Effects of Fluorinated Functionalization of Linker on Quercetin Encapsulation, Release and Hela Cell Cytotoxicity of Cu-Based MOFs as Smart pH-Stimuli Nanocarriers.

Controlled delivery of target molecules is required in many medical and chemical applications. For such purposes, metal-organic frameworks (MOFs), which possess desirable features such as high porosity, large surface area, and adjustable functionalities, hold great potential as drug carriers. Herein, Quercetin (QU), as an anticancer drug, was loaded on Cu2 (BDC)2 (DABCO) and Cu2 (F4 BDC)2 )DABCO) MOFs (BDC=1,4-benzenedicarboxylate and DABCO=1,4-diazabicyclo[2.2.2]octane). As these Cu-MOFs have a high surface area, an appropriate pore size, and biocompatible ingredients, they can be utilized to deliver QU. The loading efficiency of QU in these MOFs was 49.5 % and 41.3 %, respectively. The drug-loaded compounds displayed sustained drug release over 15 days, remarkably high drug loading capacities and pH-controlled release behavior. The prepared nanostructures were characterized by different characterization technics including FT-IR, PXRD, ZP, TEM, FE-SEM, UV-vis, and BET. In addition, MTT assays were carried out on the HEK-293 and HeLa cell lines to investigate cytotoxicity. Cellular apoptosis analysis was performed to investigate the cell death mechanisms. Grand Canonical Monte Carlo simulations were conducted to analyze the interactions between MOFs and QU. Moreover, the stability of MOFs was also investigated during and after the drug release process. Ultimately, kinetic models of drug release were evaluated.

Tailoring ammonia capture in MOFs and COFs: A multi-scale and machine learning comprehensive investigation of functional group modification.

Our study aims to examine the impact of ligand functionalization on the ammonia adsorption properties of MOFs and COFs, by combining multi-scale calculations with machine learning techniques. Density Functional Theory calculations were performed to investigate the interactions between ammonia (NH3) and a comprehensive set of 48 strategically chosen functional groups. In all of the cases, it is observed that functionalized rings exhibit a stronger interaction with ammonia molecule compared to unfunctionalized benzene, while -O2Mg demonstrates the highest interaction energy with ammonia (15 times stronger than the bare benzene). The trend obtained from the thorough DFT screening is verified via Grand Canonical Monte-Carlo calculations by employing interatomic potentials derived from quantum chemical calculations. Isosteric heat of adsorption plots provide a comprehensive elucidation of the adsorption process, and important insights can be taken for studies in fine-tuning materials for ammonia adsorption. Furthermore, a proof of concept machine learning (ML) analysis is conducted, which demonstrates that ML can accurately predict NH3 binding energies despite the limited amount of data. The findings derived from our multi-scale methodology indicate that the functionalization strategy can be utilized to guide synthesis towards MOFs, COFs, or other porous materials for enhanced NH3 adsorption capacity.

Water-Stable etb-MOFs for Methane and Carbon Dioxide Storage.

We utilized the etb platform of MOFs for the synthesis of two new water-stable compounds based on amide functionalized trigonal tritopic organic linkers H3BTBTB (L1), H3BTCTB (L2) and Al3+ metal ions, namely, Al(L1) and Al(L2). The mesoporous Al(L1) material exhibits an impressive methane (CH4) uptake at high pressures and ambient temperature. The corresponding values of 192 cm3 (STP) cm-3, 0.254 g g-1 at 100 bar, and 298 K are among the highest reported for mesoporous MOFs, while the gravimetric and volumetric working capacities (between 80 bar and 5 bar) can be well compared to the best MOFs for CH4 storage. Furthermore, at 298 K and 50 bar, Al(L1) adsorbs 50 wt % (304 cm3 (STP) cm-3) CO2, values among the best recorded for CO2 storage using porous materials. To gain insight into the mechanism accounting for the resultant enhanced CH4 storage capacity, theoretical calculations were performed, revealing the presence of strong CH4 adsorption sites near the amide groups. Our work demonstrates that amide functionalized mesoporous etb-MOFs can be valuable for the design of versatile coordination compounds with CH4 and CO2 storage capacities comparable to ultra-high surface area microporous MOFs.

Multiscale Theoretical Study of Sulfur Dioxide (SO2) Adsorption in Metal-Organic Frameworks.

In the present work, we used DFT in order to study the interaction of SO2 with 41 strategically functionalized benzenes that can be incorporated in MOF linkers. The interaction energy of phenyl phosphonic acid (-PO3H2) with SO2 was determined to be the strongest (-10.1 kcal/mol), which is about 2.5 times greater than the binding energy with unfunctionalized benzene (-4.1 kcal/mol). To better understand the nature of SO2 interactions with functionalized benzenes, electron redistribution density maps of the relevant complexes with SO2 were created. In addition, three of the top performing functional groups were selected (-PO3H2, -CNH2NOH, -OSO3H) to modify the IRMOF-8 organic linker and calculate its SO2 adsorption capacity with Grand Canonical Monte Carlo (GCMC) simulations. Our results showed a great increase in the absolute volumetric uptake at low pressures, indicating that the suggested functionalization technique can be used to enhance the SO2 uptake capability not only in MOFs but in a variety of porous materials.

Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage.

In the field of materials science, the main objective of predictive models is to provide scientists with reliable tools for fast and accurate identification of new materials with exceptional properties. Over the last few years, machine learning methods have been extensively used for the study of the gas-adsorption in nanoporous materials as an efficient alternative of molecular simulations and experiments. In several cases, the accuracy of the constructed predictive models for unknown materials is extremely high. In this study, we explored the adsorption of methane by metal organic frameworks (MOFs) and concluded that many top-performing materials often deviate significantly from the known materials used for the training of the machine learning algorithms. In such cases, the predictions of the machine learning algorithms may not be adequately accurate. For lack of the required appropriate data, we put forth a simple approach for the construction of artificial MOFs with the desired superior properties. Incorporation of such data during the training phase of the machine learning algorithms improves the predictions outstandingly. In some cases, over 96% of the unknown top-performing materials are successfully identified.

Linker Functionalization Strategy for Water Adsorption in Metal-Organic Frameworks.

Water adsorption in metal-organic frameworks has gained a lot of scientific attention recently due to the potential to be used in adsorption-based water capture. Functionalization of their organic linkers can tune water adsorption properties by increasing the hydrophilicity, thus altering the shape of the water adsorption isotherms and the overall water uptake. In this work, a large set of functional groups is screened for their interaction with water using ab initio calculations. The functional groups with the highest water affinities form two hydrogen bonds with the water molecule, acting as H-bond donor and H-bond acceptor simultaneously. Notably, the highest binding energy was calculated to be -12.7 Kcal/mol for the -OSO3H group at the RI-MP2/def2-TZVPP-level of theory, which is three times larger than the reference value. Subsequently, the effect of the functionalization strategy on the water uptake is examined on a selected set of functionalized MOF-74-III by performing Monte Carlo simulations. It was found that the specific groups can increase the hydrophilicity of the MOF and enhance the water uptake with respect to the parent MOF-74-III for relative humidity (RH) values up to 30%. The saturation water uptake exceeded 800 cm3/cm3 for all candidates, classifying them among the top performing materials for water harvesting.

Surface Modification Strategy for Enhanced NO2 Capture in Metal-Organic Frameworks.

The interaction strength of nitrogen dioxide (NO2) with a set of 43 functionalized benzene molecules was investigated by performing density functional theory (DFT) calculations. The functional groups under study were strategically selected as potential modifications of the organic linker of existing metal-organic frameworks (MOFs) in order to enhance their uptake of NO2 molecules. Among the functional groups considered, the highest interaction energy with NO2 (5.4 kcal/mol) was found for phenyl hydrogen sulfate (-OSO3H) at the RI-DSD-BLYP/def2-TZVPP level of theory-an interaction almost three times larger than the corresponding binding energy for non-functionalized benzene (2.0 kcal/mol). The groups with the strongest NO2 interactions (-OSO3H, -PO3H2, -OPO3H2) were selected for functionalizing the linker of IRMOF-8 and investigating the trend in their NO2 uptake capacities with grand canonical Monte Carlo (GCMC) simulations at ambient temperature for a wide pressure range. The predicted isotherms show a profound enhancement of the NO2 uptake with the introduction of the strongly-binding functional groups in the framework, rendering them promising modification candidates for improving the NO2 uptake performance not only in MOFs but also in various other porous materials.

Continuous Breathing Rare-Earth MOFs Based on Hexanuclear Clusters with Gas Trapping Properties.

Guest responsive porous materials represent an important and fascinating class of multifunctional solids that have attracted considerable attention in recent years. An understanding of how these structures form is essential toward their rational design, which is a prerequisite for the development of tailor-made materials for advanced applications. We herein report a novel series of stable rare-earth (RE) MOFs that show a rare continuous breathing behavior and an unprecedented gas-trapping property. We used an asymmetric 4-c tetratopic carboxylate-based organic ligand that is capable of affording highly crystalline materials upon controlled reaction with RE cations. These MOFs, denoted as RE-thc-MOF-1 (RE: Y3+, Sm3+, Eu3+, Tb3+, Dy3+, Ho3+, and Er3+), feature hexanuclear RE6 clusters that display a highly unusual connectivity and serve as unique 8-c hemi-cuboctahedral secondary building block, resulting in a new (3,3,8)-c thc topology. Extensive single-crystal to single-crystal structural analyses coupled with detailed gas (N2, Ar, Kr, CO2, CH4, and Xe) and vapor (EtOH, CH3CN, C6H6, and C6H14) sorption studies, supported by accurate theoretical calculations, shed light onto the unique swelling behavior. The results reveal a synergistic action involving steric effects, associated with coordinated solvent molecules and 2-fluorobenzoate (2-FBA) nonbridging ligands, as well as cation-framework electrostatic interactions. We were able to probe the individual role of the coordinated solvent molecules and 2-FBA ligands and found that both cooperatively control the gas-breathing and -trapping properties, while 2-FBA controls the vapor adsorption selectivity. These findings provide unique opportunities toward the design and development of tunable RE-based flexible MOFs with tailor-made properties.

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.

A Robust Machine Learning Algorithm for the Prediction of Methane Adsorption in Nanoporous Materials.

In the present study, we propose a new set of descriptors that, along with a few structural features of nanoporous materials, can be used by machine learning algorithms for accurate predictions of the gas uptake capacities of these materials. All new descriptors closely resemble the helium atom void fraction of the material framework. However, instead of a helium atom, a particle with an appropriately defined van der Waals radius is used. The set of void fractions of a small number of these particles is found to be sufficient to characterize uniquely the structure of each material and to account for the most important topological features. We assess the accuracy of our approach by examining the predictions of the random forest algorithm in the relative small dataset of the computation-ready, experimental (CoRE) MOFs (∼4700 structures) that have been experimentally synthesized and whose geometrical/structural features have been accurately calculated before. We first performed grand canonical Monte Carlo simulations to accurately determine their methane uptake capacities at two different temperatures (280 and 298 K) and three different pressures (1, 5.8, and 65 bar). Despite the high chemical and structural diversity of the CoRE MOFs, it was found that the use of the proposed descriptors significantly improves the accuracy of the machine learning algorithm, particularly at low pressures, compared to the predictions made based solely on the rest structural features. More importantly, the algorithm can be easily adapted for other types of nanoporous materials beyond MOFs. Convergence of the predictions was reached even for small training set sizes compared to what was found in previous works using the hypothetical MOF database.

Reticular Synthesis of HKUST-like tbo-MOFs with Enhanced CH4 Storage.

Successful implementation of reticular chemistry using a judiciously designed rigid octatopic carboxylate organic linker allowed the construction of expanded HKUST-1-like tbo-MOF series with intrinsic strong CH4 adsorption sites. The Cu-analogue displayed a concomitant enhancement of the gravimetric and volumetric surface area with the highest reported CH4 uptake among the tbo family, comparable to the best performing metal organic frameworks (MOFs) for CH4 storage. The corresponding gravimetric (BET) and volumetric surface areas of 3971 m(2) g(-1) and 2363 m(2) cm(-3) represent an increase of 115% and 47%, respectively, in comparison to the corresponding values for the prototypical HKUST-1 (tbo-MOF-1), and are 42% and 20% higher than that of tbo-MOF-2. High-pressure methane adsorption isotherms revealed a high total gravimetric and volumetric CH4 uptakes, reaching 372 cm(3) (STP) g(-1) and 221 cm(3) (STP) cm(-3), respectively, at 85 bar and 298 K. The corresponding working capacities between 5 and 80 bar were found to be 294 cm(3) (STP) g(-1) and 175 cm(3) (STP) cm(-3) and are placed among the best performing MOFs for CH4 storage particularly at relatively low temperature. To gain insight on the mechanism accounting for the resultant enhanced CH4 storage capacity, molecular simulation study was performed and revealed the presence of very strong CH4 adsorption sites near the organic linker with similar adsorption energetics as the open metal sites. The present findings support the potential of tbo-MOFs based on the supermolecular building layer (SBL) approach as an ideal platform to further enhance the CH4 storage capacity via expansion and functionalization of the quadrangular pillars.

Ab initio study of the adsorption of CO(2) on functionalized benzenes.

The interaction of carbon dioxide with a series of functionalized aromatic molecules was studied by using quantum mechanical methods (MP2), to examine the effect of the substituent on the adsorption of CO2 . Several different initial configurations of CO2 were taken into account for each functionalized benzene to locate the energetically most favorable configuration. To get a better estimation of the binding energies, we applied an extrapolation scheme to approach the complete basis set. CH2 N3 -, COOH-, and SO3 H-functionalized benzenes were found to have the strongest interaction with CO2 , and the corresponding binding energies were calculated to be -3.62, -3.65, and -4.3 kcal mol(-1) , respectively. Electrostatic potential maps of the functionalized benzenes and electron redistribution density plots of the complexes were also created to get a better insight into the nature of the interaction of CO2 with the functionalized benzenes. The functional groups that were examined can be potentially incorporated in organic bridging molecules that connect the inorganic corners in MOF.

Drastic enhancement of the CO2 adsorption properties in sulfone-functionalized Zr- and Hf-UiO-67 MOFs with hierarchical mesopores.

The sulfone-functionalized Zr- and Hf-UiO-67 metal-organic frameworks with hierarchical mesopores were successfully synthesized using the ligand 4,4'-dibenzoic acid-2,2'-sulfone, with acetic acid or HCl as the modulator. Compared to UiO-67, the zirconium solid shows a remarkable 122% increase in CO2 uptake, reaching 4.8 mmol g(-1) (17.4 wt %) at 1 bar and 273 K (145% at 298 K) and more than 100% increase in CO2/CH4 selectivity.

Separation of CO2-N2 mixtures in 3D carbon-based porous nanotube networks: a molecular dynamics investigation.

The separation of an equimolar CO2-N2 mixture in a 3D porous carbon nanotube network has been investigated. An enhanced CO2 adsorption selectivity has been observed. The diffusion coefficients of the adsorbed molecules have been related to their residence dynamics in the vicinity of the carbon atoms of the nanopore.

Gas adsorption meets deep learning: voxelizing the potential energy surface of metal-organic frameworks.

Intrinsic properties of metal-organic frameworks (MOFs), such as their ultra porosity and high surface area, deem them promising solutions for problems involving gas adsorption. Nevertheless, due to their combinatorial nature, a huge number of structures is feasible which renders cumbersome the selection of the best candidates with traditional techniques. Recently, machine learning approaches have emerged as efficient tools to deal with this challenge, by allowing researchers to rapidly screen large databases of MOFs via predictive models. The performance of the latter is tightly tied to the mathematical representation of a material, thus necessitating the use of informative descriptors. In this work, a generalized framework to predict gaseous adsorption properties is presented, using as one and only descriptor the capstone of chemical information: the potential energy surface (PES). In order to be machine understandable, the PES is voxelized and subsequently a 3D convolutional neural network (CNN) is exploited to process this 3D energy image. As a proof of concept, the proposed pipeline is applied on predicting [Formula: see text] uptake in MOFs. The resulting model outperforms a conventional model built with geometric descriptors and requires two orders of magnitude less training data to reach a given level of performance. Moreover, the transferability of the approach to different host-guest systems is demonstrated, examining [Formula: see text] uptake in COFs. The generic character of the proposed methodology, inherited from the PES, renders it applicable to fields other than reticular chemistry.

Polyimides as Promising Cathodes for Metal-Organic Batteries: A Comparison between Divalent (Ca2+, Mg2+) and Monovalent (Li+, Na+) Cations.

Ca- and Mg-based batteries represent a more sustainable alternative to Li-ion batteries. However, multivalent cation technologies suffer from poor cation mass transport. In addition, the development of positive electrodes enabling reversible charge storage currently represents one of the major challenges. Organic positive electrodes, in addition to being the most sustainable and potentially low-cost candidates, compared with their inorganic counterparts, currently present the best electrochemical performances in Ca and Mg cells. Unfortunately, organic positive electrodes suffer from relatively low capacity retention upon cycling, the origin of which is not yet fully understood. Here, 1,4,5,8-naphthalenetetracarboxylic dianhydride-derived polyimide was tested in Li, Na, Mg, and Ca cells for the sake of comparison in terms of redox potential, gravimetric capacities, capacity retention, and rate capability. The redox mechanisms were also investigated by means of operando IR experiments, and a parameter affecting most figures of merit has been identified: the presence of contact ion-pairs in the electrolyte.

Insight on the formation of chitosan nanoparticles through ionotropic gelation with tripolyphosphate.

