RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials

High-throughput computational screening of hypothetical metal-organic frameworks with open copper sites for CO2/H2 separation

It is challenging to identify the optimal metal-organic framework (MOF) adsorbents for gas adsorption and membrane-based separation from the large-scale material databases. The high-throughput computational screening (HTCS) method was adopted to discover the optimal materials for CO₂/H₂ separation from thousands of MOFs. First, a hierarchical strategy was used to select 1092 MOFs from 13 512 MOFs, and their adsorption capacity towards the equimolar CO₂/H₂ mixture at 298 K and 10 bar was further calculated using the grand canonical Monte Carlo (GCMC) simulations. The results show that those MOFs with lvtb topology and organic linker 1,2,4,5-tetrazine are conducive to exhibiting high performance CO₂/H₂ adsorption separation among top-100 MOFs with high performance. The MOFs with pore limited diameter (PLD), largest cavity diameter (LCD), gravimetrical surface area (GSA), and void fraction in the range of 4-12 Å, 5-12 Å, 5500-6500 m² g^-1 and 0.80-0.85, respectively, have high adsorption capacity towards CO₂. Second, the dynamic adsorption properties of the top-4 MOFs were simulated by the breakthrough curves of the binary (CO₂/H₂) and quinary (CO₂/H₂/CH₄/CO/N₂) mixtures in the fixed adsorption bed. MOF-4641 exhibits a high breakthrough time of 130 for the quinary mixture. Finally, the adsorption mechanism of CO₂ in the top-4 MOFs was investigated by the radial distribution function (RDF), the mass center probability density distribution, etc. The atomic insights from HTCS and breakthrough curve predictions in this work will be helpful in developing novel porous materials and obtaining superior CO₂ separation performance.

Atomic-Scale Quantum Sensing of Ensembles of Guest Molecules in a Metal-Organic Framework with Intrinsic Electron Spin Centers

One of the exciting applications of electron-spin-based quantum sensing is the detection of distant nuclear spins of external molecular species. Here, we explore the application of a metal-organic framework (MOF) material as a host matrix for sensing spin centers. As a sensor, we employ inherent Cu²⁺ ions in the structure of a Zn-doped HKUST-1 framework. As a target molecular species, we use butane gas that exhibits no specific chemical reactivity toward the inner surface of HKUST-1 and is thus randomly distributed inside the MOF pore network. By employing a conventional double-resonance pulse sequence, we can effectively detect the coupling of the distant ¹H nuclear spins of butane to the electron spin of the sensor and gain atomic-scale insight into their spatial distribution. Thus, our proof-of-the-concept experiment demonstrates that MOFs, the materials featuring extremely large surface area and great tunability, are perfectly suited as a key element for emerging magnetic quantum sensing solutions.

Rational Design of Smart Metal-Organic Frameworks for Light-Modulated Gas Transport

Smart metal-organic frameworks (MOFs) are constructed by introducing stimuli-responsive functional groups into MOF platforms. Through membrane systems containing smart MOFs, external field-modulated gas transport can be achieved, which finds potential applications in chemical engineering. In this work, we design a series of Mg-MOF-74-III-based frameworks functionalized by arylazopyrazole groups. Methyleneamine chains with various lengths are attached to the photoresponsive azopyrazole moiety. Molecular dynamics simulations show that CO₂ diffusion can be remarkably changed by controlling the cis-to-trans isomerization of the functional unit due to the tunable adsorbate-adsorbent and adsorbate-adsorbate interactions of the two states. With the optimal length of the functional chain, the spatial hindrance and adsorbate-adsorbent interaction exhibit a synergetic effect to maximize the stimuli-responsive kinetic separation of N₂ over CO₂. This work provides a promising strategy for elevating smart MOFs' potential in gas separation.

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.

Molecular and Artificial Neural Networks Modeling of Sorption and Diffusion of Small Alkanes, Alkenes and Their Ternary Mixtures in ZIF-8 at Different Temperatures

Numerical computations comprising of Grand Canonical-Monte Carlo (GCMC) and Canonical Statistical Ensemble (NVT) Molecular Dynamics (MD) simulations were used to study the diffusion and sorption characteristics primarily for methane, ethane, and ethene molecules and for their ternary mixtures at different temperatures in ZIF-8 porous material. Methane as pure component or in mixture proved to be the sorbed hydrocarbon with the higher molecular mobility at the temperature range of 273-373 K among alkanes and alkenes. In addtion, alkenes were the hydrocarbons with the higher self-diffusion coefficients compared to the respective alkanes. In the ternary mixtures ethane was preferentially sorbed in ZIF-8 at all temperatures studied. Direct comparisons of the self-diffusivity data obtained from the NVT-MD simulations with recently reported Magic Angle Spinning Pulsed Field Gradient Nuclear Magnetic Resonance (MAS PFG NMR) measurements showed reasonable agreement. Furthermore, the NVT-MD self-diffusivity coefficients in conjunction with the aforementioned MAS PFG NMR experimental measurements, and sorption thermodynamic data obtained from the present GCMC simulations were utilized for the development of individual Artificial Neural Networks (ANNs) predictive modeling procedures in order to provide additional quantitative and qualitative information regarding the diffusion and sorption of small alkanes, alkenes in ZIF-8. The ANNs predictions were in good agreement with the experimental measurements and with the molecular simulation data. The modeling and analysis capabilities of ANNs along with their fast computations using moderate computer resources can significantly assist the irreplaceable molecular simulation and experimental approaches to cope with complicated problems at the molecular level.

Separation of Volatile Organic Compounds in TAMOF-1

Separation of volatile organic compounds is one of the most studied processes in industry. TAMOF-1 is a homochiral metal-organic framework with a crystalline network of interconnected ≈1 nm channels and has high thermal and chemical stability. Thanks to these features, it can resolve racemic mixtures of chiral drugs as a chiral stationary phase in chromatography. Interestingly, the particular shape and size of its channels, along with the presence of metallic centers and functional groups, allow establishing weak but significant interactions with guest molecules. This opens interesting possibilities not only to resolve racemates but also to separate other organic mixtures, such as saturated/unsaturated and/or linear/branched molecules. In search of these applications, we have studied the separation of volatile organic compounds in TAMOF-1. Monte Carlo simulations in the grand-canonical ensemble have been carried out to evaluate the separation of the selected molecules. Our results predict that TAMOF-1 is able to separate xylene isomers, hexane isomers, and benzene-cyclohexane mixtures. Experimental breakthrough analysis in the gas phase and also in the liquid phase confirms these predictions. Beds of TAMOF-1 are able to recognize the substitution in xylenes and the branching in hexanes, yielding excellent separation and reproducibility, thanks to the chemical and mechanical features of this material.

Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO2 from Flue Gas

To combat global warming, as an energy-saving technology, membrane separation can be applied to capture CO₂ from flue gas. Metal–organic frameworks (MOFs) with characteristics like high porosity have great potential as membrane materials for gas mixture separation. In this work, through a combination of grand canonical Monte Carlo and molecular dynamics simulations, the permeability of three gases (CO₂, N₂, and O₂) was calculated and estimated in 6013 computation–ready experimental MOF membranes (CoRE–MOFMs). Then, the relationship between structural descriptors and permeance performance, and the importance of available permeance area to permeance performance of gas molecules with smaller kinetic diameters were found by univariate analysis. Furthermore, comparing the prediction accuracy of seven classification machine learning algorithms, XGBoost was selected to analyze the order of importance of six structural descriptors to permeance performance, through which the conclusion of the univariate analysis was demonstrated one more time. Finally, seven promising CoRE-MOFMs were selected, and their structural characteristics were analyzed. This work provides explicit directions and powerful guidelines to experimenters to accelerate the research on membrane separation for the purification of flue gas.

Interlayer Expansion in a Layered Metal-Organic Framework Enhances CO2 Capture and CO2/N2 Separation

Developing efficient CO 2 adsorbent materials and technologies is significant to reduce the increasing greenhouse gases concentration in the atmosphere. Herein, a layered MOF with a porous kagomé lattice (kgm), which owned three phases (kgm-1, kgm-2, and kgm-3) via interlayer expansion, was evaluated as a promising CO 2 capture and separation material by using grand canonical Monte Carlo simulations. Results showed that the interlayer expansion provided additional pore volume, which played a considerable role in CO 2 adsorption and separation. The CO 2 adsorption capacity and CO 2 /N 2 selectivity followed the sequence kgm-3 > kgm-2 > kgm-1, and kgm-3 exhibited an excellent CO 2 adsorption capacity of 8.7 mmol g -1 at 1 bar with a CO 2 /N 2 selectivity of 130.3 at 20 bar and 298 K. Gas distribution analysis showed that CO 2 and N 2 are adsorbed only in the channels in kgm-1, whereas they could be adsorbed between layers in kgm-2 and kgm-3 due to the interlayer expansion. The adsorption heat and interactions between CO 2 and frameworks were analyzed to elucidate the effect of interlayer expansion. Results of this work highlighted that appropriate interlayer expansion can be an effective approach for framework adsorbents to improve CO 2 capture ability and separation performance at the same time.

Permeation Characteristics of CH4 in PVDF with Crude Oil-Containing

The liner of reinforced thermoplastic composite pipes (RTPs) used for oil and gas gathering and transportation experienced blister failure due to gas permeation. Few reports have appeared on the problem of gas permeation in thermoplastics with absorbed crude oil. Accordingly, the permeability of CH₄ in polyvinylidene fluoride (PVDF) containing crude oil was studied at the normal service conditions by molecular simulations. The results showed that the solubility coefficients of CH₄ in PVDF containing crude oil were much lower than those in pure PVDF. It can be concluded that the crude oil molecules absorbed into PVDF occupied certain adsorption sites, resulting in a decrease in the adsorption capacity of CH₄ molecules in PVDF. The diffusion coefficients of CH₄ in oil-containing PVDF were significantly greater than in PVDF. This is because the absorption of oil molecules leads to the volume swelling of PVDF and then increases the free volume for diffusion. The permeation process showed that CH₄ molecules were selective-aggregate adsorbed in the region with low potential energy in oil-containing PVDF firstly, and then they vibrated within the holes of PVDF containing oil in most cases and jumped into the neighboring holes at high temperatures and pressures.

Discovery of High-Performing Metal-Organic Frameworks for On-Board Methane Storage and Delivery via LNG-ANG Coupling: High-Throughput Screening, Machine Learning, and Experimental Validation

Liquefied natural gas (LNG) gasification coupled with adsorbed natural gas (ANG) charging (LNG-ANG coupling) is an emerging strategy for efficient delivery of natural gas. However, the potential of LNG-ANG to attain the advanced research projects agency-energy (ARPA-E) target for onboard methane storage has not been fully investigated. In this work, large-scale computational screening is performed for 5446 metal-organic frameworks (MOFs), and over 193 MOFs whose methane working capacities exceed the target (315 cm³ (STP) cm^-3 ) are identified. Furthermore, structure-performance relationships are realized under the LNG-ANG condition using a machine learning method. Additional molecular dynamics simulations are conducted to investigate the effects of the structural changes during temperature and pressure swings, further narrowing down the materials, and two synthetic targets are identified. The synthesized DUT-23(Cu) and DUT-23(Co) show higher working capacities (≈373 cm³ (STP) cm^-3 ) than that of any other porous material under ANG or LNG-ANG conditions, and excellent stability during cyclic LNG-ANG operation.

Gate Opening without Volume Change Triggers Cooperative Gas Interactions, Underpins an Isotherm Step in Metal-Organic Frameworks

Three halogenated metal-organic frameworks (MOFs) reported recently exhibited a second step in their CO₂ gas adsorption isotherms. The emergence of halogen-bonding interactions beyond a threshold gas pressure between the framework halogen and the CO₂ guest was conjectured to be the underlying reason for the additional step in the isotherm. Our investigation employing periodic density functional theory calculations did not show significant interactions between the halogen and CO₂ molecules. Further, using a combination of DFT-based ab initio molecular dynamics and grand canonical Monte Carlo simulations, we find that the increased separation of framework nitrate pairs facing each other across the pore channel enables the accommodation of an additional CO₂ molecule which is further stabilized by cooperative interactions─an observation that facilely explains the second isotherm step. The increased separation between the nitrate groups can occur without any lattice expansion, consistent with experiments. The results point to a structural feature to achieve this isotherm step in MOFs that neither possess large pores nor exhibit large-scale structural changes such as breathing.

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.

Multi-Level Computational Screening of in Silico Designed MOFs for Efficient SO2 Capture

SO₂ presence in the atmosphere can cause significant harm to the human and environment through acid rain and/or smog formation. Combining the operational advantages of adsorption-based separation and diverse nature of metal-organic frameworks (MOFs), cost-effective separation processes for SO₂ emissions can be developed. Herein, a large database of hypothetical MOFs composed of >300,000 materials is screened for SO₂/CH₄, SO₂/CO₂, and SO₂/N₂ separations using a multi-level computational approach. Based on a combination of separation performance metrics (adsorption selectivity, working capacity, and regenerability), the best materials and the most common functional groups in those most promising materials are identified for each separation. The top bare MOFs and their functionalized variants are determined to attain SO₂/CH₄ selectivities of 62.4-16899.7, SO₂ working capacities of 0.3-20.1 mol/kg, and SO₂ regenerabilities of 5.8-98.5%. Regarding SO₂/CO₂ separation, they possess SO₂/CO₂ selectivities of 13.3-367.2, SO₂ working capacities of 0.1-17.7 mol/kg, and SO₂ regenerabilities of 1.9-98.2%. For the SO₂/N₂ separation, their SO₂/N₂ selectivities, SO₂ working capacities, and SO₂ regenerabilities span the ranges of 137.9-67,338.9, 0.4-20.6 mol/kg, and 7.0-98.6%, respectively. Besides, using breakdowns of gas separation performances of MOFs into functional groups, separation performance limits of MOFs based on functional groups are identified where bare MOFs (MOFs with multiple functional groups) tend to show the smallest (largest) spreads.

