Identification of Graphene Dispersion Agents through Molecular Fingerprints

• Published in: ACS nano Vol. 16; no. 10; pp. 16109 - 16117
• Main Authors: Goldie, Stuart J., Degiacomi, Matteo T., Jiang, Shan, Clark, Stewart J., Erastova, Valentina, Coleman, Karl S.
• Format: Journal Article
• Language: English
• Published: American Chemical Society 25.10.2022
• Subjects: solvent prediction; exfoliation; molecular modeling; graphene; 2D materials
• ISSN: 1936-0851; 1936-086X
• DOI: 10.1021/acsnano.2c04406

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.