Canonical Monte Carlo Simulations Research Articles

Molecular simulation on H2 adsorption in nanopores and effects of cushion gas: Implications for underground hydrogen storage in shale reservoirs

Hydrogen (H2), a renewable and clean energy source, plays an important role in the implementation of a low-carbon economy; however, hydrogen storage is challenging. Underground hydrogen storage (UHS) in shale reservoirs is a potential option for large-scale and long-term storage of H2. In this work, we used Grand Canonical Monte Carlo (GCMC) simulations to investigate the adsorption of pure H2 and H2 mixtures with CH4 or CO2 in kerogen and montmorillonite (MMT) nanopores under storage conditions. We found that pure H2 is more strongly adsorbed on kerogen, resulting in higher excess adsorption and consequently larger storage capacities. In the presence of CH4, H2 adsorption on kerogen is gradually weakened, indicating a stronger affinity between CH4 and kerogen surfaces. In contrast, co-adsorption of H2 and CH4 occurred on MMT surfaces. Moreover, H2 was almost completely desorbed by CO2, resulting in higher CO2 adsorption and selectivity (compared to CH4). Consequently, CH4 or CO2 can be used as the cushion gas as it greatly reduces the surface adsorption of H2, which is favorable for the retrieval of H2. Due to the stronger competitive adsorption ability, CO2 might perform better than CH4 in producing higher-purity H2. However, the presence of cushion gas significantly reduces H2 storage capacity, especially in smaller pores (<2 nm). This study provides important insights into H2 adsorption mechanisms and cushion gas effects in depleted shale reservoirs, thus helping to implement an industrial-scale hydrogen economy.

Molecular Simulation of Nafion Swelling in the Cathode Catalyst Layer of Polymer Electrolyte Fuel Cells

As the world moves towards carbon neutrality, polymer electrolyte fuel cells (PEFCs) have emerged as a vital technology in recent years. The cathode catalyst layer of PEFCs is characterized by its porous structure, which promotes the production of water molecules from oxygen and protons through chemical reactions. The ionomer thin films, predominantly composed of Nafion, are critical in regulating the transport and mobility of molecules and ions in the cathode catalyst layer. Sulfonic groups in the side chains of Nafion enable accumulation of water molecules, causing the ionomer thin films to swell.Previous studies have examined the swelling of Nafion under wet conditions[1][2]. However, precise swelling mechanisms remain unclear. While privious research has analyzed the relationship between relative humidity and the amount of water within bulk Nafion samples[3], the swelling behavior in films with thickness of several nanometers differs significantly, necessitating further investigation[4]. This study therefore investigates the swelling mechanisms and the properties of ionomer thin films used in the cathode catalyst layer of PEFCs. This study aims to establish guidelines for designing the molecular structures of ionomers by elucidating the mechanisms underlying ionomer swelling in the cathode catalyst layer. Grand canonical Monte Carlo (GCMC) simulations and molecular dynamics (MD) simulations were employed to reproduce swelling in Nafion thin films and examine the impact of Nafion's structure on water content and swelling. These molecular simulation techniques enable the elucidation of the influence exerted by polymer structure on water content and swelling at the molecular scale.In this study, we firstly created a simulation model of a Nafion system using LAMMPS[5], the DREIDING Force Field[6], and Mulliken charges[7] in which parameter settings were referred from literature values[8]. The Nafion system contains 10 Nafion molecules with a degree of polymerization of 10, water molecules, and 100 oxonium ions for electrical neutrality. The density of 1.9 g/cm3 achieved during simulations closely agrees with literature values[8].To reproduce Nafion swelling under humid conditions, GCMC simulations and MD simulations were employed. The rationale behind utilizing GCMC simulations lies in the fact that they enable the insertion of water molecules into the Nafion system under constant chemical potential conditions, thereby inducing Nafion swelling. Subsequent to the GCMC simulations, MD simulations were performed under the NPT ensemble, which facilitated the system to reach equilibrium. This alternating cycle of GCMC and MD simulations is repeated to realize the final swelling state. Preliminary results indicate lower water content values than those experimentally measured under similar conditions. Possible causes include inadequate force field parameters that lead to polymer morphology different from the actual Nafion structure.Future work will explore the causes of these discrepancies and examine the effects of Nafion's structure on water content and swelling under various conditions. This will involve testing alternative force field parameters and initial structures to better represent the molecular-level structure of ionomer thin films on carbon support particles in the cathode catalyst. Additionally, we will investigate how different relative humidity conditions, temperatures, and ionomer compositions influence swelling behavior, which could provide insights into optimizing PEFCs performance under various operating environments.[1] G. C. Abuin et al., J. Membr. Sci. 428, 507 (2013).[2] J. Catalano et al., Int. J. Hydrog. Energy 37, 6308 (2012).[3] T. A. Zawadzinski et al., J. Electrochem. Soc. 140, 1981 (1993).[4] M. A. Modestino et al., Macromolecules 46, 867 (2013).[5] A. P. Thompson et al., Comp. Phys. Comm. 271, 10817 (2022).[6] S. L. Mayo et al., J. Phys. Chem. 94, 8897 (1990).[7] S. S. Jang et al., J. Phys. Chem. B 108, 3149 (2004).[8] Y. Kurihara et al., J. Power Sources 414, 263 (2019).Fig. 1 An ionomer model composed of Nafion molecules, oxonium ions, and water molecules. The red and white spheres represent oxygen and hydrogen atoms making up water molecules. The size of the water molecules is emphasized for clarity. Figure 1

