设计多种无贵金属析氢催化剂

IF 2.9 Q2 ELECTROCHEMISTRY
Wissam A. Saidi, Tarak Nandi, Timothy Yang
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引用次数: 6

摘要

氢进化反应(HER)是电催化制氢的关键反应,因其简单性而具有根本性的重要意义,但对可再生能源却非常重要。尽管如此,铂仍是该反应的主要催化剂,但由于铂的高成本和稀缺性,该技术的工业应用并不现实。高熵合金(HEA)纳米粒子的成功合成为新型催化剂的开发开辟了新的领域。在此,我们研究了基于地球富集元素 Co、Mo、Fe、Ni 和 Cu 的不含二元贵金属的 HER 催化剂的设计。利用机器学习(ML)方法和第一原理方法,我们建立了一个模型,该模型可以快速、高保真地计算合金表面的氢吸附能。在 CoMoFeNiCu HEA 的巨大成分空间内,大量合金组合被证明能以很高的概率优化氢结合。此外,由于混合熵与混合焓相比较大,且元素间的晶格失配较小,因此发现这些合金组合中的大多数在解离成金属间化合物时是稳定的,因而可合成为固溶体。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吻合。我们的研究强调了计算建模和 ML 对在几乎无限的 HEA 材料设计空间中开发新的经济高效的电催化剂的重要影响,并呼吁进行实验验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Designing multinary noble metal-free catalyst for hydrogen evolution reaction

Designing multinary noble metal-free catalyst for hydrogen evolution reaction

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.

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CiteScore
3.80
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