Dynamics of Heterogeneous Catalytic Processes at Operando Conditions

Novel high-pressure/high-temperature reactor cell for in situ and operando x-ray absorption spectroscopy studies of heterogeneous catalysts at synchrotron facilities

This paper presents the development of a novel high-pressure/high-temperature reactor cell dedicated to the characterization of catalysts using synchrotron x-ray absorption spectroscopy under operando conditions. The design of the vitreous carbon reactor allows its use as a plug-flow reactor, monitoring catalyst samples in a powder form with a continuous gas flow at high-temperature (up to 1000 °C) and under high pressure (up to 1000 bar) conditions, depending on the gas environment. The high-pressure/high-temperature reactor cell incorporates an automated gas distribution system and offers the capability to operate in both transmission and fluorescence detection modes. The operando x-ray absorption spectroscopy results obtained on a bimetallic InCo catalyst during CO2 hydrogenation reaction at 300 °C and 50 bar are presented, replicating the conditions of a conventional microreactor. The complete setup is available for users and permanently installed on the Collaborating Research Groups French Absorption spectroscopy beamline in Material and Environmental (CRG-FAME) sciences and French Absorption spectroscopy beamline in Material and Environmental sciences at ultra-high dilution (FAME-UHD) beamlines (BM30 and BM16) at the European Synchrotron Radiation Facility in Grenoble, France.

Metal-Organic Framework (MOF)-Based Clean Energy Conversion: Recent Advances in Unlocking its Underlying Mechanisms

Carbon neutrality is an important goal for humanity . As an eco-friendly technology, electrocatalytic clean energy conversion technology has emerged in the 21st century. Currently, metal-organic framework (MOF)-based electrocatalysis, including oxygen reduction reaction (ORR), oxygen evolution reaction (OER), hydrogen evolution reaction (HER), hydrogen oxidation reaction (HOR), carbon dioxide reduction reaction (CO2RR), nitrogen reduction reaction (NRR), are the mainstream energy catalytic reactions, which are driven by electrocatalysis. In this paper, the current advanced characterizations for the analyses of MOF-based electrocatalytic energy reactions have been described in details, such as density function theory (DFT), machine learning, operando/in situ characterization, which provide in-depth analyses of the reaction mechanisms related to the above reactions reported in the past years. The practical applications that have been developed for some of the responses that are of application values, such as fuel cells, metal-air batteries, and water splitting have also been demonstrated. This paper aims to maximize the potential of MOF-based electrocatalysts in the field of energy catalysis, and to shed light on the development of current intense energy situations.

Structured Catalysts and Catalytic Processes: Transport and Reaction Perspectives

The structure of catalysts determines the performance of catalytic processes. Intrinsically, the electronic and geometric structures influence the interaction between active species and the surface of the catalyst, which subsequently regulates the adsorption, reaction, and desorption behaviors. In recent decades, the development of catalysts with complex structures, including bulk, interfacial, encapsulated, and atomically dispersed structures, can potentially affect the electronic and geometric structures of catalysts and lead to further control of the transport and reaction of molecules. This review describes comprehensive understandings on the influence of electronic and geometric properties and complex catalyst structures on the performance of relevant heterogeneous catalytic processes, especially for the transport and reaction over structured catalysts for the conversions of light alkanes and small molecules. The recent research progress of the electronic and geometric properties over the active sites, specifically for theoretical descriptors developed in the recent decades, is discussed at the atomic level. The designs and properties of catalysts with specific structures are summarized. The transport phenomena and reactions over structured catalysts for the conversions of light alkanes and small molecules are analyzed. At the end of this review, we present our perspectives on the challenges for the further development of structured catalysts and heterogeneous catalytic processes.

Operando Electron Microscopy of Catalysts: The Missing Cornerstone in Heterogeneous Catalysis Research?

