• 文献标题:   Identification of Graphene Dispersion Agents through Molecular Fingerprints
  • 文献类型:   Article
  • 作  者:   GOLDIE SJ, DEGIACOMI MT, JIANG S, CLARK SJ, ERASTOVA V, COLEMAN KS
  • 作者关键词:   graphene, 2d material, exfoliation, molecular modeling, solvent prediction
  • 出版物名称:   ACS NANO
  • ISSN:   1936-0851 EI 1936-086X
  • 通讯作者地址:  
  • 被引频次:   0
  • DOI:   10.1021/acsnano.2c04406 EA SEP 2022
  • 出版年:   2022

▎ 摘  要

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.