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

Atomic observation and structural evolution of covalent organic framework rotamers

Dynamic 3D covalent organic frameworks (COFs) have shown concerted structural transformation and adaptive gas adsorption due to the conformational diversity of organic linkers. However, the isolation and observation of COF rotamers constitute undergoing challenges due to their comparable free energy and subtle rotational energy barrier. Here, we report the atomic-level observation and structural evolution of COF rotamers by cryo-3D electron diffraction and synchrotron powder X-ray diffraction. Specifically, we optimize the crystallinity and morphology of COF-320 to manifest its coherent dynamic responses upon adaptive inclusion of guest molecules. We observe a significant crystal expansion of 29 vol% upon hydration and a giant swelling with volume change up to 78 vol% upon solvation. We record the structural evolution from a non-porous contracted phase to two narrow-pore intermediate phases and the fully opened expanded phase using n-butane as a stabilizing probe at ambient conditions. We uncover the rotational freedom of biphenylene giving rise to significant conformational changes on the diimine motifs from synclinal to syn-periplanar and anticlinal rotamers. We illustrate the 10-fold increment of pore volumes and 100% enhancement of methane uptake capacity of COF-320 at 100 bar and 298 K. The present findings shed light on the design of smarter organic porous materials to maximize host-guest interaction and boost gas uptake capacity through progressive structural transformation.

Predicting Ion Diffusion from the Shape of Potential Energy Landscapes

We present an efficient method to compute diffusion coefficients of multiparticle systems with strong interactions directly from the geometry and topology of the potential energy field of the migrating particles. The approach is tested on Li-ion diffusion in crystalline inorganic solids, predicting Li-ion diffusion coefficients within 1 order of magnitude of molecular dynamics simulations at the same level of theory while being several orders of magnitude faster. The speed and transferability of our workflow make it well-suited for extensive and efficient screening studies of crystalline solid-state ion conductor candidates and promise to serve as a platform for diffusion prediction even up to the density functional level of theory.

Effects of Fluorinated Functionalization of Linker on Quercetin Encapsulation, Release and Hela Cell Cytotoxicity of Cu-Based MOFs as Smart pH-Stimuli Nanocarriers

Controlled delivery of target molecules is required in many medical and chemical applications. For such purposes, metal-organic frameworks (MOFs), which possess desirable features such as high porosity, large surface area, and adjustable functionalities, hold great potential as drug carriers. Herein, Quercetin (QU), as an anticancer drug, was loaded on Cu2 (BDC)2 (DABCO) and Cu2 (F4 BDC)2 )DABCO) MOFs (BDC=1,4-benzenedicarboxylate and DABCO=1,4-diazabicyclo[2.2.2]octane). As these Cu-MOFs have a high surface area, an appropriate pore size, and biocompatible ingredients, they can be utilized to deliver QU. The loading efficiency of QU in these MOFs was 49.5 % and 41.3 %, respectively. The drug-loaded compounds displayed sustained drug release over 15 days, remarkably high drug loading capacities and pH-controlled release behavior. The prepared nanostructures were characterized by different characterization technics including FT-IR, PXRD, ZP, TEM, FE-SEM, UV-vis, and BET. In addition, MTT assays were carried out on the HEK-293 and HeLa cell lines to investigate cytotoxicity. Cellular apoptosis analysis was performed to investigate the cell death mechanisms. Grand Canonical Monte Carlo simulations were conducted to analyze the interactions between MOFs and QU. Moreover, the stability of MOFs was also investigated during and after the drug release process. Ultimately, kinetic models of drug release were evaluated.

Solvent-Directed Construction of a Nanoporous Metal-Organic Framework with Potential in Selective Adsorption and Separation of Gas Mixtures Studied by Grand Canonical Monte Carlo Simulations

In this report, a microporous metal-organic framework of [Ca(TDC)(DMA)]n (1) and a two-dimensional coordination polymer of [Ca(TDC)(DMF)2 ]n (2), (TDC2- =Thiophene-2,5-dicarboxylate, DMA=N, N'-dimethylacetamide and DMF=N, N'-dimethylformamide) based on Ca(II) were designed by the effect of solvent, and X-ray analysis was performed for the single crystals of 1 and 2. Then, compound 1 was synthesized in three different methods and identified with a set of analyses. Compared to other adsorbents, MOFs are widely used in the field of adsorption and separation of various gases due to a series of distinctive features such as diverse and adjustable structures pores with different dimensions, high porosity and surface area with regular distribution of active sites. Therefore, the ability of 1 to uptake single gases (CH4 , CO2 , C2 H2 , H2, and N2 ) and separation of several binary mixtures of gases (CO2 /CH4 , CO2 /N2 , CO2 /H2 and CO2 /C2 H2 ), were investigated using Grand Canonical Monte Carlo simulations. Volumetric and gravimetric adsorption isotherms in various operating conditions, the isosteric heat of adsorption (qst ), the chemical potential for each thermodynamic state, and snapshots during the simulation process were reported in all cases. The results obtained from the adsorption simulation indicate that compound 1 has a high capacity for uptake of H2 (16 mmol g-1 ) and N2 (12.5 mmol g-1 ), CO2 (6.6 mmol g-1 ), C2 H2 (5 mmol g-1 ) and CH4 (1.5 mmol g-1 ) gases at 1 bar. It also performs well in separating CO2 in binary mixtures, which can be attributed to the presence of open metal sites in nodes of 1 and their electrostatic tendency to interact with CO2 containing the higher quadrupole dipole moment compared to other components of the mixture.

Molecular Simulation of the Impact of Defects on Electrolyte Intrusion in Zeolites

We have investigated through molecular simulation the intrusion of electrolytes in two representative pure-silica zeolites, silicalite-1 and chabazite, in which point defects were introduced in varying amounts. We distinguish between two types of defects, considering either "weak" or "strong" silanol nest defects, resulting in different hydration behaviors. In the presence of weak defects, the hydration process occurs through a homogeneous nucleation process, while with strong defects, we observe an initial adsorption followed by a filling of the nanoporous volume at a higher pressure. However, we show that electrolytes do not penetrate the zeolites, and these defects appear to have only marginal influence on the thermodynamics of electrolyte intrusion. While replacing pure water by the electrolyte solution shifts the intrusion pressure toward higher values because of the drop of water saturation vapor pressure, an increase in hydrophilicity of the framework due to point defects has the opposite effect, showing that controlling the amount of defects in zeolites is crucial for storage energy applications.

Hierarchical Computational Screening of Quantum Metal-Organic Framework Database to Identify Metal-Organic Frameworks for Volatile Organic-Compound Capture from Air

The design and discovery of novel porous materials that can efficiently capture volatile organic compounds (VOCs) from air are critical to address one of the most important challenges of our world, air pollution. In this work, we studied a recently introduced metal-organic framework (MOF) database, namely, quantum MOF (QMOF) database, to unlock the potential of both experimentally synthesized and hypothetically generated structures for adsorption-based n-butane (C4H10) capture from air. Configurational Bias Monte Carlo (CBMC) simulations were used to study the adsorption of a quaternary gas mixture of N2, O2, Ar, and C4H10 in QMOFs for two different processes, pressure swing adsorption (PSA) and vacuum-swing adsorption (VSA). Several adsorbent performance evaluation metrics, such as C4H10 selectivity, working capacity, the adsorbent performance score, and percent regenerability, were used to identify the best adsorbent candidates, which were then further studied by molecular simulations for C4H10 capture from a more realistic seven-component air mixture consisting of N2, O2, Ar, C4H10, C3H8, C3H6, and C2H6. Results showed that the top five QMOFs have C4H10 selectivities between 6.3 × 103 and 9 × 103 (3.8 × 103 and 5 × 103) at 1 bar (10 bar). Detailed analysis of the structure-performance relations showed that low/mediocre porosity (0.4-0.6) and narrow pore sizes (6-9 Å) of QMOFs lead to high C4H10 selectivities. Radial distribution function analyses of the top materials revealed that C4H10 molecules tend to confine close to the organic parts of MOFs. Our results provided the first information in the literature about the VOC capture potential of a large variety and number of MOFs, which will be useful to direct the experimental efforts to the most promising adsorbent materials for C4H10 capture from air.

