Machine learning-guided construction of MoS2/MoO3 heterostructures on hollow carbon shells for polysulfide mitigation in lithium-sulfur batteries.

Journal: Journal of colloid and interface science
Published Date: Aug 25, 2025

Abstract

The commercialization of lithium‑sulfur (LiS) batteries faces fundamental issues from polysulfide shuttling to inefficient redox kinetics, compounded by the absence of systematic methods for catalyst design. Herein, we present a machine learning (ML)-driven strategy to design MoS2/MoO3 heterostructures anchored on nitrogen-doped hollow carbon shells (NCS) via gradient boosting decision trees modeling. The ML-guided optimization identifies critical synthesis parameters (e.g., carbonization temperature, oxidation duration) to balance adsorption capacity and catalytic activity. The resulting heterostructure exhibits superior polysulfide confinement and accelerated conversion kinetics, enabled by MoO3-induced anchoring sites and NCS-accelerated electron/ion transport. The LiS batteries integrated with the MoS2/MoO3-NCS-modified separators deliver a remarkable initial capacity of 1002 mAh g-1 at 1C and remain 661 mAh g-1 after 500 cycles, with a low capacity decay rate of 0.068 %. Even at a high sulfur loading of 8.4 mg cm-2, the pouch cell maintains 7.25 mAh cm-2 areal capacity with 90.1 % retention after 100 cycles. This work establishes a paradigm for ML-accelerated electrocatalyst design, addressing critical challenges in LiS batteries and advancing scalable synthesis strategies for next-generation energy storage.

Authors

  • Shixian Chen
    Department of Rheumatology and Immunology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
  • Gaohui Du
    Materials Institute of Atomic and Molecular Science, Shaanxi University of Science and Technology, Xi'an 710021, China; Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering, Taiyuan 030024, China. Electronic address: [email protected].
  • Kaiting Hu
    Materials Institute of Atomic and Molecular Science, Shaanxi University of Science and Technology, Xi'an 710021, China.
  • Yunting Wang
    Materials Institute of Atomic and Molecular Science, Shaanxi University of Science and Technology, Xi'an 710021, China.
  • Chengcheng Feng
    College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
  • Yunyun Liu
    Department of Respiratory Medicine, Affiliated Hospital of Jiujiang University, No. 57 East Xunyang Road, Xunyang District, Jiujiang, 332000, China.
  • Yan Cheng
    The First Clinical Medical College of Shaanxi University of Chinese Medicine, Xianyang, China.
  • Jinchao Cui
    Materials Institute of Atomic and Molecular Science, Shaanxi University of Science and Technology, Xi'an 710021, China.
  • Di Han
  • Huayu Li
    Department of Electrical and Computer Engineering, University of Arizona, Tucson.
  • Libing Yao
    School of Engineering, Westlake University and Institute of Advanced Technology, Westlake Institute for Advanced Study, Hangzhou 310024, China.
  • Dong Wang
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Qingmei Su
    Materials Institute of Atomic and Molecular Science, Shaanxi University of Science and Technology, Xi'an 710021, China. Electronic address: [email protected].

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