电解质
溶剂
玻璃化转变
分子
盐(化学)
离子电导率
离子液体
离子
化学
化学物理
聚合物
快离子导体
无机化学
离子键合
化学工程
材料科学
物理化学
有机化学
催化作用
工程类
电极
作者
Qing Zhao,Xiaotun Liu,Jingxu Zheng,Yue Deng,Alexander Warren,Qiyuan Zhang,Lynden A. Archer
标识
DOI:10.1073/pnas.2004576117
摘要
In the presence of Lewis acid salts, the cyclic ether, dioxolane (DOL), is known to undergo ring-opening polymerization inside electrochemical cells to form solid-state polymer batteries with good interfacial charge-transport properties. Here we report that LiNO3, which is unable to ring-open DOL, possesses a previously unknown ability to coordinate with and strain DOL molecules in bulk liquids, completely arresting their crystallization. The strained DOL electrolytes exhibit physical properties analogous to amorphous polymers, including a prominent glass transition, elevated moduli, and low activation entropy for ion transport, but manifest unusually high, liquidlike ionic conductivities (e.g., 1 mS/cm) at temperatures as low as -50 °C. Systematic electrochemical studies reveal that the electrolytes also promote reversible cycling of Li metal anodes with high Coulombic efficiency (CE) on both conventional planar substrates (1 mAh/cm2 over 1,000 cycles with 99.1% CE; 3 mAh/cm2 over 300 cycles with 99.2% CE) and unconventional, nonplanar/three-dimensional (3D) substrates (10 mAh/cm2 over 100 cycles with 99.3% CE). Our finding that LiNO3 promotes reversibility of Li metal electrodes in liquid DOL electrolytes by a physical mechanism provides a possible solution to a long-standing puzzle in the field about the versatility of LiNO3 salt additives for enhancing reversibility of Li metal electrodes in essentially any aprotic liquid electrolyte solvent. As a first step toward understanding practical benefits of these findings, we create functional Li||lithium iron phosphate (LFP) batteries in which LFP cathodes with high capacity (5 to 10 mAh/cm2) are paired with thin (50 μm) lithium metal anodes, and investigate their galvanostatic electrochemical cycling behaviors.
科研通智能强力驱动
Strongly Powered by AbleSci AI