Microkinetic Model Research Articles

High-Throughput Screening of Sulfur-Resistant Catalysts for Steam Methane Reforming Using Machine Learning and Microkinetic Modeling.

The catalytic activity of bimetallic catalysts for the steam methane reforming (SMR) reaction was extensively studied previously. However, the performance of these materials in the presence of sulfur-containing species is yet to be investigated. In this study, we propose a novel process aided by machine learning (ML) and microkinetic modeling for the rapid screening of sulfur-resistant bimetallic catalysts. First, various ML models were developed to predict atomic adsorption energies (C, H, O, and S) on bimetallic surfaces. Easily accessible physical and chemical properties of the metals and adsorbates were used as input features. The Ensemble learning, artificial neural network, and support vector regression models achieved the best performance with R2 values of 0.74, 0.71, and 0.70, respectively. A microkinetic model was then built based on the elementary steps of the SMR reaction. Finally, the microkinetic model, together with the atomic adsorption energies predicted by the Ensemble model, were used to screen over 500 bimetallic materials. Four Ge-based alloys (Ge3Cu1, Ge3Ni1, Ge3Co1, and Ge3Fe1) and the Ni3Cu1 alloy were identified as promising and cost-effective sulfur-resistant catalysts.

Hydrogenation of CO2 to CH3OH catalyzed by a ruthenium-triphos complex: Theoretical free energy profile and microkinetic modeling

Direct hydrogenation of carbon dioxide to methanol is an important goal for sustainability. The ruthenium-triphos complex was reported to be successful in achieving this transformation. However, a reliable free energy profile able to explain the experimental kinetics has not been published yet. This work presents a detailed investigation of this transformation using the accurate ωB97M-V functional, followed by a detailed microkinetic analysis. We have found two key intermediates. The first one is [(triphos)Ru(HCOO)(THF)]+, named MS5 and formed at the beginning of the catalyzed reaction. The second, and the most stable, is [(triphos)Ru(HCOO)(CH3OH)]+, named MS22 and formed later in the reaction time. The highest barrier in the catalytic cycle (28.1 kcal mol−1) corresponds to the steps MS22 → [(triphos)Ru(H)(H2)(THF)]+ → TS4, with TS4 being the hydride transfer to the coordinated formate. Formic acid released in the catalysis can participate in the important proton transfer steps along the catalytic cycle.

Ab initio modelling of photocatalytic CO2 reduction reactions over Cu/TiO2 semiconductors including the electronic excitation effects

Photocatalysis is a promising method for reducing CO2 in an environmentally friendly way. Despite extensive experimental studies, theoretical studies lag behind and often resort to describing catalysts in the excited state, while the reaction mechanism is mostly studied at the ground level. We theoretically calculate a full reaction mechanism of CO2 reduction to CO in the excited state for five surfaces: Cu(111), anatase(110), rutile(101) and Cu/anatase, Cu/rutile using the ΔSCF approach. We show that excited states considerably lower activation barriers, making it necessary in describing the experimental performance. The density functional theory calculations in the excited state are used to construct a microkinetic model, which is used to predict the performance of each catalytic surface. We show that Cu(111) is photocatalytically inactive, TiO2 is only active in the UV range and catalyzes water splitting. Only Cu/rutile is active for CO2 reduction to CO, while Cu/anatase produces more H2.

Importance of Site Diversity and Connectivity in Electrochemical CO Reduction on Cu.

