Polarization of ionic liquid and polymer and its implications for polymerized ionic liquids: An overview towards a new theory and simulation
Polarization of ionic liquid and polymer and its implications for polymerized ionic liquids: An overview towards a new theory and simulation
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DOI：
10.1002/pol.20210330
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发表时间：
2021-08
影响因子：
3.4
通讯作者：
Tongtong Gao;Jester N. Itliong;S. Kumar;Zackerie W Hjorth;I. Nakamura
中科院分区：
文献类型：
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作者：
Tongtong Gao;Jester N. Itliong;S. Kumar;Zackerie W Hjorth;I. Nakamura
含离子液体（IL）的聚合物在电化学应用中受到关注。本文综述了聚合物电解质的最新实验和理论研究，这些研究可能会为含IL聚合物培养新的理论和计算框架。前两部分概述了离子液体的独特性，将其与聚合物中的无机盐区分开来，并探讨了与“室温熔盐”隐喻概念的偏离。这种独特的性质包括（1）大的固有偶极矩和电子极化率，（2）氢键，（3）π相互作用，（4）电荷在整个离子上的广泛分布，以及（5）离子的各向异性。此外，当离子聚合时，这些性质的复杂性显著增加。事实上，它们的特殊特性将克服由于无机盐掺杂聚合物的离子电导率和机械鲁棒性之间的权衡而造成的障碍。鉴于这些事实，文章的其余部分侧重于研究聚合物电解质的介电响应，相分离，离子电导率和机械鲁棒性的新兴趋势，突出了可能激发现有理论和模拟的实验中的杰出观察结果。我们的讨论还包括提高含IL聚合物的计算复杂性。为此，最近的机器学习研究，考虑离子液体和聚合物液体。
Ionic liquid (IL)‐containing polymers garner attention for electrochemical applications. This article overviews recent experimental and theoretical studies of polymer electrolytes that would be likely to cultivate new theoretical and computational frameworks for IL‐containing polymers. The first two sections outline the uniqueness of ILs that differentiates them from inorganic salts in polymers and explore deviation from the concept of the metaphor “room‐temperature molten salt.” Such distinct properties include (1) large intrinsic dipole moment and electronic polarizability, (2) hydrogen bonding, (3) π‐interactions, (4) a broad distribution of charges over the entire ion, and (5) the anisotropy of the ions. Moreover, the complexity of these properties substantially increases when the ions are polymerized. Indeed, their exceptional features would overcome the hurdle due to a trade‐off between ionic conductivity and mechanical robustness in inorganic salt‐doped polymers. Given these facts, the rest of the article focuses on emerging trends in the study of the dielectric response, phase separation, ion conductivity, and mechanical robustness of the polymer electrolytes, highlighting outstanding observations in experiments that may inspire existing theory and simulation. Our discussion also includes improving computational complexity for IL‐containing polymers. To this end, recent machine learning studies that consider ILs and polymer liquids are presented.