Dual regulatory mechanisms and machine learning analysis of nitrate reduction: a triatomic heteronuclear catalyst on g-C2N.

Yushan Pang¹, Chen Hong¹, Ran Ding¹, Hong Xu², Guohong Fan³, Yi Cui⁴

¹School of Chemistry and Chemical Engineering, Anhui University of Technology, Maanshan, Anhui 243002, PR China.

²School of Chemistry and Chemical Engineering, Anhui University of Technology, Maanshan, Anhui 243002, PR China. Electronic address: hongxu@ahut.edu.cn.

³School of Chemistry and Chemical Engineering, Anhui University of Technology, Maanshan, Anhui 243002, PR China. Electronic address: ghfan8@ahut.edu.cn.

⁴i-lab, Vacuum Interconnected Nanotech Workstation (Nano-X), Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences, Suzhou 215123, China. Electronic address: ycui2015@sinano.ac.cn.

PubMed DOI

Abstract

This study systematically explores homonuclear/heteronuclear trimetallic catalysts (TM3@g-C2N) for sustainable NH₃ synthesis by integrating density functional theory (DFT) and machine learning (ML) analyses. The Fe2Mn@g-C2N heterostructure demonstrates exceptional performance with a record-low limiting potential of -0.25 V, outperforming homonuclear counterparts (Fe3@g-C2N: -0.30 V) and benchmark…

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