Ten-Electron Count Rule of MXene-Supported Single-Atom Catalysts for Sulfur Reduction in Lithium–Sulfur Batteries

Lujie Jin, Yujin Ji, Youyong Li
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Abstract

Lithium–sulfur (Li–S) batteries are proposed as next-generation energy storage devices due to their high theoretical capacity and specific energy. However, the actual capacity utilization is greatly limited by the poor reactivity of the sulfur reduction reaction (SRR), which motivates us to develop corresponding high-efficient catalysts. Inspired by the application of MXene and single-atom catalysts (SACs) in improving SRR, a virtual screening on the MXene-supported SACs from the imp2d database is carried out. Finally, six kinds of top catalysts are identified for SRR, and most of them can be considered as variants of the previous representative SRR catalysts, which reflects the rationality of our screening. Meanwhile, the stability and reactivity metrics of the SACs are calculated by density functional theory (DFT) and show obvious trends depending on the type of adatom/MXene. For the critical intermediate binding that can tune SRR activity, further electronic structure analysis reveals the so-called 10-electron count rule, whose decisive role is also reflected by the Shapley value analysis from machine learning (ML). It is noteworthy that this count rule was used to analyze the SACs for hydrogen/carbon/nitrogen-related reactions before, and our successful attempt to optimize SRR further indicates its universality in catalysis fields. Overall, the 10-electron count rule not only rationalizes the nature of SAC–adsorbate interactions but also provides intuitive design guidance for novel SRR catalysts.

Abstract Image

mxene负载单原子催化剂在锂硫电池中硫还原中的十电子计数规律
锂硫电池由于具有较高的理论容量和比能量,被提出作为下一代储能设备。然而,硫还原反应(SRR)的反应活性较差,极大地限制了实际的产能利用率,这促使我们开发相应的高效催化剂。受MXene和单原子催化剂(SACs)在提高SRR方面应用的启发,从imp2d数据库中对MXene支持的SACs进行了虚拟筛选。最后确定了6种最佳SRR催化剂,其中大部分都可以看作是之前代表性SRR催化剂的变体,这反映了我们筛选的合理性。同时,用密度泛函理论(DFT)计算了SACs的稳定性和反应性指标,结果表明,随着adatom/MXene的类型的不同,SACs的稳定性和反应性指标有明显的变化趋势。对于调节SRR活性的关键中间结合,进一步的电子结构分析揭示了所谓的10电子计数规则,其决定性作用也反映在机器学习(ML)的Shapley值分析中。值得注意的是,该计数规则之前曾用于分析氢/碳/氮相关反应的sac,我们对SRR优化的成功尝试进一步表明了其在催化领域的普遍性。总的来说,10电子计数规则不仅使sac -吸附物相互作用的性质合理化,而且为新型SRR催化剂的设计提供了直观的指导。
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