Grand Canonical Monte Carlo Simulations Research Articles

A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture

Metal-organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2m mol g−1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset.

Well‐Dispersed MOF‐5 on The Polyvinylpyrrolidone‐Coated Random Lamellas of Clinoptilolites for Adsorptive Separation Performance of CO2, CH4, and N2

AbstractThe MOF‐5@clinoptilolite (MOF@CP) composites are successfully synthesized using polyvinylpyrrolidone (PVP) for adsorption separation of CO2/CH4, CO2/N2, and CH4/N2. The effects of the PVP amounts on the dispersity of MOF‐5 on CP random lamellas of the MOF@CPs are evaluated via various characterizations. Meanwhile, their single‐component gas adsorption isotherms, breakthrough experiments, and cycling test are measured. The results elucidate that the used PVP amount has a significant influence on the particle size and the uniformity of MOF‐5 dispersed on CP random lamellas. The MOF‐5‐loaded amount in MOF@CP is estimated to be up to 42.52 wt.%. Especially, the surface fractal evolutions indicated the surfaces of MOF@CP became from rough to smooth with the increase of PVP. The CO2/CH4, CO2/N2, and CH4/N2 selectivity factors of MOF@CP are higher than that of CP, displaying a better separation performance. Their cycling test revealed that MOF@CPs could be used repetitively, highlighting efficiency for CO2 and CH4 separation. Meanwhile, the MOF‐5@CP stability under moisture is preliminarily investigated, showing higher moisture resistance stability than MOF‐5. Additionally, the grand canonical Monte Carlo simulations demonstrated the adsorption separation mechanism of the prepared MOF@CPs.

Adsorption Characteristics of CO2/CH4/H2S Mixtures in Calcite Nanopores with the Implications for CO2 Sequestration

Summary Injecting CO2 into reservoirs for storage and enhanced oil recovery (EOR) is a practical and cost-effective strategy for reducing carbon emissions. Commonly, CO2-rich industrial waste gas is used as the CO2 source, whereas contaminants such as H2S may severely impact carbon storage and EOR via competitive adsorption. Hence, the adsorption behavior of CH4, CO2, and H2S in calcite (CaCO3) micropores and the impact of H2S on CO2 sequestration and methane recovery are specifically investigated. The Grand Canonical Monte Carlo (GCMC) simulations were applied to study the adsorption characteristics of pure CO2, CH4, and H2S, and their multicomponent mixtures were also investigated in CaCO3 nanopores to reveal the impact of H2S on CO2 storage. The effects of pressure (0–20 MPa), temperature (293.15–383.15 K), pore width, buried depth, and gas mole fraction on the adsorption behaviors are simulated. Molecular dynamics (MD) simulations were performed to explore the diffusion characteristics of the three gases and their mixes. The amount of adsorbed CH4, CO2, and H2S enhances with rising pressure and declines with rising temperature. The order of adsorption quantity in CaCO3 nanopores is H2S > CO2 > CH4 based on the adsorption isotherm. At 10 MPa and 323.15 K, the interaction energies of CaCO3 with CO2, H2S, and CH4 are −2166.40 kcal/mol, −2076.93 kcal/mol, and −174.57 kcal/mol, respectively, which implies that the order of adsorption strength between the three gases and CaCO3 is CO2 > H2S > CH4. The CH4-CaCO3 and H2S-CaCO3 interaction energies are determined by van der Waals energy, whereas electrostatic energy predominates in the CO2-CaCO3 system. The adsorption loading of CH4 and CO2 are lowered by approximately 59.47% and 24.82% when the mole fraction of H2S is 20% at 323.15 K, reflecting the weakening of CH4 and CO2 adsorption by H2S due to competitive adsorption. The diffusivities of three pure gases in CaCO3 nanopore are listed in the following order: CH4 > H2S ≈ CO2. The presence of H2S in the ternary mixtures will limit diffusion and outflow of the system and each single gas, with CH4 being the gas most affected by H2S. Concerning carbon storage in CaCO3 nanopores, the CO2/CH4 binary mixture is suitable for burial in shallower formations (around 1000 m) to maximize the storage amount, while the CO2/CH4/H2S ternary mixture should be buried as deep as possible to minimize the adverse effects of H2S. The effects of H2S on CO2 sequestration and CH4 recovery in CaCO3 nanopores are clarified, which provides theoretical assistance for CO2 storage and EOR projects in carbonate formation.

