Exploring dual-iron atomic catalysts for efficient nitrogen reduction: a comprehensive study on structural and electronic optimization

The nitrogen reduction reaction (NRR), as an efficient and green pathway for ammonia synthesis, plays a crucial role in achieving on-demand ammonia production. This study proposes a novel design concept based on dual-iron atomic sites and nitrogen-boron co-doped graphene (Fe₂N_xB_y@G) catalysts, exploring their high efficiency in the NRR. By modulating the N_xB_y co-doped ratios, we found that the Fe₂N₃B@G catalyst exhibited significant activity in the adsorption and hydrogenation of N₂ molecules, especially with the lowest free energy (0.32 eV) in the NRR distal pathway, showing its excellent nitrogen activation capability and NRR performance. The computed electron localization function, crystal orbital Hamiltonian population, and the electrostatic potential map revealed that the improved NRR kinetics of the Fe₂N₃B@G catalyst derived by N₃B co-doping induced optimization of the Fe-Fe electronic environment, regulation of Fe-N bond strength, and continuous electronic support during N₂ breakage and hydrogenation. In particular, machine learning molecular dynamics (MLMD) simulations were employed to verify the high activity of the Fe₂N₃B@G catalyst in the NRR, which revealed that Fe₂N₃B@G effectively regulates the electron density of the Fe-N bond, ensuring the smooth generation and desorption of NH₃ molecules and avoiding the competition with the hydrogen evolution reaction (HER). Furthermore, the determined higher HER overpotential of the Fe₂N₃B@G catalyst can effectively inhibit the HER and enhance the selectivity toward the NRR. In addition, the Fe₂N₃B@G catalyst also showed good thermal stability by MD simulations up to 500 K, offering its feasibility in practical applications. This study demonstrates the superior performance of Fe₂N₃B@G in nitrogen reduction catalysis and provides theoretical guidance for atomic catalyst design by a co-doping strategy and in-depth electronic environment modulation.