Enhancing of CO Uptake in Metal-Organic Frameworks by Linker Functionalization: A Multi-Scale Theoretical Study

In the present work, the interaction strength of Carbon Monoxide (CO) with a set of forty-two, strategically selected, functionalized benzenes was calculated. Our ab initio calculations at the MP2/6-311++G** level of theory reveal that phenyl hydrogen sulfate (-OSO3H) showed the highest interaction with CO (−19.5 kJ/mol), which was approximately three times stronger compared with the unfunctionalized benzene (−5.3 kJ/mol). Moreover, the three top-performing functional groups (-OSO3H, -OPO3H2, -SO3H) were selected to modify the organic linker of IRMOF-8 and test their ability to capture CO at 298 K for a wide pressure range. Our Grand Canonical Monte Carlo (GCMC) simulations showed a significant increase in the CO uptake in the functionalized MOFs compared with the parent IRMOF-8. It is distinctive that for the volumetric uptake, a 60× increase was observed at 1 bar and 2× was observed at 100 bar. The proposed functionalization strategy can be applied for improving the CO uptake performance not only in MOFs but also in various other porous materials.

Sustainable Use of By-Products and Wastes from Greece to Produce Innovative Eco-Friendly Pervious Concrete

This study was based on the reduction of the extraction of natural resources and, at the same time, was focused on the use of by-products and various wastes in construction applications by following the principles of circular economy. Sterile natural rocks (limestones, basalts), industrial by-products (slags), hotel construction wastes (bathroom wastes) and electronic wastes (e-wastes) were tested for pervious concrete aggregates. For this reason, ten concrete specimens were prepared and tested petrographically, structurally, and physically. The physical properties of the tested raw materials directly depended on their petrographic characteristics and played crucial role for the permeability of the produced concrete specimens, for their mechanical behavior, and for the freeze–thaw test results. Generally, from this study, strong encouraging results were achieved as concrete made by variable wastes and by-products can be compatible for concrete production as they show similar performance both in the mechanical strength test and in the freeze–thaw test with those made by natural aggregates. Another goal of this study was to recommend to other researchers the extended use of by-products, construction wastes, and e-wastes as concrete aggregates for producing eco-friendly constructions.

VOC Mixture Sensing with a MOF Film Sensor Array: Detection and Discrimination of Xylene Isomers and Their Ternary Blends

Detection and recognition of volatile organic compounds (VOCs) are crucial in many applications. While pure VOCs can be detected by various sensors, the discrimination of VOCs in mixtures, especially of similar molecules, is hindered by cross-sensitivities. Isomer identification in mixtures is even harder. Metal-organic frameworks (MOFs) with their well-defined, nanoporous, and versatile structures have the potential to improve the VOC sensing performance by tailoring the adsorption affinities. Here, we detect and identify ternary xylene isomer mixtures by using an array of six gravimetric, quartz crystal microbalance (QCM)-based sensors coated with selected MOF films with different isomer affinities. We use classical molecular simulations to provide insights into the sensing mechanism. In addition to the attractive interaction between the analytes and the MOF film, the isomer discrimination is caused by the rigid crystalline framework sterically controlling the access of the isomers to different adsorption sites in the MOFs. The sensor array has a very low limit of detection of 1 ppm for each pure isomer and allows the isomer discrimination in mixtures. At 100 ppm, 16 different ternary o-p-m-xylene mixtures were identified with high classification accuracy (96.5%). This work shows the unprecedented performance of MOF-sensor arrays, also referred to as MOF-electronic nose (MOF-e-nose), for sensing VOC mixtures. Based on the study, guidelines for detecting and discriminating complex mixtures of volatile molecules are also provided.

Enhancing CH4 Capture from Coalbed Methane through Tuning van der Waals Affinity within Isoreticular Al-Based Metal-Organic Frameworks

Efficient separation of the CH₄/N₂ mixture is of great significance for coalbed methane purification. It is an effective strategy to separate this mixture by tuning the van der Waals interaction due to the nonpolar properties of CH₄ and N₂ molecules. Herein, we prepared several isoreticular Al-based metal-organic frameworks (MOFs) with different ligand sizes and polarities because of their high structural stability and low cost/toxicity feature of Al metal. Adsorption experiments indicated that the CH₄ uptake, Q_st of CH₄, and CH₄/N₂ selectivity are in the order of Al-FUM-Me (27.19 cm³(STP) g^-1, 24.06 kJ mol^-1 and 8.6) > Al-FUM (20.44 cm³(STP) g^-1, 20.60 kJ mol^-1 and 5.1) > Al-BDC (15.98 cm³(STP) g^-1, 18.81 kJ mol^-1 and 3.4) > Al-NDC (10.86 cm³(STP) g^-1, 14.89 kJ mol^-1 and 3.1) > Al-BPDC (5.90 cm³(STP) g^-1, 11.75 kJ mol^-1 and 2.2), confirming the synergetic effects of pore sizes and pore surface polarities. Exhilaratingly, the ideal adsorbed solution theory selectivity of Al-FUM-Me is higher than those of all zeolites, carbon materials, and most water-stable MOF materials (except Al-CDC and Co₃(C₄O₄)₂(OH)₂), which is comparable to MIL-160. Breakthrough results demonstrate its excellent separation performance for the CH₄/N₂ mixture with good regenerability. The separation mechanism of Al-FUM-Me for the CH₄/N₂ mixture was elucidated by theoretical calculations, showing that the stronger affinity of CH₄ can be attributed to its relatively shorter interaction distance with adsorption binding sites. Therefore, this work not only offers a promising candidate for CH₄/N₂ separation but also provides valuable guidance for the design of high-performance adsorbents.

Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal–organic frameworks

Energy-related descriptors in machine learning are a promising strategy to predict adsorption properties of metal–organic frameworks (MOFs) in the low-pressure regime. Interactions between hosts and guests in these systems are typically expressed as a sum of dispersion and electrostatic potentials. The energy landscape of dispersion potentials plays a crucial role in defining Henry’s constants for simple probe molecules in MOFs. To incorporate more information about this energy landscape, we introduce the Gaussian-approximated Lennard-Jones (GALJ) potential, which fits pairwise Lennard-Jones potentials with multiple Gaussians by varying their heights and widths. The GALJ approach is capable of replicating information that can be obtained from the original LJ potentials and enables efficient development of Gaussian integral (GI) descriptors that account for spatial correlations in the dispersion energy environment. GI descriptors would be computationally inconvenient to compute using the usual direct evaluation of the dispersion potential energy surface. We show that these new GI descriptors lead to improvement in ML predictions of Henry’s constants for a diverse set of adsorbates in MOFs compared to previous approaches to this task.

Rational design of mixed-matrix metal-organic framework membranes for molecular separations

Conventional separation technologies to separate valuable commodities are energy intensive, consuming 15% of the worldwide energy. Mixed-matrix membranes, combining processable polymers and selective adsorbents, offer the potential to deploy adsorbent distinct separation properties into processable matrix. We report the rational design and construction of a highly efficient, mixed-matrix metal-organic framework membrane based on three interlocked criteria: (i) a fluorinated metal-organic framework, AlFFIVE-1-Ni, as a molecular sieve adsorbent that selectively enhances hydrogen sulfide and carbon dioxide diffusion while excluding methane; (ii) tailoring crystal morphology into nanosheets with maximally exposed (001) facets; and (iii) in-plane alignment of (001) nanosheets in polymer matrix and attainment of [001]-oriented membrane. The membrane demonstrated exceptionally high hydrogen sulfide and carbon dioxide separation from natural gas under practical working conditions. This approach offers great potential to translate other key adsorbents into processable matrix.

Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66

Modeling of diffusion of adsorbates through porous materials with atomistic molecular dynamics (MD) can be a challenging task if the flexibility of the adsorbent needs to be included. This is because potentials need to be developed that accurately account for the motion of the adsorbent in response to the presence of adsorbate molecules. In this work, we show that it is possible to use accurate machine learning atomistic potentials for metal-organic frameworks in concert with classical potentials for adsorbates to accurately compute diffusivities though a hybrid potential approach. As a proof-of-concept, we have developed an accurate deep learning potential (DP) for UiO-66, a metal-organic framework, and used this DP to perform hybrid potential simulations, modeling diffusion of neon and xenon through the crystal. The adsorbate-adsorbate interactions were modeled with Lennard-Jones (LJ) potentials, the adsorbent-adsorbent interactions were described by the DP, and the adsorbent-adsorbate interactions used LJ cross-interactions. Thus, our hybrid potential allows for adsorbent-adsorbate interactions with classical potentials but models the response of the adsorbent to the presence of the adsorbate through near-DFT accuracy DPs. This hybrid approach does not require refitting the DP for new adsorbates. We calculated self-diffusion coefficients for Ne in UiO-66 from DFT-MD, our hybrid DP/LJ approach, and from two different classical potentials for UiO-66. Our DP/LJ results are in excellent agreement with DFT-MD. We modeled diffusion of Xe in UiO-66 with DP/LJ and a classical potential. Diffusion of Xe in UiO-66 is about a factor of 30 slower than that of Ne, so it is not computationally feasible to compute Xe diffusion with DFT-MD. Our hybrid DP-classical potential approach can be applied to other MOFs and other adsorbates, making it possible to use an accurate DP generated from DFT simulations of an empty adsorbent in concert with existing classical potentials for adsorbates to model adsorption and diffusion within the porous material, including adsorbate-induced changes to the framework.