Electrochemical CO2 reduction on Cu is a promising approach to produce value-added chemicals using renewable feedstocks, yet various Cu preparations have led to differences in activity and selectivity toward single and multicarbon products. Here, we find, surprisingly, that the effective catalytic activity toward ethylene improves when there is a larger fraction of less active sites acting as reservoirs of *CO on the surface of Cu nanoparticle electrocatalysts. In an adaptation of chemical transient kinetics to electrocatalysis, we measure the dynamic response of a gas diffusion electrode (GDE) cell when the feed gas is abruptly switched between Ar (inert) and CO. When switching from Ar to CO, CO reduction (COR) begins promptly, but when switching from CO to Ar, COR can be maintained for several seconds (delay time) despite the absence of the CO reactant in the gas phase. A three-site microkinetic model captures the observed dynamic behavior and shows that Cu catalysts exhibiting delay times have a less active *CO reservoir that exhibits fast diffusion to active sites. The observed delay times and the estimated *CO reservoir sizes are affected by catalyst preparation, applied potential, and microenvironment (electrolyte cation identity, electrolyte pH, and CO partial pressure). Notably, we estimate that the *CO reservoir surface coverage can be as high as 88 ± 7% on oxide-derived Cu (OD-Cu) at high overpotentials (-1.52 V vs SHE) and this increases in reservoir coverage coincide with increased turnover frequencies to ethylene. We also estimate that *CO can travel substantial distances (up to 10s of nm) prior to desorption or reaction. It appears that active C-C coupling sites by themselves do not control selectivity to C2+ products in electrochemical COR; the supply of CO to those sites is also a crucial factor. More generally, the overall activity of Cu electrocatalysts cannot be approximated from linear combinations of individual site activities. Future designs must consider the diversity of the catalyst network and account for intersite transportation pathways.

Microkinetic Modeling of Furan Hydrodeoxygenation over Transition-Metal Surfaces

Microkinetic Modeling of Furan Hydrodeoxygenation over Transition-Metal Surfaces

Potential and electric double-layer effect in electrocatalytic urea synthesis

Electrochemical synthesis is a promising way for sustainable urea production, yet the exact mechanism has not been fully revealed. Herein, we explore the mechanism of electrochemical coupling of nitrite and carbon dioxide on Cu surfaces towards urea synthesis on the basis of a constant-potential method combined with an implicit solvent model. The working electrode potential, which has normally overlooked, is found influential on both the reaction mechanism and activity. The further computational study on the reaction pathways reveals that *CO-NH and *NH-CO-NH as the key intermediates. In addition, through the analysis of turnover frequencies under various potentials, pressures, and temperatures within a microkinetic model, we demonstrate that the activity increases with temperature, and the Cu(100) shows the highest efficiency towards urea synthesis among all three Cu surfaces. The electric double-layer capacitance also plays a key role in urea synthesis. Based on these findings, we propose two essential strategies to promote the efficiency of urea synthesis on Cu electrodes: increasing Cu(100) surface ratio and elevating the reaction temperature.

Impact of palladium/palladium hydride conversion on electrochemical CO2 reduction via in-situ transmission electron microscopy and diffraction

Electrochemical conversion of CO2 offers a sustainable route for producing fuels and chemicals. Pd-based catalysts are effective for converting CO2 into formate at low overpotentials and CO/H2 at high overpotentials, while undergoing poorly understood morphology and phase structure transformations under reaction conditions that impact performance. Herein, in-situ liquid-phase transmission electron microscopy and select area diffraction measurements are applied to track the morphology and Pd/PdHx phase interconversion under reaction conditions as a function of electrode potential. These studies identify the degradation mechanisms, including poisoning and physical structure changes, occurring in PdHx/Pd electrodes. Constant potential density functional theory calculations are used to probe the reaction mechanisms occurring on the PdHx structures observed under reaction conditions. Microkinetic modeling reveals that the intercalation of *H into Pd is essential for formate production. However, the change in electrochemical CO2 conversion selectivity away from formate and towards CO/H2 at increasing overpotentials is due to electrode potential dependent changes in the reaction energetics and not a consequence of morphology or phase structure changes.

