A priori control of zeolite phase competition and intergrowth with high-throughput simulations

Virtual node graph neural network for full phonon prediction

Understanding the structure-property relationship is crucial for designing materials with desired properties. The past few years have witnessed remarkable progress in machine-learning methods for this connection. However, substantial challenges remain, including the generalizability of models and prediction of properties with materials-dependent output dimensions. Here we present the virtual node graph neural network to address the challenges. By developing three virtual node approaches, we achieve Γ-phonon spectra and full phonon dispersion prediction from atomic coordinates. We show that, compared with the machine-learning interatomic potentials, our approach achieves orders-of-magnitude-higher efficiency with comparable to better accuracy. This allows us to generate databases for Γ-phonon containing over 146,000 materials and phonon band structures of zeolites. Our work provides an avenue for rapid and high-quality prediction of phonon band structures enabling materials design with desired phonon properties. The virtual node method also provides a generic method for machine-learning design with a high level of flexibility.

Acceleration of Zeolite Crystallization: Current Status, Mechanisms, and Perspectives

Zeolites are important classes of crystalline materials and possess well-defined channels and cages with molecular dimensions. They have been extensively employed as heterogeneous catalysts and gas adsorbents due to their relatively large specific surface areas, high pore volumes, compositional flexibility, definite acidity, and hydrothermal stability. The zeolite synthesis normally undergoes high-temperature hydrothermal treatments with a relatively long crystallization time, which exhibits low synthesis efficiency and high energy consumption. Various strategies, e.g., modulation of the synthesis gel compositions, employment of special silica/aluminum sources, addition of seeds, fluoride, hydroxyl (·OH) free radical initiators, and organic additives, regulation of the crystallization conditions, development of new approaches, etc., have been developed to overcome these obstacles. And, these achievements make prominent contributions to the topic of acceleration of the zeolite crystallization and promote the fundamental understanding of the zeolite formation mechanism. However, there is a lack of the comprehensive summary and analysis on them. Herein, we provide an overview of the recent achievements, highlight the significant progress in the past decades on the developments of novel and remarkable strategies to accelerate the crystallization of zeolites, and basically divide them into three main types, i.e., chemical methods, physical methods, and the derived new approaches. The principles/acceleration mechanisms, effectiveness, versatility, and degree of reality for the corresponding approaches are thoroughly discussed and summarized. Finally, the rational design of the prospective strategies for the fast synthesis of zeolites is commented on and envisioned. The information gathered here is expected to provide solid guidance for developing a more effective route to improve the zeolite crystallization and obtain the functional zeolite-based materials with more shortened durations and lowered cost and further promote their applications.

The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture

Direct air capture (DAC) of CO2 with porous adsorbents such as metal-organic frameworks (MOFs) has the potential to aid large-scale decarbonization. Previous screening of MOFs for DAC relied on empirical force fields and ignored adsorbed H2O and MOF deformation. We performed quantum chemistry calculations overcoming these restrictions for thousands of MOFs. The resulting data enable efficient descriptions using machine learning.

Coupling Different Periodic Building Units for Intergrowth Zeolites

Multiple-phase disordered zeolites, i.e., intergrowth zeolites, are important industrial catalysts, like single-phase ordered zeolites, but little is known about their rational synthesis and phase competition, mainly due to current poor understanding of the zeolite crystallization mechanism. Here, we theoretically demonstrated that sodalite and cancrinite cage layers, the periodic building units (PerBUs) of FAU/EMT and SBT/SBS structures, respectively, could be nondefectively connected to each other across double rings of 6 tetrahedral atoms when inverted and mirrored. We then synthesized an unprecedented family of FAU/SBT/SBS intergrowths with controllable FAU portions (named as the PST-34 family of intergrowth zeolites) using a multiple inorganic cation approach, providing clear experimental evidence for the layer-by-layer crystal growth mechanism of zeolites. This study shows that control of interactive cooperation extent between different inorganic structure-directing agents in the presence of an unselective organic structure-directing agent may enable repeated stacking of different but structurally related PerBUs in intergrowth zeolite synthesis.

Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review

The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.

Synthetic Placement of Active Sites in MFI Zeolites for Selective Toluene Methylation to para-Xylene

Brønsted acidic zeolites are ubiquitous catalysts in fuel and chemical production. Broadening the catalytic diversity of a given zeolite requires strategies to manipulate the acid site placement at framework positions within distinct microporous locations. Here, we combine experiment and theory to elucidate how intermolecular interactions between organic structure-directing agents (OSDAs) and framework Al centers influence the placement of H+ sites in distinct void environments of MFI zeolites and demonstrate the catalytic consequences of active site location on kinetically controlled (403 K) toluene methylation to xylene regioisomers. Kinetic measurements, interpreted using mechanism-derived rate expressions and transition state theory, alongside density functional theory (DFT) calculations show that larger intersection environments similarly stabilize all three xylene isomer transition states without altering well-established aromatic substitution patterns (ortho/para/meta ∼ 60%:30%:10%), while smaller channel environments preferentially destabilize transition states that form bulkier ortho- and meta-isomers, thereby resulting in high intrinsic para-xylene selectivity (∼80%). DFT calculations reveal that the flexibility of nonconventional OSDAs (e.g., 1,4-diazabicyclo[2.2.2]octane) to reorient within MFI intersections and their ability to hydrogen-bond to form protonated complexes favor the placement of Al in smaller channel environments compared to conventional quaternary OSDAs (e.g., tetra-n-propylammonium). These molecular-level insights establish a mechanistic link between OSDA structure, active site placement, and transition state stability in MFI zeolites and provide active site design strategies that are orthogonal to crystallite design approaches harnessing complex reaction-diffusion phenomena to enhance regioisomer selectivity in the industrial production of valuable polymer precursors.

The Current Understanding of Mechanistic Pathways in Zeolite Crystallization

Zeolite catalysts and adsorbents have been an integral part of many commercial processes and are projected to play a significant role in emerging technologies to address the changing energy and environmental landscapes. The ability to rationally design zeolites with tailored properties relies on a fundamental understanding of crystallization pathways to strategically manipulate processes of nucleation and growth. The complexity of zeolite growth media engenders a diversity of crystallization mechanisms that can manifest at different synthesis stages. In this review, we discuss the current understanding of classical and nonclassical pathways associated with the formation of (alumino)silicate zeolites. We begin with a brief overview of zeolite history and seminal advancements, followed by a comprehensive discussion of different classes of zeolite precursors with respect to their methods of assembly and physicochemical properties. The following two sections provide detailed discussions of nucleation and growth pathways wherein we emphasize general trends and highlight specific observations for select zeolite framework types. We then close with conclusions and future outlook to summarize key hypotheses, current knowledge gaps, and potential opportunities to guide zeolite synthesis toward a more exact science.

ZeoSyn: A Comprehensive Zeolite Synthesis Dataset Enabling Machine-Learning Rationalization of Hydrothermal Parameters

Zeolites, nanoporous aluminosilicates with well-defined porous structures, are versatile materials with applications in catalysis, gas separation, and ion exchange. Hydrothermal synthesis is widely used for zeolite production, offering control over composition, crystallinity, and pore size. However, the intricate interplay of synthesis parameters necessitates a comprehensive understanding of synthesis-structure relationships to optimize the synthesis process. Hitherto, public zeolite synthesis databases only contain a subset of parameters and are small in scale, comprising up to a few thousand synthesis routes. We present ZeoSyn, a dataset of 23,961 zeolite hydrothermal synthesis routes, encompassing 233 zeolite topologies and 921 organic structure-directing agents (OSDAs). Each synthesis route comprises comprehensive synthesis parameters: 1) gel composition, 2) reaction conditions, 3) OSDAs, and 4) zeolite products. Using ZeoSyn, we develop a machine learning classifier to predict the resultant zeolite given a synthesis route with >70% accuracy. We employ SHapley Additive exPlanations (SHAP) to uncover key synthesis parameters for >200 zeolite frameworks. We introduce an aggregation approach to extend SHAP to all building units. We demonstrate applications of this approach to phase-selective and intergrowth synthesis. This comprehensive analysis illuminates the synthesis parameters pivotal in driving zeolite crystallization, offering the potential to guide the synthesis of desired zeolites. The dataset is available at https://github.com/eltonpan/zeosyn_dataset.

Cu-OFF/ERI Zeolite: Intergrowth Structure Synergistically Boosting Selective Catalytic Reduction of NOx with NH3

Cu-SSZ-13 has been commercialized for selective catalytic reduction with ammonia (NH3-SCR) to remove NOx from diesel exhaust. As its synthesis usually requires toxic and costly organic templates, the discovery of alternative Cu-based zeolite catalysts with organotemplate-free synthesis and comparable or even superior NH3-SCR activity to that of Cu-SSZ-13 is of great academic and industrial significance. Herein, we demonstrated that Cu-T with an intergrowth structure of offretite (OFF) and erionite (ERI) synthesized by an organotemplate-free method showed better catalytic performance than Cu-ERI and Cu-OFF as well as Cu-SSZ-13. Structure characterizations and density functional theory calculations indicated that the intergrowth structure promoted more isolated Cu2+ located at the 6MR of the intergrowth interface, resulting in a better hydrothermal stability of Cu-T than Cu-ERI and Cu-OFF. Strikingly, the low-temperature activity of Cu-T significantly increased after hydrothermal aging, while that of Cu-ERI and Cu-OFF substantially decreased. Based on in situ diffuse reflectance infrared Fourier transform spectra analysis and density functional theory calculations, the reason can be attributed to the fact that NH4NO3 formed on the CuxOy species within ERI polymorph of Cu-T underwent a fast SCR reaction pathway with the assistance of Brønsted acid sites at the intergrowth interfaces under standard SCR reaction conditions. Significantly, Cu-T exhibited a wider temperature window at a catalytic activity of over 90% than Cu-SSZ-13 (175-550 vs 175-500 °C for fresh and 225-500 vs 250-400 °C for hydrothermal treatment). This work provides a new direction for the design of high-performance NH3-SCR catalysts in terms of the interplay of the intergrowth structure of zeolites.

One-Pot Synthesis of CHA/ERI-Type Zeolite Intergrowth from a Single Multiselective Organic Structure-Directing Agent

We report the one-pot synthesis of a chabazite (CHA)/erionite (ERI)-type zeolite intergrowth structure characterized by adjustable extents of intergrowth enrichment and Si/Al molar ratios. This method utilizes readily synthesizable 6-azaspiro[5.6]dodecan-6-ium as the exclusive organic structure-directing agent (OSDA) within a potassium-dominant environment. High-throughput simulations were used to accurately determine the templating energy and molecular shape, facilitating the selection of an optimally biselective OSDA from among thousands of prospective candidates. The coexistence of the crystal phases, forming a distinct structure comprising disk-like CHA regions bridged by ERI-rich pillars, was corroborated via rigorous powder X-ray diffraction and integrated differential-phase contrast scanning transmission electron microscopy (iDPC S/TEM) analyses. iDPC S/TEM imaging further revealed the presence of single offretite layers dispersed within the ERI phase. The ratio of crystal phases between CHA and ERI in this type of intergrowth could be varied systematically by changing both the OSDA/Si and K/Si ratios. Two intergrown zeolite samples with different Si/Al molar ratios were tested for the selective catalytic reduction (SCR) of NOx with NH3, showing competitive catalytic performance and hydrothermal stability compared to that of the industry-standard commercial NH3-SCR catalyst, Cu-SSZ-13, prevalent in automotive applications. Collectively, this work underscores the potential of our approach for the synthesis and optimization of adjustable intergrown zeolite structures, offering competitive alternatives for key industrial processes.

Unveiling the mechanisms of carboxylic acid esterification on acid zeolites for biomass-to-energy: A review of the catalytic process through experimental and computational studies

In recent years, there has been significant interest from industrial and academic areas in the esterification of carboxylic acids catalyzed by acidic zeolites, as it represents a sustainable and economically viable approach to producing a wide range of high-value-added products. However, there is a lack of comprehensive reviews that address the intricate reaction mechanisms occurring at the catalyst interface at both the experimental and atomistic levels. Therefore, in this review, we provide an overview of the esterification reaction on acidic zeolites based on experimental and theoretical studies. The combination of infrared spectroscopy with atomistic calculations and experimental strategies using modulation excitation spectroscopy techniques combined with phase-sensitive detection is presented as an approach to detecting short-lived intermediates at the interface of zeolitic frameworks under realistic reaction conditions. To achieve this goal, this review has been divided into four sections: The first is a brief introduction highlighting the distinctive features of this review. The second addresses questions about the topology and activity of different zeolitic systems, since these properties are closely correlated in the esterification process. The third section deals with the mechanisms proposed in the literature. The fourth section presents advances in IR techniques and theoretical calculations that can be applied to gain new insights into reaction mechanisms. Finally, this review concludes with a subtle approach, highlighting the main aspects and perspectives of combining experimental and theoretical techniques to elucidate different reaction mechanisms in zeolitic systems.

Competition between Mononuclear and Binuclear Copper Sites across Different Zeolite Topologies

A key challenge for metal-exchanged zeolites is the determination of metal cation speciation and nuclearity under synthesis and reaction conditions. Copper-exchanged zeolites, which are widely used in automotive emissions control and potential catalysts for partial methane oxidation, have in particular evidenced a wide variety of Cu structures that are observed to change with exposure conditions, zeolite composition, and topology. Here, we develop predictive models for Cu cation speciation and nuclearity in CHA, MOR, BEA, AFX, and FER zeolite topologies using interatomic potentials, quantum chemical calculations, and Monte Carlo simulations to interrogate this vast configurational and compositional space. Model predictions are used to rationalize experimentally observed differences between Cu-zeolites in a wide-body of literature, including nuclearity populations, structural variations, and methanol per Cu yields. Our results show that both topological features and commonly observed Al-siting biases in MOR zeolites increase the population of binuclear Cu sites, explaining the small population of mononuclear Cu sites observed in these materials relative to other zeolites such as CHA and BEA. Finally, we used a machine learning classification model to determine the preference to form mononuclear or binuclear Cu sites at different Al configurations in 200 zeolites in the international zeolite database. Model results reveal several zeolite topologies at extreme ends of the mononuclear vs binuclear spectrum, highlighting synthetic options for realization of zeolites with strong Cu nuclearity preferences.

Zeolite encapsulated organometallic complexes as model catalysts

Heterogeneities in the structure of active centers in metal-containing porous materials are unavoidable and complicate the description of chemical events occurring along reaction coordinates at the atomic level. Metal containing zeolites include sites of varied local coordination and secondary confining environments, requiring careful titration protocols to quantify the predominant active sites. Hybrid organometallic-zeolite catalysts are useful well-defined platform materials for spectroscopic, kinetic, and computational studies of heterogeneous catalysis that avoid the complications of conventional metal-containing porous materials. Such materials have been synthesized and studied previously, but catalytic applications were mostly limited to liquid-phase oxidation and electrochemical reactions. The hydrothermal stability, time-on-stream stability, and utility of these materials in gas-phase oxidation reactions are under-studied. The potential applications for single-site heterogeneous catalysts in fundamental research are abundant and motivate future synthetic, spectroscopic, kinetic, and computational studies.

Resolving Temperature-Dependent Hydrate Nucleation Pathway: The Role of "Transition Layer"

Understanding the nucleation of natural gas hydrate (NGH) at different conditions has important implications to NGH recovery and other industrial applications, such as gas storage and separation. Herein, vast numbers of hydrate nucleation events are traced via molecular dynamics (MD) simulations at different degrees of supercooling (or driving forces). Specifically, to precisely characterize a hydrate nucleus from an aqueous system during the MD simulation, we develop an evolutionary order parameter (OP) to recognize the nucleus size and shape. Subsequently, the free energy landscapes of hydrate during nucleation are explored by using the newly developed OP. The results suggest that at 270 K (or 0.92 Tm supercooling, where Tm is the melting point), the near-rounded nucleus prevails during the nucleation, as described from the classical nucleation theory. In contrast, at relatively strong driving forces of 0.85 and 0.88 Tm, nonclassical nucleation events arise. Specifically, the pathway toward an elongated nucleus becomes as important as the pathway toward a near-rounded nucleus. To explain the distinct nucleation phenomena at different supercoolings, a notion of a "transition layer" (or liquid-blob-like layer) is proposed. Here, the transition layer is to describe the interfacial region between the nucleus and aqueous solution, and this layer entails two functionalities: (1) it tends to retain CH4 depending on the degrees of supercooling and (2) it facilitates collision among CH4, which thus promote the incorporation of CH4 into nucleus. Our simulation indicates that compared to the near-rounded nucleus, the transition layer surrounding the elongated nucleus is more evident with the higher collision rate among CH4 molecules. As such, the transition layer tends to promote the elongated nucleus pathway, while offsetting the cost of larger surface free energy associated with the elongated nucleus. At 0.92 Tm, however, the transition layer gradually disappears, and classical nucleation events dominate. Overall, the notion of "transition layer" offers deeper insight into the NGH nucleation at different degrees of supercooling and could be extended to describe other types of hydrate nucleation.

Intergrowth Zeolites, Synthesis, Characterization, and Catalysis

Microporous zeolites that can act as heterogeneous catalysts have continued to attract a great deal of academic and industrial interest, but current progress in their synthesis and application is restricted to single-phase zeolites, severely underestimating the potential of intergrowth frameworks. Compared with single-phase zeolites, intergrowth zeolites possess unique properties, such as different diffusion pathways and molecular confinement, or special crystalline pore environments for binding metal active sites. This review first focuses on the structural features and synthetic details of all the intergrowth zeolites, especially providing some insightful discussion of several potential frameworks. Subsequently, characterization methods for intergrowth zeolites are introduced, and highlighting fundamental features of these crystals. Then, the applications of intergrowth zeolites in several of the most active areas of catalysis are presented, including selective catalytic reduction of NOx by ammonia (NH3-SCR), methanol to olefins (MTO), petrochemicals and refining, fine chemicals production, and biomass conversion on Beta, and the relationship between structure and catalytic activity was profiled from the perspective of intergrowth grain boundary structure. Finally, the synthesis, characterization, and catalysis of intergrowth zeolites are summarized in a comprehensive discussion, and a brief outlook on the current challenges and future directions of intergrowth zeolites is indicated.

A Model Ensemble Approach Enables Data-Driven Property Prediction for Chemically Deconstructable Thermosets in the Low-Data Regime

Thermosets present sustainability challenges that could potentially be addressed through the design of deconstructable variants with tunable properties; however, the combinatorial space of possible thermoset molecular building blocks (e.g., monomers, cross-linkers, and additives) and manufacturing conditions is vast, and predictive knowledge for how combinations of these molecular components translate to bulk thermoset properties is lacking. Data science could overcome these problems, but computational methods are difficult to apply to multicomponent, amorphous, statistical copolymer materials for which little data exist. Here, leveraging a data set with 101 examples, we introduce a closed-loop experimental, machine learning (ML), and virtual screening strategy to enable predictions of the glass transition temperature (Tg) of polydicyclopentadiene (pDCPD) thermosets containing cleavable bifunctional silyl ether (BSE) comonomers and/or cross-linkers with varied compositions and loadings. Molecular features and formulation variables are used as model inputs, and uncertainty is quantified through model ensembling, which together with heavy regularization helps to avoid overfitting and ultimately achieves predictions within <15 °C for thermosets with compositionally diverse BSEs. This work offers a path to predicting the properties of thermosets based on their molecular building blocks, which may accelerate the discovery of promising plastics, rubbers, and composites with improved functionality and controlled deconstructability.

Direct Detection of Paired Aluminum Heteroatoms in Chabazite Zeolite Catalysts and Their Significance for Methanol Dehydration Reactivity

The distributions of heteroatoms within zeolite frameworks have important influences on the locations of exchangeable cations, which account for the diverse adsorption and reaction properties of zeolite catalysts. In particular for aluminosilicate zeolites, paired configurations of aluminum atoms separated by one or two tetrahedrally coordinated silicon atoms are important for charge-balancing pairs of H+ cations, which are active for methanol dehydration, or divalent metal cations, such as Cu2+, which selectively catalyze the reduction of NOx, both technologically important reactions. Such paired heteroatom configurations, however, are challenging to detect and probe, due to the typically nonstoichiometric compositions and nonperiodic distributions of aluminum atoms within aluminosilicate zeolite frameworks. Nevertheless, distinct configurations of paired framework aluminum atoms are unambiguously detected and resolved in solid-state 2D 27Al-29Si and 29Si-29Si NMR spectra, which are sensitive to the local environments of covalently bonded 27Al-O-29Si and 29Si-O-29Si moieties, respectively. Specifically, two H+-chabazite zeolites with the same bulk framework aluminum contents are shown to have different types and populations of closely paired aluminum species, which correlate with higher activity for methanol dehydration. The methodologies and insights are expected to be broadly applicable to analyses of heteroatom sites, their distributions, and adsorption and reaction properties in other zeolite framework types.

Analysis of Al site-directing ability of organic structure-directing agents in FER and CHA zeolites: a computational exploration of energetic preferences

This study explores the control of Al location in zeolites by organic structure-directing agents (OSDAs) using atomistic simulations. We examine several zeolite-OSDA complexes to quantify the Al site-directing ability. The results show that OSDAs induce different energetic preferences to direct Al at certain locations. In particular, these effects can be enhanced by OSDAs with N-H moieties. Our findings will be useful for the development of novel OSDAs that can modulate Al site-directing properties.

Machine learning-assisted crystal engineering of a zeolite

It is shown that Machine Learning (ML) algorithms can usefully capture the effect of crystallization composition and conditions (inputs) on key microstructural characteristics (outputs) of faujasite type zeolites (structure types FAU, EMT, and their intergrowths), which are widely used zeolite catalysts and adsorbents. The utility of ML (in particular, Geometric Harmonics) toward learning input-output relationships of interest is demonstrated, and a comparison with Neural Networks and Gaussian Process Regression, as alternative approaches, is provided. Through ML, synthesis conditions were identified to enhance the Si/Al ratio of high purity FAU zeolite to the hitherto highest level (i.e., Si/Al = 3.5) achieved via direct (not seeded), and organic structure-directing-agent-free synthesis from sodium aluminosilicate sols. The analysis of the ML algorithms' results offers the insight that reduced Na₂O content is key to formulating FAU materials with high Si/Al ratio. An acid catalyst prepared by partial ion exchange of the high-Si/Al-ratio FAU (Si/Al = 3.5) exhibits improved proton reactivity (as well as specific activity, per unit mass of catalyst) in propane cracking and dehydrogenation compared to the catalyst prepared from the previously reported highest Si/Al ratio (Si/Al = 2.8).

Approaching enzymatic catalysis with zeolites or how to select one reaction mechanism competing with others

Approaching the level of molecular recognition of enzymes with solid catalysts is a challenging goal, achieved in this work for the competing transalkylation and disproportionation of diethylbenzene catalyzed by acid zeolites. The key diaryl intermediates for the two competing reactions only differ in the number of ethyl substituents in the aromatic rings, and therefore finding a selective zeolite able to recognize this subtle difference requires an accurate balance of the stabilization of reaction intermediates and transition states inside the zeolite microporous voids. In this work we present a computational methodology that, by combining a fast high-throughput screeening of all zeolite structures able to stabilize the key intermediates with a more computationally demanding mechanistic study only on the most promising candidates, guides the selection of the zeolite structures to be synthesized. The methodology presented is validated experimentally and allows to go beyond the conventional criteria of zeolite shape-selectivity.

High-throughput phase elucidation of polycrystalline materials using serial rotation electron diffraction

Rapid phase elucidation of polycrystalline materials is essential for developing new materials of chemical, pharmaceutical and industrial interest. Yet, the size and quantity of many crystalline phases are too small for routine X-ray diffraction analysis. This has become a workflow bottleneck in materials development, especially in high-throughput synthesis screening. Here we demonstrate the application of serial rotation electron diffraction (SerialRED) for high-throughput phase identification of complex polycrystalline zeolite products. The products were prepared from a combination of multiple framework T atoms ([Si,Ge,Al] or [Si,Ge,B]) and a simple organic structure-directing agent. We show that using SerialRED, five zeolite phases can be identified from a highly complex mixture. This includes phases with ultra-low contents undetectable using X-ray diffraction and phases with identical crystal morphology and similar unit cell parameters. By automatically and rapidly examining hundreds of crystals, SerialRED enables high-throughput phase analysis and allows the exploration of complex synthesis systems. It provides new opportunities for rapid development of polycrystalline materials.

Bimetallic Sites for Catalysis: From Binuclear Metal Sites to Bimetallic Nanoclusters and Nanoparticles

Heterogeneous bimetallic catalysts have broad applications in industrial processes, but achieving a fundamental understanding on the nature of the active sites in bimetallic catalysts at the atomic and molecular level is very challenging due to the structural complexity of the bimetallic catalysts. Comparing the structural features and the catalytic performances of different bimetallic entities will favor the formation of a unified understanding of the structure-reactivity relationships in heterogeneous bimetallic catalysts and thereby facilitate the upgrading of the current bimetallic catalysts. In this review, we will discuss the geometric and electronic structures of three representative types of bimetallic catalysts (bimetallic binuclear sites, bimetallic nanoclusters, and nanoparticles) and then summarize the synthesis methodologies and characterization techniques for different bimetallic entities, with emphasis on the recent progress made in the past decade. The catalytic applications of supported bimetallic binuclear sites, bimetallic nanoclusters, and nanoparticles for a series of important reactions are discussed. Finally, we will discuss the future research directions of catalysis based on supported bimetallic catalysts and, more generally, the prospective developments of heterogeneous catalysis in both fundamental research and practical applications.

Quantum Alchemy Based Bonding Trends and Their Link to Hammett's Equation and Pauling's Electronegativity Model

We present an intuitive and general analytical approximation estimating the energy of covalent single and double bonds between participating atoms in terms of their respective nuclear charges with just three parameters, [E_AB ≈ a - bZ_AZ_B + c(Z_A^7/3 + Z_B^7/3) ]. The functional form of our expression models an alchemical atomic energy decomposition between participating atoms A and B. After calibration, reasonably accurate bond dissociation energy estimates are obtained for hydrogen-saturated diatomics composed of p-block elements coming from the same row 2 ≤ n ≤ 4 in the periodic table. Corresponding changes in bond dissociation energies due to substitution of atom B by C can be obtained via simple formulas. While being of different functional form and origin, our model is as simple and accurate as Pauling's well-known electronegativity model. Analysis indicates that the model's response in covalent bonding to variation in nuclear charge is near-linear, which is consistent with Hammett's equation.

A Six-Membered Ring Molecular Sieve Achieved by a Reconstruction Route

Zeolite molecular sieves with at least eight-membered rings are widely applied in industrial applications, while zeolite crystals with six-membered rings are normally regarded as useless products due to the occupancy of the organic templates and/or inorganic cation in the micropores that could not be removed. Herein, we showed that a novel six-membered ring molecular sieve (ZJM-9) with fully open micropores could be achieved by a reconstruction route. The mixed gas breakthrough experiments such as CH₃OH/H₂O, CH₄/H₂O, CO₂/H₂O, and CO/H₂O at 25 °C showed that this molecular sieve was efficient for selective dehydration. Particularly, a lower desorption temperature (95 °C) of ZJM-9 than that (250 °C) of the commercial 3A molecular sieve might offer an opportunity for saving more energy in dehydration processes.

Regulation of the Si/Al ratios and Al distributions of zeolites and their impact on properties

Zeolites are typically a class of crystalline microporous aluminosilicates that are constructed by SiO₄ and AlO₄ tetrahedra. Because of their unique porous structures, strong Brönsted acidity, molecular-level shape selectivity, exchangeable cations, and high thermal/hydrothermal stability, zeolites are widely used as catalysts, adsorbents, and ion-exchangers in industry. The activity, selectivity, and stability/durability of zeolites in applications are closely related to their Si/Al ratios and Al distributions in the framework. In this review, we discussed the basic principles and the state-of-the-art methodologies for regulating the Si/Al ratios and Al distributions of zeolites, including seed-assisted recipe modification, interzeolite transformation, fluoride media, and usage of organic structure-directing agents (OSDAs), etc. The conventional and newly developed characterization methods for determining the Si/Al ratios and Al distributions were summarized, which include X-ray fluorescence spectroscopy (XRF), solid state ²⁹Si/²⁷Al magic-angle-spinning nuclear magnetic resonance spectroscopy (²⁹Si/²⁷Al MAS NMR), Fourier-transform infrared spectroscopy (FT-IR), etc. The impact of Si/Al ratios and Al distributions on the catalysis, adsorption/separation, and ion-exchange performance of zeolites were subsequently demonstrated. Finally, we presented a perspective on the precise control of the Si/Al ratios and Al distributions of zeolites and the corresponding challenges.

Synthesis and Crystal Structure of a New RTH-Type Precursor and Its Interlayer Expanded Zeolite

Two dimensional zeolites have drawn a lot of attention due to their structural diversity and chemical composition, which can be used to obtain 3D zeolites, for which there is no direct synthesis. Here, a new layer silicate zeolite L was synthesized using the N, N-dimethyl-(2-methyl)-benzimidazolium as the organic structure-directing agent (OSDA) in the presence of fluoride. Structure determination by single-crystal X-ray diffraction reveals that the pure silica precursor with five-ring pores in the crystalline sheets is composed of the rth layer stacking along the (001) direction in an …AAAA… sequence with SDA⁺ cations and F^- residing within the interlayer spaces. Variable temperature powder X-ray diffraction (PXRD) results showed that the new layer could transform into a 3D RTH topology structure at 350 °C via 2D-3D topotactic transformation. Furthermore, a new 3D zeolite material is obtained by treating the original layer with a diethoxydimethylsilane agent under hydrochloric acid condition (HCl-DEDMS). Based on the PXRD results and the original layer structure, the new 3D zeolite structure expanding the rth layer with another Si atom is constructed, which possesses a 10×8×6 channel system. It displays a high BET surface area of 188 cm³ /g with an external surface area of 130 cm³ /g. The structure and textural properties pave a way for potential catalytic applications. The research not only provides a new layered zeolite, broadening the 2D zeolite framework types, but also allows for the discovery of a new stable 3D zeolite expanding the RTH structure with Si atom, which hasn't been reported yet.

Synthesis strategies to control the Al distribution in zeolites: thermodynamic and kinetic aspects

The activity and selectivity of acid-catalyzed chemistry is highly dependent on the Brønsted and Lewis acid sites generated by Al substitutions in a zeolite framework with the desired pore architecture. The siting of two Al atoms in close proximity in the framework of high-silica zeolites can also play a decisive role in improving the performance of redox catalysts by producing exchangeable positions for extra-framework multivalent cations. Thus, considerable attention has been devoted to controlling the Al incorporation through direct synthesis approaches and post-synthesis treatments to optimize the performance as (industrial) solid catalysts and to develop new acid- and redox-catalyzed reactions. This Feature Article highlights bottom-up synthetic strategies to fine-tune the Al incorporation in zeolites, interpreted with respect to thermodynamic and kinetic aspects. They include (i) variation in extra-framework components in zeolite synthesis, (ii) isomorphous substitution of other heteroatoms in the zeolite framework, and (iii) control over the (alumino)silicate network in the initial synthesis mixture via in situ and ex situ methods. Most synthetic approaches introduced here tentatively showed that the energy barriers associated with Al incorporation in zeolites can be variable during zeolite crystallization processes, occurring in complex media with multiple chemical interactions. Although the generic interpretation of each strategy and underlying crystallization mechanism remains largely unknown (and often limited to a specific framework), this review will provide guidance on more efficient methods to prepare fine-tuned zeolites with desired chemical properties.

Zeolites in Adsorption Processes: State of the Art and Future Prospects

Zeolites have been widely used as catalysts, ion exchangers, and adsorbents since their industrial breakthrough in the 1950s and continue to be state-of the-art adsorbents in many separation processes. Furthermore, their properties make them materials of choice for developing and emerging separation applications. The aim of this review is to put into context the relevance of zeolites and their use and prospects in adsorption technology. It has been divided into three different sections, i.e., zeolites, adsorption on nanoporous materials, and chemical separations by zeolites. In the first section, zeolites are explained in terms of their structure, composition, preparation, and properties, and a brief review of their applications is given. In the second section, the fundamentals of adsorption science are presented, with special attention to its industrial application and our case of interest, which is adsorption on zeolites. Finally, the state-of-the-art relevant separations related to chemical and energy production, in which zeolites have a practical or potential applicability, are presented. The replacement of some of the current separation methods by optimized adsorption processes using zeolites could mean an improvement in terms of sustainability and energy savings. Different separation mechanisms and the underlying adsorption properties that make zeolites interesting for these applications are discussed.

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.

Use of Multiscale Data-Driven Surrogate Models for Flowsheet Simulation of an Industrial Zeolite Production Process

The production of catalysts such as zeolites is a complex multiscale and multi-step process. Various material properties, such as particle size or moisture content, as well as operating parameters—e.g., temperature or amount and composition of input material flows—significantly affect the outcome of each process step, and hence determine the properties of the final product. Therefore, the design and optimization of such processes is a complex task, which can be greatly facilitated with the help of numerical simulations. This contribution presents a modeling framework for the dynamic flowsheet simulation of a zeolite production sequence consisting of four stages: precipitation in a batch reactor; concentration and washing in a block of centrifuges; formation of droplets and drying in a spray dryer; and burning organic residues in a chain of rotary kilns. Various techniques and methods were used to develop the applied models. For the synthesis in the reactor, a multistage strategy was used, comprising discrete element method simulations, data-driven surrogate modeling, and population balance modeling. The concentration and washing stage consisted of several multicompartment decanter centrifuges alternating with water mixers. The drying is described by a co–current spray dryer model developed by applying a two-dimensional population balance approach. For the rotary kilns, a multi-compartment model was used, which describes the gas–solid reaction in the counter–current solids and gas flows.

Optimal machine learning feature selection for assessing the mechanical properties of a zeolite framework

In this study, 45 and 249 critical features were discovered among 896 zeolite descriptors generated by the matminer package for estimating the shear and bulk moduli of zeolites, respectively. A database containing the mechanical properties of 873 zeolite structures, calculated using density functional theory, was used to train the machine learning regression model. The results of using these critical features with the LightGBM algorithm were rigorously compared with those from other regressors as well as with different sets of features. The comparison results indicate that the surrogate model with critical features increases the prediction accuracy of the bulk and shear moduli of zeolites by 17.3% and 10.6%, respectively, and reduces the prediction uncertainty by one-third of that achieved using previously available features. The suggested features originating from several physical and chemical groups highlight the unveiled relationships between the features and mechanical properties of zeolites. The robustness of the constructed model with 356 features was confirmed by applying a set of different training-test set ratios. We believe that the suggested critical features of zeolites can help to understand the mechanical behavior of a half million unlabeled hypothetical zeolite structures and accelerate the discovery of novel zeolites with unprecedented mechanical properties.

Synthesis and Crystal Growth Mechanism of PST-2: An Aluminosilicate SBS/SBT Zeolite Intergrowth

The synthesis of PST-2, an aluminosilicate zeolite intergrowth of cage-based, large-pore SBS and SBT topologies, and its intergrowth characteristics are presented. With the Si/Al ratio and crystallization inorganic structure-directing agent in zeolite synthesis mixtures fixed to 8.0 and Cs⁺ ions, respectively, pure PST-2 is obtained at 120 °C using tetraalkylammonium ions with C/N⁺ ratios of 5-9 as a charge density mismatch (CDM) organic structure-directing agent (OSDA). More interestingly, the intergrowth ratio between SBS and SBT in PST-2 was found to vary notably not only with the type of CDM OSDA employed but also with the crystallization time, unlike the case of other well-known zeolite intergrowths such as β and MFI/MEL. When tetraethylammonium ions are used as a CDM OSDA at 100 °C in the presence of Cs⁺, the SBS portion in PST-2 decreases from over 60% to less than 45% with increasing crystallization time from 2.5 to 14 days, suggesting that SBS formation is kinetically more favorable than SBT formation. A thorough characterization of changes in the crystallite dimension of PST-2 with crystallization time, together with those in the chemical composition, allowed us to propose a plausible crystal growth mechanism of this large-pore zeolite intergrowth.

Acidic medium synthesis of zeolites - an avenue to control the structure-directing power of organic templates

This paper deals with the extension of the synthesis field of microporous zeolite-type materials and types of organic structure-directing agents (OSDA) that can be used to promote their crystallization. The highly hydrophilic hexamethylenetetramine (urotropine), with its C/N ratio = 1.5, which is unusual to act as a structure-directing agent in the crystallization of open-framework silica polymorphs, is used to exemplify the novelty of the employed approach. Namely, the protonation of urotropine in an acidic fluorine-containing medium transforms it into a very efficient OSDA that yields dodecasil 3C (MTN-type). This novel synthesis also allows gaining insights into OSDA-framework interactions in the MTN-type structure. The comprehensive ²⁹Si and ¹⁹F MAS NMR indicate a small number of point defects of the framework T sites and the multiple bonding of F^- ions to Si in a disordered manner. Based on this finding, a unit cell model has been generated using Monte Carlo simulation and validated with Rietveld refinement using experimental powder X-ray diffraction data. In the model, protonated urotropine cations are located in the center of the big hexakaidecahedral [5¹²6⁴] cages at full occupancy with random orientations. The charge balance is provided by the disordered F^- ions.

Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure-Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx

A novel ab initio methodology based on high‐throughput simulations has permitted designing unique biselective organic structure‐directing agents (OSDAs) that allow the efficient synthesis of CHA/AEI zeolite intergrowth materials with controlled phase compositions. Distinctive local crystallographic ordering of the CHA/AEI intergrowths was revealed at the nanoscale level using integrated differential phase contrast scanning transmission electron microscopy (iDPC STEM). These novel CHA/AEI materials have been tested for the selective catalytic reduction (SCR) of NOx, presenting an outstanding catalytic performance and hydrothermal stability, even surpassing the performance of the well‐established commercial CHA‐type catalyst. This methodology opens the possibility for synthetizing new zeolite intergrowths with more complex structures and unique catalytic properties.

Unraveling templated-regulated distribution of isolated SiO4 tetrahedra in silicoaluminophosphate zeolites with high-throughput computations

Silicoaluminophosphate (SAPO) zeolites are well-known catalytic materials because of the mild acidity originating from the isolated SiO₄ tetrahedra in their frameworks. Regulating the distribution of isolated SiO₄ tetrahedra in SAPO zeolites is formidably challenging because SiO₄ tetrahedra tend to agglomerate to form Si islands and the isolated SiO₄ tetrahedra are difficult to determine using conventional characterization techniques. Here we synthesized Si-island-free SAPO-35 zeolites by using N-methylpiperidine as a new template, which exhibited excellent thermal stability compared to conventional SAPO-35 zeolites and a substantially improved methanol-to-olefins catalytic lifetime even comparable to that of commercial SAPO-34 zeolites. More strikingly, with the aid of high-throughput computations on 44 697 structure models combined with various state-of-the-art characterization techniques, for the first time, we reveal that the host–guest interactions between template molecules and SAPO frameworks determine the specific distributions of isolated SiO₄ tetrahedra, which are responsible for the improvement in the chemical properties of zeolites. Our work provides an insight into the template-based regulation of isolated SiO₄ tetrahedra in SAPO zeolites, which opens a new avenue in the discovery of promising zeolite catalysts with optimal SiO₄ distribution.

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.

Polymorph Selection in Zeolite Synthesis Occurs after Nucleation

Zeolites are porous crystals with extensive polymorphism. The hydrothermal synthesis of zeolites is a multistage process involving amorphous precursors that evolve continuously in solubility and local order toward those of the crystal. These results pose several questions: Why does a first-order transition appear as a continuous transformation? At which stage is the polymorph selected? How large are the barriers and critical sizes for zeolite nucleation? Here we address these questions using nucleation theory with experimental data. We find that the nucleation barriers and critical zeolite nuclei are extremely small at temperatures of hydrothermal synthesis, resulting in spinodal-like crystallization that produces a mosaic of tiny zeolitic crystallites that compete to grow inside each glassy precursor nanoparticle. The subnanometer size of the critical nuclei reveals that the selection between zeolite polymorphs occurs after the nucleation stage, during the growth and coarsening of the crystals around the excluded volume of the structure-directing agents.

Machine learning accelerated high-throughput screening of zeolites for the selective adsorption of xylene isomers

A combination of machine learning and high throughput simulation has identified several potential zeolite structures that appear to outperform the leading commercially used material and explained the key factors for high selectivity.

Advances in the synthesis and application of SSZ-39 zeolite

Zeolites, especially aluminosilicate zeolites, have been widely utilized in the process of petroleum refining, environmental protection, and fine chemicals. In the past decades, great attentions have been paid on the...

Controlling Nucleation Pathways in Zeolite Crystallization: Seeding Conceptual Methodologies for Advanced Materials Design

A core objective of synthesizing zeolites for widespread applications is to produce materials with properties and corresponding performances that exceed conventional counterparts. This places an impetus on elucidating and controlling processes of crystallization where one of the most critical design criteria is the ability to prepare zeolite crystals with ultrasmall dimensions to mitigate the deleterious effects of mass transport limitations. At the most fundamental level, this requires a comprehensive understanding of nucleation to address this ubiquitous materials gap. This Perspective highlights recent methodologies to alter zeolite nucleation by using seed-assisted protocols and the exploitation of interzeolite transformations to design advanced materials. Introduction of crystalline seeds in complex growth media used to synthesize zeolites can have wide-ranging effects on the physicochemical properties of the final product. Here we discuss the diverse pathways of zeolite nucleation, recent breakthroughs in seed-assisted syntheses of nanosized and hierarchical materials, and shortcomings for developing generalized guidelines to predict synthesis outcomes. We offer a critical analysis of state-of-the-art approaches to tailor zeolite crystallization wherein we conceptualize whether parallels between network theory and zeolite synthesis can be instrumental for translating key findings of individual discoveries across a broader set of zeolite crystal structures and/or synthesis conditions.

Monte carlo simulations and experiments of all-silica zeolite LTA assembly combining structure directing agents that match cage sizes

We investigated the influence of organic structure-directing agents (OSDAs) on the formation rates of all-silica zeolite LTA using both simulations and experiments, to shed light on the crystallization process. We compared syntheses using one OSDA with a diameter close to the size of the large cavity in LTA, and two OSDAs of diameters matching the sizes of both the small and large LTA cavities. Reaction-ensemble Monte Carlo (RxMC) simulations predict a speed up of LTA formation using two OSDAs matching the LTA pore sizes; this qualitative result is confirmed by experimental studies of crystallization kinetics, which find a speedup in all-silica LTA crystallization of a factor of 3. Analyses of simulated rings and their Si-O-Si angular energies during RxMC crystallizations show that all ring sizes in the faster crystallization exhibit lower angular energies, on average, than in the slower crystallization, explaining the origin of the speedup through packing effects.

Data-Driven Design of Biselective Templates for Intergrowth Zeolites

Zeolites are inorganic materials with wide industrial applications due to their topological diversity. Tailoring confinement effects in zeolite pores, for instance by crystallizing intergrown frameworks, can improve their catalytic and transport properties, but controlling zeolite crystallization often relies on heuristics. In this work, we use computational simulations and data mining to design organic structure-directing agents (OSDAs) to favor the synthesis of intergrown zeolites. First, we propose design principles to identify OSDAs which are selective toward both end members of the disordered structure. Then, we mine a database of hundreds of thousands of zeolite-OSDA pairs and downselect OSDA candidates to synthesize known intergrowth zeolites such as CHA/AFX, MTT/TON, and BEC/ISV. The computationally designed OSDAs balance phase competition metrics and shape selectivity toward the frameworks, thus bypassing expensive dual-OSDA approaches typically used in the synthesis of intergrowths. Finally, we propose potential OSDAs to obtain hypothesized disordered frameworks such as AEI/SAV. This work may accelerate zeolite discovery through data-driven synthesis optimization and design.

No more trial and error for zeolites

[Figure: see text].

A priori control of zeolite phase competition and intergrowth with high-throughput simulations

[Figure: see text].