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Updated: Mar 21, 2026

Time-resolved Photophysical Characterization of Triplet-harvesting Organic Compounds at an Oxygen-free Environment Using an iCCD Camera
Published on: December 27, 2018
Quantification and modeling of deactivation rate constant of singlet oxygen by low-molecular-weight dissolved organic
Yuta Hatano1, Zhongyu Guo2, Chihiro Yoshimura1
1Department of Civil and Environmental Engineering, Institute of Science Tokyo, Tokyo 152-8552, Japan.
Abstract:
Singlet oxygen (1O2) is a key reactive intermediate in aquatic photochemistry, but its deactivation by dissolved organic matter (DOM) remains poorly characterized. In this study, we quantitatively evaluated the deactivation rate constant of 1O2 by representative DOM compounds and developed predictive models for the corrected total deactivation rate constant ( [Formula: see text] ) based on experimentally measurable optical properties. Experiments with representative phenolic, amino, and carbohydrate compounds revealed that the deactivation rate constant of 1O2 by each of those compounds (kQ) ranged from 8.76×107 to 2.12×109 s-1 M-1. The quantitative structure-activity relationship (QSAR) determined the key determinants of kQ, which are molecular descriptors such as the HOMO-LUMO gap (an indicator of π-conjugation) and charge-related topological indices (indicators of electron-donating capacity). These findings elucidated the structural factors governing 1O2 deactivation. The mixture experiments indicated that the total 1O2 deactivation could be explained by the additive contribution of individual components. Additionally, random forest model based on optical properties, particularly a254, E4:E6, S350-400, and FI, described [Formula: see text] with high accuracy (R2=0.85). This model highlights that simple absorbance-derived optical properties can act as practical and reliable proxies for 1O2 quenching capacity of a certain range of DOM types. The proposed predictive framework provides a basis for accurately estimating the lifetime and reactivity of 1O2 in natural waters, thereby improving the prediction of contaminant fate and carbon dynamics in aquatic environments.