Machine Learning Models for Predicting Molecular Diffusion in Metal-Organic Frameworks Accounting for the Impact of Framework Flexibility

Molecular diffusion in MOFs plays an important role in determining whether equilibrium can be reached in adsorption-based chemical separations and is a key driving force in membrane-based separations. Molecular dynamics (MD) simulations have shown that in some cases inclusion of framework flexibility in MOF changes predicted molecular diffusivities by orders of magnitude relative to more efficient MD simulations using rigid structures. Despite this, all previous efforts to predict molecular diffusion in MOFs in a high-throughput way have relied on MD data from rigid structures. We use a diverse data set of MD simulations in flexible and rigid MOFs to develop a classification model that reliably predicts whether framework flexibility has a strong impact on molecular diffusion in a given MOF/molecule pair. We then combine this approach with previous high-throughput MD simulations to develop a reliable model that efficiently predicts molecular diffusivities in cases in which framework flexibility can be neglected. The use of this approach is illustrated by making predictions of molecular diffusivities in ∼70,000 MOF/molecule pairs for molecules relevant to gas separations.

CO2 and CO Capture on the ZnO Surface: A GCMC and Electronic Structure Study

The amount of polluting gases released into the atmosphere has grown drastically. Among them, it is possible to cite the release of CO2 and CO gases on a large scale as one of the products of the complete and incomplete combustion of petroleum-derived fuels. It is worth noting that the production of energy by burning fossil fuels supplies the energy demand but causes environmental damage, and several studies have addressed the reduction. One of them is using materials with the potential to capture these gases. The experimental and theoretical studies have significant contributions that promote advances in this area. Among the materials investigated, ZnO has emerged, demonstrating the considerable potential for capturing various gases, including CO2 and CO. This work used density functional theory (DFT) and Grand Canonical Monte Carlo Method (GCMC) to investigate the adsorption of CO2 and CO on the surface of Zinc oxide (ZnO) to obtain adsorption isotherms and interaction energy and the interaction nature. The results suggest that CO2 adsorption slightly changed the angle of the O-C-O to values less than 180°. For the CO, its carbon atom interacts simultaneously with Zn and O of the ZnO surface. However, CO interactions have an ionic character with a lower binding energy value than the CO2 interaction. The energies calculated using the PM6 and DFT methods generated results compatible with the experimental values. In applications involving a mixture of these two gases, the adsorption of CO2 should be favored, and there may be inhibition of the adsorption of CO for high CO2 concentrations.

Metal-Organic Framework Membrane Constructor: A Tool for High-Throughput Construction of Metal-Organic Framework Membrane Models

With the rapid development of metal-organic framework (MOF) membranes for separation applications, computational screening of their separation performance has attracted increasing interest in the design and fabrication of such materials. Although bulk crystal models in MOF databases are often used to represent MOF membrane structures, membrane models in slab geometries are still essential for researchers to simulate the separation performance, particularly to understand the effects of the surface/interface structure, pore sieving, and exposed lattice plane on guest permeability. However, to date, no database or method has been established to provide researchers with numerous membrane models, restricting the further development of related theoretical studies. Herein, we propose an algorithm and develop a tool called the "MOF-membrane constructor" to realize the high-throughput construction of membrane models based on the MOF crystal structures. Using this tool, membrane models can be generated with desired sizes, reasonable surface terminations, and assigned exposed crystal planes. The tool can also deduce the most prominent surface in the Bravais-Friedel-Donnay-Harker morphology or identify the pores in MOF crystals and automatically determine an exposed plane for each membrane model. Thus, an MOF-membrane database can be established rapidly according to user simulation requirements. This study can considerably improve the efficiency of building MOF membrane models and may be beneficial for the future development of simulation studies on MOF membranes.

Enhancing Structure-Property Relationships in Porous Materials through Transfer Learning and Cross-Material Few-Shot Learning

Porous materials have emerged as promising solutions for a wide range of energy and environmental applications. However, the asymmetric development in the field of metal-organic frameworks (MOFs) has led to a data imbalance when it comes to MOFs versus other porous materials such as covalent organic frameworks (COFs), porous polymer networks (PPNs), and zeolites. To address this issue, we introduce PMTransformer (Porous Material Transformer), a multimodal Transformer model pretrained on a vast data set of 1.9 million hypothetical porous materials, including metal-organic frameworks, covalent organic frameworks, porous polymer networks, and zeolites. PMTransformer showcases remarkable transfer learning capabilities, resulting in state-of-the-art performance in predicting various porous material properties. To address the challenge of asymmetric data aggregation, we propose cross-material few-shot learning, which leverages the synergistic effect among different porous material classes to enhance the fine-tuning performance with a limited number of examples. As a proof of concept, we demonstrate its effectiveness in predicting band gap values of COFs using the available MOF data in the training set. Moreover, we established cross-material relationships in porous materials by predicting the unseen properties of other classes of porous materials. Our approach presents a new pathway for understanding the underlying relationships among various classes of porous materials, paving the way toward a more comprehensive understanding and design of porous materials.

Accelerating Metal-Organic Framework Selection for Type III Porous Liquids by Synergizing Machine Learning and Molecular Simulation

MOF-based type III porous liquids, comprising porous MOFs dissolved in a liquid solvent, have attracted increasing attention in carbon capture. However, discovering appropriate MOFs to prepare porous liquids was still limited in experiments, wasting time and energy. In this study, we have used the density functional theory and molecular dynamics simulation methods to identify 4530 MOF candidates as the core database based on the idea of prohibiting the pore occupancy of porous liquids by the solvent, [DBU-PEG][NTf2] ionic liquid. Based on high-throughput molecular simulation, random forest machine learning models were first trained to predict the CO2 sorption and the CO2/N2 sorption selectivity of MOFs to screen the MOFs to prepare porous liquids. The feature importance was inferred based on Shapley Additive Explanations (SHAP) interpretation, and the ranking of the top 5 descriptors for sorption/selectivity trade-off (TSN) was gravimetric surface area (GSA) > porosity > density > metal fraction > pore size distribution (PSD, 3.5-4 Å). RICBEM was predicted to be one candidate for preparing porous liquid with CO2 sorption capacity of 20.87 mmol/g and CO2/N2 sorption selectivity of 16.75. The experimental results showed that the RICBEM-based porous liquid was successfully synthesized with CO2 sorption capacity of 2.21 mmol/g and CO2/N2 sorption selectivity of 63.2, the best carbon capture performance known to date. Such a screening method would advance the screening of cores and solvents for preparing type III porous liquids with different applications by addressing corresponding factors.

Understanding the influence of secondary building units on the thermal conductivity of metal-organic frameworks via high-throughput computational screening

The thermal conductivity of metal-organic frameworks (MOFs) has garnered increasing interest due to their potential applications in energy-related fields. However, due to the diversity of building units, understanding the relationship between MOF structures and their thermal conductivity remains an imperative challenge. In this study, we predicted the thermal conductivity (κ) of MOFs using equilibrium molecular dynamics (EMD) simulations and investigated the contribution of structure properties to their thermal conductivity. It is revealed that the arrangement of secondary building units (SBUs) with a closer distance of metal atoms, a larger proportion of metal elements, and transition metal elements (Fe, Mn, and Co) leads to high thermal conductivity. To generally quantify the influence of such factors on thermal conductivity, the pathway factors with SBU influence (Pm) were proposed and can be used to efficiently classify structures into high, medium, and low thermal conductivity types. It was found that Pm indicates that MOFs with met topology tend to have high thermal conductivity, while rna and pcu topologies naturally tend to possess medium and low thermal conductivity. Moreover, it was also suggested that taking Pm as a descriptor in the machine learning algorithms can significantly improve the prediction accuracy for thermal conductivity. This study offers molecular insight into the impact of various SBUs on thermal conductivity in framework-based nanomaterials, which may guide the rational design of nanoporous materials with desirable thermal conductivity.

Mechanism of Cs Immobilization within a Sodalite Framework: The Role of Alkaline Cations and the Si/Al Ratio

Conditioning of radioactive waste generated from the operation of medical institutions, nuclear cycle facilities, and nuclear facilities is important for the safety of the environment. One of the most hazardous radionuclides is radioactive cesium. There is a need for more effective solutions to contain radionuclides, especially cesium (Cs+). Geopolymers are promising inorganic materials that can provide a large active surface area with adjustable porosity and binding capacity. The existence of nanosized zeolite-like structures in aluminosilicate gels was shown earlier. These structures are candidates for immobilizing radioactive cesium (Cs+). However, the mechanisms of their interactions with the aluminosilicate framework related to radionuclide immobilization have not been well studied. In this work, the influence of alkaline cations (Na+ or K+) and the aluminosilicate framework structure on the binding capacity and mechanism of interaction of geopolymers with Cs+ is explored in the example of a sodalite framework. The local structure of the water molecules and alkaline ions in the equilibrium state and its behavior when the Si/Al ratio was changed were studied by DFT.

Exploring the Potential of Hierarchical Zeolite-Templated Carbon Materials for High-Performance Li-O2 Batteries: Insights from Molecular Simulations

The commercialization of ultrahigh capacity lithium-oxygen (Li-O2) batteries is highly dependent on the cathode architecture, and a better understanding of its role in species transport and solid discharge product (i.e., Li2O2) formation is critical to improving the discharge capacity. Tailoring the pore size distribution in the cathode structure can enhance the ion mobility and increase the number of reaction sites to improve the formation of solid Li2O2. In this work, the potential of hierarchical zeolite-templated carbon (ZTC) structures as novel electrodes for Li-O2 batteries was investigated by using reactive force field molecular dynamics simulation (reaxFF-MD). Initially, 47 microporous zeolite-templated carbon morphologies were screened based on microporosity and specific area. Among them, four structures (i.e., RHO-, BEA-, MFI-, and FAU-ZTCs) were selected for further investigation including hierarchical features in their structures. Discharge product cluster analysis, self-diffusivities, and density number profiles of Li+, O2, and dimethyl sulfoxide (DMSO) electrolyte were obtained to find that the RHO-type ZTC exhibited enhanced mass transfer compared to conventional microporous ZTC (approximately 31% for O2, 44% for Li+, and 91% for DMSO) electrodes. This is due to the promoted formation of small-sized product clusters, creating more accessible sites for oxygen reduction reaction and mass transport. These findings indicate how hierarchical ZTC electrodes with micro- and mesopores can enhance the discharge performance of aprotic Li-O2 batteries, providing molecular insights into the underlying phenomena.

Zn-Based Metal-Organic Frameworks Using Triptycene Hexacarboxylate Ligands: Synthesis, Structure, and Gas-Sorption Properties

A series of metal-organic frameworks (MOFs) based on zinc ions and two triptycene ligands of different size have been synthesized under solvothermal conditions. Structural analyses revealed that they are isostructural 3D-network MOFs. The high porosity and thermal stability of these MOFs can be attributed to the highly rigid triptycene-based ligands. Their BET specific surface areas depend on the size of the triptycene ligands. In contrast to these surface-area data, the H2 and CO2 adsorption of these MOFs is larger for MOFs with small pores. Consequently, we introduced functional groups to the bridge-head position of the triptycene ligands and investigated their effect on the gas-sorption properties. The results unveiled the role of the functional groups in the specific CO2 binding via an induced interaction between adsorbates and the functional groups. Excellent H2 and CO2 properties in these MOFs were achieved in the absence of open metal sites.

Trajectory-Extending Kinetic Monte Carlo Simulations to Evaluate Pure and Gas Mixture Diffusivities through a Dense Polymeric Membrane

With renewed interest in CO2 separations, carbon molecular sieving (CMS) membrane performance evaluation requires diffusion coefficients as inputs to have a reliable estimate of the permeability. An optimal material is desired to have both high selectivity and permeability. Gases diffusing through dense CMS and polymeric membranes experience extended subdiffusive regimes, which hinders reliable extraction of diffusion coefficients from mean squared displacement data. We improve the sampling of the diffusive landscape by implementing the trajectory-extending kinetic Monte Carlo (TEKMC) technique to efficiently extend molecular dynamics (MD) trajectories from ns to μs time scales. The obtained self-diffusion coefficient of pure CO2 in CMS membranes derived from a 6FDA/BPDA-DAM precursor polymer melt is found to linearly increase from 0.8-1.3 × 10-6 cm2 s-1 in the pressure range of 1-20 bar, which supports previous experimental findings. We also extended the TEKMC algorithm to evaluate the mixture diffusivities in binary mixtures to determine the permselectivity of CO2 in CH4 and N2 mixtures. The mixture diffusion coefficient of CO2 ranges from 1.3-7 × 10-6 cm2 s-1 in the binary mixture CO2/CH4, which is significantly higher than the pure gas diffusion coefficient. Robeson plot comparisons show that the permselectivity obtained from pure gas diffusion data is significantly lower than that predicted using mixture diffusivity data. Specifically, in the case of the CO2/N2 mixture, we find that using mixture diffusivities led to permselectivities lying above the Robeson limit highlighting the importance of using mixture diffusivity data for an accurate evaluation of the membrane performance. Combined with gas solubilities obtained from grand canonical Monte Carlo (GCMC) simulations, our work shows that simulations with the TEKMC method can be used to reliably evaluate the performance of materials for gas separations.

Prediction of the capture and utilization of atmospheric acidic gases by azo-based square-pillared fluorinated MOFs

More than the permissible limit of acidic gases like CO2, SO2, and NO2 in the atmosphere are responsible for the formation of acid rain, the greenhouse effect and many other undesirable environmental hazards. So, the capture and utilization of these gases are essential for mankind. Herein, we proposed an azo-based square pillared MOF, [Ni(MF5)(1,2-bis(4-pyridy)diazene)2]n, with the CUS metal site, i.e. M = Al/Fe, for the selective capture and conversion of acidic gas molecules into commodity chemicals such as cyclic carbonate, sulphite and nitrite. With the aid of Density Functional Theory (DFT), [Ni(MF5)(1,2-bis(4-pyridy)diazene)2]n has been optimized, and the specific force field is derived via guest-host interaction. The Grand Canonical Monte Carlo (GCMC) simulation has been used to explore the guest-host interactions over a wide range of pressures, and their respective stability under pre-humidification is evaluated. The adsorption prediction reveals that MFFIVE-Ni-apy have a higher adsorptive capacity (37.1 mmol g-1), and especially ALFFIVE-Ni-apy possesses a higher affinity towards guest molecules (CO2, SO2) rather than FEFFIVE-Ni-apy. Additionally, the adsorption of gases in the presence of humidity reveals that ALFFIVE-Ni-apy has an optimal adsorption capacity for all investigated acidic gases even at 38.5 RH%. The absorbed acidic gases on MFFIVE-Ni-apy were used for the theoretical investigations on cycloaddition with the aid of DFT as an application perspective of the toxic gases instead of expelling into atmosphere. The Climbing Image Nudged Elastic Band (CI-NEB) approach was used to discover the transition state in this scenario, in which the cycloaddition of adsorbed CO2, SO2, and NO2 gases with epoxides leads to the formation of cyclic carbonates, sulphites, and nitrates, respectively.

Leveraging Machine Learning for Metal-Organic Frameworks: A Perspective

Metal-organic frameworks (MOFs) have attracted tremendous interest because of their tunable structures, functionalities, and physiochemical properties. The nearly infinite combinations of metal nodes and organic linkers have led to the synthesis of over 100,000 experimental MOFs and the construction of millions of hypothetical counterparts. It is intractable to identify the best candidates in the immense chemical space of MOFs for applications via conventional trial-to-error experiments or brute-force simulations. Over the past several years, machine learning (ML) has substantially transformed the way of MOF discovery, design, and synthesis. Driven by the abundant data from experiments or simulations, ML can not only efficiently and accurately predict MOF properties but also quantitatively derive structure-property relationships for rational design and screening. In this Perspective, we summarize recent achievements in leveraging ML for MOFs from the aspects of data acquisition, featurization, model training, and applications. Then, current challenges and new opportunities are discussed for the future exploration of ML to accelerate the development of new MOFs in this vibrant field.

