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• W3206811452 endingPage "151018" @default.
• W3206811452 startingPage "151018" @default.
• W3206811452 abstract "UV-generated hydrated electrons play a critical role in the defluorination reaction of poly- and perfluoroalkyl substances (PFAS). However, limited experimental data hinder insight into the effects of the structural characteristics of emerging PFAS on their defluorination abilities. Therefore, in this study, we adopted quantity structure-activity relationship models based on machine learning algorithms to develop the predictive models of the relative defluorination ability of PFAS. Five-fold cross-validations were used to perform the hyperparameter tuning of the models, which suggested that the gradient boosting algorithms with PaDEL descriptors as the best model possessed superior predictive performance (R2test = 0.944 and RMSEtest = 0.114). The importance of the descriptor indicated that the electrostatic properties and topological structure of the compounds significantly affected the defluorination ability of the PFAS. For the emerging PFAS the best model showed that most compounds, such as potential alternatives of perfluorooctane sulfonic acid, were recalcitrant to reductive defluorination, whereas perfluoroalkyl ether carboxylic acids had relatively stronger defluorination abilities than perfluorooctanoic acid. The theoretical calculations implied that additional electrons on PFAS could cause molecular deconstruction, such as changes in the dihedral angle involved in the carbon chain, as well as C-F bond and ether C-O bond cleavages. In general, the current computational models could be useful for screening emerging PFAS to assess their defluorination ability for the molecular design of fluorochemical structures." @default.
• W3206811452 created "2021-10-25" @default.
• W3206811452 creator A5033848652 @default.
• W3206811452 creator A5043750772 @default.
• W3206811452 creator A5072379029 @default.
• W3206811452 creator A5078915518 @default.
• W3206811452 creator A5083502884 @default.
• W3206811452 creator A5091410060 @default.
• W3206811452 date "2022-02-01" @default.
• W3206811452 modified "2023-10-16" @default.
• W3206811452 title "Insight into the defluorination ability of per- and polyfluoroalkyl substances based on machine learning and quantum chemical computations" @default.
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