Machine-Learning-Assisted Development of Gel Polymer Electrolytes for Protecting Zn Metal Anodes from the Corrosion of Water Molecules

被引:5
|
作者
Zhu, Ruijie [1 ]
Li, Zechen [2 ]
Li, Min [3 ]
Si, Xiangru [4 ]
Yang, Huijun [5 ]
Yuan, Baoyin [6 ]
Mu, Qifeng [7 ]
Zhu, Chunyu [4 ]
Cui, Wei [3 ]
机构
[1] Hokkaido Univ, Fac Engn, Sapporo, Hokkaido 0608628, Japan
[2] Natl Univ Singapore, Coll Design & Engn, Singapore 117575, Singapore
[3] Sichuan Univ, Coll Polymer Sci & Engn, Chengdu 610065, Peoples R China
[4] China Univ Min & Technol, Sch Low Carbon Energy & Power Engn, Xuzhou 221116, Peoples R China
[5] Univ Tsukuba, Grad Sch Syst & Informat Engn, Tsukuba 3058573, Japan
[6] South China Univ Technol, Sch Math, Guangzhou 510640, Peoples R China
[7] RIKEN Ctr Emergent Matter Sci, Wako, Saitama 3510198, Japan
来源
JOURNAL OF PHYSICAL CHEMISTRY LETTERS | 2024年 / 15卷 / 19期
基金
日本学术振兴会; 中国国家自然科学基金;
关键词
CHALLENGES; BATTERIES;
D O I
10.1021/acs.jpclett.4c00698
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Rechargeable aqueous zinc-ion batteries (RAZIBs) offer low cost, high energy density, and safety but struggle with anode corrosion and dendrite formation. Gel polymer electrolytes (GPEs) with both high mechanical properties and excellent electrochemical properties are a powerful tool to aid the practical application of RAZIBs. In this work, guided by a machine learning (ML) model constructed based on experimental data, polyacrylamide (PAM) with a highly entangled structure was chosen to prepare GPEs for obtaining high-performance RAZIBs. By controlling the swelling degree of the PAM, the obtained GPEs effectively suppressed the growth of Zn dendrites and alleviated the corrosion of Zn metal caused by water molecules, thus improving the cycling lifespan of the Zn anode. These results indicate that using ML models based on experimental data can effectively help screen battery materials, while highly entangled PAMs are excellent GPEs capable of balancing mechanical and electrochemical properties.
引用
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页码:5191 / 5201
页数:11
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