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Computing Clean Water

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Illustration of a water purification membrane with computationally designed, molecular-scale patterning of surface functional groups.

Synthetically created semi-permeable polymer membranes used for contaminant solute removal can provide a level of advanced treatment and improve the energy efficiency of treating water.

Credit: Brian Long/UCSB

Researchers at the University of California, Santa Barbara (UCSB), the University of Texas at Austin, and the U.S. Department of Energy's Lawrence Berkeley National Laboratory have computationally modeled affinity between solutes and membrane surfaces, to characterize their effects on water purification.

The research team developed a genetic algorithm to repattern surfaces by reconfiguring surface chemical groups to minimize or maximize a given solute's affinity for the surface, or to maximize a solute's surface affinity relative to that of another.

Simulations demonstrated a stronger link between surface affinity and how molecules near a surface or a solute reorganize in response.

Former UCSB researcher Jacob Monroe said, "This work tackles the grand challenge of understanding how to design next-generation membranes that can handle huge yearly volumes of highly contaminated water sources."

From The Current (University of California, Santa Barbara)
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA


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