MOF‐Derived High‐Entropy Biphasic Sulfides for Zn‐I <sub>2</sub> ‐S Synergistic Batteries with Machine‐Learning‐Assisted Prediction and In Situ Synchrotron Spectroscopy

The practical application of aqueous zinc batteries remains constrained by the intrinsic limitations of monocomponent systems, such as the shuttle effect in Zn-I<sub>2</sub> batteries and the sluggish kinetics in Zn─S batteries. To overcome these limitations, we developed a Zn-S-I<sub>2</sub> synergistic battery that synergistically exploits the complementary advantages of dual redox couples. A bifunctional high-entropy sulfide (HES) catalyst anchored on N-doped carbon nanosheets was engineered via a surfactant-assisted metal-organic framework (MOF) derivatization route. The entropy-stabilized HES forms a unique wurtzite/zincblende biphasic structure, creating abundant grain boundaries and synergistic active sites. Electrochemical studies demonstrate that the synergistic battery exhibits outstanding electrochemical performance and cycling stability. Machine learning (ML) models were established to predict electrochemical behavior under various operating conditions. Operando synchrotron-based spectroscopy combined with theoretical calculations revealed that the biphasic HES synergistically catalyzes multi-step conversion reactions, simultaneously lowering energy barriers and suppressing shuttle effects. This work offers new insights into high-entropy electrocatalysts for complex multi-step energy storage reactions.

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Publication Details

Journal
Advanced Materials
Published
2026-01-02
DOI
https://doi.org/10.1002/adma.202523371
Citations
5
Primary Topic
Advanced battery technologies research
Type
article
Field-Weighted Citation Impact
109.66

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article

MOF‐Derived High‐Entropy Biphasic Sulfides for Zn‐I 2 ‐S Synergistic Batteries with Machine‐Learning‐Assisted Prediction and In Situ Synchrotron Spectroscopy

Jin Yan, Shuai Cao, Mohsen Shakouri, Xinling Lv et al.
5 citations
Advanced Materials
Advanced battery technologies research
109.66
article

MOF‐Derived High‐Entropy Biphasic Sulfides for Zn‐I 2 ‐S Synergistic Batteries with Machine‐Learning‐Assisted Prediction and In Situ Synchrotron Spectroscopy

Jin Yan, Shuai Cao, Mohsen Shakouri, Xinling Lv, Yecan Pi, Wanchang Feng, Xinyu Qin, Jianfei Huang, Bin He, Zheng Liu, Xingye Lu, Guoqiang Yuan, Ziming Qiu, Huan Pang
article en
5 citations

Abstract

The practical application of aqueous zinc batteries remains constrained by the intrinsic limitations of monocomponent systems, such as the shuttle effect in Zn-I<sub>2</sub> batteries and the sluggish kinetics in Zn─S batteries. To overcome these limitations, we developed a Zn-S-I<sub>2</sub> synergistic battery that synergistically exploits the complementary advantages of dual redox couples. A bifunctional high-entropy sulfide (HES) catalyst anchored on N-doped carbon nanosheets was engineered via a surfactant-assisted metal-organic framework (MOF) derivatization route. The entropy-stabilized HES forms a unique wurtzite/zincblende biphasic structure, creating abundant grain boundaries and synergistic active sites. Electrochemical studies demonstrate that the synergistic battery exhibits outstanding electrochemical performance and cycling stability. Machine learning (ML) models were established to predict electrochemical behavior under various operating conditions. Operando synchrotron-based spectroscopy combined with theoretical calculations revealed that the biphasic HES synergistically catalyzes multi-step conversion reactions, simultaneously lowering energy barriers and suppressing shuttle effects. This work offers new insights into high-entropy electrocatalysts for complex multi-step energy storage reactions.

Advanced MaterialsVol. 38(11)
Chongqing University of Science and Technology (CN), Huzhou Normal University (CN), Nanjing Xiaozhuang University (CN), Yangzhou University (CN), Canadian Light Source (Canada) (CA)
National Natural Science Foundation of China, Government of Jiangsu Province, Yangzhou University, Basic Research Program of Jiangsu Province
Affordable and clean energy
Openalex Percentile: Top 0%
Advanced battery technologies research
109.66
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