Database for CO2 Separation Performances of MOFs Based on Computational Materials Screening

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.

Isoreticular Metal-Organic Framework-3 (IRMOF-3): From Experimental Preparation, Functionalized Modification to Practical Applications

Isoreticular metal-organic framework-3 (IRMOF-3), a porous coordination polymer, is an MOF material with the characteristics of a large specific surface area and adjustable pore size. Due to the existence of the active amino group (-NH2) on the organic ligand, IRMOF-3 has more extensive research and application potential. Herein, the main preparation methods of IRMOF-3 in existing research were compared and discussed first. Second, we classified and summarized the functionalization modification of IRMOF-3 based on different reaction mechanisms. In addition, the expanded research and progress of IRMOF-3 and their derivatives in catalysis, hydrogen storage, material adsorption and separation, carrier materials, and fluorescence detection were discussed from an application perspective. Moreover, the industrialization prospect of IRMOF-3 and the pressing problems in its practical application were analyzed and prospected. This review is expected to provide a reference for the design and application of more new nanomaterials based on IRMOF-3 to develop more advanced functional materials in industrial production and engineering applications.

Multiscale Modeling of Physical Properties of Nanoporous Frameworks: Predicting Mechanical, Thermal, and Adsorption Behavior

ConspectusNanoporous frameworks are a large and diverse family of supramolecular materials, whose chemical building units (organic, inorganic, or both) are assembled into a 3D architecture with well-defined connectivity and topology, featuring intrinsic porosity. These materials play a key role in various industrial processes and applications, such as energy production and conversion, fluid separation, gas storage, water harvesting, and many more. The performance and suitability of nanoporous materials for each specific application are directly related to both their physical and chemical properties, and their determination is crucial for process engineering and optimization of performances. In this Account, we focus on some recent developments in the multiscale modeling of physical properties of nanoporous frameworks, highlighting the latest advances in three specific areas: mechanical properties, thermal properties, and adsorption.In the study of the mechanical behavior of nanoporous materials, the past few years have seen a rapid acceleration of research. For example, computational resources have been pooled to create a public large-scale database of elastic constants as part of the Materials Project initiative to accelerate innovation in materials research: those can serve as a basis for data-based discovery of materials with targeted properties, as well as the training of machine learning predictor models.The large-scale prediction of thermal behavior, in comparison, is not yet routinely performed at such a large scale. Tentative databases have been assembled at the DFT level on specific families of materials, such as zeolites, but prediction at larger scale currently requires the use of transferable classical force fields, whose accuracy can be limited.Finally, adsorption is naturally one of the most studied physical properties of nanoporous frameworks, as fluid separation or storage is often the primary target for these materials. We highlight the recent achievements and open challenges for adsorption prediction at a large scale, focusing in particular on the accuracy of computational models and the reliability of comparisons with experimental data available. We detail some recent methodological improvements in the prediction of adsorption-related properties: in particular, we describe the recent research efforts to go beyond the study of thermodynamic quantities (uptake, adsorption enthalpy, and thermodynamic selectivity) and predict transport properties using data-based methods and high-throughput computational schemes. Finally, we stress the importance of data-based methods of addressing all sources of uncertainty.The Account concludes with some perspectives about the latest developments and open questions in data-based approaches and the integration of computational and experimental data together in the materials discovery loop.

Machine-learning-driven discovery of metal-organic framework adsorbents for hexavalent chromium removal from aqueous environments

HYPOTHESIS

Metal-organic frameworks (MOFs) have been widely studied for Cr(VI) adsorption in water. Theoretically, numerous MOFs can be synthesised by assembling diverse metals and ligands. However, the traditional manual experimentation for screening high-performance MOFs is resource-intensive and inefficient.

EXPERIMENTS

A screening strategy for MOFs based on machine learning was proposed for the adsorption and removal of Cr(VI) from water. By collecting the characteristics of MOFs and the experimental parameters of Cr(VI) adsorption from the literature, a dataset was constructed to predict the adsorption performance. Among the six regression models, the model trained by the extreme gradient boosted tree algorithm had the best performance and was used to simulate the adsorption and screen potential high-performance adsorbents.

