Search

Experimental and simulation studies for carbon dioxide (CO2) adsorption on porous silica glass were performed to reveal how surface heterogeneity can affect the adsorption mechanism of CO2. In performing the simulation, the structure of porous silica glass was modeled as a slit pore consisting of parallel walls of connected SiO4 units. The adsorption isotherms of CO2 at 283 K were generated for a series of pore widths using a Monte Carlo ensemble. The defective surfaces created by random removal of surface atoms and the surfaces containing hydroxyl functional groups were chosen to represent the surface heterogeneity for the simulation tasks. The isotherms derived for the defective surfaces showed a rapid adsorption at low pressures because of the stronger interaction between the rough nonuniform surfaces and CO2 molecules. For the role of surface functional groups, the adsorption isotherms dramatically increased with an increasing number of functional groups. The amount of CO2 adsorbed for randomly placed functional groups was greater than that for the presence of functional groups at the pore edges. The proper control of surface heterogeneity by manipulating both the amounts of hydroxyl surface groups and surface defects should help enhance the efficient capture of CO2 in porous silica glass.

We report a systematic molecular simulation study of the behavior of Lennard‐Jones fluids inside nanopores of diverse shapes, focusing on the effect that the pore geometry and the local energetic environment have on the adsorption isotherms. Infinitely long pores with polygon (triangle, square, pentagon, hexagon, octagon, decagon and circle) cross sections are considered. Three different pore sizes commensurate with the molecular diameters along with three different values of fluid‐solid energy interactions are chosen to perform Grand Canonical Monte Carlo simulations at a subcritical temperature. Overall, the effect of nanoconfinement on the adsorption of fluids is seen to be a delicate balance between the geometric packing restrictions imposed by the hard cores of the molecules and the surfaces, the excess adsorption induced by the presence (or absence) of energetically favored “hot spots” and the overall ratio of surface/bulk fluid volume present in the pore.

The prediction of protein-ligand binding affinities using free energy perturbation (FEP) is becoming increasingly routine in structure-based drug discovery. Most FEP packages use molecular dynamics (MD) to sample the configurations of proteins and ligands, as MD is well-suited to capturing coupled motion. However, MD can be prohibitively inefficient at sampling water molecules that are buried within binding sites, which has severely limited the domain of applicability of FEP and its prospective usage in drug discovery. In this paper, we present an advancement of FEP that augments MD with grand canonical Monte Carlo (GCMC), an enhanced sampling method, to overcome the problem of sampling water. We accomplished this without degrading computational performance. On both old and newly assembled data sets of proteinligand complexes, we show that the use of GCMC in FEP is essential for accurate and robust predictions for ligand perturbations that disrupt buried water.

Networks of water molecules can play a critical role at the protein–ligand interface and can directly influence drug–target interactions. Grand canonical methods aid in the sampling of these water molecules, where conventional molecular dynamics equilibration times are often long, by allowing waters to be inserted and deleted from the system, according to the chemical potential. Here, we present our open source Python module, grand (https://github.com/essex-lab/grand), which allows molecular dynamics simulations to be performed in conjunction with grand canonical Monte Carlo sampling, using the OpenMM simulation engine. We demonstrate the accuracy of this module by reproducing the density of bulk water observed from constant pressure simulations. Application of this code to the bovine pancreatic trypsin inhibitor protein reproduces three buried crystallographic water sites that are poorly sampled using conventional molecular dynamics.

Corrosion inhibitors are one of the most effective anticorrosion techniques in reinforced concrete structures. Molecule dynamics (MD) was usually utilized to simulate the interaction between the inhibitor molecules and the surface of Fe to evaluate the inhibition effect, ignoring the influence of cement hydration products. In this paper, the adsorption characteristics of five types of common alkanol-amine inhibitors on C-S-H gel in the alkaline liquid environment were simulated via the MD and the grand canonical Monte Carlo (GCMC) methods. It is found that, in the MD system, the liquid phase environment had a certain impact on the adsorption configuration of compounds. According to the analysis of the energy, the binding ability of MEA on the surface of the C-S-H gel was the strongest. In the GCMC system, the adsorption of MEA was the largest at the same temperature. Furthermore, for the competitive adsorption in the GCMC system, the adsorption characteristics of the inhibitors on the C-S-H gel were to follow the order: MEA&gt, DEA&gt, TEA&gt, NDE&gt, DETA. Both MD and GCMC simulations confirmed that the C-S-H gel would adsorb the organic inhibitors to a different extent, which might have a considerable influence on the organic inhibitors to exert their inhibition effects.

In this paper, we report the results of hydrogen adsorption properties of a new 2D carbon-based material, consisting of pentagons and octagons (Penta-Octa-Penta-graphene or POP-graphene), based on the Grand-Canonical Monte Carlo simulations. The new material exhibits a moderately higher gravimetric uptake at cryogenic temperatures (77 K), as compared to the regular graphene. We discuss the origin of the enhanced uptake of POP-graphene and offer a consistent explanation.

Recent progress in molecular simulation technology has grown an interest to modernize usual computational methods and approaches. For instance, most of the theoretical work on hydrogen adsorption on carbon nanotubes has been conducted a decade ago. It should be insightful to reinvestigate the field and take advantage of code improvements and features implemented in contemporary software. One example of such features is the pulling simulation modules now available in many molecular dynamics programs. We conduct pulling simulations on pairs of carbon nanotubes and measure the inter&mdash, tube distance before they dissociate in water. We use this distance to set the interval size between adjacent nanotubes as we arrange them in bundle configurations. We consider bundles with triangular, intermediate and honeycomb patterns and armchair nanotubes having a chiral index from n = 5 to n = 10. Then, we simulate low pressure hydrogen adsorption isotherms at 77 K, using the grand canonical Monte Carlo method. The different bundle configurations adsorb great hydrogen amounts that may exceed 2% wt at ambient pressures. The computed hydrogen capacities are considered large for physisorption on carbon nanostructures and attributed to the ultra-microporous network and extraordinary high surface area of the configured models.

International audience; Modelling gas adsorption of porous materials is nowadays an undeniable necessary in order to complement experiment findings with the purpose to enrich our fundamental understanding of adsorption mechanisms as well as develop better performing materials for gas mixture separation. In this contribution, we explore the possibility to use first-principles molecular dynamics (FPMD) and grand canonical Monte Carlo (GCMC) simulations to target the gas adsorption of disordered nanoporous chalcogenides (i.e. chalcogels). This computational scheme allows us to take advantage of the ability of FPMD to accurately describe the structure and bonding of the disordered nature of chalcogels as well as the potential of GCMC to model the adsorption mechanisms of porous networks. We assess the versatility of such scheme by evaluating the role of pore size, chemical stoichiometry and composition for multiple chalcogenide-based systems on nitrogen adsorption isotherms.

Classical molecular simulations can provide significant insights into the gas adsorption mechanisms and binding sites in various metal-organic frameworks (MOFs). These simulations involve assessing the interactions between the MOF and an adsorbate molecule by calculating the potential energy of the MOF-adsorbate system using a functional form that generally includes nonbonded interaction terms, such as the repulsion/dispersion and permanent electrostatic energies. Grand canonical Monte Carlo (GCMC) is the most widely used classical method that is carried out to simulate gas adsorption and separation in MOFs and identify the favorable adsorbate binding sites. In this review, we provide an overview of the GCMC methods that are normally utilized to perform these simulations. We also describe how a typical force field is developed for the MOF, which is required to compute the classical potential energy of the system. Furthermore, we highlight some of the common analysis techniques that have been used to determine the locations of the preferential binding sites in these materials. We also review some of the early classical molecular simulation studies that have contributed to our working understanding of the gas adsorption mechanisms in MOFs. Finally, we show that the implementation of classical polarization for simulations in MOFs can be necessary for the accurate modeling of an adsorbate in these materials, particularly those that contain open-metal sites. In general, molecular simulations can provide a great complement to experimental studies by helping to rationalize the favorable MOF-adsorbate interactions and the mechanism of gas adsorption.

We employed the molecular dynamics and Grand Canonical Monte Carlo methods and studied effects of some non-structural cations (Na+, K+, Rb+, Cs+, Mg2+, Ca2+, and Cu2+) in ethanol adsorption by Linde type L (LTL) zeolite. Results reveal that Mg2+ donates the highest affinity to zeolite for interaction with ethanol molecules. It is shown that the hopping of ethanol molecules between the adjacent cages of the zeolite is not significant in the z-direction. In addition, increasing temperature does not change the high affinity of the Mg2+-containing zeolite toward ethanol molecules but increases displacements of the molecules in the pores of the zeolite. Higher loadings of ethanol molecules in the Mg2+-LTL zeolite structure are studied to investigate the loading dependency of ethanol displacement in the Mg2+-LTL zeolite. Consequently, we propose a mechanism for the filling process of LTL cages by ethanol.

Grand canonical Monte Carlo simulations are performed to study the adsorption of water at 300 K in realistic molecular models of three activated carbon samples, namely, CS400, CS1000, and CS1000a. ...

High-efficiency absorbent is a prerequisite for CO2 capture from the atmosphere to alleviate environmental degradation. Herein, grand canonical Monte Carlo (GCMC) was used to investigate the CO2 adsorption and separation behaviors in four alkyl anime functionalized triphenylamine-based covalent organic frameworks (TPA-xC-NH2, x = 1–4). Results show that a longer alkyl anime chain has a more remarkable effect on the CO2 adsorption capacity and separation over N2. The alkyl amine chain owning four carbon atoms of TPA-4C-NH2 exhibits the best performance. The CO2 adsorption capacity in TPA-4C-NH2 is 3.41 mmol/g at 298 K and 5.26 mmol/g at 273 K and 1 bar, thereby enhancing the CO2 adsorption capacity by 50.2% and 30.5%, respectively, with a 121% selectivity increase compared with that of the pristine TPA-COF. The pore characteristic, isosteric heat, coulomb/non-coulomb interaction, and selectivity are analyzed to elucidate the enhancement mechanisms.

Grand canonical Monte Carlo (GCMC) simulations were performed to investigate hydrogen sorption in the rht-metal–organic framework (MOF) NU-111, which is composed of Cu2+ ions coordinated to 1,3,5-tris[(1,3-carboxylate-5-(4-(ethynyl)phenyl))butadiynyl]-benzene linkers. This MOF was shown to have a high Brunauer–Emmett–Teller surface area of 5000 ± 80 m2 g–1 and a hydrogen uptake of 20.8 mg g–1 at 77 K and 1 bar. It has a similar structure to that of PCN-61, which was studied previously by our group [Forrest, K. A.; et al. J. Phys. Chem. C 2012, 116, 15538−15549]; however, NU-111 has an extra triple bond on each arm in the linker body. The purpose of this study was to investigate the effects of linker extension on the MOF–sorbate interactions using PCN-61 as a basis for comparison. Three hydrogen models of varying complexities were used in the simulations: a single-site model, which only includes van der Waals interactions, a five-site model, which includes van der Waals and permanent electrostatic interact...

Grand Canonical Monte Carlo simulation of methanol adsorption on a graphite model with two hydroxyl groups grafted on the surface has been carried out to investigate the effects of temperature in the range of 278–360 K. The spacing between the OH groups was chosen so that two hydrogen bonds could be formed with the first methanol molecule. In the Henry law region, the isosteric heat at zero loading is greater than the condensation heat. When the loading is increased, the isosteric heat at low temperatures decreases slightly and exhibits a shoulder, which is associated with the formation of a cluster of methanol molecules around one OH group. On further increase in loading, the adsorbate–adsorbate interactions decrease because methanol begins to adsorb on the other OH group, resulting in a sharp decrease in the isosteric heat to a minimum, at which point both OH groups are covered with methanol molecules. At higher temperatures the isosteric heat at zero loading decreases but remains higher than the condensation heat. The shoulder heat is progressively diminished with temperature because methanol molecules are distributed over the two OH groups, due to the entropic effects. Interestingly, the minimum heat still occurs when the functional groups are covered and is even more pronounced at high temperatures.

Exploring high-performing adsorption-driven heat pumps (AHPs) remains a challenging task owing to the low working capacity, high regeneration temperature, and low energy efficiency of conventional adsorbents. Quick discovery of the novel promising adsorbents could help to improve the coefficient of performance of AHPs for heating (COPH) and cooling (COPC). Herein, we reported an approach to identify the high-performing covalent-organic frameworks (COFs) for heating, cooling, and ice making by high-throughput computational screening based on grand canonical Monte Carlo simulations and, for the first time, machine learning. It was demonstrated that compared with metal-organic frameworks (MOFs), COFs were more suitable adsorbents of AHPs for cooling because of their weak interaction toward ethanol that favors stepwise adsorption. Structure-property relationship analysis revealed that the average enthalpy of adsorption commensurate with the enthalpy of evaporation will benefit the performance of AHPs besides the high working capacity and low step positions of adsorption isotherms. In order to reduce the computational cost of screening, a random forest model was developed to successfully predict the COPC of both COFs and MOFs.

