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锂硫电池中增强锚定和催化性能的轴向不对称机制的新见解。

New insights into axially asymmetric mechanism for enhanced anchoring and catalytic performance in lithium-sulfur batteries.

作者信息

Zhang Wenxue, Wang Shirui, He Cheng

机构信息

School of Materials Science and Engineering, Chang'an University, Xi'an 710064, China.

State Key Laboratory for Mechanical Behavior of Materials, School of Materials Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

出版信息

J Colloid Interface Sci. 2025 Dec 15;700(Pt 1):138338. doi: 10.1016/j.jcis.2025.138338. Epub 2025 Jul 5.

Abstract

Lithium‑sulfur batteries (LSBs) are extensively considered as a leading candidate for future-oriented energy storage and conversion systems, facing significant challenges in commercial application caused by the shuttle effect and the slow pace of redox kinetics. To address these challenges, the design of cathode materials with superior performance is essential. In this study, a systematic screening of axially asymmetric N-coordinated graphene-supported transition metal single-atom catalysts (SACs) is conducted using Density Functional Theory (DFT) for analyzing their anchoring and catalytic properties in lithium‑sulfur batteries. The research reveals that the axially asymmetric coordination structure breaks the limitations of traditional d-p orbital hybridization theory. Despite the presence of antibonding state occupation in the d-orbitals of Fe/Co, their unique "transfer-convergence" charge transfer mechanism and the synergistic catalytic effect of TM-N work together to regulate the d-orbitals, enabling the Fe-NC and Co-NC system to exhibit appropriate anchoring capability, efficient catalytic performance, and lower LiS decomposition barriers. Further analysis combined with machine learning (ML), identifies the d-band center (ε), electronegativity (χ), and charge transfer amount (ΔQ) as key features affecting anchoring and catalytic performance. The SISSO algorithm is used to construct descriptors for adsorption energy (E) and Gibbs free energy (ΔG), providing a feasible strategy for rapid screening and prediction of reasonable SACs. This method also provides novel perspectives for developing cathode materials in lithium‑sulfur batteries.

摘要

锂硫电池(LSBs)被广泛认为是面向未来的储能和转换系统的主要候选者,但由于穿梭效应和氧化还原动力学缓慢,在商业应用中面临重大挑战。为应对这些挑战,设计具有卓越性能的阴极材料至关重要。在本研究中,使用密度泛函理论(DFT)对轴向不对称的氮配位石墨烯负载的过渡金属单原子催化剂(SACs)进行了系统筛选,以分析它们在锂硫电池中的锚定和催化性能。研究表明,轴向不对称配位结构打破了传统d-p轨道杂化理论的限制。尽管Fe/Co的d轨道中存在反键态占据,但它们独特的“转移-收敛”电荷转移机制以及TM-N的协同催化作用共同调节d轨道,使Fe-NC和Co-NC体系表现出适当的锚定能力、高效的催化性能和较低的LiS分解势垒。结合机器学习(ML)的进一步分析确定d带中心(ε)、电负性(χ)和电荷转移量(ΔQ)是影响锚定和催化性能的关键特征。使用SISSO算法构建吸附能(E)和吉布斯自由能(ΔG)的描述符,为快速筛选和预测合理的SACs提供了一种可行的策略。该方法也为锂硫电池阴极材料的开发提供了新的视角。

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