Tailoring Hydrophobicity and Pore Environment in Physisorbents for Improved Carbon Dioxide Capture under High Humidity.

CALF-20, a Zn-triazolate-based metal-organic framework (MOF), is one of the most promising adsorbent materials for CO2 capture. However, competitive adsorption of water severely limits its performance when the relative humidity (RH) exceeds 40%, limiting the potential implementation of CALF-20 in practical settings where CO2 is saturated with moisture, such as postcombustion flue gas. In this work, three newly designed MOFs related to CALF-20, denoted as NU-220, CALF-20M-w, and CALF-20M-e that feature hydrophobic methyltriazolate linkers, are presented. Inclusion of methyl groups in the linker is proposed as a strategy to improve the uptake of CO2 in the presence of water. Notably, both CALF-20M-w and CALF-20M-e retain over 20% of their initial CO2 capture efficiency at 70% RH─a threshold at which CALF-20 shows negligible CO2 uptake. Grand canonical Monte Carlo simulations reveal that the methyl group hinders water network formation in the pores of CALF-20M-w and CALF-20M-e and enhances their CO2 selectivity over N2 in the presence of a high moisture content. Moreover, calculated radial distribution functions indicate that introducing the methyl group into the triazolate linker increases the distance between water molecules and Zn coordination bonds, offering insights into the origin of the enhanced moisture stability observed for CALF-20M-w and CALF-20M-e relative to CALF-20. Overall, this straightforward design strategy has afforded more robust sorbents that can potentially meet the challenge of effectively capturing CO2 in practical industrial applications.

Efficient CO2 Capture and Separation in MOFs: Effect from Isoreticular Double Interpenetration.

Severe CO2 emissions has posed an increasingly alarming threat, motivating the development of efficient CO2 capture materials, one of the key parts of carbon capture, utilization, and storage (CCUS). In this study, a series of metal-organic frameworks (MOFs) named Sc-X (X = S, M, L) were constructed inspired by recorded MOFs, Zn-BPZ-SA and MFU-4l-Li. The corresponding isoreticular double-interpenetrating MOFs (Sc-X-IDI) were subsequently constructed via the introduction of isoreticular double interpenetration. Grand canonical Monte Carlo (GCMC) simulations were adopted at 298 K and 0.1-1.0 bar to comprehensively evaluate the CO2 capture and separation performances in Sc-X and Sc-X-IDI, with gas distribution, isothermal adsorption heat (Qst), and van der Waals (vdW)/Coulomb interactions. It is showed that isoreticular double interpenetration significantly improved the interactions between adsorbed gases and frameworks by precisely modulating pore sizes, particularly observed in Sc-M and Sc-M-IDI. Specifically, the Qst and Coulomb interactions exhibited a substantial increase, rising from 28.38 and 22.19 kJ mol-1 in Sc-M to 43.52 and 38.04 kJ mol-1 in Sc-M-IDI, respectively, at 298 K and 1.0 bar. Besides, the selectivity of CO2 over CH4/N2 was enhanced from 55.36/107.28 in Sc-M to 3308.61/7021.48 in Sc-M-IDI. However, the CO2 capture capacity is significantly influenced by the pore size. Sc-M, with a favorable pore size, exhibits the highest capture capacity of 15.86 mmol g-1 at 298 K and 1.0 bar. This study elucidated the impact of isoreticular double interpenetration on the CO2 capture performance in MOFs.

Accelerating In Silico Discovery of Metal-Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning.

Cryogenic distillation, a currently employed method for C2H4/C2H6 and C3H6/C3H8 mixture separation, is energy-intensive, prompting the research toward alternative technologies, including adsorbent-based separation. In this work, we combine machine learning (ML) technique with high-throughput screening to screen ∼23,000 hypothetical metal-organic frameworks (MOFs) for paraffin (C2H6 and C3H8) selective adsorbent separation. First, structure-based prescreening was employed to remove MOFs with undesired geometric properties. Further, a random forest model built upon the multicomponent grand canonical Monte Carlo (m-GCMC) simulation data of training set MOFs was found to be the most successful in learning the relationship between MOF features and olefin/paraffin mixture separation. Using this technique, the separation performance of the remaining (test set) MOFs was predicted, and the top-performing MOFs were identified. We also employed active learning (AL) to evaluate its effectiveness in improving the prediction of olefin/paraffin selectivity. AL was discovered to be ∼29 times more efficient than the best-supervised ML model, as it was able to identify the top materials in limited training data and at a fraction of computational cost and time as compared to ML techniques. Among the top selected materials, framework chemistry was found to be the most important parameter. Nickel and copper (as a metal node) in a tfzd and hms topological arrangement respectively, were discovered to be a prevalent attribute in high-performing MOFs, further demonstrating the prominent significance of framework chemistry. Additionally, the top MOFs discovered were studied in detail and further compared to the previously reported MOFs. These MOFs show the highest selectivity for C2H4/C2H6 and C3H6/C3H8 mixture separation, as reported until date. The hierarchical strategy devised in this study will facilitate the quick screening of MOFs across multiple databases toward industrially significant separation processes by leveraging molecular simulations and AL.

Cage occupancies of CH4, CO2, and Xe hydrates: Mean field theory and grandcanonical Monte Carlo simulations.

We propose a statistical mechanical theory for the thermodynamic stability of clathrate hydrates, considering the influence of the guest-guest interaction on the occupancies of the cages. A mean field approximation is developed to examine the magnitude of the influence. Our new method works remarkably well, which is manifested by two sorts of grandcanonical Monte Carlo (GCMC) simulations. One is full GCMC, and the other is designed in the present study for clathrate hydrates, called lattice-GCMC, in which each guest can be adsorbed at one of the centers of the cage. In the latter simulation, only the guest-guest interaction is explicitly treated, incorporating the host-guest interaction into the free energy of the cage occupation without other guests. Critical phenomena for guest species, such as large density fluctuations, are observed when the temperature is low or the guest-guest interaction is strong.

A data-guided approach for the evaluation of zeolites for hydrogen storage with the aid of molecular simulations.

This study employs a data-guided approach to evaluate zeolites for hydrogen storage, utilizing molecular simulations. The development of efficient and practical hydrogen storage materials is crucial for advancing clean energy technologies. Zeolites have shown promise as potential candidates due to their unique porous structure and tunable properties. However, the selection and design of suitable zeolites for hydrogen storage remain challenging. Therefore, this work aims to address this materials science question by utilizing molecular simulations and data-guided approaches to evaluate zeolites' performance for hydrogen storage. The results obtained from this study provide valuable insights into the evaluation of zeolites for hydrogen storage. Through molecular simulations, we analyze the adsorption behavior of hydrogen molecules in various zeolite structures. The performance of different zeolite frameworks in terms of hydrogen storage capacity, adsorption energy, and diffusion properties is assessed. Linde type A zeolite (LTA) had the highest capacity with a hydrogen capacity of 4.8wt% out of the 233 investigated zeolites. Furthermore, we investigate the influence of different factors such as mass (M), density (D), helium void fraction (HVF), accessible pore volume (APV), gravimetric surface area (GSA), and largest overall cavity diameter (Di) on the hydrogen storage performance of zeolites. The results show that Di, D, and M have a negative effect on the percentage weight capacity, while GSA and VSA have the highest positive contribution to the percentage weight. This study, therefore, provides new insights into the factors that affect their hydrogen storage capacity by exhibiting the importance of considering multiple factors when evaluating the performance of zeolites and demonstrates the potential of combining different computational methods to provide a more comprehensive understanding of materials. The current study contributes to the understanding of zeolite-based materials for hydrogen storage applications, aiding in the development of more efficient and practical hydrogen storage systems. Computational techniques were employed to investigate the hydrogen storage properties of zeolites. Molecular simulations were performed using classical force fields and molecular dynamics methods. The calculations were carried out at a force field level of theory with the GGA functional. To accurately capture the thermodynamics and kinetics of hydrogen adsorption, enhanced sampling techniques such as Monte Carlo simulations and molecular dynamics with metadynamics were utilized. We employed Grand Canonical Monte Carlo (GCMC) simulations to model hydrogen adsorption in zeolite structures for hydrogen storage. Our approach involved performing a substantial number of Monte Carlo steps (10,000) to ensure system equilibration and precise results. We defined a cutoff distance for particle interactions as 12.5 Ǻ and considered 0.000e framework charge per cell and 0.000e sorbate charge in energy calculations. The choice of an appropriate simulation cell size (50 × 50 × 50) Ǻ was crucial, mirroring real-world conditions. We specified lower and upper fugacity values (1 to 10atm) to capture the range of gas pressures in the simulations. These methodical steps collectively enabled us to accurately model hydrogen adsorption within zeolites, forming the core of our hydrogen storage evaluation. In this research, we utilized DFT calculations to thoroughly investigate the interactions between zeolites and hydrogen. We employed pseudopotentials to describe electron behavior in zeolite systems, choosing them in line with DFT norms and basis set compatibility. Our simulation cell design replicated zeolite periodicity and eliminated boundary effects. Pre-geometry optimization was performed with HyperChem29, ensuring stable conformations with strict convergence criteria. We utilized 6-31 + G(d) and LanL2DZ basis sets for light and heavy atoms, aligning with field standards for computational efficiency and precision. A machine learning algorithm was used to rank the importance of various structural features such as mass (M), density (D), helium void fraction (HVF), accessible pore volume (APV), gravimetric surface area (GSA), and largest overall cavity diameter (Di) and how they affect the capacity of the zeolites. Machine learning analysis was performed with the Scikit-learn library, an open-source Python tool. We employed a range of machine learning models, including SVMs, random forests, and neural networks, primarily for data analysis and feature extraction. Pearson correlation analysis, a classical statistical technique, was used to evaluate linear relationships between variables and assess the strength and direction of these relationships. It served as a complementary tool to understand the interplay of variables in our dataset, distinguishing it from machine learning algorithms. Further quantum chemical calculations were also performed to calculate the adsorption energy, global reactivity electronic descriptors, and natural bond orbital analysis in order to provide insights into the interaction of the zeolites with hydrogen. The simulations and data analysis were performed using BIOVIA material studio software, Gaussian, and Origin Pro software.

Flow Channel with Recognition Corners in a Stable La-MOF for One-Step Ethylene Production.

Single-step ethylene (C2H4) production from acetylene (C2H2), ethylene (C2H4), and ethane (C2H6) mixtures was realized via the strategy of a flow channel with recognition corners in MOF NTUniv-64. Both the uptake amounts and the enthalpy of adsorption (Qst) showed the same order of C2H2 > C2H6 > C2H4. Breakthrough testing also verified the above data and the C2H4 purification ability. Grand Canonical Monte Carlo (GCMC) simulations indicated that uneven corners could precisely detain C2H2 and C2H6, in which the C-H···π interaction distance between C2H2 (2.84 Å) and C2H6 (3.03 Å) and the framework was shorter than that of C2H4 (3.85 Å).

Theoretical analysis and numerical simulation of methane adsorption behavior on rough surfaces featuring fractal property

This work focuses on the control mechanism of adsorption behavior from rough surfaces featuring fractal property numerically. For that, we develop an equivalent characterization method for rough fractal surfaces, reproduce the adsorption process of methane on the surface by combining molecular dynamics and Grand canonical Monte Carlo (GCMC) simulations, analyze the control mechanisms of complex types of surface geometry on the methane adsorption process, and clarity the ratio of vertical roughness to horizontal complexity as key parameter to describe the adsorption capability. Our numerical results indicate that the adsorption density of methane in fractal and non-fractal slit pores varies with the fractal behavior of surface geometry, while the density of gas phase methane remains unaffected (all distributed around 0.06 g/cm3). And, the complexities of surface geometry process various effects on occurrence mode, adsorption potential energy (ɛ), and adsorption amounts. The surface original complexity will influence the force field, and alter the occurrence mode from monolayer adsorption to pore filling. Meanwhile, the width-to-depth ratio of surface original complexity has a negatively effects the ɛ of methane. The behavioral complexity of the surface reflects the number of small pores on it. For fractal slit pores, the maximum adsorption amounts are 2405.77, 2634.90, and 2749.46 mol/m3, respectively. They are smaller than that of the non-fractal slit pore, which is due to the increase of non-adsorption pores for the small diameter of pores. Above results give a clear explanation to the effects of fractal property on methane adsorption, which will provide a reference to study the adsorption process of methane in Coalbed Methane reservoir.

