Carbon black is used in plastics since it is a conductive filler that
allows the production of conductive polymer composites. The addition of carbon black into a
plastic increases the electrical conductivity of an otherwise insulating polymer (Zois et al., 2001).
This filler may also be used as a pigment in certain polymers to create a black coloured material.
27
Table 2.2: Manufacturing Processes & Feedstocks of Carbon Black (redrawn after reference
Donnet & Bansal, 1993)
Chemical process Production process Feedstock
Thermal decomposition
Continuous Acetylene black
process Acetylene
Discontinuous Thermal black
process
Natural gas
(oils)
Thermal-oxidative decomposition
Open system (diffusion flames)
Channel black process Natural gas
Degussa gas black
process
Coal tar
distillates
Closed system (turbulent flow)
Lampblack process
Aromatic oils
based on coal
tar or crude oil
Furnace black process
Aromatic oils
based on coal
tar or crude oil,
natural gas
28
Table 2.3: Estimated Carbon Black Sales by Application Field (redrawn after reference Donnet
& Bansal, 1993)
Rubber and Non-
Rubber %
Non-
Rubber %
Tires 65
Mechanical Rubber
Goods 25
Non-Rubber 10
Plastics 36
Printing
Inks 30
Others 21
Coatings 9
Paper 4
29
2.3 Lignin
Lignin makes up one of the top three most abundant renewable resources in the world
together with cellulose and natural oils (Pye, 2008). It is the second greatest naturally-occurring
polymer after cellulose and it is widely available and relatively cheap (Kadla et al., 2002). It is
found in the secondary cell-walls of vascular plants and it helps to bind and stiffen the cellulose.
Lignin makes up approximately 5 to 30% of the biomass from softwood, hardwood to various
annual crops (grasses) (McKendry, 2002). Woody plant species have a greater content of lignin
than grasses making them more suitable for lignin extraction and isolation.
Different categories of lignins are found based on how they are isolated. Isolation of
lignin is done by the solubilisation or dissolution of the various lignocellulosic components
through chemical or mechanical means. Lignin is separated into the distinct categories kraft
lignin, sulfite lignin and sulfur-free lignin. Kraft lignin is derived from kraft pulping in an
alkaline medium that contains a small quantity of thiol groups that cause the lignin to become
insoluble in water (Lora & Glasser, 2002). Lignin sulfonates differ in that they are water soluble
and they are generated from a sulfite process where sulfonic acid is incorporated into the lignin
backbone (Lora & Glasser, 2002). Sulfur-free lignins can be produced by three different
methods, including biomass conversion technologies, solvent pulping (organosolv) and soda
pulping with few contaminants formed and a tendency to be hydrophobic (Lora & Glasser,
2002).
Lignin is a three-dimensional amorphous polymer. It is synthesized in the plant by the
polymerization of three monolignol (phenylpropane alcohol) monomer units known as coniferyl,
sinapyl and p-coumaryl alcohol that differ in the location of the methoxy group(s) on the
phenylpropanoid ring (Chakar & Ragauskas, 2004). The primary monomeric units are shown in
30
Figure 2.2. Lignins from these three monomeric units are known as guaiacyl (G), syringyl (S)
and p-hydroxyphenyl (H) lignin, respectively. Hardwood lignin is composed of different
percentages of G and S lignin, while softwood lignin contains over 95% G lignin (Pandey, 1999).
Grass lignin as found in plants such as wheat and switchgrass contains all three lignins in various
proportions (Higuchi, 1985). Due to randomness of the polymerization of the primary structural
units of lignin, a large number of linkages are present. The most common linkage is the aryl
glycerol β-aryl ether or β-O-4 for short (Simon & Eriksson, 1996). Figure 2.3 presents a
hardwood lignin polymer with the various linkages and monomer units.
31
Figure 2.2: The three monomers of lignin (redrawn after reference Chakar & Ragauskas, 2004)
HO
OH
R2 R1
Coniferyl alcohol/guaiacyl: R1=OMe, R2=H
Sinapyl alcohol/syringyl: R1=R2=OMe
p-Coumaryl alcohol: R1=R2=H
32
Figure 2.3: Portrayal of a lignin polymer from poplar hardwood (redrawn after reference Vanholme et al., 2010)
33
2.4 Lignin-based Carbon
A large quantity of industrial lignin is produced as a co-product in the pulp and
paper industries and in bioethanol production plants. The use for lignin is limited at
present and the majority of lignin is used as a waste material for animal feed or as a fuel
source for energy recovery in industrial facilities. Lignin use is also prevalent as a
polymer in composite applications and in the adhesive sectors (Stewart, 2008). Yet, there
has been a growing interest for carbonaceous materials prepared from lignin owing to its
high content of elemental carbon in the range of 59 to 61% (Kadla et al., 2002). Interest
in higher value-added products from thermo-chemical conversion of lignin has increased
within the past decade. Lignin has also been used as a starting material for the preparation
of carbon fibers and activated carbon (Sudo & Shimizu, 1992; Hayashi et al., 2000).
Recently, lignin has shown promise in the production of carbon nanoparticles such as
carbon nanofibers and carbon nanopowders. The ability to synthesize carbon
nanoparticles from a high volume renewable resource like lignin reduces the need for
petroleum, which will benefit the environment.
2.4.1 Lignin-based Activated Carbon
Activated carbons are very porous carbon materials that have a characteristic
structure with many pores extending from micro to macro sizes. The surface area of these
materials can vary from 500 to 2000 m2 g
-1 because of the large internal pore structure
present (Suhas et al., 2007). The applications for activated carbons with micropores
having diameters less than 2 nm include a large range of absorbents for purification of
organic and inorganic liquids and gases, while meso (2-50 nm) and macroporous ( > 50
34
nm) activated carbon have applications in capacitors, battery electrodes, catalyst supports
and biomedical engineering (Yenisoy-Karakas et al., 2004). Activated carbons from
lignin are prepared through one of two methods, including either chemical or physical
activation. Physical activation, which is also known as thermal activation, occurs by
taking a carbonaceous precursor and using carbon dioxide or steam to activate the char in
a temperature range of 825 to 975 °C (Rodriguez-Reinoso & Molina-Sabio, 1992).
Chemical activation involves a single step such that biomass is chemically treated with
dehydrating agents (KOH, ZnCl2) that modify the thermal degradation process, allowing
for pyrolysis at lower temperatures than physical activation (Williams & Reed, 2006).
Both procedures can create activated carbons with well-defined porous structures. Some
activated carbons are then further manipulated through ball milling to generate
carbonaceous powders (Welham & Williams, 1998). The activated carbon and ball milled
particle counterparts can then be used in the various applications discussed above.
35
2.5 Hypotheses
1. A bionanocomposite can be produced from the addition of low loadings of a
carbonaceous nanofiller with poly(butylene succinate) that demonstrates enhanced
mechanical, thermal and electrical conductivity without additional compatibilizers.
2. The use of a biobased nano carbon material from carbonized lignin allows for a
bionanocomposite material that performs comparably to nonrenewable resource based
carbon black with potential applications in packaging or electronic uses.
36
2.6 Objectives
The primary goal of this study was to characterize and contrast the physical and
mechanical properties associated with a bionanocomposite produced from the biopolymer
poly(butylene succinate) and a carbon-based nanofiller made from either a petroleum or
biobased resource.
Specific objectives included:
Determine the mechanical, thermal and conductive properties of the
bionanocomposite at different carbon nanofiller loadings using commercial nano
carbon black.
Prepare a carbon nanofiller from carbonized lignin via optimization of pyrolysis
temperature and ball milling time through repetitive trials and characterization.
Test bionanocomposite properties using carbonized lignin nanofiller as the
nanoreinforcement agent and compare results to commercial nano carbon black.
37
The following chapter is under minor revisions in the journal of Macromolecular
Materials and Engineering under the publisher Wiley. Snowdon, M., Mohanty, A., Misra,
M. ‘Melt Processing and Characterization of Bionanocomposites Made from
Poly(butylene succinate) Bioplastic and Carbon Black.’
Chapter 3
Melt Processing and Characterization of Bionanocomposites Made from
Poly(butylene succinate) Bioplastic and Carbon Black
38
3.1 Abstract
Traditional melt processing is used in the preparation of poly(butylene succinate),
PBS, bionanocomposites containing the carbonaceous nanomaterial known as carbon
black. Filler loadings of 1, 3, and 5 wt% carbon black were added to the polymer in
order to improve the mechanical, thermal and electrical properties. For the
bionanocomposite with the highest content of nanofiller tested, the material showed an
overall enhancement in properties. The mechanical performance of the material improved
in impact strength (131%), maximum flexural stress (17%), tensile stress at yield (5%)
and storage modulus (19%). The intrinsic properties of the bionanocomposite increase
with the thermal conductivity and electrical conductivity having a 50% and 102%
improvement, respectively. Scanning electron microscopy (SEM) and optical microscope
images along with the electrical impedance measurements confirm a good dispersion of
carbon black throughout the polymer matrix. Differential scanning calorimetry (DSC)
and dynamic mechanical analysis (DMA) results show that the carbon black particles did
not affect the crystallinity and melting behavior of the composites.
3.2 Introduction
Biopolymers have become more prevalent in the market place in recent years as a
result of the continual need for more environmentally friendly plastic alternatives
(Chivrac et al., 2008). In particular, plastics that are both compostable and made from
bio-based feedstocks are being targeted as the best solution to plastic waste problems.
The only downside is that most of these plastics lack some of the relevant properties
found in conventional petroleum based plastics that are necessary for adequate use in
39
many applications (Bordes et al., 2009). Reinforcements or fillers have been added in
order to compensate for these issues and enhance the biopolymers’ properties through the
formation of new composites (Chivrac et al., 2006).
One area of study is the use of nanoparticles within the polymer matrix to form
bionanocomposites (Chivrac et al., 2010). These materials are usually low weight and
cost effective due to the minimal loading requirements for enhanced mechanical, thermal,
optical, electrical and barrier properties (Sengupta et al., 2007). The performance
characteristics of the bionanocomposites vary based on whether the nanomaterial has one,
two or all three dimensions at the nanoscale (Alexandre & Dubois, 2000). An example of
the latter type of nanoparticle is carbon black, which has a spherical shape and diameter
in the nanometer range but an aggregate size from tens to hundreds of nanometers (Wang
et al., 2003).
Carbon black has been used as a pigment, electrical dissipater and reinforcing
agent in polymers since the start of the 20th century (Donnet, 2003). The addition of
carbon black into thermoplastic matrices has allowed for the production of an important
group of electrically conducting composites that are relatively inexpensive and that find
uses in special applications (Chodak et al., 2001). These composites are adapted for
antistatic, electromagnetic interference shielding and other electronic purposes (Zhang &
Chen, 2004). Carbon black not only improves the electrical properties of the composites
but the strength and moduli can be improved, making carbon black a reinforcement filler
as well (Wang et al., 2008). This is evident by the fact that carbon black remains the most
prominently used reinforcing filler in the rubber industry at the present time (Arroyo et
al., 2003). Another feature of using carbon black is the ability for the composite to
40
dissipate heat more readily as a result of the heightened thermal conductance that the
nanofiller provides (Moisala et al., 2006). The key benefits of using carbon black as a
nanofiller include its low cost, very fine particle size, chemical inertness and the good
compatibility it has with organic materials (Rothon, 2002).
