No Time for Nitrides: How Cobalt Alloying Promotes Iron Catalysts for Ammonia Decomposition

IF 13.1 1区 化学 Q1 CHEMISTRY, PHYSICAL
Simone Perego, , , Maximilian Purcel, , , Yannick Baum, , , Shilong Chen*, , , Astrid Sophie Müller, , , Michele Parrinello, , , Malte Behrens, , , Martin Muhler, , and , Luigi Bonati*, 
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Abstract

The increasing demand for hydrogen production has driven interest in ammonia decomposition. Iron-based catalysts, widely used for ammonia synthesis, exhibit suboptimal performance in the reverse process due to their tendency to form iron nitrides. Recent experiments have shown that alloying iron with cobalt enhances the catalytic activity (Chen et al., Nat. Commun. 15, 871, 2024), yet the microscopic origin of this promotional effect is not fully understood. To address this, we leverage recent developments in machine learning-based molecular dynamics simulations to investigate the key reactions of the catalytic cycle, fully accounting for dynamical lateral interactions on the catalyst surface. Our simulations reveal that cobalt alloying provides a dual promotional effect: it slightly lowers the free energy barrier for nitrogen recombination, which is the rate-determining step for ammonia decomposition on iron, while significantly suppressing nitrogen migration into the bulk, thereby preventing nitride formation. These insights are supported by complementary transient decomposition experiments and desorption measurements, which confirm the enhanced activity and resistance to nitridation in FeCo alloys compared to monometallic iron catalysts. Furthermore, long-term stability tests demonstrate that the FeCo catalyst sustains high ammonia conversion over extended time scales. By capturing the complex interplay of competing dynamical processes at the atomic scale, our results highlight the importance of going beyond static structure–property relationships to gain mechanistic insights that can guide the rational design of more robust and efficient catalysts.

Abstract Image

没有时间氮化物:钴合金化如何促进铁催化剂分解氨
对氢气生产的需求不断增加,推动了对氨分解的兴趣。广泛用于氨合成的铁基催化剂,由于其倾向于形成氮化铁,在逆向过程中表现出不理想的性能。最近的实验表明,将铁与钴合金化可以增强催化活性(Chen et al., Nat. common . 15,871, 2024),但这种促进作用的微观起源尚未完全了解。为了解决这个问题,我们利用基于机器学习的分子动力学模拟的最新发展来研究催化循环的关键反应,充分考虑催化剂表面的动态横向相互作用。我们的模拟表明,钴合金提供了双重促进作用:它略微降低了氮复合的自由能垒,这是铁上氨分解的速率决定步骤,同时显著抑制氮向体的迁移,从而防止氮化物的形成。这些见解得到了补充瞬态分解实验和解吸测量的支持,证实了与单金属铁催化剂相比,FeCo合金的活性和抗氮化能力增强。此外,长期稳定性测试表明,FeCo催化剂在延长的时间尺度上保持高氨转化率。通过在原子尺度上捕捉相互竞争的动态过程的复杂相互作用,我们的研究结果强调了超越静态结构-性质关系以获得机制见解的重要性,这可以指导更强大和高效催化剂的合理设计。
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来源期刊
ACS Catalysis
ACS Catalysis CHEMISTRY, PHYSICAL-
CiteScore
20.80
自引率
6.20%
发文量
1253
审稿时长
1.5 months
期刊介绍: ACS Catalysis is an esteemed journal that publishes original research in the fields of heterogeneous catalysis, molecular catalysis, and biocatalysis. It offers broad coverage across diverse areas such as life sciences, organometallics and synthesis, photochemistry and electrochemistry, drug discovery and synthesis, materials science, environmental protection, polymer discovery and synthesis, and energy and fuels. The scope of the journal is to showcase innovative work in various aspects of catalysis. This includes new reactions and novel synthetic approaches utilizing known catalysts, the discovery or modification of new catalysts, elucidation of catalytic mechanisms through cutting-edge investigations, practical enhancements of existing processes, as well as conceptual advances in the field. Contributions to ACS Catalysis can encompass both experimental and theoretical research focused on catalytic molecules, macromolecules, and materials that exhibit catalytic turnover.
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