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.

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.

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.

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.

DFT study of hydrogen storage by spillover on graphite with oxygen surface groups.

DFT modeling was used to understand the role of epoxide (C-O-C) and hydroxyl (C-OH) functional groups on the spillover mechanism for hydrogen storage on graphite oxide and oxygen-modified carbons. A primary spillover model was used, consisting of a Pt(4) cluster, a graphite substrate model, and O and OH functional groups adsorbed on graphite. The spillover mechanism was found to proceed via the migration of dissociated hydrogen atoms from the Pt cluster to epoxide groups adjacent to the cluster (to form OH), followed by H migration by hopping on the adsorbed O atoms. The low energy barriers required for the relevant elementary steps indicate that the spillover process is facile when the carbon substrate is decorated with oxygen functionalities, leading to enhanced hydrogen uptake and faster charge/discharge kinetics. However, a reaction path was also identified, in which surface OH groups can react to form water, which can have adverse consequences for hydrogen storage on oxygenated carbons via spillover.

Enhancement of hydrogen adsorption in metal-organic frameworks by the incorporation of the sulfonate group and Li cations. A multiscale computational study.

By means of ab initio methods, the effect on the H(2) storage ability of a newly proposed organic linker for IRMOF-14 has been studied. The linker comprises a negatively charged sulfonate (-SO(3)(-1)) group in combination with a Li cation. It is found that these two charged groups significantly increase the interaction energy between the hydrogen molecules and the new proposed organic linker of the MOF. The substituted group of the linker may host up to six hydrogen molecules with an average interaction energy of 1.5 kcal/mol per H(2) molecule. This value is three times larger than the binding energy over the bare linker that has been obtained from DFT calculations. GCMC atomistic simulations verified that the proposed material can be qualified among the highest adsorbing materials for volumetric capture of H(2), especially at ambient conditions. This functionalization strategy can be applied in many different MOF structures to enhance their storage abilities.

Ab initio study of the interactions between CO(2) and N-containing organic heterocycles.

In the garden of dispersion: High-accuracy ab initio calculations are performed to determine the nature of the interactions and the most favorable geometries between CO(2) and heteroaromatic molecules containing nitrogen (see figure). Dispersion forces play a key role in the stabilization of the dimer, because correlation effects represent about 50 % of the total interaction energy. The interactions between carbon dioxide and organic heterocyclic molecules containing nitrogen are studied by using high-accuracy ab initio methods. Various adsorption positions are examined for pyridine. The preferred configuration is an in-plane configuration. An electron donor-electron acceptor (EDA) mechanism between the carbon of CO(2) and the nitrogen of the heterocycle and weak hydrogen bonds stabilize the complex, with important contributions from dispersion and induction forces. Quantitative results of the binding energy of CO(2) to pyridine (C(5)H(5)N), pyrimidine, pyridazine, and pyrazine (C(4)H(4)N(2)), triazine (C(3)H(3)N(3)), imidazole (C(3)H(4)N(2)), tetrazole (CH(2)N(4)), purine (C(5)H(4)N(4)), imidazopyridine (C(6)H(5)N(3)), adenine (C(5)H(5)N(5)), and imidazopyridamine (C(6)H(6)N(4)) for the in-plane configuration are presented. For purine, three different binding sites are examined. An approximate coupled-cluster model including single and double excitations with a perturbative estimation of triple excitations (CCSD(T)) is used for benchmark calculations. The CCSD(T) basis-set limit is approximated from explicitly correlated second-order Møller-Plesset (MP2-F12) calculations in the aug-cc-pVTZ basis in conjunction with contributions from single, double, and triple excitations calculated at the CCSD(T)/6-311++G** level of theory. Extrapolations to the MP2 basis-set limit coincide with the MP2-F12 calculations. The results are interpreted in terms of electrostatic potential maps and electron density redistribution plots. The effectiveness of density functional theory with the empirical dispersion correction of Grimme (DFT-D) is also examined.

The effect of structural and energetic parameters of MOFs and COFs towards the improvement of their hydrogen storage properties.

Open-framework materials have been proposed as potential materials for hydrogen storage. Metal-organic framework (MOF) and covalent-organic framework (COF) materials are under extensive study to discover their storage abilities. In particular the IRMOF family of materials have been considered as ideal to study the effect of different factors that affect the hydrogen storage capacity. In this paper, we analyse the effect of different factors such as surface area, pore volume and the interaction of hydrogen with the molecular framework on the hydrogen uptake of such materials. Through this analysis we propose guidelines to enhance hydrogen storage capacity of already synthesized materials and recommend advanced materials for this application.

Assessing the density functional theory in the hydrogen storage problem.

A variety of high and low level ab-initio calculations have been performed to calculate hydrogen's physisorption binding energy on carbon nanotube's walls. This study focuses on the performance of several functionals on treating the H2-carbon nanotube interaction within the Density Functional Theory. Our results show that the behavior of the exchange functional in the low density region plays an important role in describing this weak van der Waals type of interaction. By comparing the binding energy values obtained on each theoretical level and interpreting the results in terms of %wt percentages of hydrogen storage using the Langmuir isotherms, we proposed possible ways to treat computationally the hydrogen storage problem within the DFT.