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• W4323566610 endingPage "2443" @default.
• W4323566610 startingPage "2443" @default.
• W4323566610 abstract "Although heterogeneous photocatalysis has shown promising results in degradation of contaminants of emerging concern (CECs), the mechanistic implications related to structural diversity of chemicals, affecting oxidative (by HO•) or reductive (by O2•-) degradation pathways are still scarce. In this study, the degradation extents and rates of selected organics in the absence and presence of common scavengers for reactive oxygen species (ROS) generated during photocatalytic treatment were determined. The obtained values were then brought into correlation as K coefficients (MHO•/MO2•-), denoting the ratio of organics degraded by two occurring mechanisms: oxidation and reduction via HO• and O2•-. The compounds possessing K >> 1 favor oxidative degradation over HO•, and vice versa for reductive degradation (i.e., if K << 1 compounds undergo reductive reactions driven by O2•-). Such empirical values were brought into correlation with structural features of CECs, represented by molecular descriptors, employing a quantitative structure activity/property relationship (QSA/PR) modeling. The functional stability and predictive power of the resulting QSA/PR model was confirmed by internal and external cross-validation. The most influential descriptors were found to be the size of the molecule and presence/absence of particular molecular fragments such as C - O and C - Cl bonds; the latter favors HO•-driven reaction, while the former the reductive pathway. The developed QSA/PR models can be considered robust predictive tools for evaluating distribution between degradation mechanisms occurring in photocatalytic treatment." @default.
• W4323566610 created "2023-03-09" @default.
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• W4323566610 date "2023-03-07" @default.
• W4323566610 modified "2023-10-17" @default.
• W4323566610 title "Structural Features Promoting Photocatalytic Degradation of Contaminants of Emerging Concern: Insights into Degradation Mechanism Employing QSA/PR Modeling" @default.
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• W4323566610 doi "https://doi.org/10.3390/molecules28062443" @default.
• W4323566610 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36985414" @default.
• W4323566610 hasPublicationYear "2023" @default.
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