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Approaches and Perspective of Coarse-Grained Modeling and Simulation for Polymer–Nanoparticle Hybrid Systems
[Image: see text] Molecular modeling and simulations have emerged as effective and indispensable tools to characterize polymeric systems. They provide fundamental and essential insights to design a product of the required properties and to improve the understanding of a phenomenon at the molecular l...
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/PMC9798744/ https://www.ncbi.nlm.nih.gov/pubmed/36591142 http://dx.doi.org/10.1021/acsomega.2c06248 |
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author | Khan, Parvez Kaushik, Rahul Jayaraj, Abhilash |
author_facet | Khan, Parvez Kaushik, Rahul Jayaraj, Abhilash |
author_sort | Khan, Parvez |
collection | PubMed |
description | [Image: see text] Molecular modeling and simulations have emerged as effective and indispensable tools to characterize polymeric systems. They provide fundamental and essential insights to design a product of the required properties and to improve the understanding of a phenomenon at the molecular level for a particular system. The polymer–nanoparticle hybrids are materials with outstanding properties and correspondingly large applications whose study has benefited from this new paradigm. However, despite the significant expansion of modern day computational powers, investigation of the long time and large length scale phenomenon in polymeric and polymer–nanoparticle systems is still a challenging task to complete through all-atom molecular dynamics (AA-MD) simulations. To circumvent this problem, a variety of coarse-grained (CG) models have been proposed, ranging from the generic CG models for qualitative properties predictions to more realistic chemically specific CG models for quantitative properties predictions. These CG models have already delivered some success stories in the study of several spatial and temporal evolutions of many processes. Some of these studies were beyond the feasibility of traditional atomistic resolution models due to either the size or the time constraints. This review captures the different types of popular CG approaches that are utilized in the investigation of the microscopic behavior of polymer–nanoparticle hybrid systems. The rationale of this article is to furnish an overview of the popular CG approaches and their applications, to review several important and most recent developments, and to delineate the perspectives on future directions in the field. |
format | Online Article Text |
id | pubmed-9798744 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-97987442022-12-30 Approaches and Perspective of Coarse-Grained Modeling and Simulation for Polymer–Nanoparticle Hybrid Systems Khan, Parvez Kaushik, Rahul Jayaraj, Abhilash ACS Omega [Image: see text] Molecular modeling and simulations have emerged as effective and indispensable tools to characterize polymeric systems. They provide fundamental and essential insights to design a product of the required properties and to improve the understanding of a phenomenon at the molecular level for a particular system. The polymer–nanoparticle hybrids are materials with outstanding properties and correspondingly large applications whose study has benefited from this new paradigm. However, despite the significant expansion of modern day computational powers, investigation of the long time and large length scale phenomenon in polymeric and polymer–nanoparticle systems is still a challenging task to complete through all-atom molecular dynamics (AA-MD) simulations. To circumvent this problem, a variety of coarse-grained (CG) models have been proposed, ranging from the generic CG models for qualitative properties predictions to more realistic chemically specific CG models for quantitative properties predictions. These CG models have already delivered some success stories in the study of several spatial and temporal evolutions of many processes. Some of these studies were beyond the feasibility of traditional atomistic resolution models due to either the size or the time constraints. This review captures the different types of popular CG approaches that are utilized in the investigation of the microscopic behavior of polymer–nanoparticle hybrid systems. The rationale of this article is to furnish an overview of the popular CG approaches and their applications, to review several important and most recent developments, and to delineate the perspectives on future directions in the field. American Chemical Society 2022-12-12 /pmc/articles/PMC9798744/ /pubmed/36591142 http://dx.doi.org/10.1021/acsomega.2c06248 Text en © 2022 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by-nc-nd/4.0/Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Khan, Parvez Kaushik, Rahul Jayaraj, Abhilash Approaches and Perspective of Coarse-Grained Modeling and Simulation for Polymer–Nanoparticle Hybrid Systems |
title | Approaches and
Perspective of Coarse-Grained Modeling
and Simulation for Polymer–Nanoparticle Hybrid Systems |
title_full | Approaches and
Perspective of Coarse-Grained Modeling
and Simulation for Polymer–Nanoparticle Hybrid Systems |
title_fullStr | Approaches and
Perspective of Coarse-Grained Modeling
and Simulation for Polymer–Nanoparticle Hybrid Systems |
title_full_unstemmed | Approaches and
Perspective of Coarse-Grained Modeling
and Simulation for Polymer–Nanoparticle Hybrid Systems |
title_short | Approaches and
Perspective of Coarse-Grained Modeling
and Simulation for Polymer–Nanoparticle Hybrid Systems |
title_sort | approaches and
perspective of coarse-grained modeling
and simulation for polymer–nanoparticle hybrid systems |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798744/ https://www.ncbi.nlm.nih.gov/pubmed/36591142 http://dx.doi.org/10.1021/acsomega.2c06248 |
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