Bromine functionalization of metal–organic frameworks to improve ethane/ethylene separation

Metal–organic frameworks (MOFs) have undergone extensive study as ethane-selective adsorbents for ethane/ethylene (C2H6/C2H4) separation, a process currently conducted through energy-intensive cryogenic distillation in the petrochemical industry. Herein, we present the effect of functionalization using bromine, known for its high polarizability, on the C2H6-selective adsorption properties of MOF. This approach, based on the slightly larger polarizability of C2H6 compared to C2H4, was systematically investigated using three isostructural MOFs: DMOF-1, DMOF-1-Br, and DMOF-1-Br2, featuring 1,4-benzenedicarboxylate, 2-bromo-1,4-benzenedicarboxylate, and 2,5-dibromo-1,4-benzenedicarboxylate ligands, respectively. While their C2H6/C2H4 selectivity, based on the ideal adsorbed solution theory, increased (from 1.51 to 1.75 at 298 K and 100 kPa for the 1/1 (v/v) mixture) with the rising Br content, breakthrough experiments revealed that DMOF-1-Br achieves the highest high-purity (>99.95 %) C2H4 productivity (9.7 LSTP kg−1 at 298 K and 100 kPa for the 1/1 mixture and 22.2 LSTP kg−1 for the 1/15 mixture). This accomplishment is attributed to the appropriate bromine functionalization. Further insight into their C2H6-selective adsorption behavior was gained through canonical Monte Carlo and dispersion-corrected density functional theory simulations.

Fluorinated Metal–Organic Framework–Polymer Mixed Matrix Membrane with Tunable Hydrophobic Channel for Efficient Pervaporation of Butanol/Water

The permeability–selectivity trade‐off of membrane is a major challenge limiting the development of pervaporation (PV) technology. Rational design of high‐performance mixed matrix membranes (MMMs) has the potential to break off the trade‐off. Herein, a solvent‐assisted linker exchange‐based strategy is reported to introduce fluoroalkyl into metal–organic framework‐808 (MOF‐808). The pore size of fluorinated MOF‐808 can be adjusted with fluoroalkyl of different chain length (like trifluoromethyl and pentafluoropropyl). Then, the fluorinated MOF‐808/polyether block amide (PEBA) MMMs are prepared for the PV of n‐butanol/water. Compared with pristine PEBA membrane, 20 wt% 3F‐MOF‐808(P)/PEBA MMMs (3F = ‐CF3 group; P = postmodification method) exhibit 69% increase in permeation flux and 33% increase in separation factor in the PV of 2.5 wt% n‐butanol aqueous solutions at 70 °C. Based on Grand Canonical Monte Carlo and molecular dynamics simulations, fluorinated MOF‐808 shows better butanol affinity (pull effect) and stronger water repulsion ability (push effect). And the “push–pull effect” between fluorinated MOF‐808 with butanol/water is helpful to enhance the PV performance of MMMs. The application of “push–pull effect” provides a new strategy for the rational design of high‐performance MMMs, which is of great significance for the in‐depth research and application of PV technology.

Lithium-Ion Doping for Enhanced Hydrogen Sulfide Adsorption on Metal–Organic Frameworks: Grand Canonical Monte Carlo and Density Functional Theory Simulations

Lithium-Ion Doping for Enhanced Hydrogen Sulfide Adsorption on Metal–Organic Frameworks: Grand Canonical Monte Carlo and Density Functional Theory Simulations

Study of supramolecular organic frameworks for purification of hydrogen through molecular dynamics simulations

The efficiency of proton exchange membrane fuel cells (PEMFCs) is heavily dependent on hydrogen purity. The production of hydrogen using methanol releases gases such as CO2 and CO, and hydrogen purification is particularly important. In this study, the performance of hydrogen purification (H2/CO2, H2/CO, and H2/CO2/CO) of three supramolecular organic frameworks (SOF-8, SOF-9a, and SOF-10a) with similar structures and different pore sizes and dimensions (two-dimensional (2D) and three-dimensional (3D)) were comprehensively investigated by molecular dynamics simulations and Grand Canonical Monte Carlo simulations. The monolayer, AA and AB stacked SOFs membranes were investigated. The results show that monolayer membranes exhibited high H2 permeability but low CO2 interception. AA-stacked SOF-10a and AB-stacked SOF-8 and SOF-10a can achieve 100 % separation of H2 from both CO2 and CO, with a minimum H2 permeability of 0.3 × 106 GPU and a maximum of up to 0.66 × 106 GPU in the H2/CO2 separation system, which is nearly 5 orders of magnitude higher than the industry standard. Microscopic analyses indicate that 3D SOFs (SOF-9a and SOF-10a) exhibit stronger interactions with CO2, while still maintain high H2 permeability in different stacking modes.

A quad-core Zn (II) cluster-based microporous metal-organic framework with nonpolar cages for efficient removal of ethane from ethane/ethylene mixtures

Ethane (C2H6) and ethylene (C2H4) have similar physical and chemical properties, which are critical and difficult for the separation of C2H6/C2H4 mixtures in the petrochemical process. Traditional C2H4-selective adsorbents can separate the C2H6/C2H4 mixtures, but need further desorption treatment to obtain pure C2H4 production. On the contrary, high-pure C2H4 can be prepared in one step by C2H6-selective porous material. Herein, we present a caged metal-organic framework (GNU-2) based on Zn (II) clusters, which has advantageous adsorption sites and cavity pockets for the reverse capture of C2H6 from C2H6/C2H4 mixtures. At 298 K and 1 bar, high C2H6 uptake (5.10 mmol g−1) and C2H6/C2H4 selectivity (1.849) were attained based on the activated GNU-2 (GNU-2a), exceeding a number of top C2H6-selective adsorbents. Grand canonical Monte Carlo (GCMC) simulations exhibit that the appropriate chemical environment of nonpolar cage in GNU-2a allows it to continuously maintain closer van der Waals contacts between C2H6 molecule, saturated Zn cluster and nonpolar pore surface through multiple CH···O hydrogen interactions and CH···π hydrogen interactions. Moreover, dynamic breakthrough studies also show that GNU-2a displays the actual separation performances of C2H6.

