Designing Better Water Filters With AI

Researchers in ACS Central Science simulated a synthetic carbon nanotube pore designed to filter boric acid-containing water, taking inspiration from the characteristics of biological membranes.

Led by M. Scott Shell, the group utilized hydroxyl (water-attracting) and methyl (water-repelling) groups connected to every atom on the inner wall. Using artificial intelligence (AI), they created thousands of functional group patterns and tested the speed at which water and boric acid would move through the said pore.

Results showed that having one or two rows of hydroxyl groups between methyl groups in a ring-like formation at the pore's midsection is the most effective. The simulations also showed that water moves through the pore twice as fast as boric acid. Moreover, the optimized carbon nanotube designs can also separate phenol, isopropanol, benzene, and other neutral solutes from water.

Another key takeaway from this study is the potential role of AI in developing new water purification membranes and filter systems.

Read the full article here to learn more about AI-optimized functional group patterns.

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