Computer simulations of 4240 MOF membranes for H2/CH4 separations: insights into structure-performance relations

Hydrogen-Bonded Metal-Organic Framework Nanosheet as a Proton Conducting Membrane for an H2/O2 Fuel Cell

Proton-conducting metal-organic frameworks (MOFs) have attracted attention as potential electrolytes for fuel cells. However, research progress in utilizing MOFs as electrolytes for fuel cells has been limited, mainly due to challenges associated with issues such as the fabrication of MOF membranes, and hydrogen crossover through the MOF's pores. Here, proton conductivity and fuel cell performance of a self-standing membrane prepared from of a bismuth subgallate MOF nanosheets with non-porous structure are reported. The fabricated MOF nanosheet membrane with no binding agent exhibits structural anisotropy. The proton conductivity in the membrane thickness direction (4.4 × 10-3 S cm-1) at 90 °C and RH 100% is observed to be higher than that in the in-plane direction of the membrane (3.3 × 10-5 S cm-1). The open circuit voltage (OCV) of a fuel cell with ≈120 µm proton conducting membrane is 1.0 V. The non-porous nature of the MOF nanosheets contributes to the relatively high OCV. A fuel cell using ≈40 µm membrane as proton conducting electrolyte records a maximum of 25 mW cm-2 power density and a maximum of 109 mA cm-2 current density with 0.91 V OCV at 80 °C in humid conditions.

Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior

ConspectusNanoporous frameworks are a large and diverse family of supramolecular materials, whose chemical building units (organic, inorganic, or both) are assembled into a 3D architecture with well-defined connectivity and topology, featuring intrinsic porosity. These materials play a key role in various industrial processes and applications, such as energy production and conversion, fluid separation, gas storage, water harvesting, and many more. The performance and suitability of nanoporous materials for each specific application are directly related to both their physical and chemical properties, and their determination is crucial for process engineering and optimization of performances. In this Account, we focus on some recent developments in the multiscale modeling of physical properties of nanoporous frameworks, highlighting the latest advances in three specific areas: mechanical properties, thermal properties, and adsorption.In the study of the mechanical behavior of nanoporous materials, the past few years have seen a rapid acceleration of research. For example, computational resources have been pooled to create a public large-scale database of elastic constants as part of the Materials Project initiative to accelerate innovation in materials research: those can serve as a basis for data-based discovery of materials with targeted properties, as well as the training of machine learning predictor models.The large-scale prediction of thermal behavior, in comparison, is not yet routinely performed at such a large scale. Tentative databases have been assembled at the DFT level on specific families of materials, such as zeolites, but prediction at larger scale currently requires the use of transferable classical force fields, whose accuracy can be limited.Finally, adsorption is naturally one of the most studied physical properties of nanoporous frameworks, as fluid separation or storage is often the primary target for these materials. We highlight the recent achievements and open challenges for adsorption prediction at a large scale, focusing in particular on the accuracy of computational models and the reliability of comparisons with experimental data available. We detail some recent methodological improvements in the prediction of adsorption-related properties: in particular, we describe the recent research efforts to go beyond the study of thermodynamic quantities (uptake, adsorption enthalpy, and thermodynamic selectivity) and predict transport properties using data-based methods and high-throughput computational schemes. Finally, we stress the importance of data-based methods of addressing all sources of uncertainty.The Account concludes with some perspectives about the latest developments and open questions in data-based approaches and the integration of computational and experimental data together in the materials discovery loop.

Fast Water Transport in UTSA-280 via a Knock-Off Mechanism

Water and other small molecules frequently coordinate within metal-organic frameworks (MOFs). These coordinated molecules may actively engage in mass transfer, moving together with the transport molecules, but this phenomenon has yet to be examined. In this study, we explore a unique water transfer mechanism in UTSA-280, where an incoming water molecule can displace a coordinated molecule for mass transfer. We refer to this process as the "knock-off" mechanism. Despite UTSA-280 possessing one-dimensional channels, the knock-off transport enables water movement along the other two axes, effectively simulating a pseudo-three-dimensional mass transfer. Even with a relatively narrow pore width, the knock-off mechanism enables a high water flux in the UTSA-280 membrane. The knock-off mechanism also renders UTSA-280 superior water/ethanol diffusion selectivity for pervaporation. To validate this unique mechanism, we conducted 1 H and 2 H solid-state NMR on UTSA-280 after the adsorption of deuterated water. We also derived potential energy diagrams from the density functional theory to gain atomic-level insight into the knock-off and the direct-hopping mechanisms. The simulation findings reveal that the energy barrier of the knock-off mechanism is marginally lower than the direct-hopping pathway, implying its potential role in enhancing water diffusion in UTSA-280.