FINDINGS

Structure-property analysis indicated that prepared MOF adsorbents with properties of 0.37 < largest cavity diameter < 0.71 nm, 0.18 < pore volume < 0.57 cm3/g, 412 < specific surface area < 1588 m2/g, 0.43 < void fraction < 0.62 will achieve enhanced adsorption of Cr(VI) in water. High-performance adsorbents were successfully screened using a combination of machine-learning prediction and analysis. Experiments were conducted to verify the exceptional adsorption capacity of UiO-66 and MOF-801. This method effectively identified adsorbents and accelerated the development of new MOF adsorbents for contaminant removal, providing a novel approach for the discovery of superior adsorbents.

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.

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.

Prediction of Xe/Kr Separation in Metal-Organic Frameworks by a Precursor-Based Neural Network Synergistic with a Polarizable Adsorbate Model

Adsorption and separation of Xe/Kr are significant for making high-density nuclear energy environmentally friendly and for meeting the requirements of the gas industry. Enhancing the accuracy of the adsorbate model for describing the adsorption behaviors of Xe and Kr in MOFs and the efficiency of the model for predicting the separation potential (SP) value of Xe/Kr separation in MOFs helps in searching for promising MOFs for Xe/Kr adsorption and separation within a short time and at a low cost. In this work, polarizable and transferable models for mimic Xe and Kr adsorption behaviors in MOFs were constructed. Using these models, SP values of 38 MOFs at various temperatures and pressures were calculated. An optimal neural network model called BPNN-SP was designed to predict SP value based on physical parameters of metal center (electronegativity and radius) and organic linker (three-dimensional size and polarizability) combined with temperature and pressure. The regression coefficient value of the BPNN-SP model for each data set is higher than 0.995. MAE, MBE, and RMSE of BPNN-SP are only 0.331, -0.002, and 0.505 mmol/g, respectively. Finally, BPNN-SP was validated by experiment data from six MOFs. The transferable adsorbate model combined with the BPNN-SP model would highly improve the efficiency for designing MOFs with high performance for Xe/Kr adsorption and separation.

Graph Transformer with Convolution Parallel Networks for Predicting Single and Binary Component Adsorption Performance of Metal-Organic Frameworks

Metal-organic frameworks (MOFs) are considered one of the most important materials for carbon capture and storage (CCS) due to the advantages of porosity, multifunction, diverse structure, and controllable chemical composition. With the continuous development of artificial intelligence (AI) technology, more and more machine learning models are used to identify MOFs with high performance within a massive search space. However, current works have yet to form a model that uses graph-structured data only, which can predict the adsorption properties of single and binary components. In this work, we proposed and developed a graph transformer, combined with convolution parallel networks, called GC-Trans. The model can accurately and efficiently predict the adsorption performance of MOFs under the single- and binary-component adsorption conditions using only the features of the crystal diagram as inputs. By extracting and fusing local and global feature information, the model has stronger expression and generalization abilities. Thus, we used it to screen the ARC-MOF database and analyze the MOF structures that meet the target requirements. Additionally, to demonstrate the transferability of the model, we applied transfer learning methods to predict the CO2/CH4 separations and CH4 uptake, both of which showed good predictive performance.

A Robust Squarate-Cobalt Metal-Organic Framework for CO2/N2 Separation

The separation of CO₂ from the industrial post-combustion flue gas is of great importance to reduce the increasingly serious greenhouse effect, yet highly challenging due to the extremely high stability, low cost, and high separation performance requirements for adsorbents under the practical operating conditions. Herein, we report a robust squarate-cobalt metal-organic framework (MOF), FJUT-3, featuring an ultra-small 1D square channel decorated with -OH groups, for CO₂/N₂ separation. Remarkably, FJUT-3 not only has excellent stability under harsh chemical conditions but also presents low-cost property for scale-up synthesis. Moreover, FJUT-3 shows excellent CO₂ separation performance under various humid and temperature conditions confirmed by the transient breakthrough experiments, thus enabling FJUT-3 with adequate potentials for industrial CO₂ capture and removal. The distinct CO₂ adsorption mechanism is well elucidated by theoretical calculations, in which the hierarchical C···O_CO2, C-O···C_CO2, and O-H···O_CO2 interactions play a vital synergistic role in the selective CO₂ adsorption process.

