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Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications

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...

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Autor principal: Chen, Jihua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8470047/
https://www.ncbi.nlm.nih.gov/pubmed/34578720
http://dx.doi.org/10.3390/nano11092405
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author Chen, Jihua
author_facet Chen, Jihua
author_sort Chen, Jihua
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description 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.
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spelling pubmed-84700472021-09-27 Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications Chen, Jihua Nanomaterials (Basel) Review 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. MDPI 2021-09-15 /pmc/articles/PMC8470047/ /pubmed/34578720 http://dx.doi.org/10.3390/nano11092405 Text en © 2021 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Chen, Jihua
Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications
title Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications
title_full Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications
title_fullStr Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications
title_full_unstemmed Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications
title_short Advanced Electron Microscopy of Nanophased Synthetic Polymers and Soft Complexes for Energy and Medicine Applications
title_sort advanced electron microscopy of nanophased synthetic polymers and soft complexes for energy and medicine applications
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8470047/
https://www.ncbi.nlm.nih.gov/pubmed/34578720
http://dx.doi.org/10.3390/nano11092405
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