NANOSCALE EFFECTS GOVERNING
MECHANICAL REINFORCEMENT IN
C O M P O S I T E M AT E R I A L S
peter may
A thesis submitted for the degree of
Doctor of Philosophy
Supervised by Prof. Jonathan Coleman
Chemical Physics of Low Dimensional Nanostructures Group
School of Physics
Trinity College Dublin
2013
To all the very special people who have helped me on my journey so far..
D E C L A R AT I O N
I declare that this thesis has not been submitted as an exercise for a degree at this or any
other university and it is entirely my own work.
I agree to deposit this thesis in the University’s open access institutional repository or allow the library to do so on my behalf, subject to Irish Copyright Legislation and
Trinity College Library conditions of use and acknowledgement.
Elements of this work that have been carried out jointly with others or by collaborators have been duly acknowledged in the text wherever included.
Peter May
iii
ABSTRACT
Nanoscale materials have been studied extensively in the scientific research community in recent years. Due to their impressive mechanical properties, one- and twodimensional nanomaterials have become candidates as reinforcing fillers for composite
materials.
In this work, the energetics of liquid phase exfoliation of graphene, boron nitride
(BN) and molybdenum disulphide (MoS2 ) via polymer aided exfoliation is examined.
It is found that both exfoliation and stabilisation of nanosheets occur in an otherwise
poor solvent using this mechanism. Using a derived model, maximum exfoliation yield
is predicted to occur within a region near where the Hildebrand solubility parameters
of nanosheet, polymer and solvent phases coincide. The model shows solid agreement
with experimental results. This facilitates an easy choice of polymer and solvent to
maximise exfoliated nanosheet yield, provided the solubility parameters of each of the
three phases are known.
Composites prepared herein with both graphene and BN nanosheets in the polymer polyvinylalcohol (PVA) achieve modulus reinforcement levels of dY/dVf = 680
and 670 GPa respectively, close to the ultimate theoretical limit. Strength increase
rates of dσB /dVf = 22 and 47 GPa are observed for graphene and BN filled films
respectively. Both rates of increase observed for each composite set are higher than any
2D-nanofiller/polymer composites in the literature to date. It is yet unclear why the
BN films exhibit such a high level of strength increase, surpassing the shear strength
of the PVA matrix itself. Comparison with the modified rule of mixtures (MRoM) and
Halpin-Tsai (HT) reinforcement models show that both composite sets agree with one
of the two utilised theories.
A survey is also carried out in a similar PVA matrix with a range of nine onedimensional filler materials. Data from this comparison reveals while some fillers show
no reinforcement, mechanical increases between dY/dVf = 415-1860 GPa and dσB /dVf
iv
= 28-38 GPa suggest that new breeds of rod-like fillers are possible replacements
for more expensive filler substances. Interfacial layer crystallisation is suggested to
be responsible for certain data exhibiting modulus increase above the theoretical
maximum. It is also shown that modulus and strength increase rates are linked via a
linear relationship.
Finally, a metal matrix composite is prepared using solvent exfoliated molybdenum
telluride (MoTe2 ) nanosheets as a filler material. It is shown that a doubling of Young’s
modulus is achieved, for composites containing only 1 vol% MoTe2 . The increase in
mechanical properties is shown to agree well with the HT model. Further investigation
into the nature of the MRoM and HT models leads to distinct differences between
them, namely that each theory is more accurate at predicting reinforcement levels for
composite systems with either a high (MRoM) or low (HT) YF /YM ratio. Despite these
differences, both models suggest that filler aspect ratio is the most critical parameter
governing efficient composite reinforcement.
v
P U B L I C AT I O N S
1. Khan, U., May, P., O’Neill, A., and Coleman, J. N. (2010) Development of stiff, strong,
yet tough composites by the addition of solvent exfoliated graphene to polyurethane.
Carbon 48 (14), 4035–4041.
2. Khan, U., May, P., O’Neill, A., Vilatela, J. J., Windle, A. H., and Coleman, J. N.
(2011) Tuning the Mechanical Properties of Composites from Elastomeric to Rigid
Thermoplastic by Controlled Addition of Carbon Nanotubes. Small 7, 1579–1586.
3. Khan, U., Porwal, H., O’Neill, A., Nawaz, K., May, P., and Coleman, J. N.
(2011) Solvent-Exfoliated Graphene at Extremely High Concentration. Langmuir 27,
9077–9082.
4. Khan, U., O’Neill, A., Porwal, H., May, P., Nawaz, K., and Coleman, J. N. (2012)
Size selection of dispersed, exfoliated graphene flakes by controlled centrifugation.
Carbon 50, 470–475.
5. May, P., Khan, U., O’Neill, A., and Coleman, J. N. (2012) Approaching the theoretical limit for reinforcing polymers with graphene. Journal of Materials Chemistry 22,
1278.
6. May, P., Khan, U., Hughes, J. M., and Coleman, J. N. (2012) Role of Solubility
Parameters in Understanding the Steric Stabilization of Exfoliated Two-Dimensional
Nanosheets by Adsorbed Polymers. The Journal of Physical Chemistry C 116, 11393–
11400.
7. Nawaz, K., Khan, U., Ul-Haq, N., May, P., O’Neill, A., and Coleman, J. N. (2012)
Observation of mechanical percolation in functionalized graphene oxide/elastomer
composites. Carbon 50, 4489–4494.
8. Sainsbury, T., Satti, A., May, P., Wang, Z., McGovern, I., Gunko, Y. K., and Coleman,
J. N. (2012) Oxygen Radical Functionalization of Boron Nitride Nanosheets. Journal of
the American Chemical Society 134, 18758–18771.
vi
9. Sainsbury, T., Satti, A., May, P., O’Neill, A., Nicolosi, V., Gunko, Y., and Coleman, J. N. (2012) Covalently Functionalized Hexagonal Boron Nitride Nanosheets by
Nitrene Addition. Chem. - Eur. J. 18 (35), 10808–10812.
10. Khan, U., May, P., O’Neill, A., Bell, A. P., Boussac, E., Martin, A., Semple, J.,
and Coleman, J. N. (2013) Polymer reinforcement using liquid-exfoliated boron nitride
nanosheets. Nanoscale 5 (2), 581–587.
11. Khan, U., and May, P., and Porwal, H., and Nawaz, K., and Coleman, J. N. (2013)
Improved Adhesive Strength and Toughness of Polyvinyl Acetate Glue on Addition of
Small Quantities of Graphene. ACS Applied Materials and Interfaces 5 (4), 1423-1428
12. May, P., Khan, U., and Coleman, J. N. (2013) Reinforcement of metal with
exfoliated inorganic layered material. Applied Physics Letters (submitted)
13. May, P., Khan, U., Gao, C., Yin, Y., Li, L., Chen, Y., and Coleman, J. N. (2013)
Survey of the mechanical properties of composites of polyvinylalcohol filled with a
range of 1-dimensional nano-fillers. Applied Physics Letters (submitted)
vii
Be kind whenever possible. It is always possible.
(Dalai Lama)
If you want to draw water you do not dig six one foot wells. You dig one six foot well.
(The Buddha)
Science is the great antidote to the poison of enthusiasm and superstition.
(Adam Smith)
ACKNOWLEDGMENTS
At the beginning of an extensive list of people who I wish to thank is Prof. Jonathan
Coleman. Without his expertise, guidance, inspiration and generosity this thesis would
not have become a reality. I wish also to express an abundance of gratitude to Dr.
Umar Khan, for his constant input, solid friendship and unfathomable memory when
it comes to all scientific values and procedures.
It is difficult to comprehend that my four years in the Coleman Group is coming to
an end. I thank Dr. Shane Bergin, who on day one led a nervous Theoretical Physicist
into a room full of experimentalists in order to become acquainted with folks that
would soon become research colleagues and dear friends. Thank you to everyone who
has since moved on to persue their careers - Darren, Paula, Denise, Rickard, Fiona,
Brian, Karen, Marguerite, Damian, Toby, Will, Sukanta, Phil, Evelyn and Mustafa, I
wish you all the best for the future. To all the current members of the group - Arlene,
Claudia, Keith, Eswar, Greg, Paul, Rónán, Sophie, Tom, Graeme, Seb, Conor, Peter,
Damien, Auren, Oana and Shaobo, thank you for your continued guidance and support.
Thanks to the MI crowd and the visiting students I have had the pleasure of working
with - Harshit, Khalid, Sweta, Manoli, Arnaud, Elodie and Rahim. Thank you to
Neal, Amanda, Clodagh and Heath in the CMA for all the help with the imaging. To
Samantha and Ciara in the finance office for all the patience dealing with four years
worth of chitties.
viii
Thank you to all the people I have met on the football pitch - to Pa, Shay, Jimmy,
Georg, Ken, Alan, Robbie and Jemmer for the Monday game. To Ronan, Brendan,
Kristian, Carsten, Graham, Rocco and Mike for the chemistry game. Thanks too to
all the Sporstco lads for some great early weekend matches, especially Rob for his
effort when organising the emails. Special mention goes to Dr. Niall McEvoy for his
outstanding man marking when on the opposition, forcing me to better myself on the
field or providing that extra heart to overcome the opposition when on the same side.
To all of the above sportsmen who have put up with my long legs!
Last but certainly not least, deepest thanks to my family for 26 years of support,
enabling me to progress to this point with great pride and satisfaction.
ix
CONTENTS
1 motivation
1
2 materials and background
3
2.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
2.2
Nanoscale Filler Materials - Carbon Based . . . . . . . . . . . . . . . . .
3
2.2.1
Graphene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5
2.2.2
Carbon Nanotubes . . . . . . . . . . . . . . . . . . . . . . . . . . .
12
2.2.3
Carbon Nanofibers . . . . . . . . . . . . . . . . . . . . . . . . . . .
16
Nanoscale Filler Materials - Inorganic . . . . . . . . . . . . . . . . . . . .
17
2.3.1
Boron Nitride Nanosheets . . . . . . . . . . . . . . . . . . . . . . .
18
2.3.2
Molybdenum Disulphide Nanosheets . . . . . . . . . . . . . . . .
19
2.3.3
Molybdenum Telluride Nanosheets . . . . . . . . . . . . . . . . .
21
2.3.4
Boron Nitride Nanotubes . . . . . . . . . . . . . . . . . . . . . . .
21
2.3.5
Tungsten Disulphide Nanotubes . . . . . . . . . . . . . . . . . . .
23
2.3.6
Silica Nanotubes . . . . . . . . . . . . . . . . . . . . . . . . . . . .
24
Matrix Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
25
2.4.1
Polymers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
25
2.4.2
Metals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
32
Composite Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
34
2.3
2.4
2.5
3 characterisation and methods
36
3.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
36
3.2
Absorption Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . .
37
3.3
Raman Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
Raman Spectroscopy of Carbon Nanomaterials . . . . . . . . . .
41
3.3.1
3.4
Tensile Testing
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
42
3.5
Transmission Electron Microscopy . . . . . . . . . . . . . . . . . . . . . .
44
x
contents
. . . . . . . . . . . . . . . . . . . . . . .
46
3.6
Scanning Electron Microscopy . . . . . . . . . . . . . . . . . . . . . . . .
48
3.7
Helium Ion Microscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . .
51
3.8
Differential Scanning Calorimetry . . . . . . . . . . . . . . . . . . . . . .
53
3.9
Thermogravimetric Analysis . . . . . . . . . . . . . . . . . . . . . . . . .
54
3.10 Focused Ion Beam Lamella Preparation . . . . . . . . . . . . . . . . . . .
55
3.11 Energy-dispersive X-ray spectroscopy . . . . . . . . . . . . . . . . . . . .
58
3.5.1
Statistical TEM Analysis
4 theory
60
4.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60
4.2
Solubility Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60
4.2.1
Thermodynamics of Solutions . . . . . . . . . . . . . . . . . . . .
61
4.2.2
Solubility Parameters . . . . . . . . . . . . . . . . . . . . . . . . .
62
Polymer States in Solution and at Surfaces . . . . . . . . . . . . . . . . .
63
Steric Stabilisation . . . . . . . . . . . . . . . . . . . . . . . . . . .
65
Mechanical Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
66
4.4.1
Mechanisms of Deformation . . . . . . . . . . . . . . . . . . . . .
66
4.4.2
Modelling Composite Reinforcement . . . . . . . . . . . . . . . .
68
4.3
4.3.1
4.4
xi
5 the role of solubility parameters in understanding the steric
stabilisation of exfoliated two-dimensional nanosheets by adsorbed polymers
71
5.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
71
5.2
Experimental Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . .
72
5.3
Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
74
5.3.1
Initial Characterisation . . . . . . . . . . . . . . . . . . . . . . . . .
74
5.3.2
Modelling Polymer Adsorption . . . . . . . . . . . . . . . . . . .
76
5.3.3
Comparing Experiment with Theory . . . . . . . . . . . . . . . .
82
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
89
5.4
6 polymer reinforcement using graphene and boron nitride nanosheets 91
6.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
91
contents
6.2
6.3
Experimental Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . .
93
6.2.1
Graphene Composites . . . . . . . . . . . . . . . . . . . . . . . . .
93
6.2.2
BN Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
94
Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
96
2D-nanofiller / polymer composites in the literature . . . . . . .
96
Sample Characterisation . . . . . . . . . . . . . . . . . . . . . . . . . . . .
99
6.4.1
Graphene Composites . . . . . . . . . . . . . . . . . . . . . . . . .
99
6.4.2
BN Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
6.3.1
6.4
6.5
6.6
xii
Mechanical Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
6.5.1
Graphene Composites . . . . . . . . . . . . . . . . . . . . . . . . . 104
6.5.2
BN Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
7 polymer composites containing a range of 1-dimensional nanofillers118
7.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
7.2
Experimental Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
7.3
Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
7.4
7.3.1
1D-nanofiller / polymer Composites in the literature . . . . . . . 120
7.3.2
Filler Structure and Morphology . . . . . . . . . . . . . . . . . . . 120
7.3.3
Mechanical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
8 metal matrix composites with exfoliated layered compounds 129
8.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
8.2
Experimental Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
8.3
Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
8.4
8.3.1
MMCs in the Literature . . . . . . . . . . . . . . . . . . . . . . . . 132
8.3.2
Characterisation
8.3.3
Mechanical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
9 conclusions
141
contents
9.1
Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
10 appendix
145
bibliography
161
xiii
LIST OF FIGURES
Figure 1.1
European and worldwide plastics demand since 1950 . . . . . . .
2
Figure 2.1
Illustration of various carbon allotropes . . . . . . . . . . . . . . .
4
Figure 2.2
Illustration of the crystal structure of graphene . . . . . . . . . .
6
Figure 2.3
Schematic of the production of chemically derived graphene . .
7
Figure 2.4
Schematic of the liquid phase exfoliation process . . . . . . . . .
12
Figure 2.5
Illustration of carbon nanotube structure . . . . . . . . . . . . . .
14
Figure 2.6
Illustration of the lattice structures of BN, TMDs and Silica combined with elements forming TMD compounds . . . . . . . . . .
18
Figure 2.7
Schematic of the repeated unit conifguration of polymers . . . .
25
Figure 2.8
Illustration of the structural configurations of polymer chains . .
26
Figure 2.9
Schematic of the different repeat unit configurations of polymers
27
Figure 2.10
Schematic of the three distinct cases of stereoisomerism in polymers 27
Figure 2.11
Illustration and micrograph image displaying the spherulitic
structure of crystalline polymer regions . . . . . . . . . . . . . . .
29
Figure 2.12
Schematic describing the classification of polymers . . . . . . . .
31
Figure 2.13
The chemical composition of polyvinylalcohol. . . . . . . . . . .
32
Figure 2.14
Crystal structures adopted by metals . . . . . . . . . . . . . . . .
33
Figure 3.1
Illustration of the Lambert-Beer Law . . . . . . . . . . . . . . . . .
38
Figure 3.2
Schematic of a UV-Vis spectrophotometer . . . . . . . . . . . . . .
39
Figure 3.3
Energy level diagram of infrared absorption and various inelastic
light scattering events . . . . . . . . . . . . . . . . . . . . . . . . .
40
Figure 3.4
Raman spectra of graphite and graphene . . . . . . . . . . . . . .
41
Figure 3.5
Stress strain curve showing characteristic parameters of tensile
Figure 3.6
testing combined with typical curves for various polymer types
43
Schematic of a TEM column . . . . . . . . . . . . . . . . . . . . .
45
xiv
List of Figures
Figure 3.7
TEM images describing length, width and thickness determination for graphene nanosheets . . . . . . . . . . . . . . . . . . . . .
Figure 3.8
Representation of the signals generated within the interaction
volume of an SEM specimen . . . . . . . . . . . . . . . . . . . . .
Figure 3.9
48
49
Representation of the ion emission process in a helium ion microscope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
51
Figure 3.10
A DSC specimen curve for a PVA film . . . . . . . . . . . . . . . .
53
Figure 3.11
Illustrations of the FiBiD process and equipment setup within
the instrument column . . . . . . . . . . . . . . . . . . . . . . . . .
56
Figure 3.12
Illustration representing the components of the silicon drift detector 58
Figure 4.1
Representation of the different states of adsorbed polymers . . .
Figure 4.2
Illustration of the deformation mechanism of semi-crystalline
materials. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure 4.3
75
TEM images of dispersed nanosheets in various polymer/solvent
solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure 5.3
67
Photograph of post-centrifugation supernatants of graphene dispersed in THF by a range of polymers . . . . . . . . . . . . . . . .
Figure 5.2
66
A stress strain curve describing the various stages of deformation
in semi-crystalline materials. . . . . . . . . . . . . . . . . . . . . .
Figure 5.1
64
76
Statistical TEM analysis averages for length, width and thickness
of nanosheets for the 8 graphene/polymer/THF samples as a
function of polymer HSP . . . . . . . . . . . . . . . . . . . . . . .
Figure 5.4
Schematics of the two scenarios examined in the derived lattice
model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Figure 5.5
77
80
Concentration of dispersed nanosheets after centrifugation as a
function of HSP of stabilising polymer . . . . . . . . . . . . . . .
83
Figure 5.6
Measured Gaussian peak widths plotted against kT/[vS (δG − δS )] 85
Figure 5.7
Dispersed concentration of graphene in PSt/THF plotted against
number of Kuhn monomers per PSt chain . . . . . . . . . . . . .
Figure 5.8
86
Normalised contour plot in δP and δS of predicted nanosheet
concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
88
xv
List of Figures
Figure 6.1
Representative TEM images and Raman spectra of both smaller
as-prepared, and larger size-selected graphene flakes . . . . . . .
Figure 6.2
99
Helium ion images of neat PVA and graphene/PVA film fracture
surfaces and stress strain curves from the same films . . . . . . . 101
Figure 6.3
TEM images of liquid exfoliated, size selected BN nanosheets . . 102
Figure 6.4
Photograph of BN/PVA composite films and helium ion images
of BN/PVA composite fracture surfaces . . . . . . . . . . . . . . . 103
Figure 6.5
Mechanical properties of graphene/PVA composites . . . . . . . 104
Figure 6.6
MRoM calculated modulus length efficiency factor versus graphene
aspect ratio for PVA composites . . . . . . . . . . . . . . . . . . . 107
Figure 6.7
Stress strain curves for neat PVA and the best performing BN/PVA
composite film . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
Figure 6.8
Mechanical properties of BN/PVA composites . . . . . . . . . . . 111
Figure 6.9
Mechanical properties of BN/PVA and graphene/PVA films
versus polymer composites in the literature . . . . . . . . . . . . 117
Figure 7.1
TEM and SEM image collage of the nine studied 1D nanofillers . 121
Figure 7.2
Representative stress strain curves for neat PVA and composites
featuring poor and successful reinforcement . . . . . . . . . . . . 123
Figure 7.3
Mechanical properties of the six composite sets showing reinforcement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
Figure 7.4
Modulus reinforcement rate versus strength increase rate for all
nine composite sets and maximum modulus versus maximum
strength values for composites containing the six reinforcing fillers127
Figure 8.1
TEM, photograph and SEM collage of the MoTe2 dispersion,
pewter ingot, composite films and fracture surfaces of pewter
only and composite samples . . . . . . . . . . . . . . . . . . . . . 130
Figure 8.2
Representative stress strain curves and mechanical properties of
pewter only and MoTe2 composite films . . . . . . . . . . . . . . 135
Figure 8.3
Comparison of modulus length efficiency factor prediction accuracy for HT and MRoM models . . . . . . . . . . . . . . . . . . . 139
xvi
List of Figures
Figure 10.1
TEM statistics of flake dimesions for size-selected and as-prepared
graphene/PVA dispersions . . . . . . . . . . . . . . . . . . . . . . 148
Figure 10.2
Comparison of mechanical properties of composites prepared
with size-selected and as-prepared flakes . . . . . . . . . . . . . . 149
Figure 10.3
Strain at break and Toughness data for size-selected graphene/PVA
composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
Figure 10.4
DSC curves for the best performing size-selected graphene/PVA
composite and neat PVA film . . . . . . . . . . . . . . . . . . . . . 150
Figure 10.5
Representative TEM images for the smaller, as-prepared BN
nanosheets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
Figure 10.6
TEM statistics of flake dimensions for the smaller, as-prepared
BN nanosheets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
Figure 10.7
Mechanical properties of PVA composites prepared with asprepared BN nanosheets . . . . . . . . . . . . . . . . . . . . . . . . 152
Figure 10.8
TEM statistics of flake dimensions for the size-selected BN/PVA
dispersion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
Figure 10.9
TGA analysis on the BN/PVA powder used to form composites
Figure 10.10
DSC spectra for the neat PVA and 3 BN/PVA composite films . 154
Figure 10.11
Representative stress strain curves for the best performing 1D-
153
filler/PVA composite films compared with neat PVA . . . . . . . 154
Figure 10.12
Mechanical properties of the three 1D-filler/PVA composite sets
showing no reinforcement . . . . . . . . . . . . . . . . . . . . . . . 156
Figure 10.13
Strain at break and Toughness data for the six 1D-filler/composite
sets showing reinforcement . . . . . . . . . . . . . . . . . . . . . . 157
Figure 10.14
Strain at break and Toughness data for the three 1D-filler/composite
sets lacking reinforcement . . . . . . . . . . . . . . . . . . . . . . . 158
Figure 10.15
DSC curves and melt enthalpy measurements for the neat PVA
and nine 1D-filler/PVA composites . . . . . . . . . . . . . . . . . 158
Figure 10.16
SEM images of the raster scan regions chosen for EDX analysis
of pewter only and MoTe2 / pewter composites . . . . . . . . . . 159
xvii
Figure 10.17
EDX scan results for the pewter only and MoTe2 / pewter composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
L I S T O F TA B L E S
Table 5.1
Hildebrand solubility parameters of studied nanosheets and
solvents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Table 10.1
73
A literature summary of the Young’s modulus of nanoscale filler
materials featured in this thesis, with some added TMD materials 146
Table 10.2
Lattice properties of filler materials used throughout this work . 147
Table 10.3
Dispersed nanosheet concentration for a range of polymer/solvent combinations, shown with polymer HSP . . . . . . . . . . . 147
Table 10.4
A summary of mechanical properties of graphene/polymer composites in the literature . . . . . . . . . . . . . . . . . . . . . . . . 155
xviii
1
M O T I VAT I O N
Polymeric materials are some of the most commonly used substances on Earth. From
product packaging, sports equipment and automotive coatings to prosthetic limbs,
super-glue and contact lens solution, plastics provide a myriad of applications in
everyday life. With worldwide plastics usage rapidly approaching 300 million tonnes
per annum (figure 1.1), the required quantity of these materials is only deemed to
increase in the future.
The recent advances in the scientific research of nanomaterials have led to many
interesting prospects with regard to plastics used for structural purposes. Materials
such as graphene and carbon nanotubes possess unparalleled levels of modulus and
strength - a material’s resistance to inital elongation and the maximum force required
to fracture the material, respectively. This makes such substances desirable as filler
materials, utilised in composites to increase the mechanical properties of the matrix
substance. Furthermore, due to their very low densities, these substances can be
incorporated into a polymer without substantially increasing the overall weight of the
composite. This advantage is vital for lightweight and high performance structural
applications.
Many challenges are present in the field of composite reinforcement using nanomaterials. Due to complex synthesis techniques, many of the nanoscale materials used in
composites are very expensive, approaching $1000/g for some carbon nanotubes. Filler
quality has a huge effect on reinforcement potential also, with desirable properties
only present at the nanoscale, an efficient production method of isolating a large
amount of high quality particles from the bulk is required. In addition, harmful and
costly solvents are often required to exfoliate these particles from the bulk. This can be
avoided, by chemically altering the material to react well with safer solvents. However,
1
motivation
Figure 1.1: Comparison of European and worldwide plastics production quantities between
1950 and 2011. Reproduced from [1].
in the case of graphene, this process lowers the mechanical properties of the material
considerably.
The main goal of this work is to fabricate composite materials with high levels of
reinforcement, using knowledge regarding nanoscale interactions that are present
between filler and matrix materials, both during the exfoliation process and through to
composite film formation. Initially, knowledge is gained into maximising the exfoliation
yield of two-dimensional layered nanomaterials using liquid phase polymer-aided
exfoliation in a poor solvent. A different, aqueous environment is then used to produce
two polymer composites sets, each filled with a layered nanomaterial, and is shown
to produce high yields of filler material with suitable thickness and dimensions for
effective reinforcement. Finally, insights into reinforcement obtained from polymer
composites are utilised to quantify filler impact on a distinctly different, metal matrix.
Upon comparison of experimental data with theoretical reinforcement models and
using several sample characterisation techniques, vital information is gained into which
filler parameters are critical for the formation of mechanically superior composite
materials. It is hoped that the results presented in this thesis can contribute to the
fabrication of stiffer, stronger composites in the future.
2
2
M AT E R I A L S A N D B A C K G R O U N D
2.1
introduction
This chapter outlines and discusses the materials examined in this thesis. The physical
structure and properties of each substance is highlighted, with particular emphasis
placed on the mechanical properties of each material. The various synthesis and
production methods for each material are also examined, with advantages and disadvantages highlighted for each procedure in the case where various techniques exist.
Special consideration is given to the liquid phase exfoliation method, the technique
utilised throughout this thesis.
The substances are categorised into two sections, nanoscale filler materials and
matrix materials, to distinguish their roles in the fabrication of efficient composite
materials. Carbon based nanofillers are discussed initially, followed by inorganic fillers
and transition metal dichalcogenides (TMDs), with the convention of 2-dimensional
(2D) preceding 1-dimensional (1D) materials. The matrix materials section contains a
brief description of the structure and properties of both polymers and metals, with
added detail given to the examples of each material family chosen for this work. Finally,
insight into the field of composite materials is given, with motivation as to why such a
hybrid system should lead to the production of favourable new materials.
2.2
nanoscale filler materials - carbon based
Carbon, the fourth most abundant material in the universe by mass, is a very special
and versatile element. This material, present in all known life forms, has six protons
and is tetravalent, with four electrons available to form covalent bonds. Many isotopes
3
2.2 nanoscale filler materials - carbon based
Figure 2.1: Allotropes of carbon, displayed in order of increasing dimensionality. Reproduced
from [9].
of carbon exist, from stable C12 , the basis from which the atomic mass of all nuclides
is measured, to radioactive C14 , an isotope used in carbon dating of organic materials
due to its long half life of 5730 years [2].
Elemental carbon exists in two natural forms, graphite and diamond, consisting of
networks of sp2 - and sp3 - hybridised carbon atoms respectively [3], as shown in figure
2.1. Both of these forms show interesting traits such as high thermal and electrical
conductivity, impresssive hardness and lubricative properties. Diamond and graphite
represented the only abundant allotropes of carbon for a very long time.
That changed significantly in 1985, when Kroto et al. observed fullerenes for the
first time, igniting an era of interest into synthetic carbon allotropes [4]. The smallest
and most stable of these fullerenes, C60 , consisted of 60 sp2 - hybridised carbon atoms,
arranged in a zero-dimensional (0D) “soccer ball” configuration, seen in figure 2.1.
Five years later the first macroscale production of 0D fullerenes was reported by
Kraetschmer and Huffman, using an arc discharge method [5]. Among other interesting
properties, it has been found that chemically functionalising these substances vastly
increases their solubility in any solvent, making fullerenes desirable for biomedical
applications [3]. Nowadays fullerenes are used as exceptional electron acceptors in solar
devices [6]. Huge scientific breakthroughs were to follow in the carbon family, with
the synthesis of carbon nanotubes (CNTs) in 1991 [7] and the discovery of graphene in
2004 [8].
4
2.2 nanoscale filler materials - carbon based
2.2.1
2.2.1.1
Graphene
Discovery
Graphene was first discovered in 2004 when Geim and co-workers at Manchester
University first isolated monolayer samples from highly oriented pyrolytic graphite,
using a mechanical cleavage method [8]. While studies had progressed for some time,
utilising fewer and fewer layers of graphite [10], a single layer of such a material was
assumed not to exist in nature [8]. This was due to the belief that such atomic planes
would be thermodynamically unstable on such a scale [11], and if unsupported, such
planes would buckle and roll-up [12]. This pioneering discovery of single layer carbon
led to an explosion of interest in the scientific community, and its impact was truly
recognised by the award of the Nobel Prize in physics for related work in 2010.
2.2.1.2
Structure and Properties
Graphene is a two-dimensional planar sheet of sp2 - hybridised carbon atoms. It is
the parent material for all carbon allotropes. It can be stacked to form bulk graphite,
rolled into cylindrical CNTs or curved into spherical buckyball type fullerenes such
as C60 . Arranged in a hexagonal “honeycomb” lattice, individual graphene sheets in
a stack are bound together by Van der Waals forces, as shown in figure 2.2. These
forces are weak relative to the covalent interatomic bonds within a given lattice, and
allow graphite to be stripped down to few or even single layer sheets, by mechanical
cleavage or other means [8, 13–15]. Originally assumed to be perfectly flat, ripples in
the lattice were found to be present, caused by thermal fluctuations [16]. Since the
advancement of high resolution transmission electron microscopy (TEM), it has been
possible to directly image graphene’s honeycomb lattice structure [17]. In terms of
optical properties, it has been found that graphene absorbs 2.3% of the incident light
per layer [18], a significant level of absorption for a single atomic layer of material.
Graphene has been dubbed a “wonder material” due to its incredible range of
impressive properties. These include its properties as a zero bandgap semiconductor
or semi-metal, a tunable bandgap [19], an ambipolar field effect [8], the quantum-
5
2.2 nanoscale filler materials - carbon based
Figure 2.2: The hexagonal crystal structure of graphene, shown with lattice constants for the
carbon-carbon bond, and interlayer spacing. In this tri-layer stack, individual sheets
are bound by Van der Waals forces. Modified from [28].
Hall effect at room temperature [20] and extremely high carrier mobility [21]. These
electrical properties suggested that graphene would be an ideal candidate for use in
solar cell devices, flexible transparent conductors and other areas such as conductive
polymer composites [22–25]. Use in gas sensor technology was also highlighted, as the
first ever detection of single molecule adsorption events were reported for graphene
[26].
With regard to studies described in this thesis, the most important report on graphene
was that of its mechanical characteristics. In 2008, Lee et al. measured the stiffness and
strength of monolayer graphene, using a nanoindentation method on an atomic force
microscope (AFM) [27]. This study showed atomically thin graphene to be the strongest
material known to man, with extremely high stiffness and strength of ~200 times that
of steel. This report resulted in graphene quickly becoming the ideal candidate as a
filler material for polymer composite systems. While CNTs were known to be extremely
strong and stiff, both the geometrical advantage of a planar sheet when considering
polymer-nanomaterial interactions and the dramatic reduction in cost compared to
CNTs made graphene the ideal choice for such a system. All that was required at this
point, was a method to successfully exfoliate graphite to produce large quantities of
high quality graphene.
6
2.2 nanoscale filler materials - carbon based
Figure 2.3: The production of chemically derived graphene. Modified from [32].
2.2.1.3
Chemistry and Functionalisation
Graphite has a rich chemistry in which it can participate in reactions as either a
reducing agent or an oxidiser. For this reason, it became apparent that by using
chemical processes, the surface chemistry of graphite could be altered in order to
change its properties. Hummer’s method, proposed in the 1950s, uses strong oxidising
agents to attach hydroxyl groups and other functionalities on the basal plane of
graphite, altering its affinity with water [29]. In 2006, members of Ruoff’s group
demonstrated a solution based process for producing monolayer graphene by initially
using Hummer’s method to produce a water dispersable graphitic intermediary
material known as Graphite Oxide (GO) [14, 30]. GO contains AB stacked sheets with
hydroxyl and epoxide functionalities on the basal plane [31]. GO was then readily
exfoliated to form graphene oxide (GrO) upon sonication, as the hydrophilicity of
the sidegroups allows water to intercalate between the sheets and disperse them
individually [14], as shown in figure 2.3.
Problems with this method include the fact that GO itself is non conducting. This
can be remedied by thermal annealing of the graphene oxide of by treatment with
hydrazine hydrate to remove the oxide groups on the surface the formation and
removal of epoxide complexes [14]. However, hydrazine itself is problematic as the
substance is both highly toxic and potentially explosive [33].
Another method of preparing graphene sheets from GO is that of thermal shock
treatment. This method involves the rapid heating of GO to 1050 o C at a rate of
2000 o C/min. This causes causes the GO to split into individual sheets through CO2
evolution. Although this process does not involve volatile chemicals such as hydrazine,
7
2.2 nanoscale filler materials - carbon based
there is still a remainder of oxygen within the graphene sheets, at a carbon to oxygen
ratio of 10:1 [15].
These methods display the difficulty in removing the oxygen completely once the
oxidation step such as Hummer’s method has been underaken. Hence, it is virtually
impossible to restore GrO to a pure sp2 - hybridised system such as that of pristine
graphene. With regard to the mechanical properties of the GrO, the remaining oxygen
groups act as defects in the lattice, reducing dramatically the mechanical properties
of the planar material. In fact, reports suggest that the modulus of monolayer GrO is
up to ~80% lower than that of single sheet pristine graphene [34]. Thus it becomes
clear how advantageous it would be to develop a method to produce large quantities
of pristine graphene for composite formation. This is discussed further later in this
chapter.
2.2.1.4
Production Methods and Challenges
It is no surprise that the demand for large amounts of high quality graphene has
skyrocketed since the material’s discovery. Although several production techniques
are available, unfortunately each route features a trade-off between scalable mass
production and material quality [35]. The performance of graphene depends on three
factors - nanosheet dimensions, nanosheet thickness / number of layers and the overall
quality of the crystal lattice, with regard to both electrical [36, 37] and mechanical [38]
properties. The various methods for graphene production will now be discussed.
The initial discovery of graphene was realised via micromechanical cleavage, or
“scotch tape” method. This involves using adhesive tape to peel thin graphitic layers
from bulk graphite and depositing them on a silicon substrate. By repeatedly using this
method, the graphite crystals can be split into increasingly thinner pieces, eventually
resulting in monolayer exfoliation. This type of exfoliation provides the highest quality,
single layer samples with no lattice damage. It is however, both arduous and extremely
low yield. In order to exfoliate a single sheet using this method, van der Waals
attraction forces between exactly the first and second layers have to be overcome
without disturbing any other weak intersheet bonds. For this reason, other avenues
were explored for graphene production.
8
2.2 nanoscale filler materials - carbon based
Many attempts have been made to produce graphene by using chemical reduction
of graphite oxide [14, 39–42], as mentioned above. However, these methods first
involve oxidation of the graphite surface, and reduction methods often use volatile
chemicals and cannot completely remove oxygen groups. Recently however, reports
have been made suggesting that annealing methods using focused solar radiation leads
to less oxygen functionalities [43]. In the past few months, Kaner and co-workers have
developed a novel route of graphite oxide reduction known as light-scribing [44]. This
involves coating a compact disc surface with a GO dispersion and reducing it into
graphene using the laser in a conventional DVD-burner drive.
Another method of producing graphene is that of epitaxial growth on silicon carbide (SiC). The SiC is heated to temperatures exceeding 1100o C in a low pressure
environment to reduce it to graphene. The size and properties of the SiC wafer govern
the properties of the formed graphene, such as its mobility and carrier density. Many
reports of successful graphene production using this method exist [45–47]. However,
disadvantages of this method include non-uniform arrays of graphene [47] and the
stringent environments required for growth, such as atomically clean polished surfaces
and ultra vacuum. Furthermore, due to the extremely high temperatures required,
substrate type is limited, as polymers and other flexible substrates cannot withstand
such conditions. Graphene has also been grown on ruthenium using these conditions
[48]. However, this substrate tends to lead to a formation of a graphene film with
non-uniform thickness.
The process of chemical vapour deposition (CVD) has emerged as a highly efficient method to produce large areas of high quality graphene. This route involves
the decomposition of a carbon containing gas (eg. methane) and a process gas (eg.
hydrogen) that catalyses a reaction between methane and a copper substrate, typically
held at ~1000 o C. Growth of predominantly monolayer graphene on copper foil has
been reported using hexane at 950 o C [49]. In recent years, reports of high quality
graphene production with very large areas have been reported, from centimetre size
with 95% monolayer coverage [50] up to 30 inch predominantly monolayer films using
roll-to-roll production and wet chemical doping [51]. There are even accounts in the
literature of graphene produced using CVD from “unusual” precursor materials [52].
9
2.2 nanoscale filler materials - carbon based
However, even with large areas of graphene possible, the CVD process always has its
graphene yield limited by the substrate size. Furthermore, bottom-up methods such as
CVD and epitaxial growth are simply not suitable for composite formation.
Another bottom-up method has also been employed for graphene formation, growing graphene directly from chemical assembly [53, 54] using polycyclic aromatic
hydrocarbons [55, 56]. Nonetheless, as with other methods there are drawbacks with
this route. Chemical syntheses have been size limited due to macromolecules becoming
insoluble and the occurance of side reactions increases with molecular weight [54].
2.2.1.5
Liquid exfoliation of pristine nanomaterials
Exfoliation of nanomaterials involves taking a bulk or aggregated material and reducing it down to its most basic atomic form, in order to harness the unique physical
properties of the material only available at the nanoscale. For 2D materials this involves
a single planar atomic sheet, whereas for 1D materials a single tube or rod-like structure is utilised. Although somewhat similar to exfoliation of graphite oxide, shown
above in figure 2.3, exfoliation of pristine materials is a distinctly different method of
producing nanomaterials than micromechanical cleavage, epitaxial growth, CVD or
chemical synthesis.
The exfoliation of compounds is a topic that dates back to the mid 19th century
[57]. At this time, it was known that graphite had a laminar orientation, although it
wasn’t until the 1920s that a detailed structural understanding of the material became
apparent [58]. When microscopy techniques became widespread in the late 1900s,
advancement in scanning tunneling microscopy (STM) and AFM allowed accurate
determination of single layers of nanomaterials. At this point widespread interest had
fueled vast amounts of research into material exfoliation.
After graphene’s discovery, interest in exfoliation surged, with efforts made to
exfoliate this new wonder material. The first reports of monolayer graphene production
involved liquid exfoliation and the oxidization of graphite [14, 30], as shown in 2.3.
With regard to exfoliation of pristine materials, nano-graphite was produced using the
solvent dichlorobenzene in 2005 [59], however no monolayer graphene was observed.
It was not until three years later when monolayer graphene was first produced using
10
2.2 nanoscale filler materials - carbon based
liquid exfoliation of pristine graphite [60]. This was reported my members of the group
and since then has been widely used to exfoliate materials in both solvent [61–64] and
aqueous surfactant [65, 66] systems. A large range of nanomaterials besides graphene
have been exfoliated using this method [57], from 1D materials [67] to 2D inorganic
nanosheets [68]. As discussed in greater detail in chapters 5, 6 and 7, this method can
be used to exfoliate materials in the presence of a stabilising polymer.
Liquid phase exfoliation involves the excitation of a nanomaterial in a suitable
solvent, using various forms of energy. While high shear mixing methods harness
mechanical energy, sonication involves sound induced material excitation. It relies on
the structural properties of layered materials, that they are covalently bonded in plane
but weakly bonded via van der Waals forces out of plane. More information on solvent
choice for this method is given in chapter 4.
Using ultrasonication, high frequency sonic energy is transferred into the sample
via a liquid medium (ie. the sample vessel is placed in a water bath). The sonic tip is
placed directly into the sample vessel. The applied sonic energy causes the solvent
molecules to oscillate. The sound waves from the tip change between compression and
rarefaction cycles vary rapidly due to a frequency >15 kHz. The solvent molecules
are drawn apart by this rarefaction cycle and cavities are created. These cavitation
bubbles cause scission in rod like materials [69]. It is noted that as yet, the breaking
mechanism of both 1D and 2D materials casued by cavitation induced scission is poorly
understood [70]. Depending on the choice of solvent, after exfoliation occurs either a
stable dispersion follows or a dispersion with aggregation and/or sedimentation. This
is shown in figure 2.4.
The advantages of this method are numerous. By using mild sonication in a sonic
bath, quantities of solvent and nanomaterial up to 1 litre can be exfoliated in a number
of days. By adding a large initial concentration of nanomaterial, large yields of few
layer exfoliated material can be obtained. Furthermore, nanomaterials such as graphene
show no defects or oxides in the lattice due to this production method [60], which
leads to no deterioration in mechanical properties of the sheets, in stark contrast to the
sheets produced by chemical reduction techniques above. Keeping the nanoparticles
in liquid phase allows removal of unexfoliated crystallites and flake size selection
11
2.2 nanoscale filler materials - carbon based
Figure 2.4: Stages of the liquid phase exfoliation process. The layered crystal is sonicated
in a solvent to form exfoliated nanosheets. In “good” with appropriate surface
energy, the dispersion is stable. Conversely, for “poor” solvents, reaggregation and
sedimentation of nanosheets will occur. Modified from [57].
via centrifugation [64], and is also ideal for mixing with polymer/solvent blends to
dropcast composite films.
Unlike other techniques, no high temperatures, ultra high vacuum conditions or
large voltages are required for this method, however this route is not without its
disadvantages. Although good concentrations of exfoliated material can be achieved
using aqueous polymer and aqueous surfactant systems, the solvents found to be
optimal for exfoliation yield are toxic and/or harmful, and also expensive. Another
drawback of liquid phase exfoliation is that of exfoliated material thickness. Although
small percentages of single layers flakes have been observed using this method, the
vast majority of exfoliated nanosheets are between 3-7 layers. However, as discussed
in further chapters, with regard to composite reinforcement it is not crucial to have a
dispersion that is majority single layer to harness the outstanding mechanical properties
of these materials, once the nanosheets possess sufficient length to thickness ratios.
2.2.2
2.2.2.1
Carbon Nanotubes
Discovery
There has been some controversy with regard to who should be accredited with the
discovery of CNTs [71], with reports in a soviet journal dating back as far as 1952 that
seemed to contain images of the nanomaterial [72]. However, it is widely believed that
12
2.2 nanoscale filler materials - carbon based
the glory goes to Iijima et al. who discovered multi-wall carbon nanotubes in material
obtained after arc-burning graphite rods [7]. After this discovery, researchers began
theorising that if a single wall version of this cylindrical material were to be obtained,
it would possess remarkable properties [73]. With this goal on the horizon, tremendous
effort was made to isolate this material. Only one year later, it had been achieved, with
independent reports from Iijima’s group themselves [74] and others [75] showcasing
the material.
2.2.2.2
Structure and Properties
Carbon nanotubes are essentially graphene atomic layers rolled into 1D cylindrical
tubules, as shown in figure 2.1. They can consist of one cylinder (single wall nanotubes
or SWNTs) or several concentric rings (multiwall nanotube or MWNTs). Depending
on how the lattice is wrapped can have a large effect on the material’s properties. The
wrapping configuration is described using the chiral vector
Cn = na1 + ma2
(2.1)
where a1 and a2 the lattice unit vectors and n, m are known as the chiral indices. This
relation is displayed in figure 2.5, along with the 3 types of nanotubes characterised by
different chiral index configurations. For chiral nanotubes, an added parameter, the
chiral angle φ is required to describe the wrapping configuration. This is the angle
between the chiral vector and the armchair line. This angle is always 0o and 30o for
armchair and zig-zag tubes respectively, and hence is only used to describe chiral
tubes, when 0o < φ < 30o . Nanotubes have been known to have very high aspect ratio,
with typical diameter and lengths shown in figure 2.5A, although tubes have been
fabricated with lengths up to ~55 cm [76, 77]. The diameter d of the tube is related to
the chiral indices via
d=
a
π
q
(n2 + nm + m2 )
(2.2)
where a = 0.246 nm. Tubes are also “capped” at the end by a hemispherical “soccer
ball” type configuration. Reports suggest that the orientation of these caps, governed
by the chirality of the nanotube that they join onto, have a large effect on the cap
formation energy [78].
13
2.2 nanoscale filler materials - carbon based
Figure 2.5: Structural properties of CNTs. A) Typical dimensions of a SWNT. B) Describing the
lattice wrapping using the chiral vector relation. C) - E) Wrapping configurations of
C) armchair, D) zig-zag and E) chiral nanotubes.
Carbon nanotubes maintain many of the impressive physical properties of the
graphene sheet when it is wrapped into this two-dimensional form. Interestingly, the
electrical properties of CNTs can change dramatically based on the nanotube type. For
instance, with regard to the chiral vector in equation 2.1, if (n − m)/3 = i where i is an
integer, then the tube is semiconducting, otherwise the tube is metallic. It follows from
this relation that approximately one third of CNTs are semiconducting. It is possible to
separate these two subgroups by chemical treatment and centrifugation [79]. Other
significant properties of CNTs include their thermal conductivity, which is nearly ten
times that of copper. This is due to ballistic conduction of charge carriers along the
tube axis. These materials have also been studied as candidates for water desalination
[80], microwave absorption [81] and for use as nanoscale electrochemical motors [82].
With regard to this work, carbon nanotubes also retain graphene’s extremely desirable mechanical properties. Reports using different testing regimes have all suggested
that CNTs are on par with graphene in terms of stiffness [83–85] and strength [86]
along the tube axis. This stiffness has been shown to decrease by a factor of 20-100 in
the radial direction, for tubes with radius >3 nm [87, 88], however. The aspect ratio
of a filler particle also greatly affects its efficiency to reinforce a composite material,
as shown in this thesis. Nanotubes have a higher aspect ratio than any 2D material,
14
2.2 nanoscale filler materials - carbon based
making them a desirable filler material for composites. SWNTs also posses very high
levels of hardness, and can withstand high pressures without deformation. Reports
suggest that a superhard phase of SWNTs possesses higher hardness levels than that
of diamond [89].
2.2.2.3
Synthesis Methods and Challenges
Nowadays there are four main synthesis techniques utilised to produce scalable quantities of carbon nanotubes. These routes involve physical processes - arc discharge,
laser ablation; and chemical techniques - CVD, high pressure carbon monoxide disproportionation (HiPco).
The physical synthesis methods make use of physical principles for carbon conversion into nanotubes. These two process are the most widely used for production of
nanotubes for experimental purposes. Arc discharge is the first of these methods, originally used by Iijima to produce the first MWNTs [7]. The principle of this technique is
to vaporise carbon in the presence of catalysts under a reduced atmosphere of inert
gas (usually argon or helium). Arc discharge tends to produce NTs that are narrower
and shorter than those produced by the next method, laser ablation.
The laser ablation method was first discovered by Smalley and coworkers at Rice
University [90]. This method involves a pulsed laser that hits a graphite target in a
high temperature reactor filled with helium, which vaporises the target. The nanotubes
form on the cooler surfaces of the reactor, as the vaporised carbon condenses. By
varying temperature, catalyst composition and other parameters, the average diameter
and lengths of the NTs can be tailored [91]. Both arc discharge and laser ablation
methods form bundles of CNTs rather than single structures. Disadvantages of these
two methods include the high energy requirements for CNT production and also
that the CNTs formed are often in tangled configurations, mixed in with unwanted
materials such as amorphous carbon or catalysts.
Two chemical synthesis routes are also used for NT production. The CVD growth
method works in a similar fashion to graphene growth for CNT growth, with one
exception. For nanotube growth a variation of metal catalysts such as iron, nickel
and cobalt is used along with a silicon substrate instead of a copper foil. The carbon
15
2.2 nanoscale filler materials - carbon based
containing gas is broken down on the surface of the catalyst particles, and carbon is
deposited on the nanotube edges, causing them to grow. The catalyst particles can
either stay on the top of the growing nanotube, or remain at the tube’s base during
growth [92]. The substrate preparation is vital for CVD growth, as it is this component
that governs the nature and type of CNTs formed.
The final NT synthesis method is known as the HiPco technique. Unlike CVD growth
where metal catalysts are embedded on a substrate before carbon deposition, HiPco
involves the introduction of a metal catalyst in the gas phase. This method is suitable
for large scale synthesis, as the nanotubes are free from catalytic supports. The carbon
monoxide gas reacts with iron pentacarbonyl, Fe(CO5 ) to form SWNTs.
Advantages of these chemical processes include simple reactor design, abundant
availability of the required gases for synthesis, cost effectiveness to to lack of expensive
targets and low energy requirements. It is worth noting however that scale up of the
CVD reactors provides a serious challenge [93].
2.2.3
2.2.3.1
Carbon Nanofibers
Structure, Properties and Applications
Carbon nanofibres (CNFs) are nanoscale cylindrical structures consisting of graphene
layers arranged as stacked cones, cups or plates. These materials are similar to carbon
fibers, which are larger diameter (few microns) materials commonly used along with
epoxy resin to form high strength composite materials. Unlike carbon nanotubes,
CNFs are not arranged by perfectly wrapping graphene sheets into cylinders. This less
efficient arrangement leads to a noticeable decrease in mechanical properties. Reports
studying the mechanical properties of CNFs have shown that these fibres only possess
a stiffness of ~5-25% of the best performing CNTs [94–96].
Nonetheless, CNFs are used in a range of applications, such as single fibres for
scanning probe microscopy tips [97] and field emission sources [98] as well as fiber
arrays for electrode materials [99] and gene delivery applications [100].
16
2.3 nanoscale filler materials - inorganic
2.2.3.2
Synthesis Methods
Both carbon fibres and CNFs are synthesis via the aforementioned CVD method. The
fibre generation process consists of six stages - gas decomposition, carbon deposition,
fiber growth, fiber thickening, graphitisation and purification. These processes result
in hollow fibres, where fibre diameter is governed by catalyst size, as is the case for
CVD synthesis of CNTs. The CVD process falls into two categories - a fixed catalyst
process and a floating catalyst process. The former produces carbon fibres, while the
latter produces carbon nanofibres, which is discussed here.
The floating catalyst process was first reported in 1983 [101], with later modifications
by Hatano et al. [102]. They utilised ultrafine catalyst particles in a hydrocarbon gas
with temperatures approaching 1100 o C. The fiber growth initiates normally on the
catalyst surface. However, only the surface area of the catalyst particle in contact
with the gas mixture promotes growth, with fibre growth ceasing when this region
is covered [103]. This “covering” occurs when the catalyst is poisoned by impurities
within the system. This phenomenon limits the size of the grown fibres and ensures
their dimensions stay in the nanofibre regime.
2.3
nanoscale filler materials - inorganic
In addition to carbon based nanomaterials, many new types of materials have been
synthesised and used for a number of physical applications. Inorganic materials such
as Boron Nitride (BN) have been fabricated as both 2D layered materials (hBN) [104]
and 1D nanotubes [104, 105]. Silica (SiO2 ) nanotubes have also recently been produced
[106]. The remainder of the materials used as filler particles in this work belong to a
host of compounds known as the transition metal dichalcogenides (TMDs). Materials
in this group have been the subject of increasing scientific research over the last few
years [57, 107–109], due to the presence of distinct properties present in different TMDs,
with some being semiconducting, semi-metallic, metallic and even superconducting
[107]. While some of these materials have been exfoliated in the liquid phase [68],
others have yet to be isolated from the bulk using this technique.
17
2.3 nanoscale filler materials - inorganic
Figure 2.6: A) - C) Typical lattice structures of A) hBN, B) TMDs and C) Silica. D) Periodic
groups from which elements are chosen to generate compounds from the zoo of
substances known as TMDs. Modified from [57, 107].
2.3.1 Boron Nitride Nanosheets
2.3.1.1
Properties and Applications
Hexagonal boron nitride (BN) is a layered material with a graphite like structure in
which networks of BN structures are regularly stacked [104]. The BN lattice is similar
to that of graphene, with sp2 - bonded layers containing alternating boron and nitrogen
atoms, as shown in figure 2.6A. However, unlike its carbon cousin, BN is an insulating
material.
In bulk form, BN has low density, high thermal conductivity, electrical insulation,
superb oxidation resistance, passivity to reactions with acids, and low coefficient
of friction [104]. The vast majority of reports regarding applications of this layered
material are those regarding its electrical properties. Because of its desirable electrical
18
2.3 nanoscale filler materials - inorganic
insulation properties, BN has been applied as a charge leakage layer for use in electronic
equipment. Graphene technologies have led to interest in this material as due to
similarity in the structures between both materials. The use of BN thin films as a
thin top dielectric layer to gate graphene and as an inert flat substrate for graphene
transistors has been shown to significantly increase device performance [110]. BN has
also been used a a lubricating material. With regard to its mechanical properties, in
addition to the work reported in this thesis, there have been few reports of this layered
material being used in a polymer composite for reinforcement purposes [111]. No
experimental analysis has been undertaken to determine the mechanical properties of
BN in its single layer form. Nevertheless, due to its similarities with graphene, with
strong bonds in plane and weak bonds out of plane, the material is promising as a
filler substance.
2.3.1.2
Production Methods and Challenges
Several years ago, BN nanosheets were first produced using the decomposition of
borazine in the form of nanomeshes on metallic substrates in the case of lattice
mismatch [112], or on metal surfaces for BN (matching lattices) [113]. Only recently
has this material been harnessed using ultrasonication of BN powder [68]. Limitations
with regard to the chemical synthesis methods of BN include the need for atomically
clean polished surfaces, ultrahigh vacuum and temperatures in excess of 1000 K [112].
With regard to the liquid exfoliation production, as discussed previously the main
drawback of this method is the very low monolayer yield, although high quantities of
few layer material can be produced in this manner.
2.3.2
2.3.2.1
Molybdenum Disulphide Nanosheets
Properties and Applications
Molybdenum disulphide (MoS2 ) is a layered compound belonging to the TMD family.
This classification consists of a wide range of compounds, containing a single transition
metal layer sandwiched between two chalcogen layers. Chalcogens consist of the
19
2.3 nanoscale filler materials - inorganic
elements sulphur, selenium and tellurium. Typical TMD structures and elements
in this material family are shown in figure 2.6B and D. The side view from figure
2.6B shows that TMDs typically adopt a trigonal prismatic structure, although lattice
parameters vary with compound as shown in Table 10.2 in the appendix. These
compounds have been observed to adopt honeycomb, “H” , and centered honeycomb,
“T”, configurations [114]. Although a single layer consists of three atomic sheets for
these materials, like graphene these materials are still bound by weak interlayer van der
Waals forces, allowing them to be exfoliated to few layer quality. Due to the presence
of metals within these compounds, TMDs tend to have much higher density than other
layered materials such as graphene and hBN.
A rippling effect similar to that observed for graphene has been examined in
monolayer MoS2 [115]. Similarly to hBN, the weak forces between MoS2 layers give
it a low coefficient of friction, giving the compound desirable lubricative properties.
Unlike hBN however, MoS2 is a direct bandgap semiconductor with a bandgap of 1.2
eV. This property along with a high surface area make MoS2 an interesting candidate
for use as an anode material in lithium ion batteries [116] and as an electrode material
in supercapacitor devices [117]. Most recently, MoS2 has emerged as a very promising
material as an advanced catalyst for hydrogen storage applications [118].
Results in the literature probing the mechanical properties of thin layers of MoS2
state its stiffness to be roughly 1/3 that of graphene for monolayer [119] and few layer
[120] samples.
2.3.2.2
Production Methods and Challenges
A number of techniques have been used to produce low thickness MoS2 nanosheets.
These include liquid phase exfoliation of the bulk powder in either solvent [68] or
aqueous surfactant [121] media. There is little to no monolayer exfoliation using this
method, however. Large areas of single layer MoS2 have been produced using CVD
[122], but high temperatures are required and yield is limited by substrate size. Li et al.
have reported a solvothermal method of producing MoS2 nanosheets on the surface of
reduced graphene oxide [118]. This method requires the use of both harmful solvent
dimethylformamide and explosive material hydrazine to function.
20
2.3 nanoscale filler materials - inorganic
2.3.3
2.3.3.1
Molybdenum Telluride Nanosheets
Properties and Applications
Molybdenum telluride (MoTe2 ) is another layered materials belonging to the TMD
classification. It also features a trigonal prismatic structure, similar to that seen in figure
2.6B. This material was chosen for work within metal matrix composites as its density
matched the metal matrix material closely, allowing for more uniform filler-matrix
interaction. Unlike MoS2 above, MoTe2 is semi-metallic, and therefore has a very small
overlap between the bottom of the valence band and the top of the conduction band.
Thin films of MoTe2 have been reported as effective photovoltaic cell materials [123].
MoTe2 is a relatively new material in terms of research, compared to other TMDs
such as MoS2 . As yet no mechanical experimentation has been carried out on the
material. However, a recent publication utilises density functional theory to predict the
stiffness of a MoTe2 at roughly 15% that of graphene and CNTs [124].
2.3.3.2
Production Methods and Challenges
MoTe2 has been exfoliated in the liquid phase using aqueous surfactant systems [121].
In this work the bulk powder is ultrasonicated in the solvent n-methylpyrrolidone.
Other methods include cathode electrodeposition on conducting substrates from
ammonaical solution of H2 MoO4 and TeO2 [123]. However, it was not possible to
achieve few layer deposition, with the thinnest layers of 800 nm observed. It has also
been shown that MoTe2 can be synthesised by annealing a molybdenum foil under
tellurium pressure [125]. This method is severely limited by the size of the foil substrate,
measuring only 10x13 mm in this case.
2.3.4
2.3.4.1
Boron Nitride Nanotubes
Properties and Applications
The production of Boron Nitride nanotubes (BNNTs) was first reported by Chopra et
al. in 1995 [105]. These new materials were found to possess a high aspect ratio and a
21
2.3 nanoscale filler materials - inorganic
similar structure to multiwall CNTs. These tubes, consisting of a wrapped atomic sheet
of boron nitride shown in figure 2.6A, are known as cylindrical BNNTs (Cyl BNNTs).
Reports have shown that like CNTs, Cyl BNNTs can conform to armchair, zig-zag and
chiral wrapping configurations [104]. There is a striking difference between CNTs and
Cyl BNNTs, however. In CNTs, topological defects manifest themselves in terms of
odd membered rings, yet even-membered rings for these BN tubes. This also leads to
characteristic “caps” at the end of Cyl BNNTs which are distinguishable from CNT
caps [104]. Single walled Cyl BNNTs are not popular in nature, due to peculiar B-N
stacked characteristics, and hence are rarely observed and studied [126].
Reports also describe a distinctly different BNNT structure, displaying a bamboo-like
appearance [127, 128]. These tubes consist of short BN nanotubular segments with
specific interfaces at the bamboo shaped joints. This type of BNNT has been observed
as a by product from different synthesis methods, and does not seem to be unique to a
certain process.
Unlike metallic or semiconducting CNTs, BNNTs are electrically insulating with a
bandgap of ~5.5 eV [129]. However, reports have shown that this can be changed to
narrow gap n- or p-type semiconductors through doping [130, 131], deformation [132]
and/or functionalisation [133]. BNNTs also possess much higher chemical and thermal
stability than CNTs [134], making them more suitable than their carbon counterpart
for nanotube-based devices or shielding layers on various nanomaterials, especially for
those functioning in high temperature or hazardous conditions. These materials have
also been found to possess superhydrophobic capabilities [135].
Some attempts have been made to examine the mechanical properties of BNNTs.
Tests on multiwall Cyl BNNTs have reported stiffness values both above [136] and
below [137] that of graphene. Bamboo BNNTs have considerably lower stiffness,
roughly 1/4 that of graphene and CNTs [138]. Interestingly, measurements on the
torsional strength and shear modulus of multiwall Cyl BNNTs have shown that these
materials have values up to ten times higher than those reported for multi-wall CNTs
[139]. This has been attributed to strong interlayer bonding, arising from the faceted
nature of the BNNTs, caused by the polarity of the B-N bond.
22
2.3 nanoscale filler materials - inorganic
2.3.4.2
Synthesis Methods and Challenges
Cyl BNNTs have been synthesised using ball milling and annealing [127, 128], CVD
[117, 140, 141], CNT substitution reaction [142], arc discharge [105] and laser ablation
[126]. Disadvantages associated with ball milling include a long duration (150 hrs) and
high annealing temperatures (>1000 o C) for successful synthesis. CNT substitution
reactions require temperatures in excess of 1500O C and also expensive CVD grown
CNTs as a precursor. The obstacles encountered for the remaining methods have been
discussed earlier in this chapter.
2.3.5
2.3.5.1
Tungsten Disulphide Nanotubes
Properties and Applications
Tungsten disulphide nanotubes (WS2 NTs) are 1D cylindrical structures formed by
wrapping one or more atomic layers of WS2 , another member of the TMD compounds.
The 2D form adopts a trigonal prismatic structure as shown in figure 2.6B. First
discovered by Tenne et al. in 1992 [143], these nanotubes were formed by annealing
WS2 films at 1000 o C in the presence of low pressure H2 S gas. Each wall of the nanotube
consists of a wrapped layer composed of a sixfold bonded tungsten layer, sandwiched
between two threefold bonded sulphur layers.
The main applications of WS2 nanotubes are those of solid lubrication and structural
enhancement [144]. Reports have shown WS2 NTs to have modulus values roughly
1/7 that of graphene [145, 146] and also that these tubes have impressive shockwave
resistance [147]. These properties could perhaps lead to efficient use of WS2 NTs in
terms of structural reinforcement, shielding or in the automotive industry.
2.3.5.2
Synthesis Methods and Challenges
Reports suggest that large quantities of WS2 NTs can be synthesised by sulphidising
tungsten oxide nanoparticles in a fluidised bed reactor [148]. Other synthesis methods
include an activation process involving nitric acid treatment [149] and atmospheric
23
2.3 nanoscale filler materials - inorganic
pressure CVD growth [150]. Although layered 2D WS2 has been exfoliated in the liquid
phase [68], no reports have surfaced for exfoliation of WS2 NTs in solvents.
2.3.6 Silica Nanotubes
2.3.6.1
Properties and Applications
The final filler material to be discussed in this work is silica nanotubes (Silica NTs).
These 1D materials are formed from the parent material shown in figure 2.6C, consisting
of a tetrahedral arrangement of oxygen atoms around silicon atoms. Silica is also known
as silicon dioxide (SiO2 ), an extremely abundant material in the form of sand and
glass. Silica NTs have recently been utilised in the biomedical sciences industry, as
their distinctive inner and outer surfaces make them ideal for gene delivery [151], cell
sorting and cell targeting, molecular imprinting and hydrogen adsorption and storage
[152]. Mechanical investigation of Silica NTs consist only of theoretical calculations
to date. These reports display stiffnesses of 75% that of CNTs and graphene, putting
them on par with values theorised for BN [153].
2.3.6.2
Synthesis Methods and Challenges
Common synthesis methods for Silica NTs all involve a templating method, with
templates consisting of organic or inorganic materials [152]. The tubes used for work
in thesis were fabricated using templating against nickel-hydrazine complex nanorods
[106]. This method allows control of the nanotube aspect ratio, a critical parameter
for effective filler materials. Other methods in the literature include aluminium oxide
template synthesis. Organic methods consist of sol-gel synthesis using surface reactions
[154], with surfactants, acids, block copolymers and gel systems all being utilised in
the literature [152].
Numerous challenges have to be overcome to provide well defined, high quality
Silica NTs, especially for biological and biomedical applications. First, the large-scale
synthesis of high-quality of Silica NTs requires a facile and reproducible process. An
unavoidable problem associated with syntheses is the availability of reliable templates.
24
2.4 matrix materials
Figure 2.7: The repeated unit configuration of polymers, shown for Teflon. Adapted from [155].
Only a small spectrum of materials is suited to act as templates, although most
inorganic templates and biological templates inspired from nature are thermally and
chemically stable with respect to specific reaction mechanisms, their versatility is
limited. The use of organic compounds as templates may require complex techniques
of synthetic chemistry where multiple-step reactions, harmful organic solvents and/or
toxic substances are involved.
2.4
2.4.1
matrix materials
Polymers
Polymers are large macromolecules consisting of very long and flexible chains of
molecules, bound together by covalent bonds [155]. Polymers are synthesised from stable molecules known as monomers, translated from the Greek “mono-meros” meaning
“one-part”. Polymer chains have repeating structural units known as “mers” which contain groups of carbon (most commonly, sometimes oxygen) atoms covalently bonded to
other atoms or groups. The term polymer is coined from this repeating unit structure
- “many-mers”. The carbon atoms comprise the backbone of the polymer, with each
carbon atom bound to two other carbon atoms, one on each side. Hence, two remaining
valence electrons are free to bond with other atoms or groups. This configuration is
most simply presented in the case of polytetrafluoroethylene or Teflon, with each mer
unit containing two carbon atoms and four fluorine atoms. This is displayed in figure
2.7
25
2.4 matrix materials
Figure 2.8: Three structural configurations of polymer chains. A) Linear, B) Branched and C)
Cross-linked configurations. Adapted from [155].
As is the case for Teflon, when all the repeating units along the chain are of the same
type, the polymer is known as a homopolymer. In contrast to this, copolymers contain
two or more different mer units.
Polymer structure an be classified into three categories, linear branched and crosslinked,
as shown in figure 2.8. Linear polymers are those in which the mer units are joined
end to end in single chains, as shown in figure 2.8A. Polymers can be synthesised so
that side branch chains are connected to the main chains, as seen in figure 2.8B. These
branched polymers have reduced packing efficiency compared to linear polymers,
and therefore a lower polymer density. In crosslinked polymers, visible in figure 2.8C,
adjacent linear chains are connected to one another at various stages by covalent bonds.
As discussed in greater detail later in the chapter, these crosslinks can either be formed
during synthesis or by an irreversible chemical reaction.
2.4.1.1
Molecular Configurations
For linear and branched polymers that possess more complex side groups (other than
H, F, Cl, Cl3 for example) bonded to the backbone, the periodicity and symmetry of the
side groups can significantly influence the polymer’s properties. Consider the repeat
unit shown in figure 2.9A. One arrangement is possible when the side groups (denoted
by R) of successive mer units bond to alternate carbon atoms in the backbone. This is
known as head-to-tail configuration, as shown in figure 2.9B. Conversely, a head-tohead configuration arises when R side groups bond to adjacent carbon atoms in the
backbone, displayed in figure 2.9C. In most polymers, the head-to-tail configuration
predominates as polar repulsion can occur between side groups for the head-to-head
configuration.
26
2.4 matrix materials
Figure 2.9: Different configurations of A) the repeat unit in a polymer. In general, head-to-tail
configuration B) predominates head-to-head configuration C) due to polar repulsion
between side groups (R). Modified from [155].
Figure 2.10: The three distinct cases of stereoisomerism. A) isotactic B) syndiotactic and C)
atactic configurations. Adapted from [155].
For molecules aligned in the head-to-tail configuration, 3 separate situations can arise
depending on the spatial arrangement of the molecules. This is known as stereoisomerism, with the 3 different scenarios shown in figure 2.10. In the first case, all the side
groups are located on the same side of the chain backbone, as seen in figure 2.10A. This
is called an isotactic configuration. In the case where the side groups alternate between
different sides of the backbone, the term syndiotactic configuration is adopted, as in
figure 2.10B. Finally, the case where side groups are randomly positioned is dubbed
an atactic configuration, as in figure 2.10C.
Conversion from one stereoisomer scenario to another is not possible by merely
rotating the bonds. This process can only occur once the bonds are initially severed,
then rotated and reformed. In reality, a specific polymer does not uniquely exhibit one
of these stereoisomerisms, although the dominant configuration can be governed by
the method used to synthesise the polymer from a monomer material.
27
2.4 matrix materials
2.4.1.2
Molecular Weight
Molecular weight is used to describe a polymer’s composition in terms of chain
bulkiness and length. Polymers with large side groups attached to the backbone or
very long chains will have large molecular weights. Most synthetic polymers are
polydisperse - i.e. they have a range of chain lengths and molecular weights. For this
reason the molecular weight must be calculated as an average molecular weight which
considers all the weights of all the chains in the sample. There are a few different
methods to calculate the molecular weight, which consider either the number average,
weight average or viscosity average of a sum of chains. In this work the weight average
molecular weight is chosen, given by MW [ g/mol ]:
MW =
∑ Wi Mi
(2.3)
where Mi represents the mean molecular weight of chain size range i and Wi denotes
the weight fraction of molecules within that same size interval. At room temperature.
polymers with very short chains exists as liquids or gases (MW < 100 g/mol). Soft
resins exist in the intermediate range (MW ∼ 1000 g/mol) while solid polymers
commonly have weights (MW ∼105 − 106 g/mol).
2.4.1.3
Polymer Crystallinity
Unlike other molecular substances such as solids which are completely crystalline
or liquids which are amorphous throughout, due to their macromolecular size and
complexity, polymers are often both crystalline and amorphous. Polymer crystallinity
is a phenomenon which occurs when polymer chains are packed together to form
linear ordered regions, These regions of ordered chains are called lamellae and act as
crystalline regions, surrounded by the remaining random, disordered chains which
make up the amorphous region. These lamellar regions form larger spheroidal regions
known as spherulites, objects approximately 10 nm thick that radiate outwards from a
single nucleation site [156]. This region is shown in figure 2.11
Any chain disorder or misalignment will result in an amorphous region. This
outcome is very common since twisting, coiling and kinking of the chains will prevent
the strict ordering of chains required for a lamella to be formed.
28
2.4 matrix materials
Figure 2.11: A) Schematic of a spherulite structure, which radiates out from the nucleation point
in all directions. A cross section of the spherulite boundary is shown to contain
a network of highly ordered crystalline lamellae separated by amorphous interlamellar links of polymer chains. B) A representative polarized light micrograph
image of a spherulitic structure, present in a poly(butylene terephthalate) thin film.
Both images reproduced from [156].
Nucleation occurs when an area of the order of a few nanometres acts as a “seed”
when some of its chains occur in a parallel conformation, due to heat motion. These
seeds can either fail, if thermal motion destroys the alignment, or grow further if the
site grain size exceeds a critical value [157].
The molecular composition of a particular polymer has a large influence on its
ability to crystallise. For example, it is very difficult to inhibit crystallisation in linear
polymers, especially those with simple side groups like Teflon in figure 2.7. This is
because there is virtually no restrictions to prevent chain alignment. Conversely, for
linear polymer with complex side groups, crystallinity is not favoured as these large
molecules act as anchor points and restrict the movement of the chains. This rigidity
towards movement is even more prevalent in both branched and crosslinked polymer
types, sometimes with no crystallisation occurring altogether. For this reason most
crosslinked polymers are completely amorphous. Temperature also has a large effect
on crystallisation probability.
Polymer crystallisation affects the optical, mechanical, thermal and chemical properties of a polymer. For this work, the mechanical affect causes the most concern.
Crystallisation can occur in a solvent due to cooling, mechanical stretching, filler
incorporation and solvent evaporation; all of which occur during the composite film
fabrication in this thesis. For this reason, characterisation methods are used to investi-
29
2.4 matrix materials
gate whether or not excess crystallisation has occurred due to the introduction of a
filler material causing one of these nucleation “seed” sites to form.
2.4.1.4
Polymer Types
Polymers can be classified into two main categories - rigid plastics and elastomers,
as shown in figure 2.12. These types are distinctly varied in terms of mechanical
properties. Rigid polymers have a high stiffness and low ductility, less than 100%
elongation in general. Elastomers on the other hand have very low stiffness yet are
much more flexible, with the potential to elongate up to 1000% of their original length.
More information on mechanical differences between these polymer types can be
found in chapter 3.
When it comes to molecular composition, thermoplastic elastomers (TPEs) tend to
have much more complex mer units than rigid plastics and elastomers, as they are
a hybrid of both polymer types. TPEs like polyurethane, have two main parts of the
repeated unit, known as the hard and soft segments. Typically the hard segment will
contain short chains with a high degree of crosslinking, leading to a stiffer, more rigid
region. The soft segment contains longer chains with little or no crosslinking, which
gives the material its high flexibility. The advantage of such elastomer compounds is
that the hard to soft segment ratios can be altered during synthesis, allowing a high
level of customisation between stiffness and ductility of the overall material.
Rigid plastics themselves are characterised into two further categories - thermoplastics and thermosets. Thermoplastics contain long chains of molecules linked by
covalent bonds, like Teflon mentioned previously. Thermosets however, have high
degrees of crosslinking present between adjacent chains. These structural differences
lead to large variations in properties. Thermoplastics are generally uncrosslinked and
are heat reversible, while thermosets like epoxy consists of a resin which is treated with
a curing agent, which adds chemical crosslinks to the chain. These thermosets are rigid
for this reason, and the chemical crosslinking process is non-reversible. Vulcanisation
is a similar chemical crosslinking method used for elastomers.
Composite materials can be produced from elastomers, TPEs, thermoplastics and
thermosets. Graphene has been used to reinforce thermoplastic polyurethane, with
30
2.4 matrix materials
Figure 2.12: Schematic describing the classification of polymers. The dashed lines represent
a synthesis method linking two material types while the solid lines denote a
subcategory. Reworked from [158].
impressive results [67]. All of the polymers used in chapter 5 are rigid thermoplastics,
along with the polymer used for composites in chapters 6 and 7, polyvinylalcohol
(PVA).
2.4.1.5
Polyvinylalcohol
PVA is a very common thermoplastic polymer, with over a million tonnes used worldwide in 2006 alone [159]. PVA is synthesised from polyvinylacetate, using hydrolysis to
remove the acetate groups. PVA’s chemical structure consists of an atactic configuration
of a mer unit featuring a carbon backbone with hydroxyl (OH ) side groups, as shown
in figure 2.13. These side groups give the polymer excellent hydrophilic properties,
making it ideal for use in an aqueous nanomaterial exfoliation environment and facilitating progress to the composite formation stage without using a more expensive,
possibly harmful solvent. PVA is also odourless, non-toxic, resistant to oil and solvents
and has excellent adhesive properties. Other advantages of choosing PVA as a matrix
material include its well understood and documented mechanical properties, with
31
2.4 matrix materials
Figure 2.13: The chemical composition of polyvinylalcohol.
numerous reports of PVA composites incorporating various nanoscale filler materials
present in the literature.
2.4.2
Metals
The term metal is derived from the Greek “métallon” which means a mine or quarry.
A metal is an element, alloy or compound renowned as an efficient conductor of both
heat and electricity. These materials have been utilised for thousands of years, with
ancient eras such as the Iron Age and Bronze Age right up to the Industrial Revolution
in the late 1700s all marking periods of great advancement in metallurgy. Nowadays,
steel is by far the most common structural material available, with over 1500 million
tonnes produced in 2011 alone [160]. Metals are distinctly different from polymers in
regards to both physical structure and properties. Nonetheless, they have emerged
recently as an interesting candidate as a matrix material in composite systems due
to interesting mechanical properties. Metals tend to be of much higher density than
polymers, and therefore are perhaps more suitable for purely structural materials
rather than lightweight applications such as sports equipment or high performance
sports car manufacturing. However, as displayed in Chapter 8, it is apparent that
relatively small amounts of nanofiller can enhance the properties of the metal matrix
considerably, without contributing extensively to overall composite weight. Since the
late 1980s, the rapid technological advances in the space industry have led to the use
of various types of metal matrix composites (MMCs).
32
2.4 matrix materials
Figure 2.14: Crystal structures adopted by metals. A) Body centered cubic (BCC) and B) Face
centered cubic (FCC).
2.4.2.1
Structure
Unlike polymers chains, the molecular structure of metals is a more rigid, crystallographic orientation. Most metals conform to a configuration based around three
unit cell types, simple cubic, body centered cubic or face centered cubic, as shown in
figure 2.14. The simple cubic has 8 atoms, each placed on a vertex of the cube. In the
BCC structure an atom is present at each of the vertices along with an atom in the
centre of the cell. For the FCC configuration, in addition to the atoms on the vertices
there are atoms in the centre of every face of the cubic cell. Depending on how many
other adjacent shells share the volume of each lattice point, the simple cubic structure
has 1 lattice point per cell, the BCC structure has 2 while the FCC has 4. Transition
metals and intermetallic compounds have been observed to adopt both BCC and FCC
structures simultaneously [161].
2.4.2.2
Properties
Metals contain electrons in their outer shells which are very loosely bound. For this
reason metals can readily give up these electrons to form electron clouds. These free
flowing electron clouds are what provide high thermal and electrical conductivity, and
what give metals a lower work function than other material types. The ease with which
metals lose these electrons means that they are inclined to form cations. These cations
can react with oxygen in the air to form oxides over various timescales, such as rust
(iron oxide). In order to slow or stop this corrosion process completely, some metals
are painted, anodized or electroplated to increase their corrosion resistance.
Metals differ greatly from polymers in both mechanical properties and appearance.
They are very reflective/shiny and although some metals are malleable and ductile,
33
2.5 composite materials
they do not possess to elongation capability of polymers. Most metals tend to fail
after less than 10% elongation. Nonetheless, there are some similarities between
these two matrix material types. As mentioned previously, the two phases present in
thermoplastic elastomers allows a tailoring of their ratios in order to choose strength
and stiffness over ductility or vice-versa. The same is true for metal alloys. An alloy
is a combination of two or more pure metals. By adjusting the ratio of the individual
components, a alloy can be fabricated that has increased corrosion resistance or
hardness, decreased brittleness or simply an aesthetic quality such as colour or luster,
compared to one of the pure metal components. Sometimes more modern versions of
alloys might substitute a component with one that is cheaper or less poisonous. This is
the case for pewter, the metal matrix material used in chapter 8.
2.4.2.3
Pewter
Pewter is an alloy of Tin, Antimony and Zinc. Antimony replaced lead in recent years,
after lead was found to be harmful. Tin makes up the majority of the alloy, with
Antimony and Zinc added to increase pewter’s hardness. The main advantages of
choosing this alloy for the work mentioned in chapter 8 is that it matches the density
of the MoTe2 filler nanosheets, and also that it had a very low melting point for an
alloy, of 230 o C. This low melting point allows it to be easily melted and cooled using a
hot-plate, and for this reason pewter is favoured among hobbyists for fabricating and
moulding toy soldiers. Tin and Antimony conform to variations of the FCC (diamond
cubic) and simple cubic (rhombohedral), respectively. Zinc adopts a hexagonal close
packed structure, which does not involve a cubic unit cell. Hence, pewter consists of a
combination of these three structures.
2.5
composite materials
A composite material consists of a system formed from two or more constituents with
distinctly different physical properties. The goal of this system is to possess properties
superior to that of each constituent. In this work, a two part system, comprised of
34
2.5 composite materials
a matrix material containing a small quantity of filler material is produced, with
the aim of enhancing the mechanical properties of the matrix substance. Composite
materials have been used for thousands of years, with the use of straw and mud bricks
documented by ancient Egyptian paintings. These systems are also found in nature
[162].
As shown in this chapter, the filler material possesses high stiffness (as shown
in table 10.1) and strength, governed by the strong lattice bonding present on the
nanoscale. However, these materials cannot be utilised to form macroscale filler only
films, as their poor ductility makes the material too fragile to be used for any structural
applications. Furthermore, it is not possible to produce such large quantities of high
quality nanoscale filler material that these substances would require. For these reasons,
a small quantity of filler material is embedded in a matrix material, typically a much
more elastic, amorphous and affordable material such as those listed previously. This
way the overall system can benefit from the addition of a small amount of mechanically
superior material while retaining the ductility of the matrix material. This subject
is further discussed in chapter 3 with regard to parameters involved in mechanical
testing of composite films. Nowadays composite materials are part of everyday life,
from fibre reinforced concrete [163] and carbon fiber sports equipment [164] to knee
replacements [165] and biodegradable packaging [166].
35
3
C H A R A C T E R I S AT I O N A N D M E T H O D S
3.1
introduction
In this chapter, the processes and equipment used to prepare and characterise the
samples will be discussed. The initial stage of production for all samples in this
work is liquid phase exfolation of the bulk crystalline material in a suitably chosen
polymer-solvent combination. Different methods of mild sonication in a sonic bath
and ultrasonication with a sonic tip are utilised throughout the study. Mild sonication
allows larger volumes of solution to be affected whereas ultrasonication involves a
much higher energy to perturb a smaller volume. This serves to decrease the exfoliation
time dramatically. Past studies within the group have shown that the concentration of
exfoliated material increases with the square root of mild sonication time [167]. These
dispersions are then centrifuged to remove unexfoliated crystallites. Centrifugation
regimes are also used to narrow the distribution of exfoliated flake sizes within our
dispersion [64], which is favourable for composite formation.
Absorption spectroscopy is used to determine the concentration of exfoliated material within our dispersion. This information is essential for the mass fraction calculation
stage of composite formation. All further characterisation methods feature solid samples. Raman spectroscopy is carried out on either a bulk powder or on a thin film
formed by filtration onto a porous polymer membrane. This technique reveals information regarding the lateral dimensions and thickness of our 2D nanosheets. Tensile
testing reveals the mechanical properties of our composite films, and whether the
introduction of our filler material has reinforced the reference polymer successfully.
Transmission electron microscopy is undertaken to access the statistical distribution
of exfoliate length, width and thickness, and hence quality of exfoliation. Scanning
electron microscopy and helium ion microscopy examine the fracture surface of our
36
3.2 absorption spectroscopy
previously tested composite films that have failed under tension, to see if our filler
materials have contributed effectively to the overall strength of the composite. Differential scanning calorimetry examines whether or not composite reinforcement is
achieved solely by filler introduction, or whether matrix crystallinity also plays a
part. For the work involving metal matrix samples, focused ion beam methods are
used to isolate a very thin slice of the composite, so that elemental analysis and grain
boundary examination can be undertaken. These characterisation techniques will now
be discussed in greater detail.
3.2
absorption spectroscopy
Many interesting phenomena occur when light interacts with materials. The incident
ray can be reflected back along its path or deflected by an interface between media
as it is slowed down by refraction. Photons can interact with a material also, causing
them to scatter, changing their direction of propagation. The most informative optical
process for our experiments is that of absorption. Absorption occurs when photons
from the incident ray interact with molecules or individual atoms within the material.
Depending on the frequency of the incident light, different interactions may occur. The
rotational or vibrational states of molecules may be changed, or electrons within the
molecules may be excited to higher energy states by the incident light. It is only the
vibrational and excitation phenomena that occur within our region of examination,
from UV (200-400 nm) through the visible region (400-700 nm) and into the near infra
red (800-2000 nm). Since a range of wavelengths is traversed during the testing, a
characteristic absorption spectra outputted gives detailed information on each material,
allowing us to recognise a particular substance based on peak position, relative peak
height or the gradient of the absorption background. The Lambert-Beer Law relates
the measured absorption value with the concentration of dispersed material within
the dispersion, typically for a characteristic absorption peak produced by interaction
procsses in a given material.
37
3.2 absorption spectroscopy
Figure 3.1: Illustration of the Lambert-Beer Law. Reproduced from [168]
Consider a beam of monochromatic light passing through a sample of known
thickness l, as shown in figure 3.1. The transmission, T, of light through a substance is
the ratio of transmitted intensity, I , to incident intensity, I0 , by:
T=
I
I0
(3.1)
The law relates this transmission to the thickness of the material the radiation passes
through, hence the path length through sample vessel , l, by:
I = I0 10−el = I0 10αlc
(3.2)
Where e is the absorption coefficient of the material, denoting the ability of a beam
of incident radiation to penetrate a given length of the solid at a certain frequency.
This can also be expressed for a liquid in terms of the extinction coefficient, α, and the
concentration of the absorbing species, c , in the dispersion. The absorbance A can be
defined as:
A = −log10
I
= −log10 T
I0
(3.3)
By comparing this to equation 3.2 the following result is obtained:
A = el = αlc
(3.4)
Where e = absorption coefficient [m−1 ], l = sample thickness [m], α = extinction
coefficient [mLmg−1 m−1 ], and c = concentration [mgmL−1 ]
The spectrometry measurements carried out in this thesis were all obtained using a
Cary Varian 6000i spectrophotometer with scanning range (190-1800 nm). A schematic
38
3.3 raman spectroscopy
Figure 3.2: Schematic of a UV-Vis spectrophotometer. Adapted from [170]
of the tool is shown in figure 3.2. This tool uses a dual beam system with a tungsten
halogen lamp for visible and NIR wavelengths and a deuterium arc for ultraviolet
wavelengths [169]. Scans were carried out on samples using a range of (200-800 nm) for
Boron Nitride and (350-800 nm) for all other layered materials. The spectrophotometer
consists of a bulb whose beam which is split into two using a half mirror, which scan
a reference sample (either polymer and solvent or solvent only depending on the
exfoliation method) and sample simultaneously to give a baseline corrected spectrum.
A baseline scan is taken initially in all cases. An average of three scans is taken for
each sample spectrum.
3.3
raman spectroscopy
The Raman Effect, named after Indian physicist Chandrasekhara Venkata Raman, is a
phenomenon involving inelastic scattering of light. Discovered in 1928 [171] in liquids
and leading to the awarding of the Nobel Prize in 1930, this method of characterisation
is used widely throughout scientific research. When a source of monochromatic light
such as laser light is incident on a material, photons interact with quantised lattice
vibrations known as phonons or other excitations within the system. This causes their
energy to be either up-shifted or down-shifted, revealing valuable information about
the vibrational modes of the material in question.
An energy level diagram of the several types of absorption event is shown in figure
3.3. Upon interaction with the incident photon, the molecule within the material is
39
3.3 raman spectroscopy
Figure 3.3: Energy level diagram of infrared absorption various inelastic light scattering events.
excited from its ground state to a virtual energy state. The molecule then relaxes,
returning to another rotational or vibrational state by emission of a photon. This
relaxed state is different to the original state before photon interaction. The energy
difference between this new state and the original determines the frequency of the
photon emitted during the relaxation of the molecule. If the final vibrational state is
more energetic than the original state, the emitted photon will be shifted to a lower
frequency in order to conserve the total energy of the system. This frequency change
is known as the Stokes shift. Conversely, an anti-Stokes shift is where the final state
is less energetic than the initial state, and the emitted photon is shifted to a higher
frequency. Due to the presence of an incident photon and an emitted photon, Raman
scattering is classified as a two photon event, in contrast to infrared absorption where
a single photon excites the surface molecule to a higher state.
A change in the polarisibility of the molecule with respect to its vibration is required
in order to cause a Raman effect. The greater the change in polarisibility of the
molecule the greater the Raman scattering intensity observed. It must be noted that
not all scattering that occurs during this method is inelastic. Elastic collisions whose
energy is matched with the initial energy of the laser beam are filtered out by the tool
whereas inelastic events pass on through to the detector. These elastic scattering events
are known as Rayleigh scattering.
40
3.3 raman spectroscopy
Figure 3.4: A) Raman spectra of bulk graphite and graphene, B) closeup of 2D peak shape variation as number of graphene layers increases. Laser wavelength 514 nm. Adapted
from [172].
3.3.1
Raman Spectroscopy of Carbon Nanomaterials
Raman spectroscopy has been used for many years to examine graphene properties
and has proven itself a powerful tool. In 2006 Ferrari et al. examined the spectra of
graphene samples produced by micromechanical cleavage of varying thicknesses using
a 514 nm laser [172]. The spectrum can be seen in figure 3.4. Graphene itself has a very
distinct spectrum with the following characteristic peaks. The D band at ~1350 cm−1 ,
G band at ~1580 cm−1 and the 2D band at ~2700 cm−1 .
The D band was named after defects or disorder within the lattice of sp2 - hybridised
carbon materials. These could include 5 or 7 membered rings, dislocations or interstitial
sites. For 2D nanosheets, lattice edges also register as defects. This result is highly
beneficial for this work as it allows a D band intensity ratio to be used to estimate
average flake lengths within our samples from a calculated relation [173, 174]. The
spectra in figure 3.4 have no observable D band suggesting that the sample area in
question was free of defects.
The G band was so called after it was first observed in graphitic systems. It originates
from normal first order scattering processes and has been seen to differ in shape and
position between graphite, graphite oxide and functionalised graphene sheets [175].
41
3.4 tensile testing
The final characteristic peak in the graphene spectrum is the 2D band, named due to
its wavenumber being a multiple of the D band. As seen in figure 3.4 above, as the
number of graphene layers increases from monolayer to bulk, both a blueshift and
change in peak shape is observed. The 2D peak in bulk graphite at ~2700 cm−1 shows
most clearly the presence of two separate sub peaks, 2D1 and 2D2 . These peaks are
approximately 1/4 and 1/2 the height of the G peak respectively. Monolayer graphene
however has a sharp single 2D peak situated at ~2640 cm−1 . Recent studies have also
shown that shifts of the 2D peak can provide information regarding the adhesion
between the filler and matrix components of composite materials. It is reported that a
redshift or blueshift in wavelength indicates either tensile or compressive strain on a
composite, respectively [176]. By measuring the shift rate of the 2D peak per % strain
on the material, the adhesion strength for composites filled with both 1D and 2D
nanomaterials can be quantified [38, 177–179].
For this work, Raman analysis was undertaken using a Horiba Jobin Yvon LabRam
HR spectrometer using a 633 nm laser excitation source. Notch filters were used to
filter the Raman scattered light along with a diffraction grating with 600 lines per mm
and a 100x magnification lens. Calibration was carried out using a white light lamp
and a silicon reference sample before every analysis session. When analysing polymer
films and filter membranes coated with powder, spectra were normalised using an
average of 10 scans per sample.
3.4
tensile testing
Of all the characterisation techniques available to the researcher, no stage provides
more information regarding the mechanical properties of a composite than tensile
testing. This process involves applying a vertical tensile stress to a sample strip of fixed
width and given thickness at a predetermined rate and examining the force required to
elongate and ultimately cause fracture in the material. The output is a curve of stress,
42
3.4 tensile testing
Figure 3.5: A) Stress strain curve showing characteristic parameters of mechanical testing. B)
Typical stress strain curves for (a) brittle, (b) composite and (c) ductile polymer
materials. A) Taken from own data. B) Modified from [155].
σ [ MPa], versus strain, ε [%]. The stress is defined as the ratio of applied force, F [ N ],
to the cross sectional area, A [m2 ], of the sample
σ=
F
A
(3.5)
The strain is simply the percentage elongation of the sample with regard to its
original length, l [m], or
ε=
4l
l
(3.6)
There are many properties to be extracted from the stress strain curve. The stiffness
or Young’s Modulus, Y [ GPa], is defined as the gradient of the curve (resistance to
applied stress) at the initial stage of the testing, namely
Y = dσ/dε
(3.7)
Other key points on the stress strain curve are the Ultimate Tensile Strength, UTS
[ MPa], the highest point on the curve and the strain at break, ε B [%], the percentage
elongation at which sample failure occurs. The Yield Point is a transitionary point
between two distinct regions of deformation, and is discussed further in section 4.4.1.
A typical stress strain curve for a PVA reference sample is shown in figure 3.5A.
The final property used to distinguish composite materials is the Toughness. This
is a measure of the total energy absorbed by the film before failure occurs. The units
are [ MJm−3 ]. The toughness is obtained by integrating the area under the stress
43
3.5 transmission electron microscopy
strain curve and hence is heavily affected by both the UTS and strain at break of the
composite. This property is critical when characterising materials such as bulletproof
vests, safety belts, shielding or any structural material. It is described by
T=
Zε B
σ dε
(3.8)
0
Brittle polymers such as thermosets or rigid plastics will have a high stress required
to break them and a high stiffness but will fail at low elongation. Conversely, elastomers
require only a low stress to cause deformation but have high ductility, sometimes
elongating to >10 times their original length before failure. A composite material
would lie somewhere in between these two extremes, with both intermediate UTS
and ductility. The desirable goal is to achieve a composite with increased stiffness and
strength, without considerable loss of ductility, hence the increasing the toughness
considerably.
Typical examples of stress strain curves are shown for these three material types in
figure 3.5B. Temperature has drastic effects on the shape of these stress strain curves
and for this reason all our testing is carried out at room temperature with no heat
sources in the vicinity. All tensile testing in this work was undertaken using a Zwick
Roell tensile tester with a 100 N load cell. The sample thickness and width are inputted
into the software before each sample strip is tested and then the sample is deformed
at a strain rate of 5 mm/min.
3.5
transmission electron microscopy
The normal resolution transmission electron microscopy (TEM) method is widely used
throughout this work to analyse the quality of our exfoliated material. The first electron
microscopes designed in the 1920s were capable of only 18x magnification whereas
nowadays normal TEMs are capable of 1,500,000x with HRTEMs achieving up to
150,000,000x magnification [180]. This method operates by placing the sample halfway
up the microscope’s column and examining electrons that pass through the sample
and are transmitted down the column to be imaged by a camera at the tool’s base.
44
3.5 transmission electron microscopy
Figure 3.6: Schematic of a TEM column. Adapted from [181].
This design makes the tool highly effective at measuring the thickness of nanoparticles.
After the sample stage has been inserted, the column is pumped down to a vacuum
pressure of ~10−6 mbar before the electron gun is activated. Some electrons will pass
through the sample without any interaction while others will scatter from atoms in the
specimen. Some scatter elastically while others undergo inelastic scattering. The image
formed on the output monitor is essentially a map generated by these events where
the contrast is governed by the loss of electron beam intensity due to scattering.
The layout of the instrument contains both electrostatic and magnetic components
to first manipulate the electron beam and then optical manipulation using a variety of
lenses, as shown in figure 3.6. The electron gun lies at the top of the column. Normal
resolution TEMs have either a tungsten filament or Lanthanum Hexaboride (LaB6 )
source [182]. This gun is connected to a high voltage source and will emit electrons
into the system by either thermionic emission or field emission. The system above the
specimen level functions by using magnetic and electrostatic fields to manipulate the
electron beam so that it is parallel to the optic axis when sample interaction occurs.
This also allows for the formation of a shift in the beam path. The condenser lenses
control how the specimen is illuminated by the incident beam. This coherent beam
45
3.5 transmission electron microscopy
then passes through the specimen sample with some of the beam being disrupted
by both elastic and inelastic scattering. Several objective parts are located under the
specimen in the back focal plane. The objective lens is crucial to the formation of a high
quality image as it must focus a wide range of scattering angles. It is essential that this
lens is properly aligned and stigmated otherwise large errors will be introduced. The
intermediate lens deals with a smaller range of angles than the objective lens and is
used to focus the beam in the first image plane. Lenses in the projector system are
then used to expand the beam onto the CCD camera for imaging.
All TEM imaging in this work was carried out using a Jeol 2100 TEM system
operating at a voltage of 200 kV. This equipment uses a LaB6 source and can operate
between 80 and 200 kV with a maximum high contrast resolution of 0.31 nm. A 5 grid
specimen holder is used to load the samples into the column. Low magnifications of
~200x are used initially to determine a representative area to image. Magnifications of
between 6000-80000x are used to image nanoparticles of varying dimensions. When
liquid dispersions of high boiling point solvents are studied, it is necessary to leave the
samples in the column overnight to ensure an acceptable level of vacuum is achieved
for the imaging session as degassing can lead to poor image clarity and focusing
problems.
3.5.1
Statistical TEM Analysis
By examining a holey carbon grid onto which a few millilitres of dispersion has been
dropped, we can ascertain statistical information about our dispersed nanomaterials.
By carefully tuning the focus, magnification and brightness, a clear dark image of our
nanoparticle can be achieved against the bright background, allowing us to quantify
particle length, width and thickness/number of layers for 2D nanosheets. For 1D
particles such as multiwall nanotubes we can examine tube length as well as inner
and outer diameters of the concentric walls within the tube. Using image analysis
software, the individual sheets/tubes are treated as rectangular objects, with the
longest dimension labelled as the length and the width recorded as the widest point
46
3.5 transmission electron microscopy
perpendicular to this measured length. The methods of length, width and thickness
calculation are shown for graphene in figure 3.7 below.
An edge counting method is utilised for the measurement of N, the number of layers
present in a 2D nanosheet. It is noted that while this technique lacks the accuracy of
methods such as AFM [8], Raman spectroscopy [172] or electron diffraction [60] for
determining flake thickness, it is not feasible to use these techniques to analyse filler
dispersions for composites as data must be obtained for a large number of flakes within
the hybrid material. In the case of multilayer flakes, upon examining the perimeter of
the sheet a terraced or step like set of edges is observed. By counting the number of
these edges the number of layers present can be quantified. To account somewhat for
human bias the following convention is adopted. If two stacks of flakes are present
that appear to have between 3 and 4 layers respectively, the observer would choose the
lower option of 3 layers for the former and 4 layers for the latter. This way a level of
consistency is applied across the whole set of data.
