Issue 22, 2023

Self-promoted ammonia selectivity for the electro-reduction of nitrogen on gt-C3N4 supported single metal catalysts: the machine learning model and physical insights

Abstract

Selectivity toward ammonia is an important indicator of a good electrocatalyst for the electrochemical nitrogen reduction reaction (eNRR). The multi-adsorption of N2 on TM/gt-C3N4 greatly decreases the possibility of H binding, thus, self-promoting the selectivity toward NRR. Furthermore, the amount of nitrogen that can be trapped on the active sites of the studied catalysts is determined by the numbers of unoccupied d-orbitals of the supported single metal atom. The NRR selectivity on TM/gt-C3N4 (TM = V, Cr, Mn, Mo, Tc, W, and Re) is predicted to be 100% while three N2 were adsorbed on TM (3N2@TM/gt-C3N4). Furthermore, 3N2@TM/gt-C3N4 is the dominant configuration under a high pressure region at room temperature. Multiple dinitrogen molecules can be stably adsorbed on the active site, which is a good indicator of thermal stability by AIMD simulation in the canonical ensemble. Machine-learning analysis indicates that the high selectivity toward ammonia is determined by the numbers of effectively bound N2 molecules, and the low limiting-potential may correlate with the charging states of the supported metal atom, adsorption energy, and N–N bond length of the adsorbed N2 molecule. W/N3–G (W atom supported on three-pyrimidine-nitrogen-doped graphene) is predicted as a potential single atom catalyst with a low limiting-potential of −0.44 V and high selectivity based on the machine learning model, which is verified by further DFT calculations. This suggests a good generalization capability of the machine learning model.

Graphical abstract: Self-promoted ammonia selectivity for the electro-reduction of nitrogen on gt-C3N4 supported single metal catalysts: the machine learning model and physical insights

Supplementary files

Article information

Article type
Research Article
Submitted
20 7 2023
Accepted
24 9 2023
First published
25 9 2023

Inorg. Chem. Front., 2023,10, 6578-6587

Self-promoted ammonia selectivity for the electro-reduction of nitrogen on gt-C3N4 supported single metal catalysts: the machine learning model and physical insights

L. Zhang, L. Chen, W. Zhao, Z. Hu, J. Chen, W. Zhang and J. Yang, Inorg. Chem. Front., 2023, 10, 6578 DOI: 10.1039/D3QI01390A

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