The impact of trace amounts of CO2 on the high-pressure adsorption of CH4 on 5A zeolite

The impact of trace amounts of CO2 on CH4 adsorption in a zeolite at elevated pressure was examined using experimental, binary adsorption isotherms, and molecular simulations. CH4 upgrading is important for a variety of applications, and the use of zeolites for CH4 upgrading has been considered previously but, to the best of our knowledge, not at pressures between 60 and 70 bar. The non-ideality of the adsorption was examined by comparing the experimental loadings to predicted loadings from the Ideal Adsorbed Solution Theory (IAST). When adsorption occurs at a total pressure of 58–66 bar, and a CO2 fraction below 0.42 mol %, there was less than 5 % error in the CH4 IAST predicted loading compared to the experimental value, but the deviation in CO2 loading between IAST and experiments, even at these low CO2 mole percentages, varied nominally from 4 to 13 %. Above 0.42 mol % deviations from IAST were more severe. Grand Canonical Monte Carlo simulations captured the trends in the adsorption data and illustrated how CO2 molecules align in the pore when adsorbed such that oxygens on both ends of the molecule can interact with cations in the zeolite. The results highlight the applicability of adsorbent materials to separate CO2 from process streams at elevated pressure and provide molecular insights that can aid the design of adsorbent structures.

Water adsorption on MoS2 under realistic atmosphere conditions and impacts on tribology.

Molybdenum disulfide (MoS2) is a 2D material widely used as a dry lubricant. However, exposure to water and oxygen is known to reduce its effectiveness, and therefore an understanding of the uptake of water is important information for mitigating these effects. Here we use grand canonical Monte Carlo simulations to rigorously study water adsorption on MoS2 surfaces and edges with different concentrations of defects under realistic atmospheric conditions (i.e. various temperatures and humidity levels). We find that the amount of water adsorbed depends strongly on the number of defects. Simulations indicate that defect sites are generally saturated with water even at low ppm levels of humidity. Water binds strongly to S vacancies on interlamellar surfaces, but generally only one water molecule can fit on each of these sites. Defects on surfaces or edges of lamellae also strongly attract water molecules that then nucleate small clusters of water bonded via hydrogen bonding. We demonstrate that water preferentially binds to surface defects, but once those are saturated at a critical humidity level of about 500-1000 ppm water, water binds to edge sites where it negatively impacts the tribological performance of MoS2.

Sodium‐Decorated SiB Monolayer for Reversible Hydrogen Storage: High‐Capacity Achieved by Carbon Doping

Based on the first‐principles calculation and grand canonical Monte Carlo (GCMC) simulation, the reversible hydrogen storage of Na‐decorated silicon boron (SiB) and C‐doped SiB monolayers have been investigated. For double side 4Na‐decorated SiB monolayer, each Na atom can adsorb five H2 molecules with the average adsorption energy of −0.18 eV/H2, yielding an H2 gravimetric density of 9.09 wt% and desorption temperature TD of 227 K. To further improve the H2 gravimetric density and desorption temperature, doping effect was employed to enhance the internal electric field between Na atom and SiB substrate. When two Si atoms in the SiB monolayer were replaced by C atoms, the adsorption energy for H2 was significantly improved. The H2 gravimetric density reaches to 10.63 wt% with the average adsorption energy of −0.22 eV/H2. Meanwhile, the desorption temperature TD was raised from 227 to 282 K, reaching the ideal condition near room temperature. In addition, the GCMC simulations further confirm that the H2 gravimetric density can fully meet the latest hydrogen storage target (5.5 wt%) for both Na‐decorated SiB monolayer and Na‐decorated C‐doped SiB monolayer. Our results indicate that both Na‐modified SiB and C‐doped SiB monolayers can be used as promising materials for reversible hydrogen storage.

Molecular simulation of [P8883][Tf2N] ionic liquid decorated silica in 6FDA-ODA based mixed matrix membrane for enhanced CO2/CH4 separation.

