Coarse grained models of graphene and graphene oxide for use in aqueous solution
Coarse grained models of graphene and graphene oxide for use in aqueous solution
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DOI：
10.1088/2053-1583/ab6f0c
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发表时间：
2020-02
期刊：
影响因子：
5.5
通讯作者：
C. D. Williams;M. Lísal
中科院分区：
文献类型：
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作者：
C. D. Williams;M. Lísal
获得石墨烯基材料的稳定水性分散体是石墨烯在纳米技术中的开发和广泛使用的主要障碍。原子模拟在获得对石墨烯/氧化石墨烯（GO）的聚集和剥离的分子水平洞察方面的功效受到长度和时间尺度限制的阻碍。在这项工作中，我们开发了粗粒（CG）模型的石墨烯/GO片，兼容的极化马提尼水模型，使用分子动力学，迭代玻尔兹曼反演和伞形采样模拟。新的CG模型精确地再现了小石墨烯（−316 kJ mol−1）和GO（−108 kJ mol −1）参考片的石墨烯/GO-水径向分布函数和片-片聚集自由能。羧酸官能团的去质子化通过静电排斥稳定剥离状态，只要它们以足够高的表面浓度存在。模拟还强调了熵在控制聚集或剥离倾向中所起的关键作用。CG模型提高了一个数量级的模拟的计算效率，提出的框架是可转移到不同尺寸和氧含量的片材。它们现在可以用于提供分散体稳定性和受控自组装的基本物理见解，支持含石墨烯纳米材料的计算设计。
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.