Further steps to increase accuracy in this data is to take a large sample set, between
100 and 200 flakes in total for one sample, in an attempt to minimise the standard
deviation in the flake properties measured. The error in length and width calculations
are much smaller than that of N, due to the nature of the image analysis. Another
source of inaccuracy lies with the structure of the sample grid. The holey carbon on
the grid acts like a web to trap particles on its surface. There are circular voids within
this web that range in diameter from ~100 nm to microns in some cases. It is therefore
an issue that any flakes smaller than this diameter could merely fall through the grid
during the sample prep stage. Only flakes that manage to lie on this web or even on
top of a large flake that is placed across the void in the mesh can be recorded. However,
while it is difficult to accurately estimate what percentage of flakes from the total
dispersion have fallen through and therefore cannot be imaged, a large number of very
small flakes (<200 nm) are present in the images and are always considered within the
datasets.
It is found that in cases for all 2D layered materials studied that the vast majority
of the flakes contain few (2-8) layers. In very rare cases is a flake that appears to be
monolayer observed, and it is impossible to be certain using this method alone. If a
47
3.6 scanning electron microscopy
Figure 3.7: Images highlighting A) length and width determination and B) the edge counting
method for graphene flakes. Images from own data.
centrifugation regime has been applied to the dispersion then large aggregates and
crystallites are not observed in the TEM.
3.6
scanning electron microscopy
Scanning Electron Microscopy (SEM) is another useful method of characterisation
for this work. Unlike TEM this method is carried out on solid samples, namely our
composite films and initial powders of our filler materials. The procedure allows us to
determine the quality of our powders by examining the quantity of pure nanomaterial
present in contrast to amorphous material . After the composite films have been tensile
tested to failure, the fracture surface (broken edge) of the films is imaged with the SEM
to determine if the filler material is present at this interface and hence contributing to
the overall mechanical properties of the composite. It also allows us to see if the filler
particles are uniformly present in the composite, that no aggregates are present and
that the filler particles are aligned along the plane of mechanical stress.
Since the first prototype of a high magnification SEM in 1935 [183], the SEM has
developed into an extremely useful, user friendly and widespread method of sample
examination. Nowadays specimens can be examined in high and low vacuum, even in
wet conditions with excellent depth of field and resolution of ~2 nm [184].
The SEM operates using a similar electron beam to that mentioned for the TEM
above. An electron gun uses either a tungsten filament for thermionic emission or
48
3.6 scanning electron microscopy
Figure 3.8: A representation of the signals generated within the interaction volume of a sample
during SEM. Reproduced from [186].
a lanthanum hexaboride crystal grown into a tip for field emission of electrons into
the system. The LaB6 crystal provides a brighter beam than the tungsten filament at
the same applied voltage, and is more expensive. The electron beam is then focused
by an electromagnetic condenser lens. The beam then passes through an aperture
which excludes electrons which are not parallel with the optic axis, this improves the
resolution of the final image. The stigmators make the final adjustments to the beam
before it interacts with the sample, correcting inconsistencies in the beam.
When the beam interacts with the sample under vacuum, many different interactions
take place. These depend both on the composition of the specimen and the energy of
the beam itself [185]. What results is a droplet shaped region within the sample that is
excited. This is referred to as the interaction volume, as shown in figure 3.8
The size of the interaction volume, or penetration depth R [µm] into the sample can
be described by the Kanaya-Okayama depth penetration formula [187]
R = 0.0276
AE1.67
ρZ0.89
(3.9)
With sample parameters including atomic weight A [ gmol −1 ], atomic number Z,
density ρ [ gcm−3 ] and electron beam energy E [kV ].
Of the many types of emitted signal, it is the secondary and backscattered electrons
that are used to form the image. Secondary electrons are electrons which are ejected
from the specimen interaction volume atoms by inelastic scattering with beam electrons.
These electrons tend to have very low energies, of <5eV and so only penetrate a few
49
3.6 scanning electron microscopy
nanometers into the sample surface. For this reason the SE2 detector is used to image
these electrons to give a high degree of surface detail/topography.
Backscattered electrons, on the other hand, originated as high energy electrons in
the beam and have been reflected or back-scattered out of the specimen interaction
volume by elastic scattering events within the specimen. These have higher energies
than secondary electrons and hence bury deeper into the interaction volume, as seen
in figure 3.8. Imaging using the AsB detector on these backscattered electrons is useful
for obtaining compositional information about the specimen. The relationship between
electron yield and sample atomic number is much more sensitive for backscattered
electrons than for secondary electrons, and hence using this detector gives a large
contrast different for heavier elements than lighter ones. However, higher energy
implies lower image resolution and hence not as sharp an image using the AsB
detector compared with SE2. Both detectors use a raster scan across the surface to
generate an image.
The SEM analysis used in this work was carried out on a Carl Zeiss Ultra Plus SEM
using a LaB6 source. This tool features the GEMINI column from Zeiss that excels at
imaging while using low emission voltages. All of the polymer composite imaging
featured in this thesis is carried out at 5 kV or less as higher voltages can cause heating
or damage of polymer samples from the beam. For this reason this setup is ideal
for our imaging purposes. It is worth noting that due to the insulating properties of
PVA, many of the polymer composite samples must first be coated with a 10 nm gold
palladium (AuPd) layer. If this step is not undertaken, a charging effect will occur as
electrons do not travel effectively out of the sample and this greatly hinders image
resolution. These metals are chosen as a coating material as their nanoparticle grain
size is very small and hence does not interfere with specimen surface topology. When
the sample are sufficiently conducting they are placed on a sticky carbon tab which is
then placed on an SEM stub which is mounted for imaging in the microscope chamber.
50
3.7 helium ion microscopy
Figure 3.9: Representation of the ion emission process in a helium ion microscope. Helium
ions (green) become ionised in the high field region (orange). The resulting ions
(yellow) accelerate away from the tip. Reproduced from [188].
3.7
helium ion microscopy
Although the advancement of SEM technology over the last 50 years has been impressive, continued improvement of the imgaing resolution has been slow. This is due to
the electron diffraction and the physics of the interactions between the electron beam
and the specimen. A new type of equipment called the Helium Ion microscope came
on the market in 2007 and seeks to overcome this resolution barrier by utilising new
technology. The key to this system is that the helium ion source is long lasting and can
generate intense ion currents from a volume no larger than a single atom [188].
The helium ion source differs greatly from the electron gun options available for the
SEM and operates as follows. The ion source in this tool consists of a sharp needle
that is kept in cryogenic temperatures and under high vacuum. A schematic of the ion
emission process is shown in figure 3.9.
When the needle is appropriately shaped, the ionisation process occurs within the
vicinity of a single atom and hence the beam appears to be produced from a region
less than 1 Å in size. This remarkable process gives the beam a very high brightness
which leads to a high degree of focusing capabilities. The beam is then manipulated
down a column which contains and array of elements that focus and align the beam.
51
3.7 helium ion microscopy
A probe size as small as 0.75 nm can be achieved using this method. A raster scan
similar to that used by the SEM then images the surface.
To understand further the advantage of this new method of microscopy, consider
the electron diffraction formula with respect to the de Broglie wavelength λ[nm]:
λ=
h
h
=
p
mo v
(3.10)
The above relation links the deBroglie wavelength with the momentum p = mo v
[kgms−1 ] of the beam particles with Planck’s constant h [ Js]. The particles are accelerated by a potential U [V ] to reach a desired speed v [ms−1 ] by
r
v=
2eU
mo
(3.11)
where e [Coulomb] is the elementary charge. By combining equations 3.10 and 3.11
we obtain
λ= √
h
2m0 eU
(3.12)
The difference between SEM and helium ion methods now becomes apparent. In
SEM electrons in the beam are limited by diffraction effects arising form the wavelike
properties of the particle. The helium ions however have much larger masses than the
electrons and hence a much smaller deBroglie wavelength using equation 3.12 above.
Hence the focused probe of ions are not strongly influenced by diffraction effects. This
method can therefore produce images with higher resolution and is more sensitive to
measuring surface topology than SEM. The manufacturer boasts image resolution of
0.5 nm and up to 10 times depth of field compared to SEM systems with this new tool
[189].
Helium ion imaging featured in chapter 6 of this work was carried out using a Zeiss
Orion Plus microscope using a voltage of 30 kV. Details regarding the beam current,
working distance and aperture are discussed in chapter 6.
52
3.8 differential scanning calorimetry
Figure 3.10: A DSC specimen curve for a PVA film. Taken from own data.
3.8
differential scanning calorimetry
Differential Scanning Calorimetry (DSC) is a thermoanalytical technique used to
characterise our composite films. By controlling several cooling and heating stages
and rates, this technique examines the amount of heat energy required to raise the
temperature of the specimen as a function of temperature. We use this method to
examine the change of glass transition temperature, melting temperature and enthalpy
between our composite films and a reference sample. This way we can decide whether
reinforcement has occurred due to the filler particles or whether polymer crystallisation
has occurred during our manufacturing process.
The DSC tool consists of 2 sample inputs, in the case of this work one for the
composite film and one for the polymer only film. Both the sample and reference are
kept at identical temperatures during the cooling and heating stages, with the machine
outputting a baseline corrected spectrum. The preinciple behind the process is that if a
material undergoes a phase transition such as melting, then more or less applied heat
will be required for the composite than the reference sample to keep both at the same
temperature. These state changes are registered as peaks on the DSC spectrum.
Films of known mass (~1 mg) are placed in special aluminium pans. An aluminium
lid is then placed on top and sealed using a mechanical press. Initially a scan is carried
out with empty aluminium pans sealed with lids in both holders. This initial scan is
53
3.9 thermogravimetric analysis
carried out at exactly the same stages and rates as the sample procedure and eliminates
the heating effects of the metal vessels. After this the reference and composite samples
are inserted and scanned.
Figure 3.10 shows a characteristic DSC curve for a PVA film. The primary features of
the DSC curve for a material are the peaks signifying the glass transition temperature
Tg [o C ] and the melting point Tm [o C ]. The first peak, Tg , signifies the transition of an
amorphous material (or in the case of this work, the amorphous polymer region of a
semi-crystalline composite) from a brittle glass-like state to a molten or rubbery state.
The peak signifying Tm occurs at much higher temperature and simply displays where
the substance changes from a solid to a liquid state. The main information obtained
from the DSC spectra is the melt enthalpy [ J/g], determined from the area under the
Tm peak. This area describes the total thermal energy required to melt the composite,
and gives information as to whether or not the incorporation of a filler material has
caused crystallinity within the polymer matrix itself. Characteristic DSC curves can be
seen for PVA composites in figure 10.10 in the appendix.
All DSC measurements in this thesis were carried out using a Perkin Elmer Diamond
DSC. A temperature range of -5 o C to 250 o C using a scan rate of 20 o Cmin−1 was
chosen in all cases.
3.9
thermogravimetric analysis
Thermogravimetric analysis (TGA) is a thermal analysis technique similar to DSC. It
provides information on a sample by examining the mass loss in a sample as a function
of temperature. Phase transitions, including vaporisation, sublimation, adsorption and
desorption can all be characterised by this analysis technique [190]. With regard to this
thesis, TGA is especially useful for analysis of composite powders, such as BN and
PVA in chapter 6. Through analysis of characteristic decomposition patterns in the
material, the ratio of the constituents in the composite powder can be calculated. It is
imperative that this parameter is accurate as errors in filler volume fraction calculations
lead to significant under or overestimation of filler reinforcement potential.
54
3.10 focused ion beam lamella preparation
The TGA operates by placing a small mass of the sample (3-5mg) in a metal crucible.
This is then inserted into the machine where it is heated by a furnace up to 1000o C.
The instrument can operate in two modes, either applying a constant heating rate and
measuring the mass loss or measuring the time taken to achieve a constant rate of
mass loss. In the case of this work the former regime is used. By comparing the mass
percentage versus temperature curves for the composite powder versus the polymer
only powder, the rate of change of mass with respect to time can lead to an estimation
for the ratio of filler material to polymer within the composite powder.
The TGA apparatus used for this work is carried out using a Perkin Elmer Pyris
TGA. The furnace on this piece of equipment is capable of heating up to 1000o C with
a temperature precision of ±5 o C [191]. In this work a range from 35 o C to 1000 o C is
used with a constant heating rate of 10 o Cmin−1 .
3.10
focused ion beam lamella preparation
For the work involving metal composites, it is required to prepare a thin slice of the
sample known as a lamella so that its elemental distribution and grain properties can
be examined. For this task we use focused ion beam (FiB) apparatus. The FiB system is
used within the Gemini column of the aforementioned SEM apparatus. Rather than
an electron beam normally used with this column FiB relies on a finely focused beam
of positively charged gallium ions. These ions can be incorporated at low current for
imaging purposes or at higher currents for milling applications.
When the energetic gallium ions bombard the sample, the momentum change causes
a chain reaction of collisions between neighbouring atoms within the target, known
as a cascade. These cascades recoil around the sample surface and if one of these
recoils possesses an energy greater than the binding energy an atom will be ejected - a
process known as sputtering. Secondary electrons are produced by the beam during
this process and it is these particles that provide the signal for the specimen image.
After ejection of the surface atoms there is a remainder of both neutral atoms and
secondary ions (both positive and negative) on the surface.
55
3.10 focused ion beam lamella preparation
Figure 3.11: Illustrations of A) the FiBiD process and B) FiB-SEM equipment setup within the
instrument column. Adapted from [192].
The FiB setup allows many other techniques to be carried out on the specimen.
Depending on what beam current is selected a desired level of control can be achieved.
The previously mentioned milling technique can be carried out at high (~nA) or low
(~pA) beam currents. Ion milling takes place at the higher currents and is quite a
violent technique, with large samples areas corroding away in real time as the user
observes the image. In order to ensure that only desired regions of the specimen are
corroding while others remain intact, a masking technique can be employed. The
process is known as focused ion beam induced deposition (FiBiD) and is represented
figure 3.11A.
The FiBiD process involves the introduction of a gaseous precursor into the vacuum
chamber. In this case a metallic precursor is used to deposit platinum onto the surface
as a protective mask. When the gas containing the platinum interacts with the gallium
beam, the beam energy is tuned to a lower setting. This value provides an ideal amount
of kinetic energy to the platinum atoms so that they are deposited onto the sample. If
too high a beam current is chosen, the platinum particles have so much energy that
they themselves can damage the surface by milling. These low energies are also used
in the gallium only case to polish the sample, a process referred to as e-beam milling.
While this process is considerably more time intensive, the beam is very focused
and allows for both precise control over which areas are affected along with a slow
corrosion regime. This allows the user to examine the thickness/smoothness of the
sample in real time and easily stop the beam when the desired structure has been
reached.
56
3.10 focused ion beam lamella preparation
In order to prepare a lamella of the sample, a suitable area is chosen on the fracture
edge of the sample, which faces upwards as the sample is adhered vertically on the
stub in a “tombstone” configuration. FiBiD is then used to coat a rectangular section
of platinum to act as a protective layer. The gallium beam energy is then increased and
ion milling is used to cut trenches on either side of our mask. The milling depth can be
set by the software. A small trench is then cut on the third side of the rectangle. Before
the final side can be cut away we must attach a contact to the platinum topside so that
the slab is not lost when the final edge is milled away. A very sensitive piezoelectronic
device is used to bring a needle into contact with the sample, as shown in figure 3.11B.
More platinum is then deposited onto the contact surface between the needle and the
slab. When a suitable thickness has been laid down the final edge of the slab is milled
away using the gallium beam and the sample can be lifted by the needle away from
the specimen.
The needle is moved towards a specialised TEM grid where the far side of the slab is
then adhered to the grid, once again by platinum deposition. When the slab is secure
milling is used to break the contact between the slab and the needle. The slab can then
be thinned into a lamella of desired thickness by using low energy ebeam milling to
polish the sample. At around 100 nm the sample becomes electron transparent, and
could be imaged on the TEM if required. For our purposes a thickness of ~200 nm
is sufficient for grain information and elemental analysis. The AsB detector is used
on the lamella to give excellent contrast ratio between lighter and heavier elements
present.
FiB processing was carried out on a Carl Zeiss Auriga FiB-SEM with a Gemini
column. A voltage of 5 kV is used during all the milling, polishing and deposition
stages. The beam currents used were 2 nA for the ion milling, 50 pA for the metal
deposition and 2 pA for the precise e-beam milling/polishing. The voltage is increased
to 30 kV for the AsB detection for optimum contrast during elemental characterisation.
57
3.11 energy-dispersive x-ray spectroscopy
Figure 3.12: The components of the Silicon Drift Detector. Adapted from [193].
3.11
energy-dispersive x-ray spectroscopy
Energy-dispersive x-ray spectroscopy (EDX) is an analytical technique used to obtain
information on the elemental composition of a specimen. It functions on the fact that
each element has a unique atomic structure or “fingerprint” that leads to a unique
set of peaks on an x-ray spectrum. It can provide data on which elements are present
and also about their relative abundances. An Oxford Instruments silicon drift detector
(SDD) is used for EDX characterisation in this work. It consists of a component that is
attached to the column of the SEM or FiB-SEM. The layout of this device can be seen
in figure 3.12.
The x-rays must pass through the collimator to reach the detector, this piece makes
sure that only electrons emitted by the excited sample area are detected and not others
from the microscope chamber. The electron trap is a magnet assembly that strongly
deflects electrons that can cause artifacts if they reach the detector. The window is
carefully designed so that it is transparent to low energy x-rays but can still maintain a
high level of vacuum with the chamber. The sensor consists of a semiconductor device
that uses ionisation to convert an x-ray of a certain energy into an electric charge
of similar magnitude. This component can be made from silicon crystals containing
58
3.11 energy-dispersive x-ray spectroscopy
lithium or newer SDD devices. The field effect transistor (FET) is connected to the
sensor that measures and amplifies the x-ray induced charge in the crystal and converts
it into a voltage. The final component is the cooling apparatus, which uses fins to
direct heat away from the detector and remove it via a heat pipe. The detector operates
at 10-20 o C and must be kept cool.
Atoms in the sample have their electrons in their ground state configurations known
as electron shells. When the beam interacts with these electrons, an electron from an
inner shell can be ejected, leaving a hole behind. This hole can then be filled by an
electron from an outer shell. The resultant energy difference between these two events
is conserved by the emission of an x-ray of that energy. If the hole is produced in
the n=1 shell then the x-rays produced are called K x-rays. Similarly, L x-rays arise
from a vacancy in the n=2 shell. If the electron travels from an adjacent shell to fill
the hole then the α prefix is incorporated whereas the β prefix denotes a transition
between nonadjacent shells such as n=3 to n=1, say. These two types of transitions
are critical to observing the spectrum correctly, as sometimes elements might have
considerable overlap in their Kα energies and hence examining the Lα peaks present
at higher energies can distinguish one component from another.
In order to maximise the counts achieved on the elemental spectrum a higher voltage
is used in the column than for normal imaging. In this case 30 kV was chosen. By
having our composite sample mounted in a tombstone configuration it ensures that
most of the X-rays interacting with the sample would be deflected at angles close to
that of the detector position. Otherwise count rates would be too low to give accurate
information on the specimen’s composition. The output is that of counts versus energy
[kV ] for the emitted X-rays.
59
4
T H E O RY
4.1
introduction
This chapter discusses the origins of theoretical models and key physical and chemical
mechanisms that are required to fully understand and quantify our experimental
results. Solvent solubility theory plays a critical role in chapter 5 when describing
the interactions between each component of our nanosheet/polymer/solvent system.
Chapters 6, 7 and 8 all investigate the level of reinforcement achieved within composite
materials and hence require some models to calculate the efficiency of our filler
materials. In this chapter theories, definitions and derivations will be covered to
provide a baseline for more concise calculations and theories covered in later chapters.
4.2
solubility theory
Since all of the composite dispersions discussed in this thesis were prepared using a
liquid phase exfoliation process it is vital to understand the mechanism behind this
method. In the simplest case where only nanomaterials and solvent are present, a
relationship between properties of both parts of the dispersion will govern whether
or not a stable solution is produced. While this liquid phase process is effective at
producing large quantities of exfoliated material, solely exciting the nanomaterial in
an arbitrary solvent using sonication is not sufficient. Studies within the group in
2008 investigated the exfoliation yield after centrifugation for CNTs in a large range of
solvents [61]. This was followed by a study of graphene’s exfoliation yield in 40 solvents
[62]. While some solvents, specifically in the amide family, succeeded in exfoliating
a large quantity of material, most of the solvent range would disperse the tubes
60
4.2 solubility theory
during sonication but they would remain aggregated and fall out as sediment during
centrifugation. To understand what governs a good solvent choice for exfoliation of
nanomaterials, a closer look at the energetics and thermodynamics of the system is
necessary.
4.2.1
Thermodynamics of Solutions
The mixing of any two chemical compounds will lead to a change in entropy and
enthalpy of the overall system. Entropy, S , is defined as the number of specific ways
a system can be arranged, and hence a measure of the disorder of a system. The
enthalpy, H , is the total energy of a thermodynamic system. In the case of the addition
of a nanomaterial solute to a solvent, the following relationship holds for constant
temperature, T [194]
∆GMix = ∆HMix − T∆S Mix
(4.1)
Here, G is the Gibbs free energy of the system. For the mixing process to be deemed
energetically favourable, the following must hold
∆GMix ≤ 0
(4.2)
The process of mixing will always increase the disorder of the system and hence
∆S Mix will be positive. It has been discovered that for large rigid particles like CNTs
and nanosheets that ∆S Mix is quite small and so ∆HMix becomes very important [13].
For the case of 2D nanosheets to be stably exfoliated from the bulk material, the net
energy change ∆HMix is very small.
In work published by the group in 2008 an approximation to link the enthalpy of
mixing to the surface energy of a 2D nanosheet was derived [60]. The energy cost of
exfoliation per unit volume of mixture, VMix , was shown to be
∆HMix
2
≈
− δsolvent )2 Vf
(δ
VMix
Tsheet sheet
(4.3)
61
4.2 solubility theory
Where Tsheet is the nanosheet thickness, δi =
q
i
Esur
f ace is the square root of the
surface energy for either phase i and Vf is the volume fraction of the sheets in the
mixture (defined in equation 4.12).
The above relation relies on the balance between surface energies of both the solute
and solvent. For 2D layered materials this parameter is defined as the energy required
to overcome the van der Waals attractive forces when peeling two sheets apart. For the
solvent phase the surface energy is reliant on the surface tension, γ , by
solvent
solvent
γ = Esur
f ace − TSsur f ace
(4.4)
solvent is the solvent surface entropy. This value was shown to be ~0.1
Where Ssur
f ace
mJm−2 K −1 for CNTs and graphene in amide solvents [13]. Examining equation 4.3
displays that using solvents with similar surface energies to the filler nanosheets will
form a mixture with minimal energy cost, and that these combinations are the most
favourable. The most effective way to choose solvents for high quality dispersions is to
examine their solubility parameters.
4.2.2
Solubility Parameters
An effective way of determining one mixture phase’s interaction with another is to
examine its solublility parameter. Observing the relationship with the δi in equation 4.3
above, similarities with the well known chemistry phrase “like dissolves like” become
apparent. This implies that two species with closely matched surface energies will
interact in a manner that is energetically favourable. The surface energy term here
is linked with the cohesive energy density, also known as the vaporisation energy.
This is the energy required to completely remove a unit volume of molecules from
their neighbours to infinite separation within a substance. The most common form of
solublility parameter takes the form of
r
δi =
Ec,i
V
(4.5)
62
4.3 polymer states in solution and at surfaces
Where Ec,i is the cohesive energy density of the species i in a mixture and V is
the molar volume of the solvent. These δi are known as the Hildebrand solubility
parameters (HSP) and are widely used to characterise solute-solvent interactions
[13, 194, 195]. By using the Hildebrand-Scratchard equation the enthalpy of mixing per
unit volume, ∆ H̄Mix , can be expressed in terms of these parameters by [13, 61, 195]
∆ H̄Mix ≈ (δA − δB )2 Vf (1 − Vf )
(4.6)
Where δA,B are the HSP of phases A and B respectively and Vf is the solute volume
fraction. The Flory-Huggins equation also expresses this quantity through [196]
∆ H̄Mix = χVf (1 − Vf )kT/v0
(4.7)
Where k is the Boltzmann constant, v0 is the solvent molecular volume or lattice site
volume in lattice theory and χ is known as the Flory-Huggins interaction parameter.
Upon comparison of equations 4.6 and 4.7, this parameter can be expressed in terms
of HSP as [196]
χ AB ≈
v0
( δ A − δB ) 2
kT
(4.8)
This approximation is successful for species with non polar interactions, which
is suitable for this work where van der Waals forces are dominant. An optimum
dispersion will have a very small value of χ and hence requires matching between the
HSP of solute and solvent.
4.3
polymer states in solution and at surfaces
When in solution, a polymer chain can resort to a number of different configurations
depending on the net inter-segment forces in the liquid. A rigid polymer will be
straight and its fully extended length or contour length, LC = nl , where n is the
number of Kuhn segments/monomers (the equivalent monomer length for an ideal
polymer chain) and l is the length of one monomer [196]. Even if the polymer adopts
this rigid rod like configuration, at finite temperature thermal fluctuations will cause
63
4.3 polymer states in solution and at surfaces
Figure 4.1: Representation of the different states of adsorbed polymers. Adapted from [197].
the chain to bend and oscillate. Such polymer chains are known as wormlike chains,
with their wavelength of fluctuation called their persistence length.
Another scenario is that of the freely jointed chain. Here the individual segments
are able to rotate freely about one another in any direction. If these rotations are
unhindered by any intersegment interactions whatsoever then the polymer chain
assumes the shape of a random coil. The effective size or lateral magnitude of the coil
in this configuration is known as the radius of gyration R g given by
r
Rg = l
n
6
(4.9)
This factor is governed by the type of solvent present in the system. In the case
of a poor solvent equation 4.9 does not hold, as segments attract one another and
the coil will shrink. If these attractive forces are very strong, for instance van der
Waals or hydrophobic then the chain can lose all of its randomness and collapse into
a compact structure, shown in figure 4.1B. In an ideal solvent or theta solvent the
equation holds as there are no interactions between segments in the solvent. In real or
nonideal solvents R g will either increase or decrease. The final configuration is adopted
for the case of a good solvent. Here we have repulsive interactions between the chain
segments and the the chain will swell and become more expanded as shown in figure
4.1C, causing an increase in R g .
64
4.3 polymer states in solution and at surfaces
Polymers can adsorb easily onto surfaces often reaching a maximum level of adsorption for concentrations as low as a few parts per million in solution [197]. If the
adsorbed, unperturbed coil’s state is the same as its state in solution the adsorbed coil
will adopt a mushroom like shape as seen in figure 4.1B. It is reasonable to quantify
that the amount of polymer adsorbed with full surface coverage without any coil overlap is equivalent to that if all the coils were to lie flat in a close packed configuration
on the surface.
If the adsorbed state differs from that in the solution then the system becomes far
more complex. Types of adsorption can vary with bulk polymer concentration or
whether the polymer is a homopolymer or copolymer. Physisorption (figure 4.1B and
C) occurs when when adsorption occurs due to physical forces while chemisorption
(figure 4.1A) takes place by grafting, anchoring or binding of a certain functional group
to the surface. If chemisorption occurs at high surface coverage then a polymer brush
will form, where the layer thickness will be greater than R g .
4.3.1
Steric Stabilisation
When two polymer coated surfaces approach one another, a force occurs when the
outer segments begin to overlap. This overlap takes place at separation distances of
less than a few R g . This force will be repulsive as the confining or compression of
the chains between the surfaces is entropically unfavourable. This process is known
as steric repulsion and this plays a critical role in systems described in chapter 5 of
this thesis. This phenomenon enables the addition of a small amount of polymer to
have a stabilisation effect on a solute solvent mix where the colloidal particles would
normally aggregate. The addition of a polymer to a colloidal system for this purpose
is known as steric stabilisation [196, 197]. Work carried out by Dolan et al. examined
the magnitude of this repulsion for two flat surfaces each with an adsorbed polymer
layer in a theta solvent [198]. Upon varying the surface separation from D = 8R g to
D = 2R g an osmotic pressure P [ Nm−2 ] of
P( D ) = A(
ΓkT − D/Rg
)e
Rg
(4.10)
65
4.4 mechanical theory
Figure 4.2: Illustration of the deformation mechanism of semi-crystalline materials.
was found to be present, where A is a constant, Γ = 1/s is the number of grafted
chains per unit area and s is the mean distance between chain-surface attachment
points. In the case of a good solvent the chains are swollen and hence a repulsive
osmotic pressure greater than that predicted by equation 4.10 is expected.
4.4
mechanical theory
4.4.1 Mechanisms of Deformation
As described in chapter 2, rigid polymers can contain both crystalline and amorphous
regions. The addition of a stiff yet brittle filler material to form a composite results
in a similar semi-crystalline system. This initial pre-stress setup can be seen in stage
1 of figure 4.2. This is similar to the case for elastomers which contain hard and soft
segments. Depending on the level of stress applied to this semi crystalline material,
two distinctly different mechanisms take place during deformation.
As stress is applied to the composite specimen, the initial regime is called elastic
deformation. This stage involves the elongation of the polymer chains in the amorphous
region from their stable configurations in the direction of the applied stress. This occurs
via the bending and alignment of the chain covalent bonds. Only the orientation of the
66
4.4 mechanical theory
Figure 4.3: A stress strain curve describing the various stages of deformation in semi-crystalline
materials.
polymer chains is altered in this process, and so the mechanism is reversible at this
point.
We now pass the yield point and move into the plastic region, as seen in figure 4.3.
We now have processes involving both the crystalline filler regions known as lamellae
and the amorphous polymer chains. In stage 2 of figure 4.2, the aligned chains now slip
past one another in the loading direction. This causes the lamellar regions to slide past
one another as the chains bound to them become extended. As more stress in applied
in stage 3 the lamellae themselves are aligned along the direction of load. In stage 4
the bonds between the groups of lamellae in the crystalline block regions separate
from one another, with each segment still being joined to some polymer chains. Stage
5 shows the alignment of these new block and chains along the tensile direction.
Failure occurs shortly after the system reaches this highly oriented configuration. Any
stress applied after the yield point is reached is irreversible, and it is only possible to
obtain the original configuration by heating the sample to near its melting point. The
extent of physical recovery will then depend on the heating temperature and degree
of elongation caused previously in the material. It is noted that hard thermosetting
plastics, like ceramics, have minimal plastic deformation ranges.
The final stage observed in figure 4.3 is the necking stage. First discovered by
Considere in 1885 [199], necking occurs during mechanical stress when a large amount
of stress is focused in a small localised area of the material. The material contains some
67
4.4 mechanical theory
infinitesimal weaknesses and strengths in this area, necking occurs at one of these
weak points. As the stress increases dramatically the cross sectional area of the sample
is decreased rapidly and hence a “neck” is formed.
Deformation of metal matrices, while a different system altogether, shares some
similarities with polymer matrix deformation. While lamella-chain interactions shown
in figure 4.3 are not present in this matrix, stress strain profiles similar to that shown
in this figure are common for metallic materials. Furthermore, ductile metals such as
copper, silver and tin have rather large plastic deformation ranges [200], with necking
also occuring in metal matrices [201].
4.4.2
Modelling Composite Reinforcement
Vital information regarding the efficiency of reinforcing nanofiller materials can be
gained by quantifying the level of mechanical improvement obtained. Initially it is
required to analyse the quantity of filler material present in each composite film. This
is achieved by fabricating a range of films with increasing amounts of filler material,
then calculating the mass fraction and volume fraction of each composite film in the
series. The mass fraction, m f , of a composite is defined as
mf =
mF
mF + mP
(4.11)
Where m F and m P are the masses of filler material and polymer respectively. In order
to investigate the effectiveness of the filler material it is more accurate to quantify the
composite in terms of filler volume fraction. This parameter, Vf , is linked to the mass
fraction, m f , by
ρF 1 − m f
Vf = 1 + (
)
ρP
mf
−1
(4.12)
Where ρ F and ρ P [kgm−3 ] are the densities of the filler material and polymer respectively. The volume fraction percent parameter is sometimes also used, simply defined
as Vf (%) = vol% = 100 × Vf . There is also a similar parameter known as the weight
percent which has a similar link to the mass fraction, namely wt% = 100 × m f . By
examining the mechanical data one can obtain information regarding the interfacial
68
4.4 mechanical theory
strength of the filler-matrix interaction, the optimum volume fractions of filler to
choose and also the point where the addition of more filler becomes degenerative on
the film properties. To quantify the level of reinforcement present, the initial region of
modulus increase before saturation is converted into a figure of merit, dY/dVf . This
is then compared with the value obtained by two well known composite theoretical
models.