High-Throughput Screening of the CoRE-MOF-2019 Database for CO2 Capture from Wet Flue Gas: A Multi-Scale Modeling Strategy

Stabilizing the escalating CO₂ levels in the atmosphere is a grand challenge in view of the increasing global demand for energy, the majority of which currently comes from the burning of fossil fuels. Capturing CO₂ from point source emissions using solid adsorbents may play a part in meeting this challenge, and metal-organic frameworks (MOFs) are considered to be a promising class of materials for this purpose. It is important to consider the co-adsorption of water when designing materials for CO₂ capture from post-combustion flue gases. Computational high-throughput screening (HTS) is a powerful tool to identify top-performing candidates for a particular application from a large material database. Using a multi-scale modeling strategy that includes a machine learning model, density functional theory (DFT) calculations, force field (FF) optimization, and grand canonical Monte Carlo (GCMC) simulations, we carried out a systematic computational HTS of the all-solvent-removed version of the computation-ready experimental metal-organic framework (CoRE-MOF-2019) database for selective adsorption of CO₂ from a wet flue gas mixture. After initial screening based on the pore diameters, a total of 3703 unique MOFs from the database were considered for screening based on the FF interaction energies of CO₂, N₂, and H₂O molecules with the MOFs. MOFs showing stronger interactions with CO₂ compared to that with H₂O and N₂ were considered for the next level of screening based on the interaction energies calculated from DFT. CO₂-selective MOFs from DFT screening were further screened using two-component (CO₂ and N₂) and finally three-component (CO₂, N₂, and H₂O) GCMC simulations to predict the CO₂ capacity and CO₂/N₂ selectivity. Our screening study identified MOFs that show selective CO₂ adsorption under wet flue gas conditions with significant CO₂ uptake capacity and CO₂/N₂ selectivity in the presence of water vapor. We also analyzed the nature of pore confinements responsible for the observed CO₂ selectivity.

Ultramicroporous iron-isonicotinate MOFs combining size-exclusion kinetics and thermodynamics for efficient CO2/N2 gas separation

Two ultramicroporous 2D and 3D iron-based Metal-Organic Frameworks (MOFs) have been obtained by solvothermal synthesis using different ratios and concentrations of precursors. Their reduced pore space decorated with pendant pyridine from tangling isonicotinic ligands enables the combination of size-exclusion kinetic gas separation, due to their small pores, with thermodynamic separation, resulting from the interaction of the linker with CO2 molecules. This combined separation results in efficient materials for dynamic breakthrough gas separation with virtually infinite CO2/N2 selectivity in a wide operando range and with complete renewability at room temperature and ambient pressure.

A high performance dual-mode biosensor based on Nd-MOF nanosheets functionalized with ionic liquid and gold nanoparticles for sensing of ctDNA

A dual-mode biosensor constructed based on photoelectrochemical (PEC) and electrochemical (EC) property was developed for assaying circulating tumor DNA (ctDNA), which is commonly used for triple-negative breast cancer diagnosis. Ionic liquid functionalized two-dimensional Nd-MOF nanosheets were successfully synthesized through a template-assisted reagent substituting reaction. Nd-MOF nanosheets integrated with gold nanoparticles (AuNPs) were able to improve photocurrent response and supply active sites for assembling sensing elements. To achieve selective detection of ctDNA, thiol-functionalized capture probes (CPs) were immobilized on the Nd-MOF@AuNPs modified glassy carbon electrode surface, thereby generating a "signal-off" photoelectrochemical biosensor for ctDNA under visible light irradiation. After the recognition of ctDNA, ferrocene-labeled signaling probes (Fc-SPs) were introduced into the biosensing interface. After hybridization between ctDNA and Fc-SPs, the oxidation peak current of Fc-SPs generated from square wave voltammetry can be employed as a "signal-on" electrochemical signal for ctDNA quantification. Under the optimized conditions, a linear relationship was obtained to the logarithm of ctDNA concentration in between 1.0 fmol L^-1 to 10 nmol L^-1 for the PEC model and 1.0 fmol L^-1 to 1.0 nmol L^-1 for the EC model. The dual-mode biosensor can provide accurate results for ctDNA assays, effectively eliminating the probable occurrence of false-positive or false-negative results in single-model assays. By switching DNA probe sequences, the proposed dual-mode biosensing platform can serve as a strategy for detecting other DNAs and possesses broad applications in bioassay and early disease diagnosis.

Process optimization for enhanced carbon capture and cyclic stability using adsorbents derived from coal fly ash

Zeolites and metal-organic frameworks (MOFs) are popular adsorbents when it comes to capturing CO₂ from the gaseous feed stream. In this study, a hybrid of zeolite and ZIF-8 adsorbent was synthesized from coal fly ash via fusion-hydrothermal process and then in-situ aqueous ZIF-8 synthesis technique. This technique of in-situ synthesis is highly cost-effective as it is done at room temperature. The hybrid adsorbent showed an enhanced microporosity as compared to zeolites synthesized from coal fly ash due to the in-situ synthesis of ZIF-8 upon coal fly ash zeolite. It was designated as CFAZ/ZIF-8. At 298 K, a maximum CO₂ uptake value of 2.83 mmol/g was observed with a constant decrease with an increase in temperature. BET surface area value of 426 m²/g was obtained for this adsorbent. Kinetics fit for the best uptake value was performed with the Avrami model kinetics, describing the adsorption well at an R² value of 0.997 for the fit. The adsorbent also showed impressive cyclic stability after five cycles of carbonation and decarbonation. The cyclic stability studies show that the as-synthesized hybrid adsorbent shows promise in CO₂ uptake studies.

Nanozyme and Stimulated Fluorescent Cu-Based Metal-Organic Frameworks (Cu-MOFs) Functionalized with Engineered Aptamers as a Molecular Recognition Element for Thrombin Detection in the Plasma of COVID-19 Patients

An essential tool in the management and control of the COVID-19 pandemic is the development of a fast, selective, sensitive, and inexpensive COVID-19 biomarkers detection method. Herein, an ultrasensitive and label-free biosensing strategy was described for the colorimetric and fluorimetric detection of thrombin. A dual-mode aptasensing method based on integrating engineered ssDNA with a stimulated fluorescent enzyme-mimetic copper-based metal-organic framework (Cu-MOF) as a molecular recognition element for thrombin was investigated. Cu-MOFs displayed stimulated fluorescence and enzyme-mimetic peroxidase activities that oxidize the chromogenic colorless substance TMB to blue-colored oxTMB. The thrombin-based aptamer (ssDNA) can be immobilized on the Cu-MOF surface to form a functionalized composite, ssDNA/MOF, and quench the stimulated fluorescence emission and the enzymatic activity of the Cu-MOF. Later, addition of thrombin recovers the fluorescence and enzymatic activity of the MOF. Thus, a turn-on colorimetry/fluorimetry aptasensing probe was designed for the detection of thrombin. Based on colorimetric assay, 350 pM was recorded as the lower limit of detection (LOD), while based on the fluorescence mode, 110 fM was recorded as the LOD (when S/N = 3). The label-free aptasensing probe was used successfully for the detection of thrombin in COVID-19 patients with satisfactory recoveries, 95-98%. Since the detection time of our aptasensor is relatively rapid (45 min) and due to the low-cost precursors and easy-to-operate characteristics, we believe that it has great potential to be used in point-of-care testing (POCT).

The HEALED SBU Library of Chemically Realistic Building Blocks for Construction of Hypothetical Metal-Organic Frameworks

Advancements in hypothetical metal-organic framework (hMOF) databases and construction tools have resulted in a rapidly expanding chemical design space for nanoporous materials. The bulk of these hypothetical structures are constructed using structural building units (SBUs) derived from experimental MOF structures, often collected from the CoRE-MOF database. Recent investigations into the state of these deposited experimental structures' chemical accuracy identified an array of common structural errors, including omitted protons, missing counterions, and disordered structures. These structural errors propagate into the SBUs mined from experimental MOFs, culminating in inaccurate hMOF structures possessing net charges or missing atoms which were not accounted for previously. This work demonstrates how manual investigation was applied to diagnose structural errors in SBUs obtained from several popular hMOF construction tools and databases. An analysis of the prevailing errors discovered during the examination process is provided along with representative cases to aid with error detection in future studies involving SBU extraction and hMOF construction. A novel repair protocol was established and employed to generate a library of SBUs that are hand-examined and labeled with enhanced detail (HEALED). This repaired library of SBUs contains 952 inorganic SBUs and 568 organic SBUs ideally suited for the generation of hypothetical frameworks that are chemically accurate and properly charge labeled. Additionally, case studies following the effects of SBU errors on electrostatic potential-fitted charges and GCMC-simulated gas adsorption predictions are presented to highlight the significance of using chemically accurate hMOF structures exclusively in all screening efforts going forward.

Discovery of High-Performing Metal-Organic Frameworks for On-Board Methane Storage and Delivery via LNG-ANG Coupling: High-Throughput Screening, Machine Learning, and Experimental Validation

Liquefied natural gas (LNG) gasification coupled with adsorbed natural gas (ANG) charging (LNG-ANG coupling) is an emerging strategy for efficient delivery of natural gas. However, the potential of LNG-ANG to attain the advanced research projects agency-energy (ARPA-E) target for onboard methane storage has not been fully investigated. In this work, large-scale computational screening is performed for 5446 metal-organic frameworks (MOFs), and over 193 MOFs whose methane working capacities exceed the target (315 cm3 (STP) cm-3 ) are identified. Furthermore, structure-performance relationships are realized under the LNG-ANG condition using a machine learning method. Additional molecular dynamics simulations are conducted to investigate the effects of the structural changes during temperature and pressure swings, further narrowing down the materials, and two synthetic targets are identified. The synthesized DUT-23(Cu) and DUT-23(Co) show higher working capacities (≈373 cm3 (STP) cm-3 ) than that of any other porous material under ANG or LNG-ANG conditions, and excellent stability during cyclic LNG-ANG operation.

High-Throughput Computational Exploration of MOFs with Open Cu Sites for Adsorptive Separation of Hydrogen Isotopes

Effective separation of hydrogen isotopes still remains one of the extremely challenging tasks in industry. Compared to the present methods that are energy- and cost-intensive, quantum sieving technology based on nanostructured materials offers a more efficient alternative approach, where metal-organic frameworks (MOFs) featuring open metal sites (OMS) can serve as an ideal platform. Herein, a combination of periodic density functional theory (DFT) with dispersive correction and high-throughput molecular simulation was employed from thermodynamic viewpoints to explore the D₂/H₂ separation properties of 929 experimental MOFs bearing a copper-paddlewheel unit. The DFT calculations showed that there is a negligible rotational energy barrier for the molecule adsorbed at the OMS, and the movement of the Cu atoms along the Cu-Cu axis vector almost has no influence on the interaction energy. On the basis of the DFT results, a new force field with a proposed cutoff scheme was developed to accurately describe the strong isotope-OMS interaction. Under practical conditions (40 K and 1.0 bar), large-scale computational material screening demonstrated that the OMS interaction plays a more important role in highly selective materials and ignoring such interactions can lead to completely wrong identification of the most promising materials. Using the adsorption selectivity and adsorbent performance score as evaluation metrics, this work demonstrated that the materials with sql topology notably outperform many benchmark adsorbents reported so far.

A neural network potential for the IRMOF series and its application for thermal and mechanical behaviors

Metal-organic frameworks (MOFs) with their exceptional porous and organized structures have been the subject of numerous applications. Predicting the bulk properties from atomistic simulations requires the most accurate force fields, which is still a major problem due to MOFs' hybrid structures governed by covalent, ionic and dispersion forces. Application of ab initio molecular dynamics to such large periodic systems is thus beyond the current computational power. Therefore, alternative strategies must be developed to reduce computational cost without losing reliability. In this work, we construct a generic neural network potential (NNP) for the isoreticular metal-organic framework (IRMOF) series trained by PBE-D4/def2-TZVP reference data of MOF fragments. We confirmed the success of the resulting NNP on both fragments and bulk MOF structures by prediction of properties such as equilibrium lattice constants, phonon density of states and linker orientation. The RMSE values of energy and force for the fragments are only 0.0017 eV atom^-1 and 0.15 eV Å^-1, respectively. The NNP predicted equilibrium lattice constants of bulk structures, even though not included in training, are off by only 0.2-2.4% from experimental results. Moreover, our fragment based NNP successfully predicts the phenylene ring torsional energy barrier, equilibrium bond distances and vibrational density of states of bulk MOFs. Furthermore, the NNP enables revealing the odd behaviors of selected MOFs such as the dual thermal expansion properties and the effect of mechanical strain on the adsorption of hydrogen and methane molecules. The NNP based molecular dynamics (MD) simulations suggest IRMOF-4 and IRMOF-7 to have positive-to-negative thermal expansion coefficients while the rest to have only negative thermal expansion at the studied temperatures of 200 K to 400 K. The deformation of the bulk structure by reduction of the unit cell volume has been shown to increase the volumetric methane uptake in IRMOF-1 but decrease the volumetric methane uptake in IRMOF-7 due to the steric hindrance. To the best of our knowledge, this study presents the first pre-trained model publicly available giving the opportunity for the researchers in the field to investigate different aspects of IRMOFs by performing large-scale simulation at the first-principles level of accuracy.

A High-Luminescence Biomimetic Nanosensor Based on N, S-GQDs-Embedded Zinc-Based Metal-Organic Framework@Molecularly Imprinted Polymer for Sensitive Detection of Octopamine in Fermented Foods

In this study, a novel fluorescent molecularly imprinted nanosensor (N, S-GQDs@ZIF-8@MIP) based on the nitrogen and sulfur co-doped graphene quantum dots decorated zeolitic imidazolate framework-8 was constructed for the detection of octopamine (OA). Herein, ZIF-8 with a large surface area was introduced as a supporter of the sensing system, which effectively shortened the response time of the sensor. Meanwhile, high green luminescent N, S-GQDs and a maximum emission wavelength of 520 nm under 460 nm excitation and a 12.5% quantum yield were modified on the surface of ZIF-8 as a signal tag that can convert the interactions between the sensor and OA into detectable fluorescent signals. Finally, N, S-GQDs@ZIF-8@MIP was acquired through the surface molecular imprinting method. Due to the synergy of N, S-GQDs, ZIF-8, and MIP, the obtained sensor not only demonstrated higher selectivity and sensitivity than N, S-GQDs@ZIF-8@NIP, but also displayed faster fluorescence response than N, S-GQDs@MIP. Under optimal conditions, the developed sensor presented a favorable linear relationship in the range of 0.1-10 mg L-1 with a detection limit of 0.062 mg L-1. Additionally, the proposed N, S-GQDs@ZIF-8@MIP strategy was effectively applied to the detection of OA in fermented samples, and the obtained results had a satisfactory correlation with those of HPLC.

Light-responsive metal-organic framework sheets constructed smart membranes with tunable transport channels for efficient gas separation

Exploring a new type of smart membrane with tunable separation performance is a promising area of research. In this study, new light-responsive metal-organic framework [Co(azpy)] sheets were prepared by a facile microwave method for the first time, and were then incorporated into a polymer matrix to fabricate smart mixed matrix membranes (MMMs) applied for flue gas desulfurization and decarburization. The smart MMMs exhibited significantly elevated SO2(CO2)/N2 selectivity by 184(166)% in comparison with an unfilled polymer membrane. The light-responsive characteristic of the smart MMMs was investigated, and the permeability and selectivity of the Co(azpy) sheets-loaded smart MMMs were able to respond to external light stimuli. In particular, the selectivity of the smart MMM at the Co(azpy) content of 20% for the SO2/N2 system could be switched between 341 and 211 in situ irradiated with Vis and UV light, while the SO2 permeability switched between 58 Barrer and 36 Barrer, respectively. This switching influence was mainly ascribed to the increased SO2 adsorption capacity in the visible light condition, as verified by adsorption test. The CO2 permeability and CO2/N2 selectivity of MMMs in the humidified state could achieve 248 Barrer and 103.2, surpassing the Robeson's upper bound reported in 2019.

Pillararene-based molecular-scale porous materials

This review discusses the design and syntheses of molecular-scale pillar[n]arene-based porous materials with promising applications and summarises the development of using pillar[n]arenes as the building blocks of porous materials. From the perspective of "role of participation" in the syntheses of molecular-scale pillar[n]arene-based porous materials, the content can be divided into pillar[n]arenes serving as supramolecular nanovalves on surfaces and as ligands for metal-organic frameworks and covalent organic polymers. By integrating pillararenes, which possess rigid pillar-like structures, electron-rich cavities and desirable host-guest properties, with porous polymers of large surface areas and abundant active sites, applications of the resulting materials in drug release platforms, molecular recognition, sensing, detection, gas adsorption, removal of water pollution, organic photovoltaic materials and heterogeneous catalysis can be realised simultaneously and efficiently. Finally, in the conclusions and perspectives part, we put forward the challenges and viewpoints of the current research on pillar[n]arene-based porous materials. We hope this article can provide a timely and valuable reference for researchers interested in synthetic macrocycles and porous materials.

Water Bridges Substitute for Defects in Amine-Functionalized UiO-66, Boosting CO2 Adsorption

The binary adsorption of CO₂ and water on an amine-functionalized UiO-66 metal-organic framework (MOF) was studied experimentally and computationally. Grand canonical Monte Carlo simulations were used to investigate three additional UiO-66 MOFs with different functionalized linkers. Each MOF was studied in a defect-free form as well as two additional forms with precise linker defects. Binary adsorption isotherms are presented for CO₂ at specific water loadings. While water loading in defect-free MOFs reduces the CO₂ uptake, the defects slightly boost the CO₂ uptake at low water loadings. It was found that water bridges form between the metal oxide cores, replacing the missing linkers. Effectively, this creates smaller pores that are more welcoming of CO₂ adsorption. Experimental measurement of the binary isotherms for UiO-66-NH₂ shows a behavior that is consistent with this enhancement.

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.

An upper bound visualization of design trade-offs in adsorbent materials for gas separations: alkene/alkane adsorbents

The last 20 years has seen an explosion in the number of publications investigating porous solids for gas adsorption and separation. The combination of external drivers such as anthropogenic climate change and industrial efficiency has been coupled with discovery of new materials such as synthetic zeolites, metal-organic frameworks, covalent organic frameworks, and non-porous adsorbents. Numerous reviews catalogue these materials and their properties. However, the field lacks a unifying resource to visually compare and analyse materials properties with regard to their utility as a scientific advance and potential for industrial use. In the related field of membrane science, the 'Robeson upper bound' empirically describes the trade-off between gas permeability and selectivity and has become a ubiquitous tool for comparing membrane materials. In this article, we propose upper and lower bounds that empirically correlate the trade-offs encountered when designing adsorbent materials for gas separation, specifically: capacity, selectivity, and heat of adsorption. We apply bound visualizations to adsorbents studied for light alkene/alkane separations and highlight their use in identifying candidate materials for examination within process models and for guiding insights to the most effective materials design strategies. Furthermore, we note the limitations of upper and lower bound visualizations and provide links to a database resource for researchers to produce and download bound visualization plots. We anticipate that introducing bound visualizations to the field of adsorbents for gas separations will allow researchers to provide context for the importance of new materials discoveries, understand trade-offs in adsorbent design, and connect process engineers with candidate materials.

Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure-performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.

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.

Aptamer-functionalized metal-organic frameworks (MOFs) for biosensing

As a class of crystalline porous materials, metal-organic frameworks (MOFs) have attracted increasing attention. Due to the nanoscale framework structure, adjustable pore size, large specific surface area, and good chemical stability, MOFs have been applied widely in many fields such as biosensors, biomedicine, electrocatalysis, energy storage and conversions. Especially when they are combined with aptamer functionalization, MOFs can be utilized to construct high-performance biosensors for numerous applications ranging from medical diagnostics and food safety inspection, to environmental surveillance. Herein, this article reviews recent innovations of aptamer-functionalized MOFs-based biosensors and their bio-applications. We first briefly introduce different functionalization methods of MOFs with aptamers, which provide a foundation for the construction of MOFs-based aptasensors. Then, we comprehensively summarize different types of MOFs-based aptasensors and their applications, in which MOFs serve as either signal probes or signal probe carriers for optical, electrochemical, and photoelectrochemical detection, with an emphasis on the former. Given recent substantial research interests in stimuli-responsive materials and the microfluidic lab-on-a-chip technology, we also present the stimuli-responsive aptamer-functionalized MOFs for sensing, followed by a brief overview on the integration of MOFs on microfluidic devices. Current limitations and prospective trends of MOFs-based biosensors are discussed at the end.

Construction of an Anion-Pillared MOF Database and the Screening of MOFs Suitable for Xe/Kr Separation

The separation of xenon/krypton (Xe/Kr) mixtures is a challenging process. Many porous materials allow the adsorption of both Xe and Kr but only with low selectivity. Anion-pillared metal-organic frameworks (MOFs), featuring the anion groups as structural pillars, show potential in gas separations, but only a limited number of them have been synthesized. Here, we describe a collection of 936 anion-pillared MOFs based on 22 experimentally available structures. We performed density functional theory (DFT) optimization and then assigned density-derived electrostatic and chemical (DDEC) charges for each MOF to make them well suited to molecular simulations. The structural properties of the MOFs vary more strongly with the choice of the organic ligand than with other aspects like fluorine groups and metal centers. We then screened the entire collection of MOFs in the context of Xe/Kr separation at room temperature. Compared with previously reported MOFs, the interpenetrated MOF SIFSIX-6-Cd-i is predicted to perform better for Xe/Kr separations, with a good balance between working capacity (1.62 mmol/g) and separation selectivity (16.4) at 298 K and 100 kPa. We also found that the heterogeneity of fluorine groups within a MOF can help to enhance Xe working capacity without reducing the Xe/Kr selectivity, suggesting that synthesis of anion-pillared MOFs with mixed fluorine groups may lead to improved Xe/Kr separation performance.

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.

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.

Do New MOFs Perform Better for CO2 Capture and H2 Purification? Computational Screening of the Updated MOF Database

High-throughput computational screening of metal organic frameworks (MOFs) enables the discovery of new promising materials for CO₂ capture and H₂ purification. The number of synthesized MOFs is increasing very rapidly, and computation-ready, experimental MOF databases are being updated. Screening the most recent MOF database is essential to identify the best performing materials among several thousands. In this work, we performed molecular simulations of the most recent MOF database and described both the adsorbent and membrane-based separation performances of 10 221 MOFs for CO₂ capture and H₂ purification. The best materials identified for pressure swing adsorption, vacuum swing adsorption, and temperature swing adsorption processes outperformed commercial zeolites and previously studied MOFs in terms of CO₂ selectivity and adsorbent performance score. We then discussed the applicability of Ideal Adsorbed Solution Theory (IAST), effects of inaccessible local pores and catenation in the frameworks and the presence of impurities in CO₂/H₂ mixture on the adsorbent performance metrics of MOFs. Very large numbers of MOF membranes were found to outperform traditional polymer and porous membranes in terms of H₂ permeability. Our results show that MOFs that are recently added into the updated MOF database have higher CO₂/H₂ separation potentials than the previously reported MOFs. MOFs with small pores were identified as potential adsorbents for selective capture of CO₂ from H₂, whereas MOFs with high porosities were the promising membranes for selective separation of H₂ from CO₂. This study reveals the importance of enriching the number of MOFs in high-throughput computational screening studies for the discovery of new promising materials for CO₂/H₂ separation.