Molecular Simulations of Zeolites: Adsorption, Diffusion, and Shape Selectivity

Adsorption Modeling Based on Classical Density Functional Theory and PC-SAFT: Temperature Extrapolation and Fluid Transfer

Adsorption is at the heart of many processes from gas separation to cooling. The design of adsorption-based processes requires equilibrium adsorption properties. However, data for adsorption equilibria are limited, and therefore, a model is desirable that uses as little data as possible for its parametrization, while allowing for data interpolation or even extrapolation. This work presents a physics-based model for adsorption isotherms and other equilibrium adsorption properties. The model is based on one-dimensional classical density functional theory (1D-DFT) and the perturbed-chain statistical associating fluid theory (PC-SAFT). The physical processes inside the pores are considered in a thermodynamically consistent approach that is computationally efficient. Once parametrized with a single isotherm, the model is able to extrapolate to other temperatures and outperforms the extrapolation capabilities of state-of-the-art models, such as the empirical isotherm models from Langmuir or Toth. Furthermore, standard combining rules can be used to transfer parameters adjusted to an adsorbent/fluid pair to other fluids. These features are demonstrated for the adsorption of N2, CH4, and CO2 in metal-organic frameworks. Thereby, the presented model can calculate temperature-dependent isotherms for various fluids by using data limited to a single isotherm as input.

Monte Carlo Simulations of Water Pollutant Adsorption at Parts-per-Billion Concentration: A Study on 1,4-Dioxane

1,4-dioxane, an emerging water pollutant with high production volumes, is a probable human carcinogen. The inadequacy of conventional treatment processes demonstrates the need for an effective remediation strategy. Crystalline nanoporous materials are cost-effective adsorbents due to their high capacity and selective separation in mixtures. This study explores the potential of all-silica zeolites for the separation of 1,4-dioxane from water. These zeolites are highly hydrophobic and can preferentially adsorb nonpolar molecules from mixtures. We investigated six zeolite frameworks (BEA, EUO, FER, IFR, MFI, and MOR) using Monte Carlo simulations in the Gibbs ensemble. The simulations indicate high selectivity by FER and EUO, especially at low pressures, which we attribute to pore sizes and shapes with a greater affinity to 1,4-dioxane. We also demonstrate a Monte Carlo simulation workflow using gauge cells to model the adsorption of an aqueous solution of 1,4-dioxane at a 0.35 ppb concentration. We quantify 1,4-dioxane and water coadsorption and observe selectivities ranging from 1.1 × 105 in MOR to 8.7 × 106 in FER. We also demonstrate that 1,4-dioxane is in the infinite dilution regime in the aqueous phase at this concentration. This simulation technique can be extended to model other emerging water contaminants such as perfluoroalkyl and polyfluoroalkyl substances (PFAS), chlorofluorocarbons, and others, which are also found in extremely low concentrations.

Double Unit-Cell Silicogermanate Nanosheets Developed by Salting-Out Mechanism for Biomass Conversion

Zeolite nanosheets with an extremely thin thickness featuring both unique pore systems and low diffusion resistance have the potential to achieve enhanced catalytic performance in the conversion of bulky molecular biomass. The preparation of unit-cell level nanosheets generally requires complex and costly multifunctional surfactants or an organic structure-directing agent (OSDA). Commercially available and environmentally friendly ionic liquids can also direct the structure of zeolite nanosheets by π-π stacking when these kinds of OSDA are used in large amount. Herein, we first report unit-cell-sized silicogermanate nanosheets of NS-IM-20 (UWY topology), 5 nm in thickness, which were synthesized at a relatively low ionic liquid concentration with the assistance of halide ion (Cl-). The Pd-loaded NS-IM-20 nanosheets with a hierarchical porosity and moderate acidity act as promising bifunctional catalysts for selective biomass conversion.

Probing Self-Diffusion of Guest Molecules in a Covalent Organic Framework: Simulation and Experiment

Covalent organic frameworks (COFs) are a class of porous materials whose sorption properties have so far been studied primarily by physisorption. Quantifying the self-diffusion of guest molecules inside their nanometer-sized pores allows for a better understanding of confinement effects or transport limitations and is thus essential for various applications ranging from molecular separation to catalysis. Using a combination of pulsed field gradient nuclear magnetic resonance measurements and molecular dynamics simulations, we have studied the self-diffusion of acetonitrile and chloroform in the 1D pore channels of two imine-linked COFs (PI-3-COF) with different levels of crystallinity and porosity. The higher crystallinity and porosity sample exhibited anisotropic diffusion for MeCN parallel to the pore direction, with a diffusion coefficient of Dpar = 6.1(3) × 10-10 m2 s-1 at 300 K, indicating 1D transport and a 7.4-fold reduction in self-diffusion compared to the bulk liquid. This finding aligns with molecular dynamics simulations predicting 5.4-fold reduction, assuming an offset-stacked COF layer arrangement. In the low-porosity sample, more frequent diffusion barriers result in isotropic, yet significantly reduced diffusivities (DB = 1.4(1) × 10-11 m2 s-1). Diffusion coefficients for chloroform at 300 K in the pores of the high- (Dpar = 1.1(2) × 10-10 m2 s-1) and low-porosity (DB = 4.5(1) × 10-12 m2 s-1) samples reproduce these trends. Our multimodal study thus highlights the significant influence of real structure effects such as stacking faults and grain boundaries on the long-range diffusivity of molecular guest species while suggesting efficient intracrystalline transport at short diffusion times.

