Accurate Characterization of the Pore Volume in Microporous Crystalline Materials

Pushing the Limits of Heat Conduction in Covalent Organic Frameworks Through High-Throughput Screening of Their Thermal Conductivity

Tailor-made materials featuring large tunability in their thermal transport properties are highly sought-after for diverse applications. However, achieving `user-defined' thermal transport in a single class of material system with tunability across a wide range of thermal conductivity values requires a thorough understanding of the structure-property relationships, which has proven to be challenging. Herein, large-scale computational screening of covalent organic frameworks (COFs) for thermal conductivity is performed, providing a comprehensive understanding of their structure-property relationships by leveraging systematic atomistic simulations of 10,750 COFs with 651 distinct organic linkers. Through the data-driven approach, it is shown that by strategic modulation of their chemical and structural features, the thermal conductivity can be tuned from ultralow (≈0.02 W m-1 K-1) to exceptionally high (≈50 W m-1 K-1) values. It is revealed that achieving high thermal conductivity in COFs requires their assembly through carbon-carbon linkages with densities greater than 500 kg m-3, nominal void fractions (in the range of ≈0.6-0.9) and highly aligned polymeric chains along the heat flow direction. Following these criteria, it is shown that these flexible polymeric materials can possess exceptionally high thermal conductivities, on par with several fully dense inorganic materials. As such, the work reveals that COFs mark a new regime of materials design that combines high thermal conductivities with low densities.

Artificial Intelligence and High-Throughput Computational Workflows Empowering the Fast Screening of Metal-Organic Frameworks for Hydrogen Storage

Metal-organic frameworks (MOFs) are one of the most promising hydrogen-storing materials due to their rich specific surface area, adjustable topological and pore structures, and modified functional groups. In this work, we developed automatically parallel computational workflows for high-throughput screening of ∼11,600 MOFs from the CoRE database and discovered 69 top-performing MOF candidates with work capacity greater than 1.00 wt % at 298.5 K and a pressure swing between 100 and 0.1 bar, which is at least twice that of MOF-5. In particular, ZITRUP, OQFAJ01, WANHOL, and VATYIZ showed excellent hydrogen storage performance of 4.48, 3.16, 2.19, and 2.16 wt %. We specifically analyzed the relationship between pore-limiting diameter, largest cavity diameter, void fraction, open metal sites, metal elements or nonmetallic atomic elements, and deliverable capacity and found that not only geometrical and physical features of crystalline but also chemical properties of adsorbate sites determined the H2 storage capacity of MOFs at room temperature. It is highlighted that we first proposed the modified crystal graph convolutional neural networks by incorporating the obtained geometrical and physical features into the convolutional high-dimensional feature vectors of period crystal structures for predicting H2 storage performance, which can improve the prediction accuracy of the neural network from the former mean absolute error (MAE) of 0.064 wt % to the current MAE of 0.047 wt % and shorten the consuming time to about 10-4 times of high-throughput computational screening. This work opens a new avenue toward high-throughput screening of MOFs for H2 adsorption capacity, which can be extended for the screening and discovery of other functional materials.

Adsorption in Pyrene-Based Metal-Organic Frameworks: The Role of Pore Structure and Topology

Pore topology and chemistry play crucial roles in the adsorption characteristics of metal-organic frameworks (MOFs). To deepen our understanding of the interactions between MOFs and CO2 during this process, we systematically investigate the adsorption properties of a group of pyrene-based MOFs. These MOFs feature Zn(II) as the metal ion and employ a pyrene-based ligand, specifically 1,3,6,8-tetrakis(p-benzoic acid)pyrene (TBAPy). Including different additional ligands leads to frameworks with distinctive structural and chemical features. By comparing these structures, we could isolate the role that pore size, the presence of open-metal sites (OMS), metal-oxygen bridges, and framework charges play in the CO2 adsorption of these MOFs. Frameworks with constricted pore structures display a phenomenon known as the confinement effect, fostering stronger MOF-CO2 interactions and higher uptakes at low pressures. In contrast, entropic effects dominate at elevated pressures, and the MOF's pore volume becomes the driving factor. Through analysis of the CO2 uptakes of the benchmark materials ─some with narrower pores and others with larger pore volumes─it becomes evident that structures with narrower pores and high binding energies excel at low pressures. In contrast, those with larger volumes perform better at elevated pressures. Moreover, this research highlights that open-metal sites and inherent charges within the frameworks of ionic MOFs stand out as CO2-philic characteristics.

Facile fabrication of recyclable robust noncovalent porous crystals from low-symmetry helicene derivative

Porous frameworks constructed via noncovalent interactions show wide potential in molecular separation and gas adsorption. However, it remains a major challenge to prepare these materials from low-symmetry molecular building blocks. Herein, we report a facile strategy to fabricate noncovalent porous crystals through modular self-assembly of a low-symmetry helicene racemate. The P and M enantiomers in the racemate first stack into right- and left-handed triangular prisms, respectively, and subsequently the two types of prisms alternatively stack together into a hexagonal network with one-dimensional channels with a diameter of 14.5 Å. Remarkably, the framework reveals high stability upon heating to 275 °C, majorly due to the abundant π-interactions between the complementarily engaged helicene building blocks. Such porous framework can be readily prepared by fast rotary evaporation, and is easy to recycle and repeatedly reform. The refined porous structure and enriched π-conjugation also favor the selective adsorption of a series of small molecules.

