Quantitative Determination of C–C Coupling Mechanisms and Detailed Analyses on the Activity and Selectivity for Fischer–Tropsch Synthesis on Co(0001): Microkinetic Modeling with Coverage Effects

Adsorption Property and Morphology Evolution of C Deposited on HCP Co Nanoparticles

Despite extensive studies of deposited carbon in Fischer-Tropsch synthesis (FTS), an atomic-level comprehension of the effect of carbon on the morphology of cobalt-based FTS catalysts remains elusive. The adsorption configurations of carbon atoms on different crystal facets of hexagonal close-packed (hcp) Co nanoparticles were studied using density functional theory (DFT) calculations to explore the interaction mechanism between C and Co surfaces. The weaker adsorption strength of C atoms on Co(0001), Co(10-10), and Co(11-20) surfaces accounted for lower diffusion energy, leading to the facile formation of C dimers. Electronic property analysis shows that more electrons are transferred from Co surfaces to C atoms on corrugated facets than on flat facets. The deposition of carbon atoms on Co nanoparticles affects surface energy by forming strong Co-C bonds, which causes the system to reach a more energetically favorable morphology with an increased proportion of exposed Co(10-12) and Co(11-20) areas as the carbon content increases slightly. This transformation in morphology implies that C deposition plays a crucial role in determining the facet proportion and stability of exposed Co surfaces, contributing to the optimization of cobalt-based catalysts with improved performance.

A DFT study towards dynamic structures of iron and iron carbide and their effects on the activity of the Fischer-Tropsch process

The Fe-based Fischer-Tropsch synthesis (FTS) catalyst shows a rich phase chemistry under pre-treatment and FTS conditions. The exact structural composition of the active site, whether iron or iron carbide (FeCx), is still controversial. Aiming to obtain an insight into the active sites and their role in affecting FTS activity, the swarm intelligence algorithm is implemented to search for the most stable Fe(100), Fe(110), Fe(210) surfaces with different carbon ratios. Then, ab initio atomistic thermodynamics and Wulffman construction were employed to evaluate the stability of these surfaces at different chemical potentials of carbon. Their FTS reactivity and selectivity were later assessed by semi-quantitative micro-kinetic equations. The results show that stability, reactivity, and selectivity of the iron are all affected by the carbonization process when the carbon ratio increases. Formation of the carbide, a rather natural process under experimental conditions, would moderately increase the turnover frequency (TOF), but both iron and iron carbide are active to the reaction.

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.

Tripodal Pd metallenes mediated by Nb2C MXenes for boosting alkynes semihydrogenation

2D metallene nanomaterials have spurred considerable attention in heterogeneous catalysis by virtue of sufficient unsaturated metal atoms, high specific surface area and surface strain. Nevertheless, the strong metallic bonding in nanoparticles aggravates the difficulty in the controllable regulation of the geometry of metallenes. Here we propose an efficient galvanic replacement strategy to construct Pd metallenes loaded on Nb₂C MXenes at room temperature, which is triggered by strong metal-support interaction based on MD simulations. The Pd metallenes feature a chair structure of six-membered ring with the coordination number of Pd as low as 3. Coverage-dependent kinetic analysis based on first-principles calculations reveals that the tripodal Pd metallenes promote the diffusion of alkene and inhibit its overhydrogenation. As a consequence, Pd/Nb₂C delivers an outstanding turnover frequency of 10372 h^-1 and a high selectivity of 96% at 25 ^oC in the semihydrogenation of alkynes without compromising the stability. This strategy is general and scalable considering the plentiful members of the MXene family, which can set a foundation for the design of novel supported-metallene catalysts for demanding transformations.

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.

Fundamental Limit of Selectivity in Photocatalytic Denitrification over Titania

Although photocatalytic decomposition of NO (deNO) into N₂ and O₂ is low-cost and non-polluting, it has a low NO conversion efficiency. Establishing the activity and selectivity trend among active sites is an important base to explore and improve the deNO processes. Because the experimental performances are determined by the reaction rate, it is worthwhile to investigate the kinetic limiting steps calculated by comparative microkinetic modeling. We found that, without illumination, N₂ production is inactive over various TiO₂ surfaces/sites, but photogenerated holes can break the scaling relation of the dark condition by weakening O₂* adsorption, leading to a significant increase in deNO activity on defective titania surfaces. However, the low N₂ selectivity can be attributed to the small strength of N₂O adsorption. In contrast, the N₂ selectivity is enhanced in Ti-modified zeolite because of a stronger N₂O* adsorption. We demonstrate here that the reaction phase diagram analysis can clearly establish a global picture of reaction activity and selectivity over various catalytic sites. In combination with microkinetic modeling, it can effectively determine the kinetic limits, providing insights to improve the design of photocatalysts.

