Number Of Metal-organic Frameworks Research Articles

High-performance metal-organic frameworks for efficient adsorption, controlled release, and membrane separation of organophosphate pesticides

Metal-organic frameworks (MOFs) are considered good choices for the absorption, separation, and release of various substances. The rapid increase in the number of MOFs being produced makes it impractical to conduct experimental research to identify the most promising candidates in these areas. Therefore, computational screening has emerged as a robust approach. We employed this approach to identify the most efficient MOFs for the removal, separation, and controlled release of organophosphorus pesticides, including diazinon, chlorpyrifos, and chlorpyrifos-methyl. From a database of approximately 14,000 MOFs, 584 MOFs with the largest cavity diameters were used in the screening process to evaluate promising candidates for each pesticide. Monte Carlo simulations were used to estimate the pesticide uptake capacities and isosteric heats of the selected MOFs. The structural properties of MOFs were correlated with their adsorption capacities and isosteric heats. Computational screening identified 325, 330, and 450 MOFs as potential absorbents and carriers for diazinon, chlorpyrifos-methyl, and chlorpyrifos, respectively. Additionally, it proposed 235, 254, and 135 MOFs as membranes for the effective separation of these organophosphorus pesticides. The reactivity of the pesticides was explored using quantum mechanics calculations. Molecular dynamics simulations of the top three MOFs with high uptake capacities for each pesticide were performed and analyzed to understand the interactions between the MOF, pesticide, and water molecules. The solvation-free energies of the pesticides were calculated for various solvents to identify the most effective solvent for desorbing pesticides from the MOFs, thereby enabling MOF regeneration.

Metal-Organic Frameworks as Potential Agents for Extraction and Delivery of Pesticides and Agrochemicals.

Pesticide contamination is a global issue, affecting nearly 44% of the global farming population, and disproportionately affecting farmers and agricultural workers in developing countries. Despite this, global pesticide usage is on the rise, with the growing demand of global food production with increasing population. Different types of porous materials, such as carbon and zeolites, have been explored for the remediation of pesticides from the environment. However, there are some limitations with these materials, especially due to lack of functional groups and relatively modest surface areas. In this regard, metal-organic frameworks (MOFs) provide us with a better alternative to conventionally used porous materials due to their versatile and highly porous structure. Recently, a number of MOFs have been studied for the extraction of pesticides from the environment as well as for targeted and controlled release of agrochemicals. Different types of pesticides and conditions have been investigated, and MOFs have proved their potential in agricultural applications. In this review, the latest studies on delivery and extraction of pesticides using MOFs are systematically reviewed, along with some recent studies on greener ways of pest control through the slow release of chemical compounds from MOF composites. Finally, we present our insights into the key issues concerning the development and translational applications of using MOFs for targeted delivery and pesticide control.

Glyco-conjugated metal-organic framework biosensor for fluorescent detection of bacteria.

Metal-organic frameworks (MOFs) are hybrid materials constructed by the linkage between an inorganic secondary building unit and an organic linker. A number of MOFs are luminescent in nature and can be structurally tuned for desirable geometry, surface functionality, and porosity. Luminescent MOFs have been endorsed for various biosensing applications. Lectins and carbohydrates have been used for the development of simple and convenient biosensing and bioimaging tools. Lectins are mostly present on the surface of microorganisms where they aid in pathogenesis. Due to this, they can be potential targets for a microbial biosensor. The present study, for the first time, explores the usage of a carbohydrate-conjugated FeMOF (Glyco-MOF) bioprobe for the selective determination of Pseudomonas aeruginosa and Escherichia coli. NH2-MIL-53(Fe) MOF was synthesized via a room temperature protocol and separately conjugated with galactose and mannose sugars via glutaraldehyde chemistry. The synthesized bioprobe is validated for structural integrity, luminescent nature, stability, and analyte assay. Electron microscopy studies validated the unhindered MOF's morphology and structural integrity, after bioconjugation. The synthesized bioprobes were able to detect P. aeruginosa and E. coli up to respective detection limits of 202 and 8CFU/mL, respectively. The bioprobes are selective even in co-presence of possible interferants as well as being environmentally stable.

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.

MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis.

The number of metal-organic frameworks (MOF) as well as the number of applications of this material are growing rapidly. With the number of characterized compounds exceeding 100,000, manual sorting becomes impossible. At the same time, the increasing computer power and established use of automated machine learning approaches makes data science tools available, that provide an overview of the MOF chemical space and support the selection of suitable MOFs for a desired application. Among the different data science tools, graph theory approaches, where data generated from numerous real-world applications is represented as a graph (network) of interconnected objects, has been widely used in a variety of scientific fields such as social sciences, health informatics, biological sciences, agricultural sciences and economics. We describe the application of a particular graph theory approach known as social network analysis to MOF materials and highlight the importance of community (group) detection and graph node centrality. In this first application of the social network analysis approach to MOF chemical space, we created MOFSocialNet. This social network is based on the geometrical descriptors of MOFs available in the CoRE-MOFs database. MOFSocialNet can discover communities with similar MOFs structures and identify the most representative MOFs within a given community. In addition, analysis of MOFSocialNet using social network analysis methods can predict MOF properties more accurately than conventional ML tools. The latter advantage is demonstrated for the prediction of gas storage properties, the most important property of these porous reticular networks.

Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening.

By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases of such hypothetical MOFs, we need to ensure that they not only contribute toward the growth of the count of structures but also add different chemistries to the existing databases. In this study, we designed a database of ∼20,000 hypothetical MOFs, which are diverse in terms of their chemical design space—metal nodes, organic linkers, functional groups, and pore geometries. Using machine learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications—post-combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post-combustion carbon capture) and MOF-5 (for hydrogen storage). All the structures developed in this study, and their properties, are provided on the Materials Cloud to encourage further use of these materials for other applications.

Non-injective gas sensor arrays: identifying undetectable composition changes

Metal-organic frameworks (MOFs) are nanoporous materials with good prospects as recognition elements for gas sensors owing to their adsorptive sensitivity and selectivity. A gravimetric, MOF-based sensor functions by measuring the mass of gas adsorbed in a MOF. Changes in the gas composition are expected to produce detectable changes in the mass of gas adsorbed in the MOF. In practical settings, multiple components of the gas adsorb into the MOF and contribute to the sensor response. As a result, there are typically many distinct gas compositions that produce the same single-sensor response. The response vector of a gas sensor array places multiple constraints on the gas composition. Still, if the number of degrees of freedom in the gas composition is greater than the number of MOFs in the sensor array, the map from gas compositions to response vectors will be non-injective (many-to-one). Here, we outline a mathematical method to determine undetectable changes in gas composition to which non-injective gas sensor arrays are unresponsive. This is important for understanding their limitations and vulnerabilities. We focus on gravimetric, MOF-based gas sensor arrays. Our method relies on a mixed-gas adsorption model in the MOFs comprising the sensor array, which gives the mass of gas adsorbed in each MOF as a function of the gas composition. The singular value decomposition of the Jacobian matrix of the adsorption model uncovers (i) the unresponsive directions and (ii) the responsive directions, ranked by sensitivity, in gas composition space. We illustrate the identification of unresponsive subspaces and ranked responsive directions for gas sensor arrays based on Co-MOF-74 and HKUST-1 aimed at quantitative sensing of CH4/N2/CO2/C2H6 mixtures relevant to natural gas sensing.

