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Competitive water adsorption can have a significant impact on metal–organic framework performance properties, ranging from occupying active sites in catalytic reactions to co-adsorbing at the most favourable adsorption sites in gas separation and storage applications. In this study, we investigate, for a metal–organic framework that is stable after moisture exposure, what are the reversible, loading-dependent structural changes that occur during water adsorption. Herein, a combination of in situ synchrotron powder and single-crystal diffraction, infrared spectroscopy and molecular modelling analysis was used to understand the important role of loading-dependent water effects in a water stable metal–organic framework. Through this analysis, insights into changes in crystallographic lattice parameters, water siting information and water-induced defect structure as a response to water loading were obtained. This work shows that, even in stable metal–organic frameworks that maintain their porosity and crystallinity after moisture exposure, important molecular-level structural changes can still occur during water adsorption due to guest–host interactions such as water-induced bond rearrangements.A stable zinc-based metal–organic framework known to retain its porosity and crystallinity after exposure to moisture has been shown to undergo structural changes at the molecular level on adsorbing water. This dynamic and reversible response to the presence of water, including the rearrangement of bonds, is suggested to be the reason for the hydrolytic stability of this particular metal–organic framework.

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.

This paper reports the results of an international interlaboratory study sponsored by the Versailles Project on Advanced Materials and Standards (VAMAS) and led by the National Institute of Standards and Technology (NIST) on the measurement of water vapor sorption isotherms at 25 °C on a pelletized nanoporous carbon (BAM-P109, a certified reference material). Thirteen laboratories participated in the study and contributed nine pure water vapor isotherms and four relative humidity isotherms, using nitrogen as the carrier gas. From these data, reference isotherms, along with the 95% uncertainty interval ( U k=2 ), were determined and are reported in a tabular format.

A microscopy technique has been used to study the formation and growth of crystals of porous solids known as metal–organic frameworks in real time. The findings will aid the design of methods for making these useful compounds.

A critical barrier to transforming separation technology from energy-intensive thermal methods to more efficient material-based adsorption methods is the limited availability of coadsorption data under mixture conditions. While experimentation under realistic conditions seems superior, the difficulty of conducting these experiments is a prevalent limitation to mainstream adoption. Ideal Adsorbed Solution Theory (IAST) and Grand Canonical Monte Carlo (GCMC) simulations offer predictive alternatives to experimental mixture adsorption; however, both have limitations in accuracy and efficiency. Here, we present a systematic study of coadsorption of binary mixtures of carbon dioxide, ethane, and n-butane as examples covering a variety of polarizability and size. Mixture adsorption is investigated in three adsorbents with different motifs: UiO-66 as a baseline material, UiO-66-NH 2 as an example with pendant functional amine groups, and HKUST-1 as an example with open metal sites (OMS). Results show near-quantitative matches of fractional loadings between IAST, GCMC, and experiments for n-butane-containing mixtures in UiO-66 and UiO-66-NH 2 with significant deviations for other systems. The observed differences can be attributed to insufficient interaction modeling in GCMC, synergetic effects in the adsorbed phase, or diffusion and flow effects under mixture conditions in a fixed bed separation, highlighting where IAST and GCMC are not adequate replacements for experimental measurements.