Researchers are now testing carbon black as filler in the new biopolymers
entering the market place to increase the electrical and thermal properties, while reducing
the amount of petro based material to give rise to green composites (Ning et al., 2008;
Wang et al., 2012; Zhijun et al., 2009). One specific biopolymer that is able to compete
with the conventional polypropylene and polyethylene is the aliphatic polyester known as
poly(butylene succinate), PBS (Liu et al., 2009). This polymer is biodegradable and is
now also being synthesized from biobased resources, providing further incentive to study
this material (Reddy et al., 2013). Yet there has not been extensive research on the use of
carbon black as filler in this material as most current research has focussed on the use of
carbon nanotubes and nano-clays with PBS (Ali & Mohan, 2010; Ojijo & Ray, 2012).
Therefore, this paper expands the bionanocomposite area by testing the mechanical,
thermal and electrical properties of carbon black filler in a PBS matrix to investigate the
potential of this bionanocomposite material for use in various applications.
3.3 Materials and Methods
3.3.1 Materials
The biopolymer used in this study was poly(butylene succinate) (PBS) injection
grade Bionolle 1020 from Showa Highpolymers Co., Ltd., Japan, density of 1.26 g cm-3
.
C-NERGY Super P Li carbon black (CB) from Timcal Ltd., Canada, density of 1.8-2.0 g
41
cm-3
, was used as the filler for the composite.
3.3.2 Processing of Composites
Prior to composite processing, neat PBS pellets were dried in a convection oven
for 4 hours at 80 °C and kept in a vacuum sealed container until use. Processing took
place in a 5g/7cc HAAKE Minilab II micro compounder conical twin screw extruder
(Thermo Scientific, Canada). The oven dried pellets were mixed with 10 wt% CB to
make a masterbatch using a processing temperature of 140 °C, a blending time of 2
minutes and a 100 rpm screw rotation (co-rotation configuration). The extrudate was then
left to solidify at room temperature before being pelletized (Strand Pelletizer, RE Scheer,
USA).
Processing of composites was carried out using the same machine and parameters
stated above to prepare 1, 3 and 5 wt% CB (0.67, 2.01, 3.37 vol% CB, respectively)
composite samples using the calculated quantity of masterbatch and neat PBS pellets.
The extrudate was collected and fabricated using a 5g/7cc HAAKE Minijet II piston
injection molding system (Thermo Scientific, Canada) with a 140 °C melt temperature
and a 30 °C mould temperature.
3.3.3 Characterization
Neat PBS polymer samples were made as control specimens and the effect of the
Super P Li carbon black content (1, 3 and 5 wt%) on the PBS was studied for property
variations. The characterization methods used are reported below. All mechanical
properties have results presented as the average of 5 specimens along with the
corresponding standard deviation (SD). Microsoft Excel 2010 was used to determine the
42
average and standard deviation following the built in commands AVERAGE and
STDEV, respectively.
3.3.3.1 Mechanical Testing
The Universal testing machine, Instron 3382, USA, was used to test the tensile
and flexural properties of the composites. The tensile measurements were done according
to ASTM standard D638-10 with a Type V specimen and a testing speed of 100 mm min-
1. The flexural measurements followed ASTM standard D790-10 using procedure B, a
support span of 36 mm and a crosshead speed of 9.6 mm min-1
. Bluehill software,
(Norwood, USA), was used to control the system and analyze the data.
A TMI Monitor Impact tester (model No. 43-02-01), USA, measured the notched
Izod impact strength of the specimens according to ASTM D256-10 with a pendulum of
5 ft lbs after being notched using a Motorized Notching Cutter (TMI 22-05-03), USA.
Refer to the Appendix for the equations used for the calculation of the mechanical
properties of the bionanocomposites.
3.3.3.2 Dynamic Mechanical Analysis (DMA)
A DMA Q800 from TA Instruments Inc., Canada, was used to measure the storage
modulus, loss modulus and tan delta relative to temperature of the polymer and
composites. Testing was done by heating specimens from -70 °C to 100 °C with a
constant heating rate of 3 °C min-1
, a frequency of 1 Hz and oscillation amplitude of 15
μm. An average of two samples was used for DMA. Refer to the Appendix for the
equation used for the calculation of the tan delta of the bionanocomposites.
43
The heat deflection temperature (HDT) was also carried out following ASTM
standard D648-07 using a 0.455 MPa load and a three point bending clamp. The
specimens were isothermally stabilized at 35 °C for 5 minutes before being heated to 100
°C at a rate of 2 °C min-1
. The average and standard deviation of three samples was
measured.
All data were analyzed using TA instrument’s Universal analysis 2000 software
version 4.5A.
3.3.3.3 Differential Scanning Calorimetry (DSC)
A DSC Q200 from TA Instruments Inc., Canada, studied the thermal transitions
of the polymer and composites. Testing was done with a nitrogen stream of 50 ml min-1
and a heat-cool-heat mode selected where the samples after being sealed in an aluminum
pan were heated from -90 to 150 °C at a constant heating rate of 10 °C min-1
followed a
cooling rate of 5 °C min-1
from 150 to -90 °C and then heated again using the same heat
cycle. With the use of the TA instrument’s Universal analysis 2000 software version
4.5A the melting and crystallization temperatures, were calculated with their respective
enthalpies by using the exotherm from the cooling run and the endotherm from the
second heating run.
The degree of crystallinity, χ (%), was determined according to Equation 1 below.
( )
, (1)
where is the weight fraction of the PBS polymer in the composite, is the
enthalpy of fusion of the polymer or composite, and is the enthalpy of fusion for
100% crystalline PBS.
44
3.3.3.4 Thermal Conductivity
A Hot Disk TPS 500 Thermal Constants Analyzer from ThermTest, Inc., Canada,
was used for measuring the thermal conductivity, thermal diffusivity and specific heat
according to the transient plane source method. A 6.378 mm diameter Kapton disk type
sensor was sandwiched between two 1.8 mm thick by 24.5 mm diameter sample disks
that were secured by the sample holder. A total of three separate measurements were
done for each composition. The heating power was set to 250 mW, a frequency of 60 Hz
and a measurement time of 10 seconds were used as the testing parameters. The average
and standard deviation based on three samples were calculated.
3.3.3.5 Electrical Resistance and Conductivity
An Autolab PGSTAT302N with an FRA32M impedance analysis module from
Metrohm Autolab B.V., Netherlands, was used to measure the resistance of the composite
specimens using alternating current impedance spectroscopy. The 1.8 mm thick by 24.5
mm diameter sample disks were slightly compressed to ensure good contact between two
square 25 x 25 mm silver electrodes which were connected to the machine at both ends
by the two probe method. A sinusoidal wave with amplitude of 10 mV and a frequency
range of 400 Hz to 600 kHz was applied. Measurements were done in the thickness
direction at room temperature with three samples tested for each composition.
Data were fitted using Nova 1.8.17 software following the model depicted in
Figure 3.1 for carbon black particles where Ra is the aggregate resistance which
represents the continuous carbon black chains, Rg being the contact resistance referring to
45
non-continuous carbon black chains containing one or more small gaps and C is the
capacitance of the gapped chains (Wang et al., 2005; Sun & Wei, 2008).
To determine the conductivity of the composite samples the same setup was used
as described above with the machine measuring the current as the potential was increased
from -5 V to 5 V. Using Nova 1.8.17 software the slope of the regression line of the IV
graph was used to calculate the conductance according to Ohm’s law and the
conductivity was then calculated according to the sample dimensions. Three samples
were used to measure the average and standard deviation.
46
Figure 3.1: Circuit model for the electrical resistance of carbon black within a polymer
matrix in the percolation region.
47
3.3.3.6 Density Measurement
The densities of the neat PBS polymer and composites were measured with an
electronic Densimeter MD-300S from Alfa Mirage, Japan, in accordance with ASTM
D792-08. An average was taken based on three sample measurements for each
composition.
3.3.3.7 Scanning Electron Microscopy (SEM)
Morphology of the composites was carried out using a FEI Inspect S50 scanning
electron microscope, Canada, at the Nanoscience facility in the University of Guelph,
Science Complex, with an accelerating voltage set to 20kV at high vacuum. All samples
were cryo-fractured using liquid nitrogen and gold coated using a Cressington sputter
coater 108auto, UK, prior to being imaged at 5000x magnification.
3.3.3.8 Optical Microscopy
A polarized optical microscope (Nikon Instruments Inc., Canada) with a hot stage
(Linkam Scientific Instruments Ltd., UK) was used to determine the carbon black
dispersion within the composites. Small samples were placed on glass slides where they
were then heated to 20 °C above melting temperature to form a thin layer before being
cooled to room temperature for imaging. Images were taken at 20x magnification.
48
3.3.3.9 Statistical Analysis
The experiment was arranged as a completely random design, with 3 experimental
units having treatments of 1, 3, and 5 wt% filler loadings. For all mechanical properties
the 3 treatments had 5 replicates, while in the case of the HDT, thermal conductivity and
electrical conductivity 3 replicates were prepared and only 2 replicates were used for
DMA measurements. Means were compared pairwise using Tukey’s test. The ANOVA
one-way variance analysis procedure of Minitab Ver. 16 (Minitab Inc., State College,
PA) was used to perform statistical computations. A Type 1 error of 0.05 was used for all
statistical tests.
The experimental design was done by preparing a 10 wt% masterbatch by mixing two
50 g sets of masterbatch pellets processed using a combination of poly(butylene
succinate) and 10 wt% carbon black on separate days. The 1, 3, and 5 wt%
bionanocomposites were processed on separate days using the 10 wt% masterbatch
material and neat poly(butylene succinate). For each wt% filler there were 5 tensile bars,
5 flexural bars, 5 impact bars, and 5 DMA/HDT bars and 3 thermal conductivity disks
and 3 electrical conductivity disks produced within 1 hour. Each bar or disk was then
measured once.
49
3.4 Results and Discussion
3.4.1 Mechanical Properties
3.4.1.1 Tensile Properties
The tensile stress at yield improved on initial loading and then remained constant,
and the Young’s modulus increased upon each additional wt% of carbon black (Fig. 3.2,
Table 3.1). The percent elongation at break showed no significant variation between the
neat polymer and the 1 and 5 wt% CB bionanocomposites (Fig. 3.3, Table 3.1). This has
also been seen by other researchers when carbon black loading was below 5 vol% for
polymers like polypropylene and polycarbonate (Huang, 2002). As the carbon black
loading varied from 0 to 5 wt% the tensile stress at yield improved by 5% while the
modulus increased by 12% (Fig 3.2, Table 3.1). The results signify that there is some
interaction occurring between the polymer matrix and the nanofiller that is improving the
strength and stiffness of the composites. The improvements in the tensile properties may
suggest that the stress transfer is good between the matrix and filler, and that interfacial
tension is low. To confirm that there is some form of adhesion between the matrix and
filler the Nocholais-Narkis model may be used. This model predicts the tensile strength
of the composites based on the adhesion of spherical fillers in a matrix. Equation 2 below
is used in the case when there is no adhesion present in between the composite and filler,
resulting in a lack of stress transfer between the two materials (Metin et al., 2004).
, (2)
where ϕf, is the volume fraction of filler, while σm and σc, are the tensile strength of the
matrix and composite, respectively. This model assumes that the spherical filler reduces
the cross-sectional area, thus lowering the tensile strength (Li et al., 2011).
50
Based on Equation 2, the tensile strength ratio for no adhesion will decrease with
filler content whereas the bionanocomposites demonstrated a constant ratio of 1.05 (Fig.
3.4). The increase in the experimental values compared to the decrease in theoretical
values emphasize that there is adhesion between the carbon black and the PBS matrix.
This adhesion has contributed to the increase in tensile strength and modulus by
increasing stress transfer through the interface of the two materials.
51
Wt.% Carbon Black
0 1 2 3 4 5
Te
nsile
Str
ess a
t Y
ield
(M
Pa
)
0
10
20
30
40
Te
nsile
Mo
du
lus (
MP
a)
640
660
680
700
720
740
760
780
Tensile Stress
Tensile Modulus
Figure 3.2: Tensile stress at yield and the tensile moduli of the PBS carbon black bionanocomposites (mean±SD).
52
Wt.% Carbon Black
0 1 2 3 4 5
% E
lon
gation
at
Bre
ak
0
50
100
150
200
250
300
350
Notc
hed
Izod
Im
pa
ct
Str
en
gth
(J m
-1)
0
20
40
60
80
100
120
% Elongation
Impact Strength
Figure 3.3: The % elongation at break and impact strength of the PBS carbon black bionanocomposites (mean±SD).
53
Wt% Carbon Black
0 1 2 3 4 5
Co
mp
osite
-Ma
trix
Te
nsile
Yie
ld S
tre
ngth
Ra
tio
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1.10
Experimental curve
Theoretical curve
Figure 3.4: Change in tensile yield strength ratio of bionanocomposites as CB wt% is increased.
54
3.4.1.2 Flexural Properties
Figure 3.5 and Table 3.1 shows that the maximum flexural stress and the flexural
modulus both increased gradually with carbon black addition. The flexural stress
increased by 13% with the flexural modulus increasing 17% when nanofiller went from 0
to 5 wt%. The enhanced stiffness of the material may be attributed to the increased
adhesion of the polymer and matrix as seen with the tensile properties. The rigidity of the
bionanocomposites may also occur since both the carbon black and the PBS matrix are
organic in nature, giving rise to good compatibility that can heighten the load transfer
between filler and matrix. Note that the increased rigidity of the material may also be a
result of a change in density from 1.26 g cm-1
to 1.30 g cm-1
for the 0 and 5 wt% carbon
black composites, respectively (data not shown).
55
Wt.% Carbon Black
0 1 2 3 4 5
Ma
x. F
lexu
ral S
tre
ss (
MP
a)
0
10
20
30
40
Fle
xu
ral M
od
ulu
s (
MP
a)
680
700
720
740
760
780
800
820
840
860
880
Flexural Stress
Flexural Modulus
Figure 3.5: Flexural stress and flexural moduli of the PBS carbon black bionanocomposites (mean±SD).
56
3.4.1.3 Impact Strength
The effects of carbon black loading on the composites impact strength are shown
in Figure 3.3 and Table 3.1. Results illustrate that there was an increase in the impact
strength of the bionanocomposites when filler was added. The nanofiller improved the
impact strength by 116% upon initial addition of 1 wt% carbon black and the higher
loadings also showed greater impact strength compared to no carbon black, which may be
connected to the random dispersion of the particles throughout the composite. Similar
observations can also be seen with polypropylene/carbon black and polypropylene/multi-
walled carbon nanotube composites at small wt% loading, where it is reported that the
introduction of the particles into the matrix helps improve the polymer resistance crack
initiation and propagation as long as the particles remain well dispersed (Zhou et al.,
2006).
57
Table 3.1: Means, standard deviation (SD), and the results of means contrasts for tensile stress (TS), tensile modulus (TM), %
elongation at break, flexural stress (FS), flexural modulus (FM), and impact strength of bionanocomposites with different carbon
black content.
Wt% Carbon
Black
TS
(MPa) SD TM (MPa) SD
%
Elongation SD
FS
(MPa) SD
FM
(MPa) SD
Impact Strength
(J m-1
)
SD
0 31.6 a 0.20 662 a 7.6 267 a 39.9 32.7 a 0.69 708 a 15.5 19 a 1.4
1 33.2 b 0.22 682 b 5.8 236 ab 40.6 34.4 b 0.54 776 b 26.5 41 ab 15.9
3 33.2 b 0.50 712 c 11.2 212 b 26.7 35.3 b 0.50 796 b 55.9 64 b 33.7
5 33.2 b 0.33 739 d 16.6 242 ab 26.9 36.9 c 0.78 832 b 37.7 44 ab 16.0
a-d Means followed by the same letter in each column are not significantly different according to Tukey's multiple range test
(P=0.05). N=5.
58
Table 3.2: Means, standard deviation (SD), and the results of
means contrasts for the heat deflection temperature (HDT) and
the storage modulus at 25 °C of PBS carbon black
bionanocomposites.
Wt% Carbon
Black
HDT
(°C) SD
Storage Modulus
(MPa) SD
0 85 a 2 666 a 21
1 90.5 b 0.4 757 b 20
3 91.7 b 0.84 762 b 14
5 91 b 1.4 790 b 14
a-b Means followed by the same letter in each column are not
significantly different according to Tukey's multiple range test
(P=0.05). N=3.
59
3.4.2 Dynamic Mechanical Analysis (DMA)
The tan delta of a composite provides information on the damping behaviour of
the composites based on the ratio of loss modulus versus storage modulus. The results for
the tan delta did not show a variation with the carbon black filler as the peak height, peak
width and peak temperature remained similar (Fig. 3.6). This implies that even though
there is a small interaction occurring between the nanoparticles and polymer as discussed
previously the nanofiller did not impede the mobility of the molecular chains within the
composite.
The storage modulus for the 5 wt% carbon black loaded composite had an overall
increase of 19% compared to the neat PBS (Fig. 3.6, Table 3.2), which is consistent with
the tensile and flexural moduli (Fig. 3.2, Fig. 3.5). Storage modulus has also been shown
to increase when carbon black has been added to other polymers such as poly(lactic acid)
and poly(propylene carbonate) (Ning et al., 2008). The gain in storage modulus is due to
the nanoparticle polymer interactions that are acting as reinforcement within the matrix.
The heat deflection temperature (HDT), which is also measured using the DMA,
is used to analyze the heat resistance of a material under load and is listed in Table 3.2
and 3.3. The data show an increase upon 1 wt% carbon black loading from 85 °C to 91
°C that remains unaltered with further carbon black content relative to the standard
deviation. The same trend has been observed to occur with other nanocomposites (Bao &
Tjong, 2008). The higher HDT value is due to greater mechanical stability provided by
the nanofiller within the composite (Manias et al., 2001).
60
Figure 3.6: Tan δ (lines) and storage moduli (symbols) at 25 °C of the PBS carbon black bionanocomposites (mean±SD).
61
3.4.3 Thermal Analysis
3.4.3.1 Differential Scanning Calorimetry (DSC)
The DSC data displayed in Table 3.3 consist of the melting behaviour and
crystallization behaviour of the composites. The results do not demonstrate any variation
when the carbon black filler is present. The nanoparticles had no effect on the glass
transition temperature (Tg), melting temperature (Tm) or crystallinity (χ) of the
composites. However, for the crystallization temperature (Tc), there was slight increase of
3-5 °C after carbon black had been added. This increased onset of crystallization at a
higher temperature may be to the carbon black acting as a nucleation agent in which the
polymer uses the nanoparticles as nucleation sites for crystal growth. Polypropylene has
also shown the same result with carbon black filler (Mucha et al., 2000).
62
Table 3.3: The glass transition temperature, Tg, melting temperature, Tm, enthalpy of fusion, ∆Hm, crystallization temperature, Tc,
enthalpy of solidification, ∆Hc, and crystallinity, χ, based on the DSC curves and heat deflection temperature (HDT) of PBS carbon
black bionanocomposites.
Wt% CB Tg (°C) Tm (°C) ∆Hm (J g-1
) Tc (°C) ∆Hc (J g-1
) χ (%) HDT (°C)
0 -32.11 114.36 64.64 83.7 62.23 30.78 85 ± 2.0
1 -31.83 115.07 62.74 86.52 59.46 30.18 91 ± 0.4
3 -32.86 114.59 66.15 88.65 63.24 32.47 92 ± 0.8
5 -32.3 115.81 60.48 87.36 61.51 30.32 91 ± 1.4
63
3.4.3.2 Thermal Conductance
Both the thermal conductivity and thermal diffusivity of the composites increased
with addition of carbon black (Table 3.4), as they are directly proportional to one another.
The thermal conductivity and diffusivity went up by 50% and 194% respectively, for the
5 wt% carbon black filled composite. This may be due to the nanofiller in the composites
forming short chain structures that act as channels to allow a greater rate of thermal
energy transfer through the composite (Agari & Uno, 1985). The greater the quantity of
the carbon particles, the larger amount of heat that can be absorbed by the composite,
since graphitic carbon as found in carbon black is known for being a good thermal
conductor (Wissler, 2006). In contrast to the increased conductivity and diffusivity, the
specific heat decreased by 50% for the highest loaded carbon black composite (Table
3.4). This can be explained by the very low specific heat of carbon black nanoparticles of
0.167 MJ m-3
K-1
.
64
Table 3.4: Means, standard deviation (SD), and the results of means contrasts for
thermal conductivity, thermal diffusivity and specific heat of PBS carbon black
bionanocomposites.
Wt%
Carbon
Black
Thermal
Conductivity
(W m-1
K-1
)
SD
Thermal
Diffusivity
(mm2 s
-1)
SD Specific Heat
(MJ m-3
K-1
) SD
0 0.56 a 0.024 0.051 a 0.0079 11 a 1.5
1 0.64 b 0.032 0.075 a 0.0039 8.6 b 0.88
3 0.710 c 0.0096 0.075 a 0.0020 9.4 ab 0.36
5 0.84 d 0.024 0.15 b 0.018 5.5 c 0.65
a-d Means followed by the same letter in each column are not significantly
different according to Tukey's multiple range test (P=0.05). N=3.
