Using voltammetry augmented with physics-based modeling and Bayesian hypothesis testing to identify analytes in electrolyte solutions

Abstract

Voltammetry is a foundational electrochemical technique that can qualitatively and quantitatively probe electroactive species in solutions and as such has been used in numerous fields of study. Recently, automation has been introduced to extend the capabilities of voltammetric analysis through approaches such as Bayesian parameter estimation and compound identification. However, opportunities exist to enable more versatile methods across a wider range of solution compositions and experimental conditions. Here, we present a protocol that uses experimental voltammetry, physics-driven models, binary hypothesis testing, and Bayesian inference to enable robust labeling of analytes in multicomponent solutions across multiple techniques. We first describe the development of this protocol, and we subsequently validate the methodology in a case study involving five N-functionalized phenothiazine derivatives. In this analysis, the protocol correctly labeled solutions each containing 10H-phenothiazine and 10-methylphenothiazine from both cyclic voltammograms and cyclic square wave voltammograms, demonstrating the ability to identify redox-active constituents of a multicomponent solution. Finally, we identify areas of further improvement—such as achieving greater detection accuracy—and future applications to potentially enhance in situ or operando diagnostic workflows.