Sun Junwei, Yu Rui, Legut Dominik, Francisco Joseph S, Zhang Ruifeng
School of Materials Science and Engineering, Beihang University, Beijing, 100191, P. R. China.
Center for Integrated Computational Materials Engineering (International Research Institute for Multidisciplinary Science) and Key Laboratory of High-Temperature Structural Materials & Coatings Technology (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, P. R. China.
Adv Mater. 2025 Jul;37(28):e2501523. doi: 10.1002/adma.202501523. Epub 2025 May 6.
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 (R = 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.
锂硫电池的实际应用受到穿梭效应和缓慢的硫转化动力学的阻碍。为应对这些挑战,本工作提出了一种有效策略,即将单原子(SAs)引入硫官能化的MXenes(S-MXenes)催化剂中,并通过第一性原理计算评估它们在锂硫电池中的潜力。通过对各种SA修饰的S-MXenes进行高通量筛选,本工作确定了73种有前景的候选物,它们表现出优异的热力学和动力学稳定性,以及对多硫化物的有效固定。值得注意的是,将Ni、Cu或Zn作为SAs掺入S-MXenes中会导致吉布斯自由能垒显著降低51%-75%,优于基于石墨烯的催化剂。这种降低源于SA诱导的表面电子密度,它影响中间体的吸附能,从而破坏了Li₂S₂与其他关键中间体之间的标度关系。通过应变工程将金属原子的d带中心移至更高能级,增加对中间体的化学亲和力,从而进一步提高催化性能。为了阐明中间体的固有吸附特性,本工作开发了一个高精度的机器学习模型(R = 0.88),该模型强调了SA电负性和局部配位环境在确定吸附强度方面的关键作用,为催化剂的合理设计提供了有价值的见解。