4.4.2.1
Modified Rule of Mixtures
The general rule of mixtures is a weighted mean used to predict the mechanical
properties of a composite material. It simply calculates the properties of the hybrid
material based on the ratios of filler and matrix phases present. It suggests the linear
increase in properties with increased addition of filler and is given by [202, 203]
Y = Vf YF + (1 − Vf )YM
(4.13)
σ = Vf σF + (1 − Vf )σM
(4.14)
for the modulus and strength increase of the composite respectively. Here Vf is the
volume fraction of filler, Y, YF , YM are the modulus of the composite, filler material
and matrix material respectively and σ,σF , σM are the strength of the composite, filler
and matrix respectively.
This general form of the rule is relevant in the case of composites reinforced by
fibres and therefore will hold for other 1D rod-like filler particles such as nanotubes.
In the case of 2D nanosheet based composites however, a Modified Rule of Mixtures
(MRoM) is used, namely
Y = (η LY YF − YM ) Vf + YM
(4.15)
σ = (η Lσ σF − σM ) Vf + σM
(4.16)
where η LY , η Lσ are known as the modulus and strength length efficiency factors of the
filler material, respectively. These parameters reflect the dependance of reinforcement
69
4.4 mechanical theory
on nanosheet length, and increase from 0 to 1 with increasing nanosheet aspect ratio
(length/thickness), L/t. It is worth noting that these expressions are valid for platelets
aligned in a plane parallel to the applied stress. The nature of these parameters is
further explored in chapter 6.
4.4.2.2
Halpin-Tsai Model
The Halpin-Tsai (HT) model is another method to predict the modulus of a unidirectional composite as a function of filler volume fraction. This model can be used for
both 1D and 2D filler materials by easily changing a geometry factor ξ = 2 ( L/d) or
ξ = 2( L/t) for fibres and flake like fillers respectively, where d is the fibre diameter.
We will adopt the latter case, giving the relation [203, 204]
Y = YM
1 + 2Vf ηL/t
1 − Vf η
(4.17)
where
η=
YF /YM − 1
YF /YM + 2L/t
(4.18)
This model assumes that all the platelets contributing to the reinforcement are
aligned in plane. Several other assumptions are made by these two models. The
aspect ratio used for our calculations is taken from the average value gained from the
statistical TEM analysis. Both models assume all filler particles are of this dimensional
magnitude. While the centrifugation regime carried out in this work attempts to
narrow the deviation in mean aspect ratio as much as possible, there will always be
a considerable number of filler particles that fall below this ratio. It is also predicted
that during the solvent removal stage of film preparation the particles are oriented in a
horizontal direction. It cannot be assumed that all particles are perfectly aligned in
plane, rather that there is a distribution of flakes tilted at a small angle with respect to
the plane of reinforcement. This will thus add a vertical component to the reinforcement
and diminish the effectiveness of the particles in terms of the plane of induced stress.
Obviously these models assume an ideal case and hence the above approximations are
considered as an upper bound for the level of reinforcement achieved.
70
5
T H E R O L E O F S O L U B I L I T Y PA R A M E T E R S I N
U N D E R S TA N D I N G T H E S T E R I C S TA B I L I S AT I O N O F
E X F O L I AT E D T W O - D I M E N S I O N A L N A N O S H E E T S B Y
A D S O R B E D P O LY M E R S
5.1
introduction
Previous work in the group has shown that by choosing a solvent whose Hildebrand
solubility parameter (HSP) is closely matched with that of CNTs, a favourable interaction can take place where successful nanotube exfolation is achieved [61, 205]. In the
case of 2D nanomaterials, dispersed, defect free graphene has been obtained using
both solvent [60, 62, 63, 167, 206, 207] and aqueous-surfactant [65, 66, 208, 209] systems.
More importantly for this work, it has been shown that graphene can be exfoliated in
otherwise poor solvents by using polymers [174, 210–214] or non-covalently attached
small molecules [215–217]. This polymer stabilisation method is particularly useful
as graphene exfoliated in the presence of a polymer makes an ideal starting point
for composite formation, as shown in chapter 6. Recently it has been reported that
inorganic layered materials such as hexagonal boron nitride (BN) and molybdenum
disulphide ( MoS2 ) can be exfoliated in solvents [19, 68, 111, 218, 219] or by using
surfactants [121]. Interestingly, there have been no reports of these materials being
exfoliated using polymers as stabilisers. This is unfortunate as both of these materials
have been shown to be promising fillers in composite materials [111, 119].
The steric repulsion mechanism mentioned in section 4.3.1 has been studied for
decades as a successful technique to stabilise a colloid [197, 220–224]. However the
majority of the reports focus on modelling the free energy of the polymer coated
colloids as a function of separation [222–224], similar to the form of equation 4.10. When
polymer adsorption is studied, simple rules describing polymer and solvent choice are
71
5.2 experimental procedure
generally not available. Information can be gained by examining the solubility of solutes
in solvents. As seen in equations 4.6 and 4.8, solubility is maximised when the HSP
of solute and solvent match. This provides a simple framework to aid solvent choice.
Unfortunately no such framework exists to aid in the choice of a polymer/solvent
combination to stabilise a colloid.
In this chapter work involving the polymer aided exfoliation of graphene and
inorganic 2D layered materials BN and MoS2 is discussed. It is shown that all three
nanomaterials can be exfoliated and stabilised against aggregation in solvents that
cannot alone exfoliate these materials, provided that dissolved polymers are present.
An attempt was also made to understand the system by deriving an expression for the
free energy of adsorption of polymer chains onto the surface of these nanosheets in
a solvent. This model involves only the HSP of each phase of the dispersion, rather
than the separation distance between adsorbed chains. An estimation of the ideal
polymer and solvent choice for each material is also produced, to maximise exfoliation
concentration in terms of the HSP of solute, solvent and polymer.
5.2
experimental procedure
Eight polymers were chosen for the stabilisation study. They are polybutadiene
(PBD), poly(styrene-co-butadiene) (PBS), polystyrene (PSt) , poly(vinyl-chloride) (PVC),
poly(vinyl- acetate) (PVAc), polycarbonate (PC), poly(methyl-methacrylate) (PMMA),
poly(vinylidene-chloride) (PVDC) and cellulose acetate (CA). All polymers had molecular weights of approximately 100 kg/mol apart from PBD which was higher, at 400
kg/mol. These polymers were chosen based on their HSP [225] with respect to those
of the nanosheets and solvents used. See tables 5.1 and 10.3 for full details on the HSP
of all the materials.
Although the solvents tetrahydrofuran (THF) and cyclohexanone (CXO) have HSP
somewhat close to those of the nanosheets investigated, they do not stably disperse
the sheets alone [60, 68]. This was ensured by sonicating each layered material in both
solvents and confirming that no nanosheets remained suspended in solution after
72
5.2 experimental procedure
Nanosheet
Solvent
δG ( MPa1/2 )
δS ( MPa1/2 )
Graphene
THF
21.25
18.6
BN
THF
21.25
18.6
MoS2
THF
22.5
18.6
Graphene
CXO
21.25
20.3
Table 5.1: Hildebrand solubility parameters of studied nanosheets and solvents. Nanosheet
HSP are extracted from the literature [62, 68], while solvent HSP are taken from the
Polymer Handbook [225].
centrifugation. This certifies that all nanosheet dispersion is due to steric effects from
the polymer rather than solvent stabilisation. All polymers were obtained in granular
form from Sigma Aldrich and initially dissolved in both solvents at concentrations of
30 mg/mL.
Graphite (Branwell Natural Graphite, grade 2369), powered hexagonal BN (Sigma
Aldrich) and powdered MoS2 (Sigma Aldrich) were added at a concentration of 3
mg/mL to 1mL of the the 30 mg/mL polymer/THF solutions. Solvent (9 mL) was
added to the layered material samples so that the concentrations were 0.3 mg/mL
layered material and 3mg/mL polymer. Additionally, graphite was added in the same
way to each polymer dissolved in CXO. These samples have a significant excess of
polymer, a factor of 10 by mass. It is expected that this quantity of polymer will lead
to an adsorption-desorption such that the majority of the polymer chains are free (i.e.
not adsorbed).
All samples were sonicated using a point probe sonic tip (VibraCell CVX 750 W at
30% amplitude for 30 min per sample), using the 10 mL in a 14 mL vial. The tip was
set to pulsed mode (7 seconds on, 10 seconds off) to avoid sample heating and solvent
evaporation. After sonication the dispersions were centrifuged at 1500 RPM for 45
minutes (Hettich Mikro 22R) and the supernatants were collected. The procedure was
repeated so that at least two independent dispersions were obtained for each layered
material/polymer/solvent combination.
73
5.3 results and discussion
The concentration C of exfoliated material was obtained by UV-Vis absorption
spectroscopy for each sample using equation 3.4 using a 1mm cuvette (l = 1 mm), and
extinction coefficients αGraphene = 3620 mLmg−1 m−1 (550 nm), α BN = 592 mLmg−1 m−1
(600 nm) and α MoS2 = 3460 mLmg−1 m−1 (672 nm) [62, 68]. It is noted that due to the
presence of scattering effects during this process [68, 121], these coefficients are not
intrinsic to the layered materials and may vary slightly based on sample preparation.
They are however sufficiently accurate to give a good estimate of the dispersed
concentration. For this analysis a relative concentration is used so a high level of
accuracy is not essential. All reported values for concentration are the average of at
least two measurements. The error bar denotes the deviation between minimum and
maximum concentration observed.
Samples were prepared for TEM analysis by dropping a few millilitres of dispersion
onto a holey carbon grid (400 mesh). No attempt was undertaken to remove excess
polymer from the samples prior to TEM imaging.
The HSP of graphene, BN and MoS2 were estimated from the literature [62, 68]. It
was previously reported that the HSP for graphene dispersed in solvents is 23 MPa1/2 .
However since then better estimates of this result have been found by fitting a Gaussian
envelope [226] to the concentration versus solvent HSP data. This fitting gives a value
of ~21.25 MPa1/2 for graphene, which is used in this work. Similar methods reveal
values of ~21.25 MPa1/2 and ~22 MPa1/2 for BN and MoS2 respectively.
5.3
results and discussion
5.3.1 Initial Characterisation
Three layered compounds - graphite, BN and MoS2 , were sonicated in solutions of a
range of polymers in the solvent THF. The graphite range was also sonicated in CXO.
The resultant dispersions were then centrifuged to remove any unexfoliated material. It
is well established that when performed in the presence of suitable solvents or aqueous
surfactant solutions, such a procedure leads to the exfoliation and stabilisation of two
74
5.3 results and discussion
Figure 5.1: Photograph of post-centrifugation supernatants of graphene dispersed in THF by a
range of polymers. Exact correlation between sample darkness and concentrations
in figure 10.3 is not apparent as these numbers are averages over a number of
batches, whereas this image is of a single batch.
dimensional nanosheets of graphene, BN and MoS2 [60, 65, 68, 121]. After centrifugation, dark-grey/black, dark-green and milky-white dispersions were obtained for all
graphene, MoS2 and BN samples, respectively. However, it was obvious, even to the
naked eye that the dispersed concentration varied between samples, with an example
of this shown for graphene dispersions in figure 5.1.
The dispersed concentration was estimated by optical absorption spectroscopy
[62, 68]. Over the range of polymers used, the measured concentrations varied from 1
to 22 µg/mL (Graphene/THF), from 2 to 34 µg/mL (BN/THF), from 17 to 33 µg/mL
(MoS2 /THF), and from 68 to 141 µg/mL (Graphene/CXO).
While it was apparent that layered material had been dispersed in the range of
polymer solutions, the exfoliation state was unknown. TEM analysis was carried out
to assess the exfoliation quality of the dispersions, The TEM images exhibited a large
number of two dimensional nanosheets with examples from various samples shown in
figure 5.2.
In all cases nanosheets appeared to be exfoliated to a degree consistent with that
observed in previous publications for solvent and aqueous surfactant exfoliation
[60, 62, 65, 66, 68, 121, 167, 209]. In addition, the vast majority of objects imaged were
electron transparent. The flake thickness can be estimated for graphene and BN using
75
5.3 results and discussion
Figure 5.2: TEM images of dispersed nanosheets in various polymer/solvent solutions, labelled
following a nanosheet/solvent/polymer convention. Polymer HSP increases from
left to right in each row. All scale bars 500 nm.
the flake edge examination method mentioned in section 3.5.1 [68, 167]. However,
this process is not reliably accurate for MoS2 . Nevertheless using flake edge analysis
combined with observed contrast suggested that the nanosheets were thin multilayers.
This was confirmed for graphene samples in THF where detailed flake edge analysis
showed nanosheets to have between 1-10 layers with a mean of 3-4. These values were
independent of polymer type. Also, lateral flake size was found to be ~500-1000 nm,
also invariant with polymer type. All of these parameters are shown in figure 5.3.
5.3.2
Modelling Polymer Adsorption
It is critical to understand what factors govern the dispersed concentration of polymer
stabilised nanosheets. As mentioned previously in section 4.3.1, polymer stabilisation
of colloids is normally through the mechanism of steric stabilisation [13, 197, 227].
Here, once chains are attached to the nanosheet, a repulsive potential will exist which
76
5.3 results and discussion
Figure 5.3: Statistical TEM analysis averages for length, width and thickness of nanosheets for
the 8 graphene/polymer/THF samples as a function of polymer HSP.
will oppose aggregation. Chains can be attached either covalently (grafted) or by weak
van der Waals forces [197]. When polymer chains are covalently attached to the basal
plane of graphene [228], such attachment causes a perturbation in the monolayers
electronic structure, resulting in significant property alteration of the material. Hence,
for graphene and other 2D materials, noncovalent attachment is preferable. It has long
been established that sonication of carbon nanotubes in a polymer solution results in
exfoliation of the nanotubes and subsequent stabilisation by the adsorbed polymer
[229–231]. Similar results for polymer stabilised graphene have been reported recently
[174, 211–214].
However, it is important to note that only partial adsorption of the polymer chains
can occur if stabilisation is to be effective. This means that the polymer may be
anchored to the nanosheet surface at a number of sites but loops of the polymer
chain must protrude out into the solvent (see figure 4.1) to provide steric stabilisation
[197, 227, 232]. Here we aim to develop a simple model that describes when partial
adsorption can occur and so leads to rules which can aid in the choice of successful
77
5.3 results and discussion
polymer/solvent combinations. Most papers assume proper polymer adsorption and
then calculate the free energy as a function of separation (see equation 4.10) between
two polymer coated particles to give information regarding the repulsive potential
barrier [222–224]. A different approach is taken in this work. We assume that the
presence of adsorbed polymer will lead to a potential barrier and so concentrate on
the conditions required for polymer adsorption.
It is known that for an arbitrary surface, competition exists between polymer and
solvent for adsorption [227, 233]. The resultant degree of adsorption and conformation
of the adsorbed chains depend on the combination of surface, polymer and solvent
and the balance of the intermolecular interactions between these three phases. Partial
polymer adsorption can only occur if it is favourable in terms of the free energy of the
system. The free energy for adsorption of a polymer chain on a surface depends on
both energetic and entropic components [196]. To estimate the energy of adsorption,
the competing intermolecular interactions involved in this process must be considered.
This task can be approached using the HSP of each phase [13, 194, 195]. It is noted that
such parameters have been used to empirically characterise polymer adsorption onto
surfaces [233–235] but have not yet been used to theoretically calculate the energetics
of polymer adsorption. The HSP of a material is linked to its cohesive energy density
as per equation 4.5. Within a lattice model, it can be shown that the intermolecular
energy of interaction, ε AB , between two lattice sites each containing a molecule or
portion of a molecule can be given by [196]
ε AB = −
2vS
δ A δB
z
(5.1)
Here the molecular size of A and B is approximated to be equal (molecular volume,
vS ) and z is the number of nearest neighbours per molecule. When dealing with
polymers, each site is considered to contain one monomer. Equation 5.1 can be used
to predict the binding energy of both like and unlike molecules although it is most
precise when the intermolecular interactions are dominated by the dispersion (London)
interaction [195, 196]. The expression breaks down for very polar or hydrogen bonding
78
5.3 results and discussion
systems and this analysis is not expected to be accurate for aqueous systems, such as
those studied in chapters 6 and 7.
In order for the steric stabilisation to occur, the polymer must partially adsorb
onto the surface of the nanosheet such that its chains can still protrude into the
solvent. For this to take place the nanosheet-polymer (GP) and polymer-solvent (PS)
interaction strengths must be similar: ε GP ∼ ε PS . If this were not true then it is
expected that the chain would either bind completely throughout its length onto the
nanosheet or else remain completely free in the solvent. The energy associated with the
polymer-nanosheet and polymer-solvent interactions can be expressed as ε GP ∝ −δG δP
and ε PS ∝ −δP δS , respectively. The similarity between the energies implies that the
nanosheets are best stabilised by adsorbed polymer when δG ∼ δS . This is the most
basic condition for stabilisation of nanosheets by adsorbed polymer and is roughly
satisfied in this case, since for THF and CXO, δS = 18.6 and 20.3 MPa1/2 while for
graphene, BN and MoS2 , δG = 21.25, 21.25 and 22.5 MPa1/2 , respectively.
However, so far there is no information regarding the requirements for the polymer
HSP. To properly describe the adsorption scenario it is necessary to analyse the
energetics of polymer adsorption in greater detail. This model considers a cube
containing solvent molecules and a single polymer chain and also where one side of
the cube is a nanosheet. We fill the cube with a cubic lattice of M×M×M sites. The
polymer chain is modelled as a linear array of m lattice sites, each containing one
monomer. All other lattice sites contain only solvent molecules. Two scenarios are
considered, as shown in figure 5.4.
In scenario 2 the chain is partially adsorbed with n lattice sites bound to the
nanosheet and hence m − n sites protruding into the solvent. The contributions from
solvent-solvent (SS), polymer-solvent (PS), nanosheet-solvent (GS) and nanosheetpolymer (GP) interactions energies for each scenario the difference between the two
cases will be calculated. The interaction energies ε SS, ε PS, ε GS, ε GP are taken to be negative.
The energy difference between scenario 1 and 2 will be considered by estimating
the energy cost of removing the red and black blocks of cells from scenario 1 (see
dashed lines in figure 5.4) and the energy gained upon replacing them in scenario 2.
79
5.3 results and discussion
Figure 5.4: Schematics of scenarios in the lattice model where (1) a polymer chain is surrounded
by solvent and (2) it is partially adsorbed on a nanosheet surface.
Only nearest neighbour “face-to-face” interactions will be considered in this 2D model.
This is sufficient as in 3D the energy cost in scenario 1 and gain in scenario 2 perfectly
match in terms of out of plane interactions. The transition from 1 to 2 is achieved as
follows:
The energy cost of removing the red block from 1
E1R = nε GS + nε PS + 2ε SS
(5.2)
Sliding the black block down into contact with the nanosheet - breaking bonds at
the top and forming new bonds at the bottom)
E1B = (n − 1)ε SS + ε PS − nε GP
(5.3)
Replacing the red box in 2
E2R = −(2n + 1)ε SS − ε PS
(5.4)
And hence the total energy difference is given by
∆E = E1R + E1B + E2R
(5.5)
By combining these 4 expressions the total energy difference is
∆E = nε GS + nε PS − nε GP − nε SS
(5.6)
80
5.3 results and discussion
This can be rewritten using equation 5.1 as
∆E
2v
= − S (δG δS − δG δP + δP δS − δS δS )
n
z
2vS
=−
(δG − δS ) (δS − δP )
z
(5.7)
This equation predicts that the adsorption energy becomes more negative and hence
more favourable as the difference between the solvent and polymer HSP increases. This
agrees with previous empirical reports [234, 235]. In general, this expression suggests
that combinations of HSP exist such that polymer chains can be driven from the bulk
solvent to adsorb onto the nanosheet surface in order to reduce their energy.
However, it is important to consider the free energy of adsorption. If this parameter
is too high, many sites along the polymer chain will be forced to bind to the surface.
This will lead to a very compact chain configuration close to surface, at high entropic
cost. On the other hand, if the free energy of adsorption is too low, there will be
very few binding sites, and hence many loops extending into the solvent. While this
configuration is entropically favourable, it is less energetically favourable. Flory wrote
the free energy per chain as the sum of an energetic and an entropic term. He then
minimised this sum to produce the free energy of adsorption in terms of the binding
energy per site. Therefore when the polymer chain adsorbs onto a surface, the free
energy per adsorbed chain is approximately given by [196]
∆E/n 2
∆F = kTN
kT
(5.8)
where N is the number of Kuhn monomers per chain. This means that the free
energy per adsorbed chain is
"
∆F = kTN
2vS
z
(δG − δS ) (δS − δP )
kT
#2
(5.9)
Equations 5.8 and 5.9 can be utilised to build rules of thumb for the relationship between nanosheet, polymer and solvent for sterically stabilised dispersions. Adsorption
is predicted to be favourable when ∆F is minimised, i.e. ∆F ≈ 0. Equation 5.9 shows
this to be the case when either δG ≈ δS or δS ≈ δP . This is consistent with the intuitive
assumption of δG ∼ δS given above. By analogy with exfoliation of graphene and other
2D layered materials in solvents and surfactants [60, 65, 66, 68, 121], it is assumed
81
5.3 results and discussion
that sonication of the layered materials in the polymer solution leads to large scale
exfoliation. Then polymer adsorption is required to stabilise the dispersed nanosheets.
It is anticipated that if favourable, polymer adsorption occurs quickly and reaches
full coverage of the nanosheet surfaces in all cases [227]. In this ideal full coverage
scenario it is proposed that the concentration of dispersed nanosheets is governed
by the probability of polymer adsorption via the Boltzmann weighting factor (i.e.,
C ∝ e−∆F/kT )
"
C = Aexp −
√
2vS (δG − δS ) (δS − δP )
N
z
kT
2 #
(5.10)
where A is a constant. This equation predicts that C will show a Gaussian peak when
plotted versus δP which is centered at δS = δP . However when C is plotted against δS ,
a double peak will be present will local maxima at δS = δP and δG = δS . It is clear that
when δG ≈ δP , this will appear as a single peak. It is worth highlighting that in the
case where solute-solvent interactions are expressed in terms of the Flory-Huggins
parameter, χ, as described in equation 4.7, equation 5.10 can be expressed in the form
C ∝ exp −4NχGS χSP /z2
(5.11)
Unfortunately, equation 5.10 cannot accurately predict the absolute concentration of
dispersed nanosheets, rather only relative concentrations. The absolute concentration,
governed by the value A, is most likely controlled by the repulsive potential energy
that stabilises the nanosheets.
5.3.3
Comparing Experiment with Theory
These predictions can by tested by plotting the dispersed nanosheet concentration as a
function of the HSP of the range of polymers, δP . This data is shown in figure 5.5 for
all four systems.
The model predicts a peak in concentration when δP ≈ δS . In each case, a peak is
observed at polymer HSP close to that of the solvent. However, it was apparent that
in all cases, the peak was centered slightly above the solvent HSP, shifted by 0.5-2.7
82
5.3 results and discussion
Figure 5.5: Concentration of dispersed nanosheets after centrifugation as a function of HSP of
stabilising polymer. The arrows denote the HSP of nanosheet, δG , and solvent, δS ,
in each case. The dashed line signifies a fit of equation 5.10 to the data.
83
5.3 results and discussion
MPa1/2 . This indicates that our simple model contains some limitations and perhaps
suggests that in practice it may be beneficial to have δP closer to δG than δS in order to
influence the polymer onto the nanosheet surface.
Some scatter is also observed in the data, this is expected due to variations in the
molecular weights between the studied polymers. This leads to a difference in N in
equation 5.10 for each polymer. It has been shown that the fluid-solid frictional forces
that govern the sonication aided exfoliation mechanism depend on the viscosity of the
dispersive medium [69, 236]. The molecular weight variation across the polymer range
could therefore lead to varying viscosities with the samples, resulting in fluctuations
in exfoliation properties and hence in the dispersed nanosheet concentration.
The main variation in the observed peaks is the width. The peaks for the graphene/THF
and BN/THF systems are quite narrow in comparison with broader peaks seen for
the graphene/CXO and MoS2 /THF systems. This can be justified by noting that the
full-width at half-maximum of the Gaussian as a function of δP , as described in
equation 5.10, is given by
γ=
1.7zkT
√
2vS (δG − δS ) N
(5.12)
This can be tested by plotting the measured width, γ, versus kT/ [vS (δG − δS )] as
shown in equation 5.6. From the slope of the linear fit, an estimation of a representative
value of N can be made. A value of 105 is obtained, which is of the same order of
magnitude as the values of 180 and 390 for the PMMA and PSt used in the study. An
estimation for N cannot be made for all polymers as information regarding the Kuhn
molecular weight is not available for the whole polymer range.
In addition, equations 5.10 and 5.12 indicate that the dispersed nanosheet concentration should scale exponentially with C ∝ − N. This has been tested by measuring the
dispersed concentration of graphene in polystyrene/THF for a range of polystyrene
molecular weights. To account for variations in dispersed concentration due to fluid
viscosity mentioned above, the sample preparation method was altered.
To counter this cause of inaccuracy, the samples for the molecular weight study were
produced using reaggregated, previously exfoliated graphene as a starting material.
This way the nanosheets in all weights of PSt have the same exfoliation history, and
84
5.3 results and discussion
Figure 5.6: Measured Gaussian peak widths plotted against kT/[vS (δG − δS )] . The linear fit is
the prediction by equation 5.12.
very light sonication is all that is required to redisperse this reaggregated powder, so
the effect of viscosity on dispersed concentration is minimised.
The most commonly used method of dispersal of reaggregated graphene is to
first exfoliate the graphite in NMP, then filter the graphene/NMP solution through
a membrane. The powder that remains on the membrane can then be dried and
redispersed in another given solvent. However, due to the high boiling point of NMP,
this solvent is extremely hard to remove completely. As residual NMP could comprise
the study at hand (by boosting the solubility for instance), it was decided to prepare
restacked graphene by sonication in water. Graphite was added to 80 mL deionised
water at a concentration of 10 mg/mL. It was then sonicated overnight (starting
graphite, sonic tip and sonication regime all as described for main study). Now, as the
solvent is water and not a stabilising solvent such as NMP [60, 62, 167], the exfoliated
graphene will not remain stably dispersed. Instead, the exfoliated graphene will weakly
aggregate into restacked graphene. This restacked graphene will eventually sediment
out of the dispersion along with any unexfoliated aggregates. This unstable dispersion
was filtered through a porous membrane (Pore Size 0.45 µm) and placed in a 60 o C
oven for 2 days.
85
5.3 results and discussion
Figure 5.7: Dispersed concentration of graphene in PSt/THF plotted against number of Kuhn
monomers per PSt chain. The top axis shows polymer molecular weight.
This dried powder consists of some unexfoliated graphite and reasonable amounts
of exfoliated graphene. Preliminary testing shows that this reaggregated graphene can
be easily resdispersed in suitable liquids without excessive amounts of sonication [63],
even when prepared in an aqueous environment. Nonetheless, care must be taken
to ensure the powder is suitably dried as residual water can destabilise any future
prepared dispersions. In general, polymer stabilised dispersions prepared with water
treated graphene are unstable over long times. However for this investigation the
advantage of ease of dispersion outweighs this problem.
The treated powder was added to solutions of PSt/THF for PSt molecular weights
of 35k, 45k, 100k, 280k and 600k. As before the concentrations are 0.3 mg graphite/3
mg PSt/mL THF. These samples are mildly sonicated for 1hour to homogenise the
dispersion and then centrifuged at 1500 RPM for 10 minutes. The supernatants were
collected swiftly and UV-vis spectroscopy was immediately carried out to ensure
maximum accuracy. The concentrations are shown in figure 5.7.
The data shows an initial increase in dispersed concentration as the polymer molecular weight is increased. More than likely this represents that a minimum molecular
weight is required for steric stabilisation to occur. However, for N ≥ 138 we see a clear
86
5.3 results and discussion
exponential decay of concentration with N, as predicted by equation 5.10. By fitting
this decay a value for d lnC/dN = −0.00113 is calculated. This can be compared with
the value predicted by equation 5.10, δG = 21.25 MPa1/2 , vS = 10−4 m3 mol −1 and z = 6.
These inputs give a value of d lnC/dN = −0.00032. These values are of the same order
of magnitude, supporting the derived model.
This analysis ignored the requirement that the solvent be capable of dissolving the
polymer chains to an extended state, so they can protrude into the solvent. This can
only occur in good solvents, in poor solvents the chain contracts to form relatively
tightly packed globules, as shown in figure 4.1B. This contraction begins to happen
when χ PS > 0.5 [196]. By expressing this in terms of HSP
χ PS ≈
vS
(δP − δS )2
kT
(5.13)
and therefore the polymer chain can extend into the solvent if and only if
s
δP − δS <
kT
2vS
(5.14)
Taking vS = 10−4 m3 mol −1 (midway between THF and CXO), it is discovered that
steric stabilisation should only work favourably if δP − δS < 4 MPa1/2 (at room
temperature). This implies that a broad window of polymer HSP exists, approximately
8 MPa1/2 wide, centered on δS . In all cases studied here, the data exists within this
window, hence all samples studied should contain polymers in extended states.
With this window in mind, a picture can be produced of the possible polymer/solvent combinations for a given nanosheet. As mentioned previously, equation 5.10
does not consider the requirement described by equation 5.14. However, this can be
n
o
remedied empirically by the following function: exp − [(δP − δS ) /3.5]2 . This is
a Gaussian function which falls to ~25% of its peak height value by moving out a
distance 4 units from its centre. Thus, this will approximately simulate the effects
87
5.3 results and discussion
Figure 5.8: Contour plot of equation 5.15 calculated with δG = 21.25 MPa1/2 , N = 200, vS = 10−4
m3 mol −1 , and z = 6. The colour - denoting the concentration, has been normalised
to a maximum value of 1. The HSP of graphene, δG = 21.25 MPa1/2 , is represented
by the white lines.
of polymer solubility on the dispersed concentration of nanosheets. Adding this to
equation 5.10 gives
"
√
2vS (δG − δS ) (δS − δP )
C = Aexp −
N
z
kT
n
o
× exp − [(δP − δS ) /3.5]2
2 #
(5.15)
Using 5.15 a contour map in δP and δS showing the predicted nanosheet concentration has been plotted. This is shown in figure 5.8.
It is acknowledged that this map is perhaps slightly shifted from its true position
due to the viscosity and varying molecular weight reasoning given above. However
it is believed that the map captures the main ideas of the situation well. The most
prominent feature of this plot is the prediction that the area of maximum concentration
occurs at δG ≈ δP ≈ δS . In addition, it is apparent that concentrations will be reasonably
high as long as δP ≈ δS , even if neither of these are equal to δG . It is important to note
also that the double peak mentioned previously is suppressed by the inclusion of the
second term in equation 5.15. Only two small lobes remain, directly above and below
the area of peak concentration.
88
5.4 conclusions
It is required to note that HSP represent a first order approximation to the intermolecular interaction energies. They only describe London interactions and do not
include contributions from polar or hydrogen bonding interactions. It is well established that more accurate results can be achieved using Hansen solubility parameters
[194]. For this work, Hildebrand parameters were chosen because equation 5.1 and all
the equations that follow it can be expressed much more simply in this format than
equivalent versions involving Hansen parameters. It is believed that the simplicity of
the described model more than outweighs this loss in accuracy.
5.4
conclusions
It has been demonstrated that two-dimensional layered materials graphene, BN and
MoS2 can be exfoliated and stabilised by a range of different polymers dissolved in
two different solvents. A simple model is proposed where the Hildebrand solubility
parameters of the nanosheet, polymer and solvent are studied in order to predict
when favourable steric stabilisation will occur. It is determined that this process is
effective only when the solubility parameters of all three phases are similar. For
each nanosheet/solvent combination, experimentally measured peaks in dispersed
nanosheet concentration are observed when plotted against polymer solubility parameter. The peak positions and widths are in very good agreement with the derived
model. Provided the solubility parameter of a given nanosheet is approximately known,
the choice of stabilising polymer and solvent becomes very simple. This enables a
critical level of control over the system, as the optimum polymer/solvent combination
can be chosen initially to ensure a high dispersion concentration and stability level.
Conversely, if the nanosheet solubility parameter is unknown, it could be estimated by
analysing the dispersed concentration in a suitable set of polymer/solvent systems.