A stable ultramicroporous Cd(II)-MOF with accessible oxygen sites for efficient separation of light hydrocarbons with high methane production

Metal-organic frameworks (MOFs) have garnered increasing attention for their effective separation of light hydrocarbons owing to their prominent separation selectivity and energy-efficient adsorption process. Here, we constructed a robust stable ultramicroporous Cd(II)-MOF ([Cd5(NTA)4(H2O)2] (Me2NH2+)2·10H2O (1)) with abundant accessible oxygen sites and investigated its adsorption performance for recovering high-purity methane (CH4) from natural gas (NG) including C1(CH4)/C2(C2H6)/C3(C3H8) mixtures. At ambient conditions, the theoretical equilibrium separation selectivity of 1 for C2H6/CH4 (v/v = 10/85) and C3H8/CH4 (v/v = 5/85) were found to be 34.3 and 223.8, respectively. The CH4/C2H6/C3H8 (v/v/v = 85/10/5) mixture breakthrough experiments for 1, conducted at 298 K, demonstrated effective separation performance with breakthrough times of up to 136 and 280 min·g−1 for C2H6 and C3H8. Particularly, the CH4 productivity (purity > 99.9 %) with 9.8 mmol·g−1 ranked the third in reported literatures, lower to the reported maximum value of 13.28 mmol·g−1 for Ni(TMBDC)(DABCO)0.5. Furthermore, Grand Canonical Monte Carlo (GCMC) simulations and first-principles density functional theory (DFT) calculations revealed that the high uptake and selectivity for C3H8 and C2H6 can be attributed to the abundant oxygen sites present in the pores. The dynamic breakthrough experiments comprehensively demonstrated that the proposed MOF can be an effective potential adsorbent for the practical separation of CH4/C2H6/C3H8 mixtures.

Machine learning aided computational exploration of metal–organic frameworks with open Cu sites for the effective separation of hydrogen isotopes

Efficient separation of hydrogen isotopes is of vital importance to develop nuclear energy industry, while it remains a significant challenge to separate D2 from H2 due to their identical physicochemical properties. As one of the efficient alternatives to conventional techniques, the thermodynamic quantum sieving technology using metal–organic frameworks (MOFs) featuring open metal sites (OMSs) has shown a great potential. However, the lack of transferable force fields in conventional molecular simulations and high expense of brute-force screening hinder the quick discovery of MOFs targeted for D2/H2 separation. Herein, based on the established force field with high accuracy and transferability, machine learning and feature engineering are applied to address these challenges. Machine learning comprehensively assesses different descriptors that influence the separation performance of 929 experimentally-reported MOFs bearing Cu(II)-OMS. By employing the same metal nodes, new Cu MOF database (6,748 MOFs) is constructed, in which 45 hypothetical MOFs are firstly identified out through feature engineering that exhibiting high performance. Furthermore, grand canonical Monte Carlo simulations are performed on these MOFs, among which the optimal one exhibits comparable selectivity (36.9) and high adsorbent performance score (315.9) that surpasses the state-of-the-art materials do. This work not only presents a cost-effective approach firstly applying in the separation of hydrogen isotopes, but also provides experimental guidance for the design of high-performance adsorbents.

Transport property of hydrogen sulfide in amorphous polyethylene using grand canonical Monte Carlo and molecular dynamics simulations

H2S corrosion poses a significant hurdle to safely transporting sulfur-containing natural gas through metal gathering pipes. Reinforced thermoplastic composite pipes have emerged as a solution, using the polyethylene inner lining layer that resists H2S corrosion. However, the permeation of H2S in the polyethylene may lead to the failure of reinforced thermoplastic composite pipes, resulting in natural gas leakage and environmental pollution. Therefore, it is crucial to investigate the gas transport properties of H2S in polyethylene materials to ensure the reliable functioning of gathering pipes. In this study, the dissolution behavior of H2S for pure H2S and H2S/CH4 mixture in amorphous PE is investigated using the grand canonical Monte Carlo method in conjunction with the NPT, with consideration given to the swelling effect. In addition, the diffusion characteristic is explored through the NVT on the amorphous PE adsorbing H2S. Subsequently, the permeability coefficient is calculated based on the obtained data. The findings reveal that with as the temperature increases, the solubility coefficient decreases, while both the diffusion and permeability coefficients increase. With increasing pressure, both the diffusion and permeability coefficients increase. Additionally, the above three coefficients all increase with increasing H2S mole fraction. The relationships between solubility, diffusion, and permeability coefficients & temperature all conform to the Arrhenius law. Finally, the diffusion mechanism of H2S in amorphous PE is revealed at the microscopic level as the “jumping” mechanism by tracking the diffusion pathway of H2S molecule.

