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Identification of Graphene Dispersion Agents through Molecular Fingerprints
[Image: see text] The scalable production and dispersion of 2D materials, like graphene, is critical to enable their use in commercial applications. While liquid exfoliation is commonly used, solvents such as N-methyl-pyrrolidone (NMP) are toxic and difficult to scale up. However, the search for alt...
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/PMC9620402/ https://www.ncbi.nlm.nih.gov/pubmed/36166830 http://dx.doi.org/10.1021/acsnano.2c04406 |
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author | Goldie, Stuart J. Degiacomi, Matteo T. Jiang, Shan Clark, Stewart J. Erastova, Valentina Coleman, Karl S. |
author_facet | Goldie, Stuart J. Degiacomi, Matteo T. Jiang, Shan Clark, Stewart J. Erastova, Valentina Coleman, Karl S. |
author_sort | Goldie, Stuart J. |
collection | PubMed |
description | [Image: see text] The scalable production and dispersion of 2D materials, like graphene, is critical to enable their use in commercial applications. While liquid exfoliation is commonly used, solvents such as N-methyl-pyrrolidone (NMP) are toxic and difficult to scale up. However, the search for alternative solvents is hindered by the intimidating size of the chemical space. Here, we present a computational pipeline informing the identification of effective exfoliation agents. Classical molecular dynamics simulations provide statistical sampling of interactions, enabling the identification of key molecular descriptors for a successful solvent. The statistically representative configurations from these simulations, studied with quantum mechanical calculations, allow us to gain insights onto the chemophysical interactions at the surface–solvent interface. As an exemplar, through this pipeline we identify a potential graphene exfoliation agent 2-pyrrolidone and experimentally demonstrate it to be as effective as NMP. Our workflow can be generalized to any 2D material and solvent system, enabling the screening of a wide range of compounds and solvents to identify safer and cheaper means of producing dispersions. |
format | Online Article Text |
id | pubmed-9620402 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-96204022022-11-01 Identification of Graphene Dispersion Agents through Molecular Fingerprints Goldie, Stuart J. Degiacomi, Matteo T. Jiang, Shan Clark, Stewart J. Erastova, Valentina Coleman, Karl S. ACS Nano [Image: see text] The scalable production and dispersion of 2D materials, like graphene, is critical to enable their use in commercial applications. While liquid exfoliation is commonly used, solvents such as N-methyl-pyrrolidone (NMP) are toxic and difficult to scale up. However, the search for alternative solvents is hindered by the intimidating size of the chemical space. Here, we present a computational pipeline informing the identification of effective exfoliation agents. Classical molecular dynamics simulations provide statistical sampling of interactions, enabling the identification of key molecular descriptors for a successful solvent. The statistically representative configurations from these simulations, studied with quantum mechanical calculations, allow us to gain insights onto the chemophysical interactions at the surface–solvent interface. As an exemplar, through this pipeline we identify a potential graphene exfoliation agent 2-pyrrolidone and experimentally demonstrate it to be as effective as NMP. Our workflow can be generalized to any 2D material and solvent system, enabling the screening of a wide range of compounds and solvents to identify safer and cheaper means of producing dispersions. American Chemical Society 2022-09-27 2022-10-25 /pmc/articles/PMC9620402/ /pubmed/36166830 http://dx.doi.org/10.1021/acsnano.2c04406 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 | Goldie, Stuart J. Degiacomi, Matteo T. Jiang, Shan Clark, Stewart J. Erastova, Valentina Coleman, Karl S. Identification of Graphene Dispersion Agents through Molecular Fingerprints |
title | Identification of
Graphene Dispersion Agents through
Molecular Fingerprints |
title_full | Identification of
Graphene Dispersion Agents through
Molecular Fingerprints |
title_fullStr | Identification of
Graphene Dispersion Agents through
Molecular Fingerprints |
title_full_unstemmed | Identification of
Graphene Dispersion Agents through
Molecular Fingerprints |
title_short | Identification of
Graphene Dispersion Agents through
Molecular Fingerprints |
title_sort | identification of
graphene dispersion agents through
molecular fingerprints |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9620402/ https://www.ncbi.nlm.nih.gov/pubmed/36166830 http://dx.doi.org/10.1021/acsnano.2c04406 |
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