Augmented Reality for Enhanced Visualization of MOF Adsorbents

Augmented reality (AR) is an emerging technique used to improve visualization and comprehension of complex 3D materials. This approach has been applied not only in the field of chemistry but also in real estate, physics, mechanical engineering, and many other areas. Here, we demonstrate the workflow for an app-free AR technique for visualization of metal–organic frameworks (MOFs) and other porous materials to investigate their crystal structures, topology, and gas adsorption sites. We think this workflow will serve as an additional tool for computational and experimental scientists working in the field for both research and educational purposes

Computer-aided discovery of a metal-organic framework with superior oxygen uptake.

Current advances in materials science have resulted in the rapid emergence of thousands of functional adsorbent materials in recent years. This clearly creates multiple opportunities for their potential application, but it also creates the following challenge: how does one identify the most promising structures, among the thousands of possibilities, for a particular application? Here, we present a case of computer-aided material discovery, in which we complete the full cycle from computational screening of metal-organic framework materials for oxygen storage, to identification, synthesis and measurement of oxygen adsorption in the top-ranked structure. We introduce an interactive visualization concept to analyze over 1000 unique structure-property plots in five dimensions and delimit the relationships between structural properties and oxygen adsorption performance at different pressures for 2932 already-synthesized structures. We also report a world-record holding material for oxygen storage, UMCM-152, which delivers 22.5% more oxygen than the best known material to date, to the best of our knowledge

DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining

The vastness of materials space, particularly that which is concerned with metal–organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties

Reverse Hierarchy of Alkane Adsorption in Metal–Organic Frameworks (MOFs) Revealed by Immersion Calorimetry

Immersion calorimetry into liquids of different dimensions is a powerful tool to learn about the pore size and shape in nanoporous solids. In general, in the absence of specific interactions with the solid surface, the accessibility of the liquid probe molecule to the inner porosity and the associated enthalpy value decreases with an increase in its kinetic diameter (bulkier molecules have lower accessibility and packing density). Although this is true for the majority of solids (e.g., activated carbons and zeolites), this study anticipates that this is not straightforward in the specific case of metal–organic frameworks (MOFs). The evaluation of different hydrocarbons and their derivatives reveals the presence of reverse selectivity for C6 isomers (2,2-dimethylbutane > 2-methylpentane > n-hexane) in UiO-66 and HKUST-1, whereas size exclusion effects take place in ZIF-8. The immersion calorimetric findings have been compared with vapor adsorption isotherms and computational studies. Monte Carlo simulations suggest that the reverse selectivity in UiO-66 is attributed to the strong confinement of the dibranched hydrocarbons in the small tetragonal cages, whereas the presence of strong interactions with the open metal sites accounts for the preferential adsorption in HKUST-1. These results open the gate toward the application of immersion calorimetry for the prescreening of MOFs to identify in an easy, fast and reliable way interesting characteristics and/or properties such as separation ability, reversed hierarchy, pore-window size, presence of unsaturated metal sites, molecular accessibility, and so on.Authors would like to acknowledge financial support from MINECO (MAT2016-80285-p), Generalitat Valenciana (PROMETEOII/2014/004) and H2020 (MSCA-RISE-2016/NanoMed Project). P.Z.M. is grateful for start-up funds from the University of Sheffield

Modulation of Pore Shape and Adsorption Selectivity by Ligand Functionalization in a Series of “rob”-like Flexible Metal-Organic Frameworks

We report the synthesis of a new family of four new isoreticular metal–organic frameworks (MOFs) based on Cu–Cu paddle-wheel building units. The four MOFs contain 1D microchannels modulated by chemical functionalisation of a dicarboxylate ligand or the use of different bis-4,40-pyridyl-like connectors behaving as ancillary linkers. A deep analysis of their CO2, H2 and CH4 adsorption properties, combining both experimental and grand canonical Monte Carlo isotherms as well as in situ synchrotron X-ray diffraction, shows variable adsorption behaviour towards the studied gases, with some materials acting as molecular sieves with virtually infinite selectivity.This work was supported by the Junta de Andalucía (FQM-1484), Red Guipuzcoana de Ciencia, Tecnolgía e Innovación (OF188/2017) and University of the Basque Country (GIU14/01, GIU17/013, EHUA16/32). The authors acknowledge technical and human support provided by SGIker of UPV/EHU and European funding (ERDF and ESF). D. F.-J. thanks the Royal Society for funding through a University Research Fellowship and the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (NanoMOFdeli), ERC-2016-COG 726380

"Explosive" synthesis of metal-formate frameworks for methane capture: an experimental and computational study.

In this work, we show a solvent-free "explosive" synthesis (SFES) method for the ultrafast and low-cost synthesis of metal-formate frameworks (MFFs). A combination of experiments and in-depth molecular modelling analysis - using grand canonical Monte Carlo (GCMC) simulations - of the adsorption performance of the synthesized nickel-formate framework (Ni-FA) revealed extremely high quality products with permanent porosity, prominent CH4/N2 selectivity (ca. 6.0), and good CH4 adsorption capacity (ca. 0.80 mmol g-1 or 33.97 cm3 cm-3) at 1 bar and 298 K. This performance is superior to those of many other state-of-the-art porous materials

Core-Shell Gold Nanorod@Zirconium-Based Metal-Organic Framework Composites as in Situ Size-Selective Raman Probes.

Nanoparticle encapsulation inside zirconium-based metal-organic frameworks (NP@MOF) is hard to control, and the resulting materials often have nonuniform morphologies with NPs on the external surface of MOFs and NP aggregates inside the MOFs. In this work, we report the controlled encapsulation of gold nanorods (AuNRs) by a scu-topology Zr-MOF, via a room-temperature MOF assembly. This is achieved by functionalizing the AuNRs with poly(ethylene glycol) surface ligands, allowing them to retain colloidal stability in the precursor solution and to seed the MOF growth. Using this approach, we achieve core-shell yields exceeding 99%, tuning the MOF particle size via the solution concentration of AuNRs. The functionality of AuNR@MOFs is demonstrated by using the AuNRs as embedded probes for selective surface-enhanced Raman spectroscopy (SERS). The AuNR@MOFs are able to both take-up or block molecules from the pores, thereby facilitating highly selective sensing at the AuNR ends. This proof-of-principle study serves to present both the outstanding level of control in the synthesis and the high potential for AuNR@Zr-MOF composites for SERS

Origin of Enantioselectivity in a Chiral Metal–Organic Framework: A Molecular Simulation Study

We used molecular simulation to examine the nature of enantioselectivity in the homochiral metal–organic framework [Ni₂(l-asp)₂(bipy)] for a number of chiral diols and compared the results to experimental enantiomeric excess (<i>ee</i>) data available in the literature. For all cases, our simulation results not only reproduced the general preference of the R-enantiomers in the framework derived from l-asp but also captured more subtle trends. Studying the adsorption process on the molecular level, we show that the R-enantioselectivity is strongly related to better packing effects at high loadings resulting in higher <i>ee</i> values for 1,3-butanediol in comparison with 1,2-butanediol and 1,2-propanediol. We demonstrate that the level of enantioselectivity is highly dependent on the chain length and the position of hydroxyl groups on the carbon chain. We show that whereas the chirality of the MOF framework assists the separation mechanism, the more dominant factor is the perfect match between guest–framework size and shape

DigiMOF: A Database of MOF Synthesis Information Generated via Text Mining

The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data mine published MOF papers to extract the materials informatics knowledge contained within the journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials and text mined over 52,680 associated properties including synthesis method, solvent, organic linker, metal precursor, and topology. This centralised, structured database reveals the MOF synthetic data embedded within thousands of MOF publications. The DigiMOF database and associated software are publicly available for other researchers to conduct further analysis of alternative MOF production pathways and create additional parsers to search for other desirable properties