Exploring the Potential of a Highly Scalable Metal-Organic Framework CALF-20 for Selective Gas Adsorption at Low Pressure

In this study, the ability of the highly scalable metal-organic framework (MOF) CALF-20 to adsorb polar and non-polar gases at low pressure was investigated using grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. The results from the simulated adsorption isotherms revealed that the highest loading was achieved for SO₂ and Cl₂, while the lowest loading was found for F₂ molecules. The analysis of interaction energies indicated that SO₂ molecules were able to form the strongest adsorbent-adsorbate interactions and had a tight molecular packing due to their polarity and angular structure. Additionally, Cl₂ gas was found to be highly adsorbed due to its large van der Waals surface and strong chemical affinity in CALF-20 pores. MD simulations showed that SO₂ and Cl₂ had the lowest mobility inside CALF-20 pores. The values of the Henry coefficient and isosteric heat of adsorption confirmed that CALF-20 could selectively adsorb SO₂ and Cl₂. Based on the results, it was concluded that CALF-20 is a suitable adsorbent for SO₂ and Cl₂ but not for F₂. This research emphasizes the importance of molecular size, geometry, and polarity in determining the suitability of a porous material as an adsorbent for specific adsorbates.

Computational investigation of multifunctional MOFs for adsorption and membrane-based separation of CF4/CH4, CH4/H2, CH4/N2, and N2/H2 mixtures

The ease of functionalization of metal-organic frameworks (MOFs) can unlock unprecedented opportunities for gas adsorption and separation applications as the functional groups can impart favorable/unfavorable regions/interactions for the desired/undesired adsorbates. In this study, the effects of the presence of multiple functional groups in MOFs on their CF₄/CH₄, CH₄/H₂, CH₄/N₂, and N₂/H₂ separation performances were computationally investigated combining grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. The most promising adsorbents showing the best combinations of selectivity, working capacity, and regenerability were identified for each gas separation. 15, 13, and 16 out of the top 20 MOFs identified for the CH₄/H₂, CH₄/N₂, and N₂/H₂ adsorption-based separation, respectively, were found to have -OCH₃ groups as one of the functional groups. The biggest improvements in CF₄/CH₄, CH₄/H₂, CH₄/N₂, and N₂/H₂ selectivities were found to be induced by the presence of -OCH₃-OCH₃ groups in MOFs. For CH₄/H₂ separation, MOFs with two and three functionalized linkers were the best adsorbent candidates while for N₂/H₂ separation, all the top 20 materials involve two functional groups. Membrane performances of the MOFs were also studied for CH₄/H₂ and CH₄/N₂ separation and the results showed that MOFs having -F-NH₂ and -F-OCH₃ functional groups present the highest separation performances considering both the membrane selectivity and permeability.

Curated Collection of More than 20,000 Experimentally Reported One-Dimensional Metal-Organic Frameworks

A collection of more than 20,000 experimentally derived crystal structures for metal-organic frameworks (MOFs) that do not have two- or three-dimensional covalently bonded networks has been developed from the materials available at the Cambridge Crystallographic Data Centre. Of these 20,000 1D MOFs, more than 12,000 structures have been verified to be solvent-free and in exact agreement with the stoichiometry of the synthesized materials. More than 10% of the complete data set comprise materials including two or more distinct metals. The band gaps of more than 12,000 1D MOFs have been computed at the density functional theory-generalized gradient approximation level, finding more than 2000 materials that have a zero band gap. Molecular simulations of CH₄ adsorption in a small number of 1D MOFs indicated that adsorbate-induced deformation plays a significant role in determining adsorption isotherms in these materials. As a result, methods that have been used previously for high-throughput predictions of molecular adsorption in 3D MOFs are not suitable for 1D MOFs.

Metal-organic frameworks and covalent organic frameworks as disruptive membrane materials for energy-efficient gas separation

In this Review we survey the molecular sieving behaviour of metal-organic framework (MOF) and covalent organic framework (COF) membranes, which is different from that of classical zeolite membranes. The nature of MOFs as inorganic-organic hybrid materials and COFs as purely organic materials is powerful and disruptive for the field of gas separation membranes. The possibility of growing neat MOFs and COFs on membrane supports, while also allowing successful blending into polymer-filler composites, has a huge advantage over classical zeolite molecular sieves. MOFs and COFs allow synthetic access to more than 100,000 different structures and tailor-made molecular gates. Additionally, soft evacuation below 100 °C is often enough to achieve pore activation. Therefore, a huge number of synthetic methods for supported MOF and COF membrane thin films, such as solvothermal synthesis, seed-mediated growth and counterdiffusion, exist. Among them, methods with high scale-up potential, for example, layer-by-layer dip- and spray-coating, chemical and physical vapour deposition, and electrochemical methods. Additionally, physical methods have been developed that involve external stimuli, such as electric fields and light. A particularly important point is their ability to react to stimuli, which has allowed the 'drawbacks' of the non-ideality of the molecular sieving properties to be exploited in a completely novel research direction. Controllable gas transport through membrane films is a next-level property of MOFs and COFs, leading towards adaptive process deviation. MOF and COF particles are highly compatible with polymers, which allows for mixed-matrix membranes. However, these membranes are not simple MOF-polymer blends, as they require improved polymer-filler interactions, such as cross-linking or surface functionalization.

X-ray Diffraction and Molecular Simulations in the Study of Metal-Organic Frameworks for Membrane Gas Separation

For more than a decade, researchers have been developing metal-organic frameworks (MOFs) in the form of pure MOF membranes as well as MOF-containing mixed-matrix membranes. MOF membranes have been used for H₂/CO₂ or C₃H₆/C₃H₈ separation, but relatively few MOF membranes enable the high-performance separation of CO₂/N₂, CO₂/CH₄, or N₂/CH₄. This article describes the use of in situ XRD analysis and molecular simulation to elucidate gas transport within MOFs and derivative membranes at the molecular level. In a review of recent studies by the authors and other research groups, this article examines the flexibility of MOFs initiated by activation, gas adsorption, and aging effects during gas permeation. This article also discusses the application of XRD analysis in conjunction with computational methods to investigate the CO₂-MOF Coulombic interaction and its effects on CO₂ separation. Note that this combined analysis approach is also useful in studying the effects of linker rotation on N₂/CH₄ separation. This article also examines the use of computational tools in identifying new MOFs for gas separation and, more importantly, in elaborating the relationship between the structure of MOFs and their corresponding gas transport properties.

Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs

Due to the enormous increase in the number of metal-organic frameworks (MOFs), combining molecular simulations with machine learning (ML) would be a very useful approach for the accurate and rapid assessment of the separation performances of thousands of materials. In this work, we combined these two powerful approaches, molecular simulations and ML, to evaluate MOF membranes and MOF/polymer mixed matrix membranes (MMMs) for six different gas separations: He/H₂, He/N₂, He/CH₄, H₂/N₂, H₂/CH₄, and N₂/CH₄. Single-component gas uptakes and diffusivities were computed by grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations, respectively, and these simulation results were used to assess gas permeabilities and selectivities of MOF membranes. Physical, chemical, and energetic features of MOFs were used as descriptors, and eight different ML models were developed to predict gas adsorption and diffusion properties of MOFs. Gas permeabilities and membrane selectivities of 5249 MOFs and 31,494 MOF/polymer MMMs were predicted using these ML models. To examine the transferability of the ML models, we also focused on computer-generated, hypothetical MOFs (hMOFs) and predicted the gas permeability and selectivity of 1000 hMOF/polymer MMMs. The ML models that we developed accurately predict the uptake and diffusion properties of He, H₂, N₂, and CH₄ gases in MOFs and will significantly accelerate the assessment of separation performances of MOF membranes and MOF/polymer MMMs. These models will also be useful to direct the extensive experimental efforts and computationally demanding molecular simulations to the fabrication and analysis of membrane materials offering high performance for a target gas separation.

Computational Screening of Metal-Organic Frameworks for Ethylene Purification from Ethane/Ethylene/Acetylene Mixture

Identification of high-performing sorbent materials is the key step in developing energy-efficient adsorptive separation processes for ethylene production. In this work, a computational screening of metal-organic frameworks (MOFs) for the purification of ethylene from the ternary ethane/ethylene/acetylene mixture under thermodynamic equilibrium conditions is conducted. Modified evaluation metrics are proposed for an efficient description of the performance of MOFs for the ternary mixture separation. Two different separation schemes are proposed and potential MOF adsorbents are identified accordingly. Finally, the relationships between the MOF structural characteristics and its adsorption properties are discussed, which can provide valuable information for optimal MOF design.

X-ray diffraction for probing free energy profiles and self-diffusivity of gases in metal–organic frameworks

This paper presents a novel methodology for measuring the free energy profiles and the self-diffusivity of gases in crystalline microporous materials.

Thermal Management for Hydrogen Charging and Discharging in a Screened Metal-Organic Framework Particle Tank

Thermal management of H₂ gas storage in a tank is crucial for determining the H₂ gas deliverable capacity. In this study, a strategy for the design of an excellent comprehensive performance fuel storage tank from the screening of microscopic materials to the design of macroscopic particle adsorption tank performance is proposed. The best metal-organic framework (MOF) for H₂ deliverable capacity in a computation-ready experimental MOF database is first screened using a grand canonical Monte Carlo (GCMC) method. An upscale model that combines the finite volume method with GCMC is then established to investigate the H₂ charging and discharging processes in a screened best MOF-filled adsorption particle tank that is integrated with a phase-change material (PCM) jacket. The process of the heat and mass transfer in the screened best MOF particle adsorption tank with and without the PCM jacket-inserted metal foam is studied. The results show that the prescreened XAWVUN has the highest gravimetric and considerable volumetric deliverable capacity among 503 MOFs, which can reach up to 23.1 mol·kg^-1 and 20.8 kg·m^-3 at 298 K and pressures between 35 000 kPa (adsorption pressure) and 160 kPa (desorption pressure), respectively. The H₂ deliverable capacity can be maximized by 3.2 and 12.1% for PCM jackets inserted with metal foam in the H₂ charging and discharging processes when it is compared with the case without the PCM jacket, respectively. The above study will facilitate the development of new equipment for hydrogen storage.

