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Progress and Challenges Toward the Rational Design of Oxygen Electrocatalysts Based on a Descriptor Approach
Author(s) -
Liu Jieyu,
Liu Hui,
Chen Haijun,
Du Xiwen,
Zhang Bin,
Hong Zhanglian,
Sun Shuhui,
Wang Weichao
Publication year - 2020
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.201901614
Subject(s) - rational design , electrocatalyst , catalysis , computer science , electrochemistry , benchmark (surveying) , biochemical engineering , nanotechnology , electrochemical energy conversion , oxygen evolution , oxygen reduction , materials science , chemistry , biological system , electrode , biochemistry , geodesy , geography , engineering , biology
Oxygen redox catalysis, including the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), is crucial in determining the electrochemical performance of energy conversion and storage devices such as fuel cells, metal–air batteries,and electrolyzers. The rational design of electrochemical catalysts replaces the traditional trial‐and‐error methods and thus promotes the R&D process. Identifying descriptors that link structure and activity as well as selectivity of catalysts is the key for rational design. In the past few decades, two types of descriptors including bulk‐ and surface‐based have been developed to probe the structure–property relationships. Correlating the current descriptors to one another will promote the understanding of the underlying physics and chemistry, triggering further development of more universal descriptors for the future design of electrocatalysts. Herein, the current benchmark activity descriptors for oxygen electrocatalysis as well as their applications are reviewed. Particular attention is paid to circumventing the scaling relationship of oxygen‐containing intermediates. For hybrid materials, multiple descriptors will show stronger predictive power by considering more factors such as interface reconstruction, confinement effect, multisite adsorption, etc. Machine learning and high‐throughput simulations can thus be crucial in assisting the discovery of new multiple descriptors and reaction mechanisms.

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