Bridging the length scales in ionic separations via data-driven science and machine learning
ORAL
Abstract
Selective ionic separations is important to wastewater remediation, mineral recovery, and bio-based products produced from organic acids derived from biomass. Central to achieving selective ionic separations is understanding how ion-exchange membranes' composition and microstructure affect ion partitioning coefficients and ionic conductivity. Our lab has engaged in thin film and bulk membrane measurements to generate structure-property relationships that enable selective ionic separations. These experiments are complemented with molecular dynamics and quantum calculations to provide further insights to ionic selectivity. The talk will briefly conclude with our future approach to advanced selective ionic separations using machine learning that captures data from molecular simulations, materials experiments, and device-level demonstrations to guide future materials design and ionic separation platforms for more effective and energy efficient ionic separations.
*This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences Separation Science program under Award No. DE-SC0022304.
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Publication:1. Q. Lei, K. Li, D. Bhattacharya, J. Xiao, S. Kole, Q. Zhang, J. Strzalka, J. Lawrence, R. Kumar, and C.G. Arges, Counterion condensation or lack of solvation? Understanding the activity of ions in thin film block copolymer electrolytes, Journal of Materials Chemistry A, 2020, 8, 15962, https://doi.org/10.1039/D0TA04266H
2.) M.V. Ramos-Garcés, K. Li, Q. Lei, D. Bhattacharya, S. Kole, Q. Zhang, J. Strzalka, P.P. Angelopoulou, G. Sakellariou, R. Kumar, and C.G. Arges, Understanding the ionic activity and conductivity value differences between random copolymer electrolytes and block copolymer electrolytes of the same chemistry, RSC Advances, 2021, 11, 15078-15084, https://doi.org/10.1039/D1RA02519H
3.) L. Briceno-Mena, G. Venugopalan, J.A. Romagnoli, and C.G. Arges, Machine learning for guiding high-temperature PEM fuel cells with greater power density, Patterns (Cell Press), 2021, 2, 100187, https://doi.org/10.1016/j.patter.2020.100187
