Canonical Monte Carlo Simulations Research Articles

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.

Tuning the hydrogen storage capacity of MOF‐650 by Mg2+/Ca2+ substitution and B, N co‐doped atoms: Grand canonical Monte Carlo simulation and periodic density functional theory

AbstractThis study used periodic density functional theory and grand canonical Monte Carlo simulations to investigate the effects of the co‐doping of B and N atoms and substituting Zn2+ with Mg2+ or Ca2+ in the organic linker groups of MOF‐650. The functionalization increased the polarity of the organic groups, stabilizing the interaction between the MOF and hydrogen molecules. The highest average binding energy of the adsorbed hydrogen in MOF‐650 NB‐C7‐azulene‐Mg was calculated to be −4.75 to 5.40 kcal/mol for the α adsorption sites. Using the substitution of NB azulene and metal cations being Mg2+ or Ca2+, The hydrogen storage capacity of functionalized MOF‐650 was increased to 22 mg/g at 90 bar/298 K, implying the modification strategy of MOF‐650 would strengthen the interaction between MOF frameworks and hydrogen molecules.

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

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

Grand Canonical Monte Carlo Simulation Experiences of Methane and Ethane Adsorption Behaviors on Simplified Organic Shale Formed by Graphene Layering

Grand Canonical Monte Carlo Simulation Experiences of Methane and Ethane Adsorption Behaviors on Simplified Organic Shale Formed by Graphene Layering

Development of atomistic graphene models for H2 adsorption from experimental data and Monte Carlo simulations

The development of adsorbent materials for the storage of hydrogen is a major research topic since this could allow effective low volume storage for mobile applications. Few-layer graphene (FLG) is a candidate to work as a template for adsorptive materials. In order to gain insight into the adsorption of hydrogen on FLG a series of Grand Canonical Monte Carlo simulations were performed to analyze the effect of the pore size and the number of layers. A semiempirical and easy-to-implement model was developed to correlate hydrogen adsorption isotherms with the morphological characteristics of the material, such as, the characteristic number of layers and the pore size. A correlation between the adsorption isotherms with the number of layers and the characteristic pore size was developed and validated against available experimental data at different temperatures showing its applicability to the description of FLG.

Nitrogen-doped carbon particles with distinctive ethylene adsorption selectivity for efficient ethylene/acetylene separation

Separation of ethylene/acetylene (C2H4/C2H2) mixtures with C2H4-selective adsorbents is of economics and energy efficiency to purify crude C2H2 from methane pyrolysis or partial oxidation (POX) process. Herein, a systematic study was carried out to investigate the structural properties and C2H4/C2H2 separation performance of novel nitrogen-doped carbon particles, as well as the mechanism of preferentially adsorbing C2H4 over C2H2 through the Grand Canonical Monte Carlo (GCMC) simulations. The nitrogen-doped carbon particles derived from coconut shell and metronidazole present regular particle sizes centering at 710 μm and excellent crushing strength of 89 N. Well-developed microporosity with pore dimension mainly ranging in 0.54–0.68 nm, and fine-tuning surface chemistry with abundant nitrogen-containing species (pyridinic N, pyrrolic N and graphitic N) were observed in nitrogen-doped carbon particles. Interestingly, nitrogen-doped carbon particles exhibit preferential adsorption of C2H4 over C2H2, and the IAST-predicted selectivity of 1.5-N-CP reaches as high as 5.2 for C2H4/C2H2 (1/9) mixture at 1.0 bar and 25 °C. Remarkably, the separation stability and recyclability with complete separation for C2H4/C2H2 were well maintained during cyclic dynamic breakthrough experiments. GCMC simulation results demonstrate that the graphitic N with great binding energy (−22.61 kJ/mol for C2H4 and −19.87 kJ/mol for C2H2) favors the adsorption of both C2H4 and C2H2. The pore dimension ranging 0.54–0.68 nm, together with pyridinic N and pyrrolic N species significantly enhance the separation of C2H4/C2H2 through the large difference in adsorption interactions between C2H4 and C2H2. Therefore, the nitrogen-doped carbon particles have the excellent application accessibility in purifying C2H2 from C2H4/C2H2 mixture.

Adsorption behaviors for clathrate hydrates of CO2 with mixed gases

Here, the preference and adsorption capacities of guest molecules in the sI and sII hydrate of water cages when CO2 + H2S, CO2 + SO2 and CO2 + N2O gas mixture form hydrates are simulated by using grand canonical Monte Carlo and molecular dynamics hybrid simulations method. The results indicate that SO2 prefers to occupy large cages, H2S prefers small cages for CO2 + H2S and CO2 + SO2 and N2O exhibits little preference between small or large or cages of CO2 + N2O sI hydrates, respectively. The abundance ratio shows hydrate can effectively achieve the separation of flue gases. Moreover, the thermodynamics calculation results show that the impurity gases H2S, SO2 and N2O favors the formation of CO2 hydrate. The stability CO2 + H2S, CO2 + SO2 and CO2 + N2O sI hydrates is higher compared to the stability of sII hydrates. The phase equilibrium analysis provides clear evidence that enclathration of H2S and SO2 molecules into the water cages of the hydrate structure plays a crucial role in stabilizing the hydrate lattices. The findings of this study suggest that removal of certain flue gas impurities is not required during the replacement of CH4 in hydrate form with the CO2 gas. The current investigation provides theoretical background for executing replacement of CH4 in hydrate form with the CO2 gas.

