Prediction of C<sub>2</sub>N-Supported Double-Atom Catalysts with Individual/Integrated Descriptors for Electrochemical and Thermochemical CO<sub>2</sub> Reduction

Electrochemical/thermochemical CO<sub>2</sub> reduction reactions (CO<sub>2</sub>RR) on double-atom catalysts (DACs) have emerged as a novel frontier in energy and environmental catalysis. However, the lack of investigation of the underlying structure-property relationship greatly limits the rational design and practical application of related catalysts. Herein, we carried out a comprehensive theoretical study on CO<sub>2</sub>RR catalyzed by a series of C<sub>2</sub>N-supported transition metals to shed light on this issue. We demonstrate that the activity of DAC can be obviously improved by judicious manipulation of metal type, and CoNi with the highest reactivity and excellent selectivity is identified as the most promising candidate for both electroreduction and thermoreduction processes. Then, based on systemically electronic and structural analysis, various quantitative structure-property relationships are established. The results reveal that key interaction and reaction properties of elementary steps can be quantitatively determined directly from individual descriptors, including key species, charge transfer, d-orbital center, bond length, spectroscopic signals, etc, while for total reaction, the integrated descriptors designed by a novel and effective three-step strategy have much better performance. The proposed ability of quantitative prediction of the catalytic property utilizing physically interpretable parameters can significantly broaden the applicability of catalytic descriptors for materials design, thus leading to indispensable guidelines for related DACs.

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Publication Details

Journal
Journal of the American Chemical Society
Published
2025-05-09
DOI
https://doi.org/10.1021/jacs.4c16377
Citations
23
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
11.91

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article

Prediction of C2N-Supported Double-Atom Catalysts with Individual/Integrated Descriptors for Electrochemical and Thermochemical CO2 Reduction

Edward Sharman, Bo Li, Chuanyi Jia, Wenhui Zhong et al.
23 citations
Journal of the American Chemical Society
Machine Learning in Materials Science
11.91
article

Prediction of C2N-Supported Double-Atom Catalysts with Individual/Integrated Descriptors for Electrochemical and Thermochemical CO2 Reduction

Edward Sharman, Bo Li, Chuanyi Jia, Wenhui Zhong, Yi Luo, Jing Yang, Jun Jiang, Zhanyong Gu, Li Sun, Shuang Jiang
article en
23 citations

Abstract

Electrochemical/thermochemical CO<sub>2</sub> reduction reactions (CO<sub>2</sub>RR) on double-atom catalysts (DACs) have emerged as a novel frontier in energy and environmental catalysis. However, the lack of investigation of the underlying structure-property relationship greatly limits the rational design and practical application of related catalysts. Herein, we carried out a comprehensive theoretical study on CO<sub>2</sub>RR catalyzed by a series of C<sub>2</sub>N-supported transition metals to shed light on this issue. We demonstrate that the activity of DAC can be obviously improved by judicious manipulation of metal type, and CoNi with the highest reactivity and excellent selectivity is identified as the most promising candidate for both electroreduction and thermoreduction processes. Then, based on systemically electronic and structural analysis, various quantitative structure-property relationships are established. The results reveal that key interaction and reaction properties of elementary steps can be quantitatively determined directly from individual descriptors, including key species, charge transfer, d-orbital center, bond length, spectroscopic signals, etc, while for total reaction, the integrated descriptors designed by a novel and effective three-step strategy have much better performance. The proposed ability of quantitative prediction of the catalytic property utilizing physically interpretable parameters can significantly broaden the applicability of catalytic descriptors for materials design, thus leading to indispensable guidelines for related DACs.

Journal of the American Chemical SocietyVol. 147(20)
University of Science and Technology of China (CN), Shijiazhuang University (CN), University of California System (US), Guizhou Education University (CN), Henan Academy of Sciences (CN), National Institute of Clean and Low-Carbon Energy (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, Ministry of Science and Technology of the People's Republic of China, Department of Education of Hebei Province, Natural Science Foundation of Hebei Province, Natural Science Foundation of Shandong Province
Openalex Percentile: Top 1%
Machine Learning in Materials Science
11.91
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