Mixed-matrix membranes (MMMs) have been reported to have considerable scope in gas separation applications because of their merged inherent strength of a durable polymer matrix and the exceptional performance capabilities of inorganic fillers. The selection of comparatively suitable polymers with fillers that can match each other and boost interfacial compatibility while ensuring uniform dispersion of filler within the polymer is still intensively demanding and is challenging at the experimental scale. Ionic liquids (ILs) are effective in promoting better dispersion and compatibility, leading to improved separation performance. A computational molecular simulation approach is employed in current work to design a hybrid membrane having Trioctapropyl phosphonium bis(trifluoromethylsulfonyl)imide [P8883][Tf2N] IL decorated silica as a filler and 4,4'-(hexafluoroisopropylidene)diphthalic anhydride-4,4'-oxydianiline (6FDA-ODA) polymer for carbon dioxide (CO2) separation from methane (CH4). Thermophysical and gas transport properties under pure and mixed gas condition (30, 50, and 70% CO2/CH4) within the MMMs with varying filler loadings (5, 10, and 15 wt% IL-silica) are examined via Grand Canonical Monte Carlo (GCMC) and Molecular Dynamics (MD) simulations. Membrane characteristics like glass transition temperature (T g), Fractional Free Volume (v f), X-Ray Diffraction (XRD), solubility, diffusivity, permeability, and selectivity for neat and IL-silica filled 6FDA-ODA are computed. The results show that the T g of the composite membrane with 5 wt% IL-silica is found to be considerably higher (with 305 °C) than that of the pure 6FDA-ODA polymer having 298 °C. A higher T g value highlights the effective dispersion and higher adhesion between the filler and polymer membrane. Additionally, CO2 permeability for 5 wt% IL-silica/6FDA-ODA MMM is significantly improved, measuring 319.0 barrer while maintaining a CO2/CH4 selectivity of 16.2. These values are 89% and 56% respectively, greater than the corresponding values of neat 6FDA-ODA membrane. Published data from the literature review is used to validate the findings and guarantee their reliability. The obtained results exhibited an error in the range of 0.7-9%. Hence, it is concluded from the study that molecular simulation can be used to design IL decorated silica incorporated within 6FDA-ODA matrix, which is able to boost the interfacial compatibility, with elevated CO2/CH4 selectivity and CO2 permeability.

Molecular recognition-induced structural flexibility in ZIF-71

The adsorption performance of ZIF-71 towards two common volatile organic compounds, chlorobenzene and phenol, has been evaluated using a number of experimental techniques and Grand Canonical Monte Carlo (GCMC) simulations.

Research on CO2/CH4/N2 competitive adsorption characteristics of anthracite coal from Shanxi Sihe coal mine.

This study aims to solve the problem of unsatisfactory development and utilization of coalbed methane and CO2 storage efficiency. It is focused on the adsorption behavior of CO2, CH4, and N2 in the macromolecular structure model of Shanxi Sihe coal mine anthracite, as well as the competitive adsorption behavior of CO2/CH4 and CH4/N2 binary gas mixtures with different ratios. Experimental analysis such as elemental analysis, solid 13C nuclear magnetic resonance (13C NMR), Fourier transform infrared (FT-IR) spectroscopy, X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) analysis were used to construct the Shanxi Sihe coal mine model of the macromolecular structure of anthracite coal. The Grand Canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulation methods were used to study the adsorption capacity and heat characteristics of CO2, CH4, and N2 at different temperatures using a molecular model of anthracite coal from Shanxi Sihe coal mine, as well as the competitive adsorption characteristics of CO2/CH4 and CH4/N2 binary mixtures. The mechanism of the influence of temperature and gas properties on the adsorption capacity and heat of adsorption was revealed from a microscopic perspective. The results indicated that the aromatic carbon content of anthracite in the Sihe coal mine, Shanxi is 81.19%, and the ratio Xbp of aromatic bridgehead carbon to surrounding carbon is 0.489. The aromatic structure is mainly composed of pyrene and anthracene. The molecular formula of the macromolecular structure model of anthracite in Shanxi Sihe coal mine is C233H157O13N2. The adsorption capacity and equivalent adsorption heat of the macromolecular model for adsorbing single-component gas CO2/CH4/N2 decrease with the increase in temperature. The temperature has the greatest impact on CO2 adsorption capacity and adsorption heat, followed by CH4 and N2. Under the competitive adsorption conditions of CO2/CH4 and CH4/N2 binary mixtures, the higher the partial pressure of a single-component gas in the mixture, the greater the adsorption capacity of the gas. The difference in the adsorption heat of CH4 and N2 is smaller than that of CH4 and CO2. The conclusions obtained from the study can provide technical and theoretical support for formulating reasonable drainage methods for coalbed methane wells.

Hydrophobic functionalization of a metal-organic framework as an ammonia visual sensing material under high humidity conditions.

Since exhaled ammonia (NH3) is one of the metabolic markers of liver and kidney diseases, ammonia visual sensing materials in humid environments have received extensive attention and investigation. Herein, through a tailor-made pore environment provided by metal-organic framework (MOF) materials (CH3-Cu(BDC)), we achieved NH3 anti-interference sensing with apparent color changing under humid conditions. With methyl (CH3-) functionalization, CH3-Cu(BDC) demonstrated a strong response for trace ammonia and showed high selectivity under a humid environment. Grand canonical Monte Carlo (GCMC) simulations indicated that CH3-Cu(BDC) showed stronger attraction towards NH3 molecules than H2O. Benefiting from the target changing coordination environment, CH3-Cu(BDC) showed a rapid response and simple analysis properties for patients' exhaled air. The strategy used in this study not only provides a demonstration case for NH3 colorimetric sensing with high humidity and anti-interference but also introduces a new method for painless and quick exhaled breath analysis for diagnosis of patients with kidney and liver diseases.