Open framework materials (OFMs) such as metal–organic frameworks (MOFs) can provide structurally and chemically tailorable nanopores. This exceptional tunability has allowed for careful positioning of optimal adsorption sites within MOF pores to enable selective CO2 physisorption, making these materials promising for energy-efficient CO2 capture. However, given the multitude of features that can be simultaneously altered within the thousands of MOFs synthesized to date, it can be daunting to elucidate the most critical features for boosting CO2 capture capabilities. Here we use a multiscale approach—density functional theory (DFT), grand canonical Monte Carlo (GCMC), and machine learning (ML)—to investigate the role of various pore chemical and topological features in the enhancement of CO2 capture metrics of MOFs. To enable a thorough “sweep” of a target region of MOF structure-space, we used computational synthesis methods to create sets of MOFs encompassing all possible combinations of 16 topologies an...

In this paper we report the use of Grand Canonical Monte Carlo (GCMC) simulations to characterize the competitive trapping of CO and N2 molecules into clathrates, for various gas compositions in the temperature range from 50 to 150 K. The simulations evidence a preferential trapping of CO with respect to N2. This leads to the formation of clathrates that are preferentially filled with CO at equilibrium, irrespective of the composition of the gas phase, the fugacity, and the temperature. Moreover, the results of the simulations show that the small cages of the clathrate structure are always filled first, independent of either the guest structure or the temperature. This issue has been associated with the rather significant differences in the calculated heats of encapsulation (∼2–3 kJ/mol) between the smallest and the largest cages. In addition, calculations with the simplified ideal adsorbed solution theory (IAST) are developed to allow a comparison with the results arising from the GCMC simulations. Inter...

Chemical warfare agents (CWAs) are regarded as a critical challenge in our society. Here, we use a high-throughput computational screening strategy backed up by experimental validation to identify and synthesize a promising porous material for CWA removal under humid conditions. Starting with a database of 2,932 existing metal–organic framework (MOF) structures, we selected those possessing cavities big enough to adsorb well-known CWAs such as sarin, soman, and mustard gas as well as their nontoxic simulants. We used Widom method to reduce significantly the simulation time of water adsorption, allowing us to shortlist 156 hydrophobic MOFs where water will not compete with the CWAs to get adsorbed. We then moved to grand canonical Monte Carlo (GCMC) simulations to assess the removal capacity of CWAs. We selected the best candidates in terms of performance but also in terms of chemical stability and moved to synthesis and experimental breakthrough adsorption to probe the predicted, excellent performance. Th...

In this paper, different types and amounts of oxygen functional groups are inserted in graphitic slit pores to investigate the individual effect of each factor, which aims to clarify the effects of functional groups in activated carbon on acetone adsorption based on molecular simulation. Adsorption isotherms of acetone are then calculated by using Grand Canonical Monte Carlo (GCMC) simulation. Simulation results show that acetone adsorption capacity is increased at low fugacity after inserting oxygen-containing functional groups, however, equilibrium adsorption capacity is decreased. Along with the increase of amounts of functional group, the acetone adsorption loading is not always increased due to the reduction of available adsorption sites on pore wall. Also, it is concluded that trace acetone (10ppmv) is adsorbed mainly in 1.0 nm pore, in which the oxygen content is 5 wt% and the functional group planted in is hydroxyl that is the most beneficial one to trace acetone adsorption.

Molecular simulations have been employed to explore at the microscopic scale the adsorption of CO in zeolites (MFI, CHA and DDR). On the basis of classical force fields, grand canonical Monte Carlo simulations are performed to predict the adsorption properties (isotherms) of these types of zeolites up to high pressure. Subsequent careful analysis yields details the microscopic mechanism in play, along the whole adsorption process, together with a considering of the arrangements of CO in MFI at high pressure. This work also summarizes an approach which uses single component diffusion data in prediction of multicomponent diffusion. Validerad;2018;Nivå 2;2018-08-07 (rokbeg)

The electrical double layer (EDL) profiles of electrical potential within charged slit-type pores is calculated in this work via a combination of grand canonical Monte Carlo (GCMC) simulations and electrodynamics concepts. The electrical potential distribution inside the pore is calculated with respect to field-free virtual bulk electrolyte solution implicit in the grand canonical ensemble. The GCMC simulations for slit-type pores performed in this work show that entropy effects lead to the exclusion of co-ions from the pore to allow densely packed counterions to efficiently neutralize surface charge. This phenomenon, in conjunction with the fact that electrical effects are long range with respect to the source charge, leads to a semioscillatory behavior of the potential profiles not predicted by classical theory. While bulk conditions in terms of ion concentrations are achieved in the center of the pore for most pore sizes studied in this work, electrical bulk conditions are not achieved except for the l...

