(159b) Molecular Simulations to Inform Polymeric Membrane Science and Technology | AIChE

(159b) Molecular Simulations to Inform Polymeric Membrane Science and Technology

Authors 

Lueptow, R. - Presenter, Northwestern University
Molecular dynamics simulations offer the opportunity to address key knowledge gaps in Angstrom-scale transport in polymeric membranes. These simulations can illuminate atomic level details for membrane/solvent/solute interactions governing membrane performance and provide new ways to model how the tortuous transport pathways, associated energy landscapes, and membrane charge structure influence solvent flux and solute rejection. However, there are many challenges. Models of polymeric membranes that accurately reflect the membrane molecular structure and still provide length and time scales that can represent real membrane processes are difficult. Appropriately representing the interactions between the membrane, solvent, and solutes sometimes requires unique modeling methods. Accurately representing the non-equilibrium physical processes that govern transport through a membrane requires special approaches. The vast number of calculations necessary to simulate even a few nanoseconds of transport through a membrane is computationally daunting. And finally, extracting meaningful information from the simulations that can lead to a better understanding of solute and solute transport through a membrane is nontrivial. While the focus of this talk will be on molecular dynamics simulations of polymeric nanofiltration and reverse osmosis membranes, the general principles can be applied to membranes more broadly. The ultimate goal is to use molecular-level simulations of polymeric membranes to not only broaden the understanding of membrane transport processes, but also to extend the approach to achieve "membrane-by-design" molecular structures for specific applications of membrane technology. Funded by the Institute for Sustainability and Energy at Northwestern with computing resources from XSEDE, which is supported by NSF grant ACI-1053575.