65
3.4.4 Electrical Resistance and Conductivity
The electrical properties of the composites were measured using impedance
spectroscopy to determine the type of conductive pathway present and how it relates to
the dispersion of the filler within the material. Upon testing, the results showed a
decrease in the impedance value as the frequency was increased (data not shown) which
occurs when the carbon black is within the percolation region where a three dimensional
network of the carbon black particles begins to form (Alexandre & Dubois, 2000; Wang
et al., 2003). In this region at low frequencies the gaps between adjacent carbon black
aggregates are not readily conductive meaning the resulting impedance is the combined
sum of the two resistances, Ra and Rg. Consequently, when the frequency is high the
small gaps begin to become active followed by larger gaps as electron tunneling occurs,
reducing the impedance to approximately Ra. This phenomenon is a product of non-
ohmic conductance that is intrinsic to the carbon black composite near the percolation
threshold, which allows the circuit model in Figure 3.1 to be used. Therefore the values
listed in Table 3.5 for the resistance due to carbon black aggregates, contact resistance
between gaps and the gap capacitance can give insight into the particle dispersion within
the composites. Both the aggregate resistance and contact resistance decreased with
carbon black loading, while the capacitance increased (Table 3.5). With the addition of
carbon black, the particles become densely packed increasing aggregate size and length
while also reducing the polymer gap between neighbouring aggregates, allowing for a
larger number of available routes for the charge to pass through. From this information, it
can be concluded that gaps are still present between carbon black aggregates in all
composite formulations as all samples retained a value for contact resistance and gap
66
capacitance, and that they have not reached a complete three dimensional network
demonstrating that the particles remain dispersed within the polymer matrix.
Correspondingly, the carbon black did increase the conductivity of the composites
for higher weight percentages of carbon black (Fig. 3.7). The 5 wt% carbon black filled
composite had a conductivity of 6.5 × 10-12
S m-1
, which is an increase of 102% over the
neat PBS that had a value of 3.2 × 10-12
S m-1
. The increase in electrical conductivity can
be attributed to the addition of the carbon black nanofiller as it is electrically conductive
due to its graphitic nature. However, the change in electrical conductivity is not a large
order of magnitude, therefore, the filler remains at the initial stages of the percolation
threshold as the electronic network has not been fully established (Wen et al., 2012). As a
three-dimensional continuous chain structure has not been reached, the nanofiller retains
good dispersion throughout the polymer matrix.
67
Table 3.5. Means, standard deviation (SD), and the results of means contrasts for the
resistance due to the aggregates, Ra, the contact resistance from the gaps between adjacent
aggregates, Rg, the capacitance of the gaps, C, and the electrical conductivity of PBS carbon
black bionanocomposites.
Wt%
Carbon
Black Ra (kΩ) SD Rg (GΩ) SD C (pF) SD
Electrical
Conductivity
(pS m-1
)
SD
0 3.8 a 0.21 1.2 a 0.21 12.3 a 0.89 3.2 a 0.61
1 3.5 a 0.22 0.6 b 0.32 13.2 a 0.59 5.6 b 0.39
3 2.957 b 0.0058 0.67 ab 0.064 15.6 b 0.20 6.0 b 0.53
5 2.44 c 0.025 0.4 b 0.11 18.4 c 0.35 6.5 b 0.25
a-c Means followed by the same letter in each column are not significantly different
according to Tukey's multiple range test (P=0.05). N=3.
68
Wt.% Carbon Black
0 1 2 3 4 5
Ele
ctr
ical C
onductivity (
pS
m-1
)
0
1
2
3
4
5
6
7
Figure 3.7: Electrical conductivity of the PBS carbon black bionanocomposites (mean±SD).
69
3.4.5 Surface Morphology and Particle Dispersion
3.4.5.1 Scanning Electron Microscopy (SEM)
The scanning electron microscopy images for the PBS composites with various
carbon black content show an increasingly smoother surface fracture upon carbon black
addition (Fig. 3.8). The neat polymer shows a brittle fracture behavior with large ridges
that becomes less pronounced as more carbon black was loaded. Another feature of these
composites is that the carbon black did not cause any gaps or holes between matrix and
filler, and some particle can be seen in the 5 wt% sample with good adhesion between the
materials. No clumping was observed in these images as evidenced by uniform
distribution of the visible particles throughout the composite.
70
Figure 3.8: SEM images of the cryo-fractured surfaces for the PBS carbon black bionanocomposites samples of 1, 3, and 5 wt% at
5000x magnification demonstrating the filler dispersion, with arrows pointing to carbon black particles within the polymer matrix.
71
3.4.5.2 Optical microscopy
Optical images provided information on the dispersion of the carbon black within
the polymer matrix (Fig. 3.9). At 1 and 3 wt% carbon black the particles were evenly
arranged throughout the composite. Compared to the 1 wt% where the polymer can be
seen readily between other adjacent particles, the 3 wt% composite had the carbon
particles packed closely together. For the 5 wt% composite, however, the carbon particles
demonstrated a combination between the 1 and 3 wt% composites with regions of high
and low carbon particle packing. This may be associated with particles bunching together
and forming larger aggregates in certain areas, as the onset of the three-dimensional chain
network started to appear at this higher loading.
72
Figure 3.9: Optical microscopy images of the PBS carbon black bionanocomposites at 20x magnification demonstrating the carbon
black dispersion within the polymer matrix at loadings of 1, 3, and 5 wt%.
73
3.5 Conclusion
Carbon black filled poly(butylene succinate) bionanocomposites were
successfully made and characterized. The composites’ mechanical properties
demonstrated improvements in tensile, flexural and impact strength upon addition up to
the 5 wt% carbon black loading. Analysis using DMA demonstrated higher storage
modulus of the composites, which matched with the tensile and flexural moduli. Thermal
conductivity and HDT were augmented with the carbon black nanoparticles as compared
to the neat PBS. Electrical conductivity was enhanced with the conductive filler. SEM
and optical microscope images along with electrical impedance measurements showed
that the nanoparticles were well dispersed within the composites. These
bionanocomposites can be beneficial in industrial applications such as antistatic plastic
and further studies on higher loadings need to be investigated to improve conductivity for
additional applications such as conductive polymer composites.
74
Adapted with permission from Snowdon, M. R., Mohanty, A., Misra, M. (2014). A Study
of Carbonized Lignin as an Alternative to Carbon Black. ACS Sustainable Chemistry &
Engineering, http://dx.doi.org/10.1021/sc500086v. Copyright 2014 American Chemical
Society.
Chapter 4
A Study of Carbonized Lignin as an Alternative to Carbon Black
75
4.1 Abstract
The production of biobased carbonaceous powder from bioethanol coproduct
lignin for use as a substitute for fossil fuel-derived conductive carbon black filler is
examined. The synthesis procedure used for the formation of biobased carbon black is
studied in order to obtain properties similar to conventional carbon black.
Characterization of the carbon material after varying carbonization temperatures and ball
milling times was investigated to optimize carbon size, surface area, and thermal and
electrical conductivity. The optimized carbonized ball milled lignin had a carbon content
greater than 90% with the majority of the carbon atoms in the sp2 hybridized state. The
carbonized ball milled lignin exhibited a surface area 882% larger and a thermal
conductivity 36% greater in comparison to the conductive carbon black tested, while the
electrical conductivity was 9.5 S m-1
lower for the carbonized ball milled lignin. This
research has demonstrated the possibility of producing biobased carbon black as a
potential substitute for commercial carbon black by using lignin as a precursor material.
4.2 Introduction
In the past decade, nanotechnology has enabled industry and academia to develop
a larger focus on nanostructured materials. Of the vast array of nanoparticles, carbon-
based nanostructures remain one of the most widely studied areas in the field of
nanotechnology as new uses are continually being developed (Shenderova et al., 2002).
These carbon nanoparticles include materials known as nanotubes, nanofibers, graphene,
carbon blacks, and fullerenes (Dresselhaus et al., 1996). In the case of carbon black, over
8 million metric tons are produced annually worldwide for a large range of applications
76
(Rahman et al., 2011). One of the primary uses of carbon black is as filler in elastomers
and plastics to enhance their overall properties (Pantea et al., 2003). Carbon black is also
well known for being one of the most commonly used fillers in the production of
conductive polymer composites as it tends to be a very good electrical conductor (Zhang
et al., 2007; Glatz-Reichenbach, 1999). There are several types of carbon blacks and they
differ based on their characteristic properties ranging from surface area to particle size to
conductivity. The types of carbon blacks that are predominantly used in the rubber and
polymer composite sectors are furnace and thermal blacks (Huang, 2002). Both furnace
and thermal carbon blacks are made by incomplete combustion or thermal degradation by
pyrolysis using liquid or gas hydrocarbons (Lahaye & Ehrburger-Dolle, 1994). Concerns
about global warming due to fossil fuels usage and the large reliance of carbon blacks on
petroleum supplies and its increasing prices have encouraged scientists to find viable
ecofriendly carbon alternatives.
Research into the use of lignin as a renewable carbon source for the production of
carbon fibers and activated carbons is still ongoing (Suhas et al., 2007; Kadla et al.,
2002). This is partially a result of the ease in which carbon structures can be made from
lignin, as the polymer has a high carbon content of approximately 60 wt% (Babel &
Jurewicz, 2008). Another feature of lignin is that it is the second most abundant natural
polymer in the world, and it is readily available, as large quantities are being produced as
a coproduct of the pulp and paper and bioethanol industries (Lora & Glasser, 2002;
Kumar et al., 2009). By finding high value-added applications that are economically
beneficial for these copious amounts of lignin, a reduction in environmental damage from
unused lignin can be avoided (Fu et al., 2013; Gonugunta et al., 2012). Utilization of this
77
waste lignin, which is primarily used as a fuel source in the production of in-plant
electricity or discarded in landfills, will allow for a more sustainable disposal method
(Hayashi et al., 2000; Poursorkhabi et al., 2013).
In the present study, the preparation of carbonized bioethanol coproduct lignin in
the absence of any metal catalysts was studied for use as a possible carbon black
alternative. The carbonization and ball milling conditions of the resulting carbonaceous
material was also investigated. The carbon structures were characterized and chosen
based on high surface area, small particle size, and electrical conductivity, as these are
considered to be the most important properties of carbon blacks (Pantea et al., 2003).
4.3 Materials and Methods
4.3.1 Materials
The hydrolysis lignin used in this study was pretreated Poplar hydrolysate solid
residue from Mascoma, Canada, bioethanol plant with a 55% to 57% dry content that had
been frozen before use. The coproduct contained approximately 62.5 wt% lignin with the
remainder being nonhydrolyzed carbohydrates (Poursorkhabi et al., 2013). C-NERGY
Super P Li carbon black (CB) from Timcal Ltd., Canada, was used for comparative
purposes.
4.3.2 Lignin Carbonization
Prior to carbonization, the material was initially thawed in an oven at 105 °C for
24 h until dry (~50% weight reduction). The material was then ground in a planetary ball
mill (Retsch PM100, Germany) with four 40 mm diameter balls at 250 rpm for 2 h with
78
counter-rotation occurring after 1 h to reduce the size of the large particles to a powder
consistency. Hydrolyzed lignin was placed in a combustion boat and inserted into the
center of a horizontal tube furnace (Carbolite 1200 °C G-range, UK). The tube was sealed
at both ends, and nitrogen gas was flushed through the tube to remove oxygen in order to
attain pyrolysis conditions. With a continual nitrogen gas flow through the tube, the
furnace was set to a heating rate of 20 °C min-1
until the respective carbonization
temperature was reached (600, 750, 900 °C) and remained isothermal at the given
temperature for 6 h before cooling to room temperature under N2 flow. The carbonized
material was removed from the furnace upon reaching room temperature and
characterized to determine the optimum carbonization temperature for the purpose of a
stable conductive particle with a large surface area.