Mild-Temperature Supercritical Water Confined in Hydrophobic Metal-Organic Frameworks

Fluids under extreme confinement show characteristics significantly different from those of their bulk counterpart. This work focuses on water confined within the complex cavities of highly hydrophobic metal-organic frameworks (MOFs) at high pressures. A combination of high-pressure intrusion-extrusion experiments with molecular dynamic simulations and synchrotron data reveals that supercritical transition for MOF-confined water takes place at a much lower temperature than in bulk water, ∼250 K below the reference values. This large shifting of the critical temperature (Tc) is attributed to the very large density of confined water vapor in the peculiar geometry and chemistry of the cavities of Cu2tebpz (tebpz = 3,3',5,5'-tetraethyl-4,4'-bipyrazolate) hydrophobic MOF. This is the first time the shift of Tc is investigated for water confined within highly hydrophobic nanoporous materials, which explains why such a large reduction of the critical temperature was never reported before, neither experimentally nor computationally.

Sorption of Soil Carbon Dioxide by Biochar and Engineered Porous Carbons

CO2 is 45 to 50 times more concentrated in soil than in air, resulting in global diffusive fluxes that outpace fossil fuel combustion by an order of magnitude. Despite the scale of soil CO2 emissions, soil-based climate change mitigation strategies are underdeveloped. Existing approaches, such as enhanced weathering and sustainable land management, show promise but continue to face deployment barriers. We introduce an alternative approach: the use of solid adsorbents to directly capture CO2 in soils. Biomass-derived adsorbents could exploit favorable soil CO2 adsorption thermodynamics while also sequestering solid carbon. Despite this potential, previous study of porous carbon CO2 adsorption is mostly limited to single-component measurements and conditions irrelevant to soil. Here, we probe sorption under simplified soil conditions (0.2 to 3% CO2 in balance air at ambient temperature and pressure) and provide physical and chemical characterization data to correlate material properties to sorption performance. We show that minimally engineered pyrogenic carbons exhibit CO2 sorption capacities comparable to or greater than those of advanced sorbent materials. Compared to textural features, sorbent carbon bond morphology substantially influences low-pressure CO2 adsorption. Our findings enhance understanding of gas adsorption on porous carbons and inform the development of effective soil-based climate change mitigation approaches.

Understanding the effect of density functional choice and van der Waals treatment on predicting the binding configuration, loading, and stability of amine-grafted metal organic frameworks

Metal organic frameworks (MOFs) are crystalline, three-dimensional structures with high surface areas and tunable porosities. Made from metal nodes connected by organic linkers, the exact properties of a given MOF are determined by node and linker choice. MOFs hold promise for numerous applications, including gas capture and storage. M2(4,4'-dioxidobiphenyl-3,3'-dicarboxylate)-henceforth simply M2(dobpdc), with M = Mg, Mn, Fe, Co, Ni, Cu, or Zn-is regarded as one of the most promising structures for CO2 capture applications. Further modification of the MOF with diamines or tetramines can significantly boost gas species selectivity, a necessity for the ultra-dilute CO2 concentrations in the direct-air capture of CO2. There are countless potential diamines and tetramines, paving the way for a vast number of potential sorbents to be probed for CO2 adsorption properties. The number of amines and their configuration in the MOF pore are key drivers of CO2 adsorption capacity and kinetics, and so a validation of computational prediction of these quantities is required to suitably use computational methods in the discovery and screening of amine-functionalized sorbents. In this work, we study the predictive accuracy of density functional theory and related calculations on amine loading and configuration for one diamine and two tetramines. In particular, we explore the Perdew-Burke-Ernzerhof (PBE) functional and its formulation for solids (PBEsol) with and without the Grimme-D2 and Grimme-D3 pairwise corrections (PBE+D2/3 and PBEsol+D2/3), two revised PBE functionals with the Grimme-D2 and Grimme-D3 pairwise corrections (RPBE+D2/3 and revPBE+D2/3), and the nonlocal van der Waals correlation (vdW-DF2) functional. We also investigate a universal graph deep learning interatomic potential's (M3GNet) predictive accuracy for loading and configuration. These results allow us to identify a useful screening procedure for configuration prediction that has a coarse component for quick evaluation and a higher accuracy component for detailed analysis. Our general observation is that the neural network-based potential can be used as a high-level and rapid screening tool, whereas PBEsol+D3 gives a completely qualitatively predictive picture across all systems studied, and can thus be used for high accuracy motif predictions. We close by briefly exploring the predictions of relative thermal stability for the different functionals and dispersion corrections.

pyCOFBuilder: A Python Package for Automated Creation of Covalent Organic Framework Models Based on the Reticular Approach

Covalent organic frameworks (COFs) have gained significant popularity in recent years due to their unique ability to provide a high surface area and customizable pore geometry and chemistry, making them an ideal choice for a wide range of applications. However, exploring COFs experimentally can be arduous and time-consuming due to their immense number of potential structures. As a result, computational high-throughput studies have become an attractive option. Nevertheless, generating COF structures can also be a challenging and time-consuming task. To address this challenge, here, we introduce the pyCOFBuilder, an open-source Python package designed to facilitate the generation of COF structures for computational studies. The pyCOFBuilder software provides an easy-to-use set of functionalities to generate COF structures following the reticular approach. In this paper, we describe the implementation, main features, and capabilities of the pyCOFBuilder, demonstrating its utility for generating COF structures with varying topologies and chemical properties. pyCOFBuilder is freely available on GitHub at https://github.com/lipelopesoliveira/pyCOFBuilder.

