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

Abstract

Emissions from vehicles contain a variety of pollutants that must be either oxidized or reduced efficiently in the catalytic converter. Improvements to the catalyst require knowledge of the microkinetics, but the complexity of the exhaust gas mixture makes it challenging to identify the reaction network. This complexity was tackled by using the "Reaction Mechanism Generator" (RMG) to automatically generate microkinetic models for the oxidation of combustion byproducts from stoichiometric gasoline direct injection engines on Pt(111). The possibilities and the limitations encountered during the generation procedure are discussed in detail. A combination of first-principles-based mechanism construction and top-down parameter refinement allows to describe experimental results obtained by kinetic testing of a Pt/Al2O3 monolith under stoichiometric conditions. The study can serve as a blueprint for the usage of RMG for other challenging heterogeneously catalyzed reactions.

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

“…This absence of reliable thermophysical properties and kinetic parameters results in flawed predictions; improvements to RMG’s nitrogen capabilities are under active development. The settings applied in the generation procedure are summarized in Table S1, and input files are provided in ref . For the temperatures and pressures considered for this study, gas-phase reactions are expected to be negligible; accordingly, although RMG can explore gas-phase reactions in parallel with surface reactions, we choose to turn off this feature to reduce computational overhead.…”

Linking Experimental and Ab-initio Thermochemistry of Adsorbates with a Generalized Thermochemical Hierarchy

“…We investigated a set of 60 adsorbates on Pt(111) used in the database of the Reaction Mechanism Generator (RMG) [41][42][43] to test the CBH method, containing all C x H y O z adsorbates with no more than 2 heavy (non-hydrogen) atoms 40 and an additional 40 adsorbates with up to 4 heavy atoms. 3 The electronic structure calculations were conducted with QuantumEspresso (QE) 44,45 using the BEEF-vdW functional. 46 All structures from Blondal et al 40 were rerelaxed on the optimized slab from the QE DFT calculations to obtain a set of consistent DFT energies.…”

“…It was used to construct thermodynamically consistent microkinetic models that could successfully describe experimental results. 3,4,25,26,40,[57][58][59] Nonetheless, the computed enthalpy of formation is subjected to the large uncertainties inherent to the DFT energies, as there is no cancellation of errors in the heat of adsorption. It further depends on the accuracy of the DFT energies for the fragments of the isogyric reaction in the gas-phase, which can contain considerable errors.…”

“…In Figure 8a, all adsorbates are referenced to H 2 , CH 4 , and H 2 O, which ensures thermodynamic consistency with the gas phase, as demonstrated in previous studies. 3,4,25,57 In this network, there are no connections between adsorbate vertices, and the enthalpies of formation for are completely independent of each other. Consequently, if the enthalpy of formation of an adsorbate is adjusted within the adsorption reaction approach, it can easily be seen that this adjustment does not affect any other species in the TN.…”

“…Accordingly, accurate parameterization of the thermophysical properties of the adsorbed intermediates -primarily enthalpy and entropy -is of paramount importance in elucidating the mechanism and microkinetic modeling. [1][2][3][4] The entropy of an adsorbate is a species-specific quantity that can be determined by the partition function using the harmonic oscillator model, or with more accurate methods that include anharmonic effects. 5,6 In contrast, the enthalpy of an adsorbate is not just a result of the partition function.…”