"Sabatier principle" of d electron number for describing the nitrogen reduction reaction performance of single-atom alloy catalysts. Issue 32 (3rd August 2022)

• Record Type: Journal Article
• Authors: Dai, Tianyi; Wang, Zhili; Lang, Xingyou; Jiang, Qing
• Is Part Of: Journal of materials chemistry. Volume 10:Issue 32(2022)
• Page Start: 16900
• Page End: 16907
• Publication Date: 2022-08-03
• Subjects: Materials -- Research -- Periodicals; Chemistry, Analytic -- Periodicals; Environmental sciences -- Research -- Periodicals; 543.0284
• DOI: 10.1039/d2ta04140e
• Languages: English
• ISSNs: 2050-7488

Abstract

Abstract : A comprehensive theoretical study on Cu-based single-atom alloys reveals their inherent structure–activity relationship relevant to performance in NRR. Abstract : 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 N2 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.