Quantitative structure-activity relationship for the photooxidation of aromatic micro-pollutants induced by graphene oxide in water

Abstract

The photocatalytic degradation behavior of aromatic micro-pollutants (AMPs) exhibits complexity and uncertainty, which mainly depends on the properties of different substituents on benzene. And with similar catalytic reaction substrates, the reaction rate constant could reveal the influence of different characteristics of molecular structure in a specific system. Therefore, the photooxidation pseudo first-order kinetic rate constants (kobs) of 30 AMPs were experimentally determined in Photo-GO system. A quantitative structure-activity relationship (QSAR) model for predicting the photooxidation reaction of AMPs has been developed by stepwise multiple linear regression (MLR) method, based on the lg kobs and representative molecule descriptors (20 in total) including physicochemical, quantum chemical and electrostatic descriptors. Afterwards, Radj2, QLOO2, and Qext2 were calculated as 0.870, 0.841, and 0.732 respectively, which exhibited the excellent goodness-of-fit, robustness, and predictability of the QSAR model, indicating its great prediction ability for photooxidation behavior of AMPs. Meanwhile, during the photooxidation process of AMPs with GO, the model revealed that the one-electron oxidation potential (Eox), molecular dipole moment (μ), and number of hydrogen bond donors (#HD) were the most important molecular structural parameters, which showed that the single electron transfer pathway and adsorption were as the significant steps. Additionally, the Hammett correlation showed that photooxidation of AMPs in Photo-GO system is of typical electrophilic reactions, which demonstrated that the electron-donating substituents could promote the photooxidation of AMPs. The QSAR model was constructed and evaluated to perform the prediction of AMPs reaction kinetics, which provided a guidance for the study of the mechanism and selective oxidation of AOPs photooxidation system based on GO.