Environment, Stability and Acidity of External Surface Sites of Silicalite-1 and ZSM-5 Micro and Nano Slabs, Sheets, and Crystals

Exploring the Interfacial Hydrogen Transfer between Pt and the Siliceous Framework and Its Promotional Effect on the Isotope Catalytic Exchange

Interfacial hydrogen transfer between metal particles and catalyst supports is a ubiquitous phenomenon in heterogeneous catalysis, and this occurrence on reducible supports has been established, yet controversies remain about how hydrogen transfer can take place on nonreducible supports, such as silica. Herein, highly dispersed Pt clusters supported on a series of porous silica materials with zeolitic or/and amorphous frameworks were prepared to interrogate the nature of hydrogen transfer and its promotional effect on H2-HDO isotope catalytic exchange. The formation of zeolitic frameworks upon these porous silica supports by hydrothermal crystallization greatly promotes the interfacial hydrogen bidirectional migration between metal clusters and supports. Benefiting from this transfer effect, the isotope exchange rate is enhanced by 10 times compared to that on the amorphous counterpart (e.g., Pt/SBA-15). In situ spectroscopic and theoretical studies suggest that the defective silanols formed within the zeolite framework serve as the reactive sites to bind HDO or H2O by hydrogen bonds. Under the electrostatic attraction interaction, the D of hydrogen-bonded HDO scrambles to the Pt site and the dissociated H on Pt simultaneously spills back to the electronegative oxygen atom of adsorbed water to attain H-D isotope exchange with an energy barrier of 0.43 eV. The reverse spillover D on Pt combines with the other H on Pt to form HD in the effluent. We anticipate that these findings are able to improve our understanding of hydrogen transfer between metal and silica supports and favor the catalyst design for the hydrogen-involving reaction.

A reactive neural network framework for water-loaded acidic zeolites

Under operating conditions, the dynamics of water and ions confined within protonic aluminosilicate zeolite micropores are responsible for many of their properties, including hydrothermal stability, acidity and catalytic activity. However, due to high computational cost, operando studies of acidic zeolites are currently rare and limited to specific cases and simplified models. In this work, we have developed a reactive neural network potential (NNP) attempting to cover the entire class of acidic zeolites, including the full range of experimentally relevant water concentrations and Si/Al ratios. This NNP has the potential to dramatically improve sampling, retaining the (meta)GGA DFT level accuracy, with the capacity for discovery of new chemistry, such as collective defect formation mechanisms at the zeolite surface. Furthermore, we exemplify how the NNP can be used as a basis for further extensions/improvements which include data-efficient adoption of higher-level (hybrid) references via Δ-learning and the acceleration of rare event sampling via automatic construction of collective variables. These developments represent a significant step towards accurate simulations of realistic catalysts under operando conditions.

Water structures on acidic zeolites and their roles in catalysis

The local reaction environment of catalytic active sites can be manipulated to modify the kinetics and thermodynamic properties of heterogeneous catalysis. Because of the unique physical-chemical nature of water, heterogeneously catalyzed reactions involving specific interactions between water molecules and active sites on catalysts exhibit distinct outcomes that are different from those performed in the absence of water. Zeolitic materials are being applied with the presence of water for heterogeneous catalytic reactions in the chemical industry and our transition to sustainable energy. Mechanistic investigation and in-depth understanding about the behaviors and the roles of water are essentially required for zeolite chemistry and catalysis. In this review, we focus on the discussions of the nature and structures of water adsorbed/stabilized on Brønsted and Lewis acidic zeolites based on experimental observations as well as theoretical calculation results. The unveiled functions of water structures in determining the catalytic efficacy of zeolite-catalyzed reactions have been overviewed and the strategies frequently developed for enhancing the stabilization of zeolite catalysts are highlighted. Recent advancement will contribute to the development of innovative catalytic reactions and the rationalization of catalytic performances in terms of activity, selectivity and stability with the presence of water vapor or in condensed aqueous phase.

