Ab Initio Multiscale Process Modeling of Ethane, Propane and Butane Dehydrogenation Reactions: A Review

Coupling acid catalysis and selective oxidation over MoO3-Fe2O3 for chemical looping oxidative dehydrogenation of propane

Redox catalysts play a vital role in chemical looping oxidative dehydrogenation processes, which have recently been considered to be a promising prospect for propylene production. This work describes the coupling of surface acid catalysis and selective oxidation from lattice oxygen over MoO₃-Fe₂O₃ redox catalysts for promoted propylene production. Atomically dispersed Mo species over γ-Fe₂O₃ introduce effective acid sites for the promotion of propane conversion. In addition, Mo could also regulate the lattice oxygen activity, which makes the oxygen species from the reduction of γ-Fe₂O₃ to Fe₃O₄ contribute to selectively oxidative dehydrogenation instead of over-oxidation in pristine γ-Fe₂O₃. The enhanced surface acidity, coupled with proper lattice oxygen activity, leads to a higher surface reaction rate and moderate oxygen diffusion rate. Consequently, this coupling strategy achieves a robust performance with 49% of propane conversion and 90% of propylene selectivity for at least 300 redox cycles and ultimately demonstrates a potential design strategy for more advanced redox catalysts.

Quantitative Evidence to Challenge the Traditional Model in Heterogeneous Catalysis: Kinetic Modeling for Ethane Dehydrogenation over Fe/SAPO-34

The production of ethylene from ethane dehydrogenation (EDH) is of great importance in the chemical industry, where zeolites are reported to be promising catalysts and kinetic simulations using the energetics from quantum mechanical calculations might provide an effective approach to speed up the development. However, the kinetic simulations with rigorous considerations of the zeolite environment are not yet advanced. In this work, EDH over Fe/SAPO-34 is investigated using quantum mechanical calculations with kinetic simulations. We show that an excellent agreement between the reaction rates from the self-consistent kinetic simulations using the coverage-dependent kinetic model developed in this work and the experimental ones can be achieved. We demonstrate that the adsorbate-adsorbate interactions are of paramount importance to the accuracy of kinetic calculations for zeolite catalysts. Our self-consistent kinetic calculations illustrate that the CH₃CH₂• radical rather than CH₃CH₂* is a favored intermediate. Perhaps more importantly, we reveal that the traditional model to describe catalytic reactions in heterogeneous catalysis cannot be used for the kinetics of the system and it may not be appropriate for many real catalytic systems. This work not only builds a framework for accurate kinetic simulations in zeolites, but also emphasizes an important concept beyond the traditional model.

In Situ Generated Boron Peroxo as Mild Oxidant in Propane Oxidative Dehydrogenation Revealed by Density Functional Theory Study

Boron-based materials catalyzing oxidative dehydrogenation is emerging as a promising protocol for efficient conversion of light alkanes to olefins, while the origin of its remarkable selectivity remains unclear. By means of density functional theory calculations, this work addresses the crucial role of boron peroxo as the mild oxidant in propane ODH: (1) Surface boron peroxo species can be generated in situ in the presence of peroxo species, preferably at the >B-O-B< sites of the zigzag edge, and show high activity to dehydrogenate propane (ΔG^⧧ = 13.5 kcal/mol, ΔG = 8.9 kcal/mol). (2) The >B-O-O· site shows high discriminability of secondary H over primary H of the propane molecule, leading to significantly higher yield of iso-propyl (CH₃ĊHCH₃) than n-propyl (CH₃CH₂ĊH₂); thus, propene formation is favored over deep oxidation. This provides physical insights into the origin of the remarkable olefin selectivity in the boron-containing ODH catalytic systems.

Mesokinetics as a Tool Bridging the Microscopic-to-Macroscopic Transition to Rationalize Catalyst Design

Heterogeneous catalysis is the workhorse of the chemical industry, and a heterogeneous catalyst possesses numerous active sites working together to drive the conversion of reactants to desirable products. Over the decades, much focus has been placed on identifying the factors affecting the active sites to gain deep insights into the structure-performance relationship, which in turn guides the design and preparation of more active, selective, and stable catalysts. However, the molecular-level interplay between active sites and catalytic function still remains qualitative or semiquantitative, ascribed to the difficulty and uncertainty in elucidating the nature of active sites for its controllable manipulation. Hence, bridging the microscopic properties of active sites and the macroscopic catalytic performance, that is, microscopic-to-macroscopic transition, to afford a quantitative description is intriguing yet challenging, and progress toward this promises to revolutionize catalyst design and preparation.In this Account, we propose mesokinetics modeling, for the first time enabling a quantitative description of active site characteristics and the related mechanistic information, as a versatile tool to guide rational catalyst design. Exemplified by a pseudo-zero-order reaction, the kinetics derivation from the Pt particle size-sensitive catalytic activity and size-insensitive activation energy suggests only one type of surface site as the dominant active site, in which the Pt(111) with almost unchanged turnover frequency (TOF₁₁₁) is further identified as the dominating active site. Such a method has been extended to identify and quantify the number (N_i) of active sites for various thermo-, electro-, and photocatalysts in chemical synthesis, hydrogen generation, environment application, etc. Then, the kinetics derivation from the kinetic compensation effects suggests a thermodynamic balance between the activation entropy and enthalpy, which exhibit linear dependences on Pt charge. Accordingly, the Pt charge can serve as a catalytic descriptor for its quantitative determination of TOF_i. This strategy has been further applied to Pt-catalyzed CO oxidation with nonzero-order reaction characteristic by taking the site coverages of surface species into consideration.Hence, substituting the above statistical correlations of N_i and TOF_i into the rate equation R = ∑N_i × TOF_i offers the mesokinetics model, which can precisely predict catalytic function and screen catalysts. Finally, based on the disentanglement of the factors underlying Pt electronic structures, a de novo strategy, from the interfacial charge distribution to reaction mechanism, kinetics, and thermodynamics parameters of the rate-determining step, and ultimately catalytic performance, is developed to map the unified mechanistic and kinetics picture of reaction. Overall, the mesokinetics not only demonstrates much potential to elucidate the quantitative interplay between active sites and catalytic activity but also provides a new research direction in kinetics analysis to rationalize catalyst design.

Alkane C-H activation and ligand exchange on silica supported d0 metal alkylidenes: relevance to alkane metathesis

In this work, we study the ligand exchange process between an alkane and a series of silica supported metal alkylidenes, which may occur by different pathways: C-H addition, σ-bond metathesis, and α-H abstraction. The results indicate that the α-H abstraction pathway is the preferred one, regardless of the catalyst and ligands. This is in contrast to the expected preference for the C-H addition route. When looking for the origin of this preference, our calculations revealed that the α-H abstraction pathway is driven by entropy, which favors the initial dissociation of the alkyl ligand from the catalyst.

Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status, and challenges

Kinetic Monte Carlo (KMC) simulations in combination with first-principles (1p)-based calculations are rapidly becoming the gold-standard computational framework for bridging the gap between the wide range of length scales and time scales over which heterogeneous catalysis unfolds. 1p-KMC simulations provide accurate insights into reactions over surfaces, a vital step toward the rational design of novel catalysts. In this Perspective, we briefly outline basic principles, computational challenges, successful applications, as well as future directions and opportunities of this promising and ever more popular kinetic modeling approach.

Dehydrogenation of Cycloalkanes over N-Doped Carbon-Supported Catalysts: The Effects of Active Component and Molecular Structure of the Substrate

Efficient dehydrogenation of cycloalkanes under mild conditions is the key to large-scale application of cycloalkanes as a hydrogen storage medium. In this paper, a series of active metals loaded on nitrogen-doped carbon (M/CN, M = Pt, Pd, Ir, Rh, Au, Ru, Ag, Ni, Cu) were prepared to learn the role of active metals in cycloalkane dehydrogenation with cyclohexane as the model reactant. Only Pt/CN, Pd/CN, Rh/CN and Ir/CN can catalyze the dehydrogenation of cyclohexane under the set conditions. Among them, Pt/CN exhibited the best catalytic activity with the TOF value of 269.32 h⁻¹ at 180 °C, followed by Pd/CN, Rh/CN and Ir/CN successively. More importantly, the difference of catalytic activity between these active metals diminishes with the increase in temperature. This implies that there is a thermodynamic effect of cyclohexane dehydrogenation with the synthetic catalysts, which was evidenced by the study on the activation energy. In addition, the effects of molecular structure on cycloalkane dehydrogenation catalyzed by Pt/CN were studied. The results reveal that cycloalkane dehydrogenation activity and hydrogen production rate can be enhanced by optimizing the type, quantity and position of alkyl substituents on cyclohexane.

Molecular Dynamics and Machine Learning in Catalysts

Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts.