Collaborative Research: Combining Models and Experiment for Quantitative Characterization of Electrocatalytic Carbon Dioxide Reduction on Doped Ceria

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The project investigates a high-temperature electrochemical approach for converting the carbon in carbon dioxide (CO2) gas to higher value carbon-containing products such as hydrocarbon fuels, thereby serving as a critical component in eventual closed carbon-cycle processes for renewable energy generation. Specifically, the project will investigate the reduction of carbon dioxide to carbon monoxide (CO) on high-temperature cerium oxide based electrodes utilizing a combination of experimental techniques, theoretical models, and statistical methods to distinguish between various reaction mechanisms that have been proposed but remain unverified. The resulting understanding will provide guidance for designing solid oxide electrodes with improved efficiency and durability.Thin-film test cells with ceria working electrodes on yttria-stabilized zirconia (YSZ) electrolytes will be fabricated, exposed to CO and CO2-containing gases and polarized at high temperature. The potential gradient in the film caused by the polarization yields a gradient in the surface potential as a function of distance from the current collector. These changes in surface potential - a quantity closely related to the concentrations of active intermediates on the ceria surface - will be measured, during cell operation, with sub-100 nm lateral resolution utilizing in-operando high-temperature scanning surface potential microscopy (HT-SSPM). These data, along with full-cell electrochemical impedance spectroscopy data (EIS), will be collected for a matrix of different experimental conditions including changes in gas composition, temperature, applied cell potential, dopant concentration, and thermal aging. Electrocatalytic phase-field modeling (EPFM), which couples mechanistic models of the catalytic reaction with the material behavior of the ceria electrode, will be quantitatively calibrated to the entire experimental dataset using Bayesian model-based analysis (BMA). Models generated and conclusions drawn during the course of this project will be useful for further development of high-temperature, mixed ionic-electronic conducting catalysts. The unique coupling of state-of-the-art in-operando HT-SSPM and EPFM methods is made possible by Bayesian calibration, which quantitatively links large experimental datasets and complex models, leading to estimates of model parameters (with uncertainty quantification) along with assessments of the model fitness in light of the data. BMA also affords the opportunity to incorporate existing measurements or calculations (including quantum calculations) of key parameters in the analysis. In addition to the technical elements, the project will include a lab activity for high school physics students that will be developed in the researchers' local communities and publicized nationally.