Ab initio selection of zeolite topology for biomass conversion to para-xylene based on transition state selectivity

Recent reports have pointed out a sustainable and highly selective formation route via the Diels–Alder (DA) cycloaddition between biomass-derived 2, 5-dimethylfuran (DMF) and ethene over zeolitic catalysts to produce para-xylene (p-xylene). Here, a systematic multi-scale simulation was carried out to identify candidate zeolite frameworks with potentially better catalytic activities than previously reported catalysts, through a combination of “ab initio” synthesis, thermodynamic and kinetic analysis, and adsorption/diffusion simulations. We first screened representative zeolites based on the limiting dimensions of their voids (windows, cages, channels) for reactants and products, the extent of stabilization of the key transition states by organic structure directing agents, and the synthesis difficulty informed by literature knowledge. Subsequently, the intrinsic and apparent free energy barriers of the rate-determining step (i.e., cycloaddition) obtained from density functional theory calculations were used to predict reaction kinetics of the potentially useful zeolites. Finally, three final candidates (MOR, MOZ, and IFR) and two experimentally validated zeolites (FAU and BEA) were employed to compare the adsorption and diffusion performance of DMF and p-xylene as the final criterion by grand canonical Monte Carlo and molecular dynamics simulations. In summary, MOR shows the fastest diffusion, IFR shows the lowest apparent energy barrier, and MOZ shows the lowest intrinsic energy barrier. Altogether, this study not only predicts MOR, IFR, and MOZ zeolites to have a good catalytic performance for the DA-dehydration process of DMF and ethene to p-xylene but also illustrates the potential of ab initio selection, through combined theoretical assessments of adsorption, reaction and diffusion, for the rational selection and design of optimal catalysts for targeted catalytic transformations.

Direct prediction of gas adsorption via spatial atom interaction learning

Physisorption relying on crystalline porous materials offers prospective avenues for sustainable separation processes, greenhouse gas capture, and energy storage. However, the lack of end-to-end deep learning model for adsorption prediction confines the rapid and precise screen of crystalline porous materials. Here, we present DeepSorption, a spatial atom interaction learning network that realizes accurate, fast, and direct structure-adsorption prediction with only information of atomic coordinate and chemical element types. The breakthrough in prediction is attributed to the awareness of global structure and local spatial atom interactions endowed by the developed Matformer, which provides the intuitive visualization of atomic-level thinking and executing trajectory in crystalline porous materials prediction. Complete adsorption curves prediction could be performed using DeepSorption with a higher accuracy than Grand canonical Monte Carlo simulation and other machine learning models, a 20-35% decline in the mean absolute error compared to graph neural network CGCNN and machine learning models based on descriptors. Since the established direct associations between raw structure and target functions are based on the understanding of the fundamental chemistry of interatomic interactions, the deep learning network is rationally universal in predicting the different physicochemical properties of various crystalline materials.

Hydrogen permeability of polyamide 6 as the liner material of Type Ⅳ hydrogen storage tanks: A molecular dynamics investigation

To attain a lightweight structure with high pressure and a high hydrogen storage mass ratio, the type IV hydrogen storage tank is the most popular choice for many fuel cell vehicle enterprises. Using polymer as the liner material prevents hydrogen embrittlement and hydrogen-induced damage of the tanks, but hydrogen permeation is inevitable. Within the service conditions (233–358 K, 0–87.5 MPa) of the Type-IV tanks, the dissolution and diffusion behaviors of H2 in polyamide 6 are comprehensively explored for the first time through the molecular dynamics and Grand Canonical Monte Carlo simulations in this study. The diffusion mechanism of H2 in polyamide 6 is further revealed. The results show that both the diffusion and permeability coefficients have positive correlations with temperature, whereas the opposite is true for the solubility coefficient. These relationships are all consistent with Arrhenius's law in the temperature range, although multiple Arrhenius regions are needed to describe them. A slight decrease in the diffusion and permeability coefficients is observed with increasing pressure. However, this effect is not as significant as that of temperature and can be ignored when the pressure range is narrow enough. The “hopping” mechanism can be used to describe the diffusion of H2 in polyamide 6, where the continuous vibrations and “hopping” allow the diffusion of H2 molecules. These findings will contribute to a more comprehensive understanding of the permeation behavior of H2 in polyamide 6 and evaluate the gas barrier property of the material in practical applications.

