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.

Structure learning of graphical models for task-oriented robot grasping

In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the main problems is the development of interaction skills with the surrounding environment, which include the ability to grasp objects. To perform this task the robot needs to sense the environment and acquire the object informations, physical attributes that may influence a grasp. Humans can solve this grasping problem easily due to their past experiences, that is why many researchers are approaching it from a machine learning perspective finding grasp of an object using information of already known objects. But humans can select the best grasp amongst a vast repertoire not only considering the physical attributes of the object to grasp but even to obtain a certain effect. This is why in our case the study in the area of robot manipulation is focused on grasping and integrating symbolic tasks with data gained through sensors. The learning model is based on Bayesian Network to encode the statistical dependencies between the data collected by the sensors and the symbolic task. This data representation has several advantages. It allows to take into account the uncertainty of the real world, allowing to deal with sensor noise, encodes notion of causality and provides an unified network for learning. Since the network is actually implemented and based on the human expert knowledge, it is very interesting to implement an automated method to learn the structure as in the future more tasks and object features can be introduced and a complex network design based only on human expert knowledge can become unreliable. Since structure learning algorithms presents some weaknesses, the goal of this thesis is to analyze real data used in the network modeled by the human expert, implement a feasible structure learning approach and compare the results with the network designed by the expert in order to possibly enhance it.

Set up of a procedure for correlation analysis between current discharge and dose delivery in a pulsed power plasma device for medical application

This thesis work aims to find a procedure for isolating specific features of the current signal from a plasma focus for medical applications. The structure of the current signal inside a plasma focus is exclusive of this class of machines and a specific analysis procedure has to be developed. The hope is to find one or more features that shows a correlation with the dose erogated. The study of the correlation between the current discharge signal and the dose delivered by a plasma focus could be of some importance not only for the practical application of dose prediction but also for expanding the knowledge anbout the plasma focus physics. Vatious classes of time-frequency analysis tecniques are implemented in order to solve the problem.