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• W4385830646 endingPage "166316" @default.
• W4385830646 startingPage "166316" @default.
• W4385830646 abstract "Hydrated electrons (eaq-) exhibit rapid degradation of diverse persistent organic contaminants (OCs) and hold great promise as a formidable reducing agent in water treatment. However, the diverse structures of compounds exert different influences on the second-order rate constant of hydrated electron reactions (keaq-), while the same OCs demonstrate notable discrepancies in keaq- values across different pH levels. This study aims to develop machine learning (ML) models that can effectively simulate the intricate reaction kinetics between eaq- and OCs. Furthermore, the introduction of the pH variable enables a comprehensive investigation into the impact of ambient conditions on this process, thereby improving the practicality of the model. A dataset encompassing 701 keaq- values derived from 351 peer-reviewed publications was compiled. To comprehensively investigate compound properties, this study introduced molecular descriptor (MD), molecular fingerprint (MF), and the integration of both (MD + MF) as model variables. Furthermore, 60 sets of predictive models were established utilizing two variable screening methodologies (MLR and RF) and ten prominent algorithms. Through statistical parameter analysis, it was determined that descriptors combined with MD and MF, the RF screening method, and the symbolism algorithm exhibited the best predictive efficacy. Importantly, the combination of descriptor models exhibited significantly superior performance compared to individual MF and MD models. Notably, the optimal model, denoted as RF - (MF + MD) - LGB, exhibited highly satisfactory predictive results (R2tra = 0.967, Q2tra = 0.840, R2ext = 0.761). The mechanistic explanation study based on Shapley Additive Explanations (SHAP) values further elucidated the crucial influences of polarity, pH, molecular weight, electronegativity, carbon-carbon double bonds, and molecular topology on the degradation of OCs by eaq-. The proposed modeling approach, particularly the integration of MF and MD, alongside the introduction of pH, may furnish innovative ideas for advanced reduction or oxidation processes (ARPs/AOPs) and machine learning applications in other domains." @default.
• W4385830646 created "2023-08-16" @default.
• W4385830646 creator A5011469016 @default.
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• W4385830646 date "2023-12-01" @default.
• W4385830646 modified "2023-10-12" @default.
• W4385830646 title "A new perspective on predicting the reaction rate constants of hydrated electrons for organic contaminants: Exploring molecular structure characterization methods and ambient conditions" @default.
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• W4385830646 doi "https://doi.org/10.1016/j.scitotenv.2023.166316" @default.
• W4385830646 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37591396" @default.
• W4385830646 hasPublicationYear "2023" @default.
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