Reports: G5
48188-G5 Molecular Simulation Study of Transport and Adsorption of Liquids through Nanoporous Block Copolymers
Membrane separation of fluid mixtures using nanoporous materials is currently a subject of great interest. The performance of membrane is strongly depended on the nanoscale chemical and structural properties of the membrane, which is essential to obtain atomic-level insights into the structure-property relation of membrane and its impact on the confined fluid behaviors under operation conditions.
In the course of our research, we have performed grand canonical Monte Carlo (GCMC) simulations to study the adsorption behavior of pure water and methanol in a copolymer poly(ethylene oxide) (PEO)-poly(propylene oxide) (PPO) matrix. The effects of PEO-to-PPO ratio of xPEO = 30%, 50%, and 70% and temperature on the adsorption of liquids in the polymer were examined. GCMC simulation results show that (i) methanol is preferentially adsorbed over water in the PEO-PPO polymer for a wide range of chemical potentials and temperatures, due to stronger dispersion interactions; (ii) the cooperative bonding mechanism results in the continuous pore filling of methanol in the PEO-PPO polymers, as compared to capillary condensation of water in the PEO-PPO polymers; (iii) the negative values of solvation force reflect the hydrophilic interactions of water and methanol with PEO dominated polymers, as compared to the positive values obtained in PPO dominated polymers.
By comparing the adsorption isotherms for both water and methanol in the PEO-PPO polymers with different PEO-PPO ratios, it is expected that water-methanol mixture can be greatly separated at xPEO = 30% and T=298 K. Based on this optimized condition, we will further explore the transport of water-methanol mixtures through a flexible copolymer membrane using the dual-control-volume grand canonical ensemble molecular dynamics (GCMD) simulations in the coming year. Selectivity and permeability of copolymers will be served as criteria to further optimize the composition of copolymer membranes. In parallel, the funds from ACS program also allow us to develop a hybrid simulation method of Monte Carlo-minimization and molecular dynamics (MCM-MD) for predicting the nanostructures of surfactants, metal clusters, and peptides.
This PRF Type G grant was my first grant. In 2009 summer, Xiang Yu, the first Ph.D student in my group, has worked on this membrane separation project. The knowledge derived from this work have delivered 2 published papers, 1 talk at ACS conference in 2008, and 1 talk at AIChE conference in 2008. The preliminary results obtained with the PRF grant were included in my NSF proposal, which is currently under review by the NSF.
