| dc.description.abstract | This thesis addresses the pressing issue of sustainable development and climate change by examining the life cycle (degradation) of electrochemical energy storage devices. Specifically, it investigates a green synthesis technique for high-performance pseudocapacitor electrodes and uses machine learning algorithms to predict and prevent degradation mechanisms in lithium-ion batteries. The research demonstrates the effectiveness of the laser irradiation technique, called ultra-short laser pulses for in situ nanostructure generation (ULPING) for fabricating a metal oxide layer on a titanium sheet under ambient conditions, as well as the potential of machine learning algorithms as a tool for constructing mathematical models to forecast the electrochemical behavior of pseudocapacitors. The thesis also highlights the importance of utilizing data-driven approaches in electrode design procedures and promoting sustainable habits in all aspects of life. In addition, the study provides insight into the modeling and prediction of the electrochemical behavior performance of pseudocapacitors, which could facilitate the development of optimal electrodes. Moreover, the research examines one of the most detrimental degradation mechanisms that occur during the fast-charging process, known as the deposition of metallic lithium or lithium plating, in lithium-ion batteries. The proposed machine learning approach based on ensemble selection accurately predicts the anode potential under various charging conditions and achieves high accuracy in preventing lithium plating. Overall, this research offers promising methods for employing ultra-short laser pulses for in situ nanostructure generation to fabricate nanostructures on transition metals that have the potential to be used in pseudocapacitor electrodes and highlights the importance of utilizing machine learning techniques in predicting and preventing degradation mechanisms in electrochemical energy storage devices. | en |
