Questing for High-Performance Electrocatalysts for Oxygen Evolution Reaction: Importance of Chemical Complexity, Active Phase, and Surface-Adsorbed Species

CHEMSUSCHEM(2024)

Cited 0|Views1
No score
Abstract
Rational design of advanced electrocatalysts for oxygen evolution reaction (OER) is of vital importance for the development of sustainable energy. Entropy engineering is emerging as a promising approach for the design of efficient OER electrocatalysts. However, other multi-anion/cation electrocatalysts with compositional complexity, particularly the medium-entropy and other non-equimolar cation/anion complex electrocatalysts, have not received noteworthy attention. In this perspective, we review and highlight the importance of compositionally complex catalysts and propose a concept of chemical complexity to correlate the OER catalytic activity with the contributions from the pairwise cation-anion interactions. Then, we offer a new view on the active catalytic sites being the hydroxylated reacting interface in an alkaline solution. Further, we argue that the common discrepancies between computationally predicted OER activities and experimental results stem from lack of considerations of surface-adsorbed species in modeling the active catalytic phases or sites. This perspective would facilitate achieving a renewed and profound understanding of the OER mechanism and promote efficient design of OER electrocatalysts for renewable energy conversion and storage. Compositionally complex electrocatalysts: Compositionally complex electrocatalysts can speed up the oxygen evolution reaction through various functions such as facilitation of the charge redistribution on the catalyst surface and changing the rate-determining step in the reaction mechanism, and thus offer great potentials for achieving high-performance electrocatalysis of oxygen evolution.image
More
Translated text
Key words
Electrocatalysis,Oxygen Evolution Reaction,Reaction Interface,Chemical Complexity,Surface-adsorbed Species
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined