We study and develop electrochemical devices containing organic materials for applications in grid energy storage and chemical separations (e.g., CO2 capture and nitrogen recovery). A critical aspect of our work involves discerning the impact of chemical reactions as well as mass and charge transport processes on device-level performance metrics. To accomplish this goal, we often conduct spectroscopic measurements of electrochemical systems while they are in operation. We apply a variety of mathematical modeling techniques to the spectroscopic data, such as multivariate curve resolution and Bayesian inference/model selection, to glean useful information about molecular transformation mechanisms and kinetics. These insights are informing closed-loop discovery of new and better-performing materials.
COntact
WebsiteLocation
Ann Arbor
Methodologies
Bayesian Methods / Mathematical and Statistical Modeling / Statistics
Applications