Interpretable Machine Learning and Reactomics Assisted Isotopically Labeled FT-ICR-MS for Exploring the Reactivity and Transformation of Natural Organic Matter during Ultraviolet Photolysis.

Isotopically labeled FT-ICR-MS combined with multiple post-analyses, including interpretable machine learning (IML) and a paired mass distance (PMD) network, was employed to unravel the reactivity and transformation of natural organic matter (NOM) during ultraviolet (UV) irradiation. FT-ICR-MS analysis was used to assign formulas, which were classified on the basis of their molecular compositions and structural categories. Isotope (deuterium, D) labeling was utilized to unequivocally determine the photochemical products and examine the development of OD radical-mediated NOM transformation. With regard to the reactive molecular formulas, CHOS formulas exhibited the highest reactivity (86.5% of precursors disappeared) followed by CHON (53.4%) and CHO (24.6%) formulas. With regard to structural categories, the degree of reactivity decreased in the following order: tannins > condensed aromatics > lignin/CRAMs. The IML algorithm demonstrated that the crucial features governing the reactivity of formulas were the molecular weight, DBE-O, NOSC, and the presence of heteroatoms (i.e., N and S), suggesting that the large and unsaturated compounds containing S and N are more prone to photodegradation. The reactomics approach using the PMD network further indicated that 11 specific molecular formulas in the CHOS and CHO class served as hubs, implying a higher photoreactivity and participation in a range of transformations. The isotope labeling analyses also found that, among the reactions observed, hydroxylation (i.e., +OD) is dominant for lignin/CRAMs and condensed aromatics, and formulas containing ≤10 D atoms were developed. Overall, this study, by adopting rigorous and interpretable techniques, could provide in-depth insights into the molecular-level dynamics of NOM under UV irradiation.

Prediction of reaction mechanism for OH radical-mediated phenol oxidation using quantum chemical calculation.

Understanding the reaction mechanism of OH•-mediated oxidation of organic micropollutants (OMPs) contributes to the assessment and development of advanced oxidation processes (AOPs) for removal of OMPs in water environment. In this study, a theoretical approach using quantum chemical calculation (QCC) was employed to investigate the prediction accuracy of the reaction mechanism (i.e., reaction site and rate) for OH•-mediated oxidation of phenol, where the hydroquinone and catechol are generated as transformation products (TPs) via radical and electrophilic reactions. We compared three different levels of theory (Hartree-Fock, B3LYP, and M06-2X) with 6-311 + G (2d,2p)/SMD, and the reaction site and rate constants were predicted by the Fukui function and transition state theory, respectively. Overall, the prediction accuracy of the TPs formation mechanism was the highest in the calculations using M06-2X. For example, the initial OH• addition to phenol was predicted to occur with a probability of 77% for the ortho position and 23% for the para position, which was consistent with the experimental observation. By applying the transition state theory, the rate constants toward TPs formation pathway can be reasonably reproduced, suggesting that M06-2X has an effective function for polycyclic reactions. However, the observed discrepancies in rate constants are inferred from dispersion effects and the multi-reference property in the computational system or derived from mismatch of target reactions between theoretical calculations and experiments. Overall, this study provides an insight into QCC application for investigating the formation mechanism of TPs in AOPs for removal of OMPs in water environment.