arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2503.03427cond-mat.mtrl-sciphysics.chem-ph
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探索双铁原子催化剂以实现高效氮还原:关于结构与电子优化的综合研究

Exploring Dual-Iron Atomic Catalysts for Efficient Nitrogen Reduction: A Comprehensive Study on Structural and Electronic Optimization

Zhe Zhang, Wenxin Ma, Jiajie Qiao, Xiaoliang Wu, Shaowen Yu, Weiye Hou, Xiang Huang, Rubin Huo, Hongbo Wu, Yusong Tu

AI总结:

本研究提出基于双铁原子位点与氮硼共掺杂石墨烯的Fe2N3B@G催化剂,通过共掺杂优化电子环境降低反应能垒,结合MLMD模拟验证其高效、高选择性的氮还原性能及热稳定性,为原子催化剂设计提供理论指导。

摘要:

氮还原反应(NRR)作为合成氨的高效绿色途径,在实现按需产氨中发挥着至关重要的作用。本研究提出了一种基于双铁原子位点和氮硼共掺杂石墨烯催化剂的新颖设计概念,探索了其在NRR中的高效性。通过调节N和B的共掺杂比例,我们发现Fe2N3B@G催化剂在N2分子的吸附和加氢中表现出显著的活性,特别是在NRR远端路径上具有最低的自由能(0.32 eV),展现了其卓越的氮活化能力和NRR性能。计算得出的电子局域函数、晶体轨道哈密顿布居和静电势图表明,Fe2N3B@G催化剂NRR动力学的改善源于N3B共掺杂诱导的Fe-Fe电子环境优化、Fe-N键强度调节,以及在N2断裂和加氢过程中的持续电子支持。特别是,采用机器学习分子动力学(MLMD)模拟验证了Fe2N3B@G催化剂在NRR中的高活性,揭示了Fe2N3B@G能有效调节Fe-N键的电子密度,确保NH3分子的顺利生成与脱附,并避免了与析氢反应(HER)的竞争。此外,测定的Fe2N3B@G催化剂较高的HER过电位能有效抑制HER并增强对NRR的选择性。另外,通过高达500 K的MD模拟,表明Fe2N3B@G催化剂也具有良好的热稳定性,为其在实际应用中提供了可行性。本研究展示了Fe2N3B@G在氮还原催化中的优异性能,并通过共掺杂策略和深度电子环境调制为原子催化剂设计提供了理论指导。

英文摘要:

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 catalysts, exploring their high efficiency in NRR. By modulating the N and B co-doped ratios, we found that Fe2N3B@G catalyst exhibited significant activity in the adsorption and hydrogenation of N2 molecules, especially with the lowest free energy (0.32 eV) on NRR distal pathway, showing its excellent nitrogen activation capability and NRR performance. The computed electron localization function, crystal orbital Hamiltonian population, electrostatic potential map revealed that the improved NRR kinetics of Fe2N3B@G catalyst derived by N3B co-doping induced optimization of Fe-Fe electronic environment, regulation of Fe-N bond strength, and the continuous electronic support during the N2 breakage and hydrogenation. In particular, machine learning molecular dynamics (MLMD) simulations were employed to verify the high activity of Fe2N3B@G catalyst in NRR, which reveal that Fe2N3B@G effectively regulates the electron density of Fe-N bond, ensuring the smooth generation and desorption of NH3 molecules and avoiding the competition with hydrogen evolution reaction (HER). Furthermore, the determined higher HER overpotential of Fe2N3B@G catalyst can effectively inhibit the HER and enhance the selectivity toward NRR. In addition, 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 the co-doping strategy and in-deep electronic environment modulation.

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