A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks

Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks (MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data. Furthermore, these methods are typically designed for single tasks, such as predicting gas adsorption capacity under specific conditions, which restricts the utilization of comprehensive datasets including all adsorption capacities. To address these challenges, we propose Uni-MOF, an innovative framework for large-scale, three-dimensional MOF representation learning, designed for multi-purpose gas prediction. Specifically, Uni-MOF serves as a versatile gas adsorption estimator for MOF materials, employing pure three-dimensional representations learned from over 631,000 collected MOF and COF structures. Our experimental results show that Uni-MOF can automatically extract structural representations and predict adsorption capacities under various operating conditions using a single model. For simulated data, Uni-MOF exhibits remarkably high predictive accuracy across all datasets. Additionally, the values predicted by Uni-MOF correspond with the outcomes of adsorption experiments. Furthermore, Uni-MOF demonstrates considerable potential for broad applicability in predicting a wide array of other properties.

Insight into the Isoreticularity of Li-MOFs for the Design of Low-Density Solid and Quasi-Solid Electrolytes

Isoreticularity in metal organic frameworks (MOFs) allows the design of the framework structure and tailoring the pore aperture at the molecular level. The optimal pore volume, long-range order of framework expansion, and crystallite size (grain size) could enable improving Li-ion conduction, thereby providing a unique opportunity to design high-performance solid and quasi-solid electrolytes. However, definitive understanding of the pore aperture, framework expansion, and crystallite size on the Li-ion conduction and its mechanism in MOFs remains at the exploratory stage. Among the different MOF subfamilies, Li-MOFs created by the isoreticular framework expansion using dicarboxylates of benzene, naphthalene, and biphenyl building blocks emerge as low-density porous solids with exceptional thermal stability to study the solid-state Li+ transport mechanisms. Herein, we report the subtle effect of the isoreticularity in Li-MOFs on the performance of solid and quasi-solid-state Li+ conduction, providing new insight into Li+ transport mechanisms in MOFs for the first time. Our experimental and computational results show that the reticular design on an isostructural extended framework structure with the optimal pore aperture and crystallite size can influence the Li+ conductivity, exhibiting comparable ionic conductivities to solid polymer electrolytes at room temperature. Aligning with the computational studies, our experimental absorption spectral traces of solid electrolytes prepared by encapsulating lithium salt (LiClO4) and the plasticizer (ethylene carbonate) with Li-MOFs confirm the participation of the free and bound states of Li+ in a pore filling-driven ion conduction mechanism. We postulate that porous channels of Li-MOFs aid free Li+ to move through the pores via a vehicle-type mechanism, in which the pore-filled plasticizer acts as a carrier for mobile Li+ while the framework's functional sites transport the bound state of Li+ via an ion hopping mechanism from one crystallite site to another. Our computational studies performed on the Li+ conduction pathway validated the postulated pore filling mechanism and confirmed the involvement of bridging complexes, formed by binding Li+ onto the framework's functional sites as well as to the pore-filled ethylene carbonates. The Li+ diffusion energy barrier profiles along with the respective conformational changes during the diffusion of Li+ in solid electrolytes prepared from Li-BDC MOF and Li-NDC MOF strongly support the cooperative movement of Li+ ions via ion hopping along the framework's edges and vehicle-type transfer, involving the pore-filled plasticizer. Our findings suggest that cooperative function of the optimal pore volume, framework expansion, and crystallite size play a unique role in Li-ion conduction, thereby providing design guidelines for the low-density solid and quasi-solid electrolytes.

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.

Atomic-Scale Elucidation of Unusually Distorted Dimeric Complexes Confined in a Zr-Based Metal-Organic Framework

Nanoconfinement in metal-organic framework (MOF) pores can lead to the isolation of unusual or reactive metal complexes. However, MOFs that support the stabilization and precise structural elucidation of metal complexes and small metal clusters are rare. Here, we report a thermally and chemically stable zirconium-based MOF (University of Adelaide Material-1001, UAM-1001) with a high density of free bis-pyrazolyl units that can confine mono- and dinuclear metal complexes. The precursor MOF, UAM-1000, has a high degree of structural flexibility, but post synthetic modification with a bracing linker, biphenyl-4,4'-dicarboxylic acid, partially rigidifies the MOF (UAM-1001). This allows "matrix isolation" and detailed structural elucidation of postsynthetically added dimeric complexes bound within a tetradentate binding site formed by two linkers. Dimeric species [Co2Cl4], [Cu2Cl4], [Ni2Cl3(H2O)2]Cl, and [Rh2(CO)3Cl2] were successfully isolated in UAM-1001 and characterized by single-crystal X-ray diffraction. Comparison of the UAM-1001 isolated species with similar complexes in the solid state reveals that UAM-1001 can significantly distort the structures and enforce notably shorter metal-metal distances. For example, MOF tethering allows isolation of a [Cu2Cl4] complex that rapidly reacts with water in the solid state. The stability, porosity, and modulated flexibility of UAM-1001 provide an ideal platform material for the isolation and study of new dimeric complexes and their reactivity.

