Ozonation of Gabapentin in Water─Investigating Reaction Kinetics and Transformation Mechanisms of a Primary Amine Using Isotopically Labeled Ozone

Isotope Techniques in Chemical Wastewater Treatment: Opportunities and Uncertainties

A comprehensive and in-depth analysis of reaction mechanisms is essential for advancing chemical water treatment technologies. However, due to the limitations of conventional experimental and analytical methods, the types of reactive species and their generation pathways are commonly debatable in many aqueous systems. As highly sensitive diagnostic tools, isotope techniques offer deeper insights with minimal interference from reaction conditions. Nevertheless, precise interpretations of isotope results remain a significant challenge. Herein, we first scrutinized the fundamentals of isotope chemistry and highlighted key changes induced by the isotope substitution. Next, we discussed the application of isotope techniques in kinetic isotope effects, presenting a roadmap for interpreting KIE in sophisticated systems. Furthermore, we summarized the applications of isotope techniques in elemental tracing to pinpoint reaction sites and identify dominant reactive species. Lastly, we propose future research directions, highlighting critical considerations for the rational design and interpretation of isotope experiments in environmental chemistry and related fields.

Integrating molecular descriptors for enhanced prediction: Shedding light on the potential of pH to model hydrated electron reaction rates for organic compounds

Hydrated electron reaction rate constant (ke-aq) is an important parameter to determine reductive degradation efficiency and to mitigate the ecological risk of organic compounds (OCs). However, OC species morphology and the concentration of hydrated electrons (e-aq) in water vary with pH, complicating OC fate assessment. This study introduced the environmental variable of pH, to develop models for ke-aq for 701 data points using 3 descriptor types: (i) molecular descriptors (MD), (ii) quantum chemical descriptors (QCD), and (iii) the combination of both (MD + QCD). Models were screened using 2 descriptor screening methods (MLR and RF) and 14 machine learning (ML) algorithms. The introduction of QCDs that characterized the electronic structure of OCs greatly improved the performance of models while ensuring the need for fewer descriptors. The optimal model MLR-XGBoost(MD + QCD), which included pH, achieved the most satisfactory prediction: R2tra = 0.988, Q2boot = 0.861, R2test = 0.875 and Q2test = 0.873. The mechanistic interpretation using the SHAP method further revealed that QCDs, polarizability, volume, and pH had a great influence on the reductive degradation of OCs by e-aq. Overall, the electrochemical parameters (QCDs, pH) related to the solvent and solute are of significance and should be considered in any future ML modeling that assesses the fate of OCs in aquatic environment.