A high-throughput screening of metal-organic framework based membranes for biogas upgrading

Applications of biomethane as a source of renewable energy and transport fuel rely heavily on successful implementation of purification methods capable of removing undesirable impurities from biogas and increasing its calorific content. Metal-organic frameworks (MOFs) are competitive candidates for biogas upgrading due to a versatile range of attractive physical and chemical properties which can be utilised in membrane materials. In this work, we present a high-throughput computational screening methodology for efficient identification of MOF structures with promising gas separation performance. The proposed screening strategy is based on initial structural analysis and predictions of the single-component permeation of CO₂, CH₄ and H₂S from adsorption and diffusion calculations at infinite dilution. The identified top performing candidates are subject to further analysis of their gas separation performance at the operating conditions of 10 bar and 298 K, using grand canonical Monte Carlo and equilibrium molecular dynamics simulations on equimolar CO₂/CH₄ and H₂S/CH₄ mixtures. The Henry constant for the adsorption of H₂O was also calculated to determine the hydrophobicity of MOF structures, as the presence of H₂O often leads to membrane instability and performance limitations. For the considered gas mixtures, the top MOF candidates exhibit superior separation capabilities over polymer-, zeolite-, and mixed matrix-based membranes as indicated by the predicted values of selectivity and permeability. The proposed screening protocol offers a powerful tool for the rational design of novel MOFs for biogas upgrading.

Combined GCMC, MD, and DFT Approach for Unlocking the Performances of COFs for Methane Purification

Covalent organic frameworks (COFs) are promising materials for gas storage and separation; however, the potential of COFs for separation of CH₄ from industrially relevant gases such as H₂, N₂, and C₂H₆ is yet to be investigated. In this work, we followed a multiscale computational approach to unlock both the adsorption- and membrane-based CH₄/H₂, CH₄/N₂, and C₂H₆/CH₄ separation potentials of 572 COFs by combining grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations and density functional theory (DFT) calculations. Adsorbent performance evaluation metrics of COFs, adsorption selectivity, working capacity, regenerability, and adsorbent performance score were calculated for separation of equimolar CH₄/H₂, CH₄/N₂, and C₂H₆/CH₄ mixtures at vacuum swing adsorption (VSA) and pressure swing adsorption (PSA) conditions to identify the best-performing COFs for each mixture. Results showed that COFs could achieve selectivities of 2-85, 1-7, and 2-23 for PSA-based CH₄/H₂, CH₄/N₂, and C₂H₆/CH₄ separations, respectively, outperforming conventional adsorbents such as zeolites and activated carbons for each mixture. Structure-performance relations revealed that COFs with pore sizes <10 Å are promising adsorbents for all mixtures. We identified the gas adsorption sites in the three top-performing COFs commonly identified for each mixture by DFT calculations and computed the binding strength of gases, which were found to be on the order of C₂H₆ > CH₄ > N₂ > H₂, supporting the GCMC results. Nucleus-independent chemical shift (NICS) indexes of aromaticity for adsorption sites were calculated, and the results revealed that the degree of linker aromaticity could be a measure for the selection or design of highly alkane-selective COF adsorbents over N₂ and H₂. Finally, COF membranes were shown to achieve high H₂ permeabilities, 4.57 × 10³ -1.25 × 10⁶ Barrer, and decent membrane selectivities, as high as 4.3, outperforming polymeric and MOF-based membranes for separation of H₂ from CH₄.

Recent advances in simulating gas permeation through MOF membranes

In the last two decades, metal organic frameworks (MOFs) have gained increasing attention in membrane-based gas separations due to their tunable structural properties. Computational methods play a critical role in providing molecular-level information about the membrane properties and identifying the most promising MOF membranes for various gas separations. In this review, we discuss the current state-of-the-art in molecular modeling methods to simulate gas permeation through MOF membranes and review the recent advancements. We finally address current opportunities and challenges of simulating gas permeation through MOF membranes to guide the development of high-performance MOF membranes in the future.

