Designing multinary noble metal‐free catalyst for hydrogen evolution reaction
Designing multinary noble metal‐free catalyst for hydrogen evolution reaction
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DOI：
10.1002/elsa.202100224
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发表时间：
2022-08
影响因子：
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通讯作者：
W. Saidi;Tarak N. Nandi;Timothy T. Yang
中科院分区：
文献类型：
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作者：
W. Saidi;Tarak N. Nandi;Timothy T. Yang
析氢反应（HER）是电催化制氢的关键反应，因其简单而具有基础性作用，同时对可再生能源也具有重要意义。尽管如此，Pt仍然是该反应的主要催化剂，由于Pt的高成本和稀缺性，这对于该技术的工业部署是不现实的。高熵合金（HEA）纳米颗粒的成功合成为新型催化剂的开发开辟了新的前沿。本文研究了基于稀土元素Co， Mo, Fe， Ni和Cu的多贵金属无HER催化剂的设计。使用机器学习（ML）方法结合第一性原理方法，我们建立了一个模型，可以快速计算高保真合金表面上的氢吸附能。在CoMoFeNiCu HEA的大组成空间内，大量的合金组合以高概率最佳结合氢。此外，这些合金成分中的大多数都是稳定的，不会分解成金属间化合物，因此可以作为固溶体合成，这是由于混合熵比混合焓大，元素之间的晶格不匹配小。这一发现与最近合成五种不同的CoMoFeNiCu HEA成分的实验结果部分一致。我们的研究强调了计算建模和机器学习可以在几乎无限的HEAs材料设计空间中开发新的具有成本效益的电催化剂，并需要实验验证。
The hydrogen evolution reaction (HER), the key reaction for electrocatalytic production of hydrogen, is of fundamental importance due to its simplicity yet is very important for renewable energy. Notwithstanding, Pt is still the main catalyst for this reaction, which is not practical for the industrial deployment of this technology owing to the high cost and scarcity of Pt. The successful synthesis of high entropy alloy (HEA) nanoparticles opens a new frontier for the development of new catalysts. Herein we investigate the design of a multinary noble metal‐free HER catalyst based on earth‐abundant elements Co, Mo, Fe, Ni, and Cu. Using a machine learning (ML) approach in conjunction with first‐principles methods, we build a model that can rapidly compute the hydrogen adsorption energy on the alloyed surfaces with high fidelity. Within the large composition space of the CoMoFeNiCu HEA, a large number of alloy combinations are shown to optimally bind hydrogen with a high probability. Further, most of these alloy compositions are found stable against dissociation into intermetallics, and hence synthesizable as a solid solution, by virtue of a large mixing entropy compared to mixing enthalpy and a small lattice mismatch between the elements. This finding is partly consistent with recent experimental results that synthesized five different CoMoFeNiCu HEA compositions. Our study underscores the significant impact that computational modeling and ML can have on developing new cost‐effective electrocatalysts in the nearly‐infinite materials design space of HEAs, and calls for experimental validation.