Interconnected Digital Solutions to Accelerate Modeling of the Reaction Kinetics in Catalysis

AbstractMicrokinetic modeling is a widely used tool in the domain of heterogeneous catalysis to gain valuable insights about the fundamental surface kinetics, crucial to designing improved catalysts. The development of a microkinetic model is a multi‐step process that demands expertise, a wide variety of experimental techniques, substantial computational resources, and extensive time and effort. In light of these challenges, automation within catalysis research is becoming increasingly important to allow exploration of a broader range of catalytic systems in a shorter timeframe. To this extent, a variety of digital tools and software have been developed to accelerate the development of microkinetic models. This work aims to highlight a selection of these tools that address the various challenges confronting the researchers in this field. These tools address diverse aspects, from the efficient storage of research data that allows easy retrieval and reuse, to the establishment of automated workflows that harness state‐of‐the‐art numerical solvers and algorithms that reduce manual effort. Through the use of automation, these aim to expedite and streamline the development and validation of models for catalytic systems, thereby reducing errors and increasing efficiency.

Atomically Dispersed Zn/Co-N-C as ORR Electrocatalysts for Alkaline Fuel Cells.

Hydrogen fuel cells have drawn increasing attention as one of the most promising next-generation power sources for future automotive transportation. Developing efficient, durable, and low-cost electrocatalysts, to accelerate the sluggish oxygen reduction reaction (ORR) kinetics, is urgently needed to advance fuel cell technologies. Herein, we report on metal-organic frameworks-derived nonprecious dual metal single-atom catalysts (SACs) (Zn/Co-N-C), consisting of Co-N4 and Zn-N4 local structures. These catalysts exhibited superior ORR activity with a half-wave potential (E1/2) of 0.938 V versus RHE (reversible hydrogen electrode) and robust stability (ΔE1/2 = -8.5 mV) after 50k electrochemical cycles. Moreover, this remarkable performance was validated under realistic fuel cell working conditions, achieving a record-high peak power density of ∼1 W cm-2 among the reported SACs for alkaline fuel cells. Operando X-ray absorption spectroscopy was conducted to identify the active sites and reveal catalytic mechanistic insights. The results indicated that the Co atom in the Co-N4 structure was the main catalytically active center, where one axial oxygenated species binds to form an Oads-Co-N4 moiety during the ORR. In addition, theoretical studies, based on a potential-dependent microkinetic model and core-level shift calculations, showed good agreement with the experimental results and provided insights into the bonding of oxygen species on Co-N4 centers during the ORR. This work provides a comprehensive mechanistic understanding of the active sites in the Zn/Co-N-C catalysts and will pave the way for the future design and advancement of high-performance single-site electrocatalysts for fuel cells and other energy applications.

A Study on the Role of Electric Field in Low-Temperature Plasma Catalytic Ammonia Synthesis via Integrated Density Functional Theory and Microkinetic Modeling.

Low-temperature plasma catalysis has shown promise for various chemical processes such as light hydrocarbon conversion, volatile organic compounds removal, and ammonia synthesis. Plasma-catalytic ammonia synthesis has the potential advantages of leveraging renewable energy and distributed manufacturing principles to mitigate the pressing environmental challenges of the energy-intensive Haber-Bosh process, towards sustainable ammonia production. However, lack of foundational understanding of plasma-catalyst interactions poses a key challenge to optimizing plasma-catalytic processes. Recent studies suggest electro- and photoeffects, such as electric field and charge, can play an important role in enhancing surface reactions. These studies mostly rely on using density functional theory (DFT) to investigate surface reactions under these effects. However, integration of DFT with microkinetic modeling in plasma catalysis, which is crucial for establishing a comprehensive understanding of the interplay between the gas-phase chemistry and surface reactions, remains largely unexplored. This paper presents a first-principles framework coupling DFT calculations and microkinetic modeling to investigate the role of electric field on plasma-catalytic ammonia synthesis. The DFT-microkinetic model shows more consistent predictions with experimental observations, as compared to the case wherein the variable effects of plasma process parameters on surface reactions are neglected. In particular, predictions of the DFT-microkinetic model indicate electric field can have a notable effect on surface reactions relative to other process parameters. A global sensitivity analysis is performed to investigate how ammonia synthesis pathways will change in relation to different plasma process parameters. The DFT-microkinetic model is then used in conjunction with active learning to systematically explore the complex parameter space of the plasma-catalytic ammonia synthesis to maximize the amount of produced ammonia while inhibiting reactions dissipating energy, such as the recombination of H2 through gas-phase H radicals and surface-adsorbed H. This paper demonstrates the importance of accounting for the effects of electric field on surface reactions when investigating and optimizing the performance of plasma-catalytic processes.

