Microkinetic Modeling of Propene Combustion on a Stepped, Metallic Palladium Surface and the Importance of Oxygen Coverage

Abstract

Better emission control of internal combustion engines can be achieved by more efficient catalytic hydrocarbon combustion at low temperatures. For a knowledge-based design of suitable catalyst candidates, a promising approach integrates theoretical models with experimental benchmarks. Studying catalytic hydrocarbon combustion theoretically is challenging, however, since hydrocarbon reaction networks are complex: even simple C2 combustion reactions include hundreds of possible elementary steps. Herein, we present a paradigm to address this challenge by (1) supplementing experimental insights with extensive density functional theory studies to derive a proxy combustion reaction network; (2) using machine learning-enhanced transition state search to rigorously scan reaction coordinates; and (3) combining ab initio thermodynamics, microkinetic modeling, and a degree of rate control analysis to unveil adsorbate coverage effects and rate-limiting reaction steps. Our systematic approach permits modeling a reaction as complex as propene combustion on palladium while maintaining the computationally efficient mean-field approximation. Using a partially oxygen-covered palladium surface model, which includes adsorbate–adsorbate interactions, we predict experimentally measured rates within 2 orders of magnitude without parameter fitting. The corresponding degree of rate control analysis yields that the second and penultimate dehydrogenation step in the reaction network limit the rate of propene combustion on palladium.

Detailed Microkinetics for the Oxidation of Exhaust Gas Emissions through Automated Mechanism Generation

“…This inclusion of a reliable submechanism as a “reaction library” to enhance RMG’s predictive capabilities is a well-known practice in RMG’s gas-phase community. , This new iteration led once more to new adsorbates in the core and triggered additional DFT calculations. Simulations with this microkinetic model revealed a too strong binding energy of CO 2 *, caused by the BEEF-vdW functional that faces issues to accurately predict the thermochemistry of gas-phase molecules with an OCO backbone. , Although the approach for the computation of the heat of formation by Blöndal et al does not depend on the DFT energy of the gas-phase precursor, we applied a correction factor of +0.41 eV ,, to the heat of formation of CO 2 *, CO 3 *, and HCO 3 * for the 5th iteration to decrease the heat of adsorption. Metrics for the generated mechanism at each stage of this iterative procedure are illustrated in Figure .…”

“…In the case of imaginary vibrational modes, the structure was further optimized until forces were below 10 meV Å –1 . If imaginary modes persisted, these were set to 12 cm –1 similar to ref .…”

Colloidal Platinum–Copper Nanocrystal Alloy Catalysts Surpass Platinum in Low-Temperature Propene Combustion

“…It is therefore expected that the addition of Cu into Pt would cause oxygen to bind more strongly and carbon to bind more weakly. As propene combustion is sensitive to the coverage and reactivity of O*, adding too much Cu to Pt can result in unreactive O* and oxygen poisoning for a sample that has a Cu-like surface (i.e., Cu content significantly larger than 50%). For the PtCu as well as for both Pt 3 Cu surface configurations, however, it was calculated that O* was slightly weakened, from 1.2 to 1.3 eV, while it remained almost unaltered compared to Pt for PtCu 3 .…”

“…In a detailed microkinetic modeling study of propene combustion over Pd, our group recently showed that the oxygen binding strength is a key factor in efficiently catalyzing propene combustion. 55 Too strong oxygen binding led to poisoning of the catalytic surface, whereas too weak oxygen binding deprived the surface of oxygen. Oxygen is needed on the surface to facilitate dehydrogenation and C−C scission reactions along the combustion pathway.…”

Detailed microkinetics for the oxidation of exhaust gas emissions through automated mechanism generation

“…Simulations with this microkinetic model revealed a too strong binding energy of CO * 2 , caused by the BEEF-vdW functional that faces issues to accurately predict the thermochemistry of gas-phase molecules with an OCO backbone. 85,86 Although the approach for the computation of the heat of formation by Blöndal et al 30 does not depend on the DFT energy of the gas-phase precursor, we applied a correction factor of +0.41 eV 52,85,86 to the heat of formation of CO * 2 , CO * 3 and HCO * 3 for the 5 th iteration to decrease the heat of adsorption. Metrics for the generated mechanism at each stage of this iterative procedure are illustrated in Figure 4.…”