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Nanoenabled Trainable Systems: From Biointerfaces to Biomimetics
[Image: see text] In the dynamic biological system, cells and tissues adapt to diverse environmental conditions and form memories, an essential aspect of training for survival and evolution. An understanding of the biological training principles will inform the design of biomimetic materials whose p...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798864/ https://www.ncbi.nlm.nih.gov/pubmed/36516872 http://dx.doi.org/10.1021/acsnano.2c08042 |
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author | Li, Pengju Kim, Saehyun Tian, Bozhi |
author_facet | Li, Pengju Kim, Saehyun Tian, Bozhi |
author_sort | Li, Pengju |
collection | PubMed |
description | [Image: see text] In the dynamic biological system, cells and tissues adapt to diverse environmental conditions and form memories, an essential aspect of training for survival and evolution. An understanding of the biological training principles will inform the design of biomimetic materials whose properties evolve with the environment and offer routes to programmable soft materials, neuromorphic computing, living materials, and biohybrid robotics. In this perspective, we examine the mechanisms by which cells are trained by environmental cues. We outline the artificial platforms that enable biological training and examine the relationship between biological training and biomimetic materials design. We place emphasis on nanoscale material platforms which, given their applicability to chemical, mechanical and electrical stimulation, are critical to bridging natural and synthetic systems. |
format | Online Article Text |
id | pubmed-9798864 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-97988642022-12-30 Nanoenabled Trainable Systems: From Biointerfaces to Biomimetics Li, Pengju Kim, Saehyun Tian, Bozhi ACS Nano [Image: see text] In the dynamic biological system, cells and tissues adapt to diverse environmental conditions and form memories, an essential aspect of training for survival and evolution. An understanding of the biological training principles will inform the design of biomimetic materials whose properties evolve with the environment and offer routes to programmable soft materials, neuromorphic computing, living materials, and biohybrid robotics. In this perspective, we examine the mechanisms by which cells are trained by environmental cues. We outline the artificial platforms that enable biological training and examine the relationship between biological training and biomimetic materials design. We place emphasis on nanoscale material platforms which, given their applicability to chemical, mechanical and electrical stimulation, are critical to bridging natural and synthetic systems. American Chemical Society 2022-12-14 2022-12-27 /pmc/articles/PMC9798864/ /pubmed/36516872 http://dx.doi.org/10.1021/acsnano.2c08042 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Li, Pengju Kim, Saehyun Tian, Bozhi Nanoenabled Trainable Systems: From Biointerfaces to Biomimetics |
title | Nanoenabled Trainable
Systems: From Biointerfaces
to Biomimetics |
title_full | Nanoenabled Trainable
Systems: From Biointerfaces
to Biomimetics |
title_fullStr | Nanoenabled Trainable
Systems: From Biointerfaces
to Biomimetics |
title_full_unstemmed | Nanoenabled Trainable
Systems: From Biointerfaces
to Biomimetics |
title_short | Nanoenabled Trainable
Systems: From Biointerfaces
to Biomimetics |
title_sort | nanoenabled trainable
systems: from biointerfaces
to biomimetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798864/ https://www.ncbi.nlm.nih.gov/pubmed/36516872 http://dx.doi.org/10.1021/acsnano.2c08042 |
work_keys_str_mv | AT lipengju nanoenabledtrainablesystemsfrombiointerfacestobiomimetics AT kimsaehyun nanoenabledtrainablesystemsfrombiointerfacestobiomimetics AT tianbozhi nanoenabledtrainablesystemsfrombiointerfacestobiomimetics |