Deposited PtGe Clusters as Active and Durable Catalysts for CO Oxidation**

AbstractControl of CO emissions raises serious environmental concerns in the current chemical industry, as well as in nascent technologies based on hydrogen such as electrolyzers and fuel cells. As for now, Pt remains one of the state‐of‐the‐art catalysts for the CO oxidation reaction, but unfortunately, it suffers from CO self‐poisoning. Recently, Pt−Ge alloys were proposed to be an excellent alternative to reduce CO poisoning. This work investigates the impact of Ge content on the CO oxidation kinetics of Pt4Gen subnanoclusters supported on MgO. A Ge concentration dependence of the reaction kinetics is found due to a strong synergy between Pt and Ge. Pt−Ge nanoalloys act as a bifunctional catalyst by displaying dual adsorption sites; i. e., CO is adsorbed on Pt whereas oxygen binds to Ge, forming an alternative oxygen source GeOx. Besides, Ge alloying modifies the electronic structure of Pt (ligand effects) and reduces the affinity to CO. In this way, the competition between CO and O2 adsorption and the overbinding of CO is alleviated, achieving a CO poisoning‐free kinetic regime. Our calculations suggest that Pt4Ge3 is the optimal catalyst, evidencing that alloying composition is a parameter of extreme importance in nanocatalyst design. The work relies on global optimization search techniques to determine the accessibility of multiple structures at different conditions, mechanistic studies and microkinetic modeling.

Elucidating the Mechanism for Oxidative Coupling of Methane Catalyzed by La2O3: Experimental and Microkinetic Modeling Studies

Elucidating the Mechanism for Oxidative Coupling of Methane Catalyzed by La₂O₃: Experimental and Microkinetic Modeling Studies

Pioneering the Future: A Trailblazing Review of the Fusion of Computational Fluid Dynamics and Machine Learning Revolutionizing Plasma Catalysis and Non-Thermal Plasma Reactor Design

The advancement of plasma technology is intricately linked with the utilization of computational fluid dynamics (CFD) models, which play a pivotal role in the design and optimization of industrial-scale plasma reactors. This comprehensive compilation encapsulates the evolving landscape of plasma reactor design, encompassing fluid dynamics, chemical kinetics, heat transfer, and radiation energy. By employing diverse tools such as FLUENT, Python, MATLAB, and Abaqus, CFD techniques unravel the complexities of turbulence, multiphase flow, and species transport. The spectrum of plasma behavior equations, including ion and electron densities, electric fields, and recombination reactions, is presented in a holistic manner. The modeling of non-thermal plasma reactors, underpinned by precise mathematical formulations and computational strategies, is further empowered by the integration of machine learning algorithms for predictive modeling and optimization. From biomass gasification to intricate chemical reactions, this work underscores the versatile potential of plasma hybrid modeling in reshaping various industrial processes. Within the sphere of plasma catalysis, modeling and simulation methodologies have paved the way for transformative progress. Encompassing reactor configurations, kinetic pathways, hydrogen production, waste valorization, and beyond, this compilation offers a panoramic view of the multifaceted dimensions of plasma catalysis. Microkinetic modeling and catalyst design emerge as focal points for optimizing CO2 conversion, while the intricate interplay between plasma and catalysts illuminates insights into ammonia synthesis, methane reforming, and hydrocarbon conversion. Leveraging neural networks and advanced modeling techniques enables predictive prowess in the optimization of plasma-catalytic processes. The integration of plasma and catalysts for diverse applications, from waste valorization to syngas production and direct CO2/CH4 conversion, exemplifies the wide-reaching potential of plasma catalysis in sustainable practices. Ultimately, this anthology underscores the transformative influence of modeling and simulation in shaping the forefront of plasma-catalytic processes, fostering innovation and sustainable applications.