4.3.3 Ball Milling of Carbonized Lignin
After temperature optimization of the carbonization process was complete, the
carbonized material from the chosen temperature of 900 °C was then tested in the
planetary ball mill (Retsch PM100, Germany) to determine the optimum ball milling time
for reduction of particle size. The various ball milling time intervals were tested using 10
mm diameter balls, a ball-to-sample weight ratio of 20:1, and a rotation speed of 300 rpm
with counter-rotation occurring halfway through the time trial. These parameters were
used to improve particle size reduction as small ball size, large ball-to-powder ratio, and
high speeds contribute to the reduction in particle size which also increases surface area
of particles. The times tested were 6, 12, 24 and 48 h.
79
4.3.4 Characterization
The characterization methods used in the temperature and ball milling time
optimizations of the carbonized hydrolysis lignin are reported below with the mean value
reported along with the standard deviation (SD) where applicable. Microsoft Excel 2010
was used to determine the average and standard deviation following the built in
commands AVERAGE and STDEV, respectively. Refer to the Appendix for the
equations used for the calculation of the thermal and electrical properties of the
carbonaceous powders.
4.3.4.1 Raman Spectroscopy
Raman spectra were acquired with a Renishaw Raman imaging microscope, UK,
using a Renishaw NIR 780TF diode laser with a wavelength of 785 nm, and an output
power of 25 mW was used for excitation with a 50x objective lens, at the Electrochemical
Technology Centre in the University of Guelph, MacNaughton building . A CCD array
detector was equipped to the machine. Calibration was done using the Raman active
vibration peak at 520 cm-1
of silicon. All spectra were obtained with the laser power set
to 100% with extended scans between 500 and 2000 cm-1
, and they were made using 10
separate measurements of 10 s each. Deconvolution of the baselined spectra was done
using PeakFit ver.4.12 software with the peak type set to Gaussian-Lorentzian area mode
with a multipeak best fit with peaks present at ~1100 and ~1400 cm-1
along with the D
and G bands (Ferrari & Robertson, 2000; Hauptman et al., 2012). Ratios of peak
intensities were determined based on amplitude height of deconvoluted peaks.
80
4.3.4.2 BET Surface Area Analysis
Brunauer-Emmet-Teller (BET) surface areas of the samples were tested in a nitrogen
gas sorption analysis at 77.3 K with a NOVA 4200e from Quantachrome Instruments,
USA, at the Nanoscience facility in the University of Guelph, Science Complex. The
calibration gas used was helium. Samples were degassed with nitrogen gas at 105 °C for
6-8 h until a stable weight was achieved before measurement. Analysis was done using
the NovaWin version 10.01 software where the BET surface area was determined from a
multipoint plot over the P/P0 range of 0.05-0.35 following ASTM standard D6556-10,
with the relative error calculated from the BET error table found in the NovaWin
Operating Manual based on the positive C constant. The relative error refers to the
uncertainty of the measurement in comparison to the size of the measurement and is a
function of the BET C constant and the relative pressure used. The pore radius and
volume were determined using the BJH pore size distribution analysis for the adsorption
isotherm.
4.3.4.3 Fourier Transform Infrared Spectroscopy (FTIR)
A Nicolet 6700 FTIR spectrometer in attenuated total reflectance infrared (ATR-
IR) mode, Thermo Scientific, Canada, was used obtain the spectra with a resolution of 4
cm-1
and 32 scans per sample. Powder samples of 0.1 g were pressed into disks using a
Specac manual hydraulic press with a 13 mm diameter dye and a 10 ton load applied.
81
4.3.4.4 Particle Size Measurement
Particle sizes were measured using a laser diffractometry with a Mastersizer 2000
with a Hydro 2000SM dispersion unit (Malvern Instruments Ltd., UK), in the Food
Science building at the University of Guelph. The refractive indices for water and carbon
were given as 1.33 and 2.42, respectively. A refractive index of 1.604 was used for lignin
based on Donaldson’s work (Donaldson, 1985). The powders were dispersed in deionized
water and sonicated for 5 min prior to being tested. Measurements were done in the range
of 0.01 to 1000 μm using a general calculation model for spherical particles. Each sample
was tested three times using a stirrer speed of 2800 rpm and an obscurance of ~5%. The
data were obtained using Mastersizer 2000 software ver. 5.60.
4.3.4.5 Electrical Conductivity
Measurements of the electrical conductivity were done at room temperature using
an Autolab PGSTAT302N equipped with an FRA32 M impedance analysis module from
Metrohm Autolab B.V., Netherlands. A frequency range from 400 Hz to 600 kHz was
used with a 10 mV amplitude sine wave. All powder samples were oven-dried at 105 °C
for 24 h and then a mass of 0.1 g of sample was tested. Powders were placed in a hollow
clear plastic cylinder with an inner diameter of 10 mm, which was then compressed
between two aluminum pistons that form the electrodes. The pressure was increased from
125 kPa (due to the weight of upper piston) to 1.12 MPa by loading additional weight on
top of the upper piston. This pressure range is low enough to prevent crushing of the
particles while being high enough for good electrical contact between the powder and
pistons. All data were acquired with Nova 1.8.17 software. Calculated averages were
82
based on three separate measurements.
4.3.4.6 Thermal Conductivity
A Hot Disk TPS 500 Thermal Constants Analyzer from ThermTest, Inc., Canada,
was used for measuring the thermal conductivity, thermal diffusivity, and volumetric heat
capacity according to the transient plane source method. A 6.378 mm diameter Kapton
disk type sensor was sandwiched between the carbon powders and secured by the sample
holder. A total of three separate measurements were done for each powder sample to
determine the average and standard deviation. The heating power was set to 250 mW; a
frequency of 60 Hz and a measurement time of 10 s were used as the testing parameters.
4.3.4.7 Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy
(SEM-EDS)
Elemental analysis of the powders was done using a FEI Inspect S50 scanning
electron microscope, Canada, at the Nanoscience facility in the University of Guelph,
Science Complex, with an accelerating voltage set to 20 kV at high vacuum, with an X-
Max 20 mm2 silicon drift detector (Oxford Instruments, UK) able to measure elements Be
and above. The EDS analysis software, Aztec ver. 2.0, with the “Point & ID mode”
feature was used to localize the beam onto five separate areas chosen manually within the
field of view. The EDS detector measured elements above lithium and gave readings of
weight percentage based on the peak heights of any element that was detected.
83
4.3.4.8 Statistical Analysis
The experiment was arranged as a completely random design, with 2 experimental
units of carbon black and carbonized ball milled lignin. For thermal conductivity 3
replicates were used for the measurement. Means were compared pairwise using Tukey’s
test. The ANOVA one-way variance analysis procedure of Minitab Ver. 16 (Minitab Inc.,
State College, PA) was used to perform statistical computations. A Type 1 error of 0.05
was used for all statistical tests.
The experimental design was done by preparing 3 samples of the carbon black and
carbonized ball milled lignin. The carbonized ball milled lignin was prepared on 3
separate days, whereas the carbon black was obtained 3 times from the same product
container. Each powder sample was then measured once.
84
4.4 Results and Discussion
4.4.1 Raman Spectroscopy
Raman spectroscopy of the 600, 750, and 900 °C carbonized lignin prior to ball
milling was characterized to determine the microscopic structure of the carbon samples
(Fig. 4.1). It has been observed that a Raman band at ~1575 cm-1
is due to a single crystal
of graphite, and it is known as the G peak. Another Raman band appears at ~1355 cm-1
in
the case of polycrystalline graphite, and it is referred to as the D peak (Tuinstra &
Koenig, 1970). A sp2 hybridized carbon structure is credited for both bands, with the D
band being a result of the turbostratic carbon where the carbon atoms are disordered and
distorted along the perimeter of the graphite sheets (Paris et al., 2005). It is reported that
the intensity ratio (ID/IG) of the two bands is inversely proportional to the crystallite size
of the graphite (La) (Tuinstra & Koenig, 1970). In this case, as the ratio increases, an
increase in disorder is also occurring as the graphene size is decreasing. Using the
equation developed by Pimenta et al., the crystallite size of the graphite, La, in
nanometers can be determined (Pimenta et al., 2007).
( ) (
)
, (1)
where λ is the wavelength of the incident laser in nanometers and the intensity ratio of the
D and G bands is unitless.
The deconvoluted spectra and the ID/IG intensity ratios for all carbon samples are
shown in Figure 4.1. The D and G band ratio for all the carbonized lignin showed a
gradual increase with carbonization temperature from 600 to 900°C and an overall
improvement of 44% between the 600 and 900 °C temperatures. This increase in the
intensity ratio of the peaks infers that there is a larger quantity of the disordered graphite
85
structure present at the higher temperatures. The band ratio is also able to show that the
graphite regions, La, are decreasing in size upon increased carbonization temperatures.
By using Equation 1 to calculate an actual value for the crystallite size, it was found that
lignin carbonized at 600 °C had a La value of 77 nm, while the 750 and 900 °C treatments
decreased 60 and 53 nm, respectively.
The carbon black spectra has a very weak G band in comparison to the carbonized
lignin samples giving rise to a larger intensity ratio of 2.86 (Fig. 4.1), implying that the
graphite regions are even smaller than those of the carbonized lignin. Again using
Equation 1, the value of La was determined to be 32 nm in size for the carbon black. The
line width for the G band of the carbon black was approximately 208 cm-1
, whereas the
900 °C carbonized lignin only had an approximate line width of 66 cm-1
, a difference of
215%, which indicates that the carbonized lignin resembles graphite more closely than
the carbon black due to the narrower line width (Tuinstra & Koenig, 1970).
86
Figure 4.1: Deconvoluted Raman spectra of carbonized lignin at 600, 750, and 900 °C and carbon black normalized to the same
height.
87
4.4.2 BET Surface Area
The results from BET isotherms analysis illustrate that the bioethanol coproduct
lignin prior to carbonization had a surface area of only 2 m2 g
-1 (Table 4.1), and after
carbonization at 600 °C, the surface area only slightly improved. However, the higher
temperature carbonization of 750 °C was able to improve the surface area to a value
similar to the carbon black. A 15-fold increase in the surface area was found upon 900 °C
carbonization, and an increase in pore volume was also observed. In another study where
coconut shell char was analyzed, the surface area was found to increase with
carbonization temperature as a result of a developing micropore structure (Li et al.,
2008). Therefore, the substantial increase in surface area in the 900 °C carbonized lignin
implies that the high temperature conditions promote the formation of pores, thus
producing an activated carbon (Cao et al., 2006).
The conductive carbon black used in this study had a surface area 955% lower
than the highest temperature carbonized lignin (Table 4.1). Even though this conductive
carbon black is within the range of surface areas associated with carbon blacks at 10 to
1000s of m2 g
-1, it has been demonstrated that those powders having large surface areas
have better electrical conductivity (Pantea et al., 2003). Therefore, we chose to continue
the study into the effects of ball milling using the 900 °C carbonized lignin as it
demonstrated the highest surface area.
After ball milling, the surface area of the carbon powder decreased (Table 4.1).
With just 6 h of ball milling, the surface area was lowered by 23%, which can be
attributed to the collapse of the pore structures during the milling process as evidenced by
a reduction in pore volume. At longer ball milling times, the surface area began to
88
increase until a maximum value of 609 m2 g
-1 was reached after 24 h, where it showed a
pore volumes exceeding the nonball milled sample. When additional ball milling was
done for 48 h a reduction in surface area was found along with a diminished pore volume.
Graphitic carbon has also produced a similar trend when being ball milled, such that an
increase in surface area is found initially due to fracturing of the particles from ball
impacts up to a critical value, which then decreases due to particle agglomeration at
longer milling times (Chen et al., 1999). Further milling does not attain surface areas
comparable to the initial maximum value (Chen et al., 1999).
89
Table 4.1: Surface area, pore radius and pore volume of carbon black, lignin treated to various carbonization temperatures, and 900 °C
carbonized lignin treated to different ball milling times.
Sample Surface Area (m
2 g
-1)
± (relative error) Pore Radius (Å) Pore Volume (cm
3 g
-1)
Untreated
Carbon
Black 62
a NA NA
Lignin 2 ± (0.20) 16.546 0.017
Carbonization Temperature of
Lignin (°C)
600 5 ± (2.25) 16.595 0.003
750 42 ± (1.68) 15.161 0.037
900 654 ± (1.96) 15.168 0.13
Ball Milling Time of 900 °C
Carbonized Lignin (hours)
6 533 ± (< 1.07) 16.457 0.076
12 563 ± (< 1.13) 16.391 0.098
24 609 ± (< 1.22) 16.425 0.135
48 580 ± (< 1.16) 16.443 0.108
aValue obtained from the manufacturer (Spahr et al., 2011).
90
4.4.3 Fourier Transform Infrared Spectroscopy (FTIR)
The spectra in Figure 4.2A shows that as the temperature was increased the FTIR
spectra smoothened to the point where the majority of the infrared peaks were removed,
providing evidence that the functional groups on the lignin are removed upon
carbonization. Kraft lignin has also shown this effect when the temperature was increased
from 350 to 800 °C (Rodriguez-Mirasol et al., 1993). The only prominent peak visible for
the 900 °C carbonized lignin sample was found at ~1600 cm-1
. This absorbance is due to
high conjugated C=O bonds as there remains residual oxygen species within the carbon
samples that are not evident in the carbon black (O’reilly & Mosher, 1983).
Figure 4.2B shows that as the ball milling time was increased up to 24 h, the
transmittance diminished along with a reduction in the 1600 cm-1
peak. The decreased
peak signifies minimal oxygen remaining throughout the carbon structure. The 48 h ball
milled sample had a transmittance similar to the 12 h sample such that the oxygen-related
peak reappears at this longer ball milling time, which can be ascribed to a lower surface
area causing a reduction in IR absorbance. The 24 h ball milled sample had the greatest
resemblance to carbon black as evidenced by no visible peaks and the lowest
transmittance of all samples.
91
Figure 4.2: FTIR spectra of A) lignin carbonized at different temperatures and B) the 900 °C carbonized lignin sample after various
ball milling times relative to carbon black.
92
4.4.4 Particle Size Analysis
The median particle diameters for the 900 °C carbonized lignin ball milled
powders were 2.185 μm, 1.742 μm, 778 nm, and 1.901 μm for the 6, 12, 24, and 48 h ball
milled samples, respectively (Fig. 4.3). These particle sizes correlate very well with the
values obtained for the surface area measurements as these two properties are inversely
proportional to one another. The 24 h ball milled powder had the smallest average
particle size with a reduction in size of 181% occurring between 6 and 24 h. The 24 h ball
milled particles contained 4% nanoparticles within the range from 1 to 100 nm, and the
remainders were submicrometer to micrometer in size. The 48 h ball milled sample
started to agglomerate as the size had increased back to the micrometer region.
The bioethanol coproduct lignin was determined to have a median particle
diameter of 19.46 μm (Fig. 4.3). The lignin particles are approximately 10 times larger
than the carbonized ball milled powders. It should be noted that Super P Li carbon black
has an aggregate size of 144 nm as calculated by the manufacturer (Spahr et al., 2011).
Only the 24 h ball milled sample had a comparable size to the carbon black at the
nanometer scale. Because the 24 h ball milled sample had the smallest particle size, the
highest surface area, and the least oxygen species present, further characterization was
only done on this powder in relation to the carbon black.
93
Figure 4.3: Distribution of particle diameters of precarbonized lignin and the 900 °C carbonized lignin after ball milling at different
time intervals.
94
4.4.5 Electrical Conductivity
The conductive carbon black demonstrated an increase in conductivity from 4.3 to 10.8
S m-1
with increasing compression pressure up to an increase of 151% (Fig. 4.4). The
optimum carbonized ball milled lignin powder only increased in conductivity from 0.3 to
0.9 S m-1
. The carbonized lignin did not show an overall improvement in its conductivity,
whereas the carbon black more than doubled over the pressure range. A difference of 9.5
S m-1
is found between the conductivities of the carbon black and optimized powder at
the highest pressure tested. The difference in the electrical conductivity of the carbonized
lignin and the carbon black may be attributed to the reduced oxygen species in carbon
black based on FTIR spectra (Fig. 4.2), whereas the ball milled carbonized lignin
contains residual oxygen. Surface elements other than carbon are known to decrease the
overall conductivity of the material (Pantea et al., 2001).
Electrical conductivity is enhanced when a reduction in the powder volume
occurs at the higher pressure loadings (Sanchez-Gonzalez et al., 2005). This helps to
explain why the carbon black had a more pronounced improvement in its conductivity
because its volume decreased by 43% at 1.12 MPa, whereas the carbonized ball milled
lignin only showed a reduction in volume of 24% (data not shown). The carbon black in
the present study showed a similar response to carbon black powder under analogous
measurement conditions (Celzard et al., 2002).
95
Figure 4.4: Electrical conductivity versus compression pressure of carbon black and 900 °C carbonized 24 h ball milled lignin.
96
4.4.6 Thermal Conductivity
Carbon structures are considered to be thermally stable, and carbon black has
been shown to improve thermal conductance (Leong & Chung, 2003). Therefore, the
optimized 900 °C carbonized 24 h ball milled lignin sample was analyzed in comparison
to the carbon black for their thermal properties. The data in Table 4.2 show that the
carbonized ball milled lignin had a 36% higher thermal conductivity than that of the
carbon black powder. This difference between the two carbon powders can be attributed
to the size difference of the particles, as the larger the particle size is the greater the
conductivity of the carbon powder is (Khizhnyak et al., 1979). This occurs because
thermal conductance relies on the thermal energy transfer within the particles, locations
of direct contact of adjacent particles, and radiant heat transferred to neighboring
particles. A bigger particle has a larger contact area than a fine particle, and it contains a
greater number of pores and cavities that contribute to the radiant heat, which will reduce
the thermal resistance and in turn improve the thermal conductance. The thermal
conductance for both carbons fall within the range of 0.01-2 W m-1
K-1
for amorphous
carbon made up of a combination of sp2 and sp
3 bonding (Balandin, 2011).
Thermal diffusivity of carbon black was approximately three times greater than
that found for the carbonized ball milled lignin sample (Table 4.2). The carbon black
more readily disperses the conducted heat because of its aciniform structure, which is
made of fused spherical primary particles, such that the fused locations act as connective
pathways in which heat can be distributed swiftly from one primary particle to the next.
The volumetric heat capacity exhibited a difference of 268% between the two powders
(Table 4.2) such that the carbonized ball milled lignin showed a relatively high specific
heat compared with that of the carbon black. The differences in the heat capacities may
97
be a result of the lower bulk density of carbon black compared with that of the carbonized
ball milled lignin.
98
Table 4.2. Means, standard deviation (SD), and the results of means contrasts for thermal conductivity, thermal
diffusivity and specific heat of carbon black and 900 °C carbonized 24 hour ball milled lignin.
Sample
Thermal
Conductivity
(W m-1
K-1
)
SD
Thermal
Diffusivity
(mm2 s
-1)
SD Specific Heat
(MJ m-3
K-1
) SD
Carbon Black 0.4924 a 0.00035 2.949 a 0.0040 0.1670 a 0.00031
Carbonized Ball Milled Lignin 0.6679 b 0.00010 1.085 b 0.0031 0.615 b 0.0017
a-b Means followed by the same letter in each column are not significantly different according to Tukey's
multiple range test (P=0.05). N=3.
99
4.4.7 Elemental Analysis
The elemental composition of carbon and oxygen in various powders was
determined using energy dispersive spectroscopy (EDS) (Fig. 4.5), although hydrogen
cannot be detected using this analysis method. The untreated lignin SEM image is
slightly distorted as a result of charging; however, this did not affect the elemental
analysis, which only showed elemental traces for carbon and oxygen when performing
the Point & ID analysis at the five locations shown in Figure 4.5. Kraft lignin also is
mainly composed of these two elements (Kumar et al., 2009). For both the untreated
lignin and the carbonized ball milled lignin, the primary constituent was carbon, and
smaller traces of oxygen remained after carbonization, indicating that not all oxygen
functional groups would be removed under this carbonization process. In the case of
carbon black, only elemental carbon was detected with no oxygen present. Other carbon
blacks have also shown minimal to no oxygen existing within the powders (Pantea et al.,
2001; Pantea et al., 2003). The difference in oxygen content between the carbonized ball
milled lignin and the carbon black explains the reduced conductivity, as surface elements
other than carbon hinder electrical conductance through the powder.
100
Figure 4.5: SEM images and elemental composition of powder samples of lignin,
carbonized ball milled lignin, and carbon black measured by EDS.
101
4.5 Conclusion
A carbonaceous powder was successfully produced using bioethanol coproduct
lignin and characterized against carbon black to optimize carbonization temperature and
ball milling time. Raman analysis demonstrated a higher graphitic nature for the
carbonized lignin than the carbon black under investigation. The BET surface area was
larger in the case of carbonized lignin as a result of the porosity of the powder. Higher
temperature carbonization and ball milling times up to 24 h reduced unwanted oxygen
species on the carbon surface as seen by FTIR. Carbonized lignin nanoparticles were
formed after 24 h of ball milling that fall within the same order of magnitude in size as
the carbon black aggregates. The conductivity measurements showed inferior electrical
conductivity while having superior thermal conductance. SEM-EDS displayed the carbon
purity of the carbonized lignin to be above 90% close to the highly pure carbon black
powder. With the only drawback of this carbonized ball milled lignin being the electrical
conductivity, the possibility of using the powder as an alternative to carbon black is
feasible when applied to nonelectrical applications. These applications include
nonconductive black ink, toner, paint, thermal paste, and thermally conductive filler.
