Events

Transient kinetic techniques such as temporal analysis of products (TAP) experiments and operando spectroscopy provide rich datasets that contain intrinsic information about multiple elementary steps of a catalytic reaction. However, the resulting data is governed by multiple physical processes arising from transport, thermodynamics, and reaction kinetics. Even in the case of TAP reactors with well-defined transport, it can be difficult to disentangle these phenomena, especially for complex multi-step reaction mechanisms. This talk will introduce the TAPSolver code, an open-source finite-element code for numerical simulation of TAP data that includes the ability to simulate and fit data based on complex reaction mechanisms and will highlight the capabilities of TAPSolver using case studies on CO oxidation and propane dehydrogenation. In addition, the impact of uncertainty in both experimental measurements and initial conditions on the intrinsic kinetic parameters extracted from TAP data will be discussed. The findings show that kinetic parameters extracted from TAP data have numerical uncertainty that is significantly lower than typical computational chemistry techniques such as DFT, at least in the case where the mechanism is well-defined and involves relatively few elementary steps. The talk will also include discussion of current and future directions in the analysis of TAP and other transient kinetic datasets, including the use of "kinetic-informed neural networks" for fitting multi-pulse and spectrokinetic data. The results indicate that the combination of transient kinetic measurements and advanced numerical methods is a powerful strategy for elucidating the intrinsic kinetic parameters of complex catalytic reactions.

Dr. AJ Medford is an Assistant Professor in the School of Chemical & Biomolecular Engineering. He attended North Carolina State University as an undergraduate, and subsequently spent a year as a Fulbright fellow at the Technical University of Denmark before attending Stanford University where he received his Ph.D in Chemical Engineering. His past research has spanned a wide range of applications including lithium-ion battery electrodes, polymer solar cells, data science, and catalysis. His thesis research focused on developing computational tools for analyzing trends in catalysis under the guidance of Prof. Jens Nørskov, and as a postdoc he worked with Prof. Surya Kalidindi on data infrastructure for materials science. His current research is at the intersection of catalysis and surface science, computational chemistry, and machine learning. Particular projects include the development of methods for automatically determining reaction mechanisms from transient kinetics experiments, applying computational chemistry to understand photocatalytic nitrogen fixation, and exploring machine-learning approaches for improving the accuracy of density functional theory calculations.