Influence of microstructural parameters of a SOFC composite cathode on the collection of reliable experimental data: A modelling analysis

Canadian Institute of Mining, Metallurgy and Petroleum
B. Kenney
Organization:
Canadian Institute of Mining, Metallurgy and Petroleum
Pages:
15
File Size:
6597 KB
Publication Date:
Jan 1, 2005

Abstract

For SOFCs, composite cathodes comprising an electronically conducting electro¬catalyst (e.g. LSM) and an conically conducting electrolyte material (e.g. YSZ) are considered to improve the electrochemical performance by extending the active reaction zone to regions beyond the electrolyte-electrode interface, as in the case of conventional cathodes. Interpretation of experimental data for composite cathodes, however, is complicated by the mixed conducting behaviour which results in local overpotential distributions throughout the composite cathode. These local overpotentials depend on many factors, such as the composition, particle size, porosity and thickness of the cathode. Reliable experimental data obtained from composite cathodes requires carefully crafted microstructural parameters in order to minimize the overpotential variations. A mathematical model of a composite cathode was developed using coupled differential equations describing transport and consumption of chemical species, oxygen ion and electrons. Simulations under various oxygen concentrations were used to obtain the reaction order of the oxygen reduction reaction. An apparent po2 dependency was then extracted from the predicted Rp vs P02 data. It was found that this p02 dependency can be significantly influenced by the micro-structural parameters of a composite cathode, primarily due to local overpotential distributions.
Citation

APA: B. Kenney  (2005)  Influence of microstructural parameters of a SOFC composite cathode on the collection of reliable experimental data: A modelling analysis

MLA: B. Kenney Influence of microstructural parameters of a SOFC composite cathode on the collection of reliable experimental data: A modelling analysis. Canadian Institute of Mining, Metallurgy and Petroleum, 2005.

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