Synergy of dual-atom catalysts deviated from the scaling relationship for oxygen evolution reaction

Dual-atom catalysts, particularly those with heteronuclear active sites, have the potential to outperform the well-established single-atom catalysts for oxygen evolution reaction, but the underlying mechanistic understanding is still lacking. Herein, a large-scale density functional theory is employ...

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Published in:Nature communications Vol. 14; no. 1; p. 4449
Main Authors: Fang, Cong, Zhou, Jian, Zhang, Lili, Wan, Wenchao, Ding, Yuxiao, Sun, Xiaoyan
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 24-07-2023
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Summary:Dual-atom catalysts, particularly those with heteronuclear active sites, have the potential to outperform the well-established single-atom catalysts for oxygen evolution reaction, but the underlying mechanistic understanding is still lacking. Herein, a large-scale density functional theory is employed to explore the feasibility of *O-*O coupling mechanism, which can circumvent the scaling relationship with improving the catalytic performance of N-doped graphene supported Fe-, Co-, Ni-, and Cu-containing heteronuclear dual-atom catalysts, namely, M’M@NC. Based on the constructed activity maps, a rationally designed descriptor can be obtained to predict homonuclear catalysts. Seven heteronuclear and four homonuclear dual-atom catalysts possess high activities that outperform the minimum theoretical overpotential. The chemical and structural origin in favor of *O-*O coupling mechanism thus leading to enhanced reaction activity have been revealed. This work not only provides additional insights into the fundamental understanding of reaction mechanisms, but also offers a guideline for the accelerated discovery of efficient catalysts. The utilization of dual-atom catalysts holds the potential in surpassing single-atom catalysts for oxygen evolution reactions. Here, the authors examine the mechanism of dual-atom catalysts for oxygen evolution reaction and identify catalyst optimization recipes via large-scale computations.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-023-40177-1