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21 results for “Porous materials”
Experimental data generated on the stability of hydrophobic porous materials
<div>/* **********</div> <div>/* This work is licensed under a Creative Commons Attribution 4.0 International License.</div> <div>/* **********</div> <div> </div> <div>Open access to experimental data generated by the project Electro-Intrusion (101017858, Horizon 2020, European Union, https://www.electro-intrusion.eu/en) along with the research to be used in intrusion-extrusion applications. Research pertaining to Task 2.1 (WP2). </div> <div>Underlying data for the publication Amayuelas, E. et al. Bimetallic Zeolitic Imidazole Frameworks for Improved Stability and Performance of Intrusion-Extrusion Energy Applications. The Journal of Physical Chemistry 2023, 127, 18310-18315. https://doi.org/10.1021/acs.jpcc.3c04368. Data related to Figures 2, 3 and 4 in the article.</div> <div> </div> <div>Dataset Identifier: 10.5281/zenodo.11273904</div> <div> </div> <div>Contact person: Eder Amayuelas (CIC energiGUNE). ORCID: </div> <div> </div> <div> </div> <div>The archive 'JPCC_3c04368.zip' contains 25 files:</div> <p> </p>
FOAM 02: Impedance tube measurements of two porous materials with diameter variation
<p>This dataset provides the data for Reference:</p> <p>[1] Alfonso Caiazzo, Florian Kraxberger, Christian Adams, Andreas Wurzinger, Jan Boysen, Giuseppe Petrone, Stefan Schoder, Sergio De Rosa, Manfred Kaltenbacher, and Christian Adams: FOAM 02: A dataset of impedance tube measurements with different materials and diameter variations. Acta Acustica 9 (50), 2025. <a href="https://doi.org/10.1051/aacus/2025033" target="_blank" rel="noopener noreferrer">https://doi.org/10.1051/aacus/2025033</a></p> <p> </p> <p>This dataset consists of three .csv files: </p> <ul> <li>alphas.csv: absorption coefficients vs. frequencies (comma-separated), 864 rows according <br>to 864 measurements </li> <li>targets.csv: one-hot encoded, i.e., binary, vectors of the parameter combinations), 864 rows <br>according to 864 measurements. The Read_Me.pdf gives further information on the one-hot <br>encoded vectors. </li> <li>diameter.csv: calliper diametric measurement in millimetres [mm]. This file contains <br>864 rows according to 864 measurements and 6 columns that, in order, represent: top <br>diameter at 0°, top diameter at 90°, bottom diameter at 0°, bottom diameter at 90°, <br>mean diameter, and standard deviation. </li> </ul> <p><br>The frequencies range from 150 Hz to 1600 Hz with resolution of 2 Hz. Note that these limits are <br>not strictly equal to the frequency limits of the impedance tube, see ISO 10534-2. </p> <p><br>The data and code are licensed under Apache License, Version 2.0 <br>https://opensource.org/licenses/Apache-2.0 </p> <p><br>Any reuse of the data must properly cite the dataset and its authors. </p> <p> </p> <p>Contact:<br>Univ.-Prof. Dr. Christian Adams<br>Graz University of Technology<br>Inffeldgasse 16c<br>8010 Graz, Austria<br>christian.adams@tugraz.at</p>
Data for the publication: High performance of porous, hierarchically structured P2- Na0.6Al0.11 – xNi0.22 – yFex+yMn0.66O2 cathode materials
<p>Data sets: SEM-images, EIS, ex situ XRD, operando XRD, electrochemical cycling.</p> <p>Abstract: Sodium-ion-batteries (SIB) are a low-cost alternative to currently used lithium-ion batteries (LIB) but suffer from poor cycling stability. Spray drying provides porous, hierarchically structured particles of cathode active material (CAM) in large amounts, suitable for up-scaling. Changing the chemical composition of the Na0.6Al0.11–xNi0.22–yFex+yMn0.66O2 layered oxides under identical synthesis conditions leads to differences in particle morphology, conductivities, sodium vacancy ordering and phase transition, therefore influencing the electrochemical performance via several mechanisms. Here, a broad overview on these changes for samples with variable nickel and iron content is presented. With increasing iron content, the particle porosity is reduced and lower of initial capacity is received for most cycling windows. Substituting half of the original Ni amount with Fe still leads to high capacities and improved cycling stability. The influence of Al as electrochemical inactive element becomes visible in stabilised cycling stability as well.</p>
A benchmark dataset for the grazing flow over porous materials
<p>Wind-tunnel data of a grazing flow over porous wall-inserts to be used as a benchmark dataset for the development and validation of numerical modeling approaches of flows over and through porous media. </p> <p>The dataset contains several profiles along the streamwise extent of the wall-insert of the mean velocity magnitude, the turbulent intensity, and the turbulent length scale. Also included are some boundary-layer parameters of these profiles. These have been derived from single-component constant temperature hot-wire measurements.</p> <p>Additionally, the spectra of the unsteady wall-pressure fluctuations at several locations on the upper and lower surfaces of the porous wall-inserts are provided. These unsteady pressure measurements have been acquired using semi-infinite waveguide-type remote-microphone probes.</p> <p>Tested are two porous media with the same <em>diamond-lattice</em> pattern structure but different permeabilities and a reference solid-walled case. All three cases are tested at three inflow velocities: 15 m/s, 20 m/s, and 25 m/s.</p> <p> </p> <p>Modification in v3: Correction of permeability values in Table 1 on page 3 of <em>AIAA_Manuscript_GrazingFlowPorousMaterials_v3.pdf</em>.</p>
Data for Functional Group Pair Distance Based Descriptor for Isomerisation in Porous Molecular Framework Materials
<p>This is a dataset of isomer structure files for pore topology: Tri2Di3, Tri4Di6, Tri4-2Di6,Tri