PRESSURE DRIVEN WATER TRANSPORT THROUGH GRAPHENE NANOCHANNEL USING MOLECULAR DYNAMICS SIMULATIONS
In broad field of nanotechnology, water desalination and purification have been topics of interest in recent times. The basis for solving these desalination problems lies in fundamental understanding of the behavior of water molecules when they are confined in nanochannels. Nanochannels made up of graphene sheets are found to be potential materials in nanofiltration due to their high strength, thinness and high flexibility as these walls have to withstand high pressures. With this motivation, molecular dynamics method is applied to study pressure driven flow through graphene nanochannel. Atomistic simulations are considered because due to small channel sizes, continuum laws are no longer applicable at these scales. The system consists of two reservoirs connected by graphene channel. By maintaining a pressure difference between two reservoirs, water molecules are transported through the nanochannel. Water molecules are modeled by TIP3P model while the interactions between graphene-sheets and water molecules are modeled by LJ potential. Long-range electrostatic interaction is modeled by particle-particle particle-mesh method (PPPM). To validate the potential used for water molecules, RDF (radial distribution function) of the $O-O$ atom pairs, mean square distance (MSD), diffusion coefficient are calculated. It is observed that the values agree well with the available literature and thus ensure the use of this potential for water molecules in this study. In a test case study, a pressure difference of 200 MPa is maintained between the two reservoirs in order to drive the water molecules through the graphene nanochannel. In order to comprehensively comment upon the flow behavior, a detailed analysis is carried out with the aid of various cases in this work. Flow properties like density, velocity are also calculated for various pressure differences. Number density, diffusion coefficient are also investigated for various channel thickness. It is also investigated how radial distribution functions vary for different interlayer distances between two graphene layers.