Zr-MOFs for CF4/CH4, CH4/H2, and CH4/N2 separation: towards the goal of discovering stable and effective adsorbents

Zirconium metal-organic frameworks (MOFs) can be promising adsorbents for various applications as they are highly stable in different chemical environments. In this work, a collection of Zr-MOFs comprised of more than 100 materials is screened for CF₄/CH₄, CH₄/H₂, and CH₄/N₂ separations using atomistic-level simulations. The top three MOFs for the CF₄/CH₄ separation are identified as PCN-700-BPDC-TPDC, LIFM-90, and BUT-67 exhibiting CF₄/CH₄ adsorption selectivities of 4.8, 4.6, and 4.7, CF₄ working capacities of 2.0, 2.0, and 2.1 mol kg^-1, and regenerabilities of 85.1, 84.2, and 75.7%, respectively. For the CH₄/H₂ separation, MOF-812, BUT-67, and BUT-66 are determined to be the top performing MOFs demonstrating CH₄/H₂ selectivities of 61.6, 36.7, and 46.2, CH₄ working capacities of 3.0, 4.1, and 3.4 mol kg^-1, and CH₄ regenerabilities of 70.7, 82.7, and 74.7%, respectively. Regarding the CH₄/N₂ separation, BUT-67, Zr-AbBA, and PCN-702 achieving CH₄/N₂ selectivities of 4.5, 3.4, and 3.8, CH₄ working capacities of 3.6, 3.9, and 3.5 mol kg^-1, and CH₄ regenerabilities of 81.1, 84.0, and 84.5%, in successive order, show the best overall separation performances. To further elucidate the adsorption in top performing adsorbents, the adsorption sites in these materials are analyzed using radial distribution functions and adsorbate density profiles. Finally, the water affinities of Zr-MOFs are explored to comment on their practical use in real gas separation applications. Our findings may inspire future studies probing the adsorption/separation mechanisms and performances of Zr-MOFs for different gases.

Connecting theory and simulation with experiment for the study of diffusion in nanoporous solids

Nanoporous solids are ubiquitous in chemical, energy, and environmental processes, where controlled transport of molecules through the pores plays a crucial role. They are used as sorbents, chromatographic or membrane materials for separations, and as catalysts and catalyst supports. Defined as materials where confinement effects lead to substantial deviations from bulk diffusion, nanoporous materials include crystalline microporous zeotypes and metal–organic frameworks (MOFs), and a number of semi-crystalline and amorphous mesoporous solids, as well as hierarchically structured materials, containing both nanopores and wider meso- or macropores to facilitate transport over macroscopic distances. The ranges of pore sizes, shapes, and topologies spanned by these materials represent a considerable challenge for predicting molecular diffusivities, but fundamental understanding also provides an opportunity to guide the design of new nanoporous materials to increase the performance of transport limited processes. Remarkable progress in synthesis increasingly allows these designs to be put into practice. Molecular simulation techniques have been used in conjunction with experimental measurements to examine in detail the fundamental diffusion processes within nanoporous solids, to provide insight into the free energy landscape navigated by adsorbates, and to better understand nano-confinement effects. Pore network models, discrete particle models and synthesis-mimicking atomistic models allow to tackle diffusion in mesoporous and hierarchically structured porous materials, where multiscale approaches benefit from ever cheaper parallel computing and higher resolution imaging. Here, we discuss synergistic combinations of simulation and experiment to showcase theoretical progress and computational techniques that have been successful in predicting guest diffusion and providing insights. We also outline where new fundamental developments and experimental techniques are needed to enable more accurate predictions for complex systems.

Microporous framework membranes for precise molecule/ion separations

Microporous framework membranes such as metal-organic framework (MOF) membranes and covalent organic framework (COF) membranes are constructed by the controlled growth of small building blocks with large porosity and permanent well-defined micropore structures, which can overcome the ubiquitous tradeoff between membrane permeability and selectivity; they hold great promise for the enormous challenging separations in energy and environment fields. Therefore, microporous framework membranes are endowed with great expectations as next-generation membranes, and have evolved into a booming research field. Numerous novel membrane materials, versatile manipulation strategies of membrane structures, and fascinating applications have erupted in the last five years. First, this review summarizes and categorizes the microporous framework membranes with pore sizes lower than 2 nm based on their chemistry: inorganic microporous framework membranes, organic-inorganic microporous framework membranes, and organic microporous framework membranes, where the chemistry, fabrications, and differences among these membranes have been highlighted. Special attention is paid to the membrane structures and their corresponding modifications, including pore architecture, intercrystalline grain boundary, as well as their diverse control strategies. Then, the separation mechanisms of membranes are covered, such as diffusion-selectivity separation, adsorption-selectivity separation, and synergetic adsorption-diffusion-selectivity separation. Meanwhile, intricate membrane design to realize synergistic separation and some emerging mechanisms are highlighted. Finally, the applications of microporous framework membranes for precise gas separation, liquid molecule separation, and ion sieving are summarized. The remaining challenges and future perspectives in this field are discussed. This timely review may provide genuine guidance on the manipulation of membrane structures and inspire creative designs of novel membranes, promoting the sustainable development and steadily increasing prosperity of this field.

