电解质
材料科学
离子电导率
锂(药物)
纳米技术
储能
金属锂
相间
工作(物理)
电导率
计算机科学
电流(流体)
电介质
快离子导体
离子键合
复合数
能量密度
合理设计
锂离子电池
作者
Zhao Yang,Limin Geng,Weijia Meng,Jiaye Ye
出处
期刊:Nano-micro Letters
[Springer Science+Business Media]
日期:2025-09-12
卷期号:18 (1): 65-65
被引量:9
标识
DOI:10.1007/s40820-025-01914-x
摘要
Lithium-ion batteries (LIBs), while dominant in energy storage due to high energy density and cycling stability, suffer from severe capacity decay, rate capability degradation, and lithium dendrite formation under low-temperature (LT) operation. Therefore, a more comprehensive and systematic understanding of LIB behavior at LT is urgently required. This review article comprehensively reviews recent advancements in electrolyte engineering strategies aimed at improving the low-temperature operational capabilities of LIBs. The study methodically examines critical performance-limiting mechanisms through fundamental analysis of four primary challenges: insufficient ionic conductivity under cryogenic conditions, kinetically hindered charge transfer processes, Li⁺ transport limitations across the solid-electrolyte interphase (SEI), and uncontrolled lithium dendrite growth. The work elaborates on innovative optimization approaches encompassing lithium salt molecular design with tailored dissociation characteristics, solvent matrix optimization through dielectric constant and viscosity regulation, interfacial engineering additives for constructing low-impedance SEI layers, and gel-polymer composite electrolyte systems. Notably, particular emphasis is placed on emerging machine learning-guided electrolyte formulation strategies that enable high-throughput virtual screening of constituent combinations and prediction of structure-property relationships. These artificial intelligence-assisted rational design frameworks demonstrate significant potential for accelerating the development of next-generation LT electrolytes by establishing quantitative composition-performance correlations through advanced data-driven methodologies.
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