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 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 mo

SUBMITTER: Jeong N 

PROVIDER: S-EPMC11686221 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature