Professor Yongsheng Chen leads a multi-university team using machine learning to discover PFAS-removing membranes.
Someday, your drinking water could be completely free of toxic "forever chemicals."
These chemicals, called PFAS (per- and polyfluoroalkyl substances), are found in common household items like makeup, nonstick cookware, dental floss, batteries, and food packaging. PFAS permeate the soil, water, food, and air, and they can remain in the environment for millennia. Once inside the human body, PFAS can persist for years, suppressing the immune system and increasing cancer risk.
Georgia Tech researchers, armed with a cutting-edge machine learning (ML) model, are spearheading a multi-university initiative. Their goal? To design a better membrane that efficiently removes PFAS from drinking water, a significant source of human exposure.
"More than 200 million Americans in all 50 states are affected by PFAS in drinking water, with 1,400 communities having levels above health experts' safety thresholds," noted the study's principal investigator Yongsheng Chen, Bonnie W. and Charles W. Moorman IV Professor in Georgia Tech's School of Civil and Environmental Engineering. Chen also directs the Nutrients, Energy, and Water Center for Agriculture Technology, or NEW Center. "Our research aims to provide a scalable, efficient, and sustainable solution for mitigating these toxic chemicals' impact on human health and the environment."
The resulting work, funded with over $10 million in multiyear grants from the U.S. Department of Agriculture (USDA), the National Science Foundation, and the Environmental Protection Agency (EPA), was recently published in Nature Communications.
Sewage Treatment Limitations
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.
"Solving one problem creates another problem," said Chen.
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's team first separated PFAS from the water stream. Success depended on finding the right membrane material to isolate the chemicals in the water.
Chen relied on a team of 10 Ph.D. 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 (UWM) validated the model with molecular simulations, while Arizona State University (ASU) trained it using data from scientific literature and their lab.