Combining Computational Modeling with Reaction Kinetics Experiments for Elucidating the In Situ Nature of the Active Site in Catalysis

Microkinetic modeling based on density functional theory (DFT) derived energetics is important for addressing fundamental questions in catalysis. The quantitative fidelity of microkinetic models (MKMs), however, is often insufficient to conclusively infer the mechanistic details of a specific catalytic system. This can be attributed to a number of factors such as an incorrect model of the active site for which DFT calculations are performed, deficiencies in the hypothesized reaction mechanism, inadequate consideration of the surface environment under reaction conditions, and intrinsic errors in the DFT exchange-correlation functional. Despite these limitations, we aim at developing a rigorous understanding of the reaction mechanism and of the nature of the active site for heterogeneous catalytic chemistries under reaction conditions. By achieving parity between experimental and modeling outcomes through robust parameter estimation and by ensuring coverage-consistency between DFT calculations and MKM predictions, it is possible to systematically refine the mechanistic model and, thereby, our understanding of the catalytic active site in situ. Our general approach consists of developing ab initio informed MKM for a given active site and then re-estimating the energies of the transition and intermediate states so that the model predictions match quantities measured in reaction kinetics experiments. If (i) model-experiment parity is high, (ii) the adjustments to the DFT-derived energetics for a given model of the active site are rationalized within the errors of standard DFT exchange-correlation functionals, and (iii) the resultant MKM predicts surface coverages that are consistent with those assumed in the DFT calculations used to initialize the MKM, we conclude that we have correctly identified the active site and the reaction mechanism. If one or more of these requirements are not met, we iteratively refine our model by updating our hypothesis for the structure of the active site and/or by incorporating coverage effects, until we obtain a high-fidelity coverage-self-consistent MKM whose final kinetic and thermodynamic parameters are within error of the values derived from DFT. Using the catalytic reaction of formic acid (FA, HCOOH) decomposition over transition-metal catalysts as an example, here we provide an account of how we applied this algorithm to study this chemistry on powder Au/SiC and Pt/C catalysts. For the case of Au catalysts, on which the FA decomposition occurred exclusively through the dehydrogenation reaction (HCOOH -> CO2+H-2), our approach was used to iteratively refine the model starting from the (111) facet until we found that specific ensembles of Au atoms present in sub-nanometer clusters can describe the active site for this catalysis. For the case of Pt catalysts, wherein both dehydrogenation (HCOOH -> CO2 + H-2) and dehydration (HCOOH -> CO + H2O) reactions were active, our approach identified that a partially CO*-covered (111) surface serves as the active site and that CO*-assisted steps contributed substantially to the overall FA decomposition activity. Finally, we suggest that once the active site and the mechanism are conclusively identified, the model can subsequently serve as a high-quality basis for designing specific goal-oriented experiments and improved catalysts.