Novel in-capsule synthesis of metal–organic framework for innovative carbon dioxide capture system

Metal-Organic Frameworks (MOFs) have been developed as solid sorbents for CO2 capture applications and their properties can be controlled by tuning the chemical blocks of their crystalline units. A number of MOFs (e.g., HKUST-1) have been developed but the question remains how to deploy them for gas–solid contact. Unfortunately, the direct use of MOFs as nanocrystals would lead to serious problems and risks. Here, for the first time, we report a novel MOF-based hybrid sorbent that is produced via an innovative in-situ microencapsulated synthesis. Using a custom-made double capillary microfluidic assembly, double emulsions of the MOF precursor solutions and UV-curable silicone shell fluid are produced. Subsequently, HKUST-1 MOF is successfully synthesized within the droplets enclosed in the gas permeable microcapsules. The developed MOF-bearing microcapsules uniquely allow the deployment of functional nanocrystals without the challenge of handling ultrafine particles, and further, can selectively reject undesired compounds to protect encapsulated MOFs.

Metal Exchange Boosts the CO2 Selectivity of Metal Organic Frameworks Having Zn-Oxide Nodes.

A large number of metal organic frameworks (MOFs) synthesized to date have nodes with a Zn metal, and a detailed understanding of their gas separation efficiency upon metal exchange is needed to pave the way for designing the next generation of MOFs. In this work, we implemented a protocol to identify MOFs with Zn nodes out of 10,221 MOFs and classified them into two main groups. Depending on the pore properties and adsorption selectivities, two MOFs from IRMOFs and two MOFs from ZnO-MOFs were selected. The metal atom (Zn) of the selected four MOFs was exchanged with eight different metals (Cd, Co, Cr, Cu, Mn, Ni, Ti, and V), and 32 different metal-exchanged MOFs (M-MOFs) were obtained. By performing grand canonical Monte Carlo simulations, we investigated the influence of the metal type on the CO2/H2 and CO2/CH4 separation performances of these 32 M-MOFs. Physical properties of the MOFs such as the pore size and surface area, and chemical properties such as the partial charges of the atoms in the framework were investigated to understand the effect of metal exchange on the gas adsorption and separation performances of materials. Exchange of Zn with V and Cr led to a remarkable increase in the CO2 uptakes of selected MOFs and these increases were reflected on the adsorption selectivity, working capacity, and the adsorbent performance score of MOFs. The exchange of Zn with V increased the selectivity of one of the MOFs from 119 to 355 and the adsorbent performance score from 70 to 444 mol/kg, while for another MOF, exchange of Zn with Cr increased the selectivity from 161 to 921 and the adsorbent performance score from 162 to 1233 mol/kg under the condition of vacuum swing adsorption. The molecular level insights we provided to explain the improvement in the gas separation performances of M-MOFs will serve as a guide to design materials with exceptional CO2 separation performances.

Recent Developments in the Synthesis and Applications of Metal Organic Framework: A Concise Review

Metal organic frameworks (MOFs) are one of those compounds which have drawn attention in various applications due to their several interesting properties like tunable shape, size, pore size, easy functionalization, high surface area, pore volume, etc. Metal organic frameworks due to their uniform structures, tunable porosity, wide variety and stability on various topology, geometry, dimension and chemical functions of the molecular network give a remarkable structural diversity in comparison to other porous materials. This enables scientists to handle numerous framework structures, porosity and functionality effectively. The unique structural architecture and tunable properties of MOF’s makes them an interesting hybrid material consisting of organic and inorganic materials. MOF can be randomly constructed like Lego bricks and superior in terms of versatility in comparisson to other porous materials. A number of MOFs containing a wide variety of metal e.g. zinc, copper, iron, aluminium, magnesium, chromium, zirconium, gadolinium, manganese are gaining rapid growth in commercial markets for gas storage, adsorption, separation and catalytic applications. This concise review emphasizes various synthetic methods e.g. solvothermal process, hydrothermal synthesis, electrochemical synthesis, microwave synthesis, sonochemical synthesis, mechanochemical synthesis, of metal organic framework developed in the last few decades. It also addresses various applications of metal organic framework e.g. hydrogen storage, gas adsorption, drug delivery systems and bioimaging agents, biocatalysts, biosensors, electrochemical sensors, etc. It also comments on various challenges and futuristic applications of metal organic frameworks in various field e.g. liquid wate management, gaseous waste management, sunlight assisted catalysis, water purification, building materials, electronic devices, battery technologies, targeted drug delivery, solar cells, etc. of science and technology in coming decades.

