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Full-Text Articles in Other Engineering Science and Materials
Computational Studies Of Structure–Function Relationships Of Supported And Unsupported Metal Nanoclusters, Hongbo Shi
Computational Studies Of Structure–Function Relationships Of Supported And Unsupported Metal Nanoclusters, Hongbo Shi
Doctoral Dissertations
Fuel cells have been demonstrated to be promising power generation devices to address the current global energy and environmental challenges. One of the many barriers to commercialization is the cost of precious catalysts needed to achieve sufficient power output. Platinum-based materials play an important role as electrocatalysts in energy conversion technologies. In order to improve catalytic efficiency and facilitate rational design and development of new catalysts, structure–function relationships that underpin catalytic activity must be understood at a fundamental level. First, we present a systematic analysis of CO adsorption on Pt nanoclusters in the 0.2-1.5 nm size range with the aim …
Correlating Long-Term Lithium Ion Battery Performance With Solid Electrolyte Interphase (Sei) Layer Properties, Seong Jin An
Correlating Long-Term Lithium Ion Battery Performance With Solid Electrolyte Interphase (Sei) Layer Properties, Seong Jin An
Doctoral Dissertations
This study was conducted to understand effects of some of key factors (i.e., anode surface properties, formation cycling conditions, and electrolyte conditions) on solid electrolyte interphase (SEI) formation in lithium ion batteries (LIBs) and the battery cycle life. The SEI layer passivates electrode surfaces and prevents electron transfer and electrolyte diffusion through it while allowing lithium ion diffusion, which is essential for stable reversible capacities. It also influences initial capacity loss, self-discharge, cycle life, rate capability and safety. Thus, SEI layer formation and electrochemical stability are primary topics in LIB development. This research involves experiments and discussions on key factors …
Data Driven Discovery Of Materials Properties., Fadoua Khmaissia
Data Driven Discovery Of Materials Properties., Fadoua Khmaissia
Electronic Theses and Dissertations
The high pace of nowadays industrial evolution is creating an urgent need to design new cost efficient materials that can satisfy both current and future demands. However, with the increase of structural and functional complexity of materials, the ability to rationally design new materials with a precise set of properties has become increasingly challenging. This basic observation has triggered the idea of applying machine learning techniques in the field, which was further encouraged by the launch of the Materials Genome Initiative (MGI) by the US government since 2011. In this work, we present a novel approach to apply machine learning …