ACS Nano: Prediction of Three-Metal Cluster Catalysts on Two-Dimensional W2N3 Support with Integrated Descriptors for Electrocatalytic Nitrogen Reduction

Title：

Prediction of Three-Metal Cluster Catalysts on Two-Dimensional W2N3 Support with Integrated Descriptors for Electrocatalytic Nitrogen Reduction

Authors：

Siyu Chen^1,#, Yongqi Gao^1,#, Wugang Wang^1,#, Oleg V. Prezhdo^2,*, and Lai Xu^1,*

Institutions：

¹Institute of Functional Nano & Soft Materials (FUNSOM), Joint International Research Laboratory of Carbon-Based Functional Materials and Devices, Soochow University, Suzhou 215123 Jiangsu, P.R. China; Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou 215123 Jiangsu, P.R. China

²Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States

Abstract：

In the electrocatalytic nitrogen reduction reaction (NRR), nitrogen (N₂) is chemically inert, it is difficult to break the triple bond, and the subsequent protonation step is very challenging. Suitable catalysts with high selectivity and high activity are needed to promote the electrocatalytic NRR. We screen a large number of clusters composed of three metal atoms embedded into a two-dimensional metal nitride, W₂N₃, with a N vacancy, and calculate the reaction energetics. The VNiCu cluster has the best catalytic activity among all the catalysts proposed so far. The Fe₃ and Fe₂Co clusters are excellent catalysts as well. In all cases, spin polarization is needed to observe the catalytic effect. We establish the optimal NRR path and confirm that it remains unchanged in the presence of a solvent. We find three groups of descriptors that can well predict the materials’ properties and exhibit linear relationships with the NRR limiting potential.

IF：

18.027

Link：

https://pubs.acs.org/doi/10.1021/acsnano.2c10607