102
Chapter 5
Physical and Mechanical Properties of Lignin Based Carbon Black As
Filler In Poly(butylene succinate)
103
5.1 Introduction: A Link between Chapters
The global carbon black market is forecasted to grow to a production level of 13
million tons per year by 2015 (Smithers Apex, 2014). A vast array of applications has
caused this rise in demand for the carbon material. Due to the high cost of present
processes including energy consumption and the reliance on crude oil, alternative sources
are being developed as starting material for the carbon black. Biomass and agricultural
wastes are paving the way for a new generation of renewable resource based carbon
blacks. Biobased carbon is now being tested for utilization in polymers and absorbents
(Khalil et al., 2007; Abdul Khalil et al., 2010; Ayyappan et al., 2005). This will allow
future production to become more sustainable and environmentally friendly.
The objective of this study was to determine the potential of the biobased carbon
black prepared in Chapter 4 as a filler in poly(butylene succinate) composites. The
properties of this biobased composite were compared to those of the bionanocomposites
prepared in Chapter 3 that used the petroleum based carbon black.
5.2 Materials and Methods
5.2.1 Materials
The same injection grade poly(butylene succinate) (PBS) Bionolle® 1020 from
Showa Highpolymers Co., Ltd., Japan from Chapter 3 is used as the biopolymer. The
optimized biobased carbon powder from Chapter 4 (900 °C carbonized lignin ball milled
for 24 hours) was used as the composite filler.
104
5.2.2 Processing and Characterization
The processing and characterization procedures were performed as described in
Chapter 3 for the carbon black bionanocomposites.
5.3 Results and Discussion
5.3.1 Mechanical Properties
5.3.1.1 Tensile Properties
As seen in Figure 5.1 and Table 5.1, the tensile stress at yield decreased upon
initial loading but after 5 wt% of the filler the tensile properties returned to that of the
neat PBS material. The tensile modulus did not vary until 5 wt% of the filler was added
in which case the modulus increased by 4% from the neat PBS. The percent elongation
did not differ between treatments (Fig. 5.2, Table 5.1). The data demonstrates a poor
interaction between the matrix and filler as there is a lack of interfacial interactions for
the 1 and 3 wt% loaded composites causing the reduction in tensile stress. The composite
with 5 wt% content did retain the same strength as the neat polymer due to the increased
availability of stress transfer locations.
105
Wt% Carbonized Lignin
0 1 2 3 4 5
Te
nsile
Str
ess a
t Y
ield
(M
Pa
)
0
10
20
30
40
Te
nsile
Mo
du
lus (
MP
a)
640
650
660
670
680
690
700
Tensile Stress
Tensile Modulus
Figure 5.1: Tensile stress at yield and the tensile moduli of the PBS carbonized lignin bionanocomposites (mean±SD).
106
Wt% Carbonized Lignin
0 1 2 3 4 5
% E
lon
gatio
n a
t B
rea
k
0
50
100
150
200
250
300
350
Notc
he
d I
zo
d I
mp
act
Str
en
gth
(J m
-1)
0
5
10
15
20
25
30
35% Elongation
Impact Strength
Figure 5.2: The % elongation at break and impact strength of the PBS carbonized lignin bionanocomposites (mean±SD).
107
5.3.1.2 Flexural Properties
The flexural stress for all composites was greater than the neat PBS, and the 5
wt% loading showed an increase of 9% (Fig. 5.3, Table 5.1). The flexural modulus also
increased when the carbonaceous filler was added, and a maximum increase of 10% was
found for the 5 wt% composite. The higher flexural strength and modulus is due to the
enhanced stiffness of the composites as a result of the particles impeding the movement
of the polymer chains. The addition of the filler also increased the density from 1.26 g
cm-1
to 1.29 g cm-1
, which can also be attributed to the greater flexural properties.
108
Wt% Carbonized Lignin
0 1 2 3 4 5
Ma
x.
Fle
xu
ral S
tress (
MP
a)
0
10
20
30
40
Fle
xu
ral M
od
ulu
s (
MP
a)
680
700
720
740
760
780
800
820
840
Flexural Stress
Flexural Modulus
Figure 5.3: Flexural stress and flexural moduli of the PBS carbonized lignin bionanocomposites (mean±SD).
109
Table 5.1: Means, standard deviation (SD), and the results of means contrasts for tensile stress (TS), tensile modulus (TM), %
elongation at break, flexural stress (FS), flexural modulus (FM), and impact strength of bionanocomposites with different carbonized
lignin content.
Wt%
Carbonized
Lignin
TS
(MPa) SD
TM
(MPa) SD
%
Elongation SD
FS
(MPa) SD
FM
(MPa) SD
Impact
Strength
(J m-1
)
SD
0 31.6 a 0.20 662 a 7.6 267 a 39.9 32.7 a 0.69 708 a 15.5 19 a 1.4
1 31.0 b 0.24 657 a 7.4 238 a 45.2 36.5 b 0.35 767 b 16.6 26 b 2.6
3 30.6 c 0.14 670 a 12.5 224 a 33.3 34.2 c 0.38 720 a 18.1 24 ab 5.1
5 31.8 a 0.38 687 b 10.2 231 a 24.2 35.7 b 1.24 778 b 42.4 25 ab 4.7
a-c Means followed by the same letter in each column are not significantly different according to Tukey's multiple range test (P=0.05).
N=5.
110
Table 5.2: Means, standard deviation (SD), and the results of means contrasts for
the heat deflection temperature (HDT) and the storage modulus at 25 °C of PBS
carbonized lignin bionanocomposites.
Wt% Carbonized Lignin HDT (°C) SD Storage Modulus (MPa) SD
0 85 a 2.0 666 a 21
1 88.10 b 0.08 688 ab 13
3 88.5 b 0.32 726 ab 35
5 88.9 b 0.64 771 b 10
a-b Means followed by the same letter in each column are not significantly
different according to Tukey's multiple range test (P=0.05). N=3.
111
5.3.1.3 Impact Strength
The addition of the biobased carbon black filler caused an increase in the impact
strength that stayed relatively uniform from 1 to 5 wt% (Fig. 5.2, Table 5.1). An
improvement of 37% was seen with the addition of 1 wt%, and subsequent loading did
not improve the properties further. For other low filler content composites it has been
found that there is an increase in impact properties, which is due to matrix shear yielding
and crack pinning of the spherical particles that can act in dissipating some of the energy
during impact (Wetzel et al., 2002).
5.3.2 Dynamic Mechanical Analysis (DMA)
The tan delta for all the composites show a faster transition compared to that of
the neat PBS (Fig. 5.4). The slight reduction in tan delta temperature (shift towards lower
peak temperature) and a narrowing of the peak for the composites in relation to the neat
PBS is a result of introduction of the filler. The interface between the particles and the
matrix change the kinetics by generating a greater number of transition growth fronts that
help speeds up this process (Cui et al., 2007).
There was a trend towards an increased storage modulus at 25 °C with the
addition of carbonized lignin (Fig. 5.4, Table 5.2), and the storage modulus for the 5 wt%
carbonized lignin filled composite was 16% greater than the neat PBS. The higher
modulus value is in accordance with the augmented values seen for both the tensile and
flexural moduli. When other composites have been tested with carbon black, a similar
increase in storage modulus is identified as filler is added (Kim, 2009). The gradual
increase can be justified by the quantity of available interfacial interactions between the
112
particles and matrix that can act as a means of reinforcing the composite.
The addition of the carbon based filler improved the heat deflection temperature
(HDT) from 85 °C to 89 °C, which is an increase of approximately 5% (Table 5.2 and5.3)
Figure . The enhanced HDT of the composites can be attributed to improvement in the
flexural modulus and high temperature stiffness resulting from the filler (Gall et al.,
2002). With the incorporation of the filler the dimensional stability of the composite is
reinforced to prevent the onset of physical deformation of the material under load at high
temperatures.
113
Figure 5.4: Tan δ (lines) and storage moduli (symbols) at 25 °C of the PBS carbonized lignin bionanocomposites (mean±SD).
114
5.3.3 Thermal Analysis
5.3.3.1 Differential Scanning Calorimetry (DSC)
Overall, the DSC data did not exhibit differences between the neat PBS and the
various carbonized lignin composites (Table 5.3). The crystallization temperature (Tc)
was the only notable variation as there was a minor increase in the temperature of up to 2
to 5 °C when the carbonized lignin filler was introduced. Other researchers have observed
the higher crystallization temperature at low loadings of carbon fillers in thermoplastic
composites, which is caused by the particles elevating the nucleation rate and reducing
the average crystal size (Zhou et al., 2006). None of the other thermal properties
including crystallinity (χ), melting temperature (Tm) and the glass transition temperature
(Tg) were affected.
115
Table 5.3: The glass transition temperature, Tg, melting temperature, Tm, enthalpy of fusion, ∆Hm, crystallization temperature, Tc,
enthalpy of solidification, ∆Hc, and crystallinity, χ, based on the DSC curves and heat deflection temperature (HDT) of PBS
carbonized lignin bionanocomposites.
Wt% CL Tg (°C) Tm (°C) ∆Hm (J g-1
) Tc (°C) ∆Hc (J g-1
) χ (%) HDT (°C)
0 -32.11 114.36 64.64 83.7 62.23 30.78 85 ± 2.0
1 -31.55 113.56 64.71 87.19 64.31 31.13 88 ± 0.1
3 -33.24 113.27 60.56 86.51 61.82 29.73 89 ± 0.3
5 -33 113.43 65.14 85.37 62.81 32.65 89 ± 0.6
116
5.3.3.2 Thermal Conductance
As the carbonized lignin content was increased there was an increase in both the
thermal conductivity and thermal diffusivity of the material (Table 5.4). The greater
thermal conductivity can be rationalized by the higher conductivity of elemental carbon
compared to the low conductivity of PBS, such that more filler in the composite is
associated with a higher thermal conductivity. The increase of the thermal diffusivity is
expected because it is directly proportional to the thermal conductivity, which is
increasing and inversely proportional to the specific heat capacity which is decreasing as
more filler is added to the composites. The change in specific heat capacity can be
explained by the polymer having an intrinsically higher specific heat than the
carbonaceous filler, which will cause a reduction in the specific heat capacity when the
carbonized lignin is inserted into the composite.
117
Table 5.4: Means, standard deviation (SD), and the results of means contrasts
for thermal conductivity, thermal diffusivity and specific heat of PBS
carbonized lignin bionanocomposites.
Wt%
Carbonized
Lignin
Thermal
Conductivity
(W m-1
K-1
)
SD
Thermal
Diffusivity
(mm2 s
-1)
SD Specific Heat
(MJ m-3
K-1
) SD
0 0.56 a 0.024 0.051 a 0.0079 11 a 1.5
1 0.59 a 0.057 0.055 a 0.010 10 a 1.1
3 0.76 b 0.023 0.092 b 0.0064 8.3 b 0.41
5 0.84 b 0.058 0.11 b 0.017 7.6 b 0.61
a-b Means followed by the same letter in each column are not significantly
different according to Tukey's multiple range test (P=0.05). N=3.
