First-principles based microkinetic modeling of transient kinetics of CO hydrogenation on cobalt catalysts

Unravelling CO Activation on Flat and Stepped Co Surfaces: A Molecular Orbital Analysis

Structure sensitivity in heterogeneous catalysis dictates the overall activity and selectivity of a catalyst whose origins lie in the atomic configurations of the active sites. We explored the influence of the active site geometry on the dissociation activity of CO by investigating the electronic structure of CO adsorbed on 12 different Co sites and correlating its electronic structure features to the corresponding C-O dissociation barrier. By including the electronic structure analyses of CO adsorbed on step-edge sites, we expand upon the current models that primarily pertain to flat sites. The most important descriptors for activation of the C-O bond are the decrease in electron density in CO's 1π orbital , the occupation of 2π anti-bonding orbitals and the redistribution of electrons in the 3σ orbital. The enhanced weakening of the C-O bond that occurs when CO adsorbs on sites with a step-edge motif as compared to flat sites is caused by a distancing of the 1π orbital with respect to Co. This distancing reduces the electron-electron repulsion with the Co d-band. These results deepen our understanding of the electronic phenomena that enable the breaking of a molecular bond on a metal surface.

A Review of Theoretical Studies on Carbon Monoxide Hydrogenation via Fischer-Tropsch Synthesis over Transition Metals

The increasing demand for clean fuels and sustainable products has attracted much interest in the development of active and selective catalysts for CO conversion to desirable products. This review maps the theoretical progress of the different facets of most commercial catalysts, including Co, Fe, Ni, Rh, and Ru. All relevant elementary steps involving CO dissociation and hydrogenation and their dependence on surface structure, surface coverage, temperature, and pressure are considered. The dominant Fischer-Tropsch synthesis mechanism is also explored, including the sensitivity to the structure of H-assisted CO dissociation and direct CO dissociation. Low-coordinated step sites are shown to enhance catalytic activity and suppress methane formation. The hydrogen adsorption and CO dissociation mechanisms are highly dependent on the surface coverage, in which hydrogen adsorption increases, and the CO insertion mechanism becomes more favorable at high coverages. It is revealed that the chain-growth probability and product selectivity are affected by the type of catalyst and its structure as well as the applied temperature and pressure.

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling

Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.

Structure Sensitivity of CO2 Conversion over Nickel Metal Nanoparticles Explained by Micro-Kinetics Simulations

Nickel metal nanoparticles are intensively researched for the catalytic conversion of carbon dioxide. They are commercially explored in the so-called power-to-methane application in which renewably resourced H₂ reacts with CO₂ to produce CH₄, which is better known as the Sabatier reaction. Previous work has shown that this reaction is structure-sensitive. For instance, Ni/SiO₂ catalysts reveal a maximum performance when nickel metal nanoparticles of ∼2-3 nm are used. Particularly important to a better understanding of the structure sensitivity of the Sabatier reaction over nickel-based catalysts is to understand all relevant elementary reaction steps over various nickel metal facets because this will tell as to which type of nickel facets and which elementary reaction steps are crucial for designing an efficient nickel-based methanation catalyst. In this work, we have determined by density functional theory (DFT) calculations and micro-kinetics modeling (MKM) simulations that the two terrace facets Ni(111) and Ni(100) and the stepped facet Ni(211) barely show any activity in CO₂ methanation. The stepped facet Ni(110) turned out to be the most effective in CO₂ methanation. Herein, it was found that the dominant kinetic route corresponds to a combination of the carbide and formate reaction pathways. It was found that the dissociation of H₂CO* toward CH₂* and O* is the most critical elementary reaction step on this Ni(110) facet. The calculated activity of a range of Wulff-constructed nickel metal nanoparticles, accounting for varying ratios of the different facets and undercoordinated atoms exposed, reveals the same trend of activity-versus-nanoparticle size, as was observed in previous experimental work from our research group, thereby providing an explanation for the structure-sensitive nature of the Sabatier reaction.

Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements

Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such purposes. However, existing NNPs are generally designed for narrow target materials, making them unsuitable for broader applications in material discovery. Here we report a development of universal NNP called PreFerred Potential (PFP), which is able to handle any combination of 45 elements. Particular emphasis is placed on the datasets, which include a diverse set of virtual structures used to attain the universality. We demonstrated the applicability of PFP in selected domains: lithium diffusion in LiFeSO₄F, molecular adsorption in metal-organic frameworks, an order–disorder transition of Cu-Au alloys, and material discovery for a Fischer–Tropsch catalyst. They showcase the power of PFP, and this technology provides a highly useful tool for material discovery.

Existing neural network potentials are generally designed for narrow target materials. Here the authors develop a neural network potential which is able to handle any combination of 45 elements and show its applicability in multiple domains.

