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104 results for “MOF”
Precision-Engineered Metal-Organic Frameworks (PE-MOFs)
<p>This Zenodo record hosts the computationally predicted structures of 94,823 Precision-Engineered Metal-Organic Frameworks (PE-MOFs), designed using a fine-tuned Reverse Topological Approach (RTA). The structures are provided as part of a large-scale effort to systematically explore the vast combinatorial design space of metal and organic building units (BUs), pairing them based on geometric signatures and topological compatibility.</p> <p>These structures are optimized and curated for applications such as post-combustion CO2 capture.</p> <p>In this repository, you will find:</p> <p>Fully optimized structures of PE-MOFs: cif files provided in standard formats compatible with molecular simulation tools for further analysis and exploration.<br>Note: This Zenodo record only provides the computational structures. For the accompanying code, tools, and data used to generate these structures, please visit the GitHub repository here. https://github.com/xiaoyu961031/Fine-tuned-RTA</p> <p>If you use this dataset in your work, please cite our related publication:<br>Wu, X., Jiang, J. (2024). Precision-engineered metal-organic frameworks: Fine-tuning reverse topological structure prediction and design. Chemical Science, 2024, DOI: 10.1039/D4SC05616G </p>
Training and validation data for MOF-801(Zr) with adsorbed water molecules.
<p>We employ MACE 0.3.5 (github.com/acesuit/mace) to train an ML potential to the extended XYZ file `data.xyz`, which contains atomic geometries and potential energy and force labels. The system is MOF-801(Zr) at various water loadings. Data was generated in an active learning fashion using psiflow (github.com/molmod/psiflow).</p>
New Azo-DMOF-1 MOF as a photo-responsive low-energy CO2 adsorbent and its exceptional CO2/N2 separation performance in mixed matrix membranes
<p>Data repository for manuscript, published in <em>ACS Applied Materials & Interfaces</em>, <strong>2018</strong>, <em>10</em> (40), pp 34291–34301, <a href="https://dx.doi.org/10.1021/acsami.8b12261">http://dx.doi.org/10.1021/acsami.8b12261</a></p> <p><strong>Abstract</strong></p> <p>A new generation-2 light-responsive metal–organic framework (MOF) has been successfully synthesized using Zn as the metal source and both 2-phenyldiazenyl terephthalic acid and 1,4-diazabicyclo[2.2.2]octane (DABCO) as the ligands. It was found that Zn-azo-dabco MOF (Azo-DMOF-1) exhibited a photoresponsive CO<sub>2</sub> adsorption both in static and dynamic condition because of the presence of azobenzene functionalities from the ligand. Further application of this MOF was evaluated by incorporating it as a filler in a mixed matrix membrane for CO<sub>2</sub>/N<sub>2</sub> gas separation. Matrimid and polymer of intrinsic microporosity-1 (PIM-1) were used as the polymer matrix. It was found that Azo-DMOF-1 could enhance both the CO<sub>2</sub> permeability and selectivity of the pristine polymer. In particular, the Azo-DMOF-1–PIM-1 composite membranes have shown a promising performance that surpassed the 2008 Robeson Upper Bound.</p>
Dataset - Removal of diclofenac by adsorption process studied in free-base porphyrins Zr-metal organic frameworks (Zr-MOFs)
<p>Dataset for the paper entitled "Removal of diclofenac by adsorption process studied in free-base porphyrins Zr-metal organic frameworks (Zr-MOFs) "</p>
Noncentrosymmetric Lanthanide-Based MOF Materials Exhibiting Strong SHG Activity and NIR Luminescence of Er3+: Application in Nonlinear Optical Thermometry
<p>Optically active luminescent materials based on lanthanide ions attract significant attention due to their unique spectroscopic properties, nonlinear optical activity, and the possibility of application as contactless sensors. Lanthanide metal-organic frameworks (Ln-MOFs) that exhibit strong second-harmonic generation (SHG) and are optically active in the NIR region are unexpectedly underrepresented. Moreover, such Ln-MOFs require ligands that are chiral and/or need multistep synthetic procedures. Here, we show that the NIR pulsed laser irradiation of the noncentrosymmetric, isostructural Ln-MOF materials (MOF-Er<sup>3+</sup> (1) and codoped MOF-Yb<sup>3+</sup>/Er<sup>3+</sup> (2)) that are constructed from simple, achiral organic substrates in a one-step procedure results in strong and tunable SHG activity. The SHG signals could be easily collected, exciting the materials in a broad NIR spectral range, from ≈800 to 1500 nm, resulting in the intense color of emission, observed in the entire visible spectral region. Moreover, upon excitation in the range of ≈900 to 1025 nm, the materials also exhibit the NIR luminescence of Er<sup>3+</sup> ions, centered at ≈1550 nm. The use of a 975 nm pulse excitation allows simultaneous observations of the conventional NIR emission of Er<sup>3+</sup> and the SHG signal, altogether tuned by the composition of the Ln-MOF materials. Taking the benefits of different thermal responses of the mentioned effects, we have developed a nonlinear optical thermometer based on lanthanide-MOF materials. In this system, the SHG signal decreases with temperature, whereas the NIR emission band of Er<sup>3+</sup> slightly broadens, allowing ratiometric (Er<sup>3+</sup> NIR 1550 nm/SHG 488 nm) temperature monitoring. Our study provides a groundwork for the rational design of readily available and self-monitoring NLO-active Ln-MOFs with the desired optical and electronic properties.</p>
Enhanced Gas Adsorption Kinetics in Supraparticle-based MOF Materials
<p>This is the raw data for the manuscript:</p> <p>Enhanced Gas Adsorption Kinetics in Supraparticle-based MOF Materials, publlished in Advanced Materials.</p> <p>A read me file containing all descriptions can be found in the main folder of the zip file</p> <p>All data are sorted according to their appearance in the figures of the main manuscript</p>
Nano MOFs as targeted drug delivery agents to combat antibiotic resistant bacterial infections
Open the record for dataset details and reuse information.
Solid-state NMR data for: Sequential pore functionalization in MOFs for enhanced carbon dioxide capture
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Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites
<p>Data for reproducibility of the paper "Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites"</p> <p>Corresponding paper:</p> <p>ShanPing Liu, Romain Dupuis, Dong Fan, Salma Benzaria, Mickaele Bonneau, Prashant Bhatt, Mohamed Eddaoudi, and Guillaume Maurin. "Machine Learning Potential for Modelling H2 Adsorption/Diffusion in MOFs with Open Metal Sites." <em>Chemical Science</em> (2024). https://doi.org/10.1039/D3SC05612K</p>
Alternative protein encapsulation with MOFs: overcoming the elusive mineralization of HKUST-1 in water
<p>Relevant data for publication with DOI: <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3CC04320G">10.1039/D3CC04320G</a></p>
Heteroepitaxial MOF-on-MOF Photocatalyst for Solar-Driven Water Splitting
<p>Relevant data for publication with doi: 10.1021/acsnano.4c03442</p> <p> </p>
A thermally/chemically robust and easily regenerable anilato-based ultramicroporous 3D MOF for CO2 uptake and separation
<p>Relevant data for the publication with DOI:</p> <table> <tbody> <tr> <td><a href="https://doi.org/10.1039/D1TA07436A"><span>10.1039/D1TA07436A</span></a></td> </tr> </tbody> </table>
Chemical Design and Magnetic Ordering in Thin Layers of 2D Metal–Organic Frameworks (MOFs)
<p>Relevant data for publication with DOI: <a title="DOI URL" href="https://doi.org/10.1021/jacs.1c07802">10.1021/jacs.1c07802</a></p>
Promoting photoswitching in mismatching mixed-linker multivariate Zr6 MOFs
<p>This is the data that accompanies the article ''Promoting photoswitching in mismatching mixed-linker multivariate Zr6 MOFs''. <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4RA07366E">https://doi.org/10.1039/D4RA07366E</a></p> <p>Information of data collection and treatment can be found in the supporting information of the article. </p>
Datasets used for Automated EffortLess MicroED Graphic User Interface (AutoLEI): Tyrosine (12), MOF SU-100 (16), protein MutT homolog 1 (38) and Lysozyme (71)
<p>The <strong>Auto</strong>mated<strong> </strong>Effort<strong>L</strong>ess<strong> </strong>Micro<strong>E</strong>D<strong> </strong>Graphic User<strong> I</strong>nterface<strong> (AutoLEI)</strong> is designed to automatically process and merge batches of rotation electron diffraction datasets using <strong>XDS[1]</strong>. This GUI aims to streamline data processing and minimize the need for manual data processing.</p> <p>The four datasets below are examples used in the Automated EffortLess MicroED Graphic User Interface (AutoLEI) paper.</p> <p>GUI available: https://zenodo.org/records/15206752 </p> <p> </p> <p><strong>A. Data information</strong></p> <p>Dataset 1: Tyrosine (Small molecule), 12 datasets in total</p> <p>Dataset 2: SU-100 (Small molecule), 16 datasets in total</p> <p>Dataset 3: MutT homolog 1 (Macro molecule), 38 datasets in total</p> <p>Dataset 4: Lysozyme (Macro molecule), 71 datasets in total</p> <p> </p> <p><strong>B. Data collection </strong></p> <p><strong>Tyrosine</strong> data was collected with an ASI Timepix hybrid detector installed on a JEOL JEM-2100 (200 kV) microscope equipped with a LaB6 filament. A Gatan 914 cryo-holder is employed to collect data at cryo temperature.</p> <p>Data collection software: Instamatic.</p> <p>Electron Microscopy Center, the Department of Materials and Environmental Chemistry, Stockholm University.</p> <table> <tbody> <tr> <th>Data</th> <th>#Frame</th> <th>Step (°)</th> <th>Start (°)</th> <th>End (°)</th> <th>Rotation axis.(°)</th> <th>WL (Å)</th> <th>Camera_l (mm)</th> <th>Size1</th> <th>Size2</th> <th>Pixel Size (1/nm)</th> </tr> </tbody> <tbody> <tr> <td>data1</td> <td>415</td> <td>0.233</td> <td>-50.82</td> <td>45.44</td> <td>129.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data2</td> <td>475</td> <td>0.233</td> <td>-57.19</td> <td>53.07</td> <td>129.4</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data3</td> <td>139</td> <td>0.232</td> <td>-51.23</td> <td>-19.17</td> <td>128.6</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data4</td> <td>421</td> <td>0.233</td> <td>-50.67</td> <td>47.01</td> <td>128.9</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data5</td> <td>448</td> <td>0.233</td> <td>-54.51</td> <td>49.43</td> <td>130.1</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data6</td> <td>330</td> <td>0.233</td> <td>-54.97</td> <td>21.54</td> <td>129.1</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data7</td> <td>41</td> <td>0.232</td> <td>-57.39</td> <td>-48.12</td> <td>128.3</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data8</td> <td>459</td> <td>0.233</td> <td>-53.60</td> <td>53.03</td> <td>128.7</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data9</td> <td>430</td> <td>0.233</td> <td>-38.49</td> <td>61.25</td> <td>129.7</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data10</td> <td>509</td> <td>0.233</td> <td>-56.38</td> <td>61.91</td> <td>128.6</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data11</td> <td>353</td> <td>0.223</td> <td>-61.69</td> <td>16.79</td> <td>130.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data12</td> <td>514</td> <td>0.233</td> <td>-52.99</td> <td>66.36</td> <td>129.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> </tbody> </table> <p> </p> <p><strong>SU-100 </strong>data was collected with an ASI Timepix hybrid detector installed on a JEOL JEM-2100 (200 kV) microscope equipped with a LaB6 filament. A Gatan 914 cryo-holder is employed to collect data at cryo temperature.</p> <p>Data collection software: Instamatic.</p> <p>Electron Microscopy Center, the Department of Materials and Environmental Chemistry, Stockholm University.</p> <table> <tbody> <tr> <th>Data</th> <th>#Frame</th> <th>Step (°)</th> <th>Start (°)</th> <th>End (°)</th> <th>Rotation axis.(°)</th> <th>WL (Å)</th> <th>Camera_l (mm)</th> <th>Size1</th> <th>Size2</th> <th>Pixel Size (1/nm)</th> </tr> </tbody> <tbody> <tr> <td>data1</td> <td>248</td> <td>0.233</td> <td>-16.82</td> <td>40.64</td> <td>131.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data2</td> <td>486</td> <td>0.233</td> <td>-55.22</td> <td>57.62</td> <td>129.4</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data3</td> <td>472</td> <td>0.232</td> <td>-51.78</td> <td>57.67</td> <td>129.3</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data4</td> <td>399</td> <td>0.232</td> <td>-56.68</td> <td>35.79</td> <td>129.8</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data5</td> <td>319</td> <td>0.233</td> <td>-15.51</td> <td>58.42</td> <td>126.1</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data6</td> <td>302</td> <td>0.233</td> <td>-50.47</td> <td>19.52</td> <td>128.6</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data7</td> <td>87</td> <td>0.233</td> <td>-50.01</td> <td>-29.93</td> <td>130.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data8</td> <td>349</td> <td>0.232</td> <td>-57.04</td> <td>23.81</td> <td>130.5</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data9</td> <td>475</td> <td>0.233</td> <td>-48.75</td> <td>61.56</td> <td>128.8</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data10</td> <td>422</td> <td>0.233</td> <td>-48.35</td> <td>49.53</td> <td>128.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data11</td> <td>415</td> <td>0.233</td> <td>-57.64</td> <td>38.62</td> <td>129.0</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data12</td> <td>351</td> <td>0.232</td> <td>-54.26</td> <td>26.95</td> <td>128.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data13</td> <td>466</td> <td>0.233</td> <td>-53.95</td> <td>54.18</td> <td>130.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data14</td> <td>450</td> <td>0.233</td> <td>-51.83</td> <td>52.56</td> <td>129.0</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data15</td> <td>443</td> <td>0.233</td> <td>-49.31</td> <td>53.57</td> <td>129.0</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data16</td> <td>374</td> <td>0.233</td> <td>-39.30</td> <td>47.46</td> <td>129.3</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> </tbody> </table> <p> </p> <p><strong>MutT homolog 1</strong> data was collected with a CMOS CetaD detector installed on a Titan Krios G3i with an autoloader. </p> <p>Data collection software: EPUD</p> <p>Cryo-EM infrastructure unit, Scilifelab, Stockholm.</p> <table> <tbody> <tr> <th>Rotation axis.(°)</th> <th>WL (Å)</th> <th>Camera_l (mm)</th> <th>Size1</th> <th>Size2</th> <th>Pixel Size (1/nm)</th> </tr> </tbody> <tbody> <tr> <td>-6.0</td> <td>0.019687</td> <td>2487.83</td> <td>2048</td> <td>2048</td> <td>5.717</td> </tr> </tbody> </table> <p> </p> <p><strong>Lysozyme</strong> data was collected with a CMOS CetaD detector installed on a Titan Krios G2 with an autoloader. </p> <p>Data collection software: EPUD</p> <p>Cryo-EM infrastructure unit, Scilifelab, Stockholm.</p> <table> <tbody> <tr> <th>Rotation axis.(°)</th> <th>WL (Å)</th> <th>Camera_l (mm)</th> <th>Size1</th> <th>Size2</th> <th>Pixel Size (1/nm)</th> </tr> <tr> <td>-174.4</td> <td>0.01968</td> <td>1155.0</td> <td>2048</td> <td>2048</td> <td>12.318</td> </tr> </tbody> </table> <p><strong>C. Reference</strong></p> <p>[1] Kabsch. W. “XDS”, <em>ACTA CRYSTALLOGRAPHICA SECTION D</em>, 2010</p>
Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and MOFs using Machine Learning
<p>Dataset containing different physical properties and gas loading capacity inside carbon molecular sieving membrane (CMSM) and metal organic frameworks (MOF). The physical properties of the adsorbent frameworks are used to train neural network (NN) and XGBoost machine learning models to accurately predict the gas loadings. This dataset corresponds to the publication, "Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and MOFs using Machine Learning", Subhadeep Dasgupta, Amal RS, Prabal K Maiti.</p>
Harvard Museum of Comparative Zoology: circa - MoF fixed
Data from the ten research collections of the Museum of Comparative Zoology at Harvard University.<p></p>
Time-efficient atmospheric water harvesting using Fluorophenyl oligomer incorporated MOFs
Open the record for dataset details and reuse information.
Data Driven Discovery of MOFs for Hydrogen Gas Adsorption
<p>This data is in accordance with the submitted article to <strong>The Journal of Chemical Theory and Computation. </strong>This article is entitled, “<strong>Data Driven Discovery of MOFs for Hydrogen Gas Adsorption</strong>” by Samrendra K. Singh, Abhishek T. Sose, Fangxi Wang, Karteek K. Bejagam, and Sanket A. Deshmukh; to be published in the special issue on “Machine Learning for Molecular Simulation” in Journal of Chemical Theory and Computation.</p>
Post-synthetic Covalent Grafting of Amines to NH2-MOF for Post-Combustion Carbon Capture
<p>Dataset for the Manuscript titled "Post-synthetic Covalent Grafting of Amines to NH2-MOF for Post-Combustion Carbon Capture".</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.