Machine learning demonstrates the impact of proton transfer and solvent dynamics on CO2 capture in liquid ammonia.

Direct air capture of CO2 using supported amines provides a promising means to achieve the net-zero greenhouse gas emissions goal; however, many mechanistic details regarding the CO2 adsorption process in condensed phase amines remain poorly understood. This work combines machine learning potentials, enhanced sampling and grand-canonical Monte Carlo simulations to directly compute experimentally relevant quantities to elucidate the mechanism of CO2 chemisorption in liquid ammonia as a model system. Our simulations suggest that CO2 capture in the liquid occurs in a sequential fashion, with the formation of a metastable zwitterion intermediate. Furthermore, we identified the importance of solvent-mediated proton transfer and solvent dynamics, not only in the reaction pathway but also in the efficiency of CO2 chemisorption. Beyond liquid ammonia, the methodology presented here can be readily extended to simulate amines with more complex chemical structures under experimental conditions, paving the way to elucidate the structure-performance of amines for CO2 capture.

Investigating the impact of pore structure and surface chemistry on CO2 adsorption in graphitic slit-pores using GCMC simulation

Carbon capture, storage, and utilization from post-combustion processes using adsorption phenomena has emerged as a promising solution to the greenhouse gas emission crisis. The capacity of solid porous adsorbents dictates the performance of such carbon capture processes. Developments of porous adsorbents with favourable structural and chemical characteristics of the pore have been a major research area. Computational tools, notably the Grand Canonical Monte Carlo (GCMC) simulation, are widely employed to characterize the adsorption process in crystalline adsorbents like metal-organic frameworks and zeolites. Activated carbons are becoming promising alternatives to conventionally-used zeolite adsorbents for CO2 adsorption, because of their abundant microporosity and cost performance. However, the amorphous nature of the activated carbon materials poses a challenge in accurately modelling their pore characteristics and their adsorption process. Therefore, this study systematically explores the effect of pore size distribution and type of functional groups on the adsorption of CO2 on activated carbons using a simplified slit-pore graphite structure representing the activated carbon adsorbent. Four different pore sizes (7 Å, 8.9 Å, 18.5 Å, and 27.9 Å) and three oxygen-containing functional groups (Carbonyl, Hydroxyl, and Carboxyl) were selected to model the graphite structures. Results from the GCMC simulation reveal a significant rise in the CO2 adsorption capacity (from 4 mmol/g to 21 mmol/g) as the pore size was increased from 7 Å to 27.9 Å. Likewise, the functional groups enhance the low-pressure adsorption process by reducing the onset pressure of the pore filling by a factor of 100, especially in ultra-micropores. Likewise, we demonstrate the increase in the isosteric heat of adsorption due to the reduction in the pore size and the presence of functional groups. Additionally, the study illustrates the adsorbed phase behaviour of CO2 concerning the pore characteristics, a facet often overlooked in the existing literature. The adsorbed phase local density and molecular orientation distribution are analysed to understand the variation in the adsorption uptake and isosteric heat of adsorption properties. The study further identifies the adsorbed phase monolayer to multilayer transition and the ‘T′-shaped orientation of the adsorbed CO2 molecules as the key contributors to the high isosteric heat of adsorption in 8.9 Å pore size. It is envisaged that this study will navigate the precision adsorbent development for an efficient carbon capture process.

Fine Tuning Metal-Organic Frameworks with Halogen Functional Groups for Ethylene Purification.

One-step C2H4 purification from a mixture of C2H2/C2H4/C2H6 could be achieved by metal-organic framework (MOF) NTUniv-70 with an F-functional group. The selectivities of C2H4/C2H6 and C2H4/C2H2 of NTUnvi-70 based on ideal adsorbed solution theory were at least twice that of the original MOF platform, which was in line with the enthalpy of adsorption (Qst) and breakthrough testing. Grand canonical Monte Carlo simulations indicated that the C-H···F interactions played an important role in enhanced C2H4/C2H6 and C2H4/C2H2 adsorption selectivities.