Computational insights into amorphous Mo-based transition metal alloys for enhanced electrocatalytic N2 reduction

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Amorphous alloys offer significant potential for enhancing the electrocatalytic dinitrogen (N 2 ) reduction reaction (NRR) due to their lack of medium- and long-range order and the abundance of unsaturated metal sites on their surface compared to their crystalline counterparts. However, their structurally complex nature presents challenges to fully understanding and characterizing their catalytic behavior. This study presents a computational investigation, combining the use of machine learning forcefields to generate atomistic models of amorphous and crystalline Mo-based transition metal catalysts , TM 125- x Mo x (with x = 1,12 and 50 and TM = Ag, Cd, Co, Cu, Pd), and density functional theory calculations to explore the adsorption and activation of dinitrogen (N 2 (g)) and its electrochemical reduction to ammonia on these materials. The study suggests that amorphous TM-Mo surfaces, in contrast to their crystalline counterparts, offer a wider range of coordination sites, leading to a multitude of active centers for N 2 adsorption. Notably, some investigated surfaces spontaneously activate N 2 , demonstrating their potential for an efficient NRR. Dopants on the alloy surface and their concentration significantly affect the position of the d -band center and thus play a crucial role in the activation of N 2 . Furthermore, the intermediates of the NRR on these surfaces exhibit enhanced stability, translating to lower overpotentials and improved selectivity . This work lays the foundation for further research into amorphous TM-Mo electrocatalysts , advancing the development of sustainable NRR conversion technologies.