High-Entropy Engineering for Multivalency-Induced Stability in SnSb-Based Anodes
Achieving high energy density and long cycle life in alloy-type anodes remains a significant challenge due to the large volume changes during cycling. Here, we introduce a high-entropy engineering approach using SnSb-based oxides codoped with Ti and Al (SSBTA-600), designed to promote the formation of efficient oxygen vacancies at a calcination temperature of 600 <i>°C</i>. This approach results in remarkable performance with a capacity of 1012 <i>mAh g</i><sup><i>-1</i></sup> at 0.5 <i>A g</i><sup><i>-1</i></sup> and 297 <i>mAh g</i><sup><i>-1</i></sup> at <i>5 A g</i><sup><i>-1</i></sup> after 500 cycles, with superior capacity retention of 99<i>%</i> and 83.5<i>%</i>, respectively. A LiFePO<sub>4</sub>||SSBTA full cell achieves 134 <i>mAh g</i><sup><i>-1</i></sup> after 100 cycles with 89.4<i>%</i> retention, demonstrating its practical potential for lithium-ion batteries. The high concentration of oxygen vacancies in SSBTA-600, induced by the multivalency of Ti and Al, is validated by electron paramagnetic resonance (EPR) and X-ray absorption spectroscopy (XAS). This high-entropy engineering approach significantly improves the cyclic stability and high-rate performance and provides a promising strategy for enhancing the energy density and cycle life in alloy-type anodes.
Authors
- Lei Shen (ORCID: https://orcid.org/0000-0001-6198-5753)
- Meng Xu (ORCID: https://orcid.org/0000-0003-1674-6037)
- Jiajin Luo
- Lin Yu (ORCID: https://orcid.org/0000-0001-6187-6514)
- Jianfang Chen (ORCID: https://orcid.org/0000-0002-6521-0266)
- Qiang Wang (ORCID: https://orcid.org/0000-0002-7396-0323)
- Pan Zeng (ORCID: https://orcid.org/0000-0003-4632-6279)
- Wei Liu (ORCID: https://orcid.org/0000-0003-1608-7270)
- Zhengyan Liu
- Gao Cheng (ORCID: https://orcid.org/0000-0002-0878-1679)
- Wei Ran
Institutions
- Guangdong University of Technology (CN)
- Southwest University (CN)
- National University of Singapore (SG)
- Sichuan University (CN)
- Key Laboratory of Guangdong Province (CN)
- Chengdu University (CN)
- Ministry of Energy (IL)
Publication Details
- Journal
- ACS Applied Materials & Interfaces
- Published
- 2025-02-10
- DOI
- https://doi.org/10.1021/acsami.4c19698
- Citations
- 5
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 2.59
Funders
- National Natural Science Foundation of China
- Chongqing Science and Technology Foundation