Heterogeneous catalysis in thermal gas-phase and electrochemical liquid-phase chemical conversion plays an important role in our modern energy landscape. However, many of the structural features that drive efficient chemical energy conversion are still unknown. These features are, in general, highly distinct on the local scale and lack translational symmetry, and thus, they are difficult to capture without the required spatial and temporal resolution. Correlating these structures to their function will, conversely, allow us to disentangle irrelevant and relevant features, explore the entanglement of different local structures, and provide us with the necessary understanding to tailor novel catalyst systems with improved productivity. This critical review provides a summary of the still immature field of operando electron microscopy for thermal gas-phase and electrochemical liquid-phase reactions. It focuses on the complexity of investigating catalytic reactions and catalysts, progress in the field, and analysis. The forthcoming advances are discussed in view of correlative techniques, artificial intelligence in analysis, and novel reactor designs.

Gaining Deep Understanding of Electrochemical CO2 RR with In Situ/Operando Techniques

Electrocatalysis for CO2 conversion has been extensively studied to mitigate the energy shortage and environmental issues, which are gaining ever-increasing attention. However, the complicated CO2 reduction process and the dynamic evolution occurring on electrocatalyst surface make it hard to understand the catalytic mechanism. The development of advanced in situ/operando techniques intelligently coupled with electrochemical cells sheds light on the related study via capturing surface atomic rearrangement, tracing chemical state change of catalysts, monitoring the behavior of intermediates and products, and depicting microenvironment near the electrode surface. In this review, fundamentals of the state-of-the-art in situ/operando techniques are clarified first. Case studies on the in situ/operando techniques performed to probe the CO2 reduction reaction processes are then discussed in detail. Finally, conclusions and outlook on this field are presented.

Accessing complex reconstructed material structures with hybrid global optimization accelerated via on-the-fly machine learning

The complex reconstructed structure of materials can be revealed by global optimization. This paper describes a hybrid evolutionary algorithm (HEA) that combines differential evolution and genetic algorithms with a multi-tribe framework. An on-the-fly machine learning calculator is adopted to expedite the identification of low-lying structures. With a superior performance to other well-established methods, we further demonstrate its efficacy by optimizing the complex oxidized surface of Pt/Pd/Cu with different facets under (4 × 4) periodicity. The obtained structures are consistent with experimental results and are energetically lower than the previously presented model.

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.

Challenges in modelling dynamic processes in realistic nanostructured materials at operating conditions

The question is addressed in how far current modelling strategies are capable of modelling dynamic phenomena in realistic nanostructured materials at operating conditions. Nanostructured materials used in applications are far from perfect; they possess a broad range of heterogeneities in space and time extending over several orders of magnitude. Spatial heterogeneities from the subnanometre to the micrometre scale in crystal particles with a finite size and specific morphology, impact the material's dynamics. Furthermore, the material's functional behaviour is largely determined by the operating conditions. Currently, there exists a huge length-time scale gap between attainable theoretical length-time scales and experimentally relevant scales. Within this perspective, three key challenges are highlighted within the molecular modelling chain to bridge this length-time scale gap. Methods are needed that enable (i) building structural models for realistic crystal particles having mesoscale dimensions with isolated defects, correlated nanoregions, mesoporosity, internal and external surfaces; (ii) the evaluation of interatomic forces with quantum mechanical accuracy albeit at much lower computational cost than the currently used density functional theory methods and (iii) derivation of the kinetics of phenomena taking place in a multi-length-time scale window to obtain an overall view of the dynamics of the process. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'.

Machine learning of atomic dynamics and statistical surface identities in gold nanoparticles

It is known that metal nanoparticles (NPs) may be dynamic and atoms may move within them even at fairly low temperatures. Characterizing such complex dynamics is key for understanding NPs' properties in realistic regimes, but detailed information on, e.g., the stability, survival, and interconversion rates of the atomic environments (AEs) populating them are non-trivial to attain. In this study, we decode the intricate atomic dynamics of metal NPs by using a machine learning approach analyzing high-dimensional data obtained from molecular dynamics simulations. Using different-shape gold NPs as a representative example, an AEs' dictionary allows us to label step-by-step the individual atoms in the NPs, identifying the native and non-native AEs and populating them along the MD simulations at various temperatures. By tracking the emergence, annihilation, lifetime, and dynamic interconversion of the AEs, our approach permits estimating a "statistical equivalent identity" for metal NPs, providing a comprehensive picture of the intrinsic atomic dynamics that shape their properties.

