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

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 elec-trolyte solutions with distinct voltammetric modes can enhance the capabilities and sensitivities of character-ization 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 com-position of an electrolyte solution may be estimated with greater accuracy, and analytes that exhibit near iden-tical 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 elec-trochemical technologies.

Automated summary

Voltammetry is a powerful analytical technique for evaluating electrochemical reactions and can be used to examine electrolyte solutions suitable for energy storage technologies. By evaluating electrolyte solutions using different voltammetry techniques, the accuracy of estimating electrolyte composition can be improved and analytes with similar electrode potentials can be differentiated more easily. This method can be further enhanced by including multiple voltammetry techniques in the training dataset. Overall, this study demonstrates the utility of sequential and simultaneous methods in evaluating multiple voltammetry datasets for diagnosing electrolyte solutions in electrochemical technologies.