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Despite significant progress in low-temperature methane (CH4) activation, commercial viability, specifically obtaining high yields of C1/C2 products, remains a challenge. High desorption energy (>2 eV) and overoxidation of the target products are key limitations in CH4 utilization. Herein, we employ first-principles density functional theory (DFT) and microkinetics simulations to investigate the CH4 activation and the feasibility of its conversion to ethylene (C2H4) on the RuO2 (1 1 0) surface. The CH activation and CH4 dehydrogenation processes are thoroughly investigated, with a particular focus on the diffusion of surface intermediates. The results show that the RuO2 (1 1 0) surface exhibits high reactivity in CH4 activation (Ea = 0.60 eV), with CH3 and CH2 are the predominant species, and CH2 being the most mobile intermediate on the surface. Consequently, self-coupling of CH2* species via CC coupling occurs more readily, yielding C2H4, a potential raw material for the chemical industry. More importantly, we demonstrate that the produced C2H4 can easily desorb under mild conditions due to its low desorption energy of 0.97 eV. Microkinetic simulations based on the DFT energetics indicate that CH4 activation can occur at temperatures below 200 K, and C2H4 can be desorbed at room temperature. Further, the selectivity analysis predicts that C2H4 is the major product at low temperatures (300-450 K) with 100 % selectivity, then competes with formaldehyde at intermediate temperatures in the CH4 conversion over RuO2 (1 1 0) surface. The present findings suggest that the RuO2 (1 1 0) surface is a potential catalyst for facilitating ethylene production under mild conditions.

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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.

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Based on first-principles calculations and microkinetic analysis, the reaction routes and origin of the activity of SmMn2O5 mullite for the selective catalytic oxidation of ammonia (NH3-SCO) are systematically investigated on three low-index surfaces under experimentally operating conditions. Key influencing factors and contributions of different iconic intermediate species (NH*, N2H4*, and HNO*) to the overall reaction process have been identified. In detail, Mn4+ serves as the primary active site for NH3 adsorption, while lattice oxygen participates in the dehydrogenation of NH3 on (010)4+ and (001)4+ surfaces. Furthermore, the (010)4+ surface shows both the best activity and the highest N2 selectivity at low temperatures via the synergy effect of exposed Mn-Mn dimers and the most labile O2 atoms. We further evaluate the potential catalytic performances of six A-site doped (010)4+ facets, among which La, Pr, and Nd dopings are predicted to possess better catalytic performances. Our study provides deep insights into the microscope reaction mechanisms and provides the specific optimization strategy for NH3-SCO on mullite oxides.

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The active site of the industrial Cu/ZnO/Al2 O3 catalyst used in CO2 hydrogenation to methanol has been debated for decades. Grand challenges remain in the characterization of structure, composition, and chemical state, both microscopically and spectroscopically, and complete theoretical calculations are limited when it comes to describing the intrinsic activity of the catalyst over the diverse range of structures that emerge under realistic conditions. Here a series of inverse model catalysts of ZnO on copper hydroxide were prepared where the size of ZnO was precisely tuned from atomically dispersed species to nanoparticles using atomic layer deposition. ZnO decoration boosted methanol formation to a rate of 877 gMeOH kgcat -1 h-1 with ≈80 % selectivity at 493 K. High pressure in situ X-ray absorption spectroscopy demonstrated that the atomically dispersed ZnO species are prone to aggregate at oxygen-deficient ZnO ensembles instead of forming CuZn metal alloys. By modeling various potential active structures, density functional theory calculations and microkinetic simulations revealed that ZnO/Cu interfaces with oxygen vacancies, rather than stoichiometric interfaces, Cu and CuZn alloys were essential to catalytic activation.

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Humins are one of the undesirable products formed during the dehydration of sugars as well as the conversion of 5-hydroxymethylfurfural (HMF) to value-added products. Thus, reducing the formation of humins is an important strategy for improving the yield of the aforementioned reactions. Even after a plethora of studies, the mechanism of formation and the structure of humins are still elusive. In this regard, we have employed density functional theory-based mechanistic studies and microkinetic analysis to identify crucial intermediates formed from glucose, fructose, and HMF that can initiate the polymerization reactions resulting in humins under Brønsted acid-catalyzed reaction conditions. This study brings light into crucial elementary reaction steps that can be targeted for controlling humins formation. Moreover, this work provides a rationale for the experimentally observed aliphatic chains and HMF condensation products in the humins structure. Different possible polymerization routes that could contribute to the structure of humins are also suggested based on the results. Importantly, the findings of this work indicate that increasing the rate of isomerization of glucose to fructose and reducing the rate of reaction between HMF molecules could be an efficient strategy for reducing humins formation.

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The concept of liquid metal membranes for hydrogen separation, based on gallium or indium, was recently introduced as an alternative to conventional palladium-based membranes. The potential of this class of gas separation materials was mainly attributed to the promise of higher hydrogen diffusivity. The postulated improvements are only beneficial to the flux if diffusion through the membrane is the rate-determining step in the permeation sequence. Whilst this is a valid assumption for hydrogen transport through palladium-based membranes, the relatively low adsorption energy of hydrogen on both liquid metals suggests that other phenomena may be relevant. In the current study, a microkinetic modeling approach is used to enable simulations based on a five-step permeation mechanism. The calculation results show that for the liquid metal membranes, the flux is limited by the dissociative adsorption over a large temperature range, and that the membrane flux is expected to be orders of magnitude lower compared to the membrane flux through pure palladium membranes. Even when accounting for the lower cost of the liquid metals compared to palladium, the latter still outperforms both gallium and indium in all realistic scenarios, in part due to the practical difficulties associated with making liquid metal thin films.

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A physicochemical model is developed for electrocatalytic reactions involving multiple electron transfer steps occurring in the electric double layer (EDL). The local reaction conditions are calculated using a mean-field EDL model, which is derived from a comprehensive grand potential that considers the steric effects, solvent polarization, and chemisorption-induced surface dipoles. Macroscopic mass transport in the so-called diffusion layer is controlled by the same set of controlling equations of the EDL model, without imposing the electroneutrality assumption as usual. The Gerischer's formulation of electron transfer theory, corrected with local reaction conditions, is used to describe the kinetics of elementary steps. Multistep kinetics of the electrocatalytic reaction is treated using microkinetics modelling, without resorting to the usual rate-determining step approximation. In formal analysis of the model, we retrieve canonical models with additional assumptions. Self-consistent numerical implementation of the model is demonstrated for oxygen reduction reaction (ORR) at Pt(111) in acidic solution, and the aptness of the model is verified by comparison with experimental data. A comparative study of the full model and its simplified versions allows us to examine how the ORR is influenced by asymmetric steric effects, finite concentration of ions, solvent polarization, surface charge effects, and metal electronic structure effects. We find that the difference in terms of the overpotential between the full model and the simplest model can be up to ∼0.1 V at a current density of -6 mAcm-2.

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As an effective method to analyze complex catalytic reaction networks, microkinetic modeling is gaining increasing popularity in the catalytic activity evaluation and rational design of heterogeneous catalysts. An automated simulator with stable and reliable performance is especially useful and in great request. Here we introduce the CATKINAS package developed for large-scale microkinetic modeling and analysis. Featuring with a multilevel solver and a multifunctional analyzer, CATKINAS can provide both accurate solutions and various quantitative and automatic analysis for a wide range of catalytic systems. The structure and the basic workflow are overviewed with the multilevel solver particularly illustrated. Also, we take the CO methanation reaction as an example to illustrate the application and efficiency of the CATKINAS package.

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Because of the high efficiency and mild reaction conditions, electrocatalytic CO2 reduction (ECR) has attracted significant attention in recent years. However, the specific mechanism of the formation of the two-electron production (CO or HCOOH) in this reaction is still unclear. Herein, with density functional theory calculation and experimental manipulation, the specific mechanism of the selective two-electron reduction of CO2 has been systematically investigated, employing the polyphenolate-substituted metalloporphyrinic frameworks, ZrPP-1-M (M = Fe, Co, Ni, Cu, and Zn), as model catalysts. Experimental observations and theoretical calculations discovered that ZrPP-1-Co is a more favorable catalyst for ECR among them. Compared with the formation of HCOOH, electroreduction of CO2 into CO has more beneficial thermodynamic and kinetic routes with ZrPP-1-Co as a catalyst. After introducing the r-GO for improving the conductivity, the Faradaic efficiency for CO formation is 82.4% at -0.6 v (vs RHE).

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Kinetic Monte Carlo method can provide valuable mechanistic insights for catalytic systems. Nonetheless, it suffers from the notorious problem of timescale disparity due to the existence of the complex catalytic network that consists of fast events and slow events. Previously, we have proposed the extended phenomenological kinetics (XPK) method that effectively deals with the timescale disparity problem between diffusion and reaction. However, it remains a great challenge to simulate systems with timescale disparity among different reaction pathways, which is important when selectivity is the major concern. In this study, we implement the enhanced XPK method to address this problem. The new algorithm works by identifying states connected through fast transitions and compressing them into a "superstate" when the chosen states satisfy a local steadystate condition. This state compression algorithm simplifies the reaction network by concealing the fast transitions. The accuracy and efficiency of the algorithm are demonstrated by two model systems: selective catalytic hydrogenation and selective catalytic decomposition. The enhanced XPK method is expected to be beneficial to the kinetic simulations of catalytic systems, especially those with complex reaction networks.

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Flow, heat, and mass transfer in fixed beds of catalyst particles are complex phenomena and, when combined with catalytic reactions, are multiscale in both time and space; therefore, advanced computational techniques are being applied to fixed bed modeling to an ever-greater extent. The fast-growing literature on the use of computational fluid dynamics (CFD) in fixed bed design reflects the rapid development of this subfield of reactor modeling. We identify recent trends and research directions in which successful methodology has been established, for example, in computer generation of packings of complex particles, and where more work is needed, for example, in the meshing of nonsphere packings and the simulation of industrial-size packed tubes. Development of fixed bed reactor models, by either using CFD directly or obtaining insight, closures, and parameters for engineering models from simulations, will increase confidence in using these methods for design along with, or instead of, expensive pilot-scale experiments.

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We show that the steady-state kinetics of a chemical reaction can be analyzed analytically in terms of proposed reaction schemes composed of series of steps with stoichiometric numbers equal to unity by calculating the maximum rates of the constituent steps, rmax,i, assuming that all of the remaining steps are quasi-equilibrated. Analytical expressions can be derived in terms of rmax,i to calculate degrees of rate control for each step to determine the extent to which each step controls the rate of the overall stoichiometric reaction. The values of rmax,i can be used to predict the rate of the overall stoichiometric reaction, making it possible to estimate the observed reaction kinetics. This approach can be used for catalytic reactions to identify transition states and adsorbed species that are important in controlling catalyst performance, such that detailed calculations using electronic structure calculations (e.g., density functional theory) can be carried out for these species, whereas more approximate methods (e.g., scaling relations) are used for the remaining species. This approach to assess the feasibility of proposed reaction schemes is exact for reaction schemes where the stoichiometric coefficients of the constituent steps are equal to unity and the most abundant adsorbed species are in quasi-equilibrium with the gas phase and can be used in an approximate manner to probe the performance of more general reaction schemes, followed by more detailed analyses using full microkinetic models to determine the surface coverages by adsorbed species and the degrees of rate control of the elementary steps.

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This review aims to illustrate the potential of kinetic analysis in general and microkinetic modeling in particular for rational catalyst design. Both ab initio calculations and experiments providing intrinsic kinetic data allow us to assess the effects of catalytic properties and reaction conditions on the activity and selectivity of the targeted reactions. Three complementary approaches for kinetic analysis of complex reaction networks are illustrated, using select examples of acid zeolite-catalyzed reactions from the authors' recent work. Challenges for future research aimed at defining targets for synthesis strategies that enable us to tune zeolite properties are identified.

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