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Data-Driven Viewpoint for Developing Next-Generation Mg-Ion Solid-State Electrolytes
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作者 Fang-Ling Yang Ryuhei Sato +5 位作者 eric jianfeng cheng Kazuaki Kisu Qian Wang Xue Jia Shin-ichi Orimo Hao Li 《电化学(中英文)》 CAS 2024年第7期38-49,共12页
Magnesium(Mg)is a promising alternative to lithium(Li)as an anode material in solid-state batteries due to its abundance and high theoretical volumetric capacity.However,the sluggish Mg-ion conduction in the lattice o... Magnesium(Mg)is a promising alternative to lithium(Li)as an anode material in solid-state batteries due to its abundance and high theoretical volumetric capacity.However,the sluggish Mg-ion conduction in the lattice of solidstate electrolytes(SSEs)is one of the key challenges that hamper the development of Mg-ion solid-state batteries.Though various Mg-ion SSEs have been reported in recent years,key insights are hard to be derived from a single literature report.Besides,the structure-performance relationships of Mg-ion SSEs need to be further unraveled to provide a more precise design guideline for SSEs.In this viewpoint article,we analyze the structural characteristics of the Mg-based SSEs with high ionic conductivity reported in the last four decades based upon data mining-we provide big-data-derived insights into the challenges and opportunities in developing next-generation Mg-ion SSEs. 展开更多
关键词 Data mining Magnesium-ion solid-state electrolytes All-solid-state batteries Magnesium-ion conductivity
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