Understanding CO2/CH4 Separation in Pristine and Defective 2D MOF CuBDC Nanosheets via Nonequilibrium Molecular Dynamics

The separation of CO₂/CH₄ gas mixtures is a key challenge for the energy sector and is essential for the efficient upgrading of natural gas and biogas. A new emerging field, that of metal-organic framework nanosheets (MONs), has shown the potential to outperform conventional separation methods and bulk metal-organic frameworks (MOFs). In this work, we model the CO₂/CH₄ separation in both defect-free and defective 2D CuBDC nanosheets and compare their performance with the bulk CuBDC MOF and experimental data. We report the results of external force nonequilibrium molecular dynamics (EF-NEMD) for pure components and binary mixtures. The EF-NEMD simulations reveal a pore blocking separation mechanism, in which the CO₂ molecules occupy adsorption sites and significantly restrict the diffusion of CH₄. The MON structure achieves a better selectivity of CO₂ over CH₄ compared to the bulk CuBDC MOF which is due to the mass transfer resistance of the methane molecules on the surface of the nanosheet. Our results show that it is essential to consider the real mixture in these systems rather than relying solely on pure component data and ideal selectivity. Furthermore, the separation is shown to be sensitive to the presence of missing linker defects in the nanosheets. Only 10% of missing linkers result in nonselective nanosheets. Hence, the importance of a defect-free synthetic method for CuBDC nanosheets is underlined.

Do New MOFs Perform Better for CO2 Capture and H2 Purification? Computational Screening of the Updated MOF Database

High-throughput computational screening of metal organic frameworks (MOFs) enables the discovery of new promising materials for CO₂ capture and H₂ purification. The number of synthesized MOFs is increasing very rapidly, and computation-ready, experimental MOF databases are being updated. Screening the most recent MOF database is essential to identify the best performing materials among several thousands. In this work, we performed molecular simulations of the most recent MOF database and described both the adsorbent and membrane-based separation performances of 10 221 MOFs for CO₂ capture and H₂ purification. The best materials identified for pressure swing adsorption, vacuum swing adsorption, and temperature swing adsorption processes outperformed commercial zeolites and previously studied MOFs in terms of CO₂ selectivity and adsorbent performance score. We then discussed the applicability of Ideal Adsorbed Solution Theory (IAST), effects of inaccessible local pores and catenation in the frameworks and the presence of impurities in CO₂/H₂ mixture on the adsorbent performance metrics of MOFs. Very large numbers of MOF membranes were found to outperform traditional polymer and porous membranes in terms of H₂ permeability. Our results show that MOFs that are recently added into the updated MOF database have higher CO₂/H₂ separation potentials than the previously reported MOFs. MOFs with small pores were identified as potential adsorbents for selective capture of CO₂ from H₂, whereas MOFs with high porosities were the promising membranes for selective separation of H₂ from CO₂. This study reveals the importance of enriching the number of MOFs in high-throughput computational screening studies for the discovery of new promising materials for CO₂/H₂ separation.

Large-Scale Computational Screening of Metal Organic Framework (MOF) Membranes and MOF-Based Polymer Membranes for H2/N2 Separations

Several thousands of metal organic frameworks (MOFs) have been reported to date, but the information on H₂/N₂ separation performances of MOF membranes is currently very limited in the literature. We report the first large-scale computational screening study that combines state-of-the-art molecular simulations, grand canonical Monte Carlo (GCMC) and molecular dynamics (MD), to predict H₂ permeability and H₂/N₂ selectivity of 3765 different types of MOF membranes. Results showed that MOF membranes offer very high H₂ permeabilities, 2.5 × 10³ to 1.7 × 10⁶ Barrer, and moderate H₂/N₂ membrane selectivities up to 7. The top 20 MOF membranes that exceed the polymeric membranes' upper bound for H₂/N₂ separation were identified based on the results of initial screening performed at infinite dilution condition. Molecular simulations were then carried out considering binary H₂/N₂ and quaternary H₂/N₂/CO₂/CO mixtures to evaluate the separation performance of MOF membranes under industrial operating conditions. Lower H₂ permeabilities and higher N₂ permeabilities were obtained at binary mixture conditions compared to the ones obtained at infinite dilution due to the absence of multicomponent mixture effects in the latter. Structure-performance relations of MOFs were also explored to provide molecular-level insights into the development of new MOF membranes that can offer both high H₂ permeability and high H₂/N₂ selectivity. Results showed that the most promising MOF membranes generally have large pore sizes (>6 Å) as well as high surface areas (>3500 m²/g) and high pore volumes (>1 cm³/g). We finally examined H₂/N₂ separation potentials of the mixed matrix membranes (MMMs) in which the best MOF materials identified from our high-throughput screening were used as fillers in various polymers. Results showed that incorporation of MOFs into polymers almost doubles H₂ permeabilities and slightly enhances H₂/N₂ selectivities of polymer membranes, which can advance the current membrane technology for efficient H₂ purification.