Grand canonical Monte Carlo (GCMC) simulations have been performed to investigate the adsorption and separation behavior of ternary and quaternary gaseous mixtures of CO2, along with H2S, SO2, and N2, in bundles of aligned double-walled carbon nanotubes with a diameter of 3 nm and an intertube distance of 0.5 nm. All of the simulations are performed at 303 K and at pressures varying between 0 and 3 bar. The GCMC results are then compared to the ideal adsorbed solution theory (IAST) predictions. For the ternary mixture H2S–CO2–N2, the results show that CO2 has the highest adsorption among the three components. The IAST predictions agree reasonably well with the GCMC data for the ternary mixture, except for H2S. For the quaternary mixture H2S–SO2–CO2–N2, it is observed that initially CO2 has the highest adsorption up until around 2 bar, whereafter there is a crossover by SO2 to have the highest adsorption. IAST fails to predict the adsorption behavior of the quaternary mixture involving SO2.

This work presents a multi-scale approach to reacting and expanding polyurethane (PU) foams modeling and simulation. The modeling strategy relies on two pillars: an atomistic model (molecular dynamics (MD)/Grand Canonical Monte Carlo (GCMC)) that provides liquid mixture density and reactant solubility and a continuum model (CFD) in which the expansion characteristics of the foam is modeled exploiting the results of the atomistic simulations. The resulting coupled model is validated for two different PU systems applied in four batches with chemical and physical blowing agents. The results demonstrate the efficacy and reliability of the developed model in the simulation of different PU foam properties such as apparent density and temperature evolutions.

Metal-organic frameworks (MOFs) have been rapidly developed in the fields of gas adsorption and storage, but are unsatisfactory in NH 3 adsorption due to their unstable structures. Considering NH 3 is both a toxic gas and the only carbon-free chemical energy carrier, MOFs exhibit great prospects for the adsorption and storage of NH 3 due to their advantages of structural diversity, modifiable structures and high surface area. In this work, we have researched, in detail, the structural characteristics, stability and NH 3 adsorption properties of MIL-53, NH 2 -MIL-53, MIL-100 and MIL-101. In addition, the NH 3 adsorption sites have been investigated using Grand canonical Monte Carlo simulations. The results showed that MIL-100 and MIL-101 have a large NH 3 uptake of 8 mmol/g and 10 mmol/g, respectively, and that the modified amino functional groups improve the NH 3 adsorption capacity in NH 2 -MIL-53. More importantly, the four MIL materials have reusable NH 3 uptake and excellent NH 3 (and NH 3 /H 2 O) stability, and are promising materials for NH 3 adsorption.

The current study utilizes Grand Canonical Monte Carlo simulations in order to calculate the amount of methane gas that can be stored inside hydrate structures sII and sH. The temperature and pressure conditions examined include those of interest to the industrial methane transportation, and the storage of methane in industrial settings or naturally-occurring hydrate deposits. To this purpose, the amount of methane stored in the different type of cages that form the hydrate structures is presented as Langmuir-type “absorption isotherms”, which are functions of pressure, temperature and hydrate structure. The obtained expressions are subsequently used in order to identify conditions (i.e., pressure temperature, hydrate promoter molecular weight, and cage fraction occupied by the hydrate promoter) where the considered hydrate structures can have storage capacities that are higher than the storage capacity of the more common sI methane hydrate. The results are also discussed in the context of storage capacities of natural hydrates that are under conditions of existing hydrate-bearing sediments.

Understanding of the absolute adsorption behavior of CH4 on shale is critically important in estimating shale gas storage in shale gas reservoirs. In this work, two approaches are applied to obtain the absolute adsorption isotherms of CH4 on shale samples. In the first approach, we first measure the excess adsorption isotherms of CH4 on two shale samples at the temperature of 298.15 K and pressures up to 12.0 MPa. Then, grand canonical Monte Carlo (GCMC) simulations are used to calculate the adsorption-phase density; such density values are consequently applied to calibrate the measured excess adsorption and obtain the accurate absolute adsorption isotherms. As for the second approach, we apply the low-field nuclear magnetic resonance (NMR) method to describe the absolute adsorption of CH4 on shale. A NMR-based setup is designed to measure the T2 spectrum distributions in shale samples by injecting CH4 into dry shale samples. The injecting pressure is set up to 12.0 MPa, which is similar to the conditions...

