| 论文简介 |
Electrocatalytic nitrogen reduction reaction (NRR) is an efficient and green way to produce ammonia, which offers an alternative option to the conventional Haber-Bosch process. Unfortunately, the large-scale industrial application of NRR processes is still hindered by poor Faraday efficiency and high overpotential, which need to be overcome urgently. Herein, combined with density functional theory and particle swarm optimization algorithm for the nitrogen carbide monolayer structural search (CmN8-m, m = 1-7), the surprising discovery is that single transition metal-atom-doped C4N4 monolayers (TM@C4N4) could effectively accelerate nitrogen reduction reaction. TM@C4N4 (TM = 29 transition metals) as single-atom catalysts are evaluated via traditional multi-step screening method, and their structures, NRR activity, selectivity and solvation effect are investigated to evaluate their NRR performance. Through the screening steps, W@C4N4 possesses the highest activity for NRR with a very low limiting potential of -0.29 V. Moreover, an intrinsic descriptor u is proposed with machine learning, which shortens the screening process and provides a new idea for finding efficient SACs. This work not only offers promising catalysts W@C4N4 for NRR process but also offers a new intrinsic and universal descriptor |
(创新港)
