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Preferable single-atom catalysts enabled by natural language processing for high energy density Na-S batteries

Author

Listed:
  • Ruilin Bai

    (University of Science and Technology of China)

  • Yu Yao

    (University of Science and Technology of China)

  • Qiaosong Lin

    (University of Chinese Academy of Sciences)

  • Lize Wu

    (Zhejiang University School of Medicine)

  • Zhen Li

    (University of Science and Technology of China)

  • Huijuan Wang

    (University of Science and Technology of China)

  • Mingze Ma

    (University of Science and Technology of China)

  • Di Mu

    (Shandong University)

  • Lingxiang Hu

    (Université Paris-Saclay)

  • Hai Yang

    (University of Science and Technology of China)

  • Weihan Li

    (Western University)

  • Shaolong Zhu

    (University of Science and Technology of China)

  • Xiaojun Wu

    (University of Science and Technology of China)

  • Xianhong Rui

    (Guangdong University of Technology)

  • Yan Yu

    (University of Science and Technology of China)

Abstract

Employing appropriate single-atom (SA) catalysts in room-temperature sodium-sulfur (Na-S) batteries is propitious to promote the performance, whereas a universal designing strategy for the highly-efficient single-atom catalysts is absent. In this work, we adopt natural language processing techniques to screen the potential single-atom catalysts, then a binary descriptor is constructed to optimize the catalyst candidates. Atomically dispersed cobalt anchored to both nitrogen and sulfur atoms (SA Co-N/S) is selected as an ideal catalyst to significantly facilitate sulfur reduction reaction. The sulfur cathode catalyzed with SA Co-N/S almost realizes complete transformation, and the corresponding pouch cell exhibits satisfactory performance with high mass loading. In-situ X-ray absorption spectroscopy reveals the dynamical interactions between SA Co-N/S and sulfur species in the sulfur reduction reaction. Our work provides a method to select the preferable SA catalyst and to understand the interfacial catalysis dynamics in the sustainable Na-S systems.

Suggested Citation

  • Ruilin Bai & Yu Yao & Qiaosong Lin & Lize Wu & Zhen Li & Huijuan Wang & Mingze Ma & Di Mu & Lingxiang Hu & Hai Yang & Weihan Li & Shaolong Zhu & Xiaojun Wu & Xianhong Rui & Yan Yu, 2025. "Preferable single-atom catalysts enabled by natural language processing for high energy density Na-S batteries," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-60931-x
    DOI: 10.1038/s41467-025-60931-x
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    References listed on IDEAS

    as
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