Formation Mechanism of Monocyclic Aromatic Hydrocarbons during Pyrolysis of Styrene Butadiene Rubber in Waste Passenger Car Tires

The production of aromatic hydrocarbons from the waste tire pyrolysis attracts more and more attention because of its tremendous potential. Based on styrene-butadiene rubber (SBR), which is the main rubber in the waste passenger car tires, this work studies the temperature influence on primary pyrolysis product distribution by experimental techniques (Py-GC/MS, TG-MS), and then, the formation mechanism of monocyclic aromatic hydrocarbons (MAHs) observed in the experiment was analyzed by first-principles calculations. The experimental results show that the MAHs during the pyrolysis mainly include styrene, toluene, and xylene, and subsequent calculations showed that these compounds were formed through a series of primary and secondary reactions. The formation pathways of these typical MAHs were studied via the reaction energy barrier analysis, respectively. It shows that the MAHs were not only derived from the benzene ring in the SBR chain but also generated from short-chain alkenes through the Diels-Alder reaction. The obtained pyrolysis reaction mechanism provides theoretical guidance for the regulation of the pyrolysis product distribution of MAHs.

Activity Self-Optimization Steered by Dynamically Evolved Fe3+@Fe2+ Double-Center on Fe2O3 Catalyst for NH3-SCR

Identification of the active centers dynamically stable under the reaction condition is of paramount importance but challenging because of the limited knowledge of steady-state chemistry on catalysts at the atomic level. Herein, focusing on the Fe₂O₃ catalyst for the selective catalytic reduction of NO with NH₃ (NH₃-SCR) as a model system, we reveal quantitatively the self-evolving Fe³⁺@Fe²⁺ (∼1:1) double-centers under the in-situ condition by the first-principles microkinetic simulations, which enables the accurate prediction of the optimal industry operating temperature (590 K). The cooperation of this double-center achieves the self-optimization of catalytic activity and rationalizes the intrinsic origin of Fe₂O₃ catalyzing NH₃-SCR at middle-high temperatures instead of high temperatures. Our findings demonstrate the atomic-level self-evolution of active sites and the dynamically adjusted activity variation of the catalyst under the in-situ condition during the reaction process and provide insights into the reaction mechanism and catalyst optimization.

Highly Efficient ZIF-67-Derived PtCo Alloy-CN Interface for Low-Temperature Aqueous-Phase Fischer-Tropsch Synthesis

Designing new materials for selective Fischer-Tropsch synthesis (FTS) is an elegant way to enhance local feedstock utilization like biomass and waste. In this approach, we have designed a thermally and chemically stable bimetallic PtCo/NC hybrid nanocomposite catalyst derived from a zeolitic imidazolate framework (ZIF-67, which contains cobalt as a metal center) through carbonization for low-temperature (413-473 K) aqueous-phase Fischer-Tropsch synthesis (AFTS). The selectivity of the desired range of hydrocarbons is adjusted using a highly dispersed PtCo bimetallic alloy, which facilitates extraordinary reduction of a metal oxide to active species by the synergic effect under the AFTS reaction conditions. The ZIF-derived catalyst tested in this study exhibited the highest activity to date for very low temperatures (433 K) in aqueous-phase Fischer-Tropsch synthesis with CO conversion rates between 0.61 and 1.20 mol_CO·mol_Co^-1·h^-1. Insights of the remarkable catalyst activity were examined by in situ X-ray photoelectron spectroscopy (XPS) studies corroborated by density functional theory (DFT) calculation. The bimetallic Co₃Pt (111) surface was found to be highly active for the C-C coupling reaction between surface-adsorbed C and CO, forming a CCO intermediate with a very low activation barrier (E_a = 0.37 eV), in comparison to the C-C coupling activation barrier obtained over the Co (111) surface (E_a = 0.87 eV). This unique approach and observations create a new path for developing next-generation advanced catalyst systems and processes for selective low-temperature FTS.

Understanding and tackling the activity and selectivity issues for methane to methanol using single atom alloys

The process for the direct oxidation of methane to methanol is investigated on single atom alloys using density functional theory. A catalyst search is performed across FCC metal single atom alloys. 7 single atom alloys are found as candidates and microkinetic modelling is performed. The activity and selectivity are remarkably improved over that of pure palladium metal, yet remain unideal.

Hydrogenation of Carbon Dioxide to Value-Added Liquid Fuels and Aromatics over Fe-Based Catalysts Based on the Fischer–Tropsch Synthesis Route

Hydrogenation of CO2 to value-added chemicals and fuels not only effectively alleviates climate change but also reduces over-dependence on fossil fuels. Therefore, much attention has been paid to the chemical conversion of CO2 to value-added products, such as liquid fuels and aromatics. Recently, efficient catalysts have been developed to face the challenge of the chemical inertness of CO2 and the difficulty of C–C coupling. Considering the lack of a detailed summary on hydrogenation of CO2 to liquid fuels and aromatics via the Fischer–Tropsch synthesis (FTS) route, we conducted a comprehensive and systematic review of the research progress on the development of efficient catalysts for hydrogenation of CO2 to liquid fuels and aromatics. In this work, we summarized the factors influencing the catalytic activity and stability of various catalysts, the strategies for optimizing catalytic performance and product distribution, the effects of reaction conditions on catalytic performance, and possible reaction mechanisms for CO2 hydrogenation via the FTS route. Furthermore, we also provided an overview of the challenges and opportunities for future research associated with hydrogenation of CO2 to liquid fuels and aromatics.

Achieving Theory-Experiment Parity for Activity and Selectivity in Heterogeneous Catalysis Using Microkinetic Modeling

Microkinetic modeling based on density functional theory (DFT) energies plays an essential role in heterogeneous catalysis because it reveals the fundamental chemistry for catalytic reactions and bridges the microscopic understanding from theoretical calculations and experimental observations. Microkinetic modeling requires building a set of ordinary differential equations (ODEs) based on the calculation results of thermodynamic properties of adsorbates and kinetic parameters for the reaction elementary steps. Solving a microkinetic model can extract information on catalytic chemistry, including critical reaction intermediates, reaction pathways, the surface species distribution, activity, and selectivity, thus providing vital guidelines for altering catalysts.

However, the quantitative reliability of traditional microkinetic models is often insufficient to conclusively extrapolate the mechanistic details of complex reaction systems. This can be attributed to several factors, the most important of which is the limitation of obtaining an accurate estimation of the energy inputs via traditional calculation methods. These limitations include the difficulty of using static DFT methods to calculate reaction energies of adsorption/desorption processes, often rate-controlling or selectivity-determining steps, and the inadequate consideration of surface coverage effects. In addition, the robust microkinetic software is rare, which also complicates the resolution of complex catalytic systems.

In this Account, we review our recent works toward refining the predictions of microkinetic modeling in heterogeneous catalysis and achieving theory–experiment parity for activity and selectivity. First, we introduce CATKINAS, a microkinetic software developed in our group, and show how it disentangles the problem that traditional microkinetic software has and how it can now be applied to obtain kinetic results for more sophisticated reaction systems. Second, we describe a molecular dynamics method developed recently to obtain the free-energy changes for the adsorption/desorption process to fill in the missing energy inputs. Third, we show that a rigorous consideration of surface coverage effects is pivotal for building more realistic models and obtaining accurate kinetic results. Following a series of studies on acetylene hydrogenation reactions on Pd catalysts, we demonstrate how this new approach can provide an improved quantitative understanding of the mechanism, active site, and intrinsic structural sensitivity. Finally, we conclude with a brief outlook and the remaining challenges in this field.

Engineering the geometric and electronic structure of Ru via Ru–TiO2 interaction for enhanced selective hydrogenation

Ru/TiO2-Vo-250H with the structure of TiO2-Vo-partially encapsulated Ru nanoparticles, balances the active sites for H2 dissociation and the adsorption sites for 6-chloroquinoline, achieving the selective hydrogenation even at room temperature.

