Cai Guorui, Chen Amanda A, Lin Sharon, Lee Dong Ju, Yu Kunpeng, Holoubek John, Yin Yijie, Mu Anthony U, Meng Ying Shirley, Liu Ping, Cohen Seth M, Pascal Tod A, Chen Zheng
Department of Nano and Chemical Engineering, University of California, San Diego, La Jolla, California 92093, United States.
Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California 92093, United States.
Nano Lett. 2023 Aug 9;23(15):7062-7069. doi: 10.1021/acs.nanolett.3c01825. Epub 2023 Jul 31.
Nonaqueous fluidic transport and ion solvation properties under nanoscale confinement are poorly understood, especially in ion conduction for energy storage and conversion systems. Herein, metal-organic frameworks (MOFs) and aprotic electrolytes are studied as a robust platform for molecular-level insights into electrolyte behaviors in confined spaces. By employing computer simulations, along with spectroscopic and electrochemical measurements, we demonstrate several phenomena that deviate from the bulk, including modulated solvent molecular configurations, aggregated solvation structures, and tunable transport mechanisms from quasi-solid to quasi-liquid in functionalized MOFs. Technologically, taking advantage of confinement effects may prove useful for addressing stability concerns associated with volatile organic electrolytes while simultaneously endowing ultrafast transport of solvates, resulting in improved battery performance, even at extreme temperatures. The molecular-level insights presented here further our understanding of structure-property relationships of complex fluids at the nanoscale, information that can be exploited for the predictive design of more efficient electrochemical systems.
人们对纳米尺度限制下的非水流体传输和离子溶剂化性质了解甚少，尤其是在能量存储和转换系统的离子传导方面。在此，金属有机框架（MOF）和非质子电解质被作为一个强大的平台进行研究，以从分子水平深入了解受限空间中电解质的行为。通过计算机模拟以及光谱和电化学测量，我们证明了几种与本体情况不同的现象，包括调制的溶剂分子构型、聚集的溶剂化结构以及功能化MOF中从准固态到准液态的可调传输机制。在技术层面，利用限制效应可能有助于解决与挥发性有机电解质相关的稳定性问题，同时赋予溶剂化物超快传输能力，即使在极端温度下也能提高电池性能。本文所呈现的分子水平见解进一步加深了我们对纳米尺度下复杂流体结构 - 性质关系的理解，这些信息可用于更高效电化学系统的预测性设计。