MOFs for next-generation cancer therapeutics through a biophysical approach-a review

Metal-organic frameworks (MOFs) have emerged as promising nanocarriers for cancer treatment due to their unique properties. Featuring high porosity, extensive surface area, chemical stability, and good biocompatibility, MOFs are ideal for efficient drug delivery, targeted therapy, and controlled release. They can be designed to target specific cellular organelles to disrupt metabolic processes in cancer cells. Additionally, functionalization with enzymes mimics their catalytic activity, enhancing photodynamic therapy and overcoming apoptosis resistance in cancer cells. The controllable and regular structure of MOFs, along with their tumor microenvironment responsiveness, make them promising nanocarriers for anticancer drugs. These carriers can effectively deliver a wide range of drugs with improved bioavailability, controlled release rate, and targeted delivery efficiency compared to alternatives. In this article, we review both experimental and computational studies focusing on the interaction between MOFs and drug, explicating the release mechanisms and stability in physiological conditions. Notably, we explore the relationship between MOF structure and its ability to damage cancer cells, elucidating why MOFs are excellent candidates for bio-applicability. By understanding the problem and exploring potential solutions, this review provides insights into the future directions for harnessing the full potential of MOFs, ultimately leading to improved therapeutic outcomes in cancer treatment.

On De Gennes narrowing of fluids confined at the molecular scale in nanoporous materials

Beyond well-documented confinement and surface effects arising from the large internal surface and severely confining porosity of nanoporous hosts, the transport of nanoconfined fluids remains puzzling in many aspects. With striking examples such as memory, i.e., non-viscous effects, intermittent dynamics, and surface barriers, the dynamics of fluids in nanoconfinement challenge classical formalisms (e.g., random walk, viscous/advective transport)-especially for molecular pore sizes. In this context, while molecular frameworks such as intermittent Brownian motion, free volume theory, and surface diffusion are available to describe the self-diffusion of a molecularly confined fluid, a microscopic theory for collective diffusion (i.e., permeability), which characterizes the flow induced by a thermodynamic gradient, is lacking. Here, to fill this knowledge gap, we invoke the concept of "De Gennes narrowing," which relates the wavevector-dependent collective diffusivity D0(q) to the fluid structure factor S(q). First, using molecular simulation for a simple yet representative fluid confined in a prototypical solid (zeolite), we unravel an essential coupling between the wavevector-dependent collective diffusivity and the structural ordering imposed on the fluid by the crystalline nanoporous host. Second, despite this complex interplay with marked Bragg peaks in the fluid structure, the fluid collective dynamics is shown to be accurately described through De Gennes narrowing. Moreover, in contrast to the bulk fluid, the departure from De Gennes narrowing for the confined fluid in the macroscopic limit remains small as the fluid/solid interactions in severe confinement screen collective effects and, hence, weaken the wavevector dependence of collective transport.

Dependence of Methane Transport on Pore Informatics in the Amorphous Nanoporous Kerogen Matrix

Fluid transport in kerogen is mainly diffusion-driven, while its dependence on pore informatics is still poorly understood. It is challenging for experiments to identify the effect of pore informatics (such as pore connectivity and tortuosity) on fluid transport therein. Therefore, in this work, we use molecular dynamics simulations to study methane transport behaviors in amorphous kerogen matrices with broad pore properties. The pore properties including porosity, pore connectivity, pore size, and diffusive tortuosity are characterized. Next, self-diffusion coefficients in the connected pores (DeffS) and in the total pores without distinguishing its connectivity (DtotS) are calculated in all the kerogen matrices based on the free volume theory. We find that both DeffS and DtotS exponentially decreases with methane loading with two controlled parameters: fitting constant αeff and DeffS(0) (DeffS at infinitely small loading) for DeffS and fitting constant αtot and DtotS(0) (DtotS at infinitely small loading) for DtotS. However, in the kerogen models with relatively low pore connectivity, αeff and αtot as well as DeffS(0) and DtotS(0) can be quite different, inducing the different estimations of DeffS and DtotS. Since methane in the unconnected pores does not contribute to the actual transport, it is important to recognize connected pores when evaluating the fluid transport in kerogen. On the other hand, DeffS(0) strongly depends on the effective limiting pore size (rlim_eff) of the dominant flow path and effective diffusive tortuosity (τeff), in which DeffS(0) linearly increases with (rlim_eff/τeff)2. We also find that αeff is a multivariable function of ϕeff, τeff, and rlim_eff, but their generalized relation requires more data to obtain. This work provides important insights into fluid transport in kerogen based on the kerogen pore informatics, which are important to shale gas development.