Fabrication of Pillar-Cage Fluorinated Anion Pillared Metal-Organic Frameworks via a Pillar Embedding Strategy and Efficient Separation of SO2 through Multi-Site Trapping

Flue gas desulfurization is crucial for both human health and ecological environments. However, developing efficient SO2 adsorbents that can break the trade-off between adsorption capacity and selectivity is still challenging. In this work, a new type of fluorinated anion-pillared metal-organic frameworks (APMOFs) with a pillar-cage structure is fabricated through pillar-embedding into a highly porous and robust framework. This type of APMOFs comprises smaller tetrahedral cages and larger icosahedral cages interconnected by embedded [NbOF5 ]2- and [TaOF5 ]2- anions acting as pillars. The APMOFs exhibits high porosity and density of fluorinated anions, ensuring exceptional SO2 adsorption capacity and ultrahigh selectivity for SO2 /CO2 and SO2 /N2 gas mixtures. Furthermore, these two structures demonstrate excellent stability towards water, acid/alkali, and SO2 adsorption. Cycle dynamic breakthrough experiments confirm the excellent separation performance of SO2 /CO2 gas mixtures and their cyclic stability. SO2 -loaded single-crystal X-ray diffraction, Grand canonical Monte Carlo (GCMC) simulations combined with density functional theory (DFT) calculations reveal the preferred adsorption domains for SO2 molecules. The multiple-site host-guest and guest-guest interactions facilitate selective recognition and dense packing of SO2 in this hybrid porous material. This work will be instructive for designing porous materials for flue gas desulfurization and other gas-purification processes.

Understanding the Heat Transfer Performance of Zeolitic Imidazolate Frameworks upon Gas Adsorption by Molecular Dynamics Simulations

Metal-organic frameworks (MOFs) with ultrahigh specific surface area and porosity have emerged as promising nanoporous materials for gas separation, storage, and adsorption-driven thermal energy conversion systems such as adsorption heat pumps. However, an inadequate understanding of the thermal transport of MOFs with adsorbed gases hampers the thermal management of such systems in practical applications. In this work, an in-depth investigation on the mechanistic heat transfer performance of three topological zeolitic imidazolate frameworks (ZIFs) upon hydrogen, methane, and ethanol adsorption was carried out by molecular dynamics simulations. It is revealed that the trade-off between the additional heat transfer pathway and phonon scattering resulting from adsorbed gases determines the thermal conductivity of ZIFs. It is found that the increased thermal conductivity with the increased number of adsorbed gases is correlated with the overlap energy between the vibrational density of states of gases and Zn atoms, suggesting the additional heat transfer pathways formed between gas molecules and frameworks. Moreover, the gas spatial distribution and diffusion also impose remarkable impacts on the heat transfer performance. Both the homogeneous gas distribution and the fast gas diffusion are conducive to form effective heat transfer pathways, leading to enhanced thermal conductivity. This study provides molecular insight into the mechanism of the improved thermal conductivity of ZIFs upon gas adsorption, which may pave the way for effective thermal management in MOF-related applications.

Surface engineering on a microporous metal-organic framework to boost ethane/ethylene separation under humid conditions

Recently, examples of metal-organic frameworks (MOFs) have been identified displaying ethane (C2H6) over ethylene (C2H4) adsorption selectivity. However, it remains a challenge to construct MOFs with both large C2H6 adsorption capacity and high C2H6/C2H4 adsorption selectivity, especially under humid conditions. Herein, we reported two isoreticular MOF-5 analogues (JNU-6 and JNU-6-CH3) and their potential applications in one-step separation of C2H4 from C2H6/C2H4 mixtures. The introduction of CH3 groups not only reduces the pore size from 5.4 Å in JNU-6 to 4.1 Å in JNU-6-CH3 but also renders an increased electron density on the pyrazolate N atoms of the organic linker. JNU-6-CH3 retains its framework integrity even after being immersed in water for six months. More importantly, it exhibits large C2H6 adsorption capacity (4.63 mmol g-1) and high C2H6/C2H4 adsorption selectivity (1.67) due to the optimized pore size and surface function. Breakthrough experiments on JNU-6-CH3 demonstrate that C2H4 can be directly separated from C2H6/C2H4 (50/50, v/v) mixtures, affording benchmark productivity of 22.06 and 18.71 L kg-1 of high-purity C2H4 (≥99.95%) under dry and humid conditions, respectively.

Prediction of Xe/Kr Separation in Metal-Organic Frameworks by a Precursor-Based Neural Network Synergistic with a Polarizable Adsorbate Model

Adsorption and separation of Xe/Kr are significant for making high-density nuclear energy environmentally friendly and for meeting the requirements of the gas industry. Enhancing the accuracy of the adsorbate model for describing the adsorption behaviors of Xe and Kr in MOFs and the efficiency of the model for predicting the separation potential (SP) value of Xe/Kr separation in MOFs helps in searching for promising MOFs for Xe/Kr adsorption and separation within a short time and at a low cost. In this work, polarizable and transferable models for mimic Xe and Kr adsorption behaviors in MOFs were constructed. Using these models, SP values of 38 MOFs at various temperatures and pressures were calculated. An optimal neural network model called BPNN-SP was designed to predict SP value based on physical parameters of metal center (electronegativity and radius) and organic linker (three-dimensional size and polarizability) combined with temperature and pressure. The regression coefficient value of the BPNN-SP model for each data set is higher than 0.995. MAE, MBE, and RMSE of BPNN-SP are only 0.331, -0.002, and 0.505 mmol/g, respectively. Finally, BPNN-SP was validated by experiment data from six MOFs. The transferable adsorbate model combined with the BPNN-SP model would highly improve the efficiency for designing MOFs with high performance for Xe/Kr adsorption and separation.

Computing Mixture Adsorption in Porous Materials through Flat Histogram Monte Carlo Methods

Mixture adsorption properties of porous materials are critical to determine their potential as adsorbents in separation applications. Toward the discovery of optimal adsorbents, in silico screening studies typically employ the grand canonical Monte Carlo (GCMC) technique to compute adsorption properties of gas mixtures in materials of interest at a given condition (i.e., composition, total pressure, and temperature) or to compute their adsorption properties for each component, followed by utilizing methods to predict mixture adsorption isotherms. However, the former approach results in the need for repeated calculations when different conditions such as compositions are considered. For the latter, the predictions may involve uncertainties, sometimes originating from the fitting quality to the pure component isotherms, and repeated simulations may also be needed for different temperatures. To this end, this study demonstrates the potential of flat histogram Monte Carlo methods in addressing the abovementioned shortfalls. Specifically, the so-called NVT + W method, first reported by Smit and co-workers, is extended herein to determine the macrostate probability distribution (MPD) of binary mixtures in porous materials. The obtained MPD can be reweighted to any conditions, yielding accurate adsorption isotherms of any desired compositions and temperatures. This approach, denoted as 2D NVT + W, is also compared with the widely adopted ideal adsorbed solution theory (IAST) method, and the former is found to offer more reliable predictions. Overall, the 2D NVT + W approach represents an efficient and effective alternative to compute mixture adsorption isotherms for porous materials, and the obtained MPD can be conveniently reused by peer researchers. A user-friendly Python code is also provided along with this article to employ this method.

Geometry and Chemistry: Influence of Pore Functionalization on Molecular Transport and Diffusion in Solvent-Filled Zirconium Metal-Organic Frameworks

Postsynthetic modification (PSM) of metal-organic frameworks (MOFs) enables incorporation of diverse functionalities in pores for chemical separations, drug delivery, and heterogeneous catalysis. However, the effect of PSM on molecular transport, which is essential for most applications of MOFs, has been rarely studied. In this paper, we used perfluoroalkane-functionalized Zr-MOF NU-1008 as a platform to systematically interrogate transport processes and mechanisms in solvated pores. We anchored perfluoroalkanes onto NU-1008 nodes by solvent-assisted ligand incorporation (SALI-n, with n = 3, 5, 7, and 9 denoting the number of fluorinated carbons). Transport of a luminescent molecule, BODIPY, through individual crystallites of four versions of methanol-filled SALI-n was monitored by confocal fluorescence microscopy as a function of time and location. In comparison with the parent NU-1008, the diffusivity of the probe molecules within SALI-n declined by 2- to 7-fold depending on chain length and loading, presumably due to the reduction in pore diameter or adsorptive interactions with perfluoroalkyl chains. Atomistic simulations were performed to uncover the microscopic behavior of the BODIPY diffusion in SALI-n. The perfluoroalkyl chains are observed to stay close to the pore walls, instead of extending toward the pore center. BODIPY molecules, which preferably interact with linkers, were pushed to the interior of the channels as the chain length increased, resulting in solvated diffusion and minor differences in the short-time mobility of BODIPY in SALI-n. This suggested that the observed decline of transport diffusivity in SALI-n mainly stemmed from the reduction in the pore size when these flexible chains are present. We anticipate that this proof of concept will assist in understanding how pore functionalization can physically and chemically affect mass transport in MOFs and will be useful in further guiding the design of PSM to realize the optimal performance of MOFs for various applications.

Does Mixed Linker-Induced Surface Heterogeneity Impact the Accuracy of IAST Predictions in UiO-66-NH2?

To move toward more energy-efficient adsorption-based processes, there is a need for accurate multicomponent data under realistic conditions. While the Ideal Adsorbed Solution Theory (IAST) has been established as the preferred prediction method due to its simplicity, limitations and inaccuracies for less ideal adsorption systems have been reported. Here, we use amine-functionalized derivatives of the UiO-66 structure to change the extent of homogeneity of the internal surface toward the adsorption of the two probe molecules carbon dioxide and ethylene. Although it might seem plausible that more functional groups lead to more heterogeneity and, thus, less accurate predictions by IAST, we find a mixed-linker system with increased heterogeneity in terms of added adsorption sites where IAST predictions and experimental loadings agree exceptionally well. We show that incorporating uncertainty analysis into predictions with IAST is important for assessing the accuracy of these predictions. Energetic investigations combined with Grand Canonical Monte Carlo simulations reveal almost homogeneous carbon dioxide but heterogeneous ethylene adsorption in the mixed-linker material, resulting in local, almost pure phases of the individual components.

Graph Transformer with Convolution Parallel Networks for Predicting Single and Binary Component Adsorption Performance of Metal-Organic Frameworks

Metal-organic frameworks (MOFs) are considered one of the most important materials for carbon capture and storage (CCS) due to the advantages of porosity, multifunction, diverse structure, and controllable chemical composition. With the continuous development of artificial intelligence (AI) technology, more and more machine learning models are used to identify MOFs with high performance within a massive search space. However, current works have yet to form a model that uses graph-structured data only, which can predict the adsorption properties of single and binary components. In this work, we proposed and developed a graph transformer, combined with convolution parallel networks, called GC-Trans. The model can accurately and efficiently predict the adsorption performance of MOFs under the single- and binary-component adsorption conditions using only the features of the crystal diagram as inputs. By extracting and fusing local and global feature information, the model has stronger expression and generalization abilities. Thus, we used it to screen the ARC-MOF database and analyze the MOF structures that meet the target requirements. Additionally, to demonstrate the transferability of the model, we applied transfer learning methods to predict the CO2/CH4 separations and CH4 uptake, both of which showed good predictive performance.

Metal-Organic Frameworks for Phthalate Capture

Indoor air contamination by phthalate ester (PAE) derivatives has become a significant concern since traces of PAEs can cause endocrine disruption, among other health issues. PAE abatement from the environment is thus mandatory to further ensure a good quality of indoor air. Herein, we explored the physisorption-based capture of volatile PAEs by metal-organic frameworks (MOFs). A high-throughput computational screening approach was first applied on databases compiling more than 20,000 MOF structures in order to identify the best MOFs for adsorbing traces of dimethyl phthalate (DMP), considered as a representative molecule of the family of PAE contaminants. Among the 20 top candidates, MOF-74(Ni), which combines substantial DMP uptake at the 10 ppm concentration level (∼0.20 g g-1) with high adsorption enthalpy at infinite dilution (-ΔHads(DMP),0 = 109.9 kJ mol-1), was revealed as an excellent porous material to capture airborne DMP. This prediction was validated by further experiments: gravimetric sorption isotherms were carried out on MOF-74(Ni), replacing DMP by dimethyl maleate (DMM), a molecule with a higher vapor pressure and indeed easier to manipulate compared to DMP while mimicking the adsorption behavior of DMP by MOFs, as evidenced by Monte Carlo calculations. Notably, saturation of DMM by MOF-74(Ni) (∼0.35 g g-1 at 343 K) occurs at very low equivalent concentration of the sorbate, i.e., 15 ppm, while half of the DMM molecules remain trapped in the MOF pores, even by heating the system up to 473 K under vacuum. This computational-experimental study reveals for the first time the potential of MOFs for the capture of phthalate ester contaminants as vapors of key importance to address indoor air quality issues.

Dynamical and electronic properties of anion-pillared metal-organic frameworks for natural gas separation

The increasing demand for natural gas as a clean energy source has emphasized the need for efficient gas separation technologies. Metal-organic frameworks (MOFs) have emerged as a promising class of materials for gas separation, with anion-pillared MOFs (APMOFs) gaining attention for their fine-tuned pore design and shape/size selectivity. In this study, we investigate the dynamical and electronic properties of three APMOFs, SIFSIX-3-Cu, SIFSIX-2-Cu-i, and SIFSIX-2-Cu, for the separation of methane from ethane, ethene, propane, propene, and N using computational simulations. Our simulations employ Grand Canonical Monte Carlo (GCMC) and Molecular Dynamics (MD) techniques combined with Density Functional Theory (DFT) calculations. We find that that all three APMOFs exhibit promising separation capabilities for methane from propane and propene based on both thermodynamics and kinetics parameters. In addition, we use Noncovalent Interaction (NCI) analysis to investigate intermolecular interactions and find that the fluorine atoms in the MOF can polarize gas molecules and establish electrostatic interactions with hydrogen atoms in the molecule. Finally, we show that SIFSIX-2-Cu-i is a potential candidate for separating N2/CH4 due to its interpenetration.

Noncovalent chemistry of xenon opens the door for anesthetic xenon recovery using Bio-MOFs

Designing an inexpensive and highly efficient recovery process for xenon (Xe) is gaining importance in the development of sustainable applications. Using metal organic frameworks (MOFs) for separating Xe from anesthetic gas mixtures has been a recent topic studied rarely and superficially in the literature. We theoretically investigated Xe recovery performances of 43 biological MOFs (Bio-MOFs) formed by biocompatible metal cations and biological endogenous linkers. Xe uptakes and Xe permeabilities in its binary mixtures with CO2, O2, and N2 were investigated by applying Grand Canonical Monte Carlo and Molecular Dynamics simulations. Materials with metalloporphyrin, hexacarboxylate, triazine, or pyrazole ligands, dimetallic paddlewheel units, relatively large pore sizes (PLD > 5 Å and LCD > 10 Å), large void fractions (≈0.8), and large surface areas (>2900 m2 g-1) have been determined as top performing Bio-MOFs for Xe recovery. By applying Density Functional Theory simulations and generating electron density difference maps, we determined that Xe-host interactions in the top performing Bio-MOFs are maximized mainly due to noncovalent interactions of Xe, such as charge-induced dipole and aerogen-π interactions. Polarized Xe atoms in the vicinity of cations/anions as well as π systems are fingerprints of enhanced guest-host interactions. Our results show examples of rarely studied aerogen interactions that play a critical role in selective adsorption of Xe in nanoporous materials.