A quantitative multiscale perspective on primary olefin formation from methanol

The formation of the first C-C bond and primary olefins from methanol over zeolite and zeotype catalysts has been studied for over 40 years. Over 20 mechanisms have been proposed for the formation of the first C-C bond. In this quantitative multiscale perspective, we decouple the adsorption, desorption, mobility, and surface reactions of early species through a combination of vacuum and sub-vacuum studies using temporal analysis of products (TAP) reactor systems, and through studies with atmospheric fixed bed reactors. These results are supplemented with density functional theory calculations and data-driven physical models, using partial differential equations, that describe the temporal and spatial evolution of species. We consider the effects of steam, early degradation species, and product masking due to the inherent autocatalytic nature of the process, which all complicate the observation of the primary olefin(s). Although quantitative spectroscopic determination of the lifetimes, surface mobility, and reactivity of adspecies is still lacking in the literature, we observe that reaction barriers are competitive with adsorption enthalpies and/or activation energies of desorption, while facile diffusion occurs in the porous structures of the zeolite/zeotype catalysts. Understanding the various processes allows for quantitative evaluation of their competing energetics, which leads to molecular insights as to what governs the catalytic activity during the conversion of methanol to primary olefins over zeolite/zeotype catalysts.

Improved Pd/CeO2 Catalysts for Low-Temperature NO Reduction: Activation of CeO2 Lattice Oxygen by Fe Doping

Developing better three-way catalysts with improved low-temperature performance is essential for cold start emission control. Density functional theory in combination with microkinetics simulations is used to predict reactivity of CO/NO/H2 mixtures on a small Pd cluster on CeO2(111). At low temperatures, N2O formation occurs via a N2O2 dimer over metallic Pd3. Part of the N2O intermediate product re-oxidizes Pd, limiting NO conversion and requiring rich conditions to obtain high N2 selectivity. High N2 selectivity at elevated temperatures is due to N2O decomposition on oxygen vacancies. Doping CeO2 by Fe is predicted to lead to more oxygen vacancies and a higher N2 selectivity, which is validated by the lower onset of N2 formation for a Pd catalyst supported on Fe-doped CeO2 prepared by flame spray pyrolysis. Activating ceria surface oxygen by transition metal doping is a promising strategy to improve the performance of three-way catalysts.

Microkinetic Modeling: A Tool for Rational Catalyst Design

The design of heterogeneous catalysts relies on understanding the fundamental surface kinetics that controls catalyst performance, and microkinetic modeling is a tool that can help the researcher in streamlining the process of catalyst design. Microkinetic modeling is used to identify critical reaction intermediates and rate-determining elementary reactions, thereby providing vital information for designing an improved catalyst. In this review, we summarize general procedures for developing microkinetic models using reaction kinetics parameters obtained from experimental data, theoretical correlations, and quantum chemical calculations. We examine the methods required to ensure the thermodynamic consistency of the microkinetic model. We describe procedures required for parameter adjustments to account for the heterogeneity of the catalyst and the inherent errors in parameter estimation. We discuss the analysis of microkinetic models to determine the rate-determining reactions using the degree of rate control and reversibility of each elementary reaction. We introduce incorporation of Brønsted-Evans-Polanyi relations and scaling relations in microkinetic models and the effects of these relations on catalytic performance and formation of volcano curves are discussed. We review the analysis of reaction schemes in terms of the maximum rate of elementary reactions, and we outline a procedure to identify kinetically significant transition states and adsorbed intermediates. We explore the application of generalized rate expressions for the prediction of optimal binding energies of important surface intermediates and to estimate the extent of potential rate improvement. We also explore the application of microkinetic modeling in homogeneous catalysis, electro-catalysis, and transient reaction kinetics. We conclude by highlighting the challenges and opportunities in the application of microkinetic modeling for catalyst design.

Mechanistic insight into carbon-carbon bond formation on cobalt under simulated Fischer-Tropsch synthesis conditions

Facile C-C bond formation is essential to the formation of long hydrocarbon chains in Fischer-Tropsch synthesis. Various chain growth mechanisms have been proposed previously, but spectroscopic identification of surface intermediates involved in C-C bond formation is scarce. We here show that the high CO coverage typical of Fischer-Tropsch synthesis affects the reaction pathways of C₂H_x adsorbates on a Co(0001) model catalyst and promote C-C bond formation. In-situ high resolution x-ray photoelectron spectroscopy shows that a high CO coverage promotes transformation of C₂H_x adsorbates into the ethylidyne form, which subsequently dimerizes to 2-butyne. The observed reaction sequence provides a mechanistic explanation for CO-induced ethylene dimerization on supported cobalt catalysts. For Fischer-Tropsch synthesis we propose that C-C bond formation on the close-packed terraces of a cobalt nanoparticle occurs via methylidyne (CH) insertion into long chain alkylidyne intermediates, the latter being stabilized by the high surface coverage under reaction conditions.

The mechanism by which C-C bonds form during Fischer-Tropsch synthesis remains debated while spectroscopic identification of reaction intermediates remains scarce. Here, the authors identify alkylidynes as reactive intermediates for C-C bond formation on cobalt terrace sites and moreover show that these intermediates are stabilized by the high surface coverage typical for Fischer-Tropsch synthesis.