Canonical Monte Carlo Simulations Research Articles

Liquid-liquid transition in a simple model fluid with competitive interactions

The possible existence of low-density and high-density fluid phase equilibria in fluids with a simple potential consisting in a combination of a square shoulder and a square well is explored by means of perturbation theory and histogram-reweighting grand canonical Monte Carlo simulation. Theory is useful as a simple and computationally inexpensive way to explore large sets of potential parameters to search for potential shapes susceptible to present multiple fluid phases, later confirmed or discarded from computer simulation. It is found that some combinations of the length and energy parameters of the potential actually give rise to such behaviour.

Machine learning-assisted high-throughput screening of MOFs for efficient adsorption and separation of CF4/N2

There is an imperative need for top-performing materials with extraordinary adsorption selectivity and working capacity, in order to achieve productive adsorption of CF4 in a CF4/N2 mixture. In this work, the High-Throughput Grand Canonical Monte Carlo (HT-GCMC) simulation method and the Machine Learning (ML) method were employed to predict and screen the adsorption performance of 10 143 computation-ready experimental metal-organic frameworks (CoRE-MOFs) for separating CF4/N2 mixed gas. Through computational simulation and ML prediction, 15 and 73 highly promising adsorbents were selected out of the 690 randomly sampled MOFs and the CoRE-MOFs database. The selection process was based on criteria that balanced favorable CF4 selectivity, working capacity, and regenerability: selectivity >60, working capacity >70 mg g−1 (0.8 mmol g−1) and regenerability >70%. The maximum observed capacity of the 15 top evaluated metal-organic frameworks (MOFs) was: 52.85 mg g−1 (0.6 mmol g−1) at 1 bar; and 204.90 mg g−1 (2.3 mmol g−1) at 10 bar. The maximum working capacity was 152.06 mg g−1 (1.7 mmol g−1) and the highest selectivity reached was 118.12 (YEGCUJ) and 101.80 (VEHLIE) at 1 bar and 10 bar, respectively. Notably, the most promising MOFs exhibited elevated Zn content relative to the overall MOF population and also possessed a significant nitrogen content. This result should serve as a compelling motivation to further investigate the utilisation of MOFs with a high Zn content (e. g. zeolitic imidazolate frameworks), for enhanced adsorption applications.

Avoiding pitfalls in molecular simulation of vapor sorption: Example of propane and isobutane in metal-organic frameworks for adsorption cooling applications.

This study introduces recommendations for conducting molecular simulations of vapor adsorption, with an emphasis on enhancing the accuracy, reproducibility, and comparability of results. The first aspect we address is consistency in the implementation of some details of typical molecular models, including tail corrections and cutoff distances, due to their significant influence on generated data. We highlight the importance of explicitly calculating the saturation pressures at relevant temperatures using methods such as Gibbs ensemble Monte Carlo simulations and illustrate some pitfalls in extrapolating saturation pressures using this method. For grand canonical Monte Carlo (GCMC) simulations, the input fugacity is usually calculated using an equation of state, which often requires the critical parameters of the fluid. We show the importance of using critical parameters derived from the simulation with the same model to ensure internal consistency between the simulated explicit adsorbate phase and the implicit bulk phase in GCMC. We show the advantages of presenting isotherms on a relative pressure scale to facilitate easier comparison among models and with experiment. Extending these guidelines to a practical case study, we evaluate the performance of various isoreticular metal-organic frameworks (MOFs) in adsorption cooling applications. This includes examining the advantages of using propane and isobutane as working fluids and identifying MOFs with a superior performance.

Li-decorated C48B12 and Li12C48B12-impregnated MOF-5 for hydrogen storage: A multi-scale simulation study

In this study, we employed density functional theory (DFT) to investigate the adsorption sites and quantities of Li atoms on the C48B12 surface. Additionally, we examined the hydrogen adsorption properties of the formed Li12C48B12 through ab initio molecular dynamics (AIMD) simulations. Our AIMD simulations confirmed the stability of adsorbed Li atoms on the C48B12 surface, computed adsorbed H2 quantities, and predicted gravimetric densities of H2 adsorption on Li12C48B12 at 77 K and 298 K. Grand Canonical Monte Carlo simulations explored the H2 gravimetric densities of pure MOF-5, C48B12-impregnated MOF-5, and Li12C48B12-impregnated MOF-5 at 77 K and 298 K within 100 bar. Li doping on the C48B12 surface did not significantly enhance the gravimetric densities of H2 in MOF-5 but enhanced the stability of H2 adsorption in MOF-5.