Ammonia Adsorption Performance of Zeolitic Imidazolate Frameworks for Cooling

Promoting the cooling performance of adsorption chillers (ACs) greatly relies on the exploration of high-performance adsorbent/refrigerant working pairs. Ammonia is not only an environmentally friendly refrigerant but also favorable for heat and mass transfer in ACs owing to its large vapor pressure and enthalpy of evaporation. Zeolite imidazolate frameworks (ZIFs) with excellent ammonia stability are identified as a class of potential adsorbents for practical ammonia-based ACs. However, high-performing ZIF/ammonia working pairs with excellent AC performance are still to be developed. In this work, the cooling performance including the coefficient of performance for cooling (COPC) and the specific cooling effects (SCEs) of 26 ZIFs with the same composites but different topologies was evaluated by combining molecular simulation and mathematical modeling. Five high-performing ZIFs with COPC > 0.45 and SCE > 250 kJ/kg were identified, among which gis-ZIF with the highest COPC of 0.51 and lta-ZIF with the highest SCE of 354 kJ/kg both are promising to be synthesized and applied further. Besides, the quantitative structure-performance relationship (QSPR) was extracted that can help quickly identify and design high-performing ZIFs according to their ammonia adsorption isotherms and structural characteristics. Moreover, "S"-shaped adsorption isotherms with high saturation adsorption capacity (>0.2 g/g), suitable step position (0.2-0.4), and relatively low Henry's constant (<1 × 10-5 mol/(kg·Pa)) are more favorable for excellent COPC and SCE. From the perspective of structure characteristics, ZIFs possessing low crystal density (<0.9 g/cm3), high accessible surface area (>2000 m2/g), balanced largest cavity diameter (∼15 Å), and accessible pore volume (∼0.65 cm3/g) are beneficial for high-efficient cooling performance.

Connectivity Analysis of Adsorption Sites in Metal-Organic Frameworks for Facilitated Water Adsorption

Metal-organic frameworks (MOFs) have recently drawn considerable attention as promising adsorbents to harvest atmospheric water. To achieve an efficient harvesting process, seeking MOFs that demonstrate sharp condensation behavior is the key. Given that the clustering of water molecules in MOFs should be driven by not only MOF-water interactions but also water-water interactions, the spatial arrangement of water adsorption sites in a MOF is therefore crucial. Specifically, this study demonstrates the critical role of continuous adsorption channels (CACs) in MOFs. Such CACs will enable water molecules to stay in proximity and in a continuous manner, thus promoting the formation of hydrogen bonds and, consequently, the clustering of water molecules. We have developed an automatic algorithm to detect CACs based on the energy grid of host-guest interactions and applied the algorithm to more than 2000 diverse structures. The results show that more than 80% of the studied MOFs displaying water condensation at 298 K and 20% relative humidity predicted by Monte Carlo simulations indeed have CACs. The developments herein are anticipated to largely facilitate the future discovery of optimal adsorbents for water harvesting or water-adsorption-related applications in general. A Python-based code for detecting CACs in porous materials is also provided along with this article to employ this approach.

Augmented Reality for Enhanced Visualization of MOF Adsorbents

Augmented reality (AR) is an emerging technique used to improve visualization and comprehension of complex 3D materials. This approach has been applied not only in the field of chemistry but also in real estate, physics, mechanical engineering, and many other areas. Here, we demonstrate the workflow for an app-free AR technique for visualization of metal-organic frameworks (MOFs) and other porous materials to investigate their crystal structures, topology, and gas adsorption sites. We think this workflow will serve as an additional tool for computational and experimental scientists working in the field for both research and educational purposes.

Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors

Metal-Organic frameworks (MOFs) have been considered for various gas storage and separation applications. Theoretically, there are an infinite number of MOFs that can be created; however, a finite amount of resources are available to evaluate each one. Computational methods can be adapted to expedite the process of evaluation. In the context of CO2 capture, this paper investigates the method of screening MOFs using machine learning trained on molecular simulation data. New descriptors are introduced to aid this process. Using all descriptors, it is shown that machine learning can predict the CO2 adsorption, with an R2 of above 0.9. The introduced Effective Point Charge (EPoCh) descriptors, which assign values to frameworks' partial charges based on the expected CO2 uptake of an equivalent point charge in isolation, are shown to be the second most important group of descriptors, behind the Henry coefficient. Furthermore, the EPoCh descriptors are hundreds of thousands of times faster to obtain compared with the Henry coefficient, and they achieve similar results when identifying top candidates for CO2 capture using pseudo-classification predictions.

Silicone oils aided fabrication of paraffin wax coated super-hydrophobic sand: A spectroscopic study

To address the global alarm of desertification and boost plant progress in arid and desert environments, super-hydrophobic sand has been suggested and fabricated in numerous researches. In the present work, sand was hydrophobized by coating with a mixture of paraffin wax and silicone oils. The contact angle (CA) of sand with 4.5 w% silicone oils increased from 143.2° to 154.2° with decreasing the chain size of silicone oil, and the further addition of 13.5 w% of paraffin wax produced a super hydrophobic sand with a CA value up to 160° comparing to 154.2° without added paraffin wax. The Fourier Transform Infrared spectra suggested the development of inter molecular forces between silicone oil and sand as well as between paraffin and silicone oil, the driving force of which was the variation in viscosity of silicone oils. The later was higher in the case of lower molecular weight silicone oil. In particular, analyzing the characteristic bands of -(CH2)n-in paraffin wax, i.e. the corresponding bands at 720, 730, 1460 and 1470 cm-1 and the two bands at 1020 and 1095 cm-1 of silicone oil revealed that two roles of paraffin were taking place. While paraffin was placed between sand and silicone oil, it coated the sand particles when lower molecular weight silicone oil was used in the first procedures, whereas it coated the higher molecular weight silicone oil in the second procedures. Molecular dynamic calculation has been performed and confirmed the previous reached conclusions and showed that paraffin molecules were encapsulated in a silicone oil shell. The average adsorption energy of paraffin and silicon oil molecules on sand particles were 29.5 and 38.9 kcal mol-1 respectively.

Combination of High-Throughput Screening and Assembly to Discover Efficient Metal-Organic Frameworks on Kr/Xe Adsorption Separation

Recycling Kr and Xe from used nuclear fuel (UNF) is conducive to regenerating economy and protecting the environment, and it is urgent to screen or design high-performance cutting-edge metal-organic framework (MOF) materials for Kr/Xe adsorption separation. After grand canonical Monte Carlo (GCMC) simulations of Kr/Xe adsorption separation on 11,000 frameworks in CoRE MOFs (2019), the important structure-adsorption property relationship (SAPR) was induced; that is, the porosity (φ) at 0.30-0.40, LCD/PLD at 1.00-1.49, density (ρ) range between 1.20 and 2.30 g/cm3, and PLD at 2.40-3.38 Å can be utilized to screen for high-performance G-MOFs and hMOFs. In addition, the key "genes" (metal nodes and linkers) of MOFs determining the Kr/Xe adsorption separation were data-mined by a machine learning technique, which were assembled into novel MOFs. After comprehensive consideration of thermal stability and the adsorbent performance score (APS), eight promising MOFs on Kr/Xe separation with the APS more than 1290.89 were screened out and assembled, which are better than most of the reported frameworks. Note that the adsorption isotherms of these MOFs on Kr and Xe belong to type I curve with the thermodynamic equilibrium mechanism on Kr/Xe based on the confinement effect. Furthermore, according to the electronic structure calculations of the independent gradient model based on Hirshfeld partition (IGMH) and energy decomposition analysis, it is found that the interactions between guests and frameworks are vdW forces with dominant induction energy (Eind). In addition, the electrostatic potential gradients of frameworks are generally linearly negative correlated with Kr uptakes. Therefore, both the geometrical and electronic structures dominate the adsorption separation performance on Kr/Xe. Interestingly, these eight MOFs are also suitable for the separation of CH4/H2 with considerable selectivities and CH4 uptakes of up to 2566.67 and 3.04 mmol/g, respectively. Herein, the accurately constructed SAPR and material genomics strategy should be helpful for the experimental discovery of novel MOFs on Kr/Xe separation experimentally.