Rational design of bimetallic alloys for effective hydrodechlorination of 4-chlorophenol

A combined density functional theory (DFT) and microkinetic model (MKM)-based approach is applied to design Pd-based A3B type bimetallic alloys – Pd3Cu, Pd3Ag and Pd3Au for hydrodechlorination (HDC) reaction of 4-chlorophenol (4-CP).

Identification of Ni3Fe alloy as a candidate catalyst for quinoline selective hydrogenation with computations

We computationally identified Ni3Fe as a promising catalyst for the quinoline (QL) selective hydrogenation to 1,2,3,4-tetrahydroquinoline (py-THQL) using density functional theory calculations and microkinetic modeling.

Development of a surrogate artificial neural network for microkinetic modeling: case study with methanol synthesis

Speeding up microkinetic modeling by a surrogate artificial neural network.

Hydroxy and surface oxygen effects on 5-hydroxymethylfurfural oxidation to 2,5-furandicarboxylic acid on β-MnO2: DFT, microkinetic and experiment studies.

Manganese dioxide, β-MnO2, has shown potential in catalyzing the oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA), a monomer of bioplastic polyethylene furanoate (PEF). Herein, the insight into the hydroxy (OH) and surface oxygen effects on the HMF-to-FDCA reaction over β-MnO2 is clarified through a comprehensive investigation using density functional theory (DFT) calculations, microkinetic modeling, and experiment. Theoretical analyses revealed that both active surface oxygen and OH species (from either base or solvent) facilitate C-H bond breaking and OH insertion, promoting the catalytic activity of β-MnO2. Microkinetic modeling demonstrated that the FFCA-to-FDCA and DFF-to-FFCA steps are the rate-limiting steps of the hydroxylated and non-hydroxylated surfaces, respectively. These theoretical results agree well with the experiment when water and dimethyl sulfoxide (DMSO) were used as solvents. In addition, the synthesized β-MnO2 catalyst showed high stability and activity, maintaining stable HMF conversion (≥99 mol%) and high FDCA yield (85-92 mol%) during continuous flow oxidation for 72 hours at pO2 of 1 MPa, 393 K and LHSV of 1 h-1. Thus, considering both hydroxy and surface oxygen species is a new strategy for enhancing the catalytic activity of Mn oxides and other metal oxide catalysts for the HMF-to-FDCA reaction.

Design of MoS2 edge-anchored single-atom catalysts for propane dehydrogenation driven by DFT and microkinetic modeling.

The design of efficient catalysts for direct propane dehydrogenation (PDH) to inhibit coke formation and deactivation of traditional Pt-based catalysts are challenging tasks. Herein, by exploiting the unique geometric feature and tunability of single atom catalysts (SACs), a wide range of 3d-5d transition metals anchored on the MoS2 edge in the single atom form (TM1-S4/edge) are comprehensively investigated for the PDH application by first-principles calculations, ab initio molecular dynamics (AIMD) simulations and microkinetic modeling. Five criteria are assessed in terms of the feasibility of preparation, practical stability, feasibility of recovery after air oxidation, activity and selectivity. We identified Ru1-S4/edge SAC as the most promising candidate with activity six times higher than that of the conventional Pt(111) catalyst. Interestingly, AIMD simulations show that the motif region of the highly reactive TM1-S4/edge SACs (such as Ru, Os, Rh, and Ir) exhibits a dynamic change, with a TM-coordinated S atom tending to flutter at reaction temperatures and return to its initial position when the species is adsorbed on TMs, thereby affecting the PDH activities. In addition to identifying the potential PDH catalyst from a practical application point of view, we believe that this study also provides a comprehensive picture for the theoretical screening of low-coordination single-atom catalysts.