Analysis of the adsorption characteristics of SF6 and its decomposition products in ZIF-67: based on GCMC and DFT

Given the superior performance of SF6 in arc extinguishing and its wide application in high voltage fields, the search for new adsorbent materials for SF6 and its main decomposition products holds significant importance in mitigating greenhouse gas potential and its implications for the environment and energy sectors. We chose ZIF-67, an organic framework with a high specific surface area and distinctive pore size structure, to address this important issue. Using the grand canonical Monte Carlo simulation approach, we investigated the adsorption of SF6 and its main decomposer gases by ZIF-67 at GPa. The interaction of ZIF-67 with SF6 and its main breakdown products was then examined at the level of atomic orbital interactions, with the adsorption energy and density of states estimated using density functional theory. The findings reveal that ZIF-67 has the best ability to capture H2S, with a saturation capacity of 3.2 mmol g−1 at room temperature. Its adsorption capacity for SOF2 and SO2F2 is about five to seven times higher than that of industrial adsorbents kdhF-03, and the working conditions are milder. The molecule radius and adsorption capacity are tightly correlated. An innovative method for the widespread recovery and treatment of SF6 and its byproducts is presented in this research. This paper aims to provide new insights and solutions to promote sustainable development in the power industry.

Neural functional theory for inhomogeneous fluids: Fundamentals and applications

We present a hybrid scheme based on classical density functional theory and machine learning for determining the equilibrium structure and thermodynamics of inhomogeneous fluids. The exact functional map from the density profile to the one-body direct correlation function is represented locally by a deep neural network. We substantiate the general framework for the hard sphere fluid and use grand canonical Monte Carlo simulation data of systems in randomized external environments during training and as reference. Functional calculus is implemented on the basis of the neural network to access higher-order correlation functions via automatic differentiation and the free energy via functional line integration. Thermal Noether sum rules are validated explicitly. We demonstrate the use of the neural functional in the self-consistent calculation of density profiles. The results outperform those from state-of-the-art fundamental measure density functional theory. The low cost of solving an associated Euler–Lagrange equation allows to bridge the gap from the system size of the original training data to macroscopic predictions upon maintaining near-simulation microscopic precision. These results establish the machine learning of functionals as an effective tool in the multiscale description of soft matter.

Molecular simulation of the impact of surface roughness on carbon dioxide adsorption in organic-rich shales

This study investigates the adsorption behavior of carbon dioxide in organic nanopores with different surface roughness. The nanopores are constructed by sinusoidally corrugating the graphite slit pore walls. By computing the density distributions, adsorption quantities and orientation of carbon dioxide under various pressure and roughness conditions, we elucidate the impacts of surface roughness on carbon dioxide adsorption in organic nanopores. The Langmuir-Freundlich adsorption model is utilized to fit the isotherms of CO2 adsorption under three different roughness conditions. the results show that increasing surface roughness led to the increase in the adsorption of carbon dioxide, as the relative roughness increased from 0% to 12.92%, the average CO2 adsorption capacity increased by 0.003 mmol/m2. Both the adsorbed layer density and monolayer maximum adsorption capacity increased concurrently with escalating roughness. Moreover, carbon dioxide molecules preferentially aligned parallel to the rough organic surface within the adsorption layer, consistent with the smooth graphitic wall configuration. All simulations, observations, and calculations were performed through grand canonical Monte Carlo (GCMC) simulations. These findings provide insights into the influence of surface roughness on CO2 adsorption, especially in organic nanopores, which has substantial implications for carbon capture and geological sequestration applications. The results could facilitate optimization of strategies for efficient, secure geological CO2 storage.

