(360w) Design of Pore Wall Chemistry to Control Solute Transport and Selectivity

Next-generation membranes for water treatment that move beyond simple desalination require materials with precisely tuned functionality. In particular, the purification and reuse of highly contaminated waters, such as oilfield-produced water, face key challenges in removing a wide array of solutes, including small neutral solutes that are difficult to separate. Multifunctional membrane surfaces potentially provide a vast, underexplored design space to improve membrane transport properties, but are difficult to design through trial-and-error. Here, we demonstrate an inverse design computational approach to efficiently identifying promising materials. We develop a combined optimization, machine learning, and molecular simulation workflow to engineer the transport of water relative to that of boric acid in a model nanopore by spatially patterning the pore wall with nonpolar methyl and polar hydroxyl groups. The genetic algorithm optimization identifies non-intuitive functionalization strategies that hinder the transport of boric acid through the pore, simply by altering the functional group patterning. Examining patterns inspired by the genetic algorithm results, we demonstrate that precise spatial functionalization of the methyl and hydroxyl groups differentially impacts the transport of water and boric acid in the pore, enabling design solutions that simultaneously improve both permeability and selectivity, a longstanding trade-off in membrane technology. This inverse design procedure and the accompanying molecular-level understanding of the effect of chemical heterogeneity on solute selectivity demonstrate new routes to the design of membrane materials with novel functionalities, and more broadly, show that surface chemical patterning can modulate water-mediated surface-solute interactions in systems relevant to many other technologies.