In Situ Neutron Diffraction of Zn-MOF-74 Reveals Nanoconfinement-Induced Effects on Adsorbed Propene

Even though confinement was identified as a common element of selective catalysis and simulations predicted enhanced properties of adsorbates within microporous materials, experimental results on the characterization of the adsorbed phase are still rare. In this study, we provide experimental evidence of the increase of propene density in the channels of Zn-MOF-74 by 16(2)% compared to the liquid phase. The ordered propene molecules adsorbed within the pores of the MOF have been localized by in situ neutron powder diffraction, and the results are supported by adsorption studies. The formation of a second adsorbate layer, paired with nanoconfinement-induced short intermolecular distances, causes the efficient packing of the propene molecules and results in an increase of olefin density.

A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture

To rank the performance of materials for a given carbon capture process, we rely on pure component isotherms from which we predict the mixture isotherms. For screening a large number of materials, we also increasingly rely on isotherms predicted from molecular simulations. In particular, for such screening studies, it is important that the procedures to generate the data are accurate, reliable, and robust. In this work, we develop an efficient and automated workflow for a meticulous sampling of pure component isotherms. The workflow was tested on a set of metal-organic frameworks (MOFs) and proved to be reliable given different guest molecules. We show that the coupling of our workflow with the Clausius-Clapeyron relation saves CPU time, yet enables us to accurately predict pure component isotherms at the temperatures of interest, starting from a reference isotherm at a given temperature. We also show that one can accurately predict the CO₂ and N₂ mixture isotherms using ideal adsorbed solution theory (IAST). In particular, we show that IAST is a more reliable numerical tool to predict binary adsorption uptakes for a range of pressures, temperatures, and compositions, as it does not rely on the fitting of experimental data, which typically needs to be done with analytical models such as dual-site Langmuir (DSL). This makes IAST a more suitable and general technique to bridge the gap between adsorption (raw) data and process modeling. To demonstrate this point, we show that the ranking of materials, for a standard three-step temperature swing adsorption (TSA) process, can be significantly different depending on the thermodynamic method used to predict binary adsorption data. We show that, for the design of processes that capture CO₂ from low concentration (0.4%) streams, the commonly used methodology to predict mixture isotherms incorrectly assigns up to 33% of the materials as top-performing.

Revisiting MMPBSA by Adoption of MC-Based Surface Area/Volume, ANI-ML Potentials, and Two-Valued Interior Dielectric Constant

Here, we report the accuracy improvements of molecular mechanics Poisson-Boltzmann surface area (MMPBSA) calculations by adoption of ANI-ML potentials in replacement of MM terms, the use of solvent-accessible surface area (SASA) and volume (SAV) values from the Monte Carlo sampling of the probe, and introducing two different interior dielectric constants for electrostatic interactions of protein-ligand (P-L) and polar solvation term in the MMPBSA calculations. Our results show that the Pearson correlation coefficients of MMPBSA-calculated values with respect to experimental binding free energies can be drastically improved from 0.48 to 0.90 by adoption of ANI-ML potentials in replacement of MM energy terms in the equation, referred to as ANI-PBSA. Moreover, we show that the SASA/SAV-combined equation in the scaled particle theory (SPT) can be a better choice to model nonpolar solvation term, reaching nearly the same accuracy by ANI-PBSA calculations. Finally, we introduce two different values of interior dielectric constants, which could be an alternative strategy between the single and variable constant definitions.

An Ecosystem for Digital Reticular Chemistry

The vastness of the materials design space makes it impractical to explore using traditional brute-force methods, particularly in reticular chemistry. However, machine learning has shown promise in expediting and guiding materials design. Despite numerous successful applications of machine learning to reticular materials, progress in the field has stagnated, possibly because digital chemistry is more an art than a science and its limited accessibility to inexperienced researchers. To address this issue, we present mofdscribe, a software ecosystem tailored to novice and seasoned digital chemists that streamlines the ideation, modeling, and publication process. Though optimized for reticular chemistry, our tools are versatile and can be used in nonreticular materials research. We believe that mofdscribe will enable a more reliable, efficient, and comparable field of digital chemistry.

Fluids and Electrolytes under Confinement in Single-Digit Nanopores

Confined fluids and electrolyte solutions in nanopores exhibit rich and surprising physics and chemistry that impact the mass transport and energy efficiency in many important natural systems and industrial applications. Existing theories often fail to predict the exotic effects observed in the narrowest of such pores, called single-digit nanopores (SDNs), which have diameters or conduit widths of less than 10 nm, and have only recently become accessible for experimental measurements. What SDNs reveal has been surprising, including a rapidly increasing number of examples such as extraordinarily fast water transport, distorted fluid-phase boundaries, strong ion-correlation and quantum effects, and dielectric anomalies that are not observed in larger pores. Exploiting these effects presents myriad opportunities in both basic and applied research that stand to impact a host of new technologies at the water-energy nexus, from new membranes for precise separations and water purification to new gas permeable materials for water electrolyzers and energy-storage devices. SDNs also present unique opportunities to achieve ultrasensitive and selective chemical sensing at the single-ion and single-molecule limit. In this review article, we summarize the progress on nanofluidics of SDNs, with a focus on the confinement effects that arise in these extremely narrow nanopores. The recent development of precision model systems, transformative experimental tools, and multiscale theories that have played enabling roles in advancing this frontier are reviewed. We also identify new knowledge gaps in our understanding of nanofluidic transport and provide an outlook for the future challenges and opportunities at this rapidly advancing frontier.

An artificial sodium-selective subnanochannel

Single-ion selectivity with high precision has long been pursued for fundamental bioinspired engineering and applications such as in ion separation and energy conversion. However, it remains a challenge to develop artificial ion channels to achieve single-ion selectivity comparable to their biological analogs, especially for high Na⁺/K⁺ selectivity. Here, we report an artificial sodium channel by subnanoconfinement of 4'-aminobenzo-15-crown-5 ethers (15C5s) into ~6-Å-sized metal-organic framework subnanochannel (MOFSNC). The resulting 15C5-MOFSNC shows an unprecedented Na⁺/K⁺ selectivity of tens to 10² and Na⁺/Li⁺ selectivity of 10³ under multicomponent permeation conditions, comparable to biological sodium channels. A co-ion-responsive single-file transport mechanism in 15C-MOFSNC is proposed for the preferential transport of Na⁺ over K⁺ due to the synergetic effects of size exclusion, charge selectivity, local hydrophobicity, and preferential binding with functional groups. This study provides an alternative strategy for developing potential single-ion selective channels and membranes for many applications.

Highly Effective H2/D2 Separation within the Stable Cu(I)Cu(II)-BTC: The Effect of Cu(I) Structure on Quantum Sieving

Realizing ideal deuterium separation from isotopic mixtures remains a daunting challenge because of their almost identical sizes, shapes, and physicochemical properties. Using the quantum sieving effect in porous materials with suitable pore size and open metal sites (OMSs) enables efficient hydrogen isotope separation. Herein, synthetic HKUST-1-derived microporous mixed-valence Cu(I)Cu(II)-BTC (BTC = benzene-1,3,5-tricarboxylate), featuring a unique network of distinct Cu(I) and Cu(II) coordination sites, can remarkably boost the D₂/H₂ isotope separation, which has a high selectivity (S_D2/H2) of 37.9 at 30 K, in comparison with HKUST-1 and other porous materials. Density functional theory (DFT) calculations indicate that the introduction of Cu(I) macrocycles in the framework decreases the pore size and further leads to relatively enhanced interaction of H₂/D₂ molecules on Cu(II) sites. The significantly enhanced selectivity of Cu(I)Cu(II)-BTC at 30 K can be mainly attributed to the synergistic effect of kinetic quantum sieving (KQS) and chemical affinity quantum sieving (CAQS). The results reveal that Cu(I) OMSs exhibit counterintuitive behaviors and play a crucial role in tuning quantum sieving without a complex structural design, which provides a deeper insight into quantum sieving mechanisms and a new strategy for the intelligent design of highly efficient isotope systems.

Multi-Scale Computer-Aided Design of Covalent Organic Frameworks for CO2 Capture in Wet Flue Gas

Discovery of remarkable porous materials for CO₂ capture from wet flue gas is of great significance to reduce the CO₂ emissions, but elucidating the most critical structure features for boosting CO₂ capture capabilities remains a great challenge. Here, machine-learning-assisted Monte Carlo computational screening on 516 experimental covalent organic frameworks (COFs) identifies the superior secondary building units (SBUs) for wet flue gas separation using COFs, which are tetraphenylporphyrin units for boosting CO₂ adsorption uptake and functional groups for boosting CO₂/N₂ selectivity. Accordingly, 1233 COFs are assembled using the identified superior SBUs. Density functional theory calculation analysis on frontier orbitals, electrostatic potential, and binding energy reveals the influencing mechanism of the SBUs on the wet flue gas separation performance. The "electron-donating-induced vdW interaction" effect is discovered to construct the better-performing COFs, which can achieve high CO₂ uptake of 4.4 mmol·g^-1 with CO₂/N₂ selectivity of 104.8. Meanwhile, the "electron-withdrawing-induced vdW + electrostatic coupling interaction" effect is unearthed to construct the better-performing COFs with superior CO₂/N₂ selectivity, which can reach 277.6 with CO₂ uptake of 2.2 mmol·g^-1; in this case, H₂O plays a positive contribution in improving CO₂/N₂ selectivity. This work provides useful guidelines for designing optimized two-dimensional-COF adsorbents for wet flue gas separation.

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

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

Mapping the Porous and Chemical Structure-Function Relationships of Trace CH3I Capture by Metal-Organic Frameworks using Machine Learning

Large-scale computational screening has become an indispensable tool for functional materials discovery. It, however, remains a challenge to adequately interrogate the large amount of data generated by a screening study. Here, we computationally screened 1087 metal-organic frameworks (MOFs), from the CoRE MOF 2014 database, for capturing trace amounts (300 ppmv) of methyl iodide (CH₃I); as a primary representative of organic iodides, CH₃¹²⁹I is one of the most difficult radioactive contaminants to separate. Furthermore, we demonstrate a simple and general approach for mapping and interrogating the high-dimensional structure-function data obtained by high-throughput screening; this involves learning two-dimensional embeddings of the high-dimensional data by applying unsupervised learning to encoded structural and chemical features of MOFs. The resulting various porous and chemical structure-function maps are human-interpretable, revealing not only top-performing MOFs but also complex structure-function correlations that are hidden when inspecting individual MOF features. These maps also alleviate the need of laborious visual inspection of a large number of MOFs by clustering similar MOFs, per the encoding features, into defined regions on the map. We also show that these structure-function maps are amenable to supervised classification of the performances of MOFs for trace CH₃I capture. We further show that the machine-learning models trained on the 1087 CoRE MOFs can be used to predict an unseen set of 250 MOFs randomly selected from a different MOF database, achieving high prediction accuracies.

Comment on the applicability of the Gurvich rule for estimation of pore volume in microporous zeolites

This comment seeks to establish a relation between two definitions of the pore volume of a microporous crystalline material. According to the first definition based on the Gurvich rule, the volume of the pores can be estimated from the saturated amount of vapour adsorbed, using the bulk liquid density of adsorbate as the conversion factor. The second definition is based on a purely geometric consideration of the porous space. With argon as the adsorbate and all-silica zeolite structures from the International Zeolite Association (IZA) database as the model adsorbents, we generate adsorption data using Grand Canonical Monte Carlo simulations and structural characteristics of the materials from the Poreblazer PB4.0 software. Under confinement in zeolitic pores, adsorbed argon forms structures very different from the liquid-like configurations. However, the pore volumes of these materials obtained from the Gurvich may deviate positively or negatively from the reference geometric value. Considering simply the geometric features of the materials, such as the pore volume itself or the pore size distribution, it proved to be difficult to anticipate how the volume from the Gurvich rule would deviate from the geometric volume for a particular structure. Overall, volume from the Gurvich rule agrees with the geometric volume within 25% error for 82% of the structures from the IZA database. As an additional outcome of this study, we provide a comprehensive database of textural characteristics and simulated argon adsorption data for all-silica zeolites, which can be used as reference values for the assessment of the quality of the microporous samples of all-silica zeolites in future experimental studies.

Surface Assessment via Grid Evaluation (SuAVE) for Every Surface Curvature and Cavity Shape

The surface assessment via grid evaluation (SuAVE) software was developed to account for the effect of curvature in the calculations of structural properties of chemical interfaces regardless of the chemical composition, asymmetry, and level of atom coarseness. It employs differential geometry techniques, enabling the representation of chemical surfaces as fully differentiable. In this article, we present novel developments of SuAVE to treat closed surfaces and complex cavity shapes. These developments expand the repertoire of curvature-dependent analyses already available in the previous version of SuAVE (e.g., area per lipid, density profiles, membrane thickness, deuterium-order parameters, volume per lipid, and surface curvature angle) to include new functionalities applicable to soft matter (e.g., sphericity, average radius, principal moment of inertia, and roundness) and crystalline porous materials (e.g., pore diameter, internal void volume, total area, and the total void volume of the unit cell structure). SuAVE can accurately handle chemical systems with high and low atom density as demonstrated for two distinct chemical systems: the lipid A vesicle and a set of selected metal–organic frameworks. The SuAVE software v2.0 is fully parallel and benefits from a compiler that supports OpenMP. SuAVE is freely available from https://github.com/SuAVE-Software/source and https://www.biomatsite.net/.

MoloVol: an easy-to-use program for analyzing cavities, volumes and surface areas of chemical structures

Cavities are a ubiquitous feature of chemical structures encountered in various fields ranging from supramolecular chemistry to molecular biology. They are involved in the encapsulation, transport and transformation of guest molecules, thus necessitating a precise and accessible tool for estimating and visualizing their size and shape. MoloVol, a free user-parametrizable open-source software, developed for calculating a range of geometric features for both unit-cell and isolated structures, is presented here. MoloVol utilizes up to two spherical probes to define cavities, surfaces and volumes. The program was optimized by combining an octree data structure with voxel-partitioned space, allowing for even high-resolution protein structure calculations on reasonable timescales. MoloVol comes with a user-friendly graphic interface along with a command-line interface for high-throughput calculations. It was written in C++ and is available on Windows, macOS and Linux distributions.

Combining Theory and Experiments To Study the Influence of Gas Sorption on the Conductivity Properties of Metal-Organic Frameworks

With a view on adding to their use in trace gas sensing, we perform a combined experimental and theoretical study of the change of the conductivity of a metal organic framework (iron (1,2,3)-triazolate, Fe(ta)2) with the uptake of chemically inert gases. To align our first-principles calculations with experimental measurements, we perform an ensemble average over different microscopic arrangements of the gas molecules in the pores of the metal-organic framework (MOF). Up to the experimentally reachable limit of gas uptake, we find a good agreement between both approaches. Thus, we can employ theory to further interpret our experimental results in terms of changes to the parameters of the Bardeen-Shockley band theory, electron-phonon coupling (in the form of the deformation potential), bulk modulus, and carrier effective mass. We find the first of these to be most strongly influenced through the gas uptake. Furthermore, we find the changes to the deformation potential to strongly depend on the individual microscopic arrangements of molecules in the pores of the MOF. This hints at a possible synthetic engineering of the material, e.g., by closing off certain pores, for a stronger, more interpretable electric response upon gas sorption.

Partial Metalation of Porphyrin Moieties in Hydrogen-Bonded Organic Frameworks Provides Enhanced CO2 Photoreduction Activity

In natural photosynthesis, the architecture of multiproteins integrates more chromophores than redox centers and simultaneously creates a well-controlled environment around the active site. Herein, we demonstrate that these features can be emulated in a prototype hydrogen-bonded organic framework (HOF) through simply varying the proportion of metalated porphyrin in the structure. Further studies demonstrate that changing the metalloporphyrin content not only realizes a fine tuning of the photosensitizer/catalyst ratio, but also alters the microenvironment surrounding the active site and the charge separation efficiency. As a result, the obtained material achieves the challenging overall CO₂ reduction with a high HCOOH production rate (29.8 μmol g^-1  h^-1 , scavenger free), standing out from existing competitors. This work unveils that the degree of metalation is vital to the catalytic activity of the porphryinic framework, presenting as a new strategy to optimize the performance of heterogeneous catalysts.

