Transition Metal Anchored Novel Holey Boron Nitride Analogues as Single-Atom Catalysts for the Hydrogen Evolution Reaction.

IF 3.5 3区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Esackraj Karthikraja, Chandra Chowdhury, Naga Venkateswara Rao Nulakani, Kothandaraman Ramanujam, V G Vaidyanathan, Venkatesan Subramanian
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引用次数: 0

Abstract

The increasing global energy demand and environmental pollution necessitate the development of alternative, sustainable energy sources. Hydrogen production through electrochemical methods offers a carbon-free energy solution. In this study, we have designed novel boron nitride analogues (BNyne) and investigated their stability and electronic properties. Furthermore, the incorporation of transition metals (TM) at holey sites in these analogues was explored, revealing their potential as promising electrocatalysts for the hydrogen evolution reaction (HER). The inclusion of transition metals significantly enhances their structural stability and electronic properties. The TM-anchored BNynes exhibit optimal Gibbs free energy changes (ΔGH) for effective HER performance. Additionally, the favorable alignment of d-band centers near the Fermi level supports efficient hydrogen adsorption. Machine learning models, particularly the Random Forest model, have also been employed to predict ΔGH values with high accuracy, capturing the complex relationships between material properties and HER efficiency. This dual approach underscores the importance of integrating advanced computational techniques with material design to accelerate the discovery of effective HER catalysts. Our findings highlight the potential of these tailored boron nitride analogues to enhance electrocatalytic applications and improve HER efficiency.

过渡金属锚定的新型 Holey 氮化硼类似物作为氢气进化反应的单原子催化剂。
全球能源需求不断增长,环境污染日益严重,因此有必要开发可持续的替代能源。通过电化学方法制氢提供了一种无碳能源解决方案。在这项研究中,我们设计了新型氮化硼类似物(BNyne),并研究了它们的稳定性和电子特性。此外,我们还探讨了在这些类似物的空穴位点加入过渡金属(TM)的问题,从而揭示了它们作为氢进化反应(HER)电催化剂的潜力。过渡金属的加入大大增强了它们的结构稳定性和电子特性。TM-anchored BNynes 表现出最佳的吉布斯自由能变化(ΔGH),从而实现了有效的 HER 性能。此外,费米级附近 d 带中心的有利排列还支持高效的氢吸附。机器学习模型,尤其是随机森林模型,也被用于高精度预测 ΔGH 值,以捕捉材料特性与 HER 效率之间的复杂关系。这种双重方法强调了将先进计算技术与材料设计相结合以加速发现有效 HER 催化剂的重要性。我们的研究结果凸显了这些量身定制的氮化硼类似物在增强电催化应用和提高 HER 效率方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Chemistry - An Asian Journal
Chemistry - An Asian Journal 化学-化学综合
CiteScore
7.00
自引率
2.40%
发文量
535
审稿时长
1.3 months
期刊介绍: Chemistry—An Asian Journal is an international high-impact journal for chemistry in its broadest sense. The journal covers all aspects of chemistry from biochemistry through organic and inorganic chemistry to physical chemistry, including interdisciplinary topics. Chemistry—An Asian Journal publishes Full Papers, Communications, and Focus Reviews. A professional editorial team headed by Dr. Theresa Kueckmann and an Editorial Board (headed by Professor Susumu Kitagawa) ensure the highest quality of the peer-review process, the contents and the production of the journal. Chemistry—An Asian Journal is published on behalf of the Asian Chemical Editorial Society (ACES), an association of numerous Asian chemical societies, and supported by the Gesellschaft Deutscher Chemiker (GDCh, German Chemical Society), ChemPubSoc Europe, and the Federation of Asian Chemical Societies (FACS).
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