Dynamic evolution of the active center driven by hemilabile coordination in Cu/CeO2 single-atom catalyst

Hemilability is an important concept in homogeneous catalysis where both the reactant activation and the product formation can occur simultaneously through a reversible opening and closing of the metal-ligand coordination sphere. However, this effect has rarely been discussed in heterogeneous catalysis. Here, by employing a theoretical study on CO oxidation over substituted Cu₁/CeO₂ single atom catalysts, we show that dynamic evolution of metal-support coordination can significantly change the electronic structure of the active center. The evolution of the active center is shown to either strengthen or weaken the metal-adsorbate bonding as the reaction proceeds from reactants, through intermediates, to products. As a result, the activity of the catalyst can be increased. We explain our observations by extending hemilability effects to single atom heterogenous catalysts and anticipate that introducing this concept can offer a new insight into the important role active site dynamics have in catalysis toward the rational design of more sophisticated single atom catalyst materials.

Nature of Catalytic Behavior of Cobalt Oxides for CO2 Hydrogenation

Cobalt oxide (CoO _x ) catalysts are widely applied in CO₂ hydrogenation but suffer from structural evolution during the reaction. This paper describes the complicated structure-performance relationship under reaction conditions. An iterative approach was employed to simulate the reduction process with the help of neural network potential-accelerated molecular dynamics. Based on the reduced models of catalysts, a combined theoretical and experimental study has discovered that CoO(111) provides active sites to break C-O bonds for CH₄ production. The analysis of the reaction mechanism indicated that the C-O bond scission of *CH₂O species plays a key role in producing CH₄. The nature of dissociating C-O bonds is attributed to the stabilization of *O atoms after C-O bond cleavage and the weakening of C-O bond strength by surface-transferred electrons. This work may offer a paradigm to explore the origin of performance over metal oxides in heterogeneous catalysis.

Dynamic Construction and Maintenance of Confined Nanoregions via Hydrogen-Bond Networks between Acetylene Reactants and a Polyoxometalate-Based Metal-Organic Framework

The nanoconfinement effect in catalysis has attracted much attention because it provides a novel means of regulating the molecular properties and related reactions. Confined nanoregions composed of both reactants and catalysts through weak interactions are expected to improve the catalytic performance and promote the mass transport of relevant molecules simultaneously. However, at reaction temperatures, the structural variation of such confined spaces constructed via weak interactions remains unclear. Herein, through density functional theory calculations combined with ab initio molecular dynamics simulations, we have systematically investigated the dynamic structural evolution of the confined space constructed by acetylene reactants and a polyoxometalate-based metal-organic framework (POMOF) via hydrogen-bond networks. It is found that, at the reaction temperature of acetylene semihydrogenation, the hydrogen-bond networks and generated confined nanoregions are not rigid but are constantly changing and dynamically maintained. The steering role played by the O atoms at the surfaces of the polyoxometalate clusters is essential for generation of the hydrogen-bond networks and maintenance of the nanoregions. Upon confinement, the acetylene reactants can be better activated than those in an unconstrained atmosphere, which is reflected by the different dynamic distributions of the ∠CHC bending magnitude. Moreover, from a comparison of the distinct interaction characteristics between acetylene/ethylene and POMOF, the different manifestations in the adsorption energy variations of the confined molecules can be interpreted. This work helps to elucidate the underlying mechanisms of confined catalysis and may promote its application in practical catalytic processes.

