Combining Cyclic Square Wave Voltammetry Experiment and Modeling to Quantify Unknown Electron Transfer Mechanisms for Applications in Relevant Electrochemical Systems

Abstract

Understanding and controlling electron transfer process(es) is of significant importance to electrochemical systems that employ Faradaic reactions, as knowledge of reaction mechanisms enables improvements on the device level (e.g., reducing the kinetic overpotential, minimizing undesirable homogeneous reactions). Therefore, developing robust and inexpensive techniques capable of diagnosing electron transfer mechanisms is highly desirable. Currently, many methods are able to quantify specific electron transfer mechanisms. For example, the Nicholson Method can determine the heterogeneous rate constant of a quasi-reversible one-electron transfer; further, voltammetric simulations can capture the behavior of more complex electron transfer phenomena.1,2 However, while these previous studies have successfully combined modeling and experiment to quantify various electrode mechanisms, such approaches require a priori knowledge of the electron transfer pathway to extract meaningful information. If a system has multiple possible electron transfer pathways, the approach used in these previous studies provides inadequate information regarding which pathway is the correct one, limiting its utility in novel or poorly understood systems. Thus, this protocol would be even more powerful if it could both determine relevant parameters for an appropriate electron transfer pathway and correctly select unknown electron transfer pathways. We successfully address this shortcoming using a robust and accessible protocol that determines electron transfer mechanisms of different redox couples by combining voltammetry, modeling, and statistical analysis. Specifically, we couple cyclic square wave voltammetry, due to its increased sensitivity to Faradaic processes as compared to conventional voltammetry,3 with model selection criteria based on statistical inference (e.g., Akaike Information Criterion, Bayesian Information Criterion), to determine the most appropriate electron transfer model. Our protocol requires only experimental data and essential inputs (e.g., simulated points per second, number of initial guesses tested) and uses an extensive library of electron transfer models derived using an approach similar to that of previous researchers.3,4 As the field becomes cognizant of the utility model selection criteria offer, researchers are starting to use them to differentiate between electron transfer mechanisms. However, the work done thus far has only been validated for moderately simple electron transfer mechanisms (at most, four reactions in series) and is ultimately targeted towards the specific application of analyzing porous electrode behavior in batteries and fuel cells.5 This protocol can be applied to an even wider variety of applications (e.g., post-mortem testing, deeper mechanistic understanding of complex electrode processes), but to do so, it must be shown to be successful for more complicated electron transfer pathways present in these applications. We pursue this goal and demonstrate its viability by extending our protocol to consider these complicated pathways. In this presentation, we first demonstrate the successful validation of our extensive model library using well-known model redox couples studied on a planar surface. We then illustrate the predictive capability of our protocol using relevant redox flow battery couples, demonstrating strong agreement with literature values. We finally discuss future work, including the addition of appropriate electron transfer models to enable this protocol’s implementation in specific, targeted applications.