Even the best water filters let some things through, but designing improved materials and then testing them is time-consuming and difficult. Now, researchers from AEC Core Sciences report that artificial intelligence (AI) could accelerate the development of promising materials. In a proof-of-concept study, they simulated different patterns of hydrophilic and hydrophobic groups covering the porous membrane of a filter and found optimal arrangements that should let water through easily and slow down certain contaminants.
Filtration systems, ranging from faucet attachments to room-sized industrial systems, clean water for drinking and other uses. However, current filtration membranes struggle if the water is extremely dirty or contains small neutral molecules, such as boric acid – an insecticide commonly used on crop plants. Indeed, synthetic porous materials are generally limited to sorting compounds by size or charge. But biological membranes have pores made of proteins, such as aquaporin, which can separate water from other molecules by size and charge due to the different types of functional groups, or collections of atoms, lining the channels. . Inspired to do the same with a synthetic porous material, Scott Shell and his colleagues wanted to use computers to design the inside of a carbon nanotube pore to filter water containing boric acid.
The researchers simulated a channel of carbon nanotubes with hydroxyl (water-attracting) and/or methyl (water-repelling) groups attached to each atom on the inner wall. Then they designed and tested thousands of functional group models with optimization algorithms and machine learning, a type of AI, to gauge how fast water and boric acid would move through pores. Here is what they found:
- Optimal models had one or two rows of hydroxyl groups sandwiched between methyl groups, forming rings around the midsection of the pore.
- In these simulations, water passed through the pore almost twice as fast as boric acid.
- Another series of simulations showed that other neutral solutes, including phenol, benzene and isopropanol, could also be separated from water with the optimized carbon nanotube designs.
According to the researchers, this study demonstrates the usefulness of AI for developing water purification membranes with new properties and could form the basis of a new type of filtration system. They add that the approach could be adapted to design surfaces that might have unique interactions with water or other molecules, such as coatings that resist fouling.
The authors acknowledge funding from the US Department of Energy (through the Center for Materials for Water and Energy Systems (M-WET), an Energy Frontier Research Center) with additional support from the US National Science Foundation, California NanoSystems Institute , from the Materials Center for Science and Engineering Research and National Science Foundation Fellowship.
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Material provided by American chemical society. Note: Content may be edited for style and length.
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Designing better water filters with AI – CNET
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