Discovery of optimal zeolites for challenging separations and chemical transformations using predictive materials modeling

Design of Co-Cured Multi-Component Thermosets with Enhanced Heat Resistance, Toughness, and Processability via a Machine Learning Approach

Designing heat-resistant thermosets with excellent comprehensive performance has been a long-standing challenge. Co-curing of various high-performance thermosets is an effective strategy, however, the traditional trial-and-error experiments have long research cycles for discovering new materials. Herein, a two-step machine learning (ML) assisted approach is proposed to design heat-resistant co-cured resins composed of polyimide (PI) and silicon-containing arylacetylene (PSA), that is, poly(silicon-alkyne imide) (PSI). First, two ML prediction models are established to evaluate the processability of PIs and their compatibility with PSA. Then, another two ML models are developed to predict the thermal decomposition temperature and flexural strength of the co-cured PSI resins. The optimal molecular structures and compositions of PSI resins are high-throughput screened. The screened PSI resins are experimentally verified to exhibit enhanced heat resistance, toughness, and processability. The research framework established in this work can be generalized to the rational design of other advanced multi-component polymeric materials.

Oriented Metal-Organic Framework Membranes for Molecular Separations

Metal-organic framework (MOF) membranes, which are recognized as state-of-the-art platforms applied in various separation processes, have attracted widespread attention. Nonetheless, to overcome the trade-off between permeability and selectivity, which is crucial for achieving efficient separation, it is important to rationally design and manipulate MOF membrane structure. Given remarkable advances in the past decade, a timely summary of recent advancement in this field has become indispensable. This review introduces major strategies for fabricating oriented MOF membranes, including in situ growth, contra-diffusion method, interface-assisted approach, and laminated nanosheet assembly. New insights into their updated progress and potential are elucidated. Of particular note, recent development and emerging applications of oriented MOF membranes, illustrating their potential to address environmental and energy challenges, are highlighted. Finally, remaining challenges facing their bath production and practical applications are discussed.

PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials Based on Crystal Graph Convolution Networks

We report a fast and easy method (PACMAN) to assign partial atomic charges on metal-organic framework (MOF) and covalent-organic framework (COF) crystal structures based on graph convolution networks (GCNs) trained on >1.8 million high-fidelity partial atomic charge data obtained from the Quantum Metal-Organic Framework (QMOF) database. The developed model shows outstanding performance, achieving a mean absolute error (MAE) of 0.0055 e (test set performance) while maintaining consistency with DDEC6, Bader, and CM5 charges across diverse chemistry and topologies of MOFs and COFs. We find that the new method accurately assigns partial atomic charges for ion-containing nanoporous materials, which has not been possible in previous machine learning (ML) models. Grand canonical Monte Carlo (GCMC) simulation results for CO2 and N2 uptakes and the Widom particle insertion calculation for Henry's law constant of water results based on PACMAN and the original DDEC6 charges show excellent agreements compared to other ML models reported in the literature. The runtime analysis of the new method demonstrates that the partial atomic charges of MOF and COF structures with up to 500 atoms can be obtained in less than 10 s. An easy-to-use web interface has been developed to facilitate the adoption of the developed model.

Understanding and Predicting the Spatially Resolved Adsorption Properties of Nanoporous Materials

Using knowledge from statistical thermodynamics and crystallography, we develop an image-image translation model, called SorbIIT, that uses three-dimensional grids of adsorbate-adsorbent interaction energies as input to predict the spatially resolved loading surface of nanoporous materials over a broad range of temperatures and pressures. SorbIIT consists of a closed-form differential model for loading-surface prediction and a U-Net to generate spatial differential distributions from the energy grids. SorbIIT is trained using the energy grids and adsorbate distributions (obtained from high-throughput simulations) of 50 synthesized and 70 hypothetical zeolites and applied for predicting the adsorption of carbon dioxide, hydrogen sulfide, n-butane, 2-methylpropane, krypton, and xenon in other zeolites from 256 to 400 K. Employing a quadratic isotherm model for the local differentiation, SorbIIT yields mean R2 values of 0.998 for total adsorption and 0.6904 for local adsorption with a resolution of 0.2 Å, and a value of 0.721 for the structural similarity of the local loading distribution.

Quantitative Simulations of Siloxane Adsorption in Metal-Organic Frameworks

We present a transferable force field (FF) for simulating the bulk properties of linear and cyclic siloxanes and the adsorption of these species in metal-organic frameworks (MOFs). Unlike previous FFs for siloxanes, our FF accurately reproduces the vapor-liquid equilibria of each species in the bulk phase. The quality of our FF combined with the Universal Force Field using standard Lorentz-Berthelot combining rules for MOF atoms was assessed in a wide range of MOFs without open metal sites, showing good agreement with dispersion-corrected density functional theory calculations. Predictions with this FF show good agreement with the limited experimental data for siloxane adsorption in MOFs that is available. As an example of using the FF to predict adsorption properties in MOFs, we present simulations examining entropy effects in binary linear and cyclic siloxane mixture coadsorption in the large-pore MOF with structure code FOTNIN.

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ∼ 0.94-0.99) for predicting single-component adsorption capacity in metal-organic frameworks (MOFs). We consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

Optimization of the wetting-drying characteristics of hydrophobic metal organic frameworks via crystallite size: The role of hydrogen bonding between intruded and bulk liquid

HYPOTHESIS

The behavior of Heterogeneous Lyophobic Systems (HLSs) comprised of a lyophobic porous material and a corresponding non-wetting liquid is affected by a variety of different structural parameters of the porous material. Dependence on exogenic properties such as crystallite size is desirable for system tuning as they are much more facilely modified. We explore the dependence of intrusion pressure and intruded volume on crystallite size, testing the hypothesis that the connection between internal cavities and bulk water facilitates intrusion via hydrogen bonding, a phenomenon that is magnified in smaller crystallites with a larger surface/volume ratio.

EXPERIMENTS

Water intrusion/extrusion pressures and intrusion volume were experimentally measured for ZIF-8 samples of various crystallite sizes and compared to previously reported values. Alongside the practical research, molecular dynamics simulations and stochastic modeling were performed to illustrate the effect of crystallite size on the properties of the HLSs and uncover the important role of hydrogen bonding within this phenomenon.

FINDINGS

A reduction in crystallite size led to a significant decrease of intrusion and extrusion pressures below 100 nm. Simulations indicate that this behavior is due to a greater number of cages being in proximity to bulk water for smaller crystallites, allowing cross-cage hydrogen bonds to stabilize the intruded state and lower the threshold pressure of intrusion and extrusion. This is accompanied by a reduction in the overall intruded volume. Simulations demonstrate that this phenomenon is linked to ZIF-8 surface half-cages exposed to water being occupied by water due to non-trivial termination of the crystallites, even at atmospheric pressure.

Unveiling Hidden Zeolitic Imidazolate Frameworks Guided by Intuition-Based Geometrical Factors

Herein, synthesizable candidate topologies to form zeolitic imidazolate frameworks (ZIFs) are efficiently identified from over 2 000 000 hypothetical structures in zeolite databases, using structural descriptors extracted from known ZIFs. A combination of intuition-based structural descriptors, such as ring patterns, node numbers, and TOT bridging angles (T = tetrahedral metal nodes in zeolites and ZIFs), is used as data filters to eliminate topologies infeasible for ZIF formation. Carefully chosen structural descriptors facilitate the prediction of plausible ZIF topologies. To investigate potential applications as porous ZIFs, this work performs hydrogen adsorption screening and suggested notable target ZIFs. The collection of new plausible ZIFs, derived from the combined descriptors, will be a structural blueprint for synthetic chemists.

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.

Quantitative Structure-Property Relationship Analysis for the Prediction of Propylene Adsorption Capacity in Pure Silicon Zeolites at Various Pressure Levels

This work is devoted to the development of quantitative structure-property relationship (QSPR) models using various regression analyses to predict propylene (C₃H₆) adsorption capacity at various pressures in zeolites from a topologically diverse International Zeolite Association database. Based on univariate and multilinear regression analysis, the accessible volume and largest cavity diameter are the most crucial factors determining C₃H₆ uptake at high and low pressures, respectively. An artificial neural network (ANN) model with five structural descriptors is sufficient to predict C₃H₆ uptake at high pressures. For combined pressures, the prediction of an ANN model with pore size distribution is pleasing. The isosteric heat of adsorption (Q _st) has a significant impact on the improvement of the prediction of low-pressure gas adsorption, which finely classifies zeolites into high or low C₃H₆ adsorbers. The conjunction of high-throughput screening and QSPR models contributes to being able to prescreen the database rapidly and accurately for top performers and perform further detailed and time-consuming computational-intensive molecular simulations on these candidates for other gas adsorption applications.

Ethanol and Water Adsorption in Conventional and Hierarchical All-Silica MFI Zeolites

Hierarchical zeolites containing both micro- (<2 nm) and mesopores (2-50 nm) have gained increasing attention in recent years because they combine the intrinsic properties of conventional zeolites with enhanced mass transport rates due to the presence of mesopores. The structure of the hierarchical self-pillared pentasil (SPP) zeolite is of interest because all-silica SPP consists of orthogonally intergrown single-unit-cell MFI nanosheets and contains hydrophilic surface silanol groups on the mesopore surface while its micropores are nominally hydrophobic. Therefore, the distribution of adsorbed polar molecules, like water and ethanol, in the meso- and micropores is of fundamental interest. Here, molecular simulation and experiment are used to investigate the adsorption of water and ethanol on SPP. Vapor-phase single-component adsorption shows that water occupies preferentially the mesopore corner and surface regions of the SPP material at lower pressures (P/P ₀ < 0.5) while loading in the mesopore interior dominates adsorption at higher pressures. In contrast, ethanol does not exhibit a marked preference for micro- or mesopores at low pressures. Liquid-phase adsorption from binary water-ethanol mixtures demonstrates a 2 orders of magnitude lower ethanol/water selectivity for the SPP material compared to bulk MFI. For very dilute aqueous solutions of ethanol, the ethanol molecules are mostly adsorbed inside the SPP micropore region due to stronger dispersion interactions and the competition from water for the surface silanols. At high ethanol concentrations (C _EtOH > 700 g L^-1), the SPP material becomes selective for water over ethanol.

Subnanometer Topological Tuning of the Liquid Intrusion/Extrusion Characteristics of Hydrophobic Micropores

Intrusion (wetting)/extrusion (drying) of liquids in/from lyophobic nanoporous systems is key in many fields, including chromatography, nanofluidics, biology, and energy materials. Here we demonstrate that secondary topological features decorating main channels of porous systems dramatically affect the intrusion/extrusion cycle. These secondary features, allowing an unexpected bridging with liquid in the surrounding domains, stabilize the water stream intruding a micropore. This reduces the intrusion/extrusion barrier and the corresponding pressures without altering other properties of the system. Tuning the intrusion/extrusion pressures via subnanometric topological features represents a yet unexplored strategy for designing hydrophobic micropores. Though energy is not the only field of application, here we show that the proposed tuning approach may bring 20-75 MPa of intrusion/extrusion pressure increase, expanding the applicability of hydrophobic microporous materials.

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.

Absorption of mechanical energy via formation of ice nanotubes in zeolites

Molecular dynamics simulations are carried out for a heterogeneous system composed of bulk water and pure-silica zeolites of the AFI type. My simulations show, for the first time, the spontaneous crystallization of water in hydrophobic zeolite channels by compression, while the water outside remains liquid. The formation of ice nanotubes results in a molecular bumper behavior in the absence of chemical reactions, although the mechanism has been explained by the appearance of silanol defects. In contrast, the same zeolite-water system exhibits a weak shock-absorber behavior at higher temperatures. My study shows that the phase transitions of confined water dramatically change its intrusion/extrusion behavior and alter the energetic performance by varying the temperature alone. The results offer a new perspective for a better design of hydrophobic nanoporous materials utilized with water.

Separating a linear C5 hydrocarbon from a branched C6 hydrocarbon: n-pentane from 2,2-dimethyl butane using levitation and blow torch effects

The separation of linear from branched hydrocarbons is often required in many situations. There are several methods through which they can be separated but none provides a very high degree of purity or works without considerable expenditure of energy. Recently, a novel method was proposed to separate a mixture of neopentane and n-pentane. The present work demonstrates that the method can be used for separating other mixtures of hydrocarbons as well, by attempting the separation of a mixture of 2,2-dimethyl butane and n-pentane. Intermolecular interaction potentials have been modified to reproduce the experimental heat of adsorption and diffusivity of 2,2-dimethyl butane and n-pentane in zeolite NaY. The method involves choosing the correct host zeolite or other porous solids and introducing hot zones at appropriate positions. This result drives both the components to the opposite ends of the zeolite column, thus leading to separation. The achieved separation factors are much higher than what can be obtained with the help of existing methods. Different properties have been computed to understand the process involved in the separation of the mixture. The approach employed here uses very little energy for separation, making it suitable for green chemistry.

Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning

Adsorptive hydrogen storage is a desirable technology for fuel cell vehicles, and efficiently identifying the optimal storage temperature requires modeling hydrogen loading as a continuous function of pressure and temperature. Using data obtained from high-throughput Monte Carlo simulations for zeolites, metal-organic frameworks, and hyper-cross-linked polymers, we develop a meta-learning model that jointly predicts the adsorption loading for multiple materials over wide ranges of pressure and temperature. Meta-learning gives higher accuracy and improved generalization compared to fitting a model separately to each material and allows us to identify the optimal hydrogen storage temperature with the highest working capacity for a given pressure difference. Materials with high optimal temperatures are found in close proximity in the fingerprint space and exhibit high isosteric heats of adsorption. Our method and results provide new guidelines toward the design of hydrogen storage materials and a new route to incorporate machine learning into high-throughput materials discovery.

Competitive Adsorption of Xylenes at Chemical Equilibrium in Zeolites

The separation of xylenes is one of the most important processes in the petrochemical industry. In this article, the competitive adsorption from a fluid-phase mixture of xylenes in zeolites is studied. Adsorption from both vapor and liquid phases is considered. Computations of adsorption of pure xylenes and a mixture of xylenes at chemical equilibrium in several zeolite types at 250 °C are performed by Monte Carlo simulations. It is observed that shape and size selectivity entropic effects are predominant for small one-dimensional systems. Entropic effects due to the efficient arrangement of xylenes become relevant for large one-dimensional systems. For zeolites with two intersecting channels, the selectivity is determined by a competition between enthalpic and entropic effects. Such effects are related to the orientation of the methyl groups of the xylenes. m-Xylene is preferentially adsorbed if xylenes fit tightly in the intersection of the channels. If the intersection is much larger than the adsorbed molecules, p-xylene is preferentially adsorbed. This study provides insight into how the zeolite topology can influence the competitive adsorption and selectivity of xylenes at reaction conditions. Different selectivities are observed when a vapor phase is adsorbed compared to the adsorption from a liquid phase. These insight have a direct impact on the design criteria for future applications of zeolites in the industry. MRE-type and AFI-type zeolites exclusively adsorb p-xylene and o-xylene from the mixture of xylenes in the liquid phase, respectively. These zeolite types show potential to be used as high-performing molecular sieves for xylene separation and catalysis.

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.

High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

Porous materials are widely employed in a large range of applications, in particular, for storage, separation, and catalysis of fine chemicals. Synthesis, characterization, and pre- and post-synthetic computer simulations are mostly carried out in a piecemeal and ad hoc manner. Whilst high throughput approaches have been used for more than 30 years in the porous material fields, routine integration of experimental and computational processes is only now becoming more established. Herein, important developments are highlighted and emerging challenges for the community identified, including the need to work toward more integrated workflows.

Generalized hydrodynamic analysis of transport through a finite open nanopore for two-component single-file systems

Single-file diffusion (SFD) in finite open nanopores is characterized by nonzero spatially varying tracer diffusion coefficients within a generalized hydrodynamic description. This contrasts with infinite SFD systems where tracer diffusivity vanishes. In standard tracer counterpermeation (TCP) analysis, two reservoirs, each containing a different species, are connected to opposite ends of a finite pore. We implement an extended TCP analysis to allow the two reservoirs to contain slightly different mixtures of the two species. Then, determination of diffusion fluxes through the pore allows extraction of diffusion coefficients for near-constant partial concentrations of the two species. This analysis is applied for a lattice-gas model describing two-component SFD through a finite linear pore represented by a one-dimensional array of cells. Two types of particles, A and B, can hop only to adjacent empty cells with generally different rates, h_{A} and h_{B}. Particles are noninteracting other than exclusion of multiple cell occupancy. Results reveal generalized hydrodynamic tracer diffusion coefficients which adopt small values inversely proportional to pore length in the pore center, but which are strongly enhanced near pore openings.

Separating Hydrocarbon Mixtures by Driving the Components in Opposite Directions: High Degree of Separation Factor and Energy Efficiency

A radically different approach for separation of molecular mixtures is proposed. A judicious combination of levitation effect observed in zeolites with a counter intuitive Landauer blow torch effect provides driving forces for the two components of the mixture to move in opposite directions. Using nonequilibrium Monte Carlo simulations, we illustrate the efficacy of the method for separating real mixtures of both linear n-pentane and its branched isomer, neopentane, and linear n-hexane and its branched isomer, 2,2-dimethylbutane. The method yields several orders of magnitude improvement in separation factor and relative energy efficiency by using submicron zeolite column. The extremely high purity of the resulting single components makes the method best suited for green chemistry.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time-consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network-specifically, a multilayer perceptron (MLP)-as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our MLP on "alchemical" species, represented only by variables derived from their force-field parameters, to predict the loadings of real adsorbates. Alchemical species used for training were small, near-spherical, and nonpolar, enabling the prediction of analogous real molecules relevant for chemical separations such as argon, krypton, xenon, methane, ethane, and nitrogen. MOFs were also represented by simple descriptors (e.g., geometric properties and chemical moieties). The trained model was shown to make accurate adsorption predictions for these six adsorbates in both hypothetical and existing MOFs. The MLP presented here is not expected to be applied "as is" to more complex adsorbates with properties not considered during its training. However, our results illustrate a new philosophy of training that can be built upon with the goal of predicting adsorption isotherms in not only a database of MOFs but also a database of adsorbates and over a range of relevant operating conditions.

Deep neural network learning of complex binary sorption equilibria from molecular simulation data

We employed deep neural networks (NNs) as an efficient and intelligent surrogate of molecular simulations for complex sorption equilibria using probabilistic modeling. Canonical (N ₁ N ₂ VT) Gibbs ensemble Monte Carlo simulations were performed to model a single-stage equilibrium desorptive drying process for (1,4-butanediol or 1,5-pentanediol)/water and 1,5-pentanediol/ethanol from all-silica MFI zeolite and 1,5-pentanediol/water from all-silica LTA zeolite. A multi-task deep NN was trained on the simulation data to predict equilibrium loadings as a function of thermodynamic state variables. The NN accurately reproduces simulation results and is able to obtain a continuous isotherm function. Its predictions can be therefore utilized to facilitate optimization of desorption conditions, which requires a laborious iterative search if undertaken by simulation alone. Furthermore, it learns information about the binary sorption equilibria as hidden layer representations. This allows for application of transfer learning with limited data by fine-tuning a pretrained NN for a different alkanediol/solvent/zeolite system.

Interplay of the adsorption of light and heavy paraffins in hydroisomerization over H-beta zeolite

Hydroisomerization: controlling selectivity by tuning the Pt/zeolite properties.

Bifunctional catalysts for the hydroisomerization of n-alkanes: the effects of metal–acid balance and textural structure

The summary of recent advances reveals excellent potentials for the preparation of novel bifunctional catalysts with excellent catalytic performances for n-alkane hydroisomerization.

Continuous flow synthesis of ordered porous materials: from zeolites to metal–organic frameworks and mesoporous silica

Herein we review the concepts, challenges and recent developments on the continuous flow synthesis of ordered porous materials.

Computational screening of structure directing agents for the synthesis of zeolites. A simplified model

Zeolite micropores become more energetically stable by the occlusion of organic structure directing agents (templates). This energetic stabilisation, if approximated by van der Waals zeo-template interactions, can be calculated in a fast way by using modern computing techniques incorporating big data handling algorithms for massive screening. A software suite is presented which calculates an arbitrarily large 2-D matrix (template×zeolite) giving the zeo-template van der Waals interaction energy corresponding to the minimum energy conformation assuming one template molecule in a pure silica zeolite unit cell. With the goal of simplicity, the software only needs two coordinate input files of template and zeolite unit cell. Though a number of approximations have been considered, the software allows to compare, for a given template, which competing zeolite phases may become more stabilised. Applied to zeolite hypothetical databases, it may be of help to suggest templates for their synthesis.

Exploring the potential and design of zeolite nanosheets as pervaporation membranes for ethanol extraction

Molecular dynamics simulations are employed to demonstrate the potential of zeolite nanosheets as pervaporation membranes for ethanol extraction. Our results show that zeolite nanosheets can provide orders of magnitude higher fluxes compared to currently available membranes and achieve outstanding separation factors. The dominant role of membrane surfaces in determining the separation performance is also identified and explored at an atomic level. Developing nanosheet membranes with hydrophobic surfaces and/or with a minimal surface silanol density represents the keys to enable highly selective separation processes.

A Complex Zeolite Containing Multiple Ring Sizes in a Single Channel: One-Dimensional Zeolite UZM-55

Zeolites are porous aluminosilicate materials utilized in a variety of sorption, separation, and catalytic applications. The oil refining industry in particular has seen a number of significant advances due to the introduction of new technologies enabled by new zeolites. Of particular importance are zeolites with 10- or 12-membered ring pores, resulting in pore shapes and sizes appropriate for the interaction with small hydrocarbon molecules. Here, the synthesis of a new zeolite UZM-55 is reported and the idealized structure thereof is presented. The most complex structure solved to date, UZM-55 possesses a large triclinic unit cell containing 52 T-sites. The material uniquely contains both 10- and 12-membered ring pores in a single, undulating one-dimensional channel, the first example in a zeolitic material of multiple delimiting rings in a single channel. This discovery opens new opportunities in shape-selective adsorption and catalysis. Demonstrated here is the unique adsorption behavior of UZM-55, shown both experimentally and computationally to adsorb one nonane molecule per unit cell in a linear conformation.

Study of Short-Chain Alcohol and Alcohol-Water Adsorption in MEL and MFI Zeolites

In this paper, we present a comparative study of the adsorption behavior of short chain alcohols (pure and in aqueous solution) into silicalite-1 (MFI-type zeolite) and silicalite-2 (MEL-type zeolite). For quite some time, silicalite-1 has been the reference material to address the problem of adsorptive-based separation, mostly for hydrocarbon mixtures. Interestingly, being structurally close to silicalite-1, adsorption studies using silicalite-2 are scarce and to the best of our knowledge, a comparative study of their behavior for alcohol-water mixtures has not been published to date. We have here resorted to molecular simulation techniques to analyze the adsorption and diffusion phenomena in both zeolites at 25 and 50 °C for pure methanol, ethanol, 1-butanol, and water, and for some relevant compositions of alcohol/water mixtures. In addition to the dilute regime in the mixture, our study ranges from intermediate alcohol concentrations to alcohol-rich phases, relevant to alcohol purification processes. Besides, we have performed volumetric and calorimetric measurements of single-component adsorption of alcohols in pure silica MEL zeolite, which were used to validate the model potentials used in the simulations. We observe that the zigzag channels of MFI zeolite are most likely responsible for its somewhat higher affinity for alcohols. This leads to higher adsorption selectivities when compared to those of MEL zeolite. We have also found that the choice of water model strongly conditions water coadsorption into the zeolites and subsequently the predictions of the adsorbent's selectivity in alcohol/water systems. Despite considerable differences for adsorbed pure components, diffusivities of alcohol and water adsorbed from mixtures are relatively similar, as a consequence of the strong hydrogen bonds between hydroxyl groups and water.

Accelerating the discovery of insensitive high-energy-density materials by a materials genome approach

Finding new high-energy-density materials with desired properties has been intensely-pursued in recent decades. However, the contradictory relationship between high energy and low mechanical sensitivity makes the innovation of insensitive high-energy-density materials an enormous challenge. Here, we show how a materials genome approach can be used to accelerate the discovery of new insensitive high-energy explosives by identification of “genetic” features, rapid molecular design, and screening, as well as experimental synthesis of a target molecule, 2,4,6-triamino-5-nitropyrimidine-1,3-dioxide. This as-synthesized energetic compound exhibits a graphite-like layered crystal structure with a high measured density of 1.95 g cm⁻³, high thermal decomposition temperature of 284 °C, high detonation velocity of 9169 m s⁻¹, and extremely low mechanical sensitivities (impact sensitivity, >60 J and friction sensitivity, >360 N). Besides the considered system of six-member aromatic and hetero-aromatic rings, this materials genome approach can also be applicable to the development of new high-performing energetic materials.

The synthesis of explosive materials that are stable, highly dense, and have low sensitivity to external stimuli is a challenge. Here, the authors use a genomic approach to accelerate the discovery of insensitive high explosive molecules with good detonation and low sensitivity properties.

Engineering of Transition Metal Catalysts Confined in Zeolites

Transition metal-zeolite composites are versatile catalytic materials for a wide range of industrial and lab-scale processes. Significant advances in fabrication and characterization of well-defined metal centers confined in zeolite matrixes have greatly expanded the library of available materials and, accordingly, their catalytic utility. In this review, we summarize recent developments in the field from the perspective of materials chemistry, focusing on synthesis, postsynthesis modification, (operando) spectroscopy characterization, and computational modeling of transition metal-zeolite catalysts.

Supramolecular Organization in Confined Nanospaces

Empty spaces are abhorred by nature, which immediately rushes in to fill the void. Humans have learnt pretty well how to make ordered empty nanocontainers, and to get useful products out of them. When such an order is imparted to molecules, new properties may appear, often yielding advanced applications. This review illustrates how the organized void space inherently present in various materials: zeolites, clathrates, mesoporous silica/organosilica, and metal organic frameworks (MOF), for example, can be exploited to create confined, organized, and self-assembled supramolecular structures of low dimensionality. Features of the confining matrices relevant to organization are presented with special focus on molecular-level aspects. Selected examples of confined supramolecular assemblies - from small molecules to quantum dots or luminescent species - are aimed to show the complexity and potential of this approach. Natural confinement (minerals) and hyperconfinement (high pressure) provide further opportunities to understand and master the atomistic-level interactions governing supramolecular organization under nanospace restrictions.

Molecular Building Block-Based Electronic Charges for High-Throughput Screening of Metal-Organic Frameworks for Adsorption Applications

Metal-organic frameworks (MOFs) are porous crystalline materials with attractive properties for gas separation and storage. Their remarkable tunability makes it possible to create millions of MOF variations but creates the need for fast material screening to identify promising structures. Computational high-throughput screening (HTS) is a possible solution, but its usefulness is tied to accurate predictions of MOF adsorption properties. Accurate adsorption simulations often require an accurate description of electrostatic interactions, which depend on the electronic charges of the MOF atoms. HTS-compatible methods to assign charges to MOF atoms need to accurately reproduce electrostatic potentials (ESPs) and be computationally affordable, but current methods present an unsatisfactory trade-off between computational cost and accuracy. We illustrate a method to assign charges to MOF atoms based on ab initio calculations on MOF molecular building blocks. A library of building blocks with built-in charges is thus created and used by an automated MOF construction code to create hundreds of MOFs with charges "inherited" from the constituent building blocks. The molecular building block-based (MBBB) charges are similar to REPEAT charges-which are charges that reproduce ESPs obtained from ab initio calculations on crystallographic unit cells of nanoporous crystals-and thus similar predictions of adsorption loadings, heats of adsorption, and Henry's constants are obtained with either method. The presented results indicate that the MBBB method to assign charges to MOF atoms is suitable for use in computational high-throughput screening of MOFs for applications that involve adsorption of molecules such as carbon dioxide.

Comparative Study of the Effect of Defects on Selective Adsorption of Butanol from Butanol/Water Binary Vapor Mixtures in Silicalite-1 Films

A promising route for sustainable 1-butanol (butanol) production is ABE (acetone, butanol, ethanol) fermentation. However, recovery of the products is challenging because of the low concentrations obtained in the aqueous solution, thus hampering large-scale production of biobutanol. Membrane and adsorbent-based technologies using hydrophobic zeolites are interesting alternatives to traditional separation techniques (e.g., distillation) for energy-efficient separation of butanol from aqueous mixtures. To maximize the butanol over water selectivity of the material, it is important to reduce the number of hydrophilic adsorption sites. This can, for instance, be achieved by reducing the density of lattice defect sites where polar silanol groups are found. The density of silanol defects can be reduced by preparing the zeolite at neutral pH instead of using traditional synthesis solutions with high pH. In this work, binary adsorption of butanol and water in two silicalite-1 films was studied using in situ attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy under equal experimental conditions. One of the films was prepared in fluoride medium, whereas the other one was prepared at high pH using traditional synthesis conditions. The amounts of water and butanol adsorbed from binary vapor mixtures of varying composition were determined at 35 and 50 °C, and the corresponding adsorption selectivities were also obtained. Both samples showed very high selectivities (100-23 000) toward butanol under the conditions studied. The sample having low density of defects, in general, showed ca. a factor 10 times higher butanol selectivity than the sample having a higher density of defects at the same experimental conditions. This difference was due to a much lower adsorption of water in the sample with low density of internal defects. Analysis of molecular simulation trajectories provides insights on the local selectivities in the zeolite channel network and at the film surface.

Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen Storage

The Materials Genome is in action: the molecular codes for millions of materials have been sequenced, predictive models have been developed, and now the challenge of hydrogen storage is targeted. Renewably generated hydrogen is an attractive transportation fuel with zero carbon emissions, but its storage remains a significant challenge. Nanoporous adsorbents have shown promising physical adsorption of hydrogen approaching targeted capacities, but the scope of studies has remained limited. Here the Nanoporous Materials Genome, containing over 850 000 materials, is analyzed with a variety of computational tools to explore the limits of hydrogen storage. Optimal features that maximize net capacity at room temperature include pore sizes of around 6 Å and void fractions of 0.1, while at cryogenic temperatures pore sizes of 10 Å and void fractions of 0.5 are optimal. Our top candidates are found to be commercially attractive as "cryo-adsorbents", with promising storage capacities at 77 K and 100 bar with 30% enhancement to 40 g/L, a promising alternative to liquefaction at 20 K and compression at 700 bar.

A Review of Biorefinery Separations for Bioproduct Production via Thermocatalytic Processing

With technological advancement of thermocatalytic processes for valorizing renewable biomass carbon, development of effective separation technologies for selective recovery of bioproducts from complex reaction media and their purification becomes essential. The high thermal sensitivity of biomass intermediates and their low volatility and high reactivity, along with the use of dilute solutions, make the bioproducts separations energy intensive and expensive. Novel separation techniques, including solvent extraction in biphasic systems and reactive adsorption using zeolite and carbon sorbents, membranes, and chromatography, have been developed. In parallel with experimental efforts, multiscale simulations have been reported for predicting solvent selection and adsorption separation. We discuss various separations that are potentially valuable to future biorefineries and the factors controlling separation performance. Particular emphasis is given to current gaps and opportunities for future development.

Irreversible Conversion of a Water-Ethanol Solution into an Organized Two-Dimensional Network of Alternating Supramolecular Units in a Hydrophobic Zeolite under Pressure

Turning disorder into organization is a key issue in science. By making use of X-ray powder diffraction and modeling studies, we show herein that high pressures in combination with the shape and space constraints of the hydrophobic all-silica zeolite ferrierite separate an ethanol-water liquid mixture into ethanol dimer wires and water tetramer squares. The confined supramolecular blocks alternate in a binary two-dimensional (2D) architecture that remains stable upon complete pressure release. These results support the combined use of high pressures and porous networks as a viable strategy for driving the organization of molecules or nano-objects towards complex, pre-defined patterns relevant for the realization of novel functional nanocomposites.

High-Throughput Screening of Metal-Organic Frameworks for CO2 Capture in the Presence of Water

Competitive coadsorption of water is a major problem in the deployment of adsorption-based CO₂ capture. Water molecules may compete for adsorption sites, reducing the capacity of the material, and dehumidification prior to separating CO₂ from N₂ increases process complexity and cost. The development of adsorbent materials that can selectively adsorb CO₂ in the presence of water would be a major step forward in the deployment of CO₂ capture materials in practice. In this study, large-scale computational screening was carried out to search for metal-organic frameworks (MOFs) with high selectivity toward CO₂ over H₂O. Calculating framework charges for thousands of MOFs is a significant challenge, so initial screening used a fast, but approximate, charge calculation method. On the basis of the initial screening, 15 MOFs were selected, and Monte Carlo simulations were carried out to compute the adsorption isotherms for these MOFs using more accurate framework charges calculated by density functional theory. A detailed investigation was performed on the effect of using different methods for calculating partial charges, and it was found that electrostatic interactions contribute the majority of the adsorption energy of H₂O in the selected MOFs.