This work reports details pertaining to the formation of chitosan nanoparticles that we prepare by the ionic gelation method. The molecular interactions of the ionic cross-linking of chitosan with tripolyphosphate have been investigated and elucidated by means of all-electron density functional theory. Solvent effects have been taken into account using implicit models. We have identified primary-interaction ionic cross-linking configurations that we define as H-link, T-link, and M-link, and we have quantified the corresponding interaction energies. H-links, which display high interaction energies and are also spatially broadly accessible, are the most probable cross-linking configurations. At close range, proton transfer has been identified, with maximum interaction energies ranging from 12.3 up to 68.3 kcal/mol depending on the protonation of the tripolyphosphate polyanion and the relative coordination of chitosan with tripolyphosphate. On the basis of our results for the linking types (interaction energies and torsion bias), we propose a simple mechanism for their impact on the chitosan/TPP nanoparticle formation process. We introduce the ß ratio, which is derived from the commonly used α ratio but is more fundamental since it additionally takes into account structural details of the oligomers.

Data-driven prediction of in situ CO2 foam strength for enhanced oil recovery and carbon sequestration.

Carbon dioxide foam injection is a promising enhanced oil recovery (EOR) method, being at the same time an efficient carbon storage technology. The strength of CO2 foam under reservoir conditions plays a crucial role in predicting the EOR and sequestration performance, yet, controlling the strength of the foam is challenging due to the complex physics of foams and their sensitivity to operational conditions and reservoir parameters. Data-driven approaches for complex fluids such as foams can be an alternative method to the time-consuming experimental and conventional modeling techniques, which often fail to accurately describe the effect of all important related parameters. In this study, machine learning (ML) models were constructed to predict the oil-free CO2 foam apparent viscosity in the bulk phase and sandstone formations. Based on previous experimental data on various operational and reservoir conditions, predictive models were developed by employing six ML algorithms. Among the applied algorithms, neural network algorithms provided the most precise predictions for bulk and porous media. The established models were then used to compute the critical foam quality under different conditions and determine the maximum apparent foam viscosity, effectively controlling CO2 mobility to co-optimize EOR and CO2 sequestration.

Designing 3D COFs with enhanced hydrogen storage capacity.

Hydrogen storage properties have been studied on newly designed three-dimensional covalent-organic framework (3D-COF). The design of these materials was based on the ctn network of the ultralow density COF-102. The structures were optimized by multiscale techniques and the optimized structures were checked for their storage capacities by grand canonical Monte Carlo simulations. Our simulations demonstrate that the gravimetric uptake of one of these new COFs can overpass the value of 25 wt % in 77 K and reach the Department of Energy's target of 6 wt % in room temperature, classifying them between the top hydrogen storage materials.

Expanding the NUIG MOF family: synthesis and characterization of new MOFs for selective CO2 adsorption, metal ion removal from aqueous systems, and drug delivery applications.

Metal organic frameworks (MOFs) have attracted considerable attention in recent years due to their use in a wide range of environmental, industrial and biomedical applications. The employment of benzophenone-4,4'-dicarboxylic acid (bphdcH2) in MOF chemistry provided access to the 3D mixed metal MOFs [CoNa2(bphdc)2(DMF)2]n (NUIG2) and [ZnK2(bphdc)2(DMF)2]n (NUIG3), and the 2D homometallic MOF [Co2(OH)(bphdcH)2(DMF)2(H2O)2]n(OH)·DMF (1·DMF). 1·DMF is based on a dinuclear SBU and consists of interpenetrating networks with an sql topology. Dc magnetic susceptibility studies were carried out in 1·DMF and revealed the presence of weak antiferomagnetic exchange interactions between the metal centres. NUIG2 and NUIG3 are structural analogues of [ZnNa2(bphdc)2(DMF)2]n (NUIG1), which has shown an exceptionally high encapsulation for ibuprophen (Ibu), NO and metal ions. Both NUIG2 and NUIG3 display high metal ion (CoII, NiII, CuII) adsorption capacity, comparable to that of NUIG1, with NUIG2 exhibiting good performance in Ibu uptake (780 mg Ibu per g NUIG2). Monte Carlo simulations were conducted in NUIG1 in order to assess its adsorption capacity for other guest molecules, and revealed that it possesses an outstanding CO2 uptake at ambient pressure, which is larger than that of the previously reported best functioning species (104 vs. 100 cm3 (stp) per cm3). Furthermore, NUIG1 exhibits high selectivity for CO2 over CH4.