A Universal Machine Learning Algorithm for Large-Scale Screening of Materials.

Application of machine learning (ML) methods for the determination of the gas adsorption capacities of nanomaterials, such as metal-organic frameworks (MOF), has been extensively investigated over the past few years as a computationally efficient alternative to time-consuming and computationally demanding molecular simulations. Depending on the thermodynamic conditions and the adsorbed gas, ML has been found to provide very accurate results. In this work, we go one step further and we introduce chemical intuition in our descriptors by using the "type" of the atoms in the structure, instead of the previously used building blocks, to account for the chemical character of the MOF. ML predictions for the methane and carbon dioxide adsorption capacities of several tens of thousands of hypothetical MOFs are evaluated at various thermodynamic conditions using the random forest algorithm. For all cases examined, the use of atom types instead of building blocks leads to significantly more accurate predictions, while the number of MOFs needed for the training of the ML algorithm in order to achieve a specified accuracy can be reduced by an order of magnitude. More importantly, since practically there are an unlimited number of building blocks that materials can be made of but a limited number of atom types, the proposed approach is more general and can be considered as universal. The universality and transferability was proved by predicting the adsorption properties of a completely different family of materials after the training of the ML algorithm in MOFs.

Current Status of Microporous Metal-Organic Frameworks for Hydrocarbon Separations.

Separation of hydrocarbon mixtures into single components is a very important industrial process because all represent very important energy resources/raw chemicals in the petrochemical industry. The well-established industrial separation technology highly relies on the energy-intensive cryogenic distillation processes. The discovery of new materials capable of separating hydrocarbon mixtures by adsorbent-based separation technologies has the potential to provide more energy-efficient industrial processes with remarkable energy savings. Porous metal-organic frameworks (MOFs), also known as porous coordination polymers, represent a new class of porous materials that offer tremendous promise for hydrocarbon separations because of their easy tunability, designability, and functionality. A number of MOFs have been designed and synthesized to show excellent separation performance on various hydrocarbon separations. Here, we summarize and highlight some recent significant advances in the development of microporous MOFs for hydrocarbon separation applications.

Identifying Highly Selective Metal Organic Frameworks for CH4/H2 Separations Using Computational Tools

The large number of metal organic frameworks (MOFs) represents both an opportunity and a challenge for identification of materials exhibiting promising properties in gas separations. We used molecular simulations to screen 250 different MOF structures in order to examine their adsorption-based CH4/H2 separation performances. Adsorption selectivity, working capacity, sorbent selection parameter, and regenerability of MOFs were calculated and compared with those of traditional nanoporous materials. The accuracy of simple models that can predict adsorption selectivity of MOFs based on structural properties of materials was discussed. With the use of molecular dynamics, gas diffusivities were computed in the MOFs which were identified as the top performing materials for adsorption-based CH4/H2 separation. Membrane selectivities of these MOFs were predicted to discuss kinetic separation performances of materials. Results showed that there is a significant number of MOFs that exhibit extraordinarily large adsor...

Metal–Organic Frameworks Built from a Linear Rigid Dicarboxylate and Different Colinkers: Trap of the Keto Form of Ethylacetoacetate, Luminescence and Ferroelectric Studies