Mixture Adsorption in Nanoporous Zeolite and at Its External Surface: In-Pore and Surface Selectivity

Using toluene, ethylene, and water as gas compounds with different representative molecular interactions, we perform atom-scale simulations for their mixtures to investigate the selectivity of the core nanoporosity and external surface in a prototypical zeolite. As expected, the overall behavior suggests that increasing the pressure of a given component promotes the desorption of the coadsorbing species. However, for water-toluene mixtures, we identify that the pseudohydrogen bonding between water and toluene leads to beneficial coadsorption as toluene adsorption in the low-pressure range promotes water adsorption. Moreover, when the zeolite is completely filled with water, toluene adsorption does not occur due to steric repulsion, and ethylene shows oversolubility as the amount of ethylene per water molecule is significantly larger than in bulk water. The underlying oversolubility mechanism is found to be due to localized ethylene adsorption in the density minima arising from the layering of water in nanoconfinement. Despite these specific effects, the relatively weak coadsorption effects in the zeolite nanoporosity, which are found to be reasonably captured using the ideal adsorbed solution theory, arise from the fact that adsorption of these gases having different molecular sizes occurs in distinct pore regions (channel type, channel intersection). Finally, in contrast to confinement in the nanoporosity, mixture adsorption at the external surface does not show coadsorption effects as it mostly follows the Henry regime. These results show that selectivity is mostly governed by the confinement effects as the external surface leads to a selectivity loss.

Effect of CNT over structural properties of SAPO-34 in MTO process: Experimental and molecular simulation studies

The hierarchical silicoaluminophosphate (SAPO-34) catalyst was synthesized using the mixtures of diethylamine (D) and butylamine (B) as a structure-directing agent (SDA), and carbon nanotube (CNT) as a secondary template in the hydrothermal method. The catalysts were characterized by Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), N2 physisorption, and temperature-programmed desorption of ammonia (NH3-TPD) techniques and evaluated for the catalytic activity in the Methanol to Olefins (MTO) process. The results showed that the use of CNT as the secondary template improved the hierarchical structure of SAPO-34 due to increasing the external surface area and mesoporosity and decreasing the particle size and as a result, made better the performance of the prepared SAPO-34 zeolite in the MTO process. Among all the prepared samples, the CNT-B-D catalyst synthesized by mixing three templates displayed the highest ethylene and propylene selectivity of 49% and 34%, respectively. Also, using CNT in the synthesis of samples increased the catalytic stability. In addition, pure, binary, and ternary adsorption isotherms and diffusivities of the main products and reactants over the SAPO-34 were investigated by theoretical measurements, because sorption and diffusion affect the catalyst stability and C2-C3 selectivity in the MTO reaction. The higher diffusion rate of ethylene leads to following the aromatic-based cycle in the MTO process.

Methanol diffusion and dynamics in zeolite H-ZSM-5 probed by quasi-elastic neutron scattering and classical molecular dynamics simulations

Zeolite ZSM-5 is a key catalyst in commercially relevant processes including the widely studied methanol to hydrocarbon reaction, and molecular diffusion in zeolite pores is known to be a crucial factor in controlling catalytic reactions. Here, we present critical analyses of recent quasi-elastic neutron scattering (QENS) data and complementary molecular dynamics (MD) simulations. The QENS experiments show that the nature of methanol diffusion dynamics in ZSM-5 pores is dependent both on the Si/Al ratio (11, 25, 36, 40 and 140), which determines the Brønsted acid site density of the zeolite, and that the nature of the type of motion observed may vary qualitatively over a relatively small temperature range. At 373 K, on increasing the ratio from 11 to 140, the observed mobile methanol fraction increases and the nature of methanol dynamics changes from rotational (in ZSM-5 with Si/Al of 11) to translational diffusion. The latter is either confined localized diffusion within a pore in zeolites with ratios up to 40 or non-localized, longer-range diffusion in zeolite samples with the ratio of 140. The complementary MD simulations conducted over long time scales (1 ns), which are longer than those measured in the present study by QENS (≈1-440 ps), at 373 K predict the occurrence of long-range translational diffusion of methanol in ZSM-5, independent of the Si/Al ratios (15, 47, 95, 191 and siliceous MFI). The rate of diffusion increases slightly by increasing the ratio from 15 to 95 and thereafter does not depend on zeolite composition. Discrepancies in the observed mobile methanol fraction between the MD simulations (100% methanol mobility in ZSM-5 pores across all Si/Al ratios) and QENS experiments (for example, ≈80% immobile methanol in ZSM-5 with Si/Al of 11) are attributed to the differences in time resolutions of the techniques. This perspective provides comprehensive information on the effect of acid site density on methanol dynamics in ZSM-5 pores and highlights the complementarity of QENS and MD, and their advantages and limitations. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 2)'.

