Microkinetic modeling has come to light as a tool for accurately predicting the outcomes of catalytic processes with the promise to bridge experiments and theory. In electrochemical systems, understanding the pivotal role of operating conditions such as pH, temperature, and applied potential (V) on the reaction microenvironment and their influence on product selectivity and yield for complex electrochemical reaction networks further necessitates its application. However, previous studies on electrocatalytic systems have solely focused on steady-state microkinetic modeling, which prevents the investigation of the transient and dynamic features of polarization curves. In this work, we address this significant knowledge gap by developing an end-to-end pipeline from modeling the unsteady behavior of electrochemical systems to using a graph-theoretic approach for finding the rate-determining step (RDS). We also incorporate the possibility of investigating potential sweeps, bringing the model closer to mimicking experimental measurements like linear sweep and staircase voltammetry. We found that unsteady state modeling removes transient artifacts and ensures a smooth transition in the current density. Moreover, using our unsteady approach, we were able to match the long-time predictions for carbon monoxide electro-reduction and the oxygen evolution reaction with results modeled using quasi-equilibrium approximations, demonstrating the accuracy of our approach. Overall, our work advances the understanding of the dynamics of electrocatalytic mechanisms by establishing a versatile framework for studying both steady-state and unsteady-state conditions.