Dynamics of Heterogeneous Catalytic Processes at Operando Conditions

The rational design of high-performance catalysts is hindered by the lack of knowledge of the structures of active sites and the reaction pathways under reaction conditions, which can be ideally addressed by an in situ/operando characterization. Besides the experimental insights, a theoretical investigation that simulates reaction conditions-so-called operando modeling-is necessary for a plausible understanding of a working catalyst system at the atomic scale. However, there is still a huge gap between the current widely used computational model and the concept of operando modeling, which should be achieved through multiscale computational modeling. This Perspective describes various modeling approaches and machine learning techniques that step toward operando modeling, followed by selected experimental examples that present an operando understanding in the thermo- and electrocatalytic processes. At last, the remaining challenges in this area are outlined.

Direct conversion of CO2 to a jet fuel over CoFe alloy catalysts

The direct conversion of carbon dioxide (CO₂) using green hydrogen is a sustainable approach to jet fuel production. However, achieving a high level of performance remains a formidable challenge due to the inertness of CO₂ and its low activity for subsequent C–C bond formation. In this study, we prepared a Na-modified CoFe alloy catalyst using layered double-hydroxide precursors that directly transforms CO₂ to a jet fuel composed of C₈–C₁₆ jet-fuel-range hydrocarbons with very high selectivity. At a temperature of 240°C and pressure of 3 MPa, the catalyst achieves an unprecedentedly high C₈–C₁₆ selectivity of 63.5% with 10.2% CO₂ conversion and a low combined selectivity of less than 22% toward undesired CO and CH₄. Spectroscopic and computational studies show that the promotion of the coupling reaction between the carbon species and inhibition of the undesired CO₂ methanation occur mainly due to the utilization of the CoFe alloy structure and addition of the Na promoter. This study provides a viable technique for the highly selective synthesis of eco-friendly and carbon-neutral jet fuel from CO₂.

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An alloy is developed for the direct CO₂ hydrogenation to jet-fuel-range hydrocarbons

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The selectivity of the hydrocarbons (63.5%) exceeds the theoretical maximum value

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The CoFe alloy is the active phase in the coupling reaction between surface carbons

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The CoFe alloy is a highly efficient catalyst in the presence of a sodium promoter

Combined Density Functional Theory and Microkinetics Study to Predict Optimum Operating Conditions of Si(100) Surface Carbonization by Acetylene for High Power Devices

The Si(100) surface carbonization mechanisms by acetylene are explored using density functional theory calculations combined with microkinetic simulations. The most stable acetylene adsorption geometries and their subsequent decomposition mechanisms to form a carbon dimer on the Si surface are investigated. Microkinetics simulations are further used to examine the optimal reaction conditions for obtaining a single-crystalline silicon carbide (SiC). We find that the carbon dimer (C₂*) as an end-bridge structure can be formed at 560 K, and the maximum of C₂* can be obtained near 640 K. The acetylene adsorbed via the di-σ configuration starts to dehydrogenate when the heating rate is too fast and will form two possible carbon dimers (di-C₂* and C₂*), which will lead to a polycrystalline SiC buffer layer. We predict that 750 K and 10^-6 bar will be the optimum temperature and pressure for obtaining a single-crystalline SiC buffer layer, respectively.

Perspective on computational reaction prediction using machine learning methods in heterogeneous catalysis

Heterogeneous catalysis plays a significant role in the modern chemical industry. Towards the rational design of novel catalysts, understanding reactions over surfaces is the most essential aspect. Typical industrial catalytic processes such as syngas conversion and methane utilisation can generate a large reaction network comprising thousands of intermediates and reaction pairs. This complexity not only arises from the permutation of transformations between species but also from the extra reaction channels offered by distinct surface sites. Despite the success in investigating surface reactions at the atomic scale, the huge computational expense of ab initio methods hinders the exploration of such complicated reaction networks. With the proliferation of catalysis studies, machine learning as an emerging tool can take advantage of the accumulated reaction data to emulate the output of ab initio methods towards swift reaction prediction. Here, we briefly summarise the conventional workflow of reaction prediction, including reaction network generation, ab initio thermodynamics and microkinetic modelling. An overview of the frequently used regression models in machine learning is presented. As a promising alternative to full ab initio calculations, machine learning interatomic potentials are highlighted. Furthermore, we survey applications assisted by these methods for accelerating reaction prediction, exploring reaction networks, and computational catalyst design. Finally, we envisage future directions in computationally investigating reactions and implementing machine learning algorithms in heterogeneous catalysis.

