Chen Jihua
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.
Nanomaterials (Basel). 2021 Sep 15;11(9):2405. doi: 10.3390/nano11092405.
After decades of developments, electron microscopy has become a powerful and irreplaceable tool in understanding the ionic, electrical, mechanical, chemical, and other functional performances of next-generation polymers and soft complexes. The recent progress in electron microscopy of nanostructured polymers and soft assemblies is important for applications in many different fields, including, but not limited to, mesoporous and nanoporous materials, absorbents, membranes, solid electrolytes, battery electrodes, ion- and electron-transporting materials, organic semiconductors, soft robotics, optoelectronic devices, biomass, soft magnetic materials, and pharmaceutical drug design. For synthetic polymers and soft complexes, there are four main characteristics that differentiate them from their inorganic or biomacromolecular counterparts in electron microscopy studies: (1) lower contrast, (2) abundance of light elements, (3) polydispersity or nanomorphological variations, and (4) large changes induced by electron beams. Since 2011, the Center for Nanophase Materials Sciences (CNMS) at Oak Ridge National Laboratory has been working with numerous facility users on nanostructured polymer composites, block copolymers, polymer brushes, conjugated molecules, organic-inorganic hybrid nanomaterials, organic-inorganic interfaces, organic crystals, and other soft complexes. This review crystalizes some of the essential challenges, successes, failures, and techniques during the process in the past ten years. It also presents some outlooks and future expectations on the basis of these works at the intersection of electron microscopy, soft matter, and artificial intelligence. Machine learning is expected to automate and facilitate image processing and information extraction of polymer and soft hybrid nanostructures in aspects such as dose-controlled imaging and structure analysis.
经过数十年的发展，电子显微镜已成为理解下一代聚合物和软质复合物的离子、电学、力学、化学及其他功能性能的强大且不可替代的工具。纳米结构聚合物和软质组装体的电子显微镜研究的最新进展，对于许多不同领域的应用都很重要，包括但不限于介孔和纳米多孔材料、吸收剂、膜、固体电解质、电池电极、离子和电子传输材料、有机半导体、软体机器人、光电器件、生物质、软磁材料以及药物设计。对于合成聚合物和软质复合物，在电子显微镜研究中，有四个主要特征将它们与无机或生物大分子对应物区分开来：（1）对比度较低；（2）轻元素含量丰富；（3）多分散性或纳米形态变化；（4）电子束引起的巨大变化。自2011年以来，橡树岭国家实验室的纳米相材料科学中心（CNMS）一直与众多设施用户合作，研究纳米结构聚合物复合材料、嵌段共聚物、聚合物刷、共轭分子、有机-无机杂化纳米材料、有机-无机界面、有机晶体及其他软质复合物。本综述总结了过去十年该过程中的一些关键挑战、成功、失败及技术。它还基于电子显微镜、软物质和人工智能交叉领域的这些工作，提出了一些展望和未来期望。机器学习有望在剂量控制成像和结构分析等方面，实现聚合物和软质杂化纳米结构的图像处理和信息提取自动化并为之提供便利。