Molecular modelling of energy storage performance on metal organic frameworks/ethane nanoparticles nanofluids mixtures and derivatives

Through the utilization of optimum working fluids, thermodynamic cycles may perform more effectively. In this study, we used molecular modeling approaches to conduct computational studies on nanofluids of ethane and its derivatives with metal organic frameworks (ZIF-8). DMol3 was used to investigate the structure and electrical behavior of nanomaterials prior to as well as following fluid adsorption. The adsorption behavior of the refrigerant gases ethane (R-170), fluroethane (R-161), and difluroethane (R-152a) on ZIF-8 was investigated using grand canonical Monte Carlo simulations at various temperatures. Additionally, R-170/ZIF-8, R-161/ZIF-8, and R-152a/ZIF-8 nanofluids' stability and thermal energy storage traits were assessed. The outcomes demonstrate a decrease in the order of R-170 > R-152a > R-161 in the adsorption of the fluids on ZIF-8. Additionally, adding ZIF-8 nanomaterial to R-170, R-161 and R-152a fluids improved their ability to store energy, but improvements at higher ZIF-8 concentrations revealed sensitive dependence on the pressure and temperature conditions, particularly the fluid under consideration's critical temperature. To validate the computational findings, they were contrasted with previously collected experimental data.

Ultramicroporous Lonsdaleite Topology MOF with High Propane Uptake and Propane/Methane Selectivity for Propane Capture from Simulated Natural Gas.

Propane (C3H8) is a widely used fuel gas. Metal-organic framework (MOF) physisorbents that are C3H8 selective offer the potential to significantly reduce the energy footprint for capturing C3H8 from natural gas, where C3H8 is typically present as a minor component. Here we report the C3H8 recovery performance of a previously unreported lonsdaleite, lon, topology MOF, a chiral metal-organic material, [Ni(S-IEDC)(bipy)(SCN)]n, CMOM-7. CMOM-7 was prepared from three low-cost precursors: Ni(SCN)2, S-indoline-2-carboxylic acid (S-IDECH), and 4,4'-bipyridine (bipy), and its structure was determined by single crystal X-ray crystallography. Pure gas adsorption isotherms revealed that CMOM-7 exhibited high C3H8 uptake (2.71 mmol g-1) at 0.05 bar, an indication of a higher affinity for C3H8 than both C2H6 and CH4. Dynamic column breakthrough experiments afforded high purity C3H8 capture from a gas mixture comprising C3H8/C2H6/CH4 (v/v/v = 5/10/85). Despite the dilute C3H8 stream, CMOM-7 registered a high dynamic uptake of C3H8 and a breakthrough time difference between C3H8 and C2H6 of 79.5 min g-1, superior to those of previous MOF physisorbents studied under the same flow rate. Analysis of crystallographic data and Grand Canonical Monte Carlo simulations provides insight into the two C3H8 binding sites in CMOM-7, both of which are driven by C-H···π and hydrogen bonding interactions.

Exploring the hydrogen and methane storage capacities of novel DUT MOFs at room temperature: A Grand Canonical Monte Carlo simulation study

Metal-Organic Frameworks (MOFs) are a significant and promising category of solid that have garnered substantial attention for their potential in storing hydrogen and methane. Grand Canonical Monte Carlo (GCMC) simulations of the usable hydrogen and methane storage capacities of five DUT MOFs (Dresden University of Technology), based on tritopic ligands and copper, have been carried out at room temperature and pressures between 0.5 and 35 MPa. These DUT MOFs exhibit high usable hydrogen and methane storage capacities, comparable or higher than the storage capacities of the best classical MOFs and the best Cu-based MOFs. The usable methane gravimetric storage capacities at 35 MPa and room temperature of these DUTs reach the Department of Energy (DOE) methane gravimetric target and their usable volumetric capacities are close to the DOE methane volumetric target.

