Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations
Abstract 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 acros...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55320-9 |
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| _version_ | 1841559340223299584 |
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| author | Nohyeong Jeong Shinyun Park Subhamoy Mahajan Ji Zhou Jens Blotevogel Ying Li Tiezheng Tong Yongsheng Chen |
| author_facet | Nohyeong Jeong Shinyun Park Subhamoy Mahajan Ji Zhou Jens Blotevogel Ying Li Tiezheng Tong Yongsheng Chen |
| author_sort | Nohyeong Jeong |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-1a29aa22f0ab4d69a38f794f85f49176 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-1a29aa22f0ab4d69a38f794f85f491762025-01-05T12:34:32ZengNature PortfolioNature Communications2041-17232024-12-0115111310.1038/s41467-024-55320-9Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulationsNohyeong Jeong0Shinyun Park1Subhamoy Mahajan2Ji Zhou3Jens Blotevogel4Ying Li5Tiezheng Tong6Yongsheng Chen7School of Civil & Environmental Engineering, Georgia Institute of TechnologyDepartment of Civil and Environmental Engineering, Colorado State UniversityDepartment of Mechanical Engineering, University of Wisconsin-MadisonDepartment of Mechanical Engineering, University of Wisconsin-MadisonDepartment of Civil and Environmental Engineering, Colorado State UniversityDepartment of Mechanical Engineering, University of Wisconsin-MadisonDepartment of Civil and Environmental Engineering, Colorado State UniversitySchool of Civil & Environmental Engineering, Georgia Institute of TechnologyAbstract 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.https://doi.org/10.1038/s41467-024-55320-9 |
| spellingShingle | Nohyeong Jeong Shinyun Park Subhamoy Mahajan Ji Zhou Jens Blotevogel Ying Li Tiezheng Tong Yongsheng Chen Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations Nature Communications |
| title | Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations |
| title_full | Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations |
| title_fullStr | Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations |
| title_full_unstemmed | Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations |
| title_short | Elucidating governing factors of PFAS removal by polyamide membranes using machine learning and molecular simulations |
| title_sort | elucidating governing factors of pfas removal by polyamide membranes using machine learning and molecular simulations |
| url | https://doi.org/10.1038/s41467-024-55320-9 |
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