Although this model was theorycrafted explicitly with nanosheets as the solute
material in mind, it is suggested that the expressions may be valid for all colloids which
are sterically stabilised by polymers bound by dispersive forces. Thus, it is genuinely
89
5.4 conclusions
believed that these results represent a fundamental advance in the knowledge of these
common, yet complex systems.
90
6
P O LY M E R R E I N F O R C E M E N T U S I N G G R A P H E N E A N D B O R O N
NITRIDE NANOSHEETS
6.1
introduction
The concept of the addition of high strength and stiffness planar filler materials to
plastics to achieve mechanical reinforcement has been present since the mid 1900s
[202]. Two dimensional fillers were predicted to be superior to rod like fillers because
of the reduced directionality of the reinforcement encountered by these 1D materials.
For instance, a composite material filled with aligned, rod like fillers will demonstrate
successful reinforment along the direction of alignment (x) but not in the other two
perpendicular directions (y and z). However, a composite material using a planar 2D
filler should demonstrate reinforcement along both x and y directions along the flake
surface, although not in the z direction.
After its discovery in 2004 [8], researchers were keen to examine the mechanical
properties of graphene now that it was possible to isolate a monolayer of the material.
In 2008, monolayer graphene was reported to possess a modulus and in-plane strength
of 1000 GPa and 130 GPa respectively, making it is strongest and stiffest material ever
recorded [27]. These mechanical properties greatly surpass many modern structural
materials such as high strength steel, and are rivalled only by carbon nanotubes.
However, when the important factor of cost is considered, graphene wins outright,
in some cases by 5 orders of magnitude over certain CNTs. Hence a surge of interest
followed as graphene became the new optimal candidate as a mechanical filler material.
Initial work within the group consisted of preparing composites of graphene in
polyurethane [214, 237]. The methods used in these reports consisted of first exfoliating
graphene in a favourable solvent - n-methyl-pyrrolidone (NMP) and then adding the
matrix material after nanosheets had been dispersed in dimethylformamide (DMF),
91
6.1 introduction
a solvent suitable for composite formation. However, after the results highlighted in
chapter 5 were obtained, the advantages of using steric stabilisation with suitable
polymers became clear. By choosing a suitable polymer and solvent it was possible
to exfoliate and disperse nanosheets and proceed directly to the composite film
formation stage without changing solvent, filtering or rewashing the sample. This
method appeared clearly advantageous in terms of sample purity and quality.
Unfortunately, graphene cannot be dispersed in water alone, and furthermore the
steric stabilisation theory breaks down due to dominant hydrogen bonding processes
present in an aqueous system. However, using knowledge gained from producing
composite fibres reinforced with CNTs [238, 239], it was found that an aqueous
polyvinylalcohol (PVA) system was successful at debundling CNTs using sonication,
and resulted in the formation of high quality composite materials. For this reason the
same polymer/solvent combination was chosen for these tests involving graphene and
BN as the filler materials. A number of benefits arise from this system choice. PVA
is cheap, its mechanical properties are well studied in the literature and due to the
presence of hydroxyl groups along its backbone, is hydrophilic. This way a system
using a safe, cost-effective solvent with an abundant polymer can be utilised.
A similar system was used in the study of Boron Nitride (BN) composites. Unlike graphene, the mechanical properties of monolayer BN have never been tested
experimentally. However, theoretical reports predict BN to have a modulus of ~750
GPa, only 25% below that of graphene [240]. Like graphene, a single layer of BN
consists of a hexagonal lattice configuration, with strong covalent bonding in plane
and weak van der Waals interlayer bonding [240, 241]. These simlarities suggest that
the two substances could possess similar mechanical properties at low thicknesses. As
mechanical data involving BN was relatively unknown, added interest and excitement
was present when attempting to quantify this material’s effectiveness as a mechanical
filler. Furthermore, as shown in chapter 5, BN has an identical solubility parameter to
graphene, and so if favourable stabilisation was to occur between graphene and PVA
with successful composites as the result, the outlook for the BN composites would also
be promising.
92
6.2 experimental procedure
6.2
6.2.1
experimental procedure
Graphene Composites
PVA (J.T. Baker, MW = 77000-79000 g/mol) was refluxed in 400 mL deionised water
in a round bottomed flask, at a concentration of 50 mg/mL. Graphite flakes (Sigma
Aldrich) were added to this solution, giving a concentration of 6 mg Grt / 50 mg PVA
/ mL H2 O. This was sonicated for 100 hours in a sonic bath (Branson 1510E-MT). The
resultant dispersion was centrifuged at 1000 RPM for 45 minutes, and the supernatant
and sediment were separated and collected. The supernatant was filtered using a 0.45
µm nylon membrane to remove any extensively sonicated (and perhaps degraded)
PVA. The sediment was redispersed in fresh PVA/H2 O and bath sonicated for 4 hours.
This sample was then re-centrifuged at 1000 RPM for 45 minutes to remove aggregates.
The supernatant was collected and TEM analysis showed it to contain small flakes (~1
µm).
The dispersed concentration was measured to be 0.9 mg/mL using absorbance
spectroscopy (using α550nm = 3620 mLmg−1 m−1 ). This corresponds to a mass fraction,
m f , of 0.082 (as described in equation 4.11). The dispersion was then blended with
a pure PVA/H2 O solution to produce a range of dispersions with graphene mass
fractions 0 < m f < 0.01. These dispersions were formed into composite films for
mechanical testing.
To isolate flakes of larger size, the sediment, which contains large exfoliated flakes
along with unexfoliated crystallites [62], was collected and topped up with fresh
PVA/H2 O . It was then sonicated in a bath for 1 hour and centrifuged at 500 RPM for
45 minutes to remove any still unexfoliated crystallites. The supernatant was taken and
its dispersed concentration was measured to be 1.33 mg/mL. TEM analysis showed
the sample contain larger flakes (~2 µm long). This supernatant was used as our filler
solution to make a range of composites with increasing mass fractions of graphene.
For each dispersion type, (large and small flakes), a portion of the final sample
was filtered through a porous mixed cellulose ester membrane to form films. Raman
93
6.2 experimental procedure
measurements were undertaken on these films. For each sample, 5 spectra were taken
at various portions of each film and averaged.
The composite dispersions were bath sonicated for a further 4 hours to homogenise
after blending. They were then drop cast into Teflon trays and dried in a vacuum
oven at 60 o C for 24 hours at a pressure of 900 mbar. It is essential to dry the samples
extensively as any solvent remaining in the films can seriously degrade the mechanical
properties. It is key to keep the PVA samples dry in an oven at all times and test them
as soon as possible after removing them, to ensure a minimum amount of moisture
is absorbed from the air. Composite films of thickness ~40 µm were peeled from the
trays and cut into 2.25 mm wide by 20 mm long strips tensile testing using a die cutter.
The composite volume fraction was calculated as per equation 4.12 with ρG = 2100
kg/m3 for graphene and ρ P = 1300 kg/m3 for PVA.
For TEM analysis, a few mL from each sample dispersion were dropped onto holey
carbon grids (400 mesh). DSC was carried out on the films to confirm that excess
PVA crystallisation [242, 243] had not been nucleated by the presence of the graphene
nanosheets. Further film samples were coated with ~10 nm AuPd using a Cressington
208 HR coater. Helium Ion microscopy was then used to image the films, with a beam
current of 1 pA and a 10 µm aperture size.
6.2.2
BN Composites
PVA (as above) was dissolved in deionised water at a concentration of 20 mg/mL by
reflux at 100 o C. Hexagonal Boron Nitride (Saint Gobain h-BN, high purity, average
crystallite size <50 µm) powder was added to 60 mL of this solution at a concentration
of 20mg/mL. This mixture was sonicated using a sonic tip (GEX600 at 25% of 600 W
for 12 hours using a flat head probe). A pulsation setting was used (5 seconds on, 5
seconds off) to minimise sample heating.
After sonication the sample was centrifuged at 1000 RPM for 45 minutes and the
supernatant was removed from the sediment to form an un-size selected “control”
sample. As mentioned for the graphene case, the supernatant here contains water, PVA
94
6.2 experimental procedure
and small exfoliated BN flakes while the sediment consists of large exfoliated flakes
and unexfoliated crystallites. The sediment was redispersed in PVA/H2 O and then
sonicated in a bath for 4 hours. It was then re-centrifuged at 500 RPM for 22 minutes,
and then supernatant collected. Finally, this supernatant was centrifuged at a higher
rate of 3000 RPM, allowing the very small flakes to be removed in the supernant. This
sediment containing large nanosheets was then dried in an oven at 60 o C and this
powder was used as our filler material.
This dried powder contains large BN flakes but also some residual PVA. TGA
analysis was used to determine the BN content within this powder, facilitating accurate
measurement of the BN content within the composite films (figure 10.9). This method
showed the BN content to be between 84 and 92% depending on the sample set.
The composite powder was redissolved in water at a solid to liquid concentration
of 1 mg/mL. A range of mass fractions identical to that of the graphene case were
produced. All composite dispersions had a total solid mass of 80 mg (PVA and BN) in
a solvent volume of 5 mL. They were sonicated as before in a bath to homogenise after
blending.
Dropcasting, film formation, film dimensions, mechanical testing and TEM analysis
parameters are all identical to those of the graphene composites. DSC was carried
out to confirm that excess PVA crystallisation [242, 243] had not been nucleated by
the presence of the BN nanosheets (figure 10.10B in appendix). The data shows no
change in PVA crystallisation with BN addition. The mass fraction was converted to
volume fraction using ρ P = 1300 kg/m3 for PVA as before, and ρ BN = 2290 kg/m3 for
BN. Helium ion microscopy was used as with the graphene composites, but on this
occasion the fracture surface was imaged using a 20 µm aperture, with a beam current
of 0.5 pA and a tilt of 15o . It is noted that because the fracture surface is not strictly
orthogonal to the film plane, the angle between the fracture surface and the beam is
poorly defined.
95
6.3 results and discussion
6.3
6.3.1
results and discussion
2D-nanofiller / polymer composites in the literature
In order to correctly quantify the mechanical results obtained by the composite materials, a comparative search through the literature was required. It was deduced that
the vast majority of the publications regarding PVA reinforcement using liquid exfoliated 2D fillers involved the oxidisation of graphite to produce exfoliated graphene
oxide (GrO) [30]. Another popular filler material was chemically modified graphenes
(CMGs), produced either by rapid thermal expansion of graphite or functionalisation
of the graphene surface. However, the degree of reinforcement achieved by composites
filled with non-pristine graphene have fallen well below the maximum theoretical
reinforcement limit [244–253].
The degree of reinforcement present in a composite material can be quantified by the
rate of increase in the Young’s Modulus or stiffness, Y, and strength, σB , with volume
fraction of filler, Vf , namely dY/dVf and dσB /dVf [254]. For a polymer matrix filled
with aligned platelets, the modulus and strength can be expressed by the modified
rule of mixtures, namely equations 4.15 and 4.16.
From these equations it is apparent that 2D filler particles should be long and
possess large YF and σF . Furthermore, for effective reinforcement, the filler must be
uniformly dispersed and it must be capable of efficiently transferring stress from
matrix to filler (i.e. the polymer must be well bonded/adhered to the platelets [254]).
In the ideal scenario, the theoretical maximum for reinforcement is bounded by the
platelet properties, or
dY/dVf ≤ YF
(6.1)
dσB /dVf ≤ σF
(6.2)
With this in mind, we can examine the promise of GrO as a filler material for
polymer composites. Due to its hydrophilic nature governed by its surface chemistry,
GrO is likely to disperse well and bond well with hydrophilic polymers. However, with
some exceptions [255], GrO flakes tend to be small, and their mechanical properties
96
6.3 results and discussion
tend to be poor [256], and in some cases inferior to those of pristine graphene by
up to 80% [34]. This likely limits the reinforcement potential significantly. In fact,
for polymer composites filled with GrO and CMGs, the measured values of dY/dVf
and dσB /dVf have fallen well below their maximum values of 1TPa and 130 GPa,
respectively, probably due to these limitations in flake size, stiffness and strength.
Hence, the maximum values reported for these composites have been dY/dVf ≈ 400
GPa and dσB /dVf ≈ 9 GPa [247], (see table 10.4 for summaries of graphene/PVA
composites from the literature).
Several other types of filler materials have been incorporated into composites in
the last 50 years. Discontinuous composites of polymers containing glass, aluminium
diboride and silicon carbide were among the first to be studied and generally resulted
in improvements in composite modulus [202]. Glass platelets, a common choice of
filler in the mid 1900s showed linear increases in modulus for volume fractions up to
Vf ∼ 0.7, with dY/dVf ≈ 70 GPa, which was very close to that of glass itself. Some of
the early results were indeed impressive. Boron carbide platelets gave linear increases
of dY/dVf ≈ 430 GPa, again nearly matching that of the filler itself [257, 258]. However,
while this value is very high, the boron carbide platelets were made by an expensive
and complex route, which is unfavourable for scalable production.
More recently much work has been carried out to study the reinforcing capabilities
of clay platelets [259, 260]. These materials were appealing as they are extremely cheap.
However, with the exception of a small number of reports [261], reinforcement values
have not been exceptional. For instance, the highest published rate of modulus increase
with clay mass fraction that could be found in the literature was for composites
of montmorillonite in nylon-6. These yielded a relatively high rate of increase of
dY/dM f = 42 GPa [262]. Here, the modulus increased linearly with mass fraction to
at least M f = 7 wt%. While these results are reasonably impressive, the vast majority
of reports on other clays show much lower reinforcement [261].
There are most likely two reasons for this. As seen in equation (6.1) above, the rate
of modulus increase can never be larger than the modulus of the filler. Clay platelets
typically have modulus ~200 GPa [261, 263, 264], making this a crude upper limit for
dY/dVf . Also, exfoliated clay platelets used in composite reinforcement are generally
97
6.3 results and discussion
small in dimension, predominantly <1 µm [261]. This can result in low levels of η LY
, as shown in equation (6.4), further reducing the reinforcement. As such, very large
rates of modulus increase are not expected for clay filled composites. Furthermore,
clay platelets are not expected to be particularly strong. While it is difficult to find
information on the platelet strength, one report gives the tensile strength of mica to be
~250 MPa [265], considerably lower even than glass platelets (~1 GPa) [202]. For these
reasons, it is not expected that ultra-strong composites can be created using clay fillers.
Consequently, it is clear that graphene surpasses these glass platelet and clay materials in terms of promise as a reinforcing material. However, graphite is just one of
a large collection of layered crystals, some of which may have similarly impressive
mechanical properties. In particular, BN nanosheets (BNNSs) are expected to have
mechanical properties similar to graphene [240], and therefore are a potentially suitable
candidate for reinforcing plastics [111, 266]. Initial reports of BNNS incorporation into
polymer composites have yield mixed results, from low reinforcement levels seen in
polybenzimidazole ( dY/dVf = 52 GPa, dσB /dVf = 1.8 GPa) [266] to more substantial
levels reported for PMMA composites ( dY/dVf = 252 GPa, dσB /dVf = 2.58 GPa) [111].
Furthermore, it has been reported recently that a wide range of layered crystals
including BN, MoS2 , WS2 to name but a few, can be exfoliated cheaply and easily by
sonication in suitable solvents to produce high aspect ratio nanosheets [68, 111, 121,
267, 268]. As seen in chapter 5, both MoS2 and BN nanosheets have been sonicated in
polymer/solvent solutions whereby exfoliation occurs by adsorbed polymer chains
via the steric stabilisation mechanism. Although this mechanism does not hold for
aqueous media, utilising polymer aided exfoliation gives an ideal starting point for
incorporating BN into polymer composites to examine its reinforcement capability.
It is apparent that significant quantities of large, defect free flakes are necessary for
successful reinforcement. Various characterisation methods are employed for this work
to examine if the reported dispersions fulfill these criteria.
98
6.4 sample characterisation
Figure 6.1: A) and B) TEM examples of A) an as-prepared smaller flake and B) a size selected
larger flake. C) and D) Raman spectra of vacuum filtered films of C) as-prepared
smaller flakes and D) size selected larger flakes.
6.4
sample characterisation
6.4.1 Graphene Composites
In order to examine the possibility of reinforcing plastics with liquid exfoliated
graphene, a graphene/PVA dispersion is prepared by sonication and centrifugation. This graphene/PVA supernatant dispersion had a graphene concentration of 0.9
mg/mL as measured by UV-Vis absorption spectroscopy. TEM analysis showed the
graphene to be present only as exfoliated flakes with no large unexfoliated crystallites,
as shown in figure 6.1A.
Detailed analysis on these images gave mean flake dimensions of < L >= 1.1 µm,
< w >= 0.56 µm, and < t >= 1 nm. Raman spectroscopy was also performed on
vacuum filtered films of these flakes, see figure 6.1C. The D/G band ratio, used to
quantise the defect content was 0.21 for these small flakes. These defects can be basal
99
6.4 sample characterisation
plane or edge defects. The edge defect content can be linked to the flake length, in the
case where there is a small D band, via the following relation [173]
0.26
ID
≈
IG
<L>
(6.3)
This allows us to estimate < L >≈1 µm. The fact that this agrees well with the
TEM statistics for the length suggests that the basal plane defect content is very small,
confirms that our processing method has not damaged the graphene. The range of
dispersions (as described in the procedure section) were formed into composites by
solution casting. Similar to previous work with nanotube polymer composites [269], it
is assumed that such solution cast films results in in-plane alignment of the graphene
flakes. Upon mechanical testing, only very slight increases in modulus, dY/dVf = 170
GPa were observed (see figure 10.2).
The lack of reinforcement present with these flakes is not surprising. Gong et al.
demonstrated that the critical length for reinforcement of polymethylmethacrylate
(PMMA) by graphene is ~3 µm [179]. This would suggest that flakes significantly larger
than 1.1 µm are necessary if the ultimate limit of reinforcement is to be approached.
Production of large graphene flakes by liquid phase exfoliation is challenging as the
flake size is governed by the sonication conditions and is inversely proportional to the
dispersed concentration [167]. Thus in order to prepare a dispersion of larger flakes
than the ones discussed above, a size separation procedure was undertaken [64], as
outlined in the experimental procedure section. Here the sediment from the initial
1000 RPM centrifugation is redispersed in PVA/water and re-centrifuged at 500 RPM.
This way the supernatant from this treatment is expected to contain much larger flakes
than before.
Analysis of TEM images from this batch (figure 6.1B) gave the mean flake size to
be < L >= 2.3 µm, < w >= 1.3 µm and < t >=1.2 nm. TEM statistics for both large
and small flake dispersions can be seen in figure 10.1. Note that the flakes are laterally
larger than before but of similar thickness.
As before the Raman spectrum was measured for this dispersion (figure 6.1D). This
time the D/G band ratio was 0.11, corresponding to < L >≈2.4 µm. Once again this
100
6.4 sample characterisation
Figure 6.2: A) and B) Helium Ion Microscopy images of fracture surfaces of A) a PVA only film
and B) a 1 vol% graphene/PVA composite film. White arrows mark the protruding
graphene flakes. Note: These fracture surfaces were coated with 10nm of AuPd to
facilitate imaging. C) Representative stress strain curves for some of the composites
fabricated using large, size selected flakes.
agrees well with the TEM analysis, showing that these flakes are also damage free.
This dispersion was blended with polymer solution and a set of composite films was
formed as before.
The resultant composite films were of good quality, appearing uniform to the naked
eye. The dispersion state of the graphene within the matrix was investigated by
examining the film fracture surface by SEM. In general, no filler material could be
resolved using this method, indicating that no aggregates were present. However,
upon more precise examination of fracture surfaces (coated with ~10 nm AuPd to
avoid charging due to the insulating nature of PVA) using a Helium Ion microscope
revealed graphene flakes protruding from the films, as shown in figure 6.2A and B. The
observed flakes are very thin, confirming the quality of the dispersion. Furthermore,
that the flakes protrude on fracture show that they are contributing to the overall
composite strength, and that failure is by pullout. This implies that the flake length is
lower than the critical length. The images also confirm that the flakes in fact aligned
along the plane of the film, supporting our previous assumption.
101
6.4 sample characterisation
Figure 6.3: A)-D) TEM images of liquid exfoliated, size selected BN nanosheets.
6.4.2 BN Composites
The initial attempts to reinforce PVA with BN involved flakes prepared using a
very basic exfoliation procedure [268]. This “control” sample contained relatively
small flakes with < L >=540 nm, < t >=1.2 nm (see figures 10.5 and 10.6 for
representative images and statistics). The resulting composites showed low levels
of reinforcement, see 10.7. Based on the modulus length efficiency model obtained
for the graphene composites (equation 6.4 and figure 6.6) it was decided that the
reinforcement was limited by flake size in this control sample. To address this issue,
a tailored centrifugation regime was applied to the control dispersion to select BN
sheets with larger lateral dimensions (see experimental methods above) [64, 268].
The dispersed, size-selected BN nanosheets were analysed using TEM. Typical
images of nanosheets are shown in figure 6.3. Note that the image quality is slightly
impaired by the presence of adsorbed PVA on the flake surface.
As the nanosheet size has significant effect on the mechanical properties of the
composites 6.6, it was required to study the flake dimensions using examination of the
TEM images. The length, L, and width, w, of 132 nanosheets were measured from the
images. Nanosheet lengths varied from ~200 nm to ~7 µm with a mean of < L >=1.35
102
6.4 sample characterisation
Figure 6.4: A) Photograph of three of the BN/PVA composite films studied in this work. B)
Helium ion image of the fracture surface of a 0.1 wt% BN/PVA composite. Large
numbers of well dispersed, well-exfoliated nanosheets can be seen protruding from
the fractured edge. C) Magnified image of the segment of B bounded by the dashed
line. D) and E) Helium ion image of the fracture surface of a 1 wt% BN/PVA
composite. In this case the BNNSs are aggregated.
± 0.1 µm, while average nanosheet with was < w >=1.0 ± 0.1 µm. The histograms
shown in figure 10.8 represent this data.
In almost all cases, these images show objects that are clearly multilayers. Estimation
of this parameter is carried out using the edge counting method (section3.5.1). In a
very small percentage of cases, only one edge was visible suggesting the presence of
monolayers. The mean number of layers per flake was found to be < N >=3, similar
to that found for the graphene composites. This implies that a mean flake thickness of
< t >≈1 nm. The aspect ratio in this case was found to be < L/t >=1400.
These size-selected BN flakes were used to make composites as described in the
methods section. In contrast to the graphene/PVA films, the BN/PVA composites were
largely transparent, with a slight milky hue as shown in figure 6.4. We investigated the
dispersion of the BNNSs within the composite by examining the fracture surface of
strips broken by mechanical testing, with helium ion microscopy. The process is ideal
for this application as it is very effective at imaging insulating surfaces without the
need for a metallic coating.
Shown in figure 6.4B is an image of a fracture surface of a 0.1 wt% (0.056 vol%)
composite. Large numbers of BNNSs can be seen protruding out of the surface. These
can be observed in more detail in figure 6.4C. For comparative purposes, fracture
103
6.5 mechanical properties
Figure 6.5: A) Young’s modulus and B) ultimate tensile strength versus volume fraction for the
graphene/PVA composites. The different symbols indicate two independent sample
sets. The dashed lines represent a linear fit to the low volume fraction regime. The
solid line in A) represents the modified rule of mixtures for platelets (MRoM) using
the parameters, YF = 1000GPa, YM = 3GPa, v P = 0.5 and L/t = 1920.
surfaces of a 1 wt% composite are shown in figure 6.4D and E. Here aggregated BNNSs
can be clearly seen. This indicates that aggregation effects occur at higher BNNSs
loadings. Furthermore, the images suggest that the BNNSs are aligned in the plane of
the polymer film.
6.5
6.5.1
mechanical properties
Graphene Composites
Shown in figure 6.2C are representative stress strain curves for the neat polymer film
and two size-selected composites. It is apparent from these curves that the modulus,
Y, and strength, σB , of the composites are significantly larger than that of the neat
polymer. This can be seen more clearly by plotting both Y and σB as a function of
graphene volume fraction Vf , as shown in figure 6.5.
104
6.5 mechanical properties
In 6.5A the modulus increases from 3 GPa for the polymer to 4.9 GPa for the 0.36
vol% composite before falling off at higher graphene loadings. Similarly the strength
increases from 100 MPa for the polymer to 190 MPa for the 0.36 vol% composite
before falling off at higher graphene contents. The reduction in mechanical properties
observed at these higher loadings is usually attributed to filler aggregation. It could
be argued that much larger volume fractions are required for high performance
applications. This could possibly be achieved by functionalisation of the graphene to
improve dispersibility. However it is worth noting that a doubling of modulus and
strength a low volume fraction is a significant result which lead to a dramatic reduction
in the mass of plastic necessary for a range of structural applications. Furthermore,
it is noted that no significant decrease in ductility or toughness was observed in
the composites (see figure 10.3). It is highlighted that these increases in mechanical
properties are large in comparison with the current literature and compare well with
the results reported for nanotube reinforced polymers [254].
It is noted that for nanotube/PVA composites, it has been demonstrated that a
significant contribution to the reinforcement arises from crystallisation of the polymer
at the nanotube interface [242, 243]. To test for this effect DSC was performed on
the neat polymer film along with several of the composite films (see figure 10.4). No
significant increase in the melt enthalpy was discovered with increasing graphene
content, in agreement with previous results for GrO/PVA composites [247]. This
implies that no significant graphene-nucleated crystallinity occurs within the samples.
This allows the notion of polymer crystallites contributing to the reinforcement to be
discounted.
With the assumption that the graphene flakes are approximately aligned along the
plane of the film [269], the reinforcement can be modelled using the modified rule of
mixtures (equation 4.15) . To a first approximation this model predicts the Y versus
Vf curve to be linear. This appears to be the case for the low Vf region of the data
shown in figure 6.5A, which is relatively linear with a slope dY/dVf = 680 GPa. As
the graphene modulus is ~1 TPa, this would suggest that η LY = 0.68.
105
6.5 mechanical properties
However, a more detailed analysis on this factor based on shear-lag theory demonstrates that η LY is given by [202]
η LY = 1 −
with
s
n=
tanh(nL/t)
nL/t
G P Vf
YF (1 − Vf )
(6.4)
(6.5)
where GP is the polymer shear modulus. This modulus can be estimated to be ~1
GPa using
YM = 2GP (1 + v P )
(6.6)
where v P is the polymer Poisson ratio. This ratio expresses the relationship between
the magnitudes of contraction/expansion in the transverse direction when a material
is stretched/compressed in the axial direction. The accepted value for PVA is v P ∼ 0.5.
As YF is known for graphene and the flake length and thickness have been measured,
equations 4.15 and 6.4 can be used to plot the theoretical modulus as a function of
graphene content. This is shown as the black curve in figure 6.5A and fits the data
extremely well.
The data can be examined in further detail using equations 6.4 and 6.5 to graph
η LY as a function of L/t. This plot is shown in figure 6.6 for a low volume fraction
composite of 0.2 vol%. In addition, the measured data for both small and large flake
composites are included (η LY = (dY/dVf )/YF = 0.17 for small flakes). This data agrees
well with the theoretical curve confirming that the length dependence is as predicted.
Some important points can be taken from this experimental curve. For the system used
in this work, the maximum rate of increase of η LY occurs for L/t ∼ 650. This is very
close to the value of L/t typically obtained for sonication exfoliated graphene. Also,
relatively small increases in flake size in this aspect ratio region results in significant
improvement in the mechanical properties. If large flakes with aspect ratio ~10000
could be produced in significant quantities, composites could be fabricated with n LY
approaching 0.9, which corresponds to a 50% increase over what has been achieved
in this study. On the other hand, flakes with L/t < 1000 with have η LY < 0.3, with
comparatively poor composites as the result.
106
6.5 mechanical properties
Figure 6.6: Modulus length efficiency factor η LY calculated using MRoM for an extremely low
volume fraction of graphene in PVA versus flake size. The parameters used for the
trend are: YF = 1000 GPa, GP = 1 GPa and Vf = 0.002. The composites studied
here are given by the open squares.
With these approximations in place, reasons to why these composites have fallen
short of the ultimate limit of reinforcement can be discussed. In an ideal case, in order
to assume that YF = 1 TPa for graphene, it is assumed that all the graphene flakes in
the composite are single layer, and that they all possess an aspect ratio on or above
a certain value (in this case L/t ≥ 1920). In reality, the samples show no evidence of
monolayers present, with the average flake observed being trilayer. Recent reports have
studied the stress transfer between the internal layers of multilayer graphene within
a polymer composite by examining the stress induced shifts in the 2D Raman band
[38]. The authors conclude that while the reinforcing capability of bilayer graphene
seems comparable to that of monolayers, there is ~15% reduction in the capability for
trilayer graphene, due to poor stress transfer between inner and outer stacked layers.
This would suggest that even if all the flakes present in the composite were exactly
three layers, the maximum reinforcement achievable would be dY/dVf = 850 GPa.
Considering that the samples in this work not only contain a spread of flake dimensions both above a thickness of 3 layers and below an aspect ratio of L/t = 1920
suggests how our samples could fall below the ultimate limit. Nonetheless, it is noted
that a reinforcement level of dY/dVf = 680 GPa is a major result in this composite
107
6.5 mechanical properties
system, and is 70% above the highest reported reinforcement in 2D filler/PVA systems
to date (table 10.4).
Finally, a simple model can be derived to describe the strength of these composites
based on the observation from the images in figure 6.2B that the composites fall
through flake pullout. For a composite that fails in this manner, it is assumed that
individual flakes only pullout if they intersect the final fracture surface. Also, the
side of the fracture surface where pullout occurs is that which results in more than
half the flake remaining embedded within the polymer. Thus, assuming the flakes are
rectangular with side a, with sides parallel to the sample. Assuming that the fracture
surface is perpendicular to the applied stress, the length of the flake that is exposed
after pullout will vary from 0 to a/2 with a mean of a/4.
Consider the force as the sum of two parts, the force required to fracture the
polymer only region of the fracture surface and the shear force required to break the
polymer-flake interface. Then the force required to break the sample is
FB = σP ( A − NF ta) + τB NF 2a ×
a
4
(6.7)
Where σP is the polymer strength, A is the sample cross sectional area, NF is the
number of flakes intersecting the final fracture surface, t is the flake thickness and τB is
the interfacial shear strength. The bracketed term in equation 6.7 is the area of polymer
which undergoes tensile fracture, while the factor of 2 in the second term accounts for
the fact that interfacial fracture occurs on both the top and bottom of each flake.
Realising that only flakes whose centres are within a/2 of the final fracture surface
will be involved in pullout allows calculation of NF , namely
NF =
N
Aa
V
(6.8)
Where N/V is the total number of flakes per volume of composite. This can be
written as
Vf
N
= 2
V
a t
(6.9)
and so using equations 6.8 and 6.9 gives
NF =
AVf
at
(6.10)
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6.5 mechanical properties
Substituting this into equation 6.7 and dividing through by A to give the strength
reveals
a
σB ≈ τB − σP Vf + σP
2t
(6.11)
For pseudo-rectangular flakes with a long axis, L and short axis, w, it is possible to
approximate a as
a≈
L+w
2
(6.12)
which gives, in terms of the size distributions obtained from TEM analysis
σB ≈
<L>+<w>
τB
− σP Vf + σP
4<t>
(6.13)
It is noted that this expression is quite similar to the equivalent expression for
fibre-reinforced composites [270]. This equation predicts the σB versus Vf curve to be
linear. Indeed, the low volume fraction of the data shows linear behaviour with a slope
dσB /dVf = 22 GPa. Using the measured values for < L >, < w > and < t > gives
τB = 29 MPa. The fact that this value is lower than the shear strength of PVA (40 MPa)
[271] suggests that the failure does indeed occur at the flake-polymer interface rather
than in the PVA matrix itself. This differs from the case for nanotube/PVA composites
where failure is thought to occur in the polymer close to the interface [272, 273].
6.5.2 BN Composites
The tensile mechanical properties of a number of BN/PVA composite films of increasing BN mass fractions as well as a PVA only sample were measured. Representative
stress-strain curves for the PVA reference and best performing composite film are
displayed in figure 6.7. It is clear from this plot that both the modulus and strength of
these composites increase on addition of extremely small quantities of BN.