Prediction of hydrogen storage in metal-organic frameworks: A neural network based approach

Gas capture, sensing, and storage systems are all within the capabilities of metal–organic frameworks (MOFs). It is common practise to choose the MOF with the best adsorption property from a large database before running an adsorption calculation. High-throughput computational research is sometimes hampered by the expense of computing thermodynamic values, slowing the progress of MOFs for separations and storage applications. When trying to predict material properties, machine learning has recently emerged as a possible alternative to more conventional methods like experiments and simulations. The H2 capacities of 918,734 MOFs drawn from 19 databases were recently predicted using ML by Ahmed and Siegel (2021). Several ML methods were utilized, and the extremely randomised tree (ERT) model emerged as the most accurate predictor of hydrogen delivery capacity in terms of both gravimetric and volumetric quantities. Interestingly we have used deep learning model (Feed-forward neural network) as well as ERT model for the prediction of H2 deliverable capacities of a huge number of MOFs developed from the previous studies and got till date best results for predictions. To verify our model’s efficacy, we also performed Grand Canonical Monte Carlo (GCMC) simulations. We show our method by forecasting the hydrogen storage capacity of MOFs during a temperature and pressure swing from 100bar/77K to 5bar/160K.

Constructing copper-organic framework with exceptionally high volumetric working capacity for efficient methane storage

Extensive efforts have been devoted to developing novel porous structures for methane storage with high volumetric uptake and volumetric working capacity. In this study, a NbO-type copper–organic framework (CMOF-1) was designed and synthesized. Notably, the CMOF-1 posses suitable nanocages decorated with carboxylate groups and nitrogen sites, exhibiting a remarkable volumetric uptake of 260 cm3 cm−3 (at 65 bar/298 K) and a volumetric working capacity of 202 cm3 cm−3 (from 5 to 65 bar). These values are comparable to those of the benchmark material HKUST-1 (267 cm3 cm−3 and 190 cm3 cm−3) and exceed those of the structural analogue ZJU-5a (249 cm3 cm−3 and 188 cm3 cm−3) under the same conditions. The grand canonical Monte Carlo simulations and density functional theory calculations reveal that the high methane volumetric uptake might be contributed by the open CuII sites and nanocages with suitable sizes/shapes and functional sites, which can enforce strong gas-framework and gas–gas interactions. The methodology presented in this work should envision potential practical applications in methane storage and could be generalized to design other novel materials for the storage of other gases like hydrogen.

Competitive Adsorption of Trace Gases on Ice at Tropospheric Temperatures: A Grand Canonical Monte Carlo Simulation Study.

The coadsorption of two atmospheric trace gases on ice is characterized by using, for the first time, grand canonical Monte Carlo (GCMC) simulations performed in conditions similar to those of the corresponding experiments. Adsorption isotherms are simulated at tropospheric temperatures by considering two different gas mixtures of 1-butanol and acetic acid molecules, and selectivity of the ice surface with respect to these species is interpreted at the molecular scale as resulting from a competition process between these molecules for being adsorbed at the ice surface. It is thus shown that the trapping of acetic acid molecules on ice is always favored with respect to that of 1-butanol at low pressures, corresponding to low coverage of the surface, whereas the adsorption of the acid species is significantly modified by the presence of the alcohol molecules in the saturated portion of the adsorption isotherm, in accordance with the experimental observations. The present GCMC simulations thus confirm that competitive adsorption effects have to be taken into consideration in real situations when gas mixtures present in the troposphere interact with the surface of ice particles.

Nanoporous Frameworks with High Porosity and Unexpected Rigid Framework Topologies Based on Odd‐Numbered Ring‐Expanded Linkers

Two tetracarboxylic linkers containing seven‐membered rings are synthesized and utilized in metal–organic framework (MOF) syntheses, leading to the two new compounds DUT‐184 and DUT‐193. The introduced odd angles between carboxylates in a new dihydro‐azepine‐containing linker lead to exotic Zn‐based MOFs: a 3D structure based on two catenated sets of 2D nets in DUT‐184 and a cubic structure with so far unknown topology in the DUT‐193 framework. The mesoporous DUT‐193 shows high porosity upon adsorption of N2, Ar, CH4, and CO2 at their standard boiling points. Grand canonical Monte Carlo simulations show good agreement between experimental and theoretical results and shine light on the microscopic adsorption mechanism at low pressure.