Issue 1, 2023

Development of a quantitative structure–activity relationship model for predicting quantum yield of hydroxyl radical generation from organic compounds

Abstract

Organic compounds are capable of generating hydroxyl radicals (˙OH) through their excited triplet states in natural water. It is of significance to reveal the underlying mechanism of the generation and obtain the generation quantum yield of ˙OH from organic compounds for better understanding of its involvement in indirect photochemical processes in the environment. In this study, the ˙OH quantum yields (Φ˙OH) of 20 organic compounds were determined by photochemical experiments. The calculated Φ˙OH values for the selected organic compounds vary from (1.2 ± 0.39) × 10−5 to (7.2 ± 0.16) × 10−4. A quantitative structure–activity relationship (QSAR) model for log Φ˙OH was developed and the established model was proven to have a proper goodness of fit, robustness, and predictive ability. The QSAR model was successfully used to predict the Φ˙OH value of organic pollutants. Mechanistic interpretation showed that the electron distribution and the electronegativity of organic compounds are the most important factors that determine the generation of ˙OH. The results are helpful for understanding the generation mechanism of ˙OH from organic compounds and also provide insights into the generation of ˙OH from dissolved organic matter in natural water.

Graphical abstract: Development of a quantitative structure–activity relationship model for predicting quantum yield of hydroxyl radical generation from organic compounds

Supplementary files

Article information

Article type
Paper
Submitted
29 Sep 2022
Accepted
23 Nov 2022
First published
24 Nov 2022

Environ. Sci.: Processes Impacts, 2023,25, 66-74

Development of a quantitative structure–activity relationship model for predicting quantum yield of hydroxyl radical generation from organic compounds

Y. Liu, X. Chen, J. Zhao, W. Jin, K. Zhang, J. Qu, Y. Zhang, G. Chen and W. J. G. M. Peijnenburg, Environ. Sci.: Processes Impacts, 2023, 25, 66 DOI: 10.1039/D2EM00396A

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