The data can be analysed in more detail by once more plotting the modulus and
strength as a function of BN volume fraction. This data is shown in figure 6.8. The
modulus increases roughly linearly with filler volume fraction from ~2.5 GPa for the
PVA film to ~3.4 GPa for the Vf = 0.12 vol% sample. Such reinforcement is similar to
109
6.5 mechanical properties
Figure 6.7: Representative stress-strain curves for the PVA only and the best performing
composite film (0.11 vol%).
that observed for the graphene reinforced composites and is relatively large compared
to clay reinforced composites [261]. This is followed by a deterioration in modulus at
higher volume fractions. This effect was observed for the graphene composites and
has also been seen in nanotube-polymer composites [254], and has been attributed to
aggregation of the filler particles [204]. DSC analysis showed no evidence of additional
PVA crystallinity caused by the introduction of the BN nanosheets (figure 10.10).
The effectiveness of the filler can be assessed by the rate of increase of modulus
with volume fraction, dY/dVf , as before, at low loading levels. Applying a linear
fit to the low volume fraction portion (Vf < 0.12 vol%) of the data in figure 6.8A
gives a slope of dY/dVf = 670 ± 250 GPa. This is significantly higher than previous
results for BN-polymer composites (dY/dVf ∼ 250 GPa in PMMA [111] and ~52
GPa in polybenzimidazole [266]) and compares reasonably well with polymers filled
with graphene in this chapter and from other sources [210]. Comparisons with other
2D-fillers such as clays, are more appropriately made via dY/dm f because of both the
large density differences between BN and most clays and also because the volume
fraction can be difficult to quantify for clay-polymer composites [263]. The dY/dVf
value obtained here is equivalent to dY/dm f = 380 ± 140 GPa. The highest published
rate of modulus increase with clay mass fraction appears to be dY/dm f = 42 GPa
for composites of montmorillonite in nylon-6 [262]. However, the vast majority of
110
6.5 mechanical properties
Figure 6.8: Plots of A) Young’s modulus and B) ultimate tensile strength as a function of BN
volume fraction. The solid lines are linear fits to the data for A) Vf < 0.12 vol%
and B) Vf < 0.06 vol%. In A) the dashed lines represent predictions made by the
modified rule of mixtures (MRoM) [equations 4.15, 6.4 and 6.5] and Halpin Tsai
(HT) [equations 4.17 and 4.18] reinforcement models.
111
6.5 mechanical properties
publications describing these systems show much lower rates of reinforcement [261].
This makes it clear that BNNSs are superior reinforcing fillers than clay platelets.
As before, the dependence of the composite modulus on the volume fraction and
aspect ratio of the BNNSs will now be examined. Applying the shear lag theory to the
MRoM (equations 6.4 and 6.5 along with equation 4.15) gives an approximation of the
reinforcement. Using the value YM = 2.6 GPa, gives GP ∼ 0.9 GPa using equation 6.6.
Assuming YF ∼ 750 GPa [240] and taking < L/t >= 1400 from figure 10.8D allows
us to plot Y as a function of Vf , as shown in figure 6.8. This line does not match the
data very well, underestimating the values for all the volume fractions in the Vf < 0.12
vol% region. This is a surprising result as the MRoM predicted the performance of the
graphene PVA composites very well. The reasoning behind this is discussed in chapter
8.
As an alternative, the in-plane mechanical properties of composites filled with
aligned platelets can be described also using the Halpin Tsai model, given by equations
4.17 and 4.18. Taking the same values as above for YM , YF and < L/t > along with
these equations allows the approximation shown in figure 6.8A to be plotted. This
time, excellent agreement between theory and data is achieved, particularly for low
volume fractions. The Halpin Tsai model also showed very good agreement with the
mechanical data found for the composites involving the un-size selected flakes, shown
in figure 10.7. This result is significant as it displays that the achieved reinforcement
values are consistent with the theoretical limit for BN nanosheets of the aspect ratio
produced here. Reasons for perhaps falling short of the dY/dVf = 750 GPa limit are
similar to those discussed for the graphene composites.
Interestingly, both models are based on continuum mechanics which appear to be
applicable even to nanoscale systems such as these. The fact that these models also
assume good interfaces between filler and matrix materials suggests that the stress
transfer at the BN/PVA interface is reasonably good. Shown in figure 6.8C is the
measured composite strength, σB , plotted as a function of BN volume fraction. As with
the modulus, the strength increases linearly from 105 MPa for the PVA only film to
~140 MPa for the Vf =0.05 vol% sample, after which a fall off is observed. This increase
is similar to the graphene composites both studied in this work and elsewhere [210],
112
6.5 mechanical properties
and relatively large compared to clay filled polymers [261]. Fitting (solid line in figure
6.8B) gives a slope in the linear region of dσB /dVf = 47 ± 19 GPa. This is significantly
higher than the increase of 22 GPa seen in the graphene PVA films. Moreover, we note
that the majority of graphene composites have demonstrated dσB /dVf < 10 GPa (see
6.9 for mechanical comparison with other literature), making this BN data much more
impressive.
As before, this data is compared with reports of clay reinforced polymers via dY/dm f .
This value of dσB /dVf is equivalent to dσB /dm f = 27 ± 11 GPa. The highest level of clay
composite strength increase published to date appears to be dσB /dm f ∼ 0.4 GPa for
montmorillonite in nylon-6, which is significantly below what has been achieved here
[260, 262]. Furthermore, as with the modulus increase, most reports how considerably
lower rates of increase [261].
To understand what governs dσB /dVf in BN/polymer composites it is necessary
to discuss the failure mechanisms. in general the composite strength can be written
as a modified rule of mixtures, namely equation 4.16. In fact, the functional form of
the length efficiency factor depends on the composite failure mechanism. There are
two possibilities, depending on whether the aspect ratio of the nanosheets is above or
below a critical value, called the critical length [155]. If the aspect ratio of the flakes
is above this value, enough stress can be transferred to the nanosheets to result in
their fracture. Conversely if the critical length is not reached then failure will occur at
the flake-polymer interface, i.e. by pullout. Following the standard derivation of the
critical aspect ratio for rod-like fillers [274], the critical aspect ratio for planar fillers
can be calculated as
σ
L
= F
t C
τ
(6.14)
where τ is the average stress transferred from polymer to nanosheets. The strength
of BNNSs has not been measured experimentally. However, theoretical studies suggest
it to be as high as 85 GPa [275]. This can be compared to the strength of graphene
of 130 GPa [27] and of individual MoS2 layers of 23 GPa [119]. It is taken that the
theoretical value is a reasonable approximation for a defect free BNNS. In addition, the
stress-transfer at the BN-PVA interface is unknown. However, Gong et al. previously
113
6.5 mechanical properties
estimated the stress transfer at a graphene-PVA interface to be ~1 MPa [179]. Assuming
this value can be used as an approximate estimate of the stress transfer in this case,
equation 6.14 gives a crude estimation that ( L/t)C ∼ 85000 . Given that the measured
value from TEM analysis is L/t ∼ 1400, it is safe to assume that in this work the aspect
ratio is well below this critical length. This is a similar conclusion to the one made for
graphene/PVA composites.
If the nanosheet aspect ratio is below the critical value, the composites will fail via
pull-out, resulting in segments of nanosheets protruding out from the fracture surface.
This is indeed the case, as shown in figure 6.4C. As shown in the derivation of equation
6.7, the protruding length on average is one quarter of the total sheet length. Crudely
assuming that the fracture surface is aligned at ~45o to the beam in figure 6.4C, the
protruding length is estimated to be 300 ± 150 nm. This is very close to the value
of < L > /4 = 340 ± 25 nm, as measured by TEM. Thus the data is consistent with
failure by pull-out.
Using the interfacial failure model described by equation 6.13 and inputting the
measured value of dσB /dVf for the BN composites gives a value of τB = 80 ± 32 MPa.
This is considerably larger than the value estimated for graphene-PVA composites
of ~29 MPa. That this value is so high, in fact higher than the reported value of PVA
strength (40 MPa) [271] is quite surprising and requires further investigation as this
value would suggest that failure should occur in the PVA matrix rather than at the
flake-polymer interface.
However, this value of τB , coupled with equation 6.13 was also found to describe
data for the composites prepared with the un-size selected flakes (see 10.7). This
suggests that for the size selected flakes, dσB /dVf (and hence dσB /dm f ) is larger than
that found for other 2D fillers, most likely because the polymer-filler interface is
particularly strong for BN-PVA. However, it is noted that this interfacial strength may
be particular to the nature of the BN-PVA interaction and may not apply to other matrix
systems. Indeed, composites prepared from BNNSs in PMMA showed dσB /dm f = 1.6
GPa [111], considerably below the value reported here. In order to successfully use
BNNSs to reinforce polymers other than PVA, it may be necessary to functionalise
the surface of the nanosheet to improve the interfacial strength and hence the stress
114
6.6 conclusions
transfer capabilities. While BN has long been assumed too inert to functionalise, recent
reports have shown that the basal plane of BNNSs can be covalently functionalised
with polymers [276]. Thus it may be possible to tailor the surface chemistry of BN to
reinforce a wide variety of polymers. Preliminary results suggest such strategies to be
reasonably successful [276–278]. Furthermore, it is worth noting that even if high τB
can be easily achieved, the form of equation 6.13 requires flakes with large lateral size
to achieve large values for dσB /dVf .
Finally, it must should be highlighted that while dY/dVf and dσB /dVf are both high,
the maximum values of composite modulus and strength are not very high (3.2 GPa
and 140 MPa respectively). This is because, similar to the graphene case, the mechanical
properties only increase with increased loadings up to a very low volume fraction
before the properties begin to deteriorate. This is different to the case for clay-polymer
composites where the mechanical properties slowly increase to clay contents >10%
[261]. increase (albeit slowly) up to clay contents of >10%. It is not certain what limits
the peak mass fraction of BN-polymer (or graphene–polymer) composites, although it
is probably aggregation related. This implies that both the flake aspect ratio and the
balance of nanosheet-solvent-polymer interaction energies are important.
6.6
conclusions
It has been shown that PVA can effectively reinforced by both graphene and BN
nanosheets, provided that the flakes in the composite have a large enough aspect
ratio. The composite modulus scales with flake size, in excellent agreement with
theoretical reinforcement models based on shear lag theory. Interestingly, although BN
is presumed to have a single-sheet modulus of only 3/4 that of graphene, it is found
that the fillers perform almost identically in terms of composite modulus increase rate
achieved. Furthermore, BN exhibited a much higher strength increase with volume
fraction compared to graphene, which suggests a stronger interfacial adhesion between
flakes and the PVA matrix. The reason why BN should have a greater affinity for this
matrix than graphene is unclear.
115
6.6 conclusions
Stiffness increases of 40% were obtained upon addition of only 0.26 and 0.12 vol%
graphene and BN, respectively. The same loadings resulted in a composite strength
increase of 50% for Graphene and 40% for BN, compared to the neat PVA films. It is
noted that the results obtained here are superior to any polymer reinforcement in the
literature using graphene nanosheets, whereas no reports of BN-polymer composites
could be found. These results are displayed in figure 6.9
Although the rates of increase for both modulus and strength are impressive, it
must be noted that for both filler materials these increases only occur up to a very
small volume fraction. When this peak is reached, the mechanical properties begin
to deteriorate rapidly. Thus, while the rates of increase are high, the absolute gain in
stiffness and strength are modest.
If unresolved, this will most likely be the single largest obstacle involved in using
high-strength nanosheets for polymer reinforcement. More research is required to
understand the nature of this peak in reinforcement at low volume fraction. Furthermore, strategies need to be developed to extend the increases to higher loading
levels, leading to stiffer, stronger composites. For example, maintaining the rates of
stiffness and strength achieved in this work for BN up to a volume fraction of only
1 vol% would yield a composite with Y ∼ 7 GPa and σB ∼ 500 MPa. This would
represent an extremely beneficial structural material enabled by a miniscule quantity
of nano-material.
116
6.6 conclusions
Figure 6.9: Comparison of the mechanical properties of the graphene (red), and BN (blue)
composites prepared in this work with graphene polymer composites examined via
tensile testing in the literature. A) Data for dY/dVf plotted as a function of dσB /dVf
In the majority of cases, the rates of increase had to be estimated from the reported
data. The grey square represents the theoretical maximum of reinforcement. B)
Summary of increase in mechanical properties for composites compared to polymer
only samples. In each case the lower data point refers to the neat polymer, whereas
the higher point represents the maximum reinforcement achieved. Note that one set
exhibits extremely good reinforcement, (38vol% graphene) while for all other cases
it was <6 vol%. See table 10.4 for details of the literature data used in this plot.
117
7
P O LY M E R C O M P O S I T E S C O N TA I N I N G A R A N G E O F
1-DIMENSIONAL NANOFILLERS
7.1
introduction
While much research effort has been devoted over the years to reinforcement of
polymer matrices with carbon nanotubes (CNTs) [254, 279], many other new 1D
nanomaterials have been reported to possess desirable mechanical properties, as
shown in table 10.1. However, these materials have yet to be examined in terms of
composite reinforcement potential. In this chapter, a range of nine 1D nanomaterials
are utilised to form composites in a PVA matrix, similar to that studied in chapter 6.
CNTs of 3 different types are examined along with carbon nanofibres (NFs) from 2
different manufacturers. Boron Nitride nanotubes (BNNTs) of two distinctly different
structures are also investigated, along with silicon dioxide nanotubes (Silica NTs).
Some of these new filler materials can be produced at a fraction of the cost of some
commercially available nanotubes, and also possess distinct traits to CNTs, which
makes them highly desirable materials if sufficient levels of reinforcement can be
achieved.
7.2
experimental procedure
Some of the filler materials were purchased commercially while others were synthesised
in house. The following commercially available powders were used - HiPCo single
wall carbon nanotubes (SWNTs) were purchased from Unidym; Tungsten disulphide
nanotubes (WS2 NTs) were obtained from www.apnano.com; Nanocyl NC3100 Thin
Multiwall Carbon Nanotubes (Nanocyl) and Very Thin Multiwall Carbon Nanotubes
118
7.2 experimental procedure
(VT MWNTs) were purchased from Nanocyl; Carbon Nanofibres were obtained from
both Grupo Antolin Nano Fibras (GANF) and Applied Sciences Inc PS1277 B1 (ASI
NFs).
SiO2 nanotubes (Silica NTs) were synthesised by templating of nickel-hydrazine complex nanorods as described in more detail elsewhere [106]. Cylindrical Boron Nitride
Nanotubes (Cyl BNNTs) and Bamboo Boron Nitride Nanotubes (Bamboo BNNTs) were
synthesised by heating a milling-activated precursor containing amorphous boron
powder and a metal nitrate Fe( NO3 )3 as described in the literature α[127].
Polyvinylalcohol (PVA, J.T. Baker, MW = 77000-79000 g/mol) was dissolved by
refluxing in deionised water at 100 o C for 6 hours, at a concentration of 30 mg/mL.
The powders were then added to this polymer solution to form 9 sets of dispersions,
all at concentrations of 0.6 mg nanomaterial / 30 mg PVA / mL water ( i.e. 2 wt%).
These samples were then sonicated using a sonic tip (VibraCell CVX at 25% of 750 W
for 3 minutes per sample) to exfoliate the material. The samples were then sonicated
in a sonic bath (Branson 1510E-MT) for 2 hours to homogenise the dispersions.
By blending these filler solutions with the PVA/water solution mentioned previously,
a set of composite dispersions with a range of volume fractions (0 - 0.6 vol%) were
fabricated for each filler material. In all cases, the total volume (filler, polymer and
solvent) was 5 mL while the total solids mass (filler and polymer) was 80 mg. The
dispersions were dropcast into Teflon trays and placed in a vacuum oven overnight at
51 c C and pressure 0.1 mbar to evaporate the solvent. When the samples had solidified
they were peeled from the trays and cut into strips using a die cutter. The sample
strips used for tensile testing were 2.25 mm wide and ~40 µm thick.
The nanomaterials were characterised by SEM and TEM. An SEM voltage of 5
kV was chosen for the 9 filler powders. To minimise inaccuracies in the imaging
due to the presence of PVA, 2 wt% samples for each nanomaterial were added to
n-methylpyrrilidone (NMP) and sonicated under the same conditions and duration
as before. A few mL of each filler/NMP solution were dropped onto a holey carbon
grid (400 mesh) and placed in a vacuum oven at 60 o C for 3 days to evaporate the
excess NMP before imaging. DSC measurements were carried out on samples from all
119
7.3 results and discussion
composite sets to investigate if excess PVA crystallisation [242, 243] had been nucleated
by the presence of the 1D filler particles.
7.3
results and discussion
7.3.1 1D-nanofiller / polymer Composites in the literature
A wide variety of polymer composites filled with nanomaterials have been reported to
date. CNTs of many forms have been added to a range of polymers, from elastomers
to rigid plastics and thermosets [279]. Impressive reinforcement has been achieved in
a PVA matrix, with a 270% increase in modulus reported upon the addition of only
1 wt% MWNTs [242]. Results also show that the addition of CNTs can substantially
improve the modulus of an elastomeric material typically having a low stiffness, with
stiffness values achieved being typical of rigid plastics [67].
There are several reports in the literature of a polypropylene matrix being reinforced
by vapor grown CNFs to form 1D composite fibres [96]. The most successful reinforcement achieved with this system has been a 350% modulus increase with 15 vol% CNFs
[280]. Although pristine BNNTs have been incorporated into polymer matrices to date
[281], there appear to be no reports of mechanical reinforcement in such a system.
However, functionalised BNNTs have been shown to reinforce a PVA matrix, with a
42% increase in modulus achieved with a mere 1 wt% addition of BNNTs [282]. There
appear to be no reports as yet of polymer composites featuring either Silica NTs or
WS2 NTs. Furthermore, it appears that no other reports involving a comparative study
of several rod-like fillers within the same matrix system exist.
7.3.2
Filler Structure and Morphology
Upon analysis by SEM and TEM, the nanofillers appeared to consists of different
structures while in powder form. SEM revealed that the SWNTs and Nanocyl powders
formed a densely aggregated material. The GANF NFs and ASI NFs consisted of a
120
7.3 results and discussion
Figure 7.1: A - D) SEM and G - I) TEM images of all the 1D nanofillers used in this study. Scale
bars are C,H,I) 100 nm; A,B,F) 200 nm; E) 300 nm and D) 1 µm.
number of rods protruding from amorphous aggregates, suggesting a decreased level
of powder purity. A smaller percentage of aggregates were discovered in the powder
of Cyl BNNTs, with the vast majority of the material consisting of rod like particles.
The Silica NTs and WS2 NTs appeared to be very pure with only rod-like objects
observed. TEM of Bamboo BNNTs and VT MWNTs showed a range of particle lengths
and diameters present with very small amounts of aggregated material. Representative
TEM and SEM images are shown in figure 7.1 for all 9 filler materials used in this
study. The image type (TEM or SEM) which gives the most information regarding filler
structure is displayed.
In order to accurately measure the level of reinforcement obtained from each material
within the composites, an accurate measurement of the filler material density was
required (for the materials that showed an increase in mechanical properties). This was
achieved by using TEM analysis of the inner and outer diameters for the multiwall
nanotubes, and the diameters for the single wall tubes and microfibers. This allowed
accurate estimation of the true volume of fillers present in the composites. This
121
7.3 results and discussion
geometrical approach yielded density values of SWNTs - 1600 kg / m3 ; WS2 NTs - 6147
kg /m3 ; Silica NTs - 1967 kg /m3 , GANF NFs 1301 kg /m3 ; VT MWNTs - 1764 kg / m3
and Bamboo BNNTs - 1670 kg / m3 . This analysis technique proved very important as
in many cases the calculated densities differed greatly (up to 28% variation) from the
bulk density values. This is hardly surprising considering the amount of aggregates
and regions of low purity in the powders analysed by microscopy techniques above.
These densities, along with that of PVA (1300 kg / m3 ) were used to calculate the filler
volume fraction from the mass fraction, as per equation 4.12.
None of the composite films showed any signs of filler aggregation during the
solvent evaporation and dropcasting process. All films were uniform in appearance,
with only colour intensity / opacity showing variation with increasing filler content.
The Bamboo BNNT and Silica NT samples were milky white in appearance, similar
to BN composites visible in figure 6.4A. The VT MWNT and WS2 NT films were
brownish-grey in colour. Both CNF sample sets and the SWNT films were a darker
grey, while the Nanocyl film became almost black as filler content increased.
DSC measurements were carried out on the best performing films from each composite samples set. Results showed that no significant increase in PVA crystallite melting
peak position occured across the range of 9 fillers. Furthermore, the enthalpy showed
no signs of increase with increased filler volume fraction, suggesting that no excess
crystallisation occurs within the polymer due the presence of the filler particles. These
results are displayed in figure 10.15 in the appendix.
7.3.3
Mechanical Results
As seen in previous in the previous chapter, the mechanical properties of these composites are examined by measuring their tensile stress strain behaviour. All nine composite
films sets had approximately the same shaped stress strain profile (see figure 10.11 for
representative curves from each film set). However, there was a considerable variation
in the mechanical performance among the filler materials. The convention “good”
and “poor” refers to filler materials where reinforcement was or was not observed
122
7.3 results and discussion
Figure 7.2: Representative stress strain curve comparison between composites containing well
(WS2 NTs 0.22 vol%) and poor (ASI NFs 0.12 vol%) performing filler materials, and
neat PVA.
compared to the reference PVA. Figure 7.2 shows stress strain curves for the polymer
only sample, together with curve examples from a good (WS2 NTs) and poor (ASI NFs)
filler. The stress strain curves can be analysed in more detail by extracting the Young’s
modulus and UTS values. The reference polymer was found to possess YM = 2.5 GPa
and σB = 100 MPa, close to the values seen for the PVA only sample for the BN/PVA
films in chapter 6.
The composite moduli varied with filler type and loading level from 1.5 GPa (0.12
vol% ASI NFs) to 3.9 GPa (0.1 vol% WS2 NTs). Maximum moduli exhibited by the
other good fillers consisted of 3.65 GPa for 0.06 vol% SWNTs, 3.68 GPa for 0.16 vol%
Bamboo BNNTs, 3.53 GPa for 0.13 vol% Silica NTs, 3.41 GPa for 0.5 vol% GANF NFs
and 3.34 GPa for 0.37 vol% VT MWNTs. Similarly, the strength ranged from 63 MPa
(ASI NFs) to 150 MPa (WS2 NTs). Maximum strength values obtained by the other
good fillers include 136 MPa for 0.08 vol% SWNTs, 135 MPa for 0.16 vol% Bamboo
BNNTs, 143 MPa for 0.33 vol% Silica NTs, 136 MPa for 0.15 vol% GANF NFs and 129
MPa for 0.37 vol% VT MWNTs. This data is more easily examinable by plotting both
modulus and strength as a function of volume fraction for each material. These data
sets are shown in figure 7.3 and figure 10.12.
For six of the nine samples (SWNTs, Bamboo BNNTs, Silica NTs, WS2 NTs, GANF
NFs and VT MWNTs), it is clear that both modulus and strength improve linearly
123
7.3 results and discussion
Figure 7.3: Young’s modulus (i) and UTS (ii) versus filler volume fraction for the six wellperforming composite sets. In all cases, a linear fit (solid line) has been applied to
the low volume fraction data. In most cases the increase in properties in this region
are relatively linear.
124
7.3 results and discussion
with increasing filler content, at least at low volume fractions. However, in all nine
cases, a threshold volume fraction is reached where this linear increase ceases and
the mechanical properties deteriorate. This, like the trends seen in chapter 6, has been
attributed to filler aggregation. Interestingly, reports of composite fibres reinforced
with CNFs show steady increases in modulus approaching 30 vol% filler addition
[96]. This suggests that composite fibres are perhaps less prone to filler aggregation,
with this phenomena occuring at much higher filler contents than planar composite
films. Furthermore, there is no clear correlation between this threshold volume fraction
and filler diameter. However, it was not possible to obtain accurate measurements
of the filler length across large numbers of tubes. For this reason, a link between
critical volume fraction and aspect ratio is not achievable. In the case of ASI NF
filled composites, both modulus and strength decreased linearly with volume fraction.
Similarly, while the data for Cyl BNNTs and Nanocyl filled composites were very
scattered (possibility due to the presence of filler aggregates), the mechanical properties
seemed to decrease with increasing loading levels. It is noted that composites which
inferior properties to even the polymer only sample is very unusual indeed, and likely
attributed to large amounts of filler aggregates arising from low initial powder purity.
It is unsurprising that both the modulus and strength of composites should increase
linearly with filler volume fraction. Such behaviour is predicted by the MRoM model
(equations 6.4, 6.5 and 6.6). However, it is found that the < L/t > value obtained via
TEM analysis for the majority of fillers is very low. Hence, the reinforcement trend
predicted by the MRoM falls well below the level of reinforcement achieved (trends
predicted not shown). The reason why the samples outperform the MRoM predicted
trend is unclear, and requires more study.
Interestingly, when plotting dσB /dVf versus dY/dVf in figure 7.4A, a linear trend is
observed for most samples. This suggests that the fillers influence the modulus and
strength of the composites in equal measure. For the most successful fillers we observe
a fall off in dσB /dVf . This saturation perhaps suggests the presence of an upper bound
for strength within these composite systems.
It is noteworthy that two of the fillers, SWNTs and Bamboo BNNTs exhibit dY/dVf
values greater than the modulus values of even the stiffest known nanomaterials them-
125
7.3 results and discussion
selves (figure 10.1). A similar situation has been observed for MWNT/PVA composites
[242]. This unexpected result was attributed to the existence of a crystalline PVA layer
at the CNT surface that has superior mechanical properties than the amorphous PVA
matrix. More recent studies have modelled the effect of the thickness of this interfacial
layer on the interfacial shear strength between rod-like filler and matrix [283]. That
said, DSC measurements showed no uniform increase in polymer crystallinity with
increased loading levels for the majority of the composite sets, shown in figure 10.15.
However, in the case of the highest dY/dVf filler, SWNTs, an increase of ~22% in the
melt enthalpy suggests this interfacial layer formation mechanism could be present.
However, the same cannot be said for the Bamboo BNNT sample, with a decrease
in enthalpy observed compared to the neat sample. The reason for why this filler
performs so well remains unclear.
By taking the peak values of Y and σB from figures 7.3 and 10.12 a plot is produced
of these maximum values versus one another, as seen in figure 7.4B. An interesting
point to take from this plot is that the maximum occurs at different loading levels
for different fillers. In addition, a high dY/dVf does not guarantee a high value for
Ymax . This comparison indicates that of all the fillers tested, the WS2 NTs give the best
reinforcement overall. Considering that according to reports, these materials possess at
most only 20% the modulus of CNTs (figure 10.1) the reasoning behind this outcome
is unclear.
Analysis of the strain at break and toughness values for all nine composite films
sets shows similar behaviour across filler type. A large increase in both ductility and
toughness is observed for Bamboo BNNTs and VT MWNTs for low volume fractions
(<0.15 vol%), before deterioration of both properties for higher loading levels. A similar
trend is displayed for the other 6 filler composite sets, although strain at break and
toughness increases for low volume fractions are more modest for these samples. Strain
at break and toughness data is given for good (figure 10.13) and poor (figure 10.14)
fillers film sets in the appendix.
This survey conveys that several of these new materials show promise as successful
fillers in composite systems. One major obstacle exists where the maximum reinforce-
126
7.4 conclusions
Figure 7.4: A) Measured values of dσB /dVf versus dY/dVf for all nine composite sets and B)
Maximum ultimate tensile strength versus maximum modulus for each of the six
composite sample sets showing reinforcement. Data taken from figures 7.3 and
10.12.
ment occurs at very low volume fractions, and increasing the filler loading level beyond
this point has no beneficial effect.
7.4
conclusions
In this chapter, the mechanical properties of PVA composite films containing a range
of 1D filler nanomaterials have been investigated. Although some of the nanomaterial
powders showed evidence of reduced purity, six of the nine filler materials examined
succeeded in increasing both the modulus and strength of the polymer matrix at
very low loading levels. Of the successful fillers, modulus increases of 31-50% are
obtained, paired with strength gains of 28-50%, compared to the reference polymer
films. Furthermore, three fillers displayed impressive rates of modulus increase with
volume fraction, similar to those obtained for graphene and BN composites in chapter
6. Variation between mechanical data could perhaps be reduced in future work using
a centrifugation regime to remove filler aggreagates.
More study is required to understand why two of the fillers possess reinforcement
rates above the theoretical limit, with possible reasons including matrix crystallisation
127
7.4 conclusions
near the filler material surface. Evidence as to why all six successful fillers surpass the
reinforcement trend predicted by the MRoM model also requires further work.
As seen in previous chapters, the obstacle of reinforcement deterioration after a
certain low volume fraction peak is observed once more in this study, and will need
to be overcome is the possibility of higher levels of reinforcement are to be achieved.
Nonetheless, inorganic filler materials such as WS2 NTs and BNNTs have emerged
as possible replacements for more expensive reinforcing materials such as CNTs.
Applications requiring an insulating filler could utilise BNNTs, while composites
containing a higher density matrix material such as metal could benefit from the
incorporation WS2 NTs, whose density is ~3.5 times that of graphene and CNTs.
128
8
M E TA L M AT R I X C O M P O S I T E S W I T H E X F O L I AT E D L AY E R E D
COMPOUNDS
8.1
introduction
Work discussed in chapters 6 and 7 has highlighted that nanoscale filler materials
can be utilised to successfully reinforce polymer matrices, due to their impressively
high strength and stiffness. In this chapter, the effect of nanomaterial addition into
a metal matrix system is investigated. Metal matrix composites (MMCs) have been
utilised for decades in the aerospace industry [284], with filler materials used to either
reinforce the reference metal, or to alter properties such as corrosion resistance or
thermal conductivity. However, it became apparent that many avenues had yet to
be explored with these materials. Most reports in the literature have involved using
expensive 1D filler materials such as CNTs to reinforce matrices such as aluminium
[285–296] and magnesium [297].
As of yet, no reports of inorganic 2D nanomaterial incorporation into MMCs have
surfaced. For this reason it was decided that an investigation into the the reinforcement
capacity of an inorganic layered compound - Molybdenum Telluride (MoTe2 ) in a metal
matrix could lead to new physical insight. A pewter matrix was chosen as its density
coincided well with that of the MoTe2 , and also because its low melting point facilitates
easy manipulation without high temperature environments. Due to its mechanical
properties, this metal is not suitable for structural applications, and is used solely as a
proof of concept of the reinforcement capabilities of the system. As the metal matrix
system differs so vastly from a that of a polymer matrix, theories such as polymer
stabilisation could not be utilised to aid exfoliation. Nonetheless, solubility theory
is utilised in the choice of an efficient solvent for exfoliation, matched with recently
measured HSP for MoTe2 nanosheets [267].
129
8.2 experimental procedure
130
Figure 8.1: A) TEM images of the MoTe2 dispersion, with many multilayer nanosheets visible.
B) Photograph of an as-received 80 g pewter ingot. C) Pewter only (top) and
composite (middle and bottom) strips used for mechanical testing. D) SEM image of
pewter surface. E) SEM image of a 1.5 wt% composite surface, with filler aggregates
visible. F) SEM image of the fracture surface of a 1.5 wt% composite film, with
protruding MoTe2 nanosheets visible.
8.2
experimental procedure
The production of the metal /MoTe2 composites began with the exfoliation of MoTe2
layered crystals to give exfoliated MoTe2 nanosheets. Powdered MoTe2 (www.materion.com,
M-1105, density 7700 kg/m3 ) was added to 80 mL n-methylpyrrolidone (NMP) at a
concentration of 15 mg/mL. This mixture was sonicated overnight (15 hours) with a
sonic tip (Sonics and Materials Inc. GEX600 at 25% of 600 W using a flat head probe).
A pulsed setting (5 seconds on, 5 seconds off) was used to minimise solvent heating. A
few mL of the resulting dispersion was dropped onto holey carbon grids (400 mesh)
and analysed using a Jeol 2100 TEM operating at 200 kV. This dispersion was then
filtered to form a powder of weakly bound nanosheets [63].
For this work a low boiling point metal is chosen to facilitate melt-mixing of the
metal and nanosheets. A high grade, lead free pewter was identified, usually used for
8.2 experimental procedure
at-home casting of toy soldiers (www.princeaugust.ie, PA-2060, 94.5% Tin / 3% Zinc /
2.5% Antimony, Melting Point 230 o C, density 7346 kg/m3 ), as it could be easily melted
and manipulated on a hot plate. An as-received 80 g ingot of this metal is shown in
figure 8.1B.
A high MoTe2 content (15 wt%) masterbatch was produced by first melting 8 g of
pewter in a glass vial on a hot plate at 240 o C. When the pewter reached a molten state,
the required mass of MoTe2 powder was added and the mixture was stirred using
a high speed rotor (a spatula blade attached to a power tool rotating at 15000 RPM)
for 5 minutes. Once homogenised, the mixture was allowed to cool to form a powder.