The ligand 2,6,2′,6′-tetranitro-biphenyl-4,4′-dicarboxylic acid (H2L) has been used alone or with a coligand to construct a number of metal organic frameworks (MOFs) with different metal ions that are X-ray crystallographically characterized. The porous three-dimensional (3D) MOF {[Gd2(L)3(DMF)4]·(4DMF)·(3H2O)}n (1) (DMF = N,N′-dimethylformamide) is found to be an excellent host to the keto form of ethylacetoacetate to produce {[Gd(L)1.5(DMF)2(H2O)2](S)(H2O)}n (1a) (S = ethyl 3-oxobutanoate) in single crystal to single crystal (SC–SC) transformation. This involves drastic rearrangement of the channels including several carboxylate shifts and concomitant movement of water molecules from the cavity to the metal center. Interestingly, the daughter framework 1a reverts back to the mother framework 1 upon keeping it in DMF for 3 days at room temperature, suggesting that the framework 1 can be used as a container. The linker H2L also forms the MOFs {[(Cd)4(L)3(HlL1)2(DMF)(H2O)2](DMF)3(H2O)2}n (2), {[Cd(L)(L2)]}...

Molecular simulations in metal–organic frameworks for diverse potential applications

As a new class of intriguing nanoporous materials, metal–organic frameworks (MOFs) have been considered for diverse potential applications. The number of MOFs synthesised to date is extremely large; thus, experimental testing alone is economically expensive and practically formidable. With rapidly growing computational resources, molecular simulation has become an indispensable tool to characterise, screen and design MOFs. Our research group has conducted comprehensive simulation studies in MOFs for carbon capture, hydrocarbon separation, alcohol adsorption and biofuel purification, water treatment, bio- and chiral separation and drug loading; furthermore, mechanical moduli, structural transition and thermal conductivity have also been examined. These systematic simulation studies are summarised in this review to demonstrate that simulation at a molecular level can secure the quantitative interpretation of experimental observation, provide the microscopic insight from bottom-up and facilitate the development of new MOFs.

Accessing Postsynthetic Modification in a Series of Metal-Organic Frameworks and the Influence of Framework Topology on Reactivity

2-Amino-1,4-benzenedicarboxylic acid (NH(2)-BDC) has been found to be a compatible building block for the construction of two new metal-organic frameworks (MOFs) that have structures isoreticular to reported MOFs that use 1,4-benzenedicarboxylic acid (BDC) as a building block. DMOF-1-NH(2) (DABCO MOF-1-NH(2)) is a derivative of a previously studied MOF that contains two-dimensional square grids based on NH(2)-BDC and zinc(II) paddle-wheel units; the grid layers are connected by DABCO (1,4-diazabicyclo[2.2.2]octane) molecules that coordinate in the axial positions of the paddlewheel secondary-building units (SBUs). UMCM-1-NH(2) is an NH(2)-BDC derivative of UMCM-1 (University of Michigan Crystalline Material-1), a highly porous MOF reported by Matzger et al., and consists of both NH(2)-BDC and BTB (BTB = 4,4',4''-benzene-1,3,5-triyl-tribenzoate) linkers with Zn(4)O SBUs. The structure of UMCM-1-NH(2) was confirmed by single-crystal X-ray diffraction. By using NH(2)-BDC to generate these MOFs, the pendant amino groups can serve as a chemical handle that can be manipulated via postsynthetic modification with alkyl anhydrides. Reactions of each MOF and different anhydrides have been performed to compare the extent of conversion, thermal and structural stability, and Brunauer-Emmett-Teller surface areas afforded by the resulting materials. Under comparable reaction conditions, (1)H NMR of digested samples show that UMCM-1-NH(2) has conversions comparable to that of IRMOF-3, while DMOF-1-NH(2) only shows high conversions with smaller anhydrides. Under specific reaction conditions, higher conversions were obtained with complete retention of crystallinity, as verified by single-crystal X-ray diffraction experiments. The results presented here demonstrate three important findings: (a) NH(2)-BDC can be used as a surrogate for BDC in a number of MOFs thereby providing a handle for postsynthetic modification, (b) postsynthetic modification is a general strategy to functionalizing MOFs that can be applied to a variety of MOF structures, and (c) the topology and chemical/thermal stability of a MOF can influence the type of chemical reactions and reagents that can be used for postsynthetic modification.