Operando Fe dissolution in Fe-N-C electrocatalysts during acidic oxygen reduction: impact of local pH change.

Atomic Fe in N-doped C (Fe-N-C) catalysts provide the most promising non-precious metal O2 reduction activity at the cathodes of proton exchange membrane fuel cells. However, one of the biggest remaining challenges to address towards their implementation in fuel cells is their limited durability. Fe demetallation has been suggested as the primary initial degradation mechanism. However, the fate of Fe under different operating conditions varies. Here, we monitor operando Fe dissolution of a highly porous and >50% FeN x electrochemical utilization Fe-N-C catalyst in 0.1 M HClO4, under O2 and Ar at different temperatures, in both flow cell and gas diffusion electrode (GDE) half-cell coupled to inductively coupled plasma mass spectrometry (ICP-MS). By combining these results with pre- and post-mortem analyses, we demonstrate that in the absence of oxygen, Fe cations diffuse away within the liquid phase. Conversely, at -15 mA cm-2 geo and more negative O2 reduction currents, the Fe cations reprecipitate as Fe-oxides. We support our conclusions with a microkinetic model, revealing that the local pH in the catalyst layer predominantly accounts for the observed trend. Even at a moderate O2 reduction current density of -15 mA cm-2 geo at 25 °C, a significant H+ consumption and therefore pH increase (pH = 8-9) within the bulk Fe-N-C layer facilitate precipitation of Fe cations. This work provides a unified view on the Fe dissolution degradation mechanism for a model Fe-N-C in both high-throughput flow cell and practical operating GDE conditions, underscoring the crucial role of local pH in regulating the stability of the active sites.

A microkinetic study of CO2 hydrogenation to methanol on Pd1-Cu(111) and Pd1-Ag(111) catalysts: a DFT analysis.

The thermochemical conversion of CO2 into methanol, a process known for its selectivity, often encounters a significant obstacle: the reverse water gas reaction. This problem emerges due to the demanding high temperatures and pressures, causing instability in catalytic performance. Recent endeavours have focused on innovatively designing catalysts capable of withstanding such conditions. Given the costliness of experimental approaches, a theoretical framework has emerged as a promising avenue for addressing the challenges in methanol production. It has been reported that transition metals, especially Pd, provide ideal binding sites for CO2 molecules and hydrogen atoms, facilitating their interactions and subsequent conversion to methanol. In the geometric single-atom form, their surface enables precise control over the reaction pathways and enhances the selectivity towards methanol. In our study, we employed density functional theory (DFT) to explore the conversion of CO2 to CH3OH on Pd1-Cu(111) and Pd1-Ag(111) single-atom alloy (SAA) catalysts. Our investigation involved mapping out the complex reaction pathways of CO2 hydrogenation to CH3OH using microkinetic reaction modelling and mechanisms. We examined three distinct pathways: the COOH* formation pathway, the HCOO* formation pathway, and the dissociation of CO2* to CO* pathway. This comprehensive analysis encompassed the determination of adsorption energies for all reactants, transition states, and resultant products. Additionally, we investigated the thermodynamic and kinetic profiles of individual reaction steps. Our findings emphasised the essential role of the Pd single atom in enhancing the activation of CO2, highlighting the key mechanism underlying this catalytic process. The favoured route for methanol generation on the Pd1-Ag(111) single-atom alloy (SAA) surface unfolds as follows: CO2* progresses through a series of transformations, transitioning successively into HCOO*, HCOOH*, H2COOH*, CH2O*, and CH2OH*, terminating in the formation of CH3OH*, due to lower activation energies and higher rate constants.