Accurate large scale modelling of graphene oxide: Ion trapping and chaotropic potential at the interface
Accurate large scale modelling of graphene oxide: Ion trapping and chaotropic potential at the interface
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DOI：
10.1016/j.carbon.2020.12.032
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发表时间：
2021-04-15
期刊：
影响因子：
10.9
通讯作者：
Lorenz, Christian D.
中科院分区：
文献类型：
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作者：
al-Badri, Mohamed Ali;Smith, Paul;Lorenz, Christian D.
氧化石墨烯（GO）具有石墨烯的许多独特的力学和电学性质，在物理、工程和医学等领域有着广泛的应用。GO的计算模拟广泛忽略了随机官能化的准确表征，放弃了空间应变和放弃边缘官能团。在这里，我们表明，分子动力学力场设计使用电子结构计算的数百个原子的GO与准确的功能化显示出良好的协议与国家的最先进的从头算分子动力学（AIMD）模拟。我们发现，定制的力场显示出更好的协议与以前的AIMD和实验结果的界面水动力学和离子吸附。也就是说，GO所描述的定制力场被发现破坏氢键网络的界面处发挥更动态的作用，从水接受和捐赠氢键。此外，与定制的力场，我们发现优先吸附的离子羧基官能团和类似的平均吸附半衰期为Nathorn和Cl-离子周围GO。这些发现对于未来GO在复杂环境中的应用研究至关重要，从脱盐到蛋白质吸附用于药物递送。(C)2020爱思唯尔有限公司保留所有权利。
Graphene oxide (GO) shares many novel mechanical and electronic properties with graphene and has been applied extensively for uses in physics, engineering and medicine. Computational simulations of GO have widely neglected accurate characterisation by random functionalisation, forsaking steric strain and abandoning edge functional groups. Here, we show that molecular dynamics forcefield design using electronic structure calculations of hundreds of atoms of GO with accurate functionalisation shows good agreement with state-of-the-art ab initio molecular dynamics (AIMD) simulations. We find that the bespoke forcefield shows better agreement with previous AIMD and experimental results in terms of the interfacial water dynamics and ion adsorption. Namely, GO described by the bespoke forcefield is found to disrupt the hydrogen bonding network at the interface by playing a more dynamic role in accepting and donating hydrogen bonds from water. Furthermore, with the bespoke forcefield, we find preferential adsorption of ions to carboxyl functional groups and a similar mean adsorption half-life for Nathorn and Cl- ions around GO. These findings are critical for future investigations of GO in complex environments in application ranging from desalination to protein adsorption for drug delivery. (C) 2020 Elsevier Ltd. All rights reserved.