Pillared graphene oxide frameworks for the adsorption and separation of polar protic and aprotic liquid solvents: The cases of pure water, methanol, dimethyl sulfoxide, and dimethyl sulfoxide-water mixtures.

Potential applications of previously synthesized pillared graphene oxide frameworks with phenyldiboronic acid linkers in the field of the adsorption and separation of polar protic and aprotic liquid solvents have been systematically explored using grand canonical Monte Carlo simulations. Particular attention was initially paid to the adsorption of pure liquid water, methanol, and dimethyl sulfoxide. The results obtained a significant increase in the isosteric heat of adsorption at low uptake in the case of dimethyl sulfoxide, which is about 17.9 and 26.8kJ/mol higher than the values corresponding to methanol and water adsorption. These findings indicated that from a thermodynamic point of view, these pillared graphene oxide frameworks could be used in the separation of dimethyl sulfoxide-water liquid mixtures. Systematic grand canonical Monte Carlo simulations were then subsequently performed for dimethyl sulfoxide-water mixtures, with a low dimethyl sulfoxide concentration. The calculated values of the separation selectivity indicate that these materials could have potential applications in the separation of these mixed liquid solvents. Molecular dynamics simulations performed for a representative adsorbed mixture have revealed a substantial slowing down of the dynamics under confinement, particularly in the case of the hydrogen bonds formed between water and dimethyl sulfoxide.

Machine learning insights into predicting biogas separation in metal-organic frameworks

Breakthroughs in efficient use of biogas fuel depend on successful separation of carbon dioxide/methane streams and identification of appropriate separation materials. In this work, machine learning models are trained to predict biogas separation properties of metal-organic frameworks (MOFs). Training data are obtained using grand canonical Monte Carlo simulations of experimental MOFs which have been carefully curated to ensure data quality and structural viability. The models show excellent performance in predicting gas uptake and classifying MOFs according to the trade-off between gas uptake and selectivity, with R2 values consistently above 0.9 for the validation set. We make prospective predictions on an independent external set of hypothetical MOFs, and examine these predictions in comparison to the results of grand canonical Monte Carlo calculations. The best-performing trained models correctly filter out over 90% of low-performing unseen MOFs, illustrating their applicability to other MOF datasets.

Capture of carbonyl sulfide trace from natural gas by adsorption on zeolitic Nanostructure: Monte Carlo molecular simulation

The economic and environmental challenge for the oil and gas industries is to eliminate carbonyl sulfide (COS) from natural gas selectively. This compound can lead to corrosion and environmental damage, even when it is present in small amounts. The zeolites have been known as the appropriate adsorbent for COS. Grand Canonical Monte Carlo (GCMC) simulation has been used to explore the suitability of zeolite frameworks with different topologies on the adsorption and separation properties of mixtures containing industrial concentrations of COS in order to overcome the experimental tests that are difficult to handle. The adsorption isotherms for pure COS and COS/methane mixture on purely siliceous zeolites such as BEA, FAU, LTL, MFI, and MOR have been recorded at 298 K within a pressure range of 0–100 kPa. MFI zeolite, with its high adsorption capacity and isosteric heat, has been identified as the most suitable candidate for COS adsorption due to its ideal pore size and shape. Examining the density distribution snapshots reveals the preferred locations of zeolites for the COS adsorption. The findings could be systematically used in various case studies, thereby providing insights into selecting the best zeolitic nanostructure for the industrial removal processes of COS.

Controlling the Uptake and Release of Semiochemicals in Channel-Type Metal-Organic Frameworks Through Pore Expansion.

Semiochemicals can be used to manipulate insect behaviour for sustainable pest management strategies, but their high volatility is a major issue for their practical implementation. Inclusion of these molecules within porous materials is a potential solution to this issue, as it can allow for a slower and more controlled release. In this work, we demonstrate that a series of Zr(IV) and Al(III) metal-organic frameworks (MOFs) with channel-type pores enable controlled release of three semiochemicals over 100 days by pore size design, with the uptake and rate of release highly dependent on the pore size. Insight from grand canonical Monte Carlo simulations indicates that this is due to weaker MOF-guest interactions per guest molecule as the pore size increases. These MOFs are all stable post-release and can be reloaded to show near-identical re-release profiles. These results provide valuable insight on the diffusion behaviour of volatile guests in MOFs, and for the further development of porous materials for sustainable agriculture applications.

Flat-Histogram Monte Carlo Simulation of Water Adsorption in Metal-Organic Frameworks.

Molecular simulations of water adsorption in porous materials often converge slowly due to sampling bottlenecks that follow from hydrogen bonding and, in many cases, the formation of water clusters. These effects may be exacerbated in metal-organic framework (MOF) adsorbents, due to the presence of pore spaces (cages) that promote the formation of discrete-size clusters and hydrophobic effects (if present), among other reasons. In Grand Canonical Monte Carlo (MC) simulations, these sampling challenges are typically manifested by low MC acceptance ratios, a tendency for the simulation to become stuck in a particular loading state (i.e., macrostates), and the persistence of specific clusters for long periods of the simulation. We present simulation strategies to address these sampling challenges, by applying flat-histogram MC (FHMC) methods and specialized MC move types to simulations of water adsorption. FHMC, in both Transition-matrix and Wang-Landau forms, drives the simulation to sample relevant macrostates by incorporating weights that are self-consistently adjusted throughout the simulation and generate the macrostate probability distribution (MPD). Specialized MC moves, based on aggregation-volume bias and configurational bias methods, separately address low acceptance ratios for basic MC trial moves and specifically target water molecules in clusters; in turn, the specialized MC moves improve the efficiency of generating new configurations which is ultimately reflected in improved statistics collected by FHMC. The combined strategies are applied to study the adsorption of water in CuBTC and ZIF-8 at 300 K, through examination of the MPD and the adsorption isotherm generated by histogram reweighting. A key result is the appearance of nontrivial oscillations in the MPD, which we show to be associated with water clusters in the adsorption system. Additionally, we show that the probabilities of certain clusters become similar in value near the boundaries of the isotherm hysteresis loop, indicating a strong connection between cluster formation/destruction and the thermodynamic limits of stability. For a hydrophobic MOF, the FHMC results show that the phase transition from low density to high density is suppressed to water pressure far above the bulk-fluid saturation pressure; this is consistent with results presented elsewhere. We also compare our FHMC simulation isotherm to one measured by a different technique but with ostensibly the same molecular interactions and comment on observed differences and the need for follow-up work. The simulation strategies presented here can be applied to the simulation of water in other MOFs using heuristic guidelines laid out in our text, which should facilitate the more consistent and efficient simulation of water adsorption in porous materials in future applications.

Exploring the Micropore Functional Mechanism of N2O Adsorption by the Eucalyptus Bark-based Porous Carbon.

Nitrous oxide (N2O), recognized as a significant greenhouse gas, has received insufficient research attention in the past. In view of their low energy consumption and cost-effectiveness, the application of porous materials in adsorption is increasingly regarded as a potent strategy to reduce N2O pollution. In this study, a series of microporous porous carbons with a preeminent specific surface area (244.54-2018.08 m2 g-1), which are derived from the fast-growing eucalyptus bark, were synthesized by KOH activation at high temperatures. The obtained materials demonstrated a relatively fine N2O capture capability (0.19-0.68 mmol g-1) at normal temperature and pressure. More importantly, the optimal pore size affecting N2O adsorption (0.8 and 1.0 nm) has been detected, which is a meaningful view that has never been put forward in previous studies. The rationality of the N2O adsorption mechanism was also validated by combining the experimental analysis and Grand Canonical Monte Carlo (GCMC) simulation. The calculated results showed that 0.8 and 1.0 nm of the porous carbon were the preferred pore sizes for N2O adsorption, and the interaction force between N2O and the pore wall decreased with the increase of distance. This study provides a significant theoretical basis for the preparation of biomass porous carbon with excellent N2O adsorption performance and practical adsorption application.

What is the best simulation approach for measuring local density fluctuations near solvo-/hydrophobes?

Measurements of local density fluctuations are crucial to characterizing the interfacial properties of equilibrium fluids. A specific case that has been well-explored involves the heightened compressibility of water near hydrophobic entities. Commonly, a spatial profile of local fluctuation strength is constructed from the measurements of the mean and variance of solvent particle number fluctuations in a set of contiguous subvolumes of the system adjacent to the solvo-/hydrophobe. An alternative measure proposed by Evans and Stewart [J. Phys.: Condens. Matter 27, 194111 (2015)] defines a local compressibility profile in terms of the chemical potential derivative of the spatial number density profile. Using Grand canonical Monte Carlo simulation, we compare and contrast the efficacy of these two approaches for a Lennard-Jones solvent at spherical and planar solvophobic interfaces and SPC/E water at a hydrophobic spherical solute. Our principal findings are as follows: (i) the local compressibility profile χ(r) of Evans and Stewart is considerably more sensitive to variations in the strength of local density fluctuations than the spatial fluctuation profile F(r) and can resolve much more detailed structure; and (ii) while the local compressibility profile is essentially independent of the choice of spatial discretization used to construct the profile, the spatial fluctuation profile exhibits a strong systematic dependence on the size of the subvolumes on which the profile is defined. We clarify the origin and nature of this finite-size effect.

