Revolutionizing Solid-State NASICON Sodium Batteries: Enhanced Ionic Conductivity Estimation through Multivariate Experimental Parameters Leveraging Machine Learning.

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作者： Yuyao,Zhang ^[1] ; Tingjie,Zhan ^[2] ; Yang,Sun ^[3] ; Lun,Lu ^[4] ; Baoliang,Chen ^[1]

作者单位： Department of Environmental Science, Zhejiang University, Hangzhou, Zhejiang, 310058, China. ^[1] Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control, Hangzhou, Zhejiang, 310058, China. ^[2] Department of Chemical & Environmental Engineering, School of Engineering and Applied Science, Yale University, New Haven, CT 06511, USA. ^[3] Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, NJ 08854, USA. ^[4] State Environmental Protection Key Laboratory of Environ Pollut Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Guangzhou, 510655, China. ^[5]

关键词 Ionic Conductivity Machine Learning NASICON Sodium-Ion Batteries Solid-State Electrolytes

DOI 10.1002/cssc.202301284

PMID 37934454

发布时间 2024-03-23

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