Exploring dual-iron atomic catalysts for efficient nitrogen reduction: a comprehensive study on structural and electronic optimization†
Abstract
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 (Fe2NxBy@G) catalysts, exploring their high efficiency in the NRR. By modulating the NxBy 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.