Efficient Exploration of Adsorption Space for Separations in Metal-Organic Frameworks Combining the Use of Molecular Simulations, Machine Learning, and Ideal Adsorbed Solution Theory

Adsorption-based separations using metal-organic frameworks (MOFs) are promising candidates for replacing common energy-intensive separation processes. The so-called adsorption space formed by the combination of billions of possible molecules and thousands of reported MOFs is vast. It is very challenging to comprehensively evaluate the performance of MOFs for chemical separation through experiments. Molecular simulations and machine learning (ML) have been widely applied to make predictions for adsorption-based separations. Previous ML approaches to these issues were typically limited to smaller molecules and often had poor accuracy in the dilute limit. To enable exploration of a wider adsorption space, we carefully selected a diverse set of 45 molecules and 335 MOFs and generated single-component isotherms of 15,075 MOF-molecule pairs by grand canonical Monte Carlo. Using this database, we successfully developed accurate (r2 > 0.9) machine learning models predicting adsorption isotherms of diverse molecules in large libraries of MOFs. With this approach, we can efficiently make predictions of large collections of MOFs for arbitrary mixture separations. By combining molecular simulation data and ML predictions with Ideal Adsorbed Solution Theory, we tested the ability of these approaches to make predictions of adsorption selectivity and loading for challenging near-azeotropic mixtures.

Methyl-Functionalized Al-Based MOF ZJU-620(Al): A Potential Physisorbent for Carbon Dioxide Capture

Developing Al-based metal-organic frameworks (MOFs) with moisture-resistance ability is a challenge for carbon dioxide (CO2) capture. Methyl-functionalized Al-based MOF ZJU-620(Al), with excellent chemical-thermal stability and specific surface area of 1347 m2/g, observed here, is a potential adsorbent for CO2 capture with good recyclability and large capacity up to 4.25 mmol/g at 298 K and 1 atm. CO2 molecules are largely trapped on two types of sites. One (site I) is near the AlO6 clusters, and another (site II) is between two parallel benzene rings with a distance of 6.64 Å. ZJU-620(Al) can be used for CO2/N2 (15/85) separation with the excellent selectivity up to 107.20 at 273 K and 31.93 at 298 K, and the separation factor of 13.68. It is also with excellent moisture-resistance ability due to 5% breakthrough time (outlet concentration reached the 5% of inlet concentration) without reduction at 80% relative humidity than under dry conditions. Water molecules occupy a small amount of CO2 adsorption site I, but they almost do not occupy the CO2 adsorption site II due to hydrophobic methyl-functional ligands. Moreover, CO2 can be adsorbed on the ZJU-620(Al) surface through C═O···H binding of water molecules with high affinity. Thus, ZJU-620(Al) is a candidate adsorbent for CO2 capture and separation especially under humidity conditions.

Adsorptive separation of CH4, H2, CO2, and N2 using fullerene pillared graphene nanocomposites: Insights from molecular simulations

CONTEXT

The adsorptive separation performances of fullerene pillared graphene nanocomposites (FPGNs) with tunable micro and meso porous morphology are investigated for the binary mixtures of CH4, H2, CO2 and N2 by using grand canonical Monte Carlo (GCMC) simulations. Different fullerene types are considered in designs as pillar to investigate the effects of porosity on the gas separation performances of FPGNs, and the GCMC simulations are performed for an equimolar binary mixture of CO2/H2, CO2/CH4, CO2/N2 and CH4/H2 inspired by industrial gas mixtures. It is found that CO2/N2, CO2/H2 and CH4/H2 selectivity of FPGNs are about 72, 410 and 145 at 298 K and 1 bar, which are higher than those for several adsorbent materials reported.

METHODS

Five different FPGN models which contain covalently bonded periodical fullerene and graphene units were constructed using C60, C180, C320, C540 and C720 fullerenes, followed by geometry optimization using Open Babel. All GCMC simulations of adsorption were performed in the RASPA. The adsorption isotherms of FPGNs for pure gases are comparatively examined, and their performances are discussed based on the pore structure and isosteric heat of adsorption. Then, the separation factors of FPGNs for equimolar binary mixtures of these gases are elucidated from the difference in the heat of adsorption and the adsorption selectivity.

In silico screening of nanoporous materials for urea removal in hemodialysis applications

The design of miniaturized hemodialysis devices, such as wearable artificial kidneys, requires regeneration of the dialysate stream to remove uremic toxins from water. Adsorption has the potential to capture such molecules, but conventional adsorbents have low urea/water selectivity. In this work, we performed a comprehensive computational study of 560 porous crystalline adsorbents comprising mainly covalent organic frameworks (COFs), as well as some siliceous zeolites, metal organic frameworks (MOFs) and graphitic materials. An initial screening using Widom insertion method assessed the excess chemical potential at infinite dilution for water and urea at 310 K, providing information on the strength and selectivity of urea adsorption. From such analysis it was observed that urea adsorption and urea/water selectivity increased strongly with fluorine content in COFs, while other compositional or structural parameters did not correlate with material performance. Two COFs, namely COF-F6 and Tf-DHzDPr were explored further through Molecular Dynamics simulations. The results agree with those of the Widom method and allow to identify the urea binding sites, the contribution of electrostatic and van der Waals interactions, and the position of preferential urea-urea and urea-framework interactions. This study paves the way for a well-informed experimental campaign and accelerates the development of novel sorbents for urea removal, ultimately advancing on the path to achieve wearable artificial kidneys.

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

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

as a Versatile and Predictable Chiral Stationary Phase for the Resolution of Racemic Mixtures

Metal-organic frameworks (MOFs) have become promising materials for multiple applications due to their controlled dimensionality and tunable properties. The incorporation of chirality into their frameworks opens new strategies for chiral separation, a key technology in the pharmaceutical industry as each enantiomer of a racemic drug must be isolated. Here, we describe the use of a combination of computational modeling and experiments to demonstrate that high-performance liquid chromatography (HPLC) columns packed with TAMOF-1 as the chiral stationary phase are efficient, versatile, robust, and reusable with a wide array of mobile phases (polar and non-polar). As proof of concept, in this article, we report the resolution with TAMOF-1 HPLC columns of nine racemic mixtures with different molecular sizes, geometries, and functional groups. Initial in silico studies allowed us to predict plausible separations in chiral compounds from different families, including terpenes, calcium channel blockers, or P-stereogenic compounds. The experimental data confirmed the validity of the models and the robust performance of TAMOF-1 columns. The added value of in silico screening is an unprecedented achievement in chiral chromatography.

Framework-Topology-Controlled Singlet Fission in Metal-Organic Frameworks

Singlet fission (SF) has been explored as a viable route to improve photovoltaic performance by producing more excitons. Efficient SF is achieved through a high degree of interchromophoric coupling that facilitates electron superexchange to generate triplet pairs. However, strongly coupled chromophores often form excimers that can serve as an SF intermediate or a low-energy trap site. The succeeding decoherence process, however, requires an optimum electronic coupling to facilitate the isolation of triplet production from the initially prepared correlated triplet pair. Conformational flexibility and dielectric modulation can provide a means to tune the SF mechanism and efficiency by modulating the interchromophoric electronic interaction. Such a strategy cannot be easily adopted in densely stacked traditional organic solids. Here, we show that the assembly of the SF-active chromophores around well-defined pores of solution-stable metal-organic frameworks (MOFs) can be a great platform for a modular SF process. A series of three new MOFs, built out from 9,10-bis(ethynylenephenyl)anthracene-derived struts, show a topology-defined packing density and conformational flexibility of the anthracene core to dictate the SF mechanism. Various steady-state and transient spectroscopic data suggest that the initially prepared singlet population can prefer either an excimer-mediated SF or a direct SF (both through a virtual charge-transfer (CT) state). These solution-stable frameworks offer the tunability of the dielectric environment to facilitate the SF process by stabilizing the CT state. Given that MOFs are a great platform for various photophysical and photochemical developments, generating a large population of long-lived triplets can expand their utilities in various photon energy conversion schemes.