Structure-Energy Relationship Prediction of the HZSM-5 Zeolite with Different Acid Site Distributions by the Neural Network Model

Zeolites are a very important family of catalysts. The catalytic activity of zeolites depends on the distribution of acid sites, which has been extensively studied. However, the relationship between the acid site distribution and catalytic efficiency remains unestablished. An onerous computational burden can be imposed when static calculations are applied to analyze the relationship between a catalyst structure and its energy. To resolve this issue, the current work uses neural network (NN) models to evaluate the relationship. By taking the typical HZSM-5 zeolite as an example, we applied the provided atomic coordinates to predict the energy. The network performances of the artificial neural network (ANN) and high-dimensional neural network (HDNN) are compared using the trained results from a dataset containing the identical number of acid sites. Furthermore, the importance of the feature is examined with the aid of a random forest model to identify the pivotal variables influencing the energy. In addition, the HDNN is employed to forecast the energy of an HZSM-5 system with varying numbers of acid sites. This study emphasizes that the energy of zeolites can be rapidly and accurately predicted through the NN, which can assist our understanding of the relationship between the structure and properties, thereby providing more accurate and efficient methods for the application of zeolite materials.

Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations

Within this Perspective, we critically reflect on the role of first-principles molecular dynamics (MD) simulations in unraveling the catalytic function within zeolites under operating conditions. First-principles MD simulations refer to methods where the dynamics of the nuclei is followed in time by integrating the Newtonian equations of motion on a potential energy surface that is determined by solving the quantum-mechanical many-body problem for the electrons. Catalytic solids used in industrial applications show an intriguing high degree of complexity, with phenomena taking place at a broad range of length and time scales. Additionally, the state and function of a catalyst critically depend on the operating conditions, such as temperature, moisture, presence of water, etc. Herein we show by means of a series of exemplary cases how first-principles MD simulations are instrumental to unravel the catalyst complexity at the molecular scale. Examples show how the nature of reactive species at higher catalytic temperatures may drastically change compared to species at lower temperatures and how the nature of active sites may dynamically change upon exposure to water. To simulate rare events, first-principles MD simulations need to be used in combination with enhanced sampling techniques to efficiently sample low-probability regions of phase space. Using these techniques, it is shown how competitive pathways at operating conditions can be discovered and how broad transition state regions can be explored. Interestingly, such simulations can also be used to study hindered diffusion under operating conditions. The cases shown clearly illustrate how first-principles MD simulations reveal insights into the catalytic function at operating conditions, which could not be discovered using static or local approaches where only a few points are considered on the potential energy surface (PES). Despite these advantages, some major hurdles still exist to fully integrate first-principles MD methods in a standard computational catalytic workflow or to use the output of MD simulations as input for multiple length/time scale methods that aim to bridge to the reactor scale. First of all, methods are needed that allow us to evaluate the interatomic forces with quantum-mechanical accuracy, albeit at a much lower computational cost compared to currently used density functional theory (DFT) methods. The use of DFT limits the currently attainable length/time scales to hundreds of picoseconds and a few nanometers, which are much smaller than realistic catalyst particle dimensions and time scales encountered in the catalysis process. One solution could be to construct machine learning potentials (MLPs), where a numerical potential is derived from underlying quantum-mechanical data, which could be used in subsequent MD simulations. As such, much longer length and time scales could be reached; however, quite some research is still necessary to construct MLPs for the complex systems encountered in industrially used catalysts. Second, most currently used enhanced sampling techniques in catalysis make use of collective variables (CVs), which are mostly determined based on chemical intuition. To explore complex reactive networks with MD simulations, methods are needed that allow the automatic discovery of CVs or methods that do not rely on a priori definition of CVs. Recently, various data-driven methods have been proposed, which could be explored for complex catalytic systems. Lastly, first-principles MD methods are currently mostly used to investigate local reactive events. We hope that with the rise of data-driven methods and more efficient methods to describe the PES, first-principles MD methods will in the future also be able to describe longer length/time scale processes in catalysis. This might lead to a consistent dynamic description of all steps-diffusion, adsorption, and reaction-as they take place at the catalyst particle level.

Molecular Views on Mechanisms of Brønsted Acid-Catalyzed Reactions in Zeolites

The Brønsted acidity of proton-exchanged zeolites has historically led to the most impactful applications of these materials in heterogeneous catalysis, mainly in the fields of transformations of hydrocarbons and oxygenates. Unravelling the mechanisms at the atomic scale of these transformations has been the object of tremendous efforts in the last decades. Such investigations have extended our fundamental knowledge about the respective roles of acidity and confinement in the catalytic properties of proton exchanged zeolites. The emerging concepts are of general relevance at the crossroad of heterogeneous catalysis and molecular chemistry. In the present review, emphasis is given to molecular views on the mechanism of generic transformations catalyzed by Brønsted acid sites of zeolites, combining the information gained from advanced kinetic analysis, in situ, and operando spectroscopies, and quantum chemistry calculations. After reviewing the current knowledge on the nature of the Brønsted acid sites themselves, and the key parameters in catalysis by zeolites, a focus is made on reactions undergone by alkenes, alkanes, aromatic molecules, alcohols, and polyhydroxy molecules. Elementary events of C-C, C-H, and C-O bond breaking and formation are at the core of these reactions. Outlooks are given to take up the future challenges in the field, aiming at getting ever more accurate views on these mechanisms, and as the ultimate goal, to provide rational tools for the design of improved zeolite-based Brønsted acid catalysts.