Free and internal energies for the adsorption of short alkanes into the zeolite SSZ-13 from ab initio molecular dynamics simulations

Electronic structure calculations have become a valuable tool in understanding chemical reactions of hydrocarbons in zeolite pores. However, commonly applied approaches to calculate free energies based on static electronic structure calculations significantly overestimate the entropic penalty for molecular adsorption into zeolite pores. Here, we use ab initio molecular dynamics (AIMD) simulations to model the adsorption of methane, ethane, and propane to purely siliceous and protonated SSZ-13. In our analyses we focus on the internal and Helmholtz free energies of adsorption of each molecule and compare our results to various approaches for the calculation of free energies based on static calculations. We find that only an approach that retains two thirds of the translational entropy of the adsorbate upon adsorption compares favorably with AIMD simulations. However, comparison to experimental measurements of Gibbs free energies of adsorption reported in the literature implies that we might not have captured the full complexity of alkane adsorption in our model. We expect that results in this work will help to develop a better understanding of alkane adsorption in zeolites, and that the provided data will serve as a benchmark for free energy calculations of alkane adsorption in zeolites in the future.

Cavity-controlled methanol conversion over zeolite catalysts

The successful development and application in industry of methanol-to-olefins (MTO) process brought about an innovative and efficient route for olefin production via non-petrochemical resources and also attracted attention of C1 chemistry and zeolite catalysis. Molecular sieve catalysts with diversified microenvironments embedding unique channel/cavity structure and acid properties, exhibit demonstrable features and advantages in the shape-selective catalysis of MTO. Especially, shape-selective catalysis over 8-MR and cavity-type zeolites with acidic supercage environment and narrow pore opening manifested special host-guest interaction between the zeolite catalyst and guest reactants, intermediates and products. This caused great differences in product distribution, catalyst deactivation and molecular diffusion, revealing the cavity-controlled methanol conversion over 8-MR and cavity-type zeolite catalyst. Furthermore, the dynamic and complicated cross-talk behaviors of catalyst material (coke)-reaction-diffusion over these types of zeolites determines the catalytic performance of the methanol conversion. In this review, we shed light on the cavity-controlled principle in the MTO reaction including cavity-controlled active intermediates formation, cavity-controlled reaction routes with the involvement of these intermediates in the complex reaction network, cavity-controlled catalyst deactivation and cavity-controlled diffusion. All these were exhibited by the MTO reaction performances and product selectivity over 8-MR and cavity-type zeolite catalysts. Advanced strategies inspired by the cavity-controlled principle were developed, providing great promise for the optimization and precise control of MTO process.

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

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

Molecular Diffusion in Nanoreactors' Pore Channel System: Measurement Techniques, Structural Regulation, and Catalytic Effects

Nanoreactors, as a new class of materials with highly enriched and ordered pore channel structures, can achieve special catalytic effects by precisely identifying and controlling the molecular diffusion behavior within the ordered pore channel system. Nanoreactors-driven molecular diffusion within the ordered pore channels can be highly dependent on the local microenvironment in the nanoreactors' pore channel system. Although the diffusion process of molecules within the ordered pore channels of nanoreactors is crucial for the regulation of catalytic behaviors, it has not yet been as clearly elucidated as it deserves to be in this study. In this review, fundamental theory and measurement techniques for molecular diffusion in the pore channel system of nanoreactors are presented, structural regulation strategies of pore channel parameters for controlling molecular diffusion are discussed, and the effects of molecular diffusion in the pore channel system on catalytic reactivity and selectivity are further analyzed. This article attempts to further develop the underlying theory of molecular diffusion within the theoretical framework of nanoreactor-driven catalysis, and the proposed perspectives may contribute to the rational design of advanced catalytic materials and the precise control of complex catalytic kinetics.

