\u201cMore than 200 million Americans in all 50 states are affected by PFAS in drinking water, with 1,400 communities having levels above health experts\u2019 safety thresholds,\u201d noted the study\u2019s principal investigator Yongsheng Chen<\/a>, Bonnie W. and Charles W. Moorman IV Professor in Georgia Tech\u2019s School of Civil and Environmental Engineering. \u201cOur research aims to provide a scalable, efficient, and sustainable solution for mitigating these toxic chemicals\u2019 impact on human health and the environment.\u201d<\/p>\n\n\n\n The resulting work, funded with more than $10 million in multiyear grants from the U.S. Department of Agriculture (USDA)<\/a>, the National Science Foundation<\/a>, and the Environmental Protection Agency (EPA)<\/a>, was recently published in Nature Communications<\/em><\/a>.<\/p>\n\n\n\n Conventional water treatment processes are ineffective at removing PFAS. Too often, traditional cleansing methods, such as using chlorine to kill pathogens in water, create harmful byproducts.<\/p>\n\n\n\n \u201cSolving one problem creates another problem,\u201d said Chen.<\/p>\n\n\n\n He has already used ML and artificial intelligence in precision agriculture to monitor nutrient levels in plants and insists that tackling PFAS removal similarly requires new approaches. Rather than treating an entire body of water, Chen\u2019s team first separated PFAS from the water stream. Success depended on finding the right membrane material to isolate the chemicals in the water.<\/p>\n\n\n\n Chen relied on a team of 10 PhD students and nine research scientists to perform the ML modeling. In addition to Georgia Tech, two other schools contributed people and laboratory expertise. The University of Wisconsin-Madison<\/a> validated the model with molecular simulations, while Arizona State University<\/a> trained it using data from scientific literature and their lab.<\/p>\n\n\n\n \u201cApplying machine learning to membrane separation represents an exciting frontier for environmental engineering,\u201d said Tiezheng Tong<\/a>, an associate professor of environmental engineering in ASU\u2019s School of Sustainable Engineering and the Built Environment.<\/p>\n\n\n\n This is another step in tackling PFAS pollution, a widespread problem that has recently received significant public attention due to PFAS\u2019 toxic nature and the recent EPA ruling on PFAS in drinking water<\/a>, he said.<\/p>\n\n\n\n \u201cBy integrating with molecular simulation tools, we can better understand PFAS transport across nanofiltration and reverse osmosis membranes, pushing the boundary of fundamental science relating to membrane separation,\u201d Tong said.<\/p>\n\n\n\n Using ML modeling significantly sped up the discovery process. For instance, one PhD student in Chen\u2019s lab used trial and error over two years to pinpoint one promising membrane. Machine learning modeling allowed the team to find eight membrane candidates 10 to 20 times faster, reducing discovery time from years to a few months.<\/p>\n\n\n\n \u201cOur molecular dynamics simulations reveal that electrostatic interactions, size exclusion, and dehydration play critical roles in governing the transport of PFAS molecules across polyamide membranes,\u201d said Ying Li<\/a>, an associate professor of mechanical engineering<\/a> at UW-Madison. \u201cThese calculations indicate that electrostatic interactions dominate PFAS rejection, with charged functional groups significantly influencing transport behavior. The simulation results provide fundamental insights that align with ML predictions, highlighting the key molecular determinants of PFAS removal efficiency.\u201d<\/p>\n\n\n\n By addressing PFAS contamination, this research could also benefit the agriculture industry, which depends on fertilizer sourced from water treatment plants. Wastewater biosolids are processed into fertilizer, offering farmers and ranchers a cheaper alternative to chemical fertilizers. Unfortunately, PFAS-tainted fertilizers from sewage sludge have contaminated significant amounts of land and livestock. Industry groups estimate that almost 70 million acres of U.S. farmland could be contaminated by these forever chemicals.<\/p>\n\n\n\n By funding this research, the USDA hopes that an effective membrane will help the United States reclaim this crucial resource.<\/p>\n\n\n\n \u201cSynthesizing a very smart membrane to get rid of PFAS also allows us to recover the fertilizer from municipal wastewater treatment plants,\u201d Chen said. \u201cSuch a membrane could enable us to get rid of things we don\u2019t want and keep the things we need, so we can keep the water for irrigation or other applications.\u201d<\/p>\n\n\n\n Eliminating PFAS in fertilizers also could help address the mismatch of food and water demand in urban versus rural areas since 80% of the demand resides in cities. PFAS removal could directly support urban area resource recovery and food production.<\/p>\n\n\n\n \u201cOur goal is achieving a circular economy where materials never become waste, and nature is regenerated,\u201d Chen said.<\/p>\n\n\n\n The team will fine-tune the model and add more data to improve its training features. Chen will synthesize membranes in his lab to further test the model’s PFAS removal predictions.<\/p>\n\n\n\n Today, scientists have found ways to remove long chains of PFAS, but the shorter chains of these chemicals persist, explained Chen.<\/p>\n\n\n\n \u201cIf we can better understand the mechanism, we\u2019ll be able to design a good material membrane to get rid of all PFAS. That could be game-changing.\u201d<\/p>\n\n\n\n This work is partially supported by the NSF (Award Nos. 2112533, 2427299, 2345543, Yongsheng Chen; 2448130, Tiezheng Tong; and 2345542, Ying Li).<\/em><\/p>\n\n\n\n Yongsheng Chen acknowledges the financial support by the USDA (Award No.2018\u221268011-28371), NSF-USDA (Award No. 2020-67021-31526), and EPA (Award No. 840080010).<\/em><\/p>\n\n\n\n Tiezheng Tong acknowledges the support of the USDA National Institute of Food and Agriculture (Hatch Project COL00799, accession 1022591).<\/em><\/p>\n\n\n\n Ying Li acknowledges the financial support by the National Alliance for Water Innovation (NAWI), funded by the US DOE, Office of Energy Efficiency and Renewable Energy (EERE), Advanced Manufacturing Office, under Funding Opportunity announcement Number DE-FOA-0001905, through a subcontract to the University of Wisconsin-Madison.<\/em><\/p>\n\n\n\nSewage treatment limitations<\/h5>\n\n\n\n
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ML accelerates membrane-material discoveries<\/h5>\n\n\n\n
Addressing PFAS exposure in agriculture<\/h5>\n\n\n\n
What\u2019s next<\/h5>\n\n\n\n