Water Stability and Adsorption in Metal–Organic Frameworks

Precise Construction of Stable Bimetallic Metal–Organic Frameworks with Single-Site Ti(IV) Incorporation in Nodes for Efficient Photocatalytic Oxygen Evolution

Imparting Superhydrophobicity to Porphyrinic Coordination Frameworks Using Organotin

Porous Metal-Organic Frameworks: Promising Materials for Methane Storage

Inspired by sarcocarps, metal–organic frameworks (MOFs) that can capture moisture spontaneously are presented as building blocks for the construction of underoil superhydrophilic surfaces. The MOF coating showed excellent self-cleaning properties to crude oil under water, and achieved on-demand emulsion separation through selective water filtration and adsorption.

Water adsorption in porous materials is important for many applications such as dehumidification, thermal batteries, and delivery of drinking water in remote areas. In this study, we have identified three criteria for achieving high performing porous materials for water adsorption. These criteria deal with condensation pressure of water in the pores, uptake capacity, and recyclability and water stability of the material. In search of an excellently performing porous material, we have studied and compared the water adsorption properties of 23 materials, 20 of which are metal-organic frameworks (MOFs). Among the MOFs are 10 zirconium(IV) MOFs with a subset of these, MOF-801-SC (single crystal form), -802, -805, -806, -808, -812, and -841 reported for the first time. MOF-801-P (microcrystalline powder form) was reported earlier and studied here for its water adsorption properties. MOF-812 was only made and structurally characterized but not examined for water adsorption because it is a byproduct of MOF-841 synthesis. All the new zirconium MOFs are made from the Zr6O4(OH)4(-CO2)n secondary building units (n = 6, 8, 10, or 12) and variously shaped carboxyl organic linkers to make extended porous frameworks. The permanent porosity of all 23 materials was confirmed and their water adsorption measured to reveal that MOF-801-P and MOF-841 are the highest performers based on the three criteria stated above; they are water stable, do not lose capacity after five adsorption/desorption cycles, and are easily regenerated at room temperature. An X-ray single-crystal study and a powder neutron diffraction study reveal the position of the water adsorption sites in MOF-801 and highlight the importance of the intermolecular interaction between adsorbed water molecules within the pores.

Owing to their highly tunable structures, metal–organic frameworks (MOFs) are considered suitable candidates for a range of applications, including adsorption, separation, sensing and catalysis. However, MOFs must be stable in water vapour to be considered industrially viable. It is currently challenging to predict water stability in MOFs; experiments involve time-intensive MOF synthesis, while modelling techniques do not reliably capture the water stability behaviour. Here, we build a machine learning-based model to accurately and instantly classify MOFs as stable or unstable depending on the target application, or the amount of water exposed. The model is trained using an empirically measured dataset of water stabilities for over 200 MOFs, and uses a comprehensive set of chemical features capturing information about their constituent metal node, organic ligand and metal–ligand molar ratios. In addition to screening stable MOF candidates for future experiments, the trained models were used to extract a number of simple water stability trends in MOFs. This approach is general and can also be used to screen MOFs for other design criteria. Metal–organic frameworks (MOFs) are attractive materials for gas capture, separation, sensing and catalysis. Determining their water stability is important, but time-intensive. Batra et al. use machine learning to screen water-stable MOFs and identify chemical features supporting their stability.

The rational design of metal-organic frameworks (MOFs) with structural stability in the presence of humid conditions is critical to the commercialization of this class of materials. However, the systematic water stability studies required to develop design criteria for the construction of water-stable MOFs are still scarce. In this work, we show that by varying the functional groups on the 1,4-benzenedicarboxylic acid (BDC) linker of DMOF [Zn(BDC)(DABCO)(0.5)], we can systematically tune the kinetic water stability of this isostructural, pillared family of MOFs. To illustrate this concept, we have performed water adsorption studies on four novel, methyl-functionalized DMOF variations along with a number of already reported functionalized analogues containing polar (fluorine) and nonpolar (methyl) functional groups on the BDC ligand. These results are distinctly different from previous reports where the apparent water stability is improved through the inclusion of functional groups such as -CH(3), -C(2)H(5), and -CF(3) which only serve to prevent significant amounts of water from adsorbing into the pores. In this study, we present the first demonstration of tuning the inherent kinetic stability of MOF structures in the presence of large amounts of adsorbed water. Notably, we demonstrate that while the parent DMOF structure is unstable, the DMOF variation containing the tetramethyl BDC ligand remains fully stable after adsorbing large amounts of water vapor during cyclic water adsorption cycles. These trends cannot be rationalized in terms of hydrophobicity alone; experimental water isotherms show that MOFs containing the same number of methyl groups per unit cell will have different kinetic stabilities and that the precise placements of the methyl groups on the BDC ligand are therefore critically important in determining their stability in the presence of water. We present the water adsorption isotherms, PXRD (powder X-ray diffraction) patterns, and BET surface areas before and after water exposure to illustrate these trends. Furthermore, we shed light on the important distinction between kinetic and thermodynamic stability in MOFs. Molecular simulations are also used to provide insight into the structural characteristics governing these trends in kinetic water stability.

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.

