452112 Molecular Simulations of Ion Permeation in Graphene Oxide Membranes

Tuesday, November 15, 2016: 1:00 PM
Plaza B (Hilton San Francisco Union Square)
Christopher Williams1, Paola Carbone2 and James Dix1, (1)The University of Manchester, School of Chemical Engineering and Analytical Science, (2)School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, United Kingdom

Molecular Simulations of Ion Permeation in Graphene Oxide Membranes

The scarcity of clean water for human consumption is a serious problem facing many communities around the world. Improving the efficiency of desalination and decontamination processes could alleviate this problem. To enable the full potential of desalination and decontamination to be harnessed, membrane materials with improved properties must be found. Graphene oxide (GO) membranes may present a breakthrough in this field because they display fast water transport and selective ion permeation, as well as being amenable to large-scale, low cost production by chemical means. Once careful control of the spacing between layers has been realised, GO is potentially an excellent candidate material for aqueous separation applications, such as desalination or decontamination.

In this work, molecular dynamics simulations are used to investigate the permeation of ions into graphene capillaries and improve understanding of the experimentally observed selectivity. By generating single ion potentials of mean force for a range of ions and pore widths, we show how the selectivity is dependent on the hydrated structures and hydration free energies of the ions.

The selectivity of anions appears to be dominated by the energetic cost of dehydration upon confinement. We predict how this property could be exploited for the use of GO to clean up weakly hydrating and problematic anionic contaminants (e.g. radioactive 99TcO4), with a high selectivity over competing species. For cations, the observed ion selectivity is dependent on the effect of dehydration, relative to the strength of cation – pi interactions with the sp2 electrons in graphene. These interactions have been accounted for in our simulations. The results have improved understanding of ion selectivity in GO and enabled us to make predictions about how to optimise the design of these membranes for a given application.


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See more of this Session: Membrane Modeling and Simulation
See more of this Group/Topical: Separations Division