Presentation from Simon R. Bare, SSRL, SLAC National Accelerator Laboratory

Initial Lessons Learned in Developing an Integrated Platform to Predict Degradation of Catalysts for Sustainable Conversion of Alternate Feedstocks to Fuels and Chemicals.

Presentation from Simon R. Bare, Distinguished Staff Scientist at Stanford Synchrotron Radiation Lightsource (SSRL) at SLAC National Accelerator Laboratory .

Through the US DOE Office of Science’s Accelerate Innovations in Emerging Technologies initiative; we are developing a platform to predict catalyst sintering and long-term performance based on based on short-term experiments. We are leveraging machine learning and artificial intelligence in an active learning workflow, whereby steady state and dynamic testing, operando and in-situ characterization, and theoretical modeling are used as input parameters to guide subsequent experiments and continuously improve the quality of the predictive models. We chose to focus the catalytic chemistry on the reverse water-gas shift (RWGS) reaction, which is critical for unlocking new routes to sustainable fuels and chemicals from CO2 feedstocks. In particular, we are investigating Rh/TiO2 as the catalyst of interest as prior work hypothesized a distinction in the selectivity towards CO vs. CH4 as a function of Rh cluster size. In this presentation I will introduce the project, and discuss several of the unexpected findings.

This will be a hybrid event online and in person with coffee and cake to follow the presentation.