Metal–organic frameworks (MOFs) with coordinatively unsaturated open metal sites (OMSs) are promising sorbent materials in atmospheric water harvesting (AWH) systems at low relative humidity (RH) due to their strong interactions with water molecules. However, accurate computational modelling of water adsorption in those materials are challenging, as standard force fields (FFs) based on the 12-6 Lennard–Jones (L–J) potential cannot properly describe the water–OMS interactions. In biomolecular simulations, the 12-6-4 L–J potential has been successfully used to model metal ion–water interactions in which the extra 1/r 4-term is for charge-induced dipole electrostatics. In this work, we adopted this strategy and used the 12-6-4 L–J potential to model water–OMS interactions in MOFs for low RH AWH applications. Without any modifications, the parameters used for aqueous metal ions were able to greatly improve the accuracy in predicting water adsorption isotherms in MOF-74, which highlights the simplicity and transferability of this method.

Enhanced water stability and catalytic activity of Fe-based metal–organic frameworks with co-ligands for 2,4-dichlorophenol degradation

The field of coordination polymers and metal–organic frameworks (MOFs) has evolved over a period of approximately 25 years to a stage where it is one of the most widely investigated areas of materials chemistry. The field has impacted many areas of science including commercial applications from

Crucial steps toward designing water sorption materials and fine-tuning their properties for specific applications include precise identification of adsorption sites and establishment of rigorous molecular-level insight into the water adsorption process. We report stepwise crystallographic mapping and density functional theory computations of adsorbed water molecules in ALP-MOF-1, a metal-organic framework decorated with distinct open metal sites and carbonyl functional groups that serve as water anchoring sites for seeding the nucleation of a complex water network. Identification of an unusual water adsorption step in ALP-MOF-1 motivated the tuning of metal ion composition to adjust water uptake. These studies provide direct evidence that the identity of the open metal sites in MOFs can dramatically affect water adsorption behavior between 0 and ∼20% RH and that multiple proximal water anchoring sites along the MOF skeleton facilitate water uptake which could be potentially useful for applications requiring rapid and energetically facile water sorption.

H2O adsorptions inside porous materials, including silica zeolites, zeolite imidazolate frameworks, and metal–organic frameworks (MOFs) using molecular simulations with different water models are investigated. Due to the existence of coordinately unsaturated metal sites, the predicted adsorption properties in M‐MOF‐74 (M = Mg, Ni, Co, Zn) and Cu‐BTC are found to be greatly sensitive to the adopted H2O models. Surprisingly, the analysis of the orientations of H2O minimum energy configuration in these materials show that three‐site H2O models predict an unusual perpendicular angle of H2O plane with respect to the Metal‐O4 plane, whereas those models with more than three sites give a more parallel angle that is in better agreement with the one obtained from density functional theory (DFT) calculations. In addition, the use of these commonly used models estimates the binding energies with the values lower than the ones computed by DFT ranging from 15 to 40%. To correct adsorption energies, simple approach to adjust metal‐O(H2O) sigma parameters to reproduce the DFT‐calculated binding energies is used. With the refined parameters, the computed water isotherms inside Mg‐MOF‐74 and Cu‐BTC are in reasonable agreement with experimental data, and provide significant improvement compared to the predictions made by the original models. Further, a detailed inspection on the water configurations at higher‐pressure region was also made, and observed that there is an interesting two‐layer water network formed using three‐ and four‐site models. © 2014 American Institute of Chemical Engineers AIChE J, 61: 677–687, 2015

Metal-organic frameworks (MOFs) are promising candidate materials for applications that would benefit from precise chemical patterning, such as desalination and atmospheric water harvesting, but many MOFs suffer from poor stability in water. In addition to water stability, high water uptake capacity in ambient conditions is expected to be necessary for water-related practical applications of MOFs, motivating large-scale search that can only be achieved computationally. Here, we take a combined machine learning and high-throughput screening approach to identifying water-stable MOFs with high water uptake capacities. Starting from a subset of previously curated MOFs with experimentally known exceptionally high stability in water, we explore the effect of linker functionalization with twelve diverse hydrophilic functional groups expected to further tune water uptake. For these 736 MOFs, we use grand canonical Monte Carlo (GCMC) simulations to compute their water uptake capacity. We observe strong positive correlations between MOF pore features (e.g., the largest cavity diameter and volumetric pore volume) and water uptake capacity, although we notice breakdowns of such correlations in MOFs with extremely hydrophobic linkers that repel water molecules despite having large pores. Finally, we develop machine learning models to screen new MOFs simultaneously for water stability and water uptake capacity. From a pool of hypothetical and experimental MOFs, we identify 74 promising materials within the domain of applicability of the machine learning models that are predicted to be both water-stable and have high water uptake.

Metal-organic frameworks (MOFs) are promising candidate materials for applications that would benefit from precise chemical patterning, such as desalination, but many MOFs suffer from poor stability in water. In addition to water stability, high water uptake capacity in ambient conditions is expected to be necessary for water-related practical applications of MOFs, motivating large-scale search that can only be achieved computationally. Here, we take a combined machine learning and high-throughput screening approach to identify water-stable MOFs with high water uptake capacities. Starting from a subset of previously curated MOFs with experimentally known exceptionally high stability in water, we explore the effect of linker functionalization with 12 diverse hydrophilic functional groups expected to further tune water uptake. For these 736 MOFs, we use grand canonical Monte Carlo (GCMC) simulations to compute their water uptake capacity. We observe strong positive correlations between MOF pore features (e.g., the largest cavity diameter and volumetric pore volume) and water uptake capacity, although we notice breakdowns of such correlations in MOFs with extremely hydrophobic linkers that repel water molecules despite having large pores. Finally, we develop machine learning models to screen new MOFs simultaneously for water stability and water uptake capacity. From a pool of hypothetical and experimental MOFs, we identify 74 promising materials within the domain of applicability of the machine learning models that are predicted to both be water-stable and have high water uptake.