Expanding the frontiers of electrocatalysis: advanced theoretical methods for water splitting

IF 13.4 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Seong Chan Cho, Jun Ho Seok, Hung Ngo Manh, Jae Hun Seol, Chi Ho Lee, Sang Uck Lee
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引用次数: 0

Abstract

Electrochemical water splitting, which encompasses the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER), offers a promising route for sustainable hydrogen production. The development of efficient and cost-effective electrocatalysts is crucial for advancing this technology, especially given the reliance on expensive transition metals, such as Pt and Ir, in traditional catalysts. This review highlights recent advances in the design and optimization of electrocatalysts, focusing on density functional theory (DFT) as a key tool for understanding and improving catalytic performance in the HER and OER. We begin by exploring DFT-based approaches for evaluating catalytic activity under both acidic and alkaline conditions. The review then shifts to a material-oriented perspective, showcasing key catalyst materials and the theoretical strategies employed to enhance their performance. In addition, we discuss scaling relationships that exist between binding energies and electronic structures through the use of charge-density analysis and d-band theory. Advanced concepts, such as the effects of adsorbate coverage, solvation, and applied potential on catalytic behavior, are also discussed. We finally focus on integrating machine learning (ML) with DFT to enable high-throughput screening and accelerate the discovery of novel water-splitting catalysts. This comprehensive review underscores the pivotal role that DFT plays in advancing electrocatalyst design and highlights its potential for shaping the future of sustainable hydrogen production.

Graphical Abstract

拓展电催化的前沿:水分解的先进理论方法。
电化学水分解包括析氢反应(HER)和析氧反应(OER),为可持续制氢提供了一条很有前途的途径。开发高效、低成本的电催化剂对于推进这项技术至关重要,特别是考虑到传统催化剂依赖昂贵的过渡金属,如Pt和Ir。本文综述了电催化剂设计和优化方面的最新进展,重点介绍了密度泛函理论(DFT)作为理解和提高HER和OER催化性能的关键工具。我们首先探索基于dft的方法来评估酸性和碱性条件下的催化活性。然后,回顾转向以材料为导向的角度,展示了关键的催化剂材料和用于提高其性能的理论策略。此外,我们通过使用电荷密度分析和d带理论讨论了结合能和电子结构之间存在的标度关系。还讨论了吸附质覆盖、溶剂化和应用潜力对催化行为的影响等先进概念。我们最后专注于将机器学习(ML)与DFT相结合,以实现高通量筛选并加速发现新型水分解催化剂。这项全面的综述强调了DFT在推进电催化剂设计方面的关键作用,并强调了其在塑造可持续氢生产未来方面的潜力。
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
Nano Convergence
Nano Convergence Engineering-General Engineering
CiteScore
15.90
自引率
2.60%
发文量
50
审稿时长
13 weeks
期刊介绍: Nano Convergence is an internationally recognized, peer-reviewed, and interdisciplinary journal designed to foster effective communication among scientists spanning diverse research areas closely aligned with nanoscience and nanotechnology. Dedicated to encouraging the convergence of technologies across the nano- to microscopic scale, the journal aims to unveil novel scientific domains and cultivate fresh research prospects. Operating on a single-blind peer-review system, Nano Convergence ensures transparency in the review process, with reviewers cognizant of authors' names and affiliations while maintaining anonymity in the feedback provided to authors.
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