Understanding the Loading Dependence of Adsorbate Diffusivities in Hierarchical Metal–Organic Frameworks

Using atomistic simulations, we studied the diffusion of n-hexane in a series of isoreticular hierarchical metal–organic frameworks (MOFs) NU-100x. Nonmonotonic diffusivity–loading relationships that depend on the pore sizes were observed, which can be explained by the spatial distribution of adsorbates at different loadings. For one of the MOFs in the series, NU-1000-M, the diffusivity–loading relationship is almost identical to the previously reported results of n-hexane diffusion in the hierarchical self-pillared pentasil (SPP) zeolite. Detailed analysis revealed that the similarity results from their similar micropore and window sizes, which was confirmed by free-energy mapping. The effects of temperature and adsorbate chain length on the diffusion were also studied, which supported our conclusion that the diffusivity in hierarchical nanoporous materials is primarily controlled by the sizes of the micropores and the connecting windows, particularly at relatively low loadings

Computational Screening of Metal-Catecholate-Functionalized Metal–Organic Frameworks for Room-Temperature Hydrogen Storage

Hydrogen is a promising alternative to fossil fuels, but the storage and transport of hydrogen for practical applications remain a significant challenge, as high pressure and/or cryogenic temperature are required. Adsorption-based storage utilizing nanoporous adsorbents such as metal–organic frameworks (MOFs) can greatly reduce the storage pressure, but cryogenic or sub-ambient temperatures are required with current adsorbents, which limits the scope of applications. In this work, we search for hydrogen storage adsorbents that allow room-temperature operation by looking at MOFs functionalized with metal-catecholate groups, which have highly unsaturated open metal sites and thus greatly enhanced binding strength for hydrogen. We screened a data set of 2736 Zr-MOFs that were constructed in a combinatorial fashion with wide varieties of topologies and linkers. By counting the possible sites that can be functionalized with metal-catecholate groups, we were able to obtain the theoretical maximum hydrogen uptake for all of the MOFs and rank them. For the top 100 MOFs, we built the functionalized structures computationally and conducted grand canonical Monte Carlo simulations to predict the hydrogen uptake at target adsorption (296 K, 100 bar) and desorption (296 K, 5 bar) conditions. We predict up to 7 wt % and 24 g/L deliverable capacities for some MOFs, which are very high for room-temperature pressure-swing adsorption cycles

Design Requirements for Metal-Organic Frameworks as Hydrogen Storage Materials

Storing an acceptable density of hydrogen in porous materials by physisorption at room temperature and reasonable pressures is a challenging problem. Metal-organic frameworks (MOFs) are a new class of nanoporous materials that have shown early promise for meeting this goal. They have extremely large specific surface areas, but the heats of adsorption to date are too low to provide significant storage at room temperature. In this work, molecular simulations are used to provide guidelines for the design of MOFs for hydrogen storage. To learn how much the heat of adsorption must be increased to meet current targets, we artificially increase the hydrogen/MOF Lennard-Jones attraction. The correlation of the amount of hydrogen adsorbed with the heat of adsorption, the surface area, and the free volume is revisited. We also review the distinction between excess and absolute adsorption and show that comparing the density of hydrogen within the free volume of materials provides useful insight. The simulation results yield a graph showing the required heats of adsorption as a function of the free volume to meet gravimetric and volumetric storage targets at room temperature and 120 bar

High-Throughput Screening of the CoRE-MOF-2019 Database for CO₂ Capture from Wet Flue Gas: A Multi-Scale Modeling Strategy

Stabilizing the escalating CO2 levels in the atmosphere is a grand challenge in view of the increasing global demand for energy, the majority of which currently comes from the burning of fossil fuels. Capturing CO2 from point source emissions using solid adsorbents may play a part in meeting this challenge, and metal–organic frameworks (MOFs) are considered to be a promising class of materials for this purpose. It is important to consider the co-adsorption of water when designing materials for CO2 capture from post-combustion flue gases. Computational high-throughput screening (HTS) is a powerful tool to identify top-performing candidates for a particular application from a large material database. Using a multi-scale modeling strategy that includes a machine learning model, density functional theory (DFT) calculations, force field (FF) optimization, and grand canonical Monte Carlo (GCMC) simulations, we carried out a systematic computational HTS of the all-solvent-removed version of the computation-ready experimental metal–organic framework (CoRE-MOF-2019) database for selective adsorption of CO2 from a wet flue gas mixture. After initial screening based on the pore diameters, a total of 3703 unique MOFs from the database were considered for screening based on the FF interaction energies of CO2, N2, and H2O molecules with the MOFs. MOFs showing stronger interactions with CO2 compared to that with H2O and N2 were considered for the next level of screening based on the interaction energies calculated from DFT. CO2-selective MOFs from DFT screening were further screened using two-component (CO2 and N2) and finally three-component (CO2, N2, and H2O) GCMC simulations to predict the CO2 capacity and CO2/N2 selectivity. Our screening study identified MOFs that show selective CO2 adsorption under wet flue gas conditions with significant CO2 uptake capacity and CO2/N2 selectivity in the presence of water vapor. We also analyzed the nature of pore confinements responsible for the observed CO2 selectivity