A computational investigation of the decomposition of acetic acid in H-SSZ-13 and its role in the initiation of the MTO process

The zeolite-catalyzed reaction of acetic acid is important in the direct utilization of biomass and also plays a role in the reactivity of oxygenates in the methanol-to-olefins (MTO) process.

Catalytic Enhancement of Cyclohexene Hydration by Ga-Doped ZSM-5 Zeolites

Ga-doped ZSM-5 zeolites were directly synthesized by a facile one-step hydrothermal method without organic templates and calcination and then investigated in the cyclohexene hydration reaction. The structure, component, textural properties, and acidity of the as-prepared samples were examined by X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray fluorescence (XRF), Brunauer-Emmett-Teller (BET), ammonia temperature-programmed desorption (NH₃-TPD), pyridine-chemisorbed IR (Py-IR), and ⁷¹Ga, ²⁷Al, ²⁹Si, and ¹H magic-angle spinning (MAS) NMR techniques. The characterization results showed that the introduction of Ga atoms into the ZSM-5 zeolite framework is much easier than Al atoms and beneficial to promote the formation of small-sized crystals. The number of Brønsted acid sites of Ga-doped ZSM-5 samples obviously increased compared with Ga0-ZSM-5. Additionally, the highest cyclohexanol yield (10.1%) was achieved over the Ga3-ZSM-5 sample, while the cyclohexanol yield of the Ga0-ZSM-5 sample was 8.6%. This result indicated that the improved catalytic performance is related to its larger external surface area, smaller particle size, and more Brønsted acid sites derived from Si-OH-Al and Si-OH-Ga of Ga3-ZSM-5. Notably, the green route reduces harmful gas emission and provides a basis for doping other heteroatoms to regulate the catalytic performance of zeolites, especially in industrial production.

Machine learning potential era of zeolite simulation

Zeolites, owing to their great variety and complexity in structure and wide applications in chemistry, have long been the hot topic in chemical research. This perspective first presents a short retrospect of theoretical investigations on zeolites using the tools from classical force fields to quantum mechanics calculations and to the latest machine learning (ML) potential simulations. ML potentials as the next-generation technique for atomic simulation open new avenues to simulate and interpret zeolite systems and thus hold great promise for finally predicting the structure-functionality relation of zeolites. Recent advances using ML potentials are then summarized from two main aspects: the origin of zeolite stability and the mechanism of zeolite-related catalytic reactions. We also discussed the possible scenarios of ML potential application aiming to provide instantaneous and easy access of zeolite properties. These advanced applications could now be accomplished by combining cloud-computing-based techniques with ML potential-based atomic simulations. The future development of ML potentials for zeolites in the respects of improving the calculation accuracy, expanding the application scope and constructing the zeolite-related datasets is finally outlooked.

DockOnSurf: A Python Code for the High-Throughput Screening of Flexible Molecules Adsorbed on Surfaces

We present the open-source python package DockOnSurf which automates the generation and optimization of low-energy adsorption configurations of molecules on extended surfaces and nanoparticles. DockOnSurf is especially geared toward handling polyfunctional flexible adsorbates. The use of this high-throughput workflow allows us to carry out the screening of adsorbate-surface configurations in a systematic, customizable, and traceable way, while keeping the focus on the chemically relevant structures. The screening strategy consists in splitting the exploration of the adsorbate-surface configurational space into chemically meaningful domains, that is, by choosing among different conformers to adsorb, surface adsorption sites, adsorbate anchoring points, and orientations and allowing dissociation of (acidic) protons. We demonstrate the performance of the main features based on varying examples, ranging from CO adsorption on a gold nanoparticle to sorbitol adsorption on hematite. Through the use of the presented program, we aim to foster efficiency, traceability, and ease of use in research within tribology, catalysis, nanoscience, and surface science in general.

Shape selectivity effects in the hydroconversion of perhydrophenanthrene over bifunctional catalysts

The zeolite pore structure dictates the formation of isomers, which in turn influences the preferred ring opening products and the distribution of cracking products.