Adsorption-Induced Deformation of Zeolites 4A and 13X: Experimental and Molecular Simulation Study

Gas adsorption in zeolites leads to adsorption-induced deformation, which can significantly affect the adsorption and diffusive properties of the system. In this study, we conducted both experimental investigations and molecular simulations to understand the deformation of zeolites 13X and 4A during carbon dioxide adsorption at 273 K. To measure the sample's adsorption isotherm and strain simultaneously, we used a commercial sorption instrument with a custom-made sample holder equipped with a dilatometer. Our experimental data showed that while the zeolites 13X and 4A exhibited similar adsorption isotherms, their strain isotherms differed significantly. To gain more insight into the adsorption process and adsorption-induced deformation of these zeolites, we employed coupled Monte Carlo and molecular dynamics simulations with atomistically detailed models of the frameworks. Our modeling results were consistent with the experimental data and helped us identify the reasons behind the different deformation behaviors of the considered structures. Our study also revealed the sensitivity of the strain isotherm of zeolites to pore size and other structural and energetic features, suggesting that measuring adsorption-induced deformation could serve as a complementary method for material characterization and provide guidelines for related technical applications.

Quantitative Simulations of Siloxane Adsorption in Metal-Organic Frameworks

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

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

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

Coordination Unsaturation and Basic Site-Immobilized Nanochannel in a Chemorobust MOF for 3-Fold-Increased High-Temperature Selectivity and Fixation of CO2 under Mild Conditions with Nanomolar Recognition of Roxarsone

A multifaceted metal-organic framework (MOF) with task-specific site-engineered pores can promise high-temperature and moisture-tolerant capture and non-redox fixation of CO2 under mild conditions as well as ultrasensitive detection of carcinogenic contaminants in water. Herein, we report a pillar-bilayered MOF that holds a nanochannel with contrasting functionalities for both these sustainable applications with improved performance characteristics. The twofold entangled robust framework exhibits CO2 adsorption at elevated temperatures with considerable MOF-gas interaction. Interestingly, CO2 selectivity unveils nearly a 3-fold improvement upon the rise of temperature, affording a CO2/N2 value of 820 at 313 K, which outperforms many porous adsorbents. Additionally, breakthrough simulation establishes complete separation and attests the potential of this MOF in the separation of flue gas mixture. Importantly, minor CO2 loss during multiple capture-release cycles and under a relative humidity of 75% promise practical usability of the material. Density functional theory (DFT) not only portrays the atomistic level snapshots of temperature-triggered CO2 inclusion inside this microporous vessel alongside the role of diverse CO2-philic sites but also validates the basis of N2-phobicity of an azo-functionalized linker on such increased selectivity. The guest-free MOF further demonstrates non-redox and recyclable CO2 fixation with wide epoxide tolerance under solvent-free mild conditions and even works at atmospheric pressure and room temperature. The crucial roles of high-density acid-base sites in both adsorption and catalysis are supported by control experiments and by comparing the activity of an unfunctionalized MOF. The hydrolytic stability and strong luminescence signature benefit the framework in aqueous-phase selective and fast responsive detection of detrimental roxarsone (ROX) with high quenching (7.56 × 104 M-1) and very low sensitivity (68 nM). Apart from varying degrees of an energy-transfer mechanism, the fluorosensing of ROX is comprehensively supported by in-depth DFT studies that manifest alteration of MOF energy levels in the presence of organoarsenic compounds and depict MOF-analyte supramolecular interactions.

Soft corrugated channel with synergistic exclusive discrimination gating for CO2 recognition in gas mixture

Developing artificial porous systems with high molecular recognition performance is critical but very challenging to achieve selective uptake of a particular component from a mixture of many similar species, regardless of the size and affinity of these competing species. A porous platform that integrates multiple recognition mechanisms working cooperatively for highly efficient guest identification is desired. Here, we designed a flexible porous coordination polymer (PCP) and realised a corrugated channel system that cooperatively responds to only target gas molecules by taking advantage of its stereochemical shape, location of binding sites, and structural softness. The binding sites and structural deformation act synergistically, exhibiting exclusive discrimination gating (EDG) effect for selective gate-opening adsorption of CO₂ over nine similar gas molecules, including N₂, CH₄, CO, O₂, H₂, Ar, C₂H₆, and even higher-affinity gases such as C₂H₂ and C₂H₄. Combining in-situ crystallographic experiments with theoretical studies, it is clear that this unparalleled ability to decipher the CO₂ molecule is achieved through the coordination of framework dynamics, guest diffusion, and interaction energetics. Furthermore, the gas co-adsorption and breakthrough separation performance render the obtained PCP an efficient adsorbent for CO₂ capture from various gas mixtures.

Advancing CH4/H2 separation with covalent organic frameworks by combining molecular simulations and machine learning

A high-throughput computational screening approach combined with machine learning (ML) was introduced to unlock the potential of both synthesized and hypothetical COFs (hypoCOFs) for adsorption-based CH₄/H₂ separation. We studied 597 synthesized COFs for adsorption of a CH₄/H₂ mixture using Grand Canonical Monte Carlo (GCMC) simulations under pressure-swing adsorption (PSA) and vacuum-swing adsorption (VSA) conditions. Based on the simulation results, the CH₄/H₂ selectivities, CH₄ working capacities, adsorbent performance scores, and regenerabilities of the synthesized COFs were assessed and the structural properties of the top-performing COFs were identified. The hypoCOF database composed of 69 840 materials was then filtered to identify 7737 hypothetical materials having similar structural properties to the top synthesized COFs. These hypothetical COFs were then examined for CH₄/H₂ separation using molecular simulations and the results showed that the top hypoCOFs have CH₄ selectivities and working capacities in the ranges of 21.9-28.7 (64.7-128.6) and 5.8-7.6 (1.3-3.1) mol kg^-1 under PSA (VSA) conditions, respectively, outperforming the synthesized COFs and metal-organic frameworks (MOFs). ML models were then developed based on the hypoCOF simulation results to accurately predict the CH₄/H₂ mixture adsorption properties of all remaining hypothetical materials when their structural and chemical properties are fed into the models. These models accurately assessed the CH₄/H₂ mixture separation performances of any hypoCOF within seconds without performing computationally demanding molecular simulations. The computational approach that we have proposed in this study will provide an accurate and efficient assessment of COF materials for CH₄/H₂ separation and significantly accelerate the experimental efforts towards the design and discovery of new high-performing COF adsorbents.

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

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

Water-Accelerated Transport: Vapor-Phase Nerve Agent Simulant Delivery within a Catalytic Zirconium Metal-Organic Framework as a Function of Relative Humidity

Zirconium-based metal-organic frameworks (MOFs) are candidate materials for effective nerve agent detoxification due to their thermo- and water stability as well as high density of catalytic Zr sites. However, as high-porosity materials, most of the active sites of Zr-MOFs can only be accessed by diffusion into the crystal interior. Therefore, the transport of nerve agents in nanopores is an important factor in the catalytic performance of Zr-MOFs. Here, we investigated the transport process and mechanism of a vapor-phase nerve agent simulant, dimethyl methyl phosphonate (DMMP), through a representative Zr-MOF, NU-1008, under practical conditions of varying humidity. Confocal Raman microscopy was used to monitor the transport of DMMP vapor through individual NU-1008 crystallites, where the relative humidity (RH) of the environment was tuned to understand the impact of water. Counterintuitively, water in the MOF channels, instead of blocking DMMP transport, assists DMMP diffusion; indeed, the transport diffusivity (D_t) of DMMP in NU-1008 is one order of magnitude higher at 70% than 0% RH. To understand the mechanism, magic angle spinning NMR and molecular dynamics simulations were performed and suggested that high water content in the channels prevents DMMP from hydrogen-bonding with the nodes, allowing for faster diffusion of DMMP in the channels. The simulated self-diffusivity (D_s) of DMMP is observed to be concentration-dependent. At low loading of DMMP, D_s is higher at 70% RH than 0% RH, while at high loadings the trend reverses due to the DMMP aggregation in water and the reduction of free volume in channels.