Leveraging graphical models to enhance in situ analyte identification via multiple voltammetric techniques

Abstract

Voltammetry is a powerful analytical technique for evaluating electrochemical reactions and holds particular promise for interrogating electrolyte solutions suitable for energy storage technologies, including examining features such as state-of-charge and state-of-health. However, individual voltammetry techniques are likely to be subcomponents of broader analytical workflows that incorporate complementary methods to diagnose evolving electrolyte solutions of uncertain composition. As such, we demonstrate that jointly evaluating electrolyte solutions with distinct voltammetric modes can enhance the capabilities and sensitivities of characterization protocols. Specifically, by considering both macroelectrode cyclic square wave and microelectrode cyclic voltammograms in sequential ("one after another") and simultaneous ("all at once") manners, the composition of an electrolyte solution may be estimated with greater accuracy, and analytes that exhibit near identical electrode potentials may be more readily differentiated. We additionally explore means of further improving this method, finding that protocol accuracy increases when multiple voltammetry techniques are included in the training dataset. We also observe that the algorithm typically becomes more confident--but not necessarily more accurate--when the number of data points increases. Overall, these studies show that the sequential and simultaneous methods may hold utility when evaluating multiple voltammetry datasets that, in turn, may be leveraged to streamline diagnostic workflows used to examine electrolyte solutions within electrochemical technologies.