118
5.3.4 Electrical Resistance and Conductivity
Parameters obtained from the impedance spectroscopy of the composite as given
in Table 5.5 are all indicative of the particle dispersion and type of network the particles
form within the polymer matrix as a result of the non-ohmic conductance the material
exhibits. The carbonized lignin is still considered to be dispersed throughout the matrix
because the percolation threshold has not been reached where the filler forms a unified
structure with no gaps present meaning no gap capacitance or contact resistance. From
Table 5.5, the gap capacitance is shown to increase while both the aggregate resistance
and the contact resistance are reduced with the additional content of carbonaceous filler.
The carbon particles more easily transfer electrical charge than the polymer and thus with
further inclusion of the filler the distance between particles is diminished and packing is
increased allowing for a greater number of avenues for electron flow, thus lowering the
resistance.
The carbonized lignin filler reduced the conductivity when 1 wt% was added
while the 3 and 5 wt% carbonized lignin filled composites did not alter the conductivity
from that of the neat polymer (Fig. 5.5). The initial decrease in conductivity can be
explained by the introduction of the filler interfering with the already existent conductive
channels of the polymer matrix and the absence of any particle agglomerates due to the
uniform distribution. For the composites with higher loadings the particles improved the
conductivity from the 1 wt% composite by increasing aggregation but with a lack of
continuous agglomerates throughout the matrix no improvement over the PBS was
possible.
119
Table 5.5: Means, standard deviation (SD), and the results of means contrasts for the resistance
due to the aggregates, Ra, the contact resistance from the gaps between adjacent aggregates, Rg,
the capacitance of the gaps, C, and the electrical conductivity of PBS carbonized lignin
bionanocomposites.
Wt%
Carbonized Lignin
Ra (kΩ) SD Rg (GΩ) SD C (pF) SD
Electrical
Conductivity (pS m
-1)
SD
0 3.8 a 0.21 1.2 a 0.21 12.3 a 0.89 3.2 a 0.61
1 3.76 a 0.047 0.79 b 0.048 13.93 b 0.058 2.1 b 0.17
3 3.52 ab 0.047 0.43 c 0.070 14.5 b 0.12 2.7 ab 0.13
5 3.33 b 0.061 0.44 c 0.035 15.0 b 0.29 3.0 ab 0.28
a-c Means followed by the same letter in each column are not significantly different according to
Tukey's multiple range test (P=0.05). N=3.
120
Wt.% Carbonized Lignin
0 1 2 3 4 5
Ele
ctr
ical C
onductivity (
pS
m-1
)
0
1
2
3
4
Figure 5.5: Electrical conductivity of the PBS carbonized lignin bionanocomposites (mean±SD).
121
5.3.5 Surface Morphology and Particle Dispersion
5.3.5.1 Scanning Electron Microscopy (SEM)
There were no visible differences in the brittle fracture surface of the composites
as more filler was added (Fig. 5.6). The filler particles were easily visible within the 3
and 5 wt% carbonized lignin composites in having sizes at the sub-micron scale. The
distinguishable particles seen in the composites are well dispersed throughout the
polymer matrix. The particle-matrix interphase shows no signs of cracks or voids
implying that the hydrophobic materials are miscible with one another.
122
Figure 5.6: SEM images of the cryo-fractured surfaces for the PBS carbonized lignin bionanocomposites samples of 1, 3, and 5 wt% at
5000x magnification demonstrating the filler dispersion, with arrows pointing to carbon black particles within the polymer matrix.
123
5.4 Conclusion
The characterization of biocomposites produced from poly(butylene succinate)
and biobased carbon black from carbonized-ball milled lignin was completed. There was
an improvement in the mechanical properties for both the flexural and impact properties
for all composite samples while only the 5 wt% loaded sample had an improvement in
the tensile modulus. The DMA showed an increase in storage modulus with the addition
of the carbonaceous filler. The HDT of the composites along with the thermal
conductivity were heightened above that of neat PBS. No gain in electrical conductivity
occurred with the incorporation of the filler. The impedance measurements with the
support of the SEM images emphasized the good dispersion of the particles within the
composites. The use of these biocomposites as alternatives to traditional carbon black
based composites is possible as they maintain similar properties in terms of mechanical
and thermal characteristics at the highest loading tested. Further filler loadings should be
explored in order to increase possible applications in other advanced composites that
require improved strength, thermal or electrical properties.
124
Chapter 6
General Discussion and Conclusions
125
In this thesis study, the preparation and characterization of bionanocomposites
was undertaken with petroleum based carbon black and a synthetized biobased carbon
black from bioethanol coproduct lignin. The bionanocomposites were tested with a low
loading of carbonaceous filler (1, 3 and 5 wt%) in a poly(butylene succinate) matrix, to
determine the effects on the mechanical, thermal and electrical properties of the material.
When the commercially available conductive carbon black was used, it was found that all
the properties tested showed improvements. In the case of the highest filler content, the
tensile stress at yield improved by 5%, the flexural stress by 13% and the impact strength
by over 100% with an increase in all moduli. There was also an improvement in the
physical properties of the composites with the thermal conductivity having a 50%
increase and the electrical conductivity growing by 102% when 5 wt% carbon black was
added to the neat poly(butylene succinate). Optical microscopy and SEM images showed
evidence of good compatibility between the polymer and filler used, with the particles
remaining uniformly distributed throughout the composites, with no agglomeration
present. The melting behavior and processability of the composites were not affected by
the addition of filler as HDT was augmented by 7% and DSC and DMA were unaffected.
For the production of the biobased carbon black, a bioethanol coproduct lignin
was used and was characterized after pyrolysis and ball milling procedures. This
carbonaceous powder was optimized for high surface area and small particle size to
enhance the mechanical properties of the composites produced using this filler. A
temperature of 900 °C during carbonization produced a product with 90% elemental
carbon that had the greatest surface area of all temperatures tested while maintaining a
graphitic structure and reducing the amount of oxygenated carbon species on the surface.
126
Once ball milling had been applied to the carbonized lignin it was found that 24 hours
created the smallest particles with an average size of 778 nm with 4% being
nanoparticles, with only a minimal loss to the surface area. The electrical conductivity of
the carbonaceous powder was 91% lower than the conductive carbon black tested in the
initial bionanocomposites but had a 36% improvement in its thermal conductivity. When
the carbonized ball milled lignin was then tested in the formation of biocomposites, the 5
wt% loaded composite had the best results with all moduli being heightened, the flexural
stress improving by 9% and 30% for the impact strength while no change was seen in the
tensile stress. The biocomposite’s electrical conductivity did not change while there was
a 50% improvement in thermal conductance. Again the filler remained well dispersed
throughout the polymer matrix as confirmed by SEM images. A 5% gain in HDT was
observed with DMA and DSC showing negligible differences ensuring thermal properties
remained consistent for normal processing conditions to be used.
Currently there remains limited studies on the use of carbon black in
poly(butylene succinate) and even less scientific research has been done on the use of
biobased carbon black as a substitute in polymer composites. Based on our current
knowledge, there has not been any mention of the use of lignin as a precursor for the
production of a biobased carbon black. The outcome of this study illustrates how the
variation in the carbonaceous fillers characteristics affects the properties of the
composites. The differences in elemental composition, size, shape, structure, surface area
and conductivity of the carbon black fillers all play a role in the features of the final
composite. Even though the industrial carbon black with its fused primary particle
structure, small aggregate size and high purity of elemental carbon show the best promise
127
in producing bionanocomposite products that improve upon neat PBS, the biobased
carbon black alternative is comparable in most aspects at these low loading levels. With
small adjustments in the development of impact strength and electrical conductivity the
biobased carbon black could be a viable replacement in the near future for various
composite applications. These applications include automotive interior parts, appliances,
packaging, and consumer goods.
Therefore, the first hypothesis referring to bionanocomposites produced from
poly(butylene succinate) and low content of carbonaceous nanofiller show improved
mechanical, thermal and electrical properties is supported when using the fossil fuel
derived carbon black. However, in the case of the carbonized ball milled lignin
alternative filler improvements in mechanical and thermal performance were evident
while electrical conductivity was not attained. In regards to the second hypothesis with
the exchange of the petro based carbon black for biobased carbon black it is possible to
create a bionanocomposite that perform similarly to that of the commercial carbon black
in terms of the mechanical and thermal properties when using a high loading of 5 wt%
filler but is unable to match the electrical conductivity.
128
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Appendix
The following equations were used in the measurements of the polymer
bionanocomposites found in Chapter 3 and Chapter 5.
Mechanical Properties:
All tensile measurements were calculated according to ASTM D638-10, Standard
Test Method for Tensile Properties of Plastics.
Tensile stress (TS) at yield: The tensile stress at yield is calculated by dividing the load at
yield by the original minimum cross sectional area and is expressed in megapascals
(MPa).
( )
( )( ) (1)
Tensile modulus (TM): The tensile modulus is calculated by using the tangent of the
initial linear portion of the stress-strain curve in the elastic region of the material and
dividing the tensile stress, at any point on this tangent, by the corresponding strain and is
expressed in megapascals (MPa).
( )
( )( )
( )
( )
(2)
% elongation at break: The elongation at break is calculated by dividing the elongation at
the moment of rupture by the initial gauge length and multiplying by 100 and is
expressed in percent.
147
( )
( ) (3)
All flexural measurements were calculated according to ASTM D790-10,
Standard Test Methods for Flexural Properties of Unreinforced and Reinforced Plastics
and Electrical Insulating Materials.
Maximum flexural stress (FS): The maximum flexural stress is calculated at the highest
stress along the stress-strain curve or when 5% strain is reached using Equation 4 and is
expressed in megapascals (MPa),
, (4)
where, P is the load at a given point on the load-deflection curve (N), L is the support
span (mm), b is the width of specimen (mm), and d is thickness of specimen (mm).
Flexural modulus (FM): The flexural modulus is calculated along the tangent line drawn
at the steepest initial linear portion of the stress-strain curve, within the elastic region,
according to Equation 5 and is expressed in megapascals (MPa),
, (5)
where, L is the support span (mm), m is the slope of the tangent at the initial linear
portion of the stress-strain curve (N mm-1
), b is the width of the specimen (mm), and d is
the thickness of the specimen (mm).
All impact strength measurements were calculated according to ASTM D256-10,
Standard Test Methods for Determining the Izod Pendulum Impact Resistance of Plastics.
148
Notched Izod Impact strength: The impact strength is calculated by taking the breaking
energy of the specimen and dividing it by the specimen’s thickness at the notch location
and is expressed in J m-1
.
( )
( ) (6)
Tan Delta (δ): The tan δ is calculated by dividing the loss modulus (energy dissipated as
heat; viscous portion) by the storage modulus (stored energy; elastic portion) and is
expressed without units.
( )
( ) (7)