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 jul 2023
Accepted
24 set 2023
First published
25 set 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

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements