Coarse grained models of graphene and graphene oxide for usein aqueous solution.

0522212 - ÚCHP 2021 RIV GB eng J - Článek v odborném periodiku
Williams, Ch.D. - Lísal, Martin
Coarse grained models of graphene and graphene oxide for usein aqueous solution.
2D Materials. Roč. 7, č. 2 (2020), č. článku 025025. ISSN 2053-1583. E-ISSN 2053-1583
Grant ostatní: EPSRC(GB) EP/R033366/1
Institucionální podpora: RVO:67985858
Klíčová slova: molecular simulation * graphene oxide * coarse graining
Obor OECD: Physical chemistry
Impakt faktor: 7.103, rok: 2020
Způsob publikování: Open access
https://iopscience.iop.org/article/10.1088/2053-1583/ab6f0c

Obtaining stable aqueous dispersions of graphene-based materials is a major obstacle in the development and widespread use of graphene in nanotechnology. The efficacy of atomistic
simulations in obtaining a molecular-level insight into aggregation and exfoliation of graphene/ graphene oxide (GO) is hindered by length and time scale limitations. In this work, we developed coarse-grained (CG) models of graphene/GO sheets, compatible with the polarizable Martini water model, using molecular dynamics, iterative Boltzmann inversion and umbrella sampling simulations. The new CG models accurately reproduce graphene/GO–water radial distribution functions and sheet–sheet aggregation free energies for small graphene (−316 kJ mol−1) and GO (−108 kJ mol−1) reference sheets. Deprotonation of carboxylic acid functionalities stabilize the exfoliated state by electrostatic repulsion, providing they are present at sufficiently high surface concentration. The simulations also highlight the pivotal role played by entropy in controlling the propensity for aggregation or exfoliation. The CG models improve the computational efficiency of simulations by an order of magnitude and the framework presented is transferrable to sheets of different sizes and oxygen contents. They can now be used to provide fundamental physical insights into the stability of dispersions and controlled self-assembly, underpinning the computational
design of graphene-containing nanomaterials.
Trvalý link: http://hdl.handle.net/11104/0308288