First principles and machine learning based superior catalytic activities and selectivities for N₂reduction in MBenes, defective 2D materials and 2D π-conjugated polymer-supported single atom catalysts

Production of ammonia through an electrochemical process suffers from two challenging issues, namely low catalytic activity and low Faraday efficiency. Here, we boost the N₂reduction to NH₃, while inhibiting the hydrogen evolution reaction (HER) using novel two-dimensional (2D) transition metal borides (MBenes), defect-engineered 2D-materials, and 2D π-conjugated polymer (2DCP)-supported single-atom catalysts (SACs). Density functional theory (DFT) calculations show that nitrogen molecules can be captured in the hollow sites of MBenes, with significant increases in the adsorption strength and N-N bond length. Also, defective 2D-materials formed by the vacancy sites of Te, Se and S expose N₂molecules to a specific environment adjacent to three transition metals, which drastically improves the catalytic activity and selectivity (by dramatic increase in the N-N bond length up to 1.38 Å). We report a new mechanism for the nitrogen reduction reaction (NRR) as a combination of dissociative and associative mechanisms. A machine-learning (ML) based fast-screening strategy to predict efficient NRR electrocatalysts is described. Overall, TaB, NbTe₂, NbB, HfTe₂, MoB, MnB, HfSe₂, TaSe₂and Nb@SAC exhibit impressive selectivities over HER with overpotentials of 0.44 V, 0.40 V, 0.24 V, 0.60 V, 0.17 V, 0.17 V, 0.64 V, 0.37 V and 0.58 V, respectively. This study opens a new doorway to overcome previous drawbacks of 2D-materials for NRR.