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103 results for “metal–organic framework”
Computation-Ready, Experimental Metal–Organic Frameworks
<p>Experimentally refined crystal structures for metal–organic frameworks (MOFs) often include solvent molecules and partially occupied or disordered atoms. This creates a major impediment to applying high-throughput computational screening to MOFs. To address this problem, we have constructed a database of MOF structures that are derived from experimental data but are immediately suitable for molecular simulations. </p> <p>The development of the CoRE MOF 2014 database is described in “Computation-ready, experimental metal-organic frameworks: A tool to enable high-throughput screening of nanoporous crystals” Chung, Y.G. et al., Chem. Mater. 2014, 26, 6185-6192 (DOI: <a href="https://doi.org/10.1021/cm502594j">10.1021/cm502594j</a>). The CoRE MOF 2014 database was developed through a collaboration of research groups participating in the Nanoporous Materials Genome Center that is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DE-FG02-12ER16362.</p>
Accelerating Discovery of Mechanically Stable Metal−Organic Frameworks for Vinylidene Fluoride Storage by Active Learning
<p><span>Supplementary data including dataset and python scripts for "<strong>Accelerating Discovery of Mechanically Stable </strong></span><strong><span>Metal−Organic Frameworks </span></strong><span><strong>for Vinylidene Fluoride Storage by Active Learning</strong>"</span></p>
Digital Design and Discovery of Biological Metal-Organic Frameworks for Gas Signaling
<p>This repository contains the structures, features and compositions of Bio-hMOFs database.</p> <ol> <li>Fragments and Composition: Contains the building block fragments used to generate the Bio-hMOFs.</li> <li>Structures-CIFs: Contains the structures of Bio-hMOFs</li> <li>Geometric and RACs: Contains the geomtric and RACs features of Bio-hMOFs</li> <li>Adsorption Capacity: Contains the adsorption uptake of NO and CO adsorption simulated under 298K under 1 bar and 10 bar.</li> <li>Mechanical Properties; Computed mechanical properties</li> </ol>
Supplementary data for "Beyond radical-rebound: Methane oxidation to methanol catalyzed by iron species in metal–organic framework nodes"
<p>Cartesian coordinates in the *.XYZ format for all the structures optimized at the M06-L/def2-TZVP in the reactivity study as part of the article "Beyond radical-rebound: Methane oxidation to methanol catalyzed by iron species in metal-organic framework nodes" (<a href="https://doi.org/10.1021/jacs.1c04766">https://doi.org/10.1021/jacs.1c04766</a>)</p>
From Sintering to Particle Discrimination: New Opportunities in Metal–Organic Frameworks Scintillators
<p>Dataset from publication</p>
Facile synthesis of magnesium-based metal-organic framework with tailored nanostructure for effective VOCs adsorption
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Data from: Antifungal activity of water-stable copper-containing metal–organic frameworks
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Synthesis, characterization and CO2 adsorption studies of DABCO based pillared Zn-BDC and Co-BDC metal organic frameworks
<p><span>This study focuses on pre-synthetic functionalized MOF material normally known as pillared layer MOFs. An additional component DABCO (1,4-diazabicyclo[2.2.2] octane) is added to the MOFs which works as a pillar to produce 3d structured MOFs. Zn-BDC-DABCO and Co-BDC-DABCO were studied for their performance in CO<sub>2</sub> capture application. The addition of DABCO turns the 2d-layered metal-BDC lattice to a 3d structure with enhance performance for CO<sub>2</sub> capture. The MOFs were characterized using XRD, SEM, TGA, FTIR and BET and the CO<sub>2</sub> capture capacity was tested at 25</span><span>°C and 0-25 bar. Zn-BDC-DABCO and Co-BDC-DABCO showed a maximum adsorption capacity of 6.3 and 4.4 mol/kg CO<sub>2</sub>.</span></p>
Scripts and Datasets for Photocatalytic Nanoscale Metal-Organic Framework for Proximity Labeling in Living Cells
<p>Please read data_process.ipynb for more details.</p>
Role of Counterions in the Structural Stabilisation of Redox-Active Metal-Organic Frameworks
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Through-space hopping transport in an iodine-doped perylene-based metal–organic framework
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Near Isotropic D4d Spin Qubits as Nodes of a Gd(III)-Based Metal–Organic Framework
<p>Data supporting the original figures 2,3,4 of the related publication.</p>
Research data for "Visualization and Quantification of Geometric Diversity in Metal-Organic Frameworks"
<p>This dataset supports the paper: "Visualization and Quantification of Geometric Diversity in Metal-Organic Frameworks".</p> <p>The coarse-grained scaled and unscaled structures are provided here in CIF format. The code introduced in this work is subject to continuing development (and can be found at: https://github.com/tcnicholas/coarse-graining), therefore we include here the version of the code used for this paper alongside the Python analysis scripts.</p> <p>The original unprocessed CIFs were extracted from the Cambridge Structural Database (CSD).</p>
Synthesis, characterization and CO2 adsorption studies of DABCO based pillared Zn-BDC and Co-BDC metal organic frameworks
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Twinning in Zr-based Metal-organic Framework Crystals
<p>Cif files for three metal-organic framework compounds. </p>
Solvent Induced Enantioselectivity Reversal in a Chiral Metal Organic Framework
<p><strong>Abstract</strong></p> <p>Solvent induced enantioselectivity reversal is a rarely reported phenomenon in porous homochiral materials. Similar behavior has been studied in chiral HPLC, where minor modifications to the mobile phase can induce elution order reversal of two enantiomers on a chiral stationary phase column. We report the first instance of solvent-induced enantioselectivity reversal in a homochiral metal organic framework. Further, we highlight the complex enantioselectivity behavior of homochiral metal organic frameworks towards racemic mixtures in the presence of solvents through racemate-solvent enantioselectivity and loading experiments as well as enantiopure-solvent loading experiments. We hypothesize that this interesting selectivity reversal behavior is likely to be observed in other competitive adsorption, non-chiral selective processes involving a solvent.</p> <p> </p> <p>This dataset contains: PXRD data</p>
Exploring the Chemical Design Space of Metal-Organic Frameworks for Photocatalysis
<p>In this work, we employ a chemical insights-based diversity-driven approach to search for metal-organic framework (MOF) photocatalysts. With an in silico design based on chemical insights, we populated areas in the chemical design space related to MOFs with photocatalytic potential. We selected a balanced dataset of DFT-based photocatalytic descriptors computed for 314 MOFs, comprising our in silico structures, a diverse subset of the QMOF database, and experimental MOF photocatalysts. With such a balanced dataset, we could fine-tune supervised machine-learning models from literature that allowed us to draw insights into relevant areas in the chemical design space for photocatalysis and potential bottlenecks.<br>Among our in silico MOFs, a few motifs stood out, such as Au-pyrazolate, Ti clusters and rod-shaped metal nodes, and a particular MOF designed with the Mn4Ca cluster, which mimics the OER center in the photosystem II of photosynthesis.<br>Overall, by combining three pillars --- the design of potential MOF photocatalysts guided by chemical insights, the DFT evaluation of photocatalytic descriptors, and the machine-learning approach --- we were able to gain insights into structure-property relationship, and identify trends in the chemical design space that can open new avenues for advancing the field of photocatalysis.</p>
Toward Understanding Drug Incorporation and Delivery from Biocompatible Metal-Organic Frameworks in View of Cutaneous Administration
<p>Although metal−organic frameworks (MOFs) have widely<br> demonstrated their convenient performances as drug-delivery systems, there is<br> still work to do to fully understand the drug incorporation/delivery processes<br> from these materials. In this work, a combined experimental and<br> computational investigation of the main structural and physicochemical<br> parameters driving drug adsorption/desorption kinetics was carried out. Two<br> model drugs (aspirin and ibuprofen) and three water-stable, biocompatible<br> MOFs (MIL-100(Fe), UiO-66(Zr), and MIL-127(Fe)) have been selected to<br> obtain a variety of drug−matrix couples with different structural and<br> physicochemical characteristics. This study evidenced that the drug-loading<br> and drug-delivery processes are mainly governed by structural parameters<br> (accessibility of the framework and drug volume) as well as the MOF/drug<br> hydrophobic/hydrophilic balance. As a result, the delivery of the drug under<br> simulated cutaneous conditions (aqueous media at 37 °C) demonstrated that<br> these systems fulfill the requirements to be used as topical drug-delivery systems, such as released payload between 1 and 7 days.<br> These results highlight the importance of the rational selection of MOFs, evidencing the effect of geometrical and chemical<br> parameters of both the MOF and the drug on the drug adsorption and release.</p>
Molecular diffusion enhanced performance evaluation of metal-organic frameworks for carbon dixoide capture
<p>This data set contains the process-level performance ranking of 982 metal-organic frameworks (MOF) which were down-selected from 10,143 structures contained in the puplic CoRE MOF 2019 data set. To rankorder MOFs for application in post-combustion carbon dioxide capture, we have used a computational workflow that combines active-learning based structure selection, molecular-level modeling, and process-level optimization. A detailed description of the repository content is provided in the README file which is included in the zip archive “Diffusion-MOF-Screening.zip”.</p>
Machine Learning Potentials for Metal-Organic Frameworks using an Incremental Learning Approach: Workflow and Data
<p>This repository contains input files, workflow scripts, and output datasets and interatomic potentials for a diverse set of metal-organic frameworks, as discussed in this <a href="https://chemrxiv.org/engage/chemrxiv/article-details/6363dbf718a8ccae675d2ac8">preprint paper</a>. In addition, we provide the scripts to compute the extended Hessian and subsequently the elastic constants using automatic differentiation.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.