A Computational Approach to Efficient Electrode Material Design for Electrochemical Oxidation of PFAS

Per- and polyfluoroalkyl substances (PFAS) are persistent contaminants of growing environmental concern due to their strong carbon-fluorine bonds and resistance to conventional degradation methods. Among destructive treatment technologies, electrochemical advanced oxidation processes (EAOP) have shown great promise for PFAS remediation in water. However, the reaction mechanisms are inadequately understood, which hinders the development of design principles for efficient electrode materials. This thesis presents a computational framework to advance the understanding of the oxidation mechanism of EAOP, focusing on perfluorooctanoic acid (PFOA) as a model contaminant. Through quantum mechanical simulations, the initial steps of EAOP, mainly the decarboxylation and chain-shortening reactions, are systematically explored. The effects of surface termination, PFOA orientation, explicit solvation, and applied potential are first evaluated on the Ti4O7 [112] surface, revealing that decarboxylation is favored for specific orientation of PFOA, and takes place under applied potential. Energetic barriers for decarboxylation were calculated next. These insights are then extended to other electrode materials such as boron-doped diamond (BDD), SnO2, Bi2O3, and RuO2, highlighting specific influences on the chain-shortening reactions. Additionally, it is shown that the major competing reaction for EAOP is the oxygen evolution reaction (OER). Prevention of OER is thus key to designing efficient electrode materials for EAOP. Building on these mechanistic insights, the thesis transitions toward accelerating discovery of materials that can prevent OER. For this purpose, graph neural network (GNN) models are shown to be an efficient pathway to screen a diverse library of electrode materials by accurately predicting the parameters involved in OER. Overall, this work bridges atomistic insight and data-driven modeling to establish a theory-guided approach for electrocatalyst design. It contributes a fundamental understanding of the mechanism of EAOP, while laying the groundwork for rapid discovery of advanced materials for PFAS remediation.