DMREF: Computational Discovery of Polymeric Membranes for Dehydration of Polar Solvents

Vanderbilt University, Nashville TN

Investigators

Abstract

NON-TECHNICAL SUMMARY Membrane-based separations have revolutionized some industries (e.g., seawater desalination) and have the potential to drive numerous applications towards more energy efficient and sustainable processes. In chemical separations, which are typically highly energy intensive and account for greater than 5% of the annual primary energy consumption in the U.S., membrane-based approaches have significant potential to reduce both energy utilization and capital cost. However, membranes for the separation of mixed solvents, while practically important, are technically challenging as differentiating between the transport of small molecules that have subtle differences in properties is required. The rational design of the next generation of membranes for such separations is a significant challenge, given the vast chemical and design space, but could be realized by Materials Genome Initiative (MGI)-inspired screening. That is, the MGI-style of this effort could alter the current, long-standing membrane development paradigm. In this work, functionality- and performance-driven screening, with close coupling between simulations and experiment, will result in the design and fabrication of high-performance membranes tailored for targeted separations. Specifically, the dehydration of polar solvents by pervaporation will be targeted as an overarching initial target. This is because the discovery and deployment of effective new materials will eliminate the need for high-cost and high-energy separations and enable effective solvent reuse for sustainable manufacturing. TECHNICAL SUMMARY This effort will develop an integrated, MGI-inspired computational and experimental screening platform with the goal of accelerating the rational design of membranes for the dehydration of polar solvents. This will be achieved through the combination of extensive molecular simulations using the Molecular Simulation and Design Framework (MoSDeF); machine learning using DeepForge; syntheses based around the combination of ring-opening metathesis polymerization chemistry combined with spin coating; experimental characterization; and in operando evaluation in a pervaporation process. This molecule-to-process approach will enable the synthesis of a wide array of polymer membrane compositions from a common central scaffold. Moreover, there will be an integral synergy between molecular-level screening simulations and experiment to molecularly design and identify new membranes that are tailored to specific dehydration separations. The goals of this project are the identification, synthesis, characterization, and testing of new candidate polymers for polar organic solvent-water membrane separations, development of a robust library of polymer membrane properties, development of machine learning models that relate chemistry to measured properties of membrane films, and the release of a generally applicable set of software tools that will enable rapid screening and machine learning studies on soft matter systems. Additionally, this effort, with its integration of computational modeling, machine learning, material synthesis, characterization, and performance evaluation for targeted separations, will serve as an excellent educational platform for participating graduate students and postdoctoral researchers to experience the full suite of interconnected components described in the MGI vision. By developing competency in the three foundational pillars of experiment, computation, and data science, the project will develop a workforce aligned with the MGI model. Also, multiple integrated educational activities at the undergraduate and K-12 levels will highlight the potential of computational materials science and the need for close coupling with experiment and data science, inspiring the next generation of the MGI workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.