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.

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.

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.

Evolution of pore characteristics and methane adsorption characteristics of Nanshan 1/3 coking coal under different stresses

To ascertain the evolution of pore characteristics and methane adsorption characteristics of the unit cell of Nanshan 1/3 coking coal under different stresses, proximate analysis, ultimate analysis, solid-state 13C nuclear magnetic resonance spectroscopy (13C-NMR) and X-ray photoelectron spectroscopy (XPS) experiments were performed on the coal samples, and a molecular unit cell model of 1/3 coking coal was established. As the increase of stress, pore diameter, proportion of larger pores, number of pores, surface area, and pore volume all decrease, the rate of decrease gradually decreases, and the smaller pores are less affected. Under 8 kinds of stress, the methane adsorption capacity and the overall system energies all conform to the Langmuir adsorption curve; as the stress increases, the methane adsorption capacity and the overall system energies both decrease, the rate of decrease gradually decreases, and the order of the adsorbed methane increases. Stress changes the methane adsorption capacity by changing the pore characteristics of the unit cell, and the stress has a more obvious effect on larger pores. As the stress increases, the speed of the stress's influence on the pores weakens. This has certain guiding significance for studying the saturated adsorption capacity of methane under different original in-situ stresses.

Incorporating Flexibility Effects into Metal-Organic Framework Adsorption Simulations Using Different Models

High-throughput calculations based on molecular simulations to predict the adsorption of molecules inside metal-organic frameworks (MOFs) have become a useful complement to experimental efforts to identify promising adsorbents for chemical separations and storage. For computational convenience, all existing efforts of this kind have relied on simulations in which the MOF is approximated as rigid. In this paper, we use extensive adsorption-relaxation simulations that fully include MOF flexibility effects to explore the validity of the rigid framework approximation. We also examine the accuracy of several approximate methods to incorporate framework flexibility that are more computationally efficient than adsorption-relaxation calculations. We first benchmark various models of MOF flexibility for four MOFs with well-established CO₂ experimental consensus isotherms. We then consider a range of adsorption properties, including Henry's constants, nondilute loadings, and adsorption selectivity, for seven adsorbates in 15 MOFs randomly selected from the CoRE MOF database. Our results indicate that in many MOFs adsorption-relaxation simulations are necessary to make quantitative predictions of adsorption, particularly for adsorption at dilute concentrations, although more standard calculations based on rigid structures can provide useful information. Finally, we investigate whether a correlation exists between the elastic properties of empty MOFs and the importance of including framework flexibility in making accurate predictions of molecular adsorption. Our results did not identify a simple correlation of this type.

Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening

By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases of such hypothetical MOFs, we need to ensure that they not only contribute toward the growth of the count of structures but also add different chemistries to the existing databases. In this study, we designed a database of ∼20,000 hypothetical MOFs, which are diverse in terms of their chemical design space─metal nodes, organic linkers, functional groups, and pore geometries. Using machine learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications─post-combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post-combustion carbon capture) and MOF-5 (for hydrogen storage). All the structures developed in this study, and their properties, are provided on the Materials Cloud to encourage further use of these materials for other applications.

Microporous Volumes from Nitrogen Adsorption at 77 K: When to Use a Different Standard Isotherm?

This work reviews the application of various standard isotherms to evaluate the micropore volume in a range of microporous materials. The selected materials have quite different surface chemistry, and are relevant due to their properties for adsorption and catalysis: zeolites, activated carbons, clay-based materials and MOFs. Some cases were analysed before and after being used as supports in the heterogenization of homogeneous catalysts. The discussion is centred, but not limited, to the three standard isotherms that are mostly employed in the literature (t-curve, non-porous carbon and non-porous hydroxylated silica) for the assessment of the micropore volume. For a given material the values of the micropore volumes from the different standard isotherms were compared, particularly against the values from the largely used t-curve. The cases where major discrepancies were found could normally be ascribed to samples that have a broad micropore size distribution.

Mechanical properties and pore size distribution in athermal porous glasses

We study the mechanical properties and pore structure in a three-dimensional molecular dynamics model of porous glass under athermal quasistatic shear. The vitreous samples are prepared by rapid thermal quenching from a high-temperature molten state. The pore structures form via solid-gas phase separation. The quiescent samples exhibit a wide range of pore topography, from inter-connected pore networks to randomly distributed compact pores depending on the material density. We find that the shear modulus strongly depends on the density and porosity. Under mechanical loading, the pore structure rearranges which is reflected in the pore size distribution function. Our results show that with increase in strain the distribution widens as the adjacent pores coalesce and form larger pores. We also propose a universal scaling law for the pore size distribution function which offers excellent data collapse for highly porous materials in the undeformed case. From the data scaling, we identify a critical density that can be attributed to the transition point from a porous-type to bulk-type material. The validity of the scaling law under finite deformation is also analyzed.

Proof of concept for CUK family metal-organic frameworks as environmentally-friendly adsorbents for benzene vapor

The utility of metal-organic frameworks (MOFs) such as the CUK family (CUK - Cambridge University-KRICT) has been explored intensively for adsorption/separation of airborne volatile organic compounds (VOCs). In this article, three M-CUK analogs (M = Mg, Co, or Ni) were synthesized hydrothermally under similar conditions to assess the effects of their isostructural properties and metal centers on adsorption of benzene vapor (0.05-1 Pa). A list of performance metrics (e.g., breakthrough volume (BTV) and partition coefficient (PC)) were used to assess the role of the metal type (in M-CUK-1s) in the adsorption of VOCs. Specifically, Co-CUK-1 (average pore size of 8.98 nm) showed 2-3 times greater performance (e.g., in terms of 10% BTV (2012 L atm g^-1) and PC (6 mol kg^-1 Pa^-1)) over other analogs when exposed up to 0.05 Pa benzene vapor. The superiority of mesoporous Co-CUK-1 (e.g., enhanced adsorption diffusion mechanism through favorable metal-π and π- π interactions) can be attributed to the presence of cobalt metal centers (e.g., in reference to Mg- or Ni-CUK-1).

Predicting hydrogen storage in MOFs via machine learning

The H2 capacities of a diverse set of 918,734 metal-organic frameworks (MOFs) sourced from 19 databases is predicted via machine learning (ML). Using only 7 structural features as input, ML identifies 8,282 MOFs with the potential to exceed the capacities of state-of-the-art materials. The identified MOFs are predominantly hypothetical compounds having low densities (<0.31 g cm-3) in combination with high surface areas (>5,300 m2 g-1), void fractions (∼0.90), and pore volumes (>3.3 cm3 g-1). The relative importance of the input features are characterized, and dependencies on the ML algorithm and training set size are quantified. The most important features for predicting H2 uptake are pore volume (for gravimetric capacity) and void fraction (for volumetric capacity). The ML models are available on the web, allowing for rapid and accurate predictions of the hydrogen capacities of MOFs from limited structural data; the simplest models require only a single crystallographic feature.

Confinement inside a Crystalline Sponge Induces Pyrrole To Form N-H⋅⋅⋅π Bonded Tetramers

Based on the DFT-level-calculated molecular volume (V_mol ) of pyrrole and its liquid density, pyrrole manifests the highest liquid density coefficient LD_c (defined as [V_mol ×density ×0.6023]/FW) value of 0.7. Normal liquids have LD_c <0.63. This very high LD_c is due to the strong N-H⋅⋅⋅π interactions in solution, and hence pyrrole can be considered to be a pseudo-crystalline liquid. When trapped inside the confined space of a crystalline sponge, a reorientation of the N-H⋅⋅⋅π interaction is observed leading to specific cyclic N-H⋅⋅⋅π tetramers and N-H⋅⋅⋅π dimers, as verified by single-crystal X-ray crystallographic and computational methods. These tetramers are of the same size as four pyrrole molecules in the solid-state of pyrrole, yet the cyclic N-H⋅⋅⋅π intermolecular interactions are circularly oriented instead of being in the linear zigzag structure found in the X-ray structure of a solid pyrrole. The confinement thus acts as an external driving force for tetramer formation.

Understanding the origin of serrated stacking motifs in planar two-dimensional covalent organic frameworks

Covalent organic frameworks (COFs) have attracted significant attention due to their chemical versatility combined with a significant number of potential applications. Of particular interest are two-dimensional COFs, where the organic building units are linked by covalent bonds within a plane. Most properties of these COFs are determined by the relative arrangement of neighboring layers. These are typically found to be laterally displaced, which, for example, reduces the electronic coupling between the layers. In the present contribution we use dispersion-corrected density-functional theory to elucidate the origin of that displacement, showing that the common notion that the displacement is a consequence of electrostatic repulsions of polar building blocks can be misleading. For the representative case of COF-1 we find that electrostatic and van der Waals interactions would, actually, favor a cofacial arrangement of the layers and that Pauli repulsion is the crucial factor causing the serrated AA-stacking. A more in-depth analysis of the electrostatic contribution reveals that the "classical" Coulomb repulsion between the boroxine building blocks of COF-1 suggested by chemical intuition does exist, but is overcompensated by attractive effects due to charge-penetration in the phenylene units. The situation becomes more involved, when additionally allowing the interlayer distance to relax for each displacement, as then the different distance-dependences of the various types of interactions come into play. The overall behavior calculated for COF-1 is recovered for several additional COFs with differently sized π-systems and topologies, implying that the presented results are of more general relevance.

Effect of the Ethanol/BTC Ratio on the Methane Uptake of Mechanochemically Synthesized MOF-199

We report on a detailed textural analysis of mechanochemically synthesized MOF-199 including N₂ adsorption-desorption and CO₂ adsorption isotherms data at 77 K and 273 K (up to atmospheric pressure), respectively, and CH₄ adsorption data at 298 K (up to 35 bar). We used the isotherm adsorption data to determine the micropore volume of the MOF-199 structures, to establish their methane uptake capacity and to understand how these properties depended on the Ethanol/BTC ratio used during the synthesis. The maximum methane uptake capacity for our specimens was recorded at 130 v/v at 35 bars. These results open an avenue for a better understanding of alternative manufacturing processes of MOF structures for gas storage applications.