Design and screening of single transition-metal atom anchored on G/BP heterostructure as NORR electrocatalysts: A DFT...

Electrocatalytic NO reduction reaction (NORR) enables the conversion of hazardous NO gases into valuable NH₃ under ambient conditions. However, due to the slow reaction kinetics and low Faraday efficiency of NORR, it is essential to explore active and selective catalysts to promote NORR enhancement. Herein, a series of transition-metal atoms anchored on the graphene and blue phosphorus (TM-G/BP) heterostructures as efficient NORR catalysts are systematically examined using Density Functional Theory (DFT) calculations. Consequently, the Fe-G/BP and Rh-G/BP heterostructures were screened as effective NORR electrocatalysts for their high activity and selectivity with U_L of 0.31 and 0.46 V, respectively. We also investigated the origin of NORR activity. The electronic analysis reveals that Fe-3d and Rh-4d orbitals can be well hybridized with the NO-2p orbitals, sufficiently activating the adsorbed NO molecule. In addition, SISSO as a machine learning approach is utilized to construct descriptors of U_L that considers properties of TM atoms, coordination environment, changes in key step free energy to predict activity. The finally constructed descriptor exhibited a high correlation coefficient of 0.90 and a mean squared error as low as 0.06. Our work not only identifies promising NORR catalysts in TM-G/BP heterostructures but also provides valuable insights for the mechanism of NO-to-NH₃ conversion.