Time-resolved oxidation state changes are key to elucidating the bifunctionality of perovskite catalysts for oxygen evolution and reduction

In a unified regenerative fuel cell (URFC) or reversible fuel cell the oxygen bifunctional catalyst must switch reversibly between the oxygen reduction reaction (ORR), fuel cell mode, and the oxygen evolution reaction (OER), electrolyzer mode. However, it is often unclear what effect alternating between ORR and OER has on the electrochemical behavior and physiochemical properties of the catalyst. Herein, operando X-ray absorption spectroscopy (XAS) is utilized to monitor the continuous and dynamic evolution of the Co, Mn, and Fe oxidation states of perovskite catalysts Ba0.5Sr0.5Co0.8Fe0.2O3-δ (BSCF) and La0.4Sr0.6MnO3-δ (LSM), while the potential is oscillated between reducing and oxidizing potentials with cyclic voltammetry. The results reveal the importance of investigating bifunctional catalysts by alternating between fuel cell and electrolyzer operation and highlight the limitations and challenges of bifunctional catalysts. It is shown that the requirements for ORR and OER performance are divergent and that the oxidative potentials of OER are detrimental to ORR activity. These findings are used to give guidelines for future bifunctional catalyst design. Additionally, it is demonstrated how sunlight can be used to reactivate the ORR activity of LSM after rigorous cycling.

Identifier
Source https://archive.materialscloud.org/record/2023.121
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:1845
Provenance
Creator Beall, Casey E.; Fabbri, Emiliana; Clark, Adam H.; Meier, Vivian; Yüzbasi, Nur Sena; Sjølin, Benjamin H.; Castelli, Ivano E.; Aegerter, Dino; Graule, Thomas; Schmidt, Thomas J.
Publisher Materials Cloud
Publication Year 2023
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type Dataset
Discipline Materials Science and Engineering