CATKINAS: A large-scale catalytic microkinetic analysis software for mechanism auto-analysis and catalyst screening

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.

Influence of surface defects on activity and selectivity: a quantitative study of structure sensitivity of Pd catalysts for acetylene hydrogenation

The structure sensitivity of Pd catalysed acetylene hydrogenation is quantitatively examined using a coverage-dependent microkinetic model. Pd(211) was found to be more active than Pd(111), but present a poorer selectivity toward ethylene.

First-principles microkinetic simulations revealing the scaling relations and structure sensitivity of CO2 hydrogenation to C1 & C2 oxygenates on Pd surfaces

CO₂ hydrogenation to alcohols and other oxygenates on Pd(211) and Pd(111) surfaces was studied by microkinetic modelling. Energy scaling relations on two surfaces were established. Activity plots as a function of reaction conditions were identified.

Mechanistic insight into methane dry reforming over cobalt: a density functional theory study

Cobalt-based catalysts are a potential candidate among non-noble metal catalysts in dry reformation of methane (DRM), while the detailed mechanism of the DRM reaction is still largely unknown. In this contribution, the rather complicated reaction network for DRM is explored by density functional theory calculations. The most favorable adsorption structures of all species involved in the DRM reaction over Co(0001) have been identified. For CO2 activation, its direct dissociation to generate CO and O is the dominant reaction pathway. For CH4 direct dissociation, CH dehydrogenation into atomic C and H is the rate-determining step (RDS). It is predicted that the CH is the most abundant species among CHx (x = 0-3) over Co(0001). O acts as an oxidant and reacts with CH to produce CHO, and subsequently, CHO decomposes into CO and H. Atomic C may directly react with O to produce CO, or be oxidized by OH to COH, followed by the COH decomposition to CO and H. Thus, three possible pathways for DRM over the Co(0001) surface are proposed in our study, and the oxidation step is suggested as the RDS. The dominant route is identified as CH4 successive dissociation into CH, and CH oxidizing by O to form CHO, then CHO decomposition to CO and H.

Machine learning in chemical reaction space

Chemical compound space refers to the vast set of all possible chemical compounds, estimated to contain 1060 molecules. While intractable as a whole, modern machine learning (ML) is increasingly capable of accurately predicting molecular properties in important subsets. Here, we therefore engage in the ML-driven study of even larger reaction space. Central to chemistry as a science of transformations, this space contains all possible chemical reactions. As an important basis for 'reactive' ML, we establish a first-principles database (Rad-6) containing closed and open-shell organic molecules, along with an associated database of chemical reaction energies (Rad-6-RE). We show that the special topology of reaction spaces, with central hub molecules involved in multiple reactions, requires a modification of existing compound space ML-concepts. Showcased by the application to methane combustion, we demonstrate that the learned reaction energies offer a non-empirical route to rationally extract reduced reaction networks for detailed microkinetic analyses.

How coverage influences thermodynamic and kinetic isotope effects for H2/D2 dissociative adsorption on transition metals

Hydrogen isotope effects are influenced by adsorbate coverage: at high coverages, isotope effects are lower than at low coverages. This helps to rationalize observed isotope effects, allowing more precise elucidation of reaction mechanisms.

Mechanistic insights of selective syngas conversion over Zn grafted on ZSM-5 zeolite

On the basis of syngas conversion mechanism over Zn²⁺-ion exchanged ZSM-5 zeolite, the reaction pathways, reaction intermediates and transition states were determined clearly.

CO activation and methanation mechanism on hexagonal close-packed Co catalysts: effect of functionals, carbon deposition and surface structure

Regardless of the functionals used and the presence of graphitic carbon, the CO methanation rate on Co(0001) is mainly controlled by CHO decomposition.