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

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

Theoretical Investigation of Inorganic Particulate Matter: The Case of Water Adsorption on a NaCl Particle Model Studied Using Grand Canonical Monte Carlo Simulations

Sodium chloride (NaCl) represents the principal component of atmospheric particulates of marine origin. To gain a molecular-level understanding of the adsorption process of water vapor on the NaCl surface, Monte Carlo simulations performed in the Grand Canonical ensemble were carried out, considering the water adsorption at different water pressures on a NaCl(001) surface. The calculated adsorption isotherm shows four different regions, whose coverages correspond to those of the low-, transition-, high-, and pre-solution-coverage regions experimentally observed. Detailed analysis reveals how the structure of the adsorbed water molecules (islands, layer, and multi-layer) changes depending on water pressure, and how their orientation with respect to the surface varies with the distance from the surface. This detailed information further supports the picture coming from previous experimental IR absorption spectroscopy studies.

Investigation of the effect of the key pore size on the radon adsorption performance by combining grand canonical Monte Carlo and activated carbon modification experiments

Radon (Rn) is an important contributing factor to lung cancer. Activated carbon (AC) is currently the only Rn adsorbent for industrial applications. However, the effect of the key pore size of AC on Rn adsorption remains unclear, and enhancement of the Rn adsorption performance when using AC is still a challenge. In this work, grand canonical Monte Carlo (GCMC) simulations were performed to simulate the Rn adsorption ability of AC with different pore sizes. The adsorption selectivity of AC with pore sizes from 0.42 nm to 0.60 nm for Rn in the atmospheric environment was much greater than that for nitrogen and oxygen. The Rn adsorption performance was also investigated using KOH-modified activated carbon (KAC) with different pore sizes experimentally prepared using high-temperature roasting. The results showed that when the pore volume of KAC was significantly increased to approximately 0.55 nm, the Rn adsorption performance was obviously improved. Its adsorption coefficient reached 6.50 ± 0.11 m3 kg−1, which was 46.7 % higher than that of AC. After heating and regeneration in air, the Rn adsorption performance of KAC was basically unchanged. Combined with the comparative analysis using theoretical and experimental results, the relationship between the Rn adsorption performance of KAC and the pore volume at 0.55 nm is determined, and the accuracy of the GCMC calculation results was substantiated. This research provides a theoretical basis and an experimental method for improving the Rn adsorption performance of AC.

Titration in Canonical and Grand-Canonical Ensembles.

We discuss problems associated with the notion of pH in heterogeneous systems. For homogeneous systems, standardization protocols lead to a well-defined quantity, which, although different from Sørensen's original idea of pH, is well reproducible and has become accepted as the measure of the "hydrogen potential". On the other hand, for heterogeneous systems, pH defined in terms of the chemical part of the electrochemical activity is thermodynamically inconsistent and runs afoul of the Gibbs-Guggenheim principle that forbids splitting of the electrochemical potential into separate chemical and electrostatic parts, since only the sum of two has any thermodynamic meaning. The problem is particularly relevant for modern simulation methods which involve charge regulation of proteins, polyelectrolytes, nanoparticles, colloidal suspensions, and so forth. In this paper, we show that titration isotherms calculated using semigrand canonical simulations can be very different from the ones obtained using canonical reactive Monte Carlo simulations.

Exploring the inhibitory effect of H2O on CO2/CH4 adsorption in coal: Insights from experimental and simulation approaches

To investigate the inhibitory effect of H2O on the adsorption of CH4/CO2 in coal, the anthracite was selected for physical adsorption experiments on CH4/CO2 gases in this study. Combined with Materials Studio software, the effect of moisture on the isothermal adsorption characteristics of CH4/CO2 in coal was investigated by using density functional theory and giant canonical Monte Carlo simulation. The intermolecular interaction energies were obtained, as well as the effects of electrostatic potentials, adsorption sites, and hydrogen bonding of oxygen-containing functional groups. The results show that: ①According to the CH4/CO2 isothermal adsorption experiments, it was found that moisture significantly inhibited the adsorption of CH4/CO2 by coal. For CH4, the relationship between adsorption constants a/b and moisture content can be expressed by the equations y = 43.447-3.348x and y = 3.225-0.751x. For CO2, adsorption constant a follows a power function relationship with moisture content, expressed as y = 29.114x-0.063, while adsorption constant b is represented by the polynomial fitting equation y = 1.672+0.508x+0.083x2. ② The findings have revealed correlations between the moisture influence coefficient and moisture content. The exponential functional equation η = exp(-0.188ω) was found to be more accurate for coal adsorbing CH4, whereas the linear functional equation η = 1/(1 + 0.243ω) was considered more appropriate for CO2. ③ The van der Waals and electrostatic energies between coal and CH4/CO2 decreased with an increase in moisture content, whereas the van der Waals and electrostatic energies between coal and H2O increased. ④ The orders of hydrogen bond formation ability between CH4/CO2 and functional groups were as follows: –CHO > –COOH > –OH > –CH3 and –OH > –COOH > –CHO > –CH3. The ability of H2O to form hydrogen bonds with –COOH, –OH, –CHO, and –CH3 is as follows: –COOH > –OH > –CHO> –CH3. This study provides a theoretical basis for the integrated use of hydraulic fracturing techniques and coalbed gas injection and replacement.

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

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