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.

Tin disulfide (SnS2) is a two-dimensional (2D) material with excellent properties and high prospects for low-cost solutions to catalytic and optoelectronic applications. In this work, vertical nanoflakes of SnS2 have been synthesized using custom-designed close space sublimation (CSS) system and investigated for applications in photoelectrochemical (PEC) water oxidation and metal-semiconductor-metal (MSM) photodetector. For the PEC application, vertical SnS2 nanoflakes grown directly on transparent conductive substrates have been used as photoanodes, which produce record photocurrents of 4.5 mA cm−2 for oxidation of a sulfite hole scavenger and 2.6 mA cm−2 for water oxidation without any hole scavenger, both at 1.23 VRHE in neutral electrolyte under simulated AM1.5G sunlight, and stable photocurrents for iodide oxidation in acidic electrolyte. This remarkable performance has been attributed to three main reasons: (1) high intrinsic carrier mobility of 330 cm2 V−1 s−1 and long photoexcited carrier lifetime of 1.3 ns in the nanoflakes, (2) the nanoflake height that balances the competing requirements of light absorption and charge transport, and (3) the unique stepped morphology of these nanoflakes that improves photocurrent by exposing multiple edge sites in every nanoflake. In another application, these SnS2 nanoflakes have been used to enhance the performance of lead sulfide quantum dot (PbS QDs) photodetectors by providing a high-mobility channel for photoexcited charges from PbS QDs, which results in 2 orders of magnitude enhancement in responsivity. The physical models and experimental findings presented in this dissertation can help engineer more cost-effective solutions for PEC water splitting and optoelectronics based on 2D metal dichalcogenides.

Our vision is as yet unsurpassed by machines because of the sophisticated representations of objects in our brains. This representation is vastly different from a pixel-based representation used in machine storages. It is this sophisticated representation that enables us to perceive two faces as very different, i.e, they are far apart in the “perceptual space”, even though they are close to each other in their pixel-based representations. Neuroscientists have proposed distances between responses of neurons to the images (as measured in macaque monkeys) as a quantification of the “perceptual distance” between the images. Let us call these neuronal dissimilarity indices of perceptual distances. They have also proposed behavioural experiments to quantify these perceptual distances. Human subjects are asked to identify, as quickly as possible, an oddball image embedded among multiple distractor images. The reciprocal of the search times for identifying the oddball is taken as a measure of perceptual distance between the oddball and the distractor. Let us call such estimates as behavioural dissimilarity indices. In this thesis, we describe a decision-theoretic model for visual search that suggests a connection between these two notions of perceptual distances. In the first part of the thesis, we model visual search as an active sequential hypothesis testing problem. Our analysis suggests an appropriate neuronal dissimilarity index which correlates strongly with the reciprocal of search times. We also consider a number of alternative possibilities such as relative entropy (Kullback-Leibler divergence), the Chernoff entropy and the L1-distance associated with the neuronal firing rate profiles. We then come up with a means to rank the various neuronal dissimilarity indices based on how well they explain the behavioural observations. Our proposed dissimilarity index does better than the other three, followed by relative entropy, then Chernoff entropy and then L1 distance. In the second part of the thesis, we consider a scenario where the subject has to find an oddball image, but without any prior knowledge of the oddball and distractor images. Equivalently, in the neuronal space, the task for the decision maker is to find the image that elicits firing rates different from the others. Here, the decision maker has to “learn” the underlying statistics and then make a decision on the oddball. We model this scenario as one of detecting an odd Poisson point process having a rate different from the common rate of the others. The revised model suggests a new neuronal dissimilarity index. The new dissimilarity index is also strongly correlated with the behavioural data. However, the new dissimilarity index performs worse than the dissimilarity index proposed in the first part on existing behavioural data. The degradation in performance may be attributed to the experimental setup used for the current behavioural tasks, where search tasks associated with a given image pair were sequenced one after another, thereby possibly cueing the subject about the upcoming image pair, and thus violating the assumption of this part on the lack of prior knowledge of the image pairs to the decision maker. In conclusion, the thesis provides a framework for connecting the perceptual distances in the neuronal and the behavioural spaces. Our framework can possibly be used to analyze the connection between the neuronal space and the behavioural space for various other behavioural tasks.