Computational and Experimental Characterization of the Ligand Environment of a Ni-Oxo Catalyst Supported in the Metal-Organic Framework NU-1000

Heterogeneous catalysts exhibit significant changes in composition due to the influence of operating conditions, and these compositional changes can have dramatic effects on catalytic performance. For traditional bulk metal heterogeneous catalysts, relationships between composition and catalytic operating conditions are well documented. However, the influence of operating conditions on the compositions of single-site heterogeneous catalysts remains largely unresolved. To address this, we report a combined computational and experimental characterization of a Ni oxo catalyst under catalytic hydrogenation conditions. Specifically, pair distribution function (PDF) analysis is combined with ab initio thermodynamic modeling to investigate ligand environments present on a Ni oxo cluster supported in the metal-organic framework NU-1000. Comparisons of the experimentally observed and simulated Ni-O coordination numbers and Ni-O, Ni···Ni, and Ni···Zr distances provide insight into the Ni ligand environment under H₂ (g). These comparisons suggest significant OH and H₂O content and, further, that different Ni ions within the cluster and/or NU-1000 structure may comprise subtly different numbers of these ligands. Further, the observation of significant H₂O content under H₂ (g) suggests that the NU-1000 support supplies H₂O to the cluster. Examples of ligand environments that could lead to the observed PDFs are provided. The combination of simulations and experiments provides new insights into the ligand environment for Ni-NU-1000 catalysts that will be useful for understanding the ligand environments of other single-site Ni catalysts as well.

Charge-separation driven mechanism via acylium ion intermediate migration during catalytic carbonylation in mordenite zeolite

By employing ab initio molecular dynamic simulations, solid-state NMR spectroscopy, and two-dimensional correlation analysis of rapid scan Fourier transform infrared spectroscopy data, a new pathway is proposed for the formation of methyl acetate (MA) via the acylium ion (i.e.,CH3 − C ≡ O+) in 12-membered ring (MR) channel of mordenite by an integrated reaction/diffusion kinetics model, and this route is kinetically and thermodynamically more favorable than the traditional viewpoint in 8MR channel. From perspective of the complete catalytic cycle, the separation of these two reaction zones, i.e., the C-C bond coupling in 8MR channel and MA formation in 12MR channel, effectively avoids aggregation of highly active acetyl species or ketene, thereby reducing undesired carbon deposit production. The synergistic effect of different channels appears to account for the high carbonylation activity in mordenite that has thus far not been fully explained, and this paradigm may rationalize the observed catalytic activity of other reactions.

Dynamic Electrochemical Interfaces for Energy Conversion and Storage

Electrochemical energy conversion and storage are central to developing future renewable energy systems. For efficient energy utilization, both the performance and stability of electrochemical systems should be optimized in terms of the electrochemical interface. To achieve this goal, it is imperative to understand how a tailored electrode structure and electrolyte speciation can modify the electrochemical interface structure to improve its properties. However, most approaches describe the electrochemical interface in a static or frozen state. Although a simple static model has long been adopted to describe the electrochemical interface, atomic and molecular level pictures of the interface structure should be represented more dynamically to understand the key interactions. From this perspective, we highlight the importance of understanding the dynamics within an electrochemical interface in the process of designing highly functional and robust energy conversion and storage systems. For this purpose, we explore three unique classes of dynamic electrochemical interfaces: self-healing, active-site-hosted, and redox-mediated interfaces. These three cases of dynamic electrochemical interfaces focusing on active site regeneration collectively suggest that our understanding of electrochemical systems should not be limited to static models but instead expanded toward dynamic ones with close interactions between the electrode surface, dissolved active sites, soluble species, and reactants in the electrolyte. Only when we begin to comprehend the fundamentals of these dynamics through operando analyses can electrochemical conversion and storage systems be advanced to their full potential.

Manganese Oxide as a Promoter for Copper Catalysts in CO2 and CO Hydrogenation

In this work, we discuss the role of manganese oxide as a promoter in Cu catalysts supported on graphitic carbon during hydrogenation of CO₂ and CO. MnO_x is a selectivity modifier in an H₂/CO₂ feed and is a highly effective activity promoter in an H₂/CO feed. Interestingly, the presence of MnO_x suppresses the methanol formation from CO₂ (TOF of 0.7 ⋅ 10^-3 s^-1 at 533 K and 40 bar) and enhances the low-temperature reverse water-gas shift reaction (TOF of 5.7 ⋅ 10^-3 s^-1) with a selectivity to CO of 87 %_C. Using time-resolved XAS at high temperatures and pressures, we find significant absorption of CO₂ to the MnO, which is reversed if CO₂ is removed from the feed. This work reveals fundamental differences in the promoting effect of MnO_x and ZnO_x and contributes to a better understanding of the role of reducible oxide promoters in Cu-based hydrogenation catalysts.