In this study, new metal organic frameworks have been designed by modifying the linker of IRMOF-1 with different carbon flakes (circular, rectangular and rhombus). The adsorption of gas molecules such as H2, CH4 and CO2 has been investigated at different temperatures and pressures using Grand Canonical Monte Carlo (GCMC) simulation. It is interesting to observe from the results that the modification of the IRMOF-1 linker with carbon flakes enhances the gas storage capacity. It is possible to note that IRMOF-1 modified with circular carbon flakes is highly suitable for CO2 storage. On the other hand, IRMOF-1 with a rhombus linker has considerable potential for H2 molecule storage. These findings form a solid foundation for designing aesthetically pleasing as well as functional new metal organic framework molecules for gas storage applications.

The current study employs Grand Canonical Monte Carlo simulations in order to calculate the process efficiency of separating CH4 + CO2 gas mixtures by utilizing structure sI clathrate hydrates. The temperature and pressure conditions examined in the current study resemble those used in industry. The simulation results are compared with experimental measurements and very good agreement is found. In addition, hydrate cage occupancies are compared with experimental measurements and calculations using a commercial simulator. Excellent agreement is found only for the case of large cages, while for the case of small cages significant disagreement is observed.

The separation of ethane (C2H6) from ethylene (C2H4) is one of the most challenging and important tasks in chemical industry. Herein we report two interpenetrated diamondoid (dia) coordination networks, Dia-4-M [M(pba)2] (pba = 4-(4-pyridyl)benzoate); M = Ni or Co), that can directly capture ethane from ethane-ethylene mixtures with reverse C2H6/C2H4 separation. Both materials not only exhibit ultra-high C2H6 uptake (100 cm3 g−1 for Dia-4-Ni; 103 cm3 g−1 for Dia-4-Co) but also display good C2H6/C2H4 selectivity (1.76 for Dia-4-Ni; 2.04 for Dia-4-Co). Such C2H6/C2H4 separation performance was confirmed by dynamic breakthrough experiments. Dia-4-M could extract low concentrated of C2H6 from C2H6/C2H4 mixture (v(C2H6)/v(C2H4) = 1:9 and 1:15) and produce high purity (99.9%) of C2H4 under ambient conditions. The mechanism for selective C2H6/C2H4 separation was clarified through Grand Canonical Monte Carlo (GCMC) simulations and Density functional theory (DFT) calculations. Overall, this research demonstrates that Dia-4-M has a significant potential as effective C2H6-selective adsorbents for the purification of ethylene in practice.

The slight differences between ternary components of C2 hydrocarbons make the separation of C2H2/C2H6/C2H4 mixtures one of the most challenging tasks in the petrochemical industry. In this study, a family of saturated coordination titanium-based metal–organic frameworks with high stability is constructed for one-step C2H4 purification. Single component adsorption measurements along with dynamic breakthrough experiments illustrated that these MOFs clearly exhibited large adsorption for C2H2 and C2H6 compared to C2H4, and could efficiently extract low concentrations of C2H6 and C2H2 from ternary C2H2/C2H6/C2H4 mixtures simultaneously. Grand canonical Monte Carlo simulations revealed the stronger affinity of the MOFs toward C2H6 and C2H2 compared to C2H4. Furthermore, the mostly intact structure of these MOFs after various examinations revealed their distinctive stability. The reasonably high C2H6 and C2H2 adsorption capacity, ultrahigh structural stability, and excellent separation performance rendered these materials good candidates for C2H4 purification from C2H2/C2H6/C2H4 mixtures in industry.

In this study, the adsorption behaviors of methanol, methanal, toluene, ethylbenzene, and styrene molecules in FAU, FER, CON, and MWW zeolites were investigated. The adsorption isotherms of the five adsorbates in the four zeolites at 298 and 350 K were simulated using grand canonical Monte Carlo simulations. Moreover, binary component adsorptions were considered. The results revealed that the saturated adsorption capacity of single components in different zeolites decreased in the order of FAU > MWW > CON > FER, and the adsorption capacity of the five adsorbates in the same zeolite decreased in the order of methanal > methanol > toluene > styrene > ethylbenzene. The equilibrium adsorption capacity slightly decreased with increasing temperature. In terms of binary component adsorption, intense competition existed between the smaller adsorbed molecules. As the differences among the molecular structures increased, the competition in adsorption became more intense.

