Duan Tengfei, Wang Li, Ma Zhongyun, Pei Yong
Department of Chemistry, Key Laboratory of Environmentally Friendly Chemistry and Applicationics of Ministry of Education, Key Laboratory for Green Organic Synthesis and Application of Hunan Province, Xiangtan University, Xiangtan, Hunan, 411105, China.
The National Center for Applied Mathematics in Hunan, Xiangtan, 411105, China.
Small. 2023 Oct;19(42):e2303760. doi: 10.1002/smll.202303760. Epub 2023 Jun 20.
Single-atom catalysts are proven to be an effective strategy for suppressing shuttle effect at the source by accelerating the redox kinetics of intermediate polysulfides in lithium-sulfur (Li-S) batteries. However, only a few 3d transition metal single-atom catalysts (Ti, Fe, Co, Ni) are currently applied for sulfur reduction/oxidation reactions (SRR/SOR), which remains challenging for screening new efficient catalysts and understanding the relationship between structure-activity of catalysts. Herein, N-doped defective graphene (NG) supported 3d, 4d, and 5d transition metals are used as single-atom catalyst models to explore electrocatalytic SRR/SOR in Li-S batteries by using density functional theory calculations. The results show that M /NG (M = Ru, Rh, Ir, Os) exhibits lower free energy change of rate-determining step and Li S decomposition energy barrier, which significantly enhance the SRR and SOR activity compared to other single-atom catalysts. Furthermore, the study accurately predicts the by machine learning based on various descriptors and reveals the origin of the catalyst activity by analyzing the importance of the descriptors. This work provides great significance for understanding the relationships between the structure-activity of catalysts, and manifests that the employed machine learning approach is instructive for theoretical studies of single-atom catalytic reactions.
单原子催化剂已被证明是一种有效的策略，可通过加速锂硫（Li-S）电池中中间多硫化物的氧化还原动力学来抑制源端的穿梭效应。然而，目前仅有少数3d过渡金属单原子催化剂（Ti、Fe、Co、Ni）应用于硫还原/氧化反应（SRR/SOR），这在筛选新型高效催化剂以及理解催化剂结构-活性之间的关系方面仍然具有挑战性。在此，以氮掺杂缺陷石墨烯（NG）负载的3d、4d和5d过渡金属作为单原子催化剂模型，通过密度泛函理论计算来探索Li-S电池中的电催化SRR/SOR。结果表明，M/NG（M = Ru、Rh、Ir、Os）表现出较低的决速步自由能变化和Li-S分解能垒，与其他单原子催化剂相比，显著增强了SRR和SOR活性。此外，该研究基于各种描述符通过机器学习准确预测了 ，并通过分析描述符的重要性揭示了催化剂活性的起源。这项工作对于理解催化剂结构-活性之间的关系具有重要意义，并表明所采用的机器学习方法对单原子催化反应的理论研究具有指导意义。