Insights into hydrogen and methane storage capacities: Grand canonical Monte Carlo simulations of SIGSUA.

In the pursuit of sustainable energy solutions, the development of materials with efficient hydrogen and methane storage capacities is imperative, particularly for advancing hydrogen-powered vehicles. Metal-organic frameworks (MOFs) have emerged as promising candidates to meet the stringent targets set by the Department of Energy for both hydrogen and methane storage. This study employs Grand Canonical Monte Carlo simulations to investigate the usable hydrogen and methane gravimetric and volumetric storage capacities of the recently synthesized SIGSUA. A comparative analysis encompasses the selected MOFs with similar metal compositions, those with comparable density and average pore radius, and classical benchmarks, such as IRMOF-15 and IRMOF-20, all evaluated at room temperature and moderate pressures ranging from 25 to 35 MPa. The results reveal that SIGSUA demonstrates noteworthy gravimetric and volumetric storage capacities for both hydrogen and methane, rivaling or surpassing those of the selected MOFs for analysis. These findings underscore the potential of SIGSUA in advancing clean energy storage technologies.

Optimizing Trace Acetylene Removal from Acetylene/Ethylene Mixture in a Flexible Metal-Organic Framework by Crystal Downsizing.

Improving the gas separation performance of metal-organic frameworks (MOFs) by crystal downsizing is an important but often overlooked issue. Here, we report three different-sized flexible ZUL-520 MOFs (according to the crystal size from large to small, the three samples are, respectively, named ZUL-520-0, ZUL-520-1, and ZUL-520-2) with the same chemical structure for optimizing trace acetylene (C2H2) removal from acetylene/ethylene (C2H2/C2H4) mixture. The three differently sized activated ZUL-520 (denoted as ZUL-520a) exhibited almost identical C2H2 uptake of 4.8 mmol/g at 100 kPa, while the C2H2 uptake at 1 kPa increased with a downsizing crystal. The C2H2 uptake of activated ZUL-520-2 (denoted as ZUL-520-2a) at 1 kPa was ∼55% higher than that of activated ZUL-520-0 (denoted as ZUL-520-0a). The adsorption isotherms and adsorption kinetics validated that gas adsorptive separation is governed not only by adsorption thermodynamics but also by adsorption kinetics. In addition, all three different-sized ZUL-520a MOFs showed high C2H2/C2H4 selectivity. Grand canonical Monte Carlo (GCMC) simulations and dispersion-corrected density functional theory (DFT-D) computations illustrated a plausible mechanism of C2H2 adsorption in MOFs. Importantly, breakthrough experiments demonstrated that ZUL-520a can effectively separate the C2H2/C2H4 (1/99, v/v) mixture and the C2H4 productivity obtained by ZUL-520-2a was much higher than that by ZUL-520-0a. Our work may provide an easy but powerful strategy for upgrading the performance of gas adsorptive separation in MOFs.

Molecular-Level Insight into the Chlorofluorocarbons Adsorption by Defective Covalent Organic Polymers.

Halocarbons have important industrial applications, but because of their contribution to global warming and the fact that they can cause ozone depletion, they are considered highly toxic. Hence, the techniques that can capture and recover the used halocarbons with energy-efficient methods have been recently received greater attention. In this contribution, we report the capture of dichlorodifluoromethane (R12), which has high global warming and ozone depletion potential, using covalent organic polymers (COPs). The defect-engineered COPs were synthesized and demonstrated outstanding sorption capacities, ~226 wt % of R12 combined with linear-shaped adsorption isotherms. We further identified the plausible microscopic adsorption mechanism of the investigated COPs via grand canonical Monte Carlo simulations applied to non-defective and a collection of atomistic models of the defective COPs. The modeling work suggests that significant R12 adsorption performance is attributed to a gradual increment of porosities due to isolated/interconnected micro-/meso-pore channels and the change of the long-range ordering of both COPs. The successive hierarchical-pore-filling mechanism promotes R12 molecular adsorption via moderate van der Waals adsorbate-adsorbent interactions in the micropores of both COPs at low pressure followed by adsorbate-adsorbate interactions in the extra-voids created at moderate to high pressure ranges. This continuous pore-filling mechanism makes defective COPs as promising sorbents for halocarbon adsorption.

Double-walled Al-based MOF with large microporous specific surface area for trace benzene adsorption.

Double-walled metal-organic frameworks (MOFs), synthesized using Zn and Co, are potential porous materials for trace benzene adsorption. Aluminum is with low-toxicity and abundance in nature, in comparison with Zn and Co. Therefore, a double-walled Al-based MOF, named as ZJU-520(Al), with large microporous specific surface area of 2235 m2 g-1, pore size distribution in the range of 9.26-12.99 Å and excellent chemical stability, was synthesized. ZJU-520(Al) is consisted by helical chain of AlO6 clusters and 4,6-Di(4-carboxyphenyl)pyrimidine ligands. Trace benzene adsorption of ZJU-520(Al) is up to 5.98 mmol g-1 at 298 K and P/P0 = 0.01. Adsorbed benzene molecules are trapped on two types of sites. One (site I) is near the AlO6 clusters, another (site II) is near the N atom of ligands, using Grand Canonical Monte Carlo simulations. ZJU-520(Al) can effectively separate trace benzene from mixed vapor flow of benzene and cyclohexane, due to the adsorption affinity of benzene higher than that of cyclohexane. Therefore, ZJU-520(Al) is a potential adsorbent for trace benzene adsorption and benzene/cyclohexane separation.

Postsynthetically Modified Alkoxide-Exchanged Ni2(OR)2BTDD: Synergistic Interactions of CO2 with Open Metal Sites and Functional Groups.

Postsynthetic modifications (PSMs) of metal-organic frameworks (MOFs) play a crucial role in enhancing material performance through open metal site (OMS) functionalization or ligand exchange. However, a significant challenge persists in preserving open metal sites during ligand exchange, as these sites are inherently bound by incoming ligands. In this study, for the first time, we introduced alkoxides by exchanging bridging chloride in Ni2Cl2BTDD (BTDD=bis (1H-1,2,3,-triazolo [4,5-b],-[4',5'-i]) dibenzo[1,4]dioxin) through PSM. Rietveld refinement of synchrotron X-ray diffraction data indicated that the alkoxide oxygen atom bridges Ni(II) centers while the OMSs of the MOF are preserved. Due to the synergy of the existing OMS and introduced functional group, the alkoxide-exchanged MOFs showed CO2 uptakes superior to the pristine MOF. Remarkably, the tert-butoxide-substituted Ni_T exhibited a nearly threefold and twofold increase in CO2 uptake compared to Ni2Cl2BTDD at 0.15 and 1 bar, respectively, as well as high water stability relative to the other exchanged frameworks. Furthermore, the Grand Canonical Monte Carlo simulations for Ni_T suggested that CO2 interacts with the OMS and the surrounding methyl groups of tert-butoxide groups, which is responsible for the enhanced CO2 capacity. This work provides a facile and unique synthetic strategy for realizing a desirable OMS-incorporating MOF platform through bridging ligand exchange.

Postsynthetically Modified Alkoxide‐Exchanged Ni2(OR)2BTDD: Synergistic Interactions of CO2 with Open Metal Sites and Functional Groups

AbstractPostsynthetic modifications (PSMs) of metal–organic frameworks (MOFs) play a crucial role in enhancing material performance through open metal site (OMS) functionalization or ligand exchange. However, a significant challenge persists in preserving open metal sites during ligand exchange, as these sites are inherently bound by incoming ligands. In this study, for the first time, we introduced alkoxides by exchanging bridging chloride in Ni2Cl2BTDD (BTDD=bis (1H‐1,2,3,–triazolo [4,5‐b],–[4′,5′‐i]) dibenzo[1,4]dioxin) through PSM. Rietveld refinement of synchrotron X‐ray diffraction data indicated that the alkoxide oxygen atom bridges Ni(II) centers while the OMSs of the MOF are preserved. Due to the synergy of the existing OMS and introduced functional group, the alkoxide‐exchanged MOFs showed CO2 uptakes superior to the pristine MOF. Remarkably, the tert‐butoxide‐substituted Ni_T exhibited a nearly threefold and twofold increase in CO2 uptake compared to Ni2Cl2BTDD at 0.15 and 1 bar, respectively, as well as high water stability relative to the other exchanged frameworks. Furthermore, the Grand Canonical Monte Carlo simulations for Ni_T suggested that CO2 interacts with the OMS and the surrounding methyl groups of tert‐butoxide groups, which is responsible for the enhanced CO2 capacity. This work provides a facile and unique synthetic strategy for realizing a desirable OMS‐incorporating MOF platform through bridging ligand exchange.

