A new perspective on predicting the reaction rate constants of hydrated electrons for organic contaminants: Exploring molecular structure characterization methods and ambient conditions

Tengyi Zhu; Shuyin Li; Lili Li; Cuicui Tao

doi:10.1016/j.scitotenv.2023.166316

A new perspective on predicting the reaction rate constants of hydrated electrons for organic contaminants: Exploring molecular structure characterization methods and ambient conditions

Sci Total Environ. 2023 Dec 15:904:166316. doi: 10.1016/j.scitotenv.2023.166316. Epub 2023 Aug 15.

Authors

Tengyi Zhu¹, Shuyin Li², Lili Li², Cuicui Tao²

Affiliations

¹ School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China. Electronic address: tyzhu@yzu.edu.cn.
² School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, Jiangsu, China.

PMID: 37591396
DOI: 10.1016/j.scitotenv.2023.166316

Abstract

Hydrated electrons (e_aq^-) 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 (k_eaq^-), while the same OCs demonstrate notable discrepancies in k_eaq^- values across different pH levels. This study aims to develop machine learning (ML) models that can effectively simulate the intricate reaction kinetics between e_aq^- 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 k_eaq^- 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 (R²_tra = 0.967, Q²_tra = 0.840, R²_ext = 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 e_aq^-. 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.

Keywords: Advanced reduction; Hydrated electrons; Machine learning; Molecular fingerprints; Molecular structure descriptors; Organic contaminants.