Charge-Transfer-Resistance-Informed Kinetic Simulation of Pollutant Degradation Using Functionalized Graphene Electrodes in an Electrochemical Batch Reactor: A Dynamic Modelling Framework for Electrode Screening in Water Treatment

Abstract

Electrochemical advanced oxidation processes (EAOPs) are increasingly recognised as effective technologies for the degradation of persistent organic pollutants in water, with electrode material selection critically determining treatment performance. Functionalised graphene electrodes including graphene oxide (GO), reduced graphene oxide (rGO), and nitrogen-doped graphene oxide (N-GO) offer tunable surface chemistry and charge-transfer properties relevant to electrochemical pollutant degradation. However, the relationship between electrode electrochemical impedance characteristics and pollutant removal kinetics has not been evaluated through dynamic systems modelling. In this study, a charge-transfer-resistance-informed pseudo-first-order kinetic model was implemented in OpenModelica to simulate pollutant concentration decay and removal efficiency in an electrochemical batch reactor over a 180-minute treatment period. Electrode charge-transfer resistance values were used to derive effective degradation rate constants through an Rct-scaling relationship, enabling direct linkage between electrochemical characterisation data and kinetic performance prediction. The model predicted pollutant concentration, cumulative removal efficiency, and kinetic rate constants for GO, N-GO, and rGO electrodes. Results demonstrated that rGO achieved the highest simulated removal efficiency of 94.39% at 180 minutes, with an effective rate constant of 0.0160 min⁻¹ a 3.2-fold improvement over GO (0.0050 min⁻¹, 59.34% removal). N-GO exhibited intermediate performance (0.0089 min⁻¹, 79.81% removal). Sensitivity analysis confirmed that charge-transfer resistance is the dominant kinetic parameter governing electrode performance, with removal efficiency decreasing sharply as Rct increases from 10 to 120 Ω. A time-to-threshold analysis further demonstrated that rGO achieves 50% pollutant removal 3.2 times faster than GO and is the only electrode to achieve 90% removal within the 180-minute window.