(366c) Molecular Simulations of Ion Permeation in Graphene Oxide Membranes

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.