Surface Modification Strategy for Enhanced NO2 Capture in Metal-Organic Frameworks

The interaction strength of nitrogen dioxide (NO₂) with a set of 43 functionalized benzene molecules was investigated by performing density functional theory (DFT) calculations. The functional groups under study were strategically selected as potential modifications of the organic linker of existing metal–organic frameworks (MOFs) in order to enhance their uptake of NO₂ molecules. Among the functional groups considered, the highest interaction energy with NO₂ (5.4 kcal/mol) was found for phenyl hydrogen sulfate (-OSO₃H) at the RI-DSD-BLYP/def2-TZVPP level of theory—an interaction almost three times larger than the corresponding binding energy for non-functionalized benzene (2.0 kcal/mol). The groups with the strongest NO₂ interactions (-OSO₃H, -PO₃H₂, -OPO₃H₂) were selected for functionalizing the linker of IRMOF-8 and investigating the trend in their NO₂ uptake capacities with grand canonical Monte Carlo (GCMC) simulations at ambient temperature for a wide pressure range. The predicted isotherms show a profound enhancement of the NO₂ uptake with the introduction of the strongly-binding functional groups in the framework, rendering them promising modification candidates for improving the NO₂ uptake performance not only in MOFs but also in various other porous materials.

A neural network potential for the IRMOF series and its application for thermal and mechanical behaviors

Metal-organic frameworks (MOFs) with their exceptional porous and organized structures have been the subject of numerous applications. Predicting the bulk properties from atomistic simulations requires the most accurate force fields, which is still a major problem due to MOFs' hybrid structures governed by covalent, ionic and dispersion forces. Application of ab initio molecular dynamics to such large periodic systems is thus beyond the current computational power. Therefore, alternative strategies must be developed to reduce computational cost without losing reliability. In this work, we construct a generic neural network potential (NNP) for the isoreticular metal-organic framework (IRMOF) series trained by PBE-D4/def2-TZVP reference data of MOF fragments. We confirmed the success of the resulting NNP on both fragments and bulk MOF structures by prediction of properties such as equilibrium lattice constants, phonon density of states and linker orientation. The RMSE values of energy and force for the fragments are only 0.0017 eV atom^-1 and 0.15 eV Å^-1, respectively. The NNP predicted equilibrium lattice constants of bulk structures, even though not included in training, are off by only 0.2-2.4% from experimental results. Moreover, our fragment based NNP successfully predicts the phenylene ring torsional energy barrier, equilibrium bond distances and vibrational density of states of bulk MOFs. Furthermore, the NNP enables revealing the odd behaviors of selected MOFs such as the dual thermal expansion properties and the effect of mechanical strain on the adsorption of hydrogen and methane molecules. The NNP based molecular dynamics (MD) simulations suggest IRMOF-4 and IRMOF-7 to have positive-to-negative thermal expansion coefficients while the rest to have only negative thermal expansion at the studied temperatures of 200 K to 400 K. The deformation of the bulk structure by reduction of the unit cell volume has been shown to increase the volumetric methane uptake in IRMOF-1 but decrease the volumetric methane uptake in IRMOF-7 due to the steric hindrance. To the best of our knowledge, this study presents the first pre-trained model publicly available giving the opportunity for the researchers in the field to investigate different aspects of IRMOFs by performing large-scale simulation at the first-principles level of accuracy.

High-Throughput Screening of COF Membranes and COF/Polymer MMMs for Helium Separation and Hydrogen Purification

Hundreds of covalent organic frameworks (COFs) have been synthesized, and thousands of them have been computationally designed. However, it is impractical to experimentally test each material as a membrane for gas separations. In this work, we focused on the membrane-based gas separation performances of experimentally synthesized COFs and hypothetical COFs (hypoCOFs). Gas permeabilities of COFs were computed by combining the results of grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations, and many COF membranes were found to overcome the upper bound of polymeric membranes for He/H₂, N₂/CH₄, H₂/N₂, He/CH₄, H₂/CH₄, and He/N₂ separations. We then examined the structure-permeability relations of the COF membranes that are above the upper bound for each of the six gas separations, and based on these relations, we proposed an efficient approach for the selection of the best hypoCOFs from a very large database. Molecular simulations showed that 120 hypoCOFs that we identified to be promising based on these structure-performance relations exceed the upper bound for He/CH₄, He/N₂, H₂/CH₄, and H₂/N₂ separations. Both real and hypothetical COFs were then studied as fillers in 25 different polymers, leading to a total of 29 020 COF/polymer and hypoCOF/polymer mixed matrix membranes (MMMs), representing the largest number of COF-based MMMs investigated to date. Permeabilities and selectivities of COF/polymer MMMs were computed for six different gas separations, and results revealed that 18 of the 25 polymers can be carried above the upper bound when COFs were used as fillers. The comprehensive analysis of COFs provided in this work will fully unlock the potential of COF membranes and COF/polymer MMMs for helium separation and hydrogen purification.

