Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations
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Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models. Utilizing the Shapley additive explanation method for XGBoost model interpretation unveils the impacts of both PFAS characteristics and membrane properties on model predictions. The examination of the impacts of chemical structure involves interpreting the multimodal transformer model incorporated with simplified molecular input line entry system strings through heat maps, providing a visual representation of the attention score assigned to each atom of PFAS molecules. Both ML interpretation methods highlight the dominance of electrostatic interaction in governing PFAS transport across polyamide membranes. The roles of functional groups in altering PFAS transport across membranes are further revealed by molecular simulations. The combination of ML with computer simulations not only advances our knowledge of PFAS removal by polyamide membranes, but also provides an innovative approach to facilitate data-driven feature selection for the development of high-performance membranes with improved PFAS removal efficiency.
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The prevalence of PFCAs in water can be attributed to their comparatively higher aqueous solubility and decreased sediment affinity.
“Therefore, the prevalence of PFCAs in water can be attributed to their comparatively higher aqueous solubility and decreased sediment affinity.”
From Land use and seasonal controls on Per- and Polyfluoroalkyl Substances (PFAS) distribution and partitioning from diffuse sources in subtropical urban waterways (2026) · cites this paper as [35]
The Abstract describes PFAS in water generally, while “Probing key factors for PFAS removal by transformer model” compares PFCAs and PFSAs in their interactions with membranes. Neither passage explains PFCA prevalence in water through higher aqueous solubility or lower sediment affinity; searches for those terms found no such account in our copy. (LLM, conf=0.76)
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Koongolla, J. B., Egodawatta, P., Goonetilleke, A., & Ayoko, G. A. (2026) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2026.143434
- Authors
- Jeong, N., Park, S., Mahajan, S., Zhou, J., Blotevogel, J., Li, Y., Tong, T., & Chen, Y.
- Affiliations
- Georgia Institute of Technology (US)
- Arizona State University (US)
- University of Wisconsin–Madison (US)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Corresponding author
- Li, Ying
- Tong, Tiezheng
- Chen, Yongsheng
- ORCID
- Venue
- Nature Communications
- Published
- 2024-12-30
- Volume
- 15
- Issue
- 1
- Pages
- 10918-10918
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11686221
- OpenAlex
- W4405895621
- Cited by
- 98
- References
- 7
- Citations by year
- 2026: 66 · 2025: 32
- FWCI
- 12.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (2448130, 2020-67021-31526, 2345542, 0001905, 2018-68011-28371, 2112533, 2345543, 2427299, 1022591)
- U.S. Department of Energy (DE‐FOA‐0001905)
- U.S. Environmental Protection Agency (840080010)
- U.S. Department of Agriculture (accession 1022591, 2018-68011-28371, 2020-67021-31526)
- University of Wisconsin-Madison
- National Institute of Food and Agriculture (2112533, 67021, 2345543, 2020-67021-31526, COL00799, 2018-68011-28371)
- Office of Energy Efficiency and Renewable Energy (DE-FOA-0001905)
- Office of Energy Efficiency (DE-FOA-0001905)
- Topics
- Physical Sciences › Environmental Science › Environmental Chemistry › Per- and polyfluoroalkyl substances research
- Physical Sciences › Environmental Science › Water Science and Technology › Membrane Separation Technologies
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- per- and polyfluoroalkyl substances, PFAS removal, polyamide membranes, membrane separation, electrostatic interactions, functional groups, structure-property relationships, SHAP, XGBoost, multimodal transformer, attention maps
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