In Silico Design and Characterization of Graphene Oxide Membranes with Variable Water Content and Flake Oxygen Content

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Abstract

Graphene oxide (GO) membranes offer exceptional promise for certain aqueous separation challenges, such as desalination. Central to unlocking this promise and optimizing performance for a given separation is the establishment of a detailed molecular-level understanding of how the membrane’s composition affects its structural and transport properties. This understanding is currently lacking, in part due to the fact that, until recently, molecular models with a realistic distribution of oxygen-functionalities and interlayer flake structure were unavailable. To understand the effect of composition on the properties of GO membranes, models with water contents and oxygen contents, varying between 0% and 40% by weight, were prepared in this work using classical molecular dynamics simulations. The change in membrane interlayer distance distribution, water connectivity and water diffusivity with water and oxygen content was quantified. Interlayer distance distribution analysis showed that the swelling of GO membranes could be controlled by separately tuning both the flake oxygen content and membrane water content. Water molecule cluster analysis showed that a continuous and fully connected network of water nanopores is not formed until the water content reaches ~20%. The diffusivity of water in the membrane was also found to strongly depend on both the water and oxygen content. These insights help to understand the structure and transport properties of GO membranes with sub-nanometer interlayer distances and could be exploited to enhance the performance of GO membranes for aqueous separation applications. More broadly, the high-throughput in silico approach adopted could be applied to other nanomaterials with intrinsic non-stoichiometry and structural heterogeneity.

Bibliographical metadata

Original languageEnglish
Pages (from-to)2995-3004
Number of pages10
JournalACS Nano
Volume13
Issue number3
Early online date20 Feb 2019
DOIs
Publication statusPublished - 26 Mar 2019

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