Computational analysis and experiments on oxide ionic conductors

Abstract

Oxide ionic conductors have found applications across many fields, including solid oxide fuel cells (SOFCs) and solid-state batteries. While these oxides have many differences, they often share similar conduction mechanisms. This dissertation will first study O ion conductors with applications in SOFCs and then extend to Li solid electrolytes for batteries. O ion conduction is one of the processes enabling the operation of SOFCs. It is therefore vital to understand the O diffusion and its coupling with the oxygen catalysis at the active sites of the materials that allows for O diffusion. In our study, oxygen transport properties are calculated from molecular simulations, while the catalytic activity is assessed thanks to computational descriptors. On the other hand, recent progress in the Li battery community has shown growing interest in applying Li-conducting oxide ceramic material as the electrolytes in order to increase the safety, boost the energy density, and extend the lifetime of the battery. In this dissertation, several O ion conducting and Li ion conducting oxides are studied from primarily a computational point of view and some experiments are carried out to support computational findings. First, a series of BaFeO₃ derivatives are studied. Their ability to conduct O ions and catalyze oxygen reduction at high temperature is assessed. Our results show remarkable potential for inexpensive Fe-based materials as SOFC cathodes and open new possibilities for catalysts design. Based on molecular dynamics simulations, we develop a data-mining framework to analyze the diffusion patterns. We further extend this approach to study Li ion diffusion in Li₇La₃Zr₂O₁₂. Hopping between different sites is determined to be the basic principle underpinning ionic transport in both types of conductors. This work puts forward a perspective of combining computational design with data science and experiments for the analysis of functional materials.

Date of Award	2016
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology