Computational Screening of Metal-Organic Frameworks for Membrane-Based CO2/N2/H2O Separations: Best Materials for Flue Gas Separation

Computational Simulations of Metal-Organic Frameworks to Enhance Adsorption Applications

Metal-organic frameworks (MOFs), renowned for their exceptional porosity and crystalline structure, stand at the forefront of gas adsorption and separation applications. Shortly after their discovery through experimental synthesis, computational simulations quickly become an important method in broadening the use of MOFs by offering deep insights into their structural, functional, and performance properties. This review specifically addresses the pivotal role of molecular simulations in enlarging the molecular understanding of MOFs and enhancing their applications, particularly for gas adsorption. After reviewing the historical development and implementation of molecular simulation methods in the field of MOFs, high-throughput computational screening (HTCS) studies used to unlock the potential of MOFs in CO2 capture, CH4 storage, H2 storage, and water harvesting are visited and recent advancements in these adsorption applications are highlighted. The transformative impact of integrating artificial intelligence with HTCS on the prediction of MOFs' performance and directing the experimental efforts on promising materials is addressed. An outlook on current opportunities and challenges in the field to accelerate the adsorption applications of MOFs is finally provided.

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.

Enhanced water permeation through the terahertz-induced phase and diffusion transition in metal-organic framework membranes

Freshwater scarcity is a pressing global concern, and water desalination has emerged as a promising solution. Metal-organic framework (MOF) membranes have demonstrated exceptional potential in this regard. However, previous efforts to improve the permeability of MOFs have primarily focused on chemical modifications and synthesis rather than exploring physical methods. Using molecular dynamics simulations, we propose that the use of terahertz waves at a specific frequency of 7.5 ± 1.0 THz significantly enhances water permeability across MOF membranes, up to 27-fold, while maintaining effective ion rejection capabilities throughout the process. The mechanism behind this enhancement involves the resonance between the terahertz wave and the hydrogen bond vibrations of water within the MOF. This resonance amplifies the rotational kinetic energy of water molecules, disrupting the hydrogen bonds and causing a phase transition from quasi 1D square ice to a gas-like phase. Additionally, the diffusion behavior shifts from Fickian diffusion to sub-diffusion, resulting in improved water permeation across the MOF membrane. This study highlights the potential of terahertz waves as a physical tool to enhance the permeability of MOFs in water desalination, providing new avenues for efficient water treatment and resource sustainability.

The green synthesis and applications of biological metal-organic frameworks for targeted drug delivery and tumor treatments

Biological metal-organic frameworks (bio-MOFs) constitute a growing subclass of MOFs composed of metals and bio-ligands derived from biology, such as nucleobases, peptides, saccharides, and amino acids. Bio-ligands are more abundant than other traditional organic ligands, providing multiple coordination sites for MOFs. However, bio-MOFs are typically prepared using hazardous or harmful solvents or reagents, as well as laborious processes that do not conform to environmentally friendly standards. To improve biocompatibility and biosafety, eco-friendly synthesis and functionalization techniques should be employed with mild conditions and safer materials, aiming to reduce or avoid the use of toxic and hazardous chemical agents. Recently, bio-MOF applications have gained importance in some research areas, including imaging, tumor therapy, and targeted drug delivery, owing to their flexibility, low steric hindrances, low toxicity, remarkable biocompatibility, surface property refining, and degradability. This has led to an exponential increase in research on these materials. This paper provides a comprehensive review of updated strategies for the synthesis of environmentally friendly bio-MOFs, as well as an examination of the current progress and accomplishments in green-synthesized bio-MOFs for drug delivery aims and tumor treatments. In conclusion, we consider the challenges of applying bio-MOFs for biomedical applications and clarify the possible research orientation that can lead to highly efficient therapeutic outcomes.

Machine Learning Models for Predicting Molecular Diffusion in Metal-Organic Frameworks Accounting for the Impact of Framework Flexibility

Molecular diffusion in MOFs plays an important role in determining whether equilibrium can be reached in adsorption-based chemical separations and is a key driving force in membrane-based separations. Molecular dynamics (MD) simulations have shown that in some cases inclusion of framework flexibility in MOF changes predicted molecular diffusivities by orders of magnitude relative to more efficient MD simulations using rigid structures. Despite this, all previous efforts to predict molecular diffusion in MOFs in a high-throughput way have relied on MD data from rigid structures. We use a diverse data set of MD simulations in flexible and rigid MOFs to develop a classification model that reliably predicts whether framework flexibility has a strong impact on molecular diffusion in a given MOF/molecule pair. We then combine this approach with previous high-throughput MD simulations to develop a reliable model that efficiently predicts molecular diffusivities in cases in which framework flexibility can be neglected. The use of this approach is illustrated by making predictions of molecular diffusivities in ∼70,000 MOF/molecule pairs for molecules relevant to gas separations.

Connectivity Analysis of Adsorption Sites in Metal-Organic Frameworks for Facilitated Water Adsorption

Metal-organic frameworks (MOFs) have recently drawn considerable attention as promising adsorbents to harvest atmospheric water. To achieve an efficient harvesting process, seeking MOFs that demonstrate sharp condensation behavior is the key. Given that the clustering of water molecules in MOFs should be driven by not only MOF-water interactions but also water-water interactions, the spatial arrangement of water adsorption sites in a MOF is therefore crucial. Specifically, this study demonstrates the critical role of continuous adsorption channels (CACs) in MOFs. Such CACs will enable water molecules to stay in proximity and in a continuous manner, thus promoting the formation of hydrogen bonds and, consequently, the clustering of water molecules. We have developed an automatic algorithm to detect CACs based on the energy grid of host-guest interactions and applied the algorithm to more than 2000 diverse structures. The results show that more than 80% of the studied MOFs displaying water condensation at 298 K and 20% relative humidity predicted by Monte Carlo simulations indeed have CACs. The developments herein are anticipated to largely facilitate the future discovery of optimal adsorbents for water harvesting or water-adsorption-related applications in general. A Python-based code for detecting CACs in porous materials is also provided along with this article to employ this approach.

Microscopic Origin of Electrochemical Capacitance in Metal-Organic Frameworks

Electroconductive metal-organic frameworks (MOFs) have emerged as high-performance electrode materials for supercapacitors, but the fundamental understanding of the underlying chemical processes is limited. Here, the electrochemical interface of Cu3(HHTP)2 (HHTP = 2,3,6,7,10,11-hexahydroxytriphenylene) with an organic electrolyte is investigated using a multiscale quantum-mechanics/molecular-mechanics (QM/MM) procedure and experimental electrochemical measurements. Our simulations reproduce the observed capacitance values and reveals the polarization phenomena of the nanoporous framework. We find that excess charges mainly form on the organic ligand, and cation-dominated charging mechanisms give rise to greater capacitance. The spatially confined electric double-layer structure is further manipulated by changing the ligand from HHTP to HITP (HITP = 2,3,6,7,10,11-hexaiminotriphenylene). This minimal change to the electrode framework not only increases the capacitance but also increases the self-diffusion coefficients of in-pore electrolytes. The performance of MOF-based supercapacitors can be systematically controlled by modifying the ligating group.

Interpretable Machine-Learning and Big Data Mining to Predict Gas Diffusivity in Metal-Organic Frameworks

For gas separation and catalysis by metal-organic frameworks (MOFs), gas diffusion has a substantial impact on the process' overall rate, so it is necessary to determine the molecular diffusion behavior within the MOFs. In this study, an interpretable machine learing (ML) model, light gradient boosting machine (LGBM), is trained to predict the molecular diffusivity and selectivity of 9 gases (Kr, Xe, CH4 , N2 , H2 S, O2 , CO2 , H2 , and He). For these 9 gases, LGBM displays high accuracy (average R2 = 0.962) and superior extrapolation for the diffusivity of C2 H6 . And this model calculation is five orders of magnitude faster than molecular dynamics (MD) simulations. Subsequently, using the trained LGBM model, an interactive desktop application is developed that can help researchers quickly and accurately calculate the diffusion of molecules in porous crystal materials. Finally, the authors find the difference in the molecular polarizability (ΔPol) is the key factor governing the diffusion selectivity by combining the trained LGBM model with the Shapley additive explanation (SHAP). By the calculation of interpretable ML, the optimal MOFs are selected for separating binary gas mixtures and CO2 methanation. This work provides a new direction for exploring the structure-property relationships of MOFs and realizing the rapid calculation of molecular diffusivity.

Mixed-linker strategy for suppressing structural flexibility of metal-organic framework membranes for gas separation

Structural flexibility is a critical issue that limits the application of metal-organic framework (MOF) membranes for gas separation. Herein we propose a mixed-linker approach to suppress the structural flexibility of the CAU-10-based (CAU = Christian-Albrechts-University) membranes. Specifically, pure CAU-10-PDC membranes display high separation performance but at the same time are highly unstable for the separation of CO₂/CH₄. A partial substitution (30 mol.%) of the linker PDC with BDC significantly improves its stability. Such an approach also allows for decreasing the aperture size of MOFs. The optimized CAU-10-PDC-H (70/30) membrane possesses a high separation performance for CO₂/CH₄ (separation factor of 74.2 and CO₂ permeability of 1,111.1 Barrer under 2 bar of feed pressure at 35°C). A combination of in situ characterization with X-ray diffraction (XRD) and diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy, as well as periodic density functional theory (DFT) calculations, unveils the origin of the mixed-linker approach to enhancing the structural stability of the mixed-linker CAU-10-based membranes during the gas permeation tests.

Computational investigation of multifunctional MOFs for adsorption and membrane-based separation of CF4/CH4, CH4/H2, CH4/N2, and N2/H2 mixtures

The ease of functionalization of metal-organic frameworks (MOFs) can unlock unprecedented opportunities for gas adsorption and separation applications as the functional groups can impart favorable/unfavorable regions/interactions for the desired/undesired adsorbates. In this study, the effects of the presence of multiple functional groups in MOFs on their CF₄/CH₄, CH₄/H₂, CH₄/N₂, and N₂/H₂ separation performances were computationally investigated combining grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. The most promising adsorbents showing the best combinations of selectivity, working capacity, and regenerability were identified for each gas separation. 15, 13, and 16 out of the top 20 MOFs identified for the CH₄/H₂, CH₄/N₂, and N₂/H₂ adsorption-based separation, respectively, were found to have -OCH₃ groups as one of the functional groups. The biggest improvements in CF₄/CH₄, CH₄/H₂, CH₄/N₂, and N₂/H₂ selectivities were found to be induced by the presence of -OCH₃-OCH₃ groups in MOFs. For CH₄/H₂ separation, MOFs with two and three functionalized linkers were the best adsorbent candidates while for N₂/H₂ separation, all the top 20 materials involve two functional groups. Membrane performances of the MOFs were also studied for CH₄/H₂ and CH₄/N₂ separation and the results showed that MOFs having -F-NH₂ and -F-OCH₃ functional groups present the highest separation performances considering both the membrane selectivity and permeability.

Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs

Due to the enormous increase in the number of metal-organic frameworks (MOFs), combining molecular simulations with machine learning (ML) would be a very useful approach for the accurate and rapid assessment of the separation performances of thousands of materials. In this work, we combined these two powerful approaches, molecular simulations and ML, to evaluate MOF membranes and MOF/polymer mixed matrix membranes (MMMs) for six different gas separations: He/H₂, He/N₂, He/CH₄, H₂/N₂, H₂/CH₄, and N₂/CH₄. Single-component gas uptakes and diffusivities were computed by grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations, respectively, and these simulation results were used to assess gas permeabilities and selectivities of MOF membranes. Physical, chemical, and energetic features of MOFs were used as descriptors, and eight different ML models were developed to predict gas adsorption and diffusion properties of MOFs. Gas permeabilities and membrane selectivities of 5249 MOFs and 31,494 MOF/polymer MMMs were predicted using these ML models. To examine the transferability of the ML models, we also focused on computer-generated, hypothetical MOFs (hMOFs) and predicted the gas permeability and selectivity of 1000 hMOF/polymer MMMs. The ML models that we developed accurately predict the uptake and diffusion properties of He, H₂, N₂, and CH₄ gases in MOFs and will significantly accelerate the assessment of separation performances of MOF membranes and MOF/polymer MMMs. These models will also be useful to direct the extensive experimental efforts and computationally demanding molecular simulations to the fabrication and analysis of membrane materials offering high performance for a target gas separation.

Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO2 from Flue Gas

To combat global warming, as an energy-saving technology, membrane separation can be applied to capture CO₂ from flue gas. Metal–organic frameworks (MOFs) with characteristics like high porosity have great potential as membrane materials for gas mixture separation. In this work, through a combination of grand canonical Monte Carlo and molecular dynamics simulations, the permeability of three gases (CO₂, N₂, and O₂) was calculated and estimated in 6013 computation–ready experimental MOF membranes (CoRE–MOFMs). Then, the relationship between structural descriptors and permeance performance, and the importance of available permeance area to permeance performance of gas molecules with smaller kinetic diameters were found by univariate analysis. Furthermore, comparing the prediction accuracy of seven classification machine learning algorithms, XGBoost was selected to analyze the order of importance of six structural descriptors to permeance performance, through which the conclusion of the univariate analysis was demonstrated one more time. Finally, seven promising CoRE-MOFMs were selected, and their structural characteristics were analyzed. This work provides explicit directions and powerful guidelines to experimenters to accelerate the research on membrane separation for the purification of flue gas.

Dye contaminated wastewater treatment through metal–organic framework (MOF) based materials

A complete discussion of MOFs and MOF composites such as MOF-based membranes, magnetic MOFs, and metal–organic gels (MOGs) used for dye removal along with their adsorption efficiency has been done.

Nanoconfinement and mass transport in metal-organic frameworks

The ubiquity of metal-organic frameworks in recent scientific literature underscores their highly versatile nature. MOFs have been developed for use in a wide array of applications, including: sensors, catalysis, separations, drug delivery, and electrochemical processes. Often overlooked in the discussion of MOF-based materials is the mass transport of guest molecules within the pores and channels. Given the wide distribution of pore sizes, linker functionalization, and crystal sizes, molecular diffusion within MOFs can be highly dependent on the MOF-guest system. In this review, we discuss the major factors that govern the mass transport of molecules through MOFs at both the intracrystalline and intercrystalline scale; provide an overview of the experimental and computational methods used to measure guest diffusivity within MOFs; and highlight the relevance of mass transfer in the applications of MOFs in electrochemical systems, separations, and heterogeneous catalysis.

A high-throughput screening of metal-organic framework based membranes for biogas upgrading

Applications of biomethane as a source of renewable energy and transport fuel rely heavily on successful implementation of purification methods capable of removing undesirable impurities from biogas and increasing its calorific content. Metal-organic frameworks (MOFs) are competitive candidates for biogas upgrading due to a versatile range of attractive physical and chemical properties which can be utilised in membrane materials. In this work, we present a high-throughput computational screening methodology for efficient identification of MOF structures with promising gas separation performance. The proposed screening strategy is based on initial structural analysis and predictions of the single-component permeation of CO₂, CH₄ and H₂S from adsorption and diffusion calculations at infinite dilution. The identified top performing candidates are subject to further analysis of their gas separation performance at the operating conditions of 10 bar and 298 K, using grand canonical Monte Carlo and equilibrium molecular dynamics simulations on equimolar CO₂/CH₄ and H₂S/CH₄ mixtures. The Henry constant for the adsorption of H₂O was also calculated to determine the hydrophobicity of MOF structures, as the presence of H₂O often leads to membrane instability and performance limitations. For the considered gas mixtures, the top MOF candidates exhibit superior separation capabilities over polymer-, zeolite-, and mixed matrix-based membranes as indicated by the predicted values of selectivity and permeability. The proposed screening protocol offers a powerful tool for the rational design of novel MOFs for biogas upgrading.

Interconnected channels through polypropylene and cellulose acetate by utilizing lactic acid for stable separators

In this study, an eco-friendly and inexpensive cellulose acetate (CA) separator was fabricated and a method of making a single film by combining a polypropylene (PP) film and cellulose was proposed. The CA solution was coated on the PP film with a doctor blade and water treatment was applied to the bonded polymer to create interconnected pores and completely bond the CA onto the PP. In addition, lactic acid was added to CA to induce a plasticizing effect for abundant pore formation. The binding was confirmed using FT-IR and SEM, and the pore size generated from the CA side was found to be less than 1 μm on average. TGA was used to measure the thermal stability of the connected polymers.

Recent advances in simulating gas permeation through MOF membranes

In the last two decades, metal organic frameworks (MOFs) have gained increasing attention in membrane-based gas separations due to their tunable structural properties. Computational methods play a critical role in providing molecular-level information about the membrane properties and identifying the most promising MOF membranes for various gas separations. In this review, we discuss the current state-of-the-art in molecular modeling methods to simulate gas permeation through MOF membranes and review the recent advancements. We finally address current opportunities and challenges of simulating gas permeation through MOF membranes to guide the development of high-performance MOF membranes in the future.

Deep learning combined with IAST to screen thermodynamically feasible MOFs for adsorption-based separation of multiple binary mixtures

The structures of metal-organic frameworks (MOFs) can be tuned to reproducibly create adsorption properties that enable the use of these materials in fixed-adsorption beds for non-thermal separations. However, with millions of possible MOF structures, the challenge is to find the MOF with the best adsorption properties to separate a given mixture. Thus, computational, rather than experimental, screening is necessary to identify promising MOF structures that merit further examination, a process traditionally done using molecular simulation. However, even molecular simulation can become intractable when screening an expansive MOF database for their separation properties at more than a few composition, temperature, and pressure combinations. Here, we illustrate progress toward an alternative computational framework that can efficiently identify the highest-performing MOFs for separating various gas mixtures at a variety of conditions and at a fraction of the computational cost of molecular simulation. This framework uses a "multipurpose" multilayer perceptron (MLP) model that can predict single component adsorption of various small adsorbates, which, upon coupling with ideal adsorbed solution theory (IAST), can predict binary adsorption for mixtures such as Xe/Kr, CH₄/CH₆, N₂/CH₄, and Ar/Kr at multiple compositions and pressures. For this MLP+IAST framework to work with sufficient accuracy, we found it critical for the MLP to make accurate predictions at low pressures (0.01-0.1 bar). After training a model with this capability, we found that MOFs in the 95th and 90th percentiles of separation performance determined from MLP+IAST calculations were 65% and 87%, respectively, the same as MOFs in the simulation-predicted 95th percentile across several mixtures at diverse conditions (on average). After validating our MLP+IAST framework, we used a clustering algorithm to identify "privileged" MOFs that are high performing for multiple separations at multiple conditions. As an example, we focused on MOFs that were high performing for the industrially relevant separations 80/20 Xe/Kr at 1 bar and 80/20 N₂/CH₄ at 5 bars. Finally, we used the MOF free energies (calculated on our entire database) to identify privileged MOFs that were also likely synthetically accessible, at least from a thermodynamic perspective.

Modelling drug adsorption in metal-organic frameworks: the role of solvent

Solvent plays a key role in biological functions, catalysis, and drug delivery. Metal-organic frameworks (MOFs) due to their tunable functionalities, porosities and surface areas have been recently used as drug delivery vehicles. To investigate the effect of solvent on drug adsorption in MOFs, we have performed integrated computational and experimental studies in selected biocompatible MOFs, specifically, UiO-AZB, HKUST-1 (or CuBTC) and NH2-MIL-53(Al). The adsorption of three drugs, namely, 5-fluorouracil (5-FU), ibuprofen (IBU), and hydroxyurea (HU) were performed in the presence and absence of the ethanol. Our computational predictions, at 1 atmospheric pressure, showed a reasonable agreement with experimental studies performed in the presence of ethanol. We find that in the presence of ethanol the drug molecules were adsorbed at the interface of solvent and MOFs. Moreover, the computationally calculated adsorption isotherms suggested that the drug adsorption was driven by electrostatic interactions at lower pressures (<10-4 Pa). Our computational predictions in the absence of ethanol were higher compared to those in the presence of ethanol. The MOF-adsorbate interaction (U HA) energy decreased with decrease in the size of a drug molecule in all three MOFs at all simulated pressures. At high pressure the interaction energy increases with increase in the MOFs pore size as the number of molecules adsorbed increases. Thus, our research shows the important role played by solvent in drug adsorption and suggests that it is critical to consider solvent while performing computational studies.

Effect of Metal-Organic Framework (MOF) Database Selection on the Assessment of Gas Storage and Separation Potentials of MOFs

Development of computation-ready metal-organic framework databases (MOF DBs) has accelerated high-throughput computational screening (HTCS) of materials to identify the best candidates for gas storage and separation. These DBs were constructed using structural curations to make MOFs directly usable for molecular simulations, which caused the same MOF to be reported with different structural features in different DBs. We examined thousands of common materials of the two recently updated, very widely used MOF DBs to reveal how structural discrepancies affect simulated CH4 , H2 , CO2 uptakes and CH4 /H2 separation performances of MOFs. Results showed that DB selection has a significant effect on the calculated gas uptakes and ideal selectivities of materials at low pressure. A detailed analysis on the curated structures was provided to isolate the critical elements of MOFs determining the gas uptakes. Identification of the top-performing materials for gas separation was shown to strongly depend on the DB used in simulations.

Enrofloxacin/florfenicol loaded cyclodextrin metal-organic-framework for drug delivery and controlled release

We presented an antibiotic-loaded γ-cyclodextrin metal-organic framework that delivered antibiotics suitable for the treatment of bacterial infections. The γ-cyclodextrin metal-organic framework was developed using γ-cyclodextrin and potassium ion via the ultrasonic method. The antibiotic (florfenicol and enrofloxacin) was primarily encapsulated into the pore structures of γ-CD-MOF, which allowed the sustained release of antibiotics over an extended period of time in vitro and in vivo. Notably, antibiotics-loaded γ-CD-MOF showed much superior activity against bacteria than free antibiotics (lower MIC value) and displayed better long-lasting activity (longer antibacterial time). The antibiotics-loaded γ-CD-MOF showed nontoxic and perfect biocompatibility to mammalian cells and tissues both in vitro and in vivo. These materials thus represent a novel drug-delivery device suitable for antibiotic therapy. This research is of great significance for reducing the generation of bacterial resistance and providing new ideas for the application of γ-CD-MOF.

H2S Stability of Metal-Organic Frameworks: A Computational Assessment

The H₂S stability of a range of metal-organic frameworks (MOFs) was systematically assessed by first-principles calculations. The most likely degradation mechanism was first determined and we identified the rate constant of the degradation reaction as a reliable descriptor for characterizing the H₂S stability of MOFs. A qualitative H₂S stability ranking was thus established for the list of investigated materials. Structure-stability relationships were further envisaged considering several variables including the nature of the linkers and their grafted functional groups, the pore size, the nature of metal sites, and the presence/nature of coordinatively unsaturated sites. This knowledge enabled the anticipation of the H₂S stability of one prototypical MOF, e.g., MIL-91(Ti), which has been previously proposed as a good candidate for CO₂ capture. This computational strategy enables an accurate and easy handling assessment of the H₂S stability of MOFs and offers a solid alternative to experimental characterizations that require the manipulation of a highly toxic and corrosive molecule.

Fluorinated metal organic frameworks, MFFIVE-Ni-L (M = Fe/Al, L = pyr), with coordinatively unsaturated metal site for CO2 separation from flue gas in the presence of humidity by computational methods

The anthropogenic emission of greenhouse gases, mainly CO₂, is considered to be one of the most challenging environmental threats related to global climatic change. Herein, for the first time, we accurately interpreted the interaction of guest molecules such as H₂O, CO₂ and N₂, the main constituent of flue gas, to a coordinatively unsaturated (CUS) square pillared fluorinated metal organic framework (MOF) using a grand canonical Monte Carlo (GCMC) simulation with the help of a specific forcefield. This specific forcefield is derived from the interaction energy profile of the guest molecules to the framework attained from the periodic-density functional theory (DFT) calculations. The DFT-derived forcefield effectively safeguarded the ability of the coordinatively unsaturated square pillared fluorinated MOF for CO₂ separation in the presence of moisture.

In-silico identification of adsorbent for separation of ethane/ethylene mixture

We present here a high-throughput computational screening of 4,821 real metal-organic framework (MOF) structures that do not contain any open metal sites to isolate the best performing candidate for separation of ethane/ethylene mixture at ambient conditions. The MOF structures were assessed on the basis of several adsorption-based separation performance metrics. Some of these metrics were found to correlate strongly among themselves. We have presented various structures-property correlations which unfold useful insights. MOF ATAGEJ is found to be the top performing MOF with highest adsorbent performance score 12.38 mol/kg and regenerability 93.88%. Several other MOFs OTOLIU (MIL-167), UMUMOG (UBMOF-8), and TOVGES (PCN-230) containing tetravalent metal cations such as Zr⁴⁺ and Ti⁴⁺ are found to be potential structures that are thermally, mechanically, and chemically stable and performs better than zeolites. Adsorption selectivity shows exponential correlation with difference of heat of adsorption of ethane and ethene at 0.1 bar and 298 K. We have also presented how various performance metrics correlate among themselves. These correlations unfold useful insights. Graphical abstract.

Understanding CO2/CH4 Separation in Pristine and Defective 2D MOF CuBDC Nanosheets via Nonequilibrium Molecular Dynamics

The separation of CO₂/CH₄ gas mixtures is a key challenge for the energy sector and is essential for the efficient upgrading of natural gas and biogas. A new emerging field, that of metal-organic framework nanosheets (MONs), has shown the potential to outperform conventional separation methods and bulk metal-organic frameworks (MOFs). In this work, we model the CO₂/CH₄ separation in both defect-free and defective 2D CuBDC nanosheets and compare their performance with the bulk CuBDC MOF and experimental data. We report the results of external force nonequilibrium molecular dynamics (EF-NEMD) for pure components and binary mixtures. The EF-NEMD simulations reveal a pore blocking separation mechanism, in which the CO₂ molecules occupy adsorption sites and significantly restrict the diffusion of CH₄. The MON structure achieves a better selectivity of CO₂ over CH₄ compared to the bulk CuBDC MOF which is due to the mass transfer resistance of the methane molecules on the surface of the nanosheet. Our results show that it is essential to consider the real mixture in these systems rather than relying solely on pure component data and ideal selectivity. Furthermore, the separation is shown to be sensitive to the presence of missing linker defects in the nanosheets. Only 10% of missing linkers result in nonselective nanosheets. Hence, the importance of a defect-free synthetic method for CuBDC nanosheets is underlined.

A Rational Design of Microporous Nitrogen-Rich Lanthanide Metal-Organic Frameworks for CO2/CH4 Separation

Three new lanthanide metal-organic frameworks IRHs-(1-3) supported by cyamelurate linkers have been synthesized and structurally characterized. The incorporation of numerous heteroatoms (N and O) into the pore walls and the relatively small microchannels of these porous solids enhance bonding force of the host-guest interactions, thus promoting the adsorption of carbon dioxide (CO₂) over methane (CH₄). The nonpolar covalent bonds in methane also favor the less uptake due to the hydrophilic walls of these frameworks. Grand canonical Monte Carlo simulations were performed to determine the origin of the adsorption. The density isocontour surfaces show that CO₂ is mainly adsorbed on the walls composed of organic linkers and around the metal sites, whereas no specific adsorption site is observed for CH₄, which indicates weak interactions between the framework and the adsorbed gas. As expected, the simulations show that CH₄ is not observed around the metal center due to the presence of H₂O molecules. The excellent selectivity of CO₂/CH₄ binary mixture was predicted by the ideal adsorbed solution theory (IAST) via correlating pure component adsorption isotherms with the Toth model. At 25 °C and 1 bar, the CO₂ and CH₄ uptakes for IRH-3 were 2.7 and 0.07 mol/kg, respectively, and the IAST predicated selectivity for CO₂/CH₄ (1:1) reached 27, which is among the best value for MOF materials.

High-throughput screening of metal-organic frameworks for kinetic separation of propane and propene

We apply molecular simulations to screen a database of reported metal-organic framework structures from the computation-ready, experimental (CoRE) MOF database to identify materials potentially capable of separating propane and propene by diffusion. We report a screening workflow that uses descriptor analysis, conventional molecular dynamics (MD), and Nudged Elastic Band (NEB) energy barrier calculations at both classical force field and Density Functional Theory (DFT) levels. For the first time, the effects of framework flexibility on guest transport properties were fully considered in a screening process and led to the identification of candidate MOFs. The hits identified by this proof-of-concept workflow include ZIF-8 and ZIF-67 previously shown to have large differences in propane and propene diffusivities as well as two other materials that have not been tested experimentally yet. This work emphasises the importance of taking into account framework flexibility when studying guest transport in porous materials, demonstrates the potential of the data-driven identification of high-performance materials and highlights the ways of improving the predictive power of the screening workflow.

Multicomponent gas separation and purification using advanced 2D carbonaceous nanomaterials

Multicomponent gas separation and purification is an important pre- or post-processing step in industry. Herein, we employed a multiscale computational approach to investigate the possibility of multicomponent low-weight gas (H₂, O₂, N₂, CO₂, CH₄) separation and purification using novel porous 2D carbonaceous nanomaterials, namely Graphdiyne (GD), Graphenylene (GN), and Rhombic-Graphyne (RG). The dispersion-corrected plane-wave density functional theory (DFT) calculation combined with the Climbing Image Nudged Elastic Band (CI-NEB) method was employed to study the gas/membrane interaction energy and diffusion barrier of different gases passing through the geometrically optimized membranes. The results from CI-NEB calculations were then fitted to the Morse potential function to construct a bridge between quantum mechanics calculations and non-equilibrium molecular dynamics (NEMD) simulation. The selectivity of each membrane for all binary mixtures was calculated using the estimated diffusion energy barriers based on the Arrhenius equation. Finally, a series of extensive NEMD simulations were carried out to evaluate the real word and time dependent separation process. According to the results, CH₄ molecules can be completely separated from the other gases using a GD membrane, O₂ molecules from CH₄, N₂, and CO₂ by a GN membrane, and H₂ molecules from all other gases using a RG membrane.

Metal-Organic Framework Nanoparticles Induce Pyroptosis in Cells Controlled by the Extracellular pH

Ion homeostasis is essential for cellular survival, and elevated concentrations of specific ions are used to start distinct forms of programmed cell death. However, investigating the influence of certain ions on cells in a controlled way has been hampered due to the tight regulation of ion import by cells. Here, it is shown that lipid-coated iron-based metal-organic framework nanoparticles are able to deliver and release high amounts of iron ions into cells. While high concentrations of iron often trigger ferroptosis, here, the released iron induces pyroptosis, a form of cell death involving the immune system. The iron release occurs only in slightly acidic extracellular environments restricting cell death to cells in acidic microenvironments and allowing for external control. The release mechanism is based on endocytosis facilitated by the lipid-coating followed by degradation of the nanoparticle in the lysosome via cysteine-mediated reduction, which is enhanced in slightly acidic extracellular environment. Thus, a new functionality of hybrid nanoparticles is demonstrated, which uses their nanoarchitecture to facilitate controlled ion delivery into cells. Based on the selectivity for acidic microenvironments, the described nanoparticles may also be used for immunotherapy: the nanoparticles may directly affect the primary tumor and the induced pyroptosis activates the immune system.

Prediction of MOF Performance in Vacuum Swing Adsorption Systems for Postcombustion CO2 Capture Based on Integrated Molecular Simulations, Process Optimizations, and Machine Learning Models

Postcombustion CO₂ capture and storage (CCS) is a key technological approach to reducing greenhouse gas emission while we transition to carbon-free energy production. However, current solvent-based CO₂ capture processes are considered too energetically expensive for widespread deployment. Vacuum swing adsorption (VSA) is a low-energy CCS that has the potential for industrial implementation if the right sorbents can be found. Metal-organic framework (MOF) materials are often promoted as sorbents for low-energy CCS by highlighting select adsorption properties without a clear understanding of how they perform in real-world VSA processes. In this work, atomistic simulations have been fully integrated with a detailed VSA simulator, validated at the pilot scale, to screen 1632 experimentally characterized MOFs. A total of 482 materials were found to meet the 95% CO₂ purity and 90% CO₂ recovery targets (95/90-PRTs)-365 of which have parasitic energies below that of solvent-based capture (∼290 kWh_e/MT CO₂) with a low value of 217 kWh_e/MT CO₂. Machine learning models were developed using common adsorption metrics to predict a material's ability to meet the 95/90-PRT with an overall prediction accuracy of 91%. It was found that accurate parasitic energy and productivity estimates of a VSA process require full process simulations.

Structure and electronic properties of rare earth DOBDC metal-organic-frameworks

Here, we apply density functional theory (DFT) to investigate rare-earth metal organic frameworks (RE-MOFs), RE12(μ3-OH)16(C8O6H4)8(C8O6H5)4 (RE = Y, Eu, Tb, Yb), and characterize the level of theory needed to accurately predict structural and electronic properties in MOF materials with 4f-electrons. A two-step calculation approach of geometry optimization with spin-restricted DFT and large core potential (LCPs), and detailed electronic structures with spin-unrestricted DFT with a full valence potential + Hubbard U correction is investigated. Spin-restricted DFT with LCPs resulted in good agreement between experimental lattice parameters and optimized geometries, while a full valence potential is necessary for accurate representation of the electronic structure. The electronic structure of Eu-DOBDC MOF indicated a strong dependence on the treatment of highly localized 4f-electrons and spin polarization, as well as variation within a range of Hubbard corrections (U = 1-9 eV). For Hubbard corrected spin-unrestricted calculations, a U value of 1-4 eV maintains the non-metallic character of the band gap with slight deviations in f-orbital energetics. When compared with experimentally reported results, the importance of the full valence calculation and the Hubbard correction in correctly predicting the electronic structure is highlighted.

Large-Scale Computational Screening of Metal Organic Framework (MOF) Membranes and MOF-Based Polymer Membranes for H2/N2 Separations

Several thousands of metal organic frameworks (MOFs) have been reported to date, but the information on H₂/N₂ separation performances of MOF membranes is currently very limited in the literature. We report the first large-scale computational screening study that combines state-of-the-art molecular simulations, grand canonical Monte Carlo (GCMC) and molecular dynamics (MD), to predict H₂ permeability and H₂/N₂ selectivity of 3765 different types of MOF membranes. Results showed that MOF membranes offer very high H₂ permeabilities, 2.5 × 10³ to 1.7 × 10⁶ Barrer, and moderate H₂/N₂ membrane selectivities up to 7. The top 20 MOF membranes that exceed the polymeric membranes' upper bound for H₂/N₂ separation were identified based on the results of initial screening performed at infinite dilution condition. Molecular simulations were then carried out considering binary H₂/N₂ and quaternary H₂/N₂/CO₂/CO mixtures to evaluate the separation performance of MOF membranes under industrial operating conditions. Lower H₂ permeabilities and higher N₂ permeabilities were obtained at binary mixture conditions compared to the ones obtained at infinite dilution due to the absence of multicomponent mixture effects in the latter. Structure-performance relations of MOFs were also explored to provide molecular-level insights into the development of new MOF membranes that can offer both high H₂ permeability and high H₂/N₂ selectivity. Results showed that the most promising MOF membranes generally have large pore sizes (>6 Å) as well as high surface areas (>3500 m²/g) and high pore volumes (>1 cm³/g). We finally examined H₂/N₂ separation potentials of the mixed matrix membranes (MMMs) in which the best MOF materials identified from our high-throughput screening were used as fillers in various polymers. Results showed that incorporation of MOFs into polymers almost doubles H₂ permeabilities and slightly enhances H₂/N₂ selectivities of polymer membranes, which can advance the current membrane technology for efficient H₂ purification.

Automated Multiscale Approach To Predict Self-Diffusion from a Potential Energy Field

For large-scale screening studies there is a need to estimate the diffusion of gas molecules in nanoporous materials more efficiently than (brute force) molecular dynamics. In particular for systems with low diffusion coefficients molecular dynamics can be prohibitively expensive. An alternative is to compute the hopping rates between adsorption sites using transition state theory. For large-scale screening this requires the automatic detection of the transition states between the adsorption sites along the different diffusion paths. Here an algorithm is presented that analyzes energy grids for the moving particles. It detects the energies at which diffusion paths are formed, together with their directions. This allows for easy identification of nondiffusive systems. For diffusive systems, it partitions the grid coordinates assigned to energy basins and transitions states, permitting a transition state theory based analysis of the diffusion. We test our method on CH₄ diffusion in zeolites, using a standard kinetic Monte Carlo simulation based on the output of our grid analysis. We find that it is accurate, fast, and rigorous without limitations to the geometries of the diffusion tunnels or transition states.