Silicon (in the form of lithium silicides) has almost ten times the theoretical charge storage capacity of graphite, the anode material used in most commercially-available lithium-ion batteries. Replacing graphite with silicon therefore promises a substantial improvement over the state-of-the-art in electrochemical energy storage. However, it has proved difficult to realise this high theoretical capacity in a practical electrochemical cell and maintain it over repeated charge-discharge cycles. This dissertation presents experimental work probing the changes in local structure occurring during the electrochemical reactions of lithium with silicon, using neutron total scattering and nuclear magnetic resonance, together with novel processing methodologies for analysing the resulting data, in the hope of suggesting ways of improving the performance of silicon-based lithium-ion batteries. Neutron total scattering patterns were obtained from silicon-based anode materials extracted from cells at various states of charge. These samples were composed of a heterogeneous mixture of amorphous, crystalline and disordered crystalline materials. Reverse Monte Carlo is a technique for obtaining structural information from experimental data (particularly total scattering patterns) from amorphous and disordered crystalline materials. However, previously existing Reverse Monte Carlo software could only handle homogeneous materials. Therefore, the RMCprofile software package was extended to handle data from heterogeneous samples. The improved RMCprofile was applied to the aforementioned total scattering patterns, but the much stronger scattering from the other components (themselves not well-characterised) swamped that from the lithium silicide. Future work should attempt to reduce the scattering from the inactive components, particularly the hard-to-model incoherent scattering. NMR data were acquired in situ from silicon-nanowire-based lithium-ion batteries during repeated charge-discharge cycles, achieving much better electrochemical performance than had been seen in previous in situ experiments with silicon. Owing to the large quantities of data obtained, an automated, model-free dimensionality reduction technique was needed. The NMR data were processed using principal component analysis and a variant of non-negative matrix factorisation. With both of these methods, one of the components was found to be associated with high voltages vs. ${Li \vert{} Li^{+}}$ (i.e. a fully discharged anode). This region has seen very little interest by comparison with the low voltage (high levels of lithiation) region of the charge-discharge cycle, so this discovery suggests a new avenue for future research.

Lithium-ion batteries (LIBs) are an integral part of consumer electronic devices and electric vehicles. There is a growing need for LIBs with higher capacity, rate performance and cycling stability. At the anode electrode these challenges are being addressed for instance by utilising materials with higher theoretical capacity compared to graphite (372 mAh/g) or by optimising the morphology of materials through nanostructuring of the electrode. In this thesis the former is investigated by synthesising a reduced graphene oxide (rGO) tin sulphide (SnS2) hybrid, and the latter by self-assembly of rGO sodium titanate and rGO titanium dioxide (TiO2) nanorods. In Chapter 2, SnS2 is investigated due to its high theoretical capacity as an anode material (645 mAh/g), low cost and environmental benignity. SnS2 nanoparticles were grown directly on rGO sheets which provide a conductive framework and limit the detachment of tin particles which undergo large volume changes during alloying reactions. However, a fast decrease in capacity was observed. Post-mortem analysis of the electrodes showed that rGO becomes irreversibly passivated suggesting that additional measures to retain effective charge transport between the low weight percent conductive additive and the active phase during cycling are required. An alternative material system based on nanorods of intercalation materials (sodium titanate and TiO2) wrapped by rGO sheets was chosen to investigate self-assembly in anodes to address the low packing density of nanomaterials. A drop-casting method was used to align rGO-sodium titanate nanorods through evaporation driven self-assembly (Chapter 3) which relies on a combination of electrostatic repulsive forces originating from the rGO coating, and liquid crystal phase formation at high concentrations, facilitated by the high aspect ratio nanorods. As reference, non-aligned films were prepared by adjusting the pH of the nanorod dispersion. Freestanding aligned and non-aligned films were converted to rGO-TiO2 (Chapter 4). The volumetric capacity of the self-assembled films was double that of non-aligned films, and up to 4.5 times higher than traditional casted electrodes using the same material. Further, up to rates of 4 C, the self-assembled films outperformed the non-aligned films. These films showed no sign of capacity fading up to 1000 cycles, which together with post-mortem analysis confirms that these assembled structures are maintained during battery cycling.

Self-activation is a process that takes advantage of the gases emitted from the pyrolysis process of biomass to activate the converted carbon. The pyrolytic gases from the biomass contain CO2 and H2O, which can be used as activating agents. As two common methods, both of physical activation using CO2 and chemical activation using ZnCl2 introduce additional gas (CO2) or chemical (ZnCl2), in which the CO2 emission from the activation process or the zinc compound removal by acid from the follow-up process will cause environmental concerns. In comparison with these conventional activation processes, the self-activation process could avoid the cost of activating agents and is more environmentally friendly, since the exhaust gases (CO and H2) can be used as fuel or feedstock for the further synthesis in methanol production. In this research, many types of biomass were successfully converted into activated carbon through the self-activation process. An activation model was developed to describe the changes of specific surface area and pore volume during the activation. The relationships between the activating temperature, dwelling time, yield, specific surface area, and specific pore volume were detailed investigated. The highest specific surface area and pore volume of the biomass-derived activated carbon through the self-activation process were up to 2738 m2 g-1 and 2.209 cm3 g-1, respectively. Moreover, the applications of the activated carbons from the self-activation process have been studied, including lithium-ion battery (LIB) manufacturing, water cleaning, oil absorption, and electromagnetic interference (EMI) shielding.