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

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

Zeolite Supported Pt for Depolymerization of Polyethylene by Induction Heating

We demonstrate that for polyethylene depolymerization with induction heating (IH), using a bifunctional (Pt- or Pt-Sn-containing zeolite) hydrocracking catalyst, we can obtain high hydrocarbon product yields (up to 95 wt % in 2 h) at a relatively low surface temperature (375 °C) and with a tunable product distribution ranging from light gas products to gasoline- to diesel-range hydrocarbons. Four zeolite types [MFI, LTL, CHA(SSZ-13), and TON] were chosen as the supports due to their varying pore sizes and structures. These depolymerization results are obtained at atmospheric pressure and without the use of H₂ and result in an alkane/alkene mixture with virtually no methane, aromatics, or coke formation. We also demonstrate how IH helps overcome diffusional resistances associated with conventional thermal heating and thereby shortens reaction times.

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.

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.

Mechanistic classification and benchmarking of polyolefin depolymerization over silica-alumina-based catalysts

Carbon-carbon bond cleavage mechanisms play a key role in the selective deconstruction of alkanes and polyolefins. Here, we show that the product distribution, which encompasses carbon range and formation of unsaturated and isomerization products, serves as a distinctive feature that allows the reaction pathways of different catalysts to be classified. Co, Ni, or Ru nanoparticles immobilized on amorphous silica-alumina, Zeo-Y and ZSM-5, were evaluated as catalysts in the deconstruction of n-hexadecane model substrate with hydrogen to delineate between different mechanisms, i.e., monofunctional- (acid site dominated) or bifunctional-hydrocracking (acid site & metal site) versus hydrogenolysis (metal site dominated), established from the product distributions. The ZSM-5-based catalysts were further studied in the depolymerization of polyethylene. Based on these studies, the catalysts are plotted on an activity-mechanism map that functions as an expandable basis to benchmark catalytic activity and to identify optimal catalysts that afford specific product distributions. The systematic approach reported here should facilitate the acceleration of catalyst discovery for polyolefin depolymerization.

Product distributions have been used to classify the depolymerization pathways of polyolefins catalyzed by silica-alumina-based catalysts to construct an activity-mechanism map as a benchmarking tool to facilitate catalyst discovery.

Multi-scale dynamical cross-talk in zeolite-catalyzed methanol and dimethyl ether conversions

Establishing a comprehensive understanding of the dynamical multiscale diffusion and reaction process is crucial for zeolite shape-selective catalysis and is urgently demanded in academia and industry. So far, diffusion and reaction for methanol and dimethyl ether (DME) conversions have usually been studied separately and focused on a single scale. Herein, we decipher the dynamical molecular diffusion and reaction process for methanol and DME conversions within the zeolite material evolving with time, at multiple scales, from the scale of molecules to single catalyst crystal and catalyst ensemble. Microscopic intracrystalline diffusivity is successfully decoupled from the macroscopic experiments and verified by molecular dynamics simulation. Spatiotemporal analyses of the confined carbonaceous species allow us to track the migratory reaction fronts in a single catalyst crystal and the catalyst ensemble. The constrained diffusion of DME relative to methanol alleviates the high local chemical potential of the reactant by attenuating its local enrichment, enhancing the utilization efficiency of the inner active sites of the catalyst crystal. In this context, the dynamical cross-talk behaviors of material, diffusion and reaction occurring at multiple scales is uncovered. Zeolite catalysis not only reflects the reaction characteristics of heterogeneous catalysis, but also provides enhanced, moderate or suppressed local reaction kinetics through the special catalytic micro-environment, which leads to the heterogeneity of diffusion and reaction at multiple scales, thereby realizing efficient and shape-selective catalysis.

Unravelling the origin of long-term stability for Cs+ and Sr2+ solidification inside sodalite

Cesium (Cs⁺) and strontium (Sr²⁺) ions are the main fission byproducts in the reprocessing of spent nuclear fuels for nuclear power plants. Their long half-live period (30.17 years for ¹³⁷Cs and 28.80 years for ⁹⁰Sr) makes them very dangerous radionuclides. Hence the solidification of Cs⁺ and Sr²⁺ is of paramount importance for preventing them from entering the human food chain through water. Despite tremendous efforts for solidification, the long-term stability remains a great challenge due to the experimental limitation and lack of good evaluation indicators for such long half-life radionuclides. Using density functional theory (DFT), we investigate the origin of long-term stability for the solidification of Cs⁺ and Sr²⁺ inside sodalite and establish that the exchange energy and the diffusion barrier play an important role in gaining the long-term stability both thermodynamically and kinetically. The acidity/basicity, solvation, temperature, and diffusion effect are comprehensively studied. It is found that solidification of Cs⁺ and Sr²⁺ is mainly attributed to the solvation effect, zeolitic adsorption ability, and diffusion barriers. The present study provides theoretical evidence to use geopolymers to adsorb Cs⁺ and Sr²⁺ and convert the adsorbed geopolymers to zeolites to achieve solidification of Cs⁺ and Sr²⁺ with long-term stability.

Fischer-Tropsch synthesis to olefins boosted by MFI zeolite nanosheets

Catalytic reactions are severely restricted by the strong adsorption of product molecules on the catalyst surface, where promoting desorption of the product and hindering its re-adsorption benefit the formation of free sites on the catalyst surface for continuous substrate conversion^1,2. A solution to this issue is constructing a robust nanochannel for the rapid escape of products. We demonstrate here that MFI zeolite crystals with a short b-axis of 90-110 nm and a finely controllable microporous environment can effectively boost the Fischer-Tropsch synthesis to olefins by shipping the olefin molecules. The ferric carbide catalyst (Na-FeC_x) physically mixed with a zeolite promoter exhibited a CO conversion of 82.5% with an olefin selectivity of 72.0% at the low temperature of 260 °C. By contrast, Na-FeC_x alone without the zeolite promoter is poorly active under equivalent conditions, and shows the significantly improved olefin productivity achieved through the zeolite promoter. These results show that the well-designed zeolite, as a promising promoter, significantly boosts Fischer-Tropsch synthesis to olefins by accelerating escape of the product from the catalyst surface.

Computational Study of Alkane Adsorption in Brønsted Acid Zeolites for More Efficient Alkane Cracking

Alkane cracking using Brønsted acid zeolites, catalytically converting long-chain molecules into smaller ones, is critical to fuel and chemical production. To enable more energy-efficient cracking processes, developing zeolite catalysts with enhanced performance (i.e., a faster reaction rate with reduced methane formation) plays a substantial role. Given the adsorption thermodynamics of alkanes onto the protons of Brønsted acid zeolites is a key step in the overall cracking reactions; therefore, catalysts possessing a more negative Gibbs free energy of adsorption for alkanes with a larger central-to-terminal bond adsorption selectivity to promote central cracking are of particular interest. This Feature Article discusses recent computational developments and discoveries by Lin and co-workers in studying the adsorption of alkanes in Brønsted acid zeolites. Their developed approach, employing configurational bias Monte Carlo with domain decomposition, with a newly parametrized molecular potential to compute the adsorption properties is first introduced. With these developments, the roles of the Si/Al ratio and Al sitting are explored and discussed. Subsequently, the Feature Article discusses the key findings obtained from a large-scale computational screening of studying more than 100 000 possible zeolite structures. The performance of identified top candidates and associated key structural features leading to desirable adsorption properties are highlighted.

Leveraging Nitrogen Linkages in the Formation of a Porous Thorium-Organic Nanotube Suitable for Iodine Capture

We report the synthesis, characterization, and iodine capture application of a novel thorium-organic nanotube, TSN-626, [Th₆O₄(OH)₄(C₆H₄NO₂)₇(CHO₂)₅(H₂O)₃]·3H₂O. The classification as a metal-organic nanotube (MONT) distinguishes it as a rare and reduced dimensionality subset of metal-organic frameworks (MOFs); the structure is additionally hallmarked by low node connectivity. TSN-626 is composed of hexameric thorium secondary building units and mixed O/N-donor isonicotinate ligands that demonstrate selective ditopicity, yielding both terminating and bridging moieties. Because hard Lewis acid tetravalent metals have a propensity to bind with electron donors of rival hardness (e.g., carboxylate groups), such Th-N coordination in a MOF is uncommon. However, the formation of key structural Th-N bonds in TSN-626 cap some of the square antiprismatic metal centers, a position usually occupied by terminal water ligands. TSN-626 was characterized by using complementary analytical and computational techniques: X-ray diffraction, vibrational spectroscopy, N₂ physisorption isotherms, and density functional theory. TSN-626 satisfies design aspects for the chemisorption of iodine. The synergy between accessibility through pores, vacancies at the metal-oxo nodes, and pendent N-donor sites allowed a saturated iodine loading of 955 mg g^-1 by vapor methods. The crystallization of TSN-626 diversifies actinide-MOF linker selection to include soft electron donors, and these Th-N linkages can be leveraged for the investigation of metal-to-ligand bonding and unconventional topological expressions.