Rapid and Accurate Screening of the COF Space for Natural Gas Purification: COFInformatics.

In this work, we introduced COFInformatics, a computational approach merging molecular simulations and machine learning (ML) algorithms, to evaluate all synthesized and hypothetical covalent organic frameworks (COFs) for the CO2/CH4 mixture separation under four different adsorption-based processes: pressure swing adsorption (PSA), vacuum swing adsorption (VSA), temperature swing adsorption (TSA), and pressure-temperature swing adsorption (PTSA). We first extracted structural, chemical, energy-based, and graph-based molecular fingerprint features of every single COF structure in the very large COF space, consisting of nearly 70,000 materials, and then performed grand canonical Monte Carlo simulations to calculate the CO2/CH4 mixture adsorption properties of 7540 COFs. These features and simulation results were used to develop ML models that accurately and rapidly predict CO2/CH4 mixture adsorption and separation properties of all 68,614 COFs. The most efficient separation process and the best adsorbent candidates among the entire COF spectrum were identified and analyzed in detail to reveal the most important molecular features that lead to high-performance adsorbents. Our results showed that (i) many hypoCOFs outperform synthesized COFs by achieving higher CO2/CH4 selectivities; (ii) the top COF adsorbents consist of narrow pores and linkers comprising aromatic, triazine, and halogen groups; and (iii) PTSA is the most efficient process to use COF adsorbents for natural gas purification. We believe that COFInformatics promises to expedite the evaluation of COF adsorbents for CO2/CH4 separation, thereby circumventing the extensive, time- and resource-intensive molecular simulations.

Large‐scale computational screening of highly effective zeolites for trace PH3 capture from hydrogen

AbstractDeveloping highly efficient adsorbents to remove trace PH3 from circulating H2 is essential for electronic‐grade silica production. Here, the pure PH3 adsorption and PH3/H2 separation performances of 103 all‐silica zeolites under various operating conditions were simulated using grand canonical Monte Carlo simulation. Based on our well‐validated force field, the optimal ranges of zeolites and the most decisive factors for pure PH3 adsorption and separation were identified by the combination of quantitative structure–activity relationship, Pearson coefficient, and adsorption density profile. Finally, the top 17 zeolites were identified through TSA, PSA, and VSA processes, which typically feature 10‐membered rings as limiting pores. Structures with 1D channels show the best performance. Some 2D and 3D zeolites (i.e., IMF, ITH, ITR, MEL, TUN), which are characterized by interconnected straight channels with cavities at channel intersections, also exhibit high selective adsorption capacities for PH3. This work provides direction for the design of novel zeolites.

Thermodynamically and Kinetically Enhanced Benzene Vapor Sensor Based on the Cu-TCPP-Cu MOF with Extremely Low Limit of Detection.

As a carcinogenic and highly neurotoxic hazardous gas, benzene vapor is particularly difficult to be distinguished in BTEX (benzene, toluene, ethylbenzene, xylene) atmosphere and be detected in low concentrations due to its chemical inertness. Herein, we develop a depth-related pore structure in Cu-TCPP-Cu to thermodynamically and kinetically enhance the adsorption of benzene vapor and realize the detection of ultralow-temperature benzene gas. We find that the in-plane π electronic nature and proper pore sizes in Cu-TCPP-Cu can selectively induce the adsorption and diffusion of BTEX. Interestingly, the theoretical calculations (including density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations) exhibit that benzene molecules are preferred to adsorb and array as a consecutive arrangement mode in the Cu-TCPP-Cu pore, while the TEX (toluene, ethylbenzene, xylene) dominate the jumping arrangement model. The differences in distribution behaviors can allow adsorption and diffusion of more benzene molecules within limited room. Furthermore, the optimal pore-depth range (60-65 nm) of Cu-TCPP-Cu allows more exposure of active sites and hinders the gas-blocking process. The optimized sensor exhibits ultrahigh sensitivity to benzene vapor (155 Hz/μg@1 ppm), fast response time (less than 10 s), extremely low limit of detection (65 ppb), and excellent selectivity (83%). Our research thus provides a fundamental understanding to design and optimize two-dimensional metal-organic framework (MOF)-based gas sensors.