Advancing NASICON-type materials through high-entropy strategy: Synthesis and applications
High-entropy materials (HEMs) have emerged as promising frontiers in electrochemical energy storage systems because of their unique compositional versatility and tunable physicochemical properties. By incorporating multiple principal elements with distinct chemical functionalities, HEMs exhibit tail...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2025-05-01
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| Series: | Journal of Advanced Ceramics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/JAC.2025.9221079 |
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| Summary: | High-entropy materials (HEMs) have emerged as promising frontiers in electrochemical energy storage systems because of their unique compositional versatility and tunable physicochemical properties. By incorporating multiple principal elements with distinct chemical functionalities, HEMs exhibit tailored electronic/ionic configurations, enabling unprecedented structural adaptability and application potential. This review systematically analyzes the fundamental principles underpinning the entropy-driven optimization of the electrochemical performance of battery materials, with a focus on the interplay between compositional disorder and functional enhancements. For the first time, we comprehensively review recent advances in Na superionic conductor (NASICON)-type HEMs spanning cathodes, solid-state electrolytes, and anodes. Through investigations, the profound impacts of high-entropy strategies on critical material parameters, including lattice strain modulation, interfacial stability reinforcement, charge-transfer kinetics optimization, and ion transport pathway regulation, were elucidated. Furthermore, we evaluate the current challenges in high-entropy NASICON-type battery design and propose actionable strategies for advancing next-generation high-entropy battery systems, emphasizing rational compositional screening, entropy-stabilized interface design, and machine learning-assisted property prediction. |
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| ISSN: | 2226-4108 2227-8508 |