Quantitatively Determining Surface-Adsorbate Properties from Vibrational Spectroscopy with Interpretable Machine Learning

Learning microscopic properties of a material from its macroscopic measurables is a grand and challenging goal in physical science. Conventional wisdom is to first identify material structures exploiting characterization tools, such as spectroscopy, and then to infer properties of interest, often with assistance of theory and simulations. This indirect approach has limitations due to the accumulation of errors from retrieving structures from spectral signals and the lack of quantitative structure-property relationship. A new pathway directly from spectral signals to microscopic properties is highly desirable, as it would offer valuable guidance toward materials evaluation and design via spectroscopic measurements. Herein, we exploit machine-learned vibrational spectroscopy to establish quantitative spectrum-property relationships. Key interaction properties of substrate-adsorbate systems, including adsorption energy and charge transfer, are quantitatively determined directly from Infrared and Raman spectroscopic signals of the adsorbates. The machine-learned spectrum-property relationships are presented as mathematical formulas, which are physically interpretable and therefore transferrable to a series of metal/alloy surfaces. The demonstrated ability of quantitative determination of hard-to-measure microscopic properties using machine-learned spectroscopy will significantly broaden the applicability of conventional spectroscopic techniques for materials design and high throughput screening under operando conditions.

Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation

The coupling of high-throughput calculations with catalyst informatics is proposed as an alternative way to design heterogeneous catalysts. High-throughput first-principles calculations for the oxidative coupling of methane (OCM) reaction are designed and performed where 1972 catalyst surface planes for the CH₄ to CH₃ reaction are calculated. Several catalysts for the OCM reaction are designed based on key elements that are unveiled via data visualization and network analysis. Among the designed catalysts, several active catalysts such as CoAg/TiO₂, Mg/BaO, and Ti/BaO are found to result in high C₂ yield. Results illustrate that designing catalysts using high-throughput calculations is achievable in principle if appropriate trends and patterns within the data generated via high-throughput calculations are identified. Thus, high-throughput calculations in combination with catalyst informatics offer a potential alternative method for catalyst design.

Deep Learning-Assisted Investigation of Electric Field-Dipole Effects on Catalytic Ammonia Synthesis

External electric fields can modify binding energies of reactive surface species and enhance catalytic performance of heterogeneously catalyzed reactions. In this work, we used density functional theory (DFT) calculations-assisted and accelerated by a deep learning algorithm-to investigate the extent to which ruthenium-catalyzed ammonia synthesis would benefit from application of such external electric fields. This strategy allows us to determine which electronic properties control a molecule's degree of interaction with external electric fields. Our results show that (1) field-dependent adsorption/reaction energies are closely correlated to the dipole moments of intermediates over the surface, (2) a positive field promotes ammonia synthesis by lowering the overall energetics and decreasing the activation barriers of the potential rate-limiting steps (e.g., NH₂ hydrogenation) over Ru, (3) a positive field (>0.6 V/Å) favors the reaction mechanism by avoiding kinetically unfavorable N≡N bond dissociation over Ru(1013), and (4) local adsorption environments (i.e., dipole moments of the intermediates in the gas phase, surface defects, and surface coverage of intermediates) influence the resulting surface adsorbates' dipole moments and further modify field-dependent reaction energetics. The deep learning algorithm developed here accelerates field-dependent energy predictions with acceptable accuracies by five orders of magnitudes compared to DFT alone and has the capacity of transferability, which can predict field-dependent energetics of other catalytic surfaces with high-quality performance using little training data.