高稳定、高活性的单原子修饰S - MXene锂电池阴极催化剂的合理设计

IF 27.4 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Junwei Sun, Rui Yu, Dominik Legut, Joseph S. Francisco, Ruifeng Zhang
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引用次数: 0

摘要

锂硫电池的实际应用受到穿梭效应和硫转化动力学缓慢的阻碍。为了解决这些挑战,本研究提出了一种有效的策略,即将单原子(SAs)引入硫功能化MXenes (S - MXenes)催化剂中,并通过第一性原理计算评估其在Li - S电池中的潜力。通过对各种SA修饰的S - MXenes进行高通量筛选,本研究确定了73种有希望的候选材料,它们具有优异的热力学和动力学稳定性,以及有效的多硫化物固定化。值得注意的是,将Ni、Cu或Zn作为SAs加入到S - MXenes中,可以显著降低吉布斯自由能垒51%-75%,优于石墨烯基催化剂。这种还原是由于SA诱导的表面电子密度影响了中间体的吸附能,从而破坏了Li₂S₂与其他关键中间体之间的结垢关系。进一步提高催化性能是通过应变工程实现的,通过将金属原子的d波段中心转移到更高的能级,增加对中间体的化学亲和力。为了阐明中间体的内在吸附特性,本工作建立了一个高精度(R2 = 0.88)的机器学习模型,该模型强调了SA电负性和局部配位环境在决定吸附强度中的关键作用,为催化剂的合理设计提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rational Design of Highly Stable and Active Single‐Atom Modified S‐MXene as Cathode Catalysts for Li‐S Batteries
The practical application of Li‐S batteries is hindered by the shuttle effect and sluggish sulfur conversion kinetics. To address these challenges, this work proposes an efficient strategy by introducing single atoms (SAs) into sulfur‐functionalized MXenes (S‐MXenes) catalysts and evaluate their potential in Li‐S batteries through first‐principles calculations. Using high‐throughput screening of various SA‐modified S‐MXenes, this work identifies 73 promising candidates that exhibit exceptional thermodynamic and kinetic stability, along with the effective immobilization of polysulfides. Notably, the incorporation of Ni, Cu, or Zn as SAs into S‐MXenes results in a significant Gibbs free energy barrier reduction by 51%–75%, outperforming graphene‐based catalysts. This reduction arises from SA‐induced surface electron density that influences the adsorption energies of intermediates and thereby disrupts the scaling relations between Li₂S₂ and other key intermediates. Further enhancement in catalytic performance is achieved through strain engineering by shifting the d‐band center of metal atoms to higher energy levels, increasing the chemical affinity for intermediates. To elucidate the intrinsic adsorption properties of intermediates, this work develops a machine learning model with high accuracy (R2 = 0.88), which underscores the pivotal roles of SA electronegativity and local coordination environment in determining adsorption strength, offering valuable insights for the rational design of catalysts.
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来源期刊
Advanced Materials
Advanced Materials 工程技术-材料科学:综合
CiteScore
43.00
自引率
4.10%
发文量
2182
审稿时长
2 months
期刊介绍: Advanced Materials, one of the world's most prestigious journals and the foundation of the Advanced portfolio, is the home of choice for best-in-class materials science for more than 30 years. Following this fast-growing and interdisciplinary field, we are considering and publishing the most important discoveries on any and all materials from materials scientists, chemists, physicists, engineers as well as health and life scientists and bringing you the latest results and trends in modern materials-related research every week.
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