The degree of dispersion of nanomaterials in metal matrices is usually controlled by
a number of parameters. The effectiveness of the mixing procedure, the degree with
which the molten metal wets the nanomaterial surface and the matching of the filler to
metal densities all have an effect on the outcome of the composite material. Because of
the matching achieved here (within 5%), it is presumed that the degree of dispersion of
the MoTe2 nanosheets in the masterbatch to be limited by the crudeness of the mixing
procedure as well as the interfacial interactions at the pewter/MoTe2 interface.
Different mass fraction composites were made by adding the required amounts of
pewter to the masterbatch powder in separate vials. Each sample was heated until the
mixture was molten, stirred at high speed and cooled to form a powder as before. The
various composite powders were then placed onto a high melting point plastic sheet
(DuPont™ Kapton® 200HN polyimide film, 50 µm thickness, melting point 400 o C)
and heated using the hot plate. When molten once more, the samples were pressed
and cooled to form thin metal sheets. These were cut into strips of width 2.25 mm and
average thickness ~100 µm for tensile testing using a die cutter, similar to those shown
in figure 8.1C.
To examine the composition of the films, thin lamellae of both pewter only and 1.5
wt% composite films were prepared using FiB microscopy. The surface features of the
lamelae were examined using SEM operating at 30 kV. This increased voltage gives
enhanced contrast with the backscatter detector when identifying various metals based
on their atomic number. In addition, SEM was also used (at 5 kV) to examine fracture
surfaces of the pewter only and composite films. Performing EDX on the FiB prepared
131
8.3 results and discussion
lamellae gave insight into what metals were present in the pewter only and composite
samples.
Mechanical measurements were performed on a piece of as-bought pewter, strips of
pewter processed in the same way as the composites and composites of various mass
fractions. Between 5 and 8 strips were tested for each mass fraction and the results
averaged.
8.3
8.3.1
results and discussion
MMCs in the Literature
The vast majority of reports on nanocomposites describe using nanomaterials to reinforce polymer matrices [210]. Nonetheless, many other types of composites exist;
including composite mixtures of two types of nanomaterial [23, 298, 299]. One very
interesting type of nanocomposite which is of great technological importance is the
MMC. These composites employ filler materials to improve the thermal, electrical and
mechanical properties of metals, often aluminium and magnesium. Of nanomaterial
reinforced MMCs, CNTs are the most widely used filler material [285–296]. Additionally, BNNTs have been used to reinforce aluminium [300]. Only recently have report
surfaced where 2D filler materials have been utilised in MMCs. In each case the filler
was graphene [301], or graphene oxide [285, 302]. Impressive results were acheived
with a near doubling of tensile strength observed in one report [302].
However, there is reason to believe that inorganic 2D materials may offer some
advantages as fillers in MMCs. Several reports from the literature highlight the presence
of aggregation of CNTs either on the metal surface [291–293] or within fissures in the
metal matrix [296]. Such agglomerations may arise due to large density differences
between the filler and matrix phases within the composite, leading to buoyancyinduced separation during mixing in the molten state [303]. This is a particular
problem when attempting to disperse low density materials such as CNTs (densities <
2000 kg/m3 ) in denser metals such as aluminium (2700 kg/m3 ). TMDs, on the other
132
8.3 results and discussion
hand, have a wide range of densities which allows the matching of the filler density to
that of the matrix, thus reducing the probability of de-mixing.
8.3.2
Characterisation
Recent studies within the group have quantified the HSP of MoTe2 to be δMoTe2 ∼ 21.1
MPa1/2 [267]. This is very close to the value for graphene shown in chapter 5. This and
other reports led to the use of solvent NMP, due to its successful graphene exfoliation
ability when paired with sonication techniques [63]. The TEM images showed the
presence of partially exfoliated nanosheets in the dispersion, as shown in figure 8.1A.
These nanosheets were found to have an average length of < L >= 620 ± 420 nm. It is
difficult to quantify the thickness of TMD nanosheets using the edge counting method
described in chapter 3 , as each sheet consists of several atomic layers. However, it is
clear from the TEM images that while the MoTe2 has been exfoliated to some degree,
the platelets still consist of a a number of stacked flakes and hence have thicknesses of
the order of a few nm.
SEM images on the lamellae prepared with the FiB showed a number of islands
dotted on the lamella surface (see appendix fig 10.16). Elemental analysis confirmed
that these islands were Zinc, with Tin and Antimony present on the surface around
these islands, as shown in figure 10.17. There was no difference in grain boundary
or island concentration between the pewter only and composite samples. No MoTe2
material was discovered on the surface of the composite lamella.
SEM imaging was also carried out on the fracture surfaces of both pewter only
and composite films. The fracture surface of the pewter only sample was relatively
featureless, as shown in figure 8.1D. However, for the composite film, clumps of
aggregated platelets (1-2 µm in size) were observed uniformly distributed throughout
the fracture surface, as seen in figure 8.1E. This suggests that the MoTe2 are not
perfectly dispersed within the metal matrix. However, some regions of the composite
fracture surface were found where non-aggregated platelet like objects could be seen
133
8.3 results and discussion
protruding from the surface, shown in figure 8.1F. EDX confirmed these objects to be
MoTe2 , as seen in figure 10.17.
8.3.3
Mechanical Results
Representative stress strain curves are shown in 8.2A for as-bought pewter, processed
pewter and composite samples. The stress strain curves for the pewter samples are
similar to those reported for both Tin alloys [304] and Zinc-Antimony alloys [305].
However, processing tends to increase the tensile strength, reduce the strain at break
while leaving the modulus largely unchanged. Analysis of the processed pewter stress
strain curves gave values of Young’s modulus, tensile strength and strain at break
of: Y = 0.97 ± 0.37 GPa, σB = 45 ± 11 MPa and ε B = 13 ± 3 % respectively. It is clear
from the curves in figure 8.2A that adding MoTe2 results in a noteworthy increase in
modulus coupled with a significant decrease in strain at break.
The Young’s modulus is plotted as a function of MoTe2 volume fraction in figure
8.2B. The as-bought pewter modulus is shown for comparison. The modulus increases
linearly with a slope of dY/dVf = 110 ± 10 GPa, reaching a maximum value of
Y = 1.93 ± 0.6 GPa for a MoTe2 content of 1 vol%, before falling off at higher loadings.
This decrease is similar to those observed for the polymer composites in chapters 6 and
7 and is more than likely due to aggregation effects. The maximum value achieved here
corresponds to a doubling of modulus relative to both the as-bought and processed
pewter samples. This appears to be the first report of a modulus increase for MMCs
filled with a 2D nanomaterial to date.
It is beneficial to consider whether such increases in modulus are feasible. The data
is analysed using the Halpin-Tsai (HT) model for reinforcement (equations 4.17 and
4.18) for matrices with nanosheets that are aligned in plane. Such in plane alignment
has been observed for both graphene and BN nanosheets in polymer matrices, in
chapter 6. Data from the BN composites lies in agreement with the HT model, and
134
8.3 results and discussion
Figure 8.2: A) Representative stress strain curves for a piece of as-bought pewter, a pewter
only film processed in the same manner as the composites and a composite film
(1 vol%). B-D) Plots of B) Modulus, C) ultimate tensile strength and C) strain at
break versus filler volume fraction for the processed pewter and composite films.
For comparison, the properties of the as-bought pewter are also displayed (hollow
circle). The dashed line in B represents a slope of dY/dVf = 110 GPa.
135
8.3 results and discussion
reports have shown that the same is the case for some MMCs [306]. By examining
equation 4.18, some alterations can be made to the expression
Y = YM
1 + 2Vf ηL/t
1 − Vf η
(8.1)
where
η=
YF /YM − 1
YF /YM + 2L/t
(8.2)
For most composite systems, the ratio of filler modulus to matrix modulus satisfies
the relation YF /YM >> 1, and hence equation 8.2 can be simplified as
L/t
η ≈ 1+2
YF /YM
−1
(8.3)
By inspection, it can be determined that this expression limits the range of values for
η to 0 ≤ η ≤ 1. As a result, because most nanocomposites have very low filler volume
fractions, the assumption that Vf η << 1 holds, and so equations 8.1 and 8.2 can be
combined in a simpler form:
Y ≈ YM + h
YF Vf
YF /YM
2L/t
+1
i
(8.4)
This expression is very useful as it predicts the rate of increase in modulus with
volume fraction, dY/dVf , to depend on the properties of the matrix and filler in a
simple way:
dY/dVf ≈ h
YF
YF /YM
2L/t
+1
i
(8.5)
From figure 8.2B, it is known that dY/dVf = 110 ± 10 GPa and YM ≈ 1 GPa. Therefore, equation 8.5 defines the relationship between YF and L/t that must be fulfilled
for the HT model to correctly predict the properties of these composites. Unfortunately,
there appears to be no direct experimental measurements in the literature that quantify
the modulus of MoTe2 nanosheets. However, a number of publications have shown
the stiffness of MoS2 nanosheets to be in the range 270 < Y < 330 GPa [119, 120].
In addition, theoretical calculations of the in-plane stiffness of a range of layered
compounds show that (when adjusted for differences in monolayer thickness [307])
the modulus of MoTe2 is approximately 60% of that of MoS2 [114]. This allows an
estimation of modulus of MoTe2 nanosheets to be in the range 160-200 GPa. Using
136
8.3 results and discussion
equation 8.5 with the above values of dY/dVf and YM shows that for the HT model to
be accurate, the average nanosheet aspect ratio should be in the range 100 ≤ L/t ≤ 160.
Given that the mean flake length measured by TEM was < L >= 620 ± 470 nm and
the thickness of an MoTe2 monolayer is 0.7 nm [307], it follows that the nanosheets
would have to consist of <15 stacked monolayers on average for the model to hold.
This seems reasonable in this case. Solvent exfoliated graphene tends to consist of
flakes with thickness in the range 1-5 layers [167]. It is clear from the TEM images in
figure 8.1A that the MoTe2 nanosheets are relatively poorly exfoliated compared to the
graphene sheets shown in figure 6.1 produced by polymer aided exfoliation. However,
it is certainly possible that the MoTe2 sheets here conists of <15 monolayers. It is noted
that if the true mean thickness of these sheets is more than 15 layers, then this implies
that the mean aspect ratio is <100. Therefore, for the HT model to accurately describe
the data, the MoTe2 modulus would have to exceed 200 GPa. On the other hand, if the
flakes turned out to be thicker than 15 monolayers, it could also imply that the HT
model does not apply in this case, resulting in larger than expected stiffness within
the composites.
The tensile strength does not increase significantly compared to the processed
pewter as MoTe2 is added, shown in figure 8.2C. Furthermore, as the filler content is
increased beyond 1 vol% the strength falls off, reaching ~10 MPa for 6 vol% MoTe2 .
Nonetheless, it is noted that the strength of all samples with less than 5 vol% filler
had strength greater than the as-bought pewter. Previous reports have observed
strength increases at relatively low loading levels followed by a fall off at higher
filler content for MMCs filled with both CNTs [286, 287, 293] and graphene [285]. A
strength increase, often at higher loading levels is commonly observed for polymer
composites filled with nanomaterials [308], and is the case for both graphene and
BN composites seen in chapter 6 and some of the 1D fillers in chapter 7. As is the
case for the modulus behaviour, this decrease is often attributed to filler aggregation
[254]. As such, the degradation of strength with MoTe2 content is expected given the
platelet aggregation visible in figure 8.1E. The strain at break also decreases relatively
uniformly with increased loading levels. This behaviour has been observed for several
137
8.3 results and discussion
other MMCs [285, 286, 289, 291, 293, 295, 302] and indeed polymer nanocomposite
materials [174, 245, 247, 249, 251, 252, 308–310].
An additional advantage of expressing the HT in its simplified form (equation 8.4) is
that now both HT and MRoM models can be expressed in very similar arrangements:
MRoM :
Hal pin − Tsai :
Y = η LY YF Vf + YM (1 − Vf )
(8.6)
Y = η LY YF Vf + YM
(8.7)
where η LY for MRoM and Halpin-Tsai models are given by equations 6.4 and 8.3,
respectively. The benefit of examining the models in these similar forms is that it
becomes clear that both rely on the ratio of filler to matrix moduli, YF /YM . In fact,
further investigation into this dependence has shown that for composites with ~1 vol%
filler material (for BN/PVA, graphene/PVA and MoTe2 /pewter samples maximum
reinforcement occurs around this loading level), the MRoM model requires a much
higher filler aspect ratio than the HT model for the same length efficiency contribution
to the modulus. This can be seen in figure 8.3A for the MoTe2 / pewter system, where
a length efficiency factor of 0.5 for HT corresponds to an aspect ratio of ~90, while
flakes with L/t ∼ 425 are required for the MRoM model to achieve the same efficiency.
In addition, the region of rapid length efficiency increase occurs over a much higher
L/t range for MRoM than is the case for HT (highest gradient of increase between
40-220 for HT and 250-580 for MRoM). It is noted that this is a considerable difference
when it comes to sample preparation and production of nanosheets with desirable
dimensions.
Furthermore, it has been found that for composite systems with a low YF /YM
ratio (<200, figure 8.3A), the HT gives the most accurate representation of the data,
while systems with higher ratios (>300, figure 8.3B) have data which lies closer to
that predicted by the MRoM model. It appears that systems with intermediate ratios
(200-300, figure 8.3C) are accurately described by both theories. This regime is shown
in figure 8.3 for both polymer composite systems discussed in chapter 6, in addition
to the MMCs studied here. By comparing the length efficiency factor (equations 6.4,
6.5 and 6.6) for MRoM with that for HT (equation 8.3), it is clear that only the former
138
8.3 results and discussion
Figure 8.3: A) - C) Comparison between experimental data for 3 studied systems and trends
predicted by the Halpin-Tsai (dashed line) and shear lag modifed Rule of Mixtures
(solid line) models for length efficiency versus nanofiller aspect ratio. The ratio of
YF /YM for each system is A) 185 (Low), B) 333 (High) and C) 288 (Intermediate). D)
Variation of the length efficiency factor with filler volume fraction for the MRoM
model with YF /YM = 250, GP = 1 GPa.
depends on the filler volume fraction. Figure 8.3D displays how this model responds
to variations in this parameter for a system with an intermediate YF /YM ratio of 250.
139
8.4 conclusions
8.4
conclusions
In this chapter a method of preparing reinforced composites of exfoliated MoTe2
nanosheets in a metal matrix has been demonstrated. Use of a low melting point
matrix such as pewter simplifies the process of melt mixing while matching the filler
and matrix densities reduces the probability of buoyancy driven de-mixing. While such
processing results in sub-optimal dispersion of the nano-platelets, some well-dispersed
MoTe2 nanosheets were observed. It is worth noting that by utilising a centrifugation
regime to narrow the distribution of flake sizes, it is believed that a higher quality
dispersion can be obtained [64]. Nonetheless, the Young’s modulus of a 1 vol% MoTe2
composite was found to be double that of the pewter itself. Such an increase agrees
reasonably well with the predictions of theory. However, both strength and strain at
break fell with increasing MoTe2 content, possibly due to aggregation effects. Given that
the matrix metal in this study was chosen purely on its low melting point, and not for
its favourable mechanical properties, it is highly possible that superior reinforcement
can be obtained by choosing a mechanically impressive metal, and selecting a TMD
filler material that matches well with the metal’s density. An example of such a system
is an ultra high strength steel matrix (7800 kg/m3 ) filled with WS2 nanosheets (7500
kg/m3 ).
Finally, simplification of the Halpin-Tsai reinforcement model enabled an effective
comparison with the modified Rule of Mixtures model. This highlighted differences
between the two theories involving required filler aspect ratios for effective composite
modulus increase, and gave valuable insight into which theory best describes composites systems of different filler and matrix combinations. This result is very useful when
attempting to accurately ascertain the level of reinforcement achieved in an arbitrary
dual-component composite system.
140
9
CONCLUSIONS
The aim of this work was to prepare nanocomposite materials possessing high levels of
mechanical reinforcement over the reference materials. To begin, an investigation into
the steric stabilisation mechanism of nanosheets by adsorbed polymers was undertaken.
It was found that graphene, BN and MoS2 can be exfoliated and stabilised by this
mechanism with a range of polymers dissolved in otherwise poor solvents THF and
CXO. A derived model predicted maximum exfoliated nanosheet concentrations to
exist within a region where δG ∼ δP ∼ δS , with reasonable concentrations achievable
where parameters of polymer and solvent are similar, namely δP − δS < 4 MPa1/2 ,
even if both are far from δG . The model showed solid agreement with Gaussian
concentration profiles displayed by experimental data. Statistical analysis of TEM
images confirmed a high quality of exfoliation, with all samples possessing an average
nanosheet layer number of < N >= 3, independent of stabilising polymer type. These
predictions are important, as a level of control over maximising nanosheet quantities is
now possible, provided the solubility parameters of both polymer and solvent (and
ideally nanomaterial) are known.
Attention was then shifted to the production of mechanically superior composite
materials harnessing nanomaterials as reinforcing fillers. For this work, an aqueous
PVA environment was chosen for exfoliation and composite formation. It was shown
that both high quality graphene and BN nanosheets were produced via polymer aided
exfoliation, with desirable aspect ratios of < L/t >=1920 and 1400 for graphene and
BN, respectively. These nanosheets enabled the production of composites showing
impressive levels of reinforcement, with modulus increases of 40% achieved with
only 0.26 and 0.12 vol% graphene and BN, respectively. Strength increases of 50%
for graphene and 40% for BN were also present, compared to neat PVA. The levels
of reinforcement achieved by both fillers in this work - dY/dVf =680 and 670 GPa;
141
conclusions
dσB /dVf =22 and 47 GPa for graphene and BN, respectively, is higher than any other
polymer composites reported in the literature. Furthermore, these levels are close to the
ultimate reinforcement limit, with data showing agreement with comparison against
theoretical reinforcement models. Composite quality was confirmed by helium ion
imaging, with both composite types showing uniform dispersion of nanosheets within
the polymer matrix, with sheets aligned along the plane of reinforcement.
After the mechanical properties of 2D nanosheet/PVA composites was assessed, a
survey on the reinforcement capabilities of several 1D rod-like fillers was undertaken,
in the same matrix environment. Microscopy techniques showed that some initial
powders had low purity, with large aggregates visible in some cases. Nonetheless of
the nine filler materials tested, six showed promise in terms of composite reinforcement
with modulus and strength increases of 28-50% and 31-50%, respectively. Reinforcement
levels varied from dY/dVf ∼400-1800 GPa for modulus increase and dσB /dVf =28-38
GPa for strength gain. DSC analysis suggested that interfacial layer crystallisation was
perhaps responsible for SWNT/PVA composites exhibiting modulus reinforcement
above the theoretical limit. In addition, it was observed that levels of modulus and
strength increase scaled linearly, facilitating easy identification of the most efficient 1D
filler materials. This survey displays that WS2 nanotubes enable the highest increases
in modulus and strength compared to the neat PVA, achieved with only 0.1vol% filler
content.
Finally, the reinforcement capabilities of a 2D nanofiller in a metal matrix were
examined. By incorporating solvent-only exfoliated MoTe2 nanosheets into a pewter
matrix, an easily manipulable composite material was formed. While SEM images
of the composite showed evidence of filler aggregation within the fracture surface,
some well dispersed nanosheets were observed. Mechanical testing revealed that a
doubling of composite modulus was achieved using only 1 vol% MoTe2 . Elemental
analysis confirmed that the grain boundary of the pewter material was not altered
by the incorporation of the filler material, and that the sheets protruding from the
composite fracture surface were indeed MoTe2 .
Valuable insight was also obtained into the differences between the accuracies of
the MRoM and HT reinforcement models. It was found that the HT model was more
142
9.1 future work
accurate at modelling reinforcement for composite systems with a filler to matrix
modulus ratio of < 180, while the MRoM had more successful predictions for systems
with a ratio of > 300. Composite systems lying in between these values are shown to
be predicted quite effectively by both theories. This observation is highly useful when
choosing an accurate method of quantifying reinforcement for any dual component
composite system.
9.1
future work
It is hoped that the results showcased in this thesis provide convincing evidence of
the effective composite reinforcing capabilities of nanoscale materials. While miniscule
quantities of nanofiller result in large percentage increases in both modulus and
strength compared to neat matrix material, there are still many challenges that need to
be overcome if these hybrid materials are to be improved further.
Further studies are needed to investigate the cause of the saturation point in the
increase in composite mechanical properties. As seen in chapters 6,7 and 8, the point
where the modulus and strength increase ceases and both properties begin to deteriorate occurs at a very low volume fraction of filler and is independent of filler
dimensionality and matrix type. As the achieveable rates of dY/dVf and dσB /dVf
within the composites are exceptionally high, even the ability to extend this saturation
point to a mere 1 vol% filler content would result in a huge improvement in both
modulus and strength. Evidence as to why the interfacial shear strength of the BN
nanosheets is so high is also required. As it stands, results suggest that failure in
these BN composites should occur within the PVA matrix, rather than the at the
sheet-polymer interface, while fracture surface imaging confirms this not to be the
case.
Another avenue of exploration is that of the results for the 1D composites in chapter
7, which show modulus increases above the maximum theoretical limit for the Bamboo
BNNT and SWNT samples. While DSC measurements suggest an interfacial layer
143
9.1 future work
of crystallised PVA to have formed at the SWNT-PVA interface, no such increase is
present for the Bamboo BNNT filled composites.
Much effort will be directed towards the optimisation of MMCs in the near future.
As the pewter material chosen for the work in chapter 8 was favourable due to its
ease of manipulation via its low melting point, it is noted that many other metals
and alloys possess much higher mechanical properties and hence have much more
scope for improvement. For instance, silicon carbide reinforced aluminium composites
have been reported with Y = 98 GPa and σB = 661 MPa, an order of magnitude
higher than those observed for the pewter composites displayed in this work [311].
Furthermore, the vast range of densities possessed by TMD materials suggests that
choosing a filler material with a suitable density for an arbitrary metal matrix should
be straightforward. As the metal matrix results in chapter 8 are purely a proof of
concept, many areas of composite optimisation are also possible, such as isolation of
larger aspect ratio nanosheets via centrifugation regimes. Composite formation using
both filler and matrix materials in powder states is also predicted to generate a higher
quality composite material, compared to homogenisation of filler powder with molten
metal reported herein.
The final route towards superior composite materials is that of filler parameters. The
comparative analysis of the two theoretical reinforcement models in figure 8.3 confirms
that in both scenarios, small increases in the aspect ratio of nanofiller particles has a
highly beneficial effect on the overall composite stiffness. Hence, if processes isolating
longer, thinner nanosheets from the bulk material, or higher quantities of debundled
rod-like filler particles from the aggregated initial material could be developed, the
resulting composites would possess highly superior mechanical properties.
144
10
APPENDIX
145
appendix
Material
Characterisation Method
< Y > (GPa)
Ref
Graphene
Nanoindentation (AFM)
1000 (Monolayer)
[27]
GrO
Nanoindentation (AFM)
208 (Monolayer)
[34]
CNTs
Temperature Vibrations
1250 (SW)
[83]
Temperature Vibrations
1800 (Unknown)
[312]
Nanoindentation (AFM)
810 (SW Ropes)
[313]
Nanoindentation (AFM)
1280 (MW)
[84]
Electrostatic Deflection
1000 (MW)
[85]
Tensile Load Stage (SEM)
270-950 (MW)
[314]
Cantilever Forces
43 (Single CNF)
[94]
MEMs-based Testing
180 (Single CNF)
[95]
CNFs
245 (Single Heat-Treated CNF)
Compressive Load
90 (CNF Array)
[315]
BN
Theoretical (DFT)
750
[240]
BNNTs
Temperature Vibrations
1220 (Cyl MW)
[136]
E-Field Resonance (TEM)
720 (Cyl MW)
[137]
Theoretical (MD) & Tensile Testing (TEM)
225 (Bamboo)
[138]
Nanoindentation (AFM)
270 (Monolayer)
[119]
Nanoindentation (AFM)
330 (Few layer)
[120]
X-ray of linear compressibilities
240 (Bulk)
[316]
Theoretical (DFT)
223 (Monolayer)
[124]
MoTe2
Theoretical (DFT)
125
[124]
MoTe2 NTs
Theoretical (DFT)
53-82 (Chirality Dependent)
[317]
MoSe2
Theoretical (DFT)
179
[124]
WS2
Theoretical (DFT)
244
[124]
WS2 NTs
AFM and SEM Measurements
160
[146]
Euler’s Equation
171
[145]
WSe2
Theoretical (DFT)
197
[124]
WTe2
Theoretical (DFT)
137
[124]
Silica NTs
Theoretical (MD)
990 (Bulk)
[153]
MoS2
780 (Nanoscale, varies for ring and layer #)
Table 10.1: A literature summary of the Young’s modulus of nanoscale filler materials featured
in this thesis, with some added TMD materials. In addition, the above data for
CNTs can be expressed as [318] < Y >= 1 − 1.3 TPa for SWNTs and < Y >=
(1.36 − 1.76)/d TPa where d is the tube diameter [nm]. For theoretical calculations,
MD refers to molecular dynamics simulations while DFT denotes density functional
theory.
146
appendix
Filler
Lattice Structure
Bond Spacing (Å)
Interlayer Spacing (Å)
Reference
Graphene
Hexagonal
2.46
3.4
[319]
h-BN
Hexagonal
2.52
6.6
[320]
MoS2
Trigonal Prismatic
3.12 (Mo-Mo), 2.38 (Mo-S)
5.99
[321]
MoTe2
Trigonal Prismatic
3.52 (Mo-Mo), 2.71 (Mo-Te)
6.99
[322]
WS2
Trigonal Prismatic
3.13 (W-W), 2.48 (W-S)
6.075
[323]
Silica
Tetrahedral
4.9
5.4
[324, 325]
Material
Table 10.2: Lattice properties of filler materials used throughout this work. Bond spacing refers
to interatom distance within the crystal lattice, with interlayer spacing describing
the z direction distance between stacked monolayers.
Polymer
HSP
Graphene/THF
BN/THF
MoS2 /THF
Graphene/CXO
( MPa1/2 )
< C > (µg/mL)
< C > (µg/mL)
< C > (µg/mL)
< C > (µg/mL)
PBD
17.2
17
8
17
68
PBS
17.7
17
11
23
79
PSt
18.1
16
17
29
110
PVAc
19.3
22
3
32
119
PVC
19.8
6
34
33
92
PC
21.9
-
-
-
129
PMMA
22.7
20
13
28
141
PVDC
25
-
2
20
-
CA
26.7
1
10
-
126
Table 10.3: Polymers used in the work discussed in chapter 5 and their HSP along with mean
dispersed nanosheet concentrations. HSP were taken from the Polymer Handbook
[225].
147
appendix
Figure 10.1: A) - C) TEM statistical breakdown of flake dimensions for graphene/PVA dispersions containing both i) size-selected large flakes and ii) as-prepared smaller flakes.
148
appendix
Figure 10.2: Comparison of mechanical properties of composites prepared with size selected
(large) flakes (open triangles) with as-exfoliated (smaller) flakes (closed squares).
The dashed lines are linear fits to the large-flake data while the solid line is a linear
fit to the small-flake data.
149
appendix
Figure 10.3: A) Strain at break and B) toughness as a function of graphene volume fraction
for the size selected (large) flake composites. Note that slightly different strain at
break results were found for the two independent sets of composites measured.
Note that the variations in toughness are caused by the variations in the strain at
break. The reasons for these variations are unclear.
Figure 10.4: DSC data for some of the size selected (large) flake Graphene/PVA composites
studied in chapter 6. A) The DSC curves for the PVA and the 0.36 vol% sample
(i.e. that with the highest mechanical properties). The peak at ~220 o C represents
melting of PVA crystallites. B) Melting enthalpy as a function of graphene content.
This shows that the crystallinity is constant with graphene content.
150
appendix
Figure 10.5: Representative TEM images for the smaller, as-prepared BN nanosheets. Scale bars
are both 100 nm.
Figure 10.6: TEM statistics for the smaller, un-size selected BN nanosheets. Total count is 97
flakes.
151
appendix
Figure 10.7: Mechanical properties of PVA composites prepared with as-prepared BN
nanosheets. Superimposed are theoretical models describing the mechanical properties. The line in A is a plot of the Halpin –Tsai equations (4.17 and 4.18) using
YM = 2.6 GPa, YF = 750 GPa and < L/t >= 497. The line in B is a plot of equation
8 with τB = 80 MPa, < L >= 0.54µm, < w >= 0.4µm and < t >= 1.2nm. All size
data are taken from figure 10.6.
152
appendix
Figure 10.8: TEM statistics for flake length, width, thickness and flake aspect ratio for the
size-selected BN/PVA dispersion.
Figure 10.9: TGA analysis on the BN/PVA powder used to form composites in chapter 6.
153
appendix
Figure 10.10: A) The DSC scans for PVA reference and 3 BN/PVA composite samples. The
peak at ~220o C represents melting of PVA crystallites. B) Melting enthalpy as a
function of BN content. No evidence of BN nanosheet nucleated crystallinity is
present.
Figure 10.11: Representative stress strain curves for the best performing 1D-filler/PVA composite films compared with neat PVA, as studied in chapter 7.
154
appendix
Filler
Polymer Matrix
dY/dVf
dσB /dVf
Poly Y,
Poly σB ,
(max Vf )
(GPa)
(MPa)
Ymax (GPa)
σB,max (MPa)
Graphene Oxide
PVA
55
1300
0.1
17
(GrO)
(0.02)
1.2
43
Functionalised
PMMA
2.1
70
Graphene Sheets
(0.0062)
3.8
84
GrO
PVA
2.45
50
3.45
86
2.6
28
36.4
80
0.67
12.2
7.5
148
N/A
23
271
400
2258
8180
(0.0025)
GrO
PVA
88
136
(0.383)
PMMA
11
209
(0.65)
GrO
PVA
N/A
8833
(0.003)
GrO
PVA
Epoxy
Graphite
(0.016)
Exfoliated
p-lactide
Graphite
(0.043)
GrO
PC
(0.06)
3.2
318
22
29
5.3
381
2666
N/A
0.45
22
0.52
29
0.78
17.7
1.83
23.8
3.0
62
4.25
70
2.35
N/A
2.67
Table 10.4: A summary of mechanical properties of graphene/polymer composites in the
literature, as used in figure 6.9.
Ref
[252]
[250]
[247]
[249]
[245]
49.5
(0.0022)
Expanded
155
[251]
[248]
[246]
[253]
appendix
Figure 10.12: Young’s modulus (i) and UTS (ii) versus filler volume fraction for the 3 poorperforming composite sets. In all cases, a linear fit (solid line) has been applied
to the low volume fraction data. However, for A and B the level of scatter makes
the fits unreliable.
156
appendix
Figure 10.13: Strain at break (i) and Toughness (ii) data versus filler volume fraction for the 6
composite sets showing reinforcement from chapter 7.
157
appendix
Figure 10.14: Strain at break (i) and Toughness (ii) versus filler volume fraction for the 3 “poor”
filler composite sets from chapter 7.
Figure 10.15: A) DSC curves and B) Melt enthalpy measurements for the PVA only and nine
composite samples. The peak at ~220o C represents the melting of the PVA
crystallites. No significant change in A) peak position for different fillers or
B) Increased crystallisation nucleated by increased filler content is observed.
158
appendix
Figure 10.16: SEM images of the raster scan regions chosen for EDX analysis. A) Background
surface of pewter only lamella. B) “Island” on surface of pewter only lamella.
C) “Island” on surface of 1.5 wt% composite lamella. D) Flake protruding from
fracture surface of 1.5 wt% composite film.
159
appendix
Figure 10.17: EDX scan results corresponding to the regions in 10.16. A) Tin (Sn) and Antimony
(Sb) present in pewter only background as expected. B) Zinc (Zn) peaks present
suggesting that the islands consist of this metal. Due to the wide area scanned
during this technique, Sn and Sb are also present from the surroundings. C) Zn
also present in the islands seen in the composite sample. The peak marked by the
arrow (~3.7 eV) is believed to be Sb, as in B, although not marked by the software.
D) Evidence that the observed flakes protruding from the composite surface
are indeed MoTe2 . All the metals are present in the fracture surface, including
Sb, once again around 3.7 eV, shown by the arrow. Other elements recorded
are copper (Cu) from the grid where the lamella is affixed, Gallium (Ga) and
Platinum from FiB milling and deposition and trace amounts of silicon (Si) and
oxygen (O).
160
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