This paper sheds light on the potential of carbon storage and gas recovery from shale using pure and multicomponent sorption studies. A multi scale approach was adopted where experimental characterization at the core level was linked with a digital laboratory through molecular modeling to simulate and predict pure and multicomponent adsorption conditions that were hard to achieve in the lab. The sensitivity of shale physical properties to the gas mixture saturating the pore space was examined for possible carbon storage and enhanced gas shale recovery applications. A novel high precision volumetric gas adsorption apparatus was constructed in house to investigate multi-component gas sorption at the core level. The thermodynamically closed system allowed for simultaneous measurements of three fundamental rock properties at the core level including porosity, permeability and excess adsorption. Experiments were carried out to measure the preferential adsorption of different components in a gas mixture on intact core samples from the Haynesville and Barnett shale plays. Based on the pore size distribution (PSD) of the sample, the experimental observations were confirmed using a grand canonical Monte Carlo (GCMC) simulation employing a slit pore model composed of parallel planar graphitic surfaces. Experimental results revealed multi-layer adsorption coverage with CO2 in comparison to a mono-layer coverage with N2 and CH4. Mixed gas sorption measurements showed preferential adsorption of CO2 over CH4 as indicated by a selectivity coefficient greater than 1. GCMC simulations on pore sizes ranging from 1 nm to 50 nm were consistent with the core-scale experimental results and revealed an increasing selectivity for CO2 over CH4 in model shale pores. N2 injection, however, proved to be an unsuccessful enhanced gas recovery technique due to a preferential adsorption of CH4 over N2.

Compared with traditional product-based prediction model, predicting adsorption and separation performance (nSP and nAPI) of Xe/Kr mixture in metal–organic frameworks (MOFs) using precursor-based prediction model can obviously enhance efficiency and save cost. In this study, nSP and nAPI of MOFs in M−IRMOF analogues database (MIAD) were calculated using Grand Canonical Monte Carlo (GCMC), Ideal Adsorbed Solution Theory (IAST). Combined with density functional theory and high-throughput screening, two novel artificial neural network models (BPNN-nSP and BPNN-nAPI) with low errors and high regression coefficients were built to predict nSP and nAPI of Xe/Kr in MOFs just using physical parameters of MOFs precursors (organic linker and metal center). In addition, tested by data of experimental MOFs, RMSE values of BPNN-nSP and BPNN-nAPI models are only 0.013 and 0.08. Therefore, these two models are reliable and generalizable for predicting nSP and nAPI, which contribute to development of nuclear power technology and satisfy industrial requirements.

Liquid-liquid phase separation (LLPS) is widely utilized by the cell to organize and regulate various biochemical processes. Although the LLPS of proteins is known to occur in a sequence-dependent manner, it is unclear how sequence properties dictate the nature of the phase transition and thereby influence condensed phase morphology. In this work, we have utilized grand canonical Monte Carlo simulations for a simple coarse-grained model of disordered proteins to systematically investigate how sequence distribution, sticker fraction, and chain length impact the formation of finite-size aggregates, which can preempt macroscopic phase separation for some sequences. We demonstrate that a normalized sequence charge decoration (SCD) parameter establishes a "soft" predictive criterion for distinguishing when a model protein undergoes macroscopic phase separation vs finite aggregation. Additionally, we find that this order parameter is strongly correlated with the critical density for phase separation, highlighting an unambiguous connection between sequence distribution and condensed phase density. Results obtained from an analysis of the order parameter reveal that at sufficiently long chain lengths, the vast majority of sequences are likely to phase separate. Our results suggest that classical LLPS should be the primary phase transition for disordered proteins when short-ranged attractive interactions dominate and suggest a possible reason behind recent findings of widespread phase separation throughout living cells.

In our original work, the computation of the density of liquid in a silica hydrophilic nanopore was executed by the grand canonical Monte Carlo (GCMC) simulation to investigate the spatial dielectric properties of water in a confined phase. We found that the average values of the dielectric constants were very close and almost independent of the number of concentric radial shells. In response to the comment by S. Mondal and B. Bagchi, we clarify the issues of reproducibility of bulk value of the dielectric constant of water and dielectric anisotropy.