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.

Energy-efficient recovery of fermented butyric acid using octyl acetate extraction

Background

A butyric acid recovery process using octyl acetate is proposed, and the design details of the extraction and subsequent distillation processes were investigated. Ternary equilibrium data for the extractor design were derived from molecular simulations and experimental measurements.

Results

A new procedure for estimating the thermodynamic parameters was introduced to determine the effect of the parameters on extractor design by comparison with previously reported parameters. Using the proposed recovery process with the newly estimated thermodynamic model, 99.8% butyric acid was recovered from the fermentation broth at a recovery rate of 99%. The energy demand for the proposed process was found to be lower than the average demand for several reported butyric acid recovery processes.

Conclusions

The investment cost is projected to be lower than that of other butyric acid processes due to the high efficiency of extraction solvent. The recovery cost of butyric acid was comparable to its selling price.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13068-022-02146-6.

MIL-101(Cr) MOF as an Effective Siloxane Sensor

Volatile methylsiloxanes (VMSs) are common silicone degradation byproducts that cause serious concern for the contamination of sensitive electronics and optics, among others. With the goal of fast, online detection of VMS, we herein highlight the mesoporous MIL-101(Cr) MOF as a promising mass sensing layer for integration with a quartz crystal microbalance (QCM), using an in-house modified gravimetric adsorption system capable of achieving extremely low concentrations of siloxane D4 (down to 0.04 ppm), targeting applications for monitoring in indoor spaces and spacecraft. Our developed MIL-101(Cr)@QCM sensor achieves near-perfect reversibility with no hysteresis alongside excellent repeatability over cycling and fast response/recovery times under 1 min. We attribute this capability to optimum host/guest interactions as uncovered through molecular simulations.

Linker Functionalization Strategy for Water Adsorption in Metal-Organic Frameworks

Water adsorption in metal-organic frameworks has gained a lot of scientific attention recently due to the potential to be used in adsorption-based water capture. Functionalization of their organic linkers can tune water adsorption properties by increasing the hydrophilicity, thus altering the shape of the water adsorption isotherms and the overall water uptake. In this work, a large set of functional groups is screened for their interaction with water using ab initio calculations. The functional groups with the highest water affinities form two hydrogen bonds with the water molecule, acting as H-bond donor and H-bond acceptor simultaneously. Notably, the highest binding energy was calculated to be -12.7 Kcal/mol for the -OSO₃H group at the RI-MP2/def2-TZVPP-level of theory, which is three times larger than the reference value. Subsequently, the effect of the functionalization strategy on the water uptake is examined on a selected set of functionalized MOF-74-III by performing Monte Carlo simulations. It was found that the specific groups can increase the hydrophilicity of the MOF and enhance the water uptake with respect to the parent MOF-74-III for relative humidity (RH) values up to 30%. The saturation water uptake exceeded 800 cm³/cm³ for all candidates, classifying them among the top performing materials for water harvesting.

Isotope-selective pore opening in a flexible metal-organic framework

Flexible metal-organic frameworks that show reversible guest-induced phase transitions between closed and open pore phases have enormous potential for highly selective, energy-efficient gas separations. Here, we present the gate-opening process of DUT-8(Ni) that selectively responds to D₂, whereas no response is observed for H₂ and HD. In situ neutron diffraction directly reveals this pressure-dependent phase transition. Low-temperature thermal desorption spectroscopy measurements indicate an outstanding D₂-over-H₂ selectivity of 11.6 at 23.3 K, with high D₂ uptake. First-principles calculations coupled with statistical thermodynamics predict the isotope-selective gate opening, rationalized by pronounced nuclear quantum effects. Simulations suggest DUT-8(Ni) to remain closed in the presence of HT, while it also opens for DT and T₂, demonstrating gate opening as a highly effective approach for isotopolog separation.