Computational Screening of Metal⁻Organic Framework Membranes for the Separation of 15 Gas Mixtures

The Monte Carlo and molecular dynamics simulations are employed to screen the separation performance of 6013 computation-ready, experimental metal⁻organic framework membranes (CoRE-MOFMs) for 15 binary gas mixtures. After the univariate analysis, principal component analysis is used to reduce 44 performance metrics of 15 mixtures to a 10-dimension set. Then, four machine learning algorithms (decision tree, random forest, support vector machine, and back propagation neural network) are combined with k times repeated k-fold cross-validation to predict and analyze the relationships between six structural feature descriptors and 10 principal components. Based on the linear correlation value R and the root mean square error predicted by the machine learning algorithm, the random forest algorithm is the most suitable for the prediction of the separation performance of CoRE-MOFMs. One descriptor, pore limiting diameter, possesses the highest weight importance for each principal component index. Finally, the 30 best CoRE-MOFMs for each binary gas mixture are screened out. The high-throughput computational screening and the microanalysis of high-dimensional performance metrics can provide guidance for experimental research through the relationships between the multi-structure variables and multi-performance variables.

Computational Analysis of Linker Defective Metal-Organic Frameworks for Membrane Separation Applications

Intracrystalline defects in metal-organic frameworks (MOFs) are known to have crucial roles in dictating their material properties. In this work, computational simulations were used to induce linker vacancy defects in MOF membranes and to investigate their influence on H₂/CH₄ separation. Linker defective structures were created for the 228 candidate MOFs, and their separation performances were compared between the defective and the nondefective structures. Our results show that the existence of linker vacancy defects can lead to significant performance changes in the MOF membranes, and more importantly, the ranking of the best materials can differ with the defects present. This suggests the importance of taking into account the potential for defects when it comes to materials design for various membrane separation applications.

Simulation of H2/CH4 mixture permeation through MOF membranes using non-equilibrium molecular dynamics

Grand canonical Monte Carlo (GCMC) simulations are widely used with equilibrium molecular dynamics (EMD) to predict gas adsorption and diffusion in single-crystals of metal-organic frameworks (MOFs). Adsorption and diffusion data obtained from these simulations are then combined to predict gas permeabilities and selectivities of MOF membranes. This GCMC + EMD approach is highly useful to screen a large number of MOFs for a target membrane-based gas separation process. External field non-equilibrium molecular dynamics (NEMD) simulations, on the other hand, can directly compute gas permeation by providing an accurate representation of MOF membranes but they are computationally demanding and require long simulation times. In this work, we performed NEMD simulations to investigate H₂/CH₄ separation performances of MOF membranes. Both single-component and binary mixture permeabilities of H₂ and CH₄ were computed using the NEMD approach and results were compared with the predictions of the GCMC + EMD approach and experimental measurements reported in the literature. Our results showed that there is a good agreement between NEMD simulations and experiments for the permeability and selectivity of MOF membranes. NEMD simulations provided the direct observation of the mass transfer resistances on the pore mouth of MOF membranes, which is neglected in the GCMC + EMD approach. Our results suggested that once the very large numbers of MOF materials were screened using the GCMC + EMD approach, more detailed NEMD calculations can be performed for the best membrane candidates to unlock the actual gas transport mechanism before the experimental fabrication of MOF membranes.

Molecular Simulations of MOF Membranes and Performance Predictions of MOF/Polymer Mixed Matrix Membranes for CO2/CH4 Separations

Efficient separation of CO₂ from CO₂/CH₄ mixtures using membranes has economic, environmental and industrial importance. Membrane technologies are currently dominated by polymers due to their processing abilities and low manufacturing costs. However, polymeric membranes suffer from either low gas permeabilities or low selectivities. Metal organic frameworks (MOFs) are suggested as potential membrane candidates that offer both high selectivity and permeability for CO₂/CH₄ separation. Experimental testing of every single synthesized MOF material as membranes is not practical due to the availability of thousands of different MOF materials. A multilevel, high-throughput computational screening methodology was used to examine the MOF database for membrane-based CO₂/CH₄ separation. MOF membranes offering the best combination of CO₂ permeability (>10⁶ Barrer) and CO₂/CH₄ selectivity (>80) were identified by combining grand canonical Monte Carlo and molecular dynamics simulations. Results revealed that the best MOF membranes are located above the Robeson's upper bound indicating that they outperform polymeric membranes for CO₂/CH₄ separation. The impact of framework flexibility on the membrane properties of the selected top MOFs was studied by comparing the results of rigid and flexible molecular simulations. Relations between structures and performances of MOFs were also investigated to provide atomic-level insights into the design of novel MOFs which will be useful for CO₂/CH₄ separation processes. We also predicted permeabilities and selectivities of the mixed matrix membranes (MMM) in which the best MOF candidates are incorporated as filler particles into polymers and found that MOF-based MMMs have significantly higher CO₂ permeabilities and moderately higher selectivities than pure polymers.

High-Throughput Screening of MOF Adsorbents and Membranes for H2 Purification and CO2 Capture

Metal organic frameworks (MOFs) have emerged as great adsorbent and membrane candidates for separation of CO2/H2 mixtures. The main challenge is the existence of thousands of MOFs, which requires computational screening methods to identify the best materials prior to experimental efforts. In this study, we performed high-throughput computational screening of MOFs to examine their adsorbent and membrane performances for CO2/H2 separation. Grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations were used to compute various adsorbent and membrane performance metrics of 3857 MOFs. CO2/H2 adsorption selectivities of MOFs at pressure swing adsorption (PSA) and vacuum swing adsorption (VSA) conditions were calculated to be in the range of 2.5-25 000 and 2.5-85 000, respectively, outperforming many zeolite adsorbents. Correlations between the ranking of MOF adsorbents at the PSA and VSA conditions were examined. H2/CO2 selectivities and H2 permeabilities of MOF membranes were computed as 2.1 × 10-5-6.3 and 230-1.7 × 106 Barrer, respectively. A high number of MOF membranes was identified to surpass the upper bound defined for polymers due to high gas permeabilities of MOFs. Structure-performance relations revealed that MOFs with narrow pore sizes and low porosities are the best adsorbent materials for separation of CO2 from H2, whereas MOFs with large pore sizes and high porosities are the best membrane materials for selective separation of H2. Our results will guide the selection of MOF adsorbents and membranes for efficient H2 purification and CO2 capture processes.

Computational Screening of Metal-Organic Frameworks for Membrane-Based CO2/N2/H2O Separations: Best Materials for Flue Gas Separation

It has become a significant challenge to select the best metal-organic frameworks (MOFs) for membrane-based gas separations because the number of synthesized MOFs is growing exceptionally fast. In this work, we used high-throughput computational screening to identify the top MOF membranes for flue gas separation. Grand canonical Monte Carlo and molecular dynamics simulations were performed to assess adsorption and diffusion properties of CO₂ and N₂ in 3806 different MOFs. Using these data, selectivities and permeabilities of MOF membranes were predicted and compared with those of conventional membranes, polymers, and zeolites. The best performing MOF membranes offering CO₂/N₂ selectivity > 350 and CO₂ permeability > 10⁶ Barrer were identified. Ternary CO₂/N₂/H₂O mixture simulations were then performed for the top MOFs to unlock their potential under industrial operating conditions, and results showed that the presence of water decreases CO₂/N₂ selectivity and CO₂ permeability of some MOF membranes. As a result of this stepwise screening procedure, the number of promising MOF membranes to be investigated for flue gas separation in future experimental studies was narrowed down from thousands to tens. We finally examined the structure-performance relations of MOFs to understand which properties lead to the greatest promise for flue gas separation and concluded that lanthanide-based MOFs with narrow pore openings (<4.5 Å), low porosities (<0.75), and low surface areas (<1000 m²/g) are the best materials for membrane-based CO₂/N₂ separations.

Database for CO2 Separation Performances of MOFs Based on Computational Materials Screening

Metal-organic frameworks (MOFs) are potential adsorbents for CO2 capture. Because thousands of MOFs exist, computational studies become very useful in identifying the top performing materials for target applications in a time-effective manner. In this study, molecular simulations were performed to screen the MOF database to identify the best materials for CO2 separation from flue gas (CO2/N2) and landfill gas (CO2/CH4) under realistic operating conditions. We validated the accuracy of our computational approach by comparing the simulation results for the CO2 uptakes, CO2/N2 and CO2/CH4 selectivities of various types of MOFs with the available experimental data. Binary CO2/N2 and CO2/CH4 mixture adsorption data were then calculated for the entire MOF database. These data were then used to predict selectivity, working capacity, regenerability, and separation potential of MOFs. The top performing MOF adsorbents that can separate CO2/N2 and CO2/CH4 with high performance were identified. Molecular simulations for the adsorption of a ternary CO2/N2/CH4 mixture were performed for these top materials to provide a more realistic performance assessment of MOF adsorbents. The structure-performance analysis showed that MOFs with Δ Qst0 > 30 kJ/mol, 3.8 Å < pore-limiting diameter < 5 Å, 5 Å < largest cavity diameter < 7.5 Å, 0.5 < ϕ < 0.75, surface area < 1000 m2/g, and ρ > 1 g/cm3 are the best candidates for selective separation of CO2 from flue gas and landfill gas. This information will be very useful to design novel MOFs exhibiting high CO2 separation potentials. Finally, an online, freely accessible database https://cosmoserc.ku.edu.tr was established, for the first time in the literature, which reports all of the computed adsorbent metrics of 3816 MOFs for CO2/N2, CO2/CH4, and CO2/N2/CH4 separations in addition to various structural properties of MOFs.