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Identification of Graphene Dispersion Agents through Molecular Fingerprints

Goldie, Stuart J.; Degiacomi, Matteo T.; Jiang, Shan; Clark, Stewart J.; Erastova, Valentina; Coleman, Karl S.

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Authors

Stuart J. Goldie

Shan Jiang

Valentina Erastova



Abstract

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.

Citation

Goldie, S. J., Degiacomi, M. T., Jiang, S., Clark, S. J., Erastova, V., & Coleman, K. S. (2022). Identification of Graphene Dispersion Agents through Molecular Fingerprints. ACS Nano, 16(10), https://doi.org/10.1021/acsnano.2c04406

Journal Article Type Article
Acceptance Date Sep 20, 2022
Online Publication Date Sep 27, 2022
Publication Date 2022
Deposit Date Feb 6, 2023
Publicly Available Date Feb 6, 2023
Journal ACS Nano
Print ISSN 1936-0851
Electronic ISSN 1936-086X
Publisher American Chemical Society
Peer Reviewed Peer Reviewed
Volume 16
Issue 10
DOI https://doi.org/10.1021/acsnano.2c04406

Files

Published Journal Article (6.8 Mb)
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

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