4.6 Article

Sabatier principle of d electron number for describing the nitrogen reduction reaction performance of single-atom alloy catalysts

期刊

JOURNAL OF MATERIALS CHEMISTRY A
卷 10, 期 32, 页码 16900-16907

出版社

ROYAL SOC CHEMISTRY
DOI: 10.1039/d2ta04140e

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资金

  1. National Key R&D Program of China [2020YFB1505603]
  2. National Natural Science Foundation of China [21673095]

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In this study, 108 Cu-based single-atom alloy (SAA) catalysts were constructed and their inherent structure-activity relationship for nitrogen reduction reactions (NRR) was investigated. The number of d electrons in the doped transition metal atom was identified as a key descriptor for evaluating NRR activity. The best performing SAA showed the lowest overpotential, and a machine-learning method was also presented to describe and regulate the characteristics of these catalysts.
The recently emerged single-atom alloy (SAA) catalysts have the combined merits of single-atom catalysts (SACs) and alloy catalysts, thus showing great potential for driving nitrogen reduction reactions (NRR). However, a rigorous design principle for novel SAAs toward achieving efficient NRR is still lacking. Herein, by means of density functional theory (DFT) calculations, we constructed 108 Cu-based SAAs to screen their inherent structure-activity relationship for driving electrochemical NRR. We found a quintuple degenerate d electron state in SAAs, and the d electrons could redistribute to the functional orbitals within the frame of the acceptance-donation mechanism for N-2 activation. The d electron number (N-e) of the doped transition metal (TM) atom has been identified as a descriptor for evaluating the NRR activity with a relationship akin to the Sabatier principle, and a moderate N-e of 5 is optimal. Among all the SAAs, the best NRR was realized by Re-Cu(553) with the lowest overpotential of 0.17 V. Moreover, a machine-learning (ML) method to describe and thus regulate all characteristics of the Cu-based SAAs is presented, which unveiled the intrinsic correlations between their structure and catalytic performance. This work provides a comprehensive insight for NRR applied by SAAs, paving the way to discovering novel catalysts toward high NRR performance.

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