Gas Adsorption and Diffusion Behaviors in Interfacial Systems Composed of a Polymer of Intrinsic Microporosity and Amorphous Silica: A Molecular Simulation Study

We investigate the adsorption and diffusion behaviors of CO₂, CH₄, and N₂ in interfacial systems composed of a polymer of intrinsic microporosity (PIM-1) and amorphous silica using grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. We build model systems of mixed matrix membranes (MMMs) with PIM-1 chains sandwiched between silica surfaces. Gas adsorption analysis using GCMC simulations shows that gas molecules are preferentially adsorbed in microcavities distributed near silica surfaces, resulting in an increase in the solubility coefficients of CO₂, CH₄, and N₂ compared to bulk PIM-1. In contrast, diffusion coefficients obtained from MD simulations and then calibrated using the dual-mode sorption model show different tendencies depending on gas species: CO₂ diffusivity decreases in MMMs compared to PIM-1, whereas CH₄ and N₂ diffusivities increase. These differences are attributed to competing effects of silica surfaces: the emergence of larger pores as a result of chain packing disruption, which enhances gas diffusion, and a quadrupole-dipole interaction between gas molecules and silica surface hydroxyl groups, which retards gas diffusion. The former has a greater impact on CH₄ and N₂ diffusivities, whereas the latter has a greater impact on CO₂ diffusivity due to the strong quadrupole-dipole interaction between CO₂ and surface hydroxyls. These findings add to our understanding of gas adsorption and diffusion behaviors in the vicinity of PIM-1/silica interfaces, which are unobtainable in experimental studies.

Gas Adsorption in Zeolite and Thin Zeolite Layers: Molecular Simulation, Experiment, and Adsorption Potential Theory

Molecular simulations and experiments are used to investigate methane adsorption in bulk and thin layers of MFI zeolite (silicalite-1). After comparing the theoretical adsorption data obtained using Grand Canonical Monte Carlo simulations for bulk MFI at various temperatures against experiments, zeolite layers with different crystalline orientations and levels of surface flexibility are considered. The data obtained for such prototypical systems allow us to rationalize both the qualitative and quantitative impact of external surface in nanoporous solids. In particular, due to strong confinement in zeolite pores, methane is found to adsorb at low pressures in the core of the zeolite while external surface adsorption occurs at pressures where the internal porosity of zeolite is saturated. Using Polanyi's adsorption potential theory, which is derived here from Hill's general scheme for adsorption, we provide a simple thermodynamic formalism to predict consistently adsorption both in the internal porosity and at the external surface of nanoporous solids. While this seminal theory has been already applied for gases in nanoporous solids, its extension to describe both surface and volume adsorption is important to provide a general rational framework for fluid adsorption in finely divided materials. We also discuss the applicability of this formalism for gas adsorption data under supercritical conditions.

Effects of Degrees of Freedom on Calculating Diffusion Properties in Nanoporous Materials

If one carries out a molecular simulation of N particles using periodic boundary conditions, linear momentum is conserved, and hence, the number of degrees of freedom is set to 3N - 3. In most programs, this number of degrees of freedom is the default setting. However, if one carries out a molecular simulation in an external field, one needs to ensure that degrees of freedom are changed from this default setting to 3N, as in an external field the velocity of the center of mass can change. Using the correct degrees of freedom is important in calculating the temperature and in some algorithms to simulate at constant temperature. For sufficiently large systems, the difference between 3N and 3N - 3 is negligible. However, there are systems in which the comparison with experimental data requires molecular dynamics simulations of a small number of particles. In this work, we illustrate the effect of an incorrect setting of degrees of freedom in molecular dynamic simulations studying the diffusion properties of guest molecules in nanoporous materials. We show that previously published results have reported a surprising diffusion dependence on the loading, which could be traced back to an incorrect setting of the degrees of freedom. As the correct settings are convoluted and counterintuitive in some of the most commonly used molecular dynamics programs, we carried out a systematic study on the consequences of the various commonly used (incorrect) settings. Our conclusion is that for systems smaller than 50 particles the results are most likely unreliable as these are either performed at an incorrect temperature or the temperature is incorrectly used in some of the results. Furthermore, a novel and efficient method to calculate diffusion coefficients of guest molecules into nanoporous materials at zero-loading conditions is introduced.

Diffusive Skin Effect in Zeolites

Effective contact and collision between reactants and active sites are essential for heterogeneous catalysis. Herein, we investigated molecular diffusion in more than 200 kinds of zeolites, and an intriguing "diffusive skin effect" was observed, whereby molecules migrated along the pore walls of zeolites (i.e., diffusion trajectories) because of the effect of the guest-host interaction and diffusion barrier. Furthermore, it was found that such a "diffusive skin effect" of zeolites would strongly promote the contacts and collisions between reactants and active sites in the reaction process, which might effectively promote the zeolite-catalyzed performance. These new findings will provide some new fundamental understanding of zeolite catalytic mechanisms under confinement effect.

