Correlation between structure and electrochemistry of LiMO2 cathode materials (M = Ni, Co)

Technical diversity and various knowledge is required for the understanding of undoubtedly complex system such as a Lithium-ion battery. The peculiarity is to combine different techniques that allow a complete investigation while the battery is working. Nowadays, research on Li-ion batteries (LIBs) is experiencing an exponential growth in the development of new cathode materials. Accordingly, Li-rich and Ni-rich NMCs, which have similar layered structure of LiMO2 oxides, have been recently proposed. Despite the promising performance on them, still a lot of issues have to be resolved and the materials need a more in depth characterisation for further commercial applications. In this study LiMO2 material, in particular M = Co and Ni, will be presented. We have focused on the synthesis of pure LiCoO2 and LiNiO2 at first, followed by the mixed LiNi0.5Co0.5O2. Different ways of synthesis were investigated for LCO but the sol-gel-water method showed the best performances. An accurate and systematic structural characterization followed by the appropriate electrochemical tests were done. Moreover, the in situ techniques (in-situ XRD and in situ OEMS) allowed a deep investigation in the structural change and gas evolution upon the electrochemically driven processes.

Mitigating non-Lambertian surfaces issues in Stereo Matching with Neural Radiance Fields

Depth estimation from images has long been regarded as a preferable alternative compared to expensive and intrusive active sensors, such as LiDAR and ToF. The topic has attracted the attention of an increasingly wide audience thanks to the great amount of application domains, such as autonomous driving, robotic navigation and 3D reconstruction. Among the various techniques employed for depth estimation, stereo matching is one of the most widespread, owing to its robustness, speed and simplicity in setup. Recent developments has been aided by the abundance of annotated stereo images, which granted to deep learning the opportunity to thrive in a research area where deep networks can reach state-of-the-art sub-pixel precision in most cases. Despite the recent findings, stereo matching still begets many open challenges, two among them being finding pixel correspondences in presence of objects that exhibits a non-Lambertian behaviour and processing high-resolution images. Recently, a novel dataset named Booster, which contains high-resolution stereo pairs featuring a large collection of labeled non-Lambertian objects, has been released. The work shown that training state-of-the-art deep neural network on such data improves the generalization capabilities of these networks also in presence of non-Lambertian surfaces. Regardless being a further step to tackle the aforementioned challenge, Booster includes a rather small number of annotated images, and thus cannot satisfy the intensive training requirements of deep learning. This thesis work aims to investigate novel view synthesis techniques to augment the Booster dataset, with ultimate goal of improving stereo matching reliability in presence of high-resolution images that displays non-Lambertian surfaces.