Profiling the Photosensitizing Properties of Atmospheric Brown Carbon

Upon light absorption, some atmospheric brown carbons (BrC) can be converted to triplet excited states (3C*), which initiate reactions with other atmospheric molecules in a process known as photosensitization. While 3C* have emerged as critical atmospheric oxidants, parametrizing their reactivities remains challenging due to the complex speciation of photosensitizers (PSs) and their diverse reactivity. In this study, we report machine learning models to predict and classify the key properties of BrC governing photosensitization, including triplet-state energy (ET), one-electron reduction potential (Eground°), and intersystem crossing (ISC) quantum yield (ΦISC), based on molecular structures. The regression models effectively predicted ET (R2 = 0.77) and Eground° (R2 = 0.75), while the classification model for ΦISC achieved an F1 score of 0.66. We first applied models on commonly used PSs in the literature to profile their chemical reactivities toward typical atmospheric substrates. Moreover, we employed the models on identified potential PSs in biomass-burning organic aerosols (BBOA). Our findings provide new insights into the speciation and reactivities of atmospheric PSs.