SCM-25: A Zeolite with Ordered Meso-cavities Interconnected by 12 × 12 × 10-Ring Channels Determined by 3D Electron Diffraction

Zeolites with large cavities that are accessible via wide pore windows are desirable but very rare. They have been dominantly used as catalysts in industry. Here we report a novel porous germanosilicate SCM-25, the zeolite structure containing ordered meso-cavities (29.9 × 7.6 × 6.0 Å3) interconnected by 10- and 12-ring channels. SCM-25 was synthesized as nanosized crystals by using a simple organic structure-directing agent (OSDA). Three-dimensional (3D) electron diffraction shows that SCM-25 crystallizes in the orthorhombic space group Cmmm with a = 14.62 Å, b = 51.82 Å, c = 13.11 Å, which is one of the zeolites with the largest unit cell dimensions. We demonstrate that 3D electron diffraction is a powerful technique for determining the complex structure of SCM-25, including the disorders and distributions of framework atoms silicon and germanium. SCM-25 has a high surface area (510 m2/g) and high thermal stability (700 °C). Furthermore, we propose a potential postsynthetic strategy for the preparation of zeolites with ordered meso-cavities by applying the ADOR (assembly-disassembly-organization-reassembly) approach.

Monomolecular Cracking of Propane: Effect of Zeolite Confinement and Acidity

The effect of zeolite pore geometry and intrinsic acidity on the activation energy of propane monomolecular cracking was investigated for six topologically distinct zeolites with different pore sizes. Periodic density functional theory calculations were used to calculate the activation energy, while cluster models were used to calculate deprotonation energies. The computed intrinsic activation energies showed a smaller variation with topology than the adsorption energies. No correlation was found between the computed deprotonation and ammonia adsorption energies at the acid site and the intrinsic activation energy. Detailed analysis of the computed structures and properties suggests that acid sites with different pore topologies impose geometrical constraints on the ion-pair formed by the ammonium molecule, which differs significantly from those that affect the propane reaction.

An atomistic view on the uptake of aromatic compounds by cyclodextrin immobilized on mesoporous silica

The effect of immobilized $$\upbeta$$ β -cyclodextrin (bCD) molecules inside a mesoporous silica support on the uptake of benzene and p-nitrophenol from aqueous solution was investigated using all-atom molecular dynamics (MD) simulations. The calculated adsorption isotherms are discussed with respect to the free energies of binding for a 1:1 complex of bCD and the aromatic guest molecule. The adsorption capacity of the bCD-containing material significantly exceeds the amount corresponding to a 1:1 binding scenario, in agreement with experimental observations. Beside the formation of 1:2 and, to a lesser extent, 1:3 host:guest complexes, also host–host interactions on the surface as well as more unspecific host–guest interactions govern the adsorption process. The demonstrated feasibility of classical all-atom MD simulations to calculate liquid phase adsorption isotherms paves the way to a molecular interpretation of experimental data that are too complex to be described by empirical models.

Unravelling Channel Structure-Diffusivity Relationships in Zeolite ZSM-5 at the Single-Molecule Level

The development of improved zeolite materials for applications in separation and catalysis requires understanding of mass transport. Herein, diffusion of single molecules is tracked in the straight and sinusoidal channels of the industrially relevant ZSM-5 zeolites using a combination of single-molecule localization microscopy and uniformly oriented zeolite thin films. Distinct motion behaviors are observed in zeolite channels with the same geometry, suggesting heterogeneous guest-host interactions. Quantification of the diffusion heterogeneities in the sinusoidal and straight channels suggests that the geometry of zeolite channels dictates the mobility and motion behavior of the guest molecules, resulting in diffusion anisotropy. The study of hierarchical zeolites shows that the addition of secondary pore networks primarily enhances the diffusivity of sinusoidal zeolite channels, and thus alleviating the diffusion limitations of microporous zeolites.

Applications of machine learning in computational nanotechnology

Machine learning (ML) has gained extensive attention in recent years due to its powerful data analysis capabilities. It has been successfully applied to many fields and helped the researchers to achieve several major theoretical and applied breakthroughs. Some of the notable applications in the field of computational nanotechnology are ML potentials, property prediction, and material discovery. This review summarizes the state-of-the-art research progress in these three fields. ML potentials bridge the efficiency versus accuracy gap between density functional calculations and classical molecular dynamics. For property predictions, ML provides a robust method that eliminates the need for repetitive calculations for different simulation setups. Material design and drug discovery assisted by ML greatly reduce the capital and time investment by orders of magnitude. In this perspective, several common ML potentials and ML models are first introduced. Using these state-of-the-art models, developments in property predictions and material discovery are overviewed. Finally, this paper was concluded with an outlook on future directions of data-driven research activities in computational nanotechnology.

Simple structure descriptors quantifying the diffusion of ethene in small-pore zeolites: Insights from molecular dynamic simulations

Small-pore zeolites with 8-rings are pivotal catalytic materials to produce light olefins from non-petroleum resources employing methanol-to-olefins or syngas-to-olefins processes. The constraints of cage openings on the diffusion of light...