Estimating the Thermochemistry of Adsorbates Based Upon Gas-Phase Properties

Implementing the Blowers-Masel Approximation to Scale Activation Energy Based on Reaction Enthalpy in Mean-Field Microkinetic Modeling for Catalytic Methane Partial Oxidation

Mean-field microkinetic modeling is a powerful tool for catalyst design and the simulation of catalytic processes. The reaction enthalpies in a microkinetic model often need to be adjusted when changing species' binding energies to model different catalysts, when performing thermodynamic sensitivity analyses, and when fitting experimental data. When altering reaction enthalpies, the activation energies should also be reasonably altered to ensure realistic reaction rates. The Blowers-Masel approximation (BMA) relates the reaction barrier to the reaction enthalpy. Unlike the Brønsted-Evans-Polani relationship, the BMA requires less data because only one parameter, the intrinsic activation energy, needs to be determined. We validate this application of BMA relations to model surface reactions by comparing against density functional theory data taken from the literature. By incorporating the BMA rate description into the open-source Cantera software, we enable a new workflow, demonstrated herein, allowing rapid screening of catalysts using linear scaling relationships and BMA kinetics within the process simulation software. For demonstration purposes, a catalyst screening for catalytic methane partial oxidation on 81 hypothetical metals is conducted. We compared the results with and without BMA-corrected rates. The heat maps of various descriptors (e.g., CH4 conversion, syngas yield) show that using BMA rates instead of Arrhenius rates (with constant activation energies) changes which metals are most active. Heat maps of sensitivity analyses can help identify which reactions or species are the most influential in shaping the descriptor map patterns. Our findings indicate that while using BMA-adjusted rates did not markedly affect the most sensitive reactions, it did change the most influential species.

Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties

The study presents an ab-initio based framework for the automated construction of microkinetic mechanisms considering correlated uncertainties in all energetic parameters and estimation routines. 2000 unique microkinetic models were generated within the uncertainty space of the BEEF-vdW functional for the oxidation reactions of representative exhaust gas emissions from stoichiometric combustion engines over Pt(111) and compared to experiments through multiscale modeling. The ensemble of simulations stresses the importance of considering uncertainties. Within this set of first-principles-based models, it is possible to identify a microkinetic mechanism that agrees with experimental data. This mechanism can be traced back to a single exchange-correlation functional, and it suggests that Pt(111) could be the active site for the oxidation of light hydrocarbons. The study provides a universal framework for the automated construction of reaction mechanisms with correlated uncertainty quantification, enabling a DFT-constrained microkinetic model optimization for other heterogeneously catalyzed systems.

Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111)

Automatic mechanism generation is used to determine mechanisms for the CO2 hydrogenation on Ni(111) in a two-stage process while considering the correlated uncertainty in DFT-based energetic parameters systematically. In a coarse stage, all the possible chemistry is explored with gas-phase products down to the ppb level, while a refined stage discovers the core methanation submechanism. Five thousand unique mechanisms were generated, which contain minor perturbations in all parameters. Global uncertainty assessment, global sensitivity analysis, and degree of rate control analysis are performed to study the effect of this parametric uncertainty on the microkinetic model predictions. Comparison of the model predictions with experimental data on a Ni/SiO2 catalyst find a feasible set of microkinetic mechanisms within the correlated uncertainty space that are in quantitative agreement with the measured data, without relying on explicit parameter optimization. Global uncertainty and sensitivity analyses provide tools to determine the pathways and key factors that control the methanation activity within the parameter space. Together, these methods reveal that the degree of rate control approach can be misleading if parametric uncertainty is not considered. The procedure of considering uncertainties in the automated mechanism generation is not unique to CO2 methanation and can be easily extended to other challenging heterogeneously catalyzed reactions.

Facet engineering accelerates spillover hydrogenation on highly diluted metal nanocatalysts

Hydrogen spillover is a well-known phenomenon in heterogeneous catalysis; it involves H₂ cleavage on an active metal followed by the migration of dissociated H species over an 'inert' support^1-5. Although catalytic hydrogenation using the spilled H species, namely, spillover hydrogenation, has long been proposed, very limited knowledge has been obtained about what kind of support structure is required to achieve spillover hydrogenation^1,5. By dispersing Pd atoms onto Cu nanomaterials with different exposed facets, Cu(111) and Cu(100), we demonstrate in this work that while the hydrogen spillover from Pd to Cu is facet independent, the spillover hydrogenation only occurs on Pd₁/Cu(100), where the hydrogen atoms spilled from Pd are readily utilized for the semi-hydrogenation of alkynes. This work thus helps to create an effective method for fabricating cost-effective nanocatalysts with an extremely low Pd loading, at the level of 50 ppm, toward the semi-hydrogenation of a broad range of alkynes with extremely high activity and selectivity.

Rates of adsorption and desorption: Entropic contributions and errors due to mean-field approximations

We have performed exact classical rate calculations to compute adsorption and desorption rate constants with a model representative of a real system. We compute the desorption rate using transition-state theory by taking the dividing-surface far from the surface of the solid. We find that using a mean-field assumption, i.e., applying potential of mean force to transition state theory, could lead to two orders-of-magnitude errors in the rate constant owing to large fluctuations in the desorption barrier. Furthermore, we compute the adsorption rate by including a dynamical factor which reflects the probability of sticking to the solid surface. We find that the sticking probability is highly sensitive to the coverage. Also, we find that the adsorption rate computed from the mean-field assumption is not very different from the exact adsorption rate. We also compute entropic contribution to desorption rates and compare it to that obtained from two limiting models of adsorption-2D ideal gas and 2D ideal lattice gas. We show that at high temperatures (700 K), the entropic contribution to desorption rates computed from the exact calculations is very close to that obtained from the 2D ideal gas model. However, for lower to intermediate temperatures from 200 K to 500 K, the entropic contributions cover a wide range which lies in between the two limiting models and could lead to over two-orders-of-magnitude errors in the rate coefficient.

The entropies of adsorbed molecules

Adsorbed molecules are involved in many reactions on solid surface that are of great technological importance. As such, there has been tremendous effort worldwide to learn how to predict reaction rates and equilibrium constants for reactions involving adsorbed molecules. Theoretical calculation of both the rate and equilibrium constants for such reactions requires knowing the entropy and enthalpy of the adsorbed molecule. While much effort has been devoted to measuring and calculating the enthalpies of well-defined adsorbates, few measurements of the entropies of adsorbates have been reported. We present here a new way to determine the standard entropies of adsorbed molecules (S(ad)(0)) on single crystal surfaces from temperature programmed desorption data, prove its accuracy by comparison to entropies measured by equilibrium methods, and apply it to published data to extract new entropies. Most importantly, when combined with reported entropies, we find that at high coverage, they linearly track the entropy of the gas-phase molecule at the same temperature (T), such that S(ad)(0)(T) = 0.70 S(gas)(0)(T) - 3.3R (R = the gas constant), with a standard deviation of only 2R over a range of 50R. These entropies, which are ~2/3 of the gas, are huge compared to most theoretical predictions. This result can be extended to reliably predict prefactors in the Arrhenius rate constant for surface reactions involving such species, as proven here for desorption.