Engineering, Physical Science
Bayesian Methods, Mathematical and Statistical Modeling, Statistics

David Kwabi

Assistant Professor

Mechanical Engineering, College of Engineering

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.