Computational Screening of Metal-Catecholate-Functionalized Metal–Organic Frameworks for Room-Temperature Hydrogen Storage

Hydrogen is a promising alternative to fossil fuels, but the storage and transport of hydrogen for practical applications remain a significant challenge, as high pressure and/or cryogenic temperature are required. Adsorption-based storage utilizing nanoporous adsorbents such as metal–organic frameworks (MOFs) can greatly reduce the storage pressure, but cryogenic or sub-ambient temperatures are required with current adsorbents, which limits the scope of applications. In this work, we search for hydrogen storage adsorbents that allow room-temperature operation by looking at MOFs functionalized with metal-catecholate groups, which have highly unsaturated open metal sites and thus greatly enhanced binding strength for hydrogen. We screened a data set of 2736 Zr-MOFs that were constructed in a combinatorial fashion with wide varieties of topologies and linkers. By counting the possible sites that can be functionalized with metal-catecholate groups, we were able to obtain the theoretical maximum hydrogen uptake for all of the MOFs and rank them. For the top 100 MOFs, we built the functionalized structures computationally and conducted grand canonical Monte Carlo simulations to predict the hydrogen uptake at target adsorption (296 K, 100 bar) and desorption (296 K, 5 bar) conditions. We predict up to 7 wt % and 24 g/L deliverable capacities for some MOFs, which are very high for room-temperature pressure-swing adsorption cycles

High-Throughput Screening of the CoRE-MOF-2019 Database for CO₂ Capture from Wet Flue Gas: A Multi-Scale Modeling Strategy

Stabilizing the escalating CO2 levels in the atmosphere is a grand challenge in view of the increasing global demand for energy, the majority of which currently comes from the burning of fossil fuels. Capturing CO2 from point source emissions using solid adsorbents may play a part in meeting this challenge, and metal–organic frameworks (MOFs) are considered to be a promising class of materials for this purpose. It is important to consider the co-adsorption of water when designing materials for CO2 capture from post-combustion flue gases. Computational high-throughput screening (HTS) is a powerful tool to identify top-performing candidates for a particular application from a large material database. Using a multi-scale modeling strategy that includes a machine learning model, density functional theory (DFT) calculations, force field (FF) optimization, and grand canonical Monte Carlo (GCMC) simulations, we carried out a systematic computational HTS of the all-solvent-removed version of the computation-ready experimental metal–organic framework (CoRE-MOF-2019) database for selective adsorption of CO2 from a wet flue gas mixture. After initial screening based on the pore diameters, a total of 3703 unique MOFs from the database were considered for screening based on the FF interaction energies of CO2, N2, and H2O molecules with the MOFs. MOFs showing stronger interactions with CO2 compared to that with H2O and N2 were considered for the next level of screening based on the interaction energies calculated from DFT. CO2-selective MOFs from DFT screening were further screened using two-component (CO2 and N2) and finally three-component (CO2, N2, and H2O) GCMC simulations to predict the CO2 capacity and CO2/N2 selectivity. Our screening study identified MOFs that show selective CO2 adsorption under wet flue gas conditions with significant CO2 uptake capacity and CO2/N2 selectivity in the presence of water vapor. We also analyzed the nature of pore confinements responsible for the observed CO2 selectivity

Insights into Catalytic Gas-Phase Hydrolysis of Organophosphate Chemical Warfare Agents by MOF-Supported Bimetallic Metal-Oxo Clusters

Zirconium-based metal–organic frameworks (Zr-MOFs) have been reported to be efficient catalysts for the hydrolysis of organophosphate chemical warfare agents (CWAs) in buffered solutions. However, for the gas-phase reaction, which is more relevant to the situation in a battlefield gas mask application, the kinetics of Zr-MOF catalysts may be severely hindered by strong product inhibition. To improve the catalytic performance, we computationally screened a series of synthetically accessible Zr-MOF-supported bimetallic metal-oxo clusters in which the metal–oxygen–metal active motif is preserved, aiming to find catalysts that have lower binding affinities to the hydrolysis product. For the promising catalyst Al2O2(OH)2@NU-1000 identified from the screening using density functional theory, we mapped out the full reaction pathway of gas-phase dimethyl p-nitrophenolphosphate (DMNP) hydrolysis and analyzed the free energy profile as well as the turnover frequency (TOF). We found that the catalytic mechanism on the new catalyst is slightly different from the one on NU-1000, which also led to a different TOF-limiting step. Additional factors that can affect the overall catalytic performance in practical application, such as the amount of ambient moisture and the existence of acid gases that may poison the catalyst, have also been evaluated

High-Throughput Screening of the CoRE-MOF-2019 Database for CO₂ Capture from Wet Flue Gas: A Multi-Scale Modeling Strategy

Stabilizing the escalating CO2 levels in the atmosphere is a grand challenge in view of the increasing global demand for energy, the majority of which currently comes from the burning of fossil fuels. Capturing CO2 from point source emissions using solid adsorbents may play a part in meeting this challenge, and metal–organic frameworks (MOFs) are considered to be a promising class of materials for this purpose. It is important to consider the co-adsorption of water when designing materials for CO2 capture from post-combustion flue gases. Computational high-throughput screening (HTS) is a powerful tool to identify top-performing candidates for a particular application from a large material database. Using a multi-scale modeling strategy that includes a machine learning model, density functional theory (DFT) calculations, force field (FF) optimization, and grand canonical Monte Carlo (GCMC) simulations, we carried out a systematic computational HTS of the all-solvent-removed version of the computation-ready experimental metal–organic framework (CoRE-MOF-2019) database for selective adsorption of CO2 from a wet flue gas mixture. After initial screening based on the pore diameters, a total of 3703 unique MOFs from the database were considered for screening based on the FF interaction energies of CO2, N2, and H2O molecules with the MOFs. MOFs showing stronger interactions with CO2 compared to that with H2O and N2 were considered for the next level of screening based on the interaction energies calculated from DFT. CO2-selective MOFs from DFT screening were further screened using two-component (CO2 and N2) and finally three-component (CO2, N2, and H2O) GCMC simulations to predict the CO2 capacity and CO2/N2 selectivity. Our screening study identified MOFs that show selective CO2 adsorption under wet flue gas conditions with significant CO2 uptake capacity and CO2/N2 selectivity in the presence of water vapor. We also analyzed the nature of pore confinements responsible for the observed CO2 selectivity

High-Throughput Screening of the CoRE-MOF-2019 Database for CO₂ Capture from Wet Flue Gas: A Multi-Scale Modeling Strategy

Stabilizing the escalating CO2 levels in the atmosphere is a grand challenge in view of the increasing global demand for energy, the majority of which currently comes from the burning of fossil fuels. Capturing CO2 from point source emissions using solid adsorbents may play a part in meeting this challenge, and metal–organic frameworks (MOFs) are considered to be a promising class of materials for this purpose. It is important to consider the co-adsorption of water when designing materials for CO2 capture from post-combustion flue gases. Computational high-throughput screening (HTS) is a powerful tool to identify top-performing candidates for a particular application from a large material database. Using a multi-scale modeling strategy that includes a machine learning model, density functional theory (DFT) calculations, force field (FF) optimization, and grand canonical Monte Carlo (GCMC) simulations, we carried out a systematic computational HTS of the all-solvent-removed version of the computation-ready experimental metal–organic framework (CoRE-MOF-2019) database for selective adsorption of CO2 from a wet flue gas mixture. After initial screening based on the pore diameters, a total of 3703 unique MOFs from the database were considered for screening based on the FF interaction energies of CO2, N2, and H2O molecules with the MOFs. MOFs showing stronger interactions with CO2 compared to that with H2O and N2 were considered for the next level of screening based on the interaction energies calculated from DFT. CO2-selective MOFs from DFT screening were further screened using two-component (CO2 and N2) and finally three-component (CO2, N2, and H2O) GCMC simulations to predict the CO2 capacity and CO2/N2 selectivity. Our screening study identified MOFs that show selective CO2 adsorption under wet flue gas conditions with significant CO2 uptake capacity and CO2/N2 selectivity in the presence of water vapor. We also analyzed the nature of pore confinements responsible for the observed CO2 selectivity