Bionanocomposites from Renewable Resources for ...

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.

iii

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.

iv

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

v

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

vi

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

vii

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

viii

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

ix

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


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


conductivity, thermal diffusivity and specific heat of PBS carbonized lignin

bionanocomposites………………………………………………………………….…………..117



the capacitance of the gaps, C, and the electrical conductivity of PBS carbonized lignin

bionanocomposites……………………………………………………………………………...119

xi

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

xii

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

xiii

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

xiv

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

2

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

4

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)

10

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.

11

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

12

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

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Year of Publication

15

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


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


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).


4.3.1 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


4.4.1 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.


5.2.1 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.


5.3.1 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


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


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


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


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


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


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


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



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


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.

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( )

( ) (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.

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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)

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