Putting Forward NUS-8-CO2H/PIM-1 as a Mixed Matrix Membrane for CO2 Capture

Mixed matrix membranes (MMMs) composed of NUS-8 metal-organic framework (MOF) nanosheets dispersed into a polymer of intrinsic microporosity 1 (PIM-1) polymer matrix are known to be promising candidates for CO₂/N₂ separation because of a solubility-driven separation mechanism. In this work, we predict that a chemical functionalization of the organic linker of NUS-8 by a CO₂-philic function confers an even better separation performance to the resulting MMM. Our simulations revealed that the NUS-8-CO₂H/PIM-1 composite exhibits a 3-fold increase in CO₂/N₂ selectivity versus the NUS-8/PIM-1 analogue while achieving a high CO₂ permeability (6700 barrer). We demonstrated that this improved level of performance is due to an increase both in the total MOF/polymer interfacial pore volume and in the CO₂-affinity due to the chemical functionalization. These results suggest that an appropriate choice of chemical functionalization of a MOF is a promising strategy to improve gas separation performances for MMM composites that exhibit a solubility-driven separation mechanism.

Evaluation of Gas Adsorption in Nanoporous Shale by Simplified Local Density Model Integrated with Pore Structure and Pore Size Distribution

Simplified local density (SLD) model has been widely used to describe the gas adsorption behaviors in porous media. However, the slit pore geometry and constant pore width associated with the SLD model may fail to represent the heterogeneous pore network structure in shale. In this study, a new method to integrate the SLD model with the slit and cylindrical pore structures as well as the pore size distribution (PSD) is proposed and validated by the grand canonical Monte Carlo (GCMC) simulations and the experimentally measured adsorption of methane on shale with complex pore network. Comparison results show that reasonably good agreement is achieved between the SLD model and GCMC simulations for both the gas adsorption isotherms and discrete-density profiles in multiwalled carbon nanoslit and nanotube. The corresponding average absolute percentage deviations (% AADs) are below 0.3 and 9.3 for gas adsorption isotherm and discrete-density profile, respectively. In addition, the SLD model coupled with the PSD of slit and cylindrical pores ranging from micro- to macropores properly characterizes the measured excess adsorption of methane on Wolfcamp shale core sample with % AADs between 1.7 and 3.6. It is found that when the pore volume is fixed, the gas adsorption isotherm and gas density profile are heavily dependent on the pore geometry and pore size. Furthermore, integrating the PSD into the SLD model can guarantee the valid identification of the adsorbed- and free-gas regions in flow channels with different sizes based on the gas density profiles. The findings of this study shed light on the effects of pore structure on gas adsorption in nanopores and enable us to precisely evaluate and predict the gas adsorption behaviors in slit and cylindrical pores over a wide range of pore sizes.

Anion Regulates Topological Porous Coordination Polymers into the Acetylene Trap

The development of efficient porous absorbents with high uptake and selectivity remains a great challenge, especially for the recovery of acetylene (C₂H₂) from its carbon dioxide (CO₂)-containing mixtures. Here, we propose and report an anion-planting strategy for regulating the scu topological porous coordination polymers (PCPs) into the C₂H₂ trap. The three electronegative anions SiF₆^2-, TiF₆^2-, and ZrF₆^2-, in addition to the ligand of 3,5-di(1H-imidazol-1-yl)benzoic acid (HL) and Cu²⁺ ion, were employed to construct highly porous PCPs (NTU-60, NTU-61, and NTU-62) with varied window aperture. Especially, due to a matching distance (d_F-F) of 5.7 Å along the c-axis, the limited space that can be assigned as a single C₂H₂ trap enables NTU-61 to show optimal ability for C₂H₂ (van der Waals (vdW) parameters of the two H atoms: ∼5.72 Å) recognition, validated by Grand Canonical Monte Carlo (GCMC) calculations and Raman spectra. These characteristics allow the NTU-series to show higher C₂H₂ uptake, as well as excellent C₂H₂/CO₂ separation performance under dynamic conditions. The molecular insight and strategy here not only permit balanced adsorption and separation in a single domain but also exhibit an opportunity to develop advanced adsorbents in nearly all frameworks with lattice or coordinated ions, which may act as the platforms for various selective guest trappings with on-demand time and/or spatial resolution.

Three-Dimensional Triptycene-Functionalized Covalent Organic Frameworks with hea Net for Hydrogen Adsorption

Owing to the finite building blocks and difficulty in structural identification, it remains a tremendous challenge to elaborately design and synthesize three-dimensional covalent organic frameworks (3D COFs) with predetermined topologies. Herein, we report the first two cases of 3D COFs with the non-interpenetrated hea net, termed JUC-596 and JUC-597, by using the combination of tetrahedral and triangular prism building units. Due to the presence of triptycene functional groups and fluorine atoms, JUC-596 exhibits an exceptional performance in the H₂ adsorption up to 305 cm³  g^-1 (or 2.72 wt%) at 77 K and 1 bar, which is higher than previous benchmarks from porous organic materials reported so far. Furthermore, the strong interaction between H₂ and COF materials is verified through the DFT theoretical calculations. This work represents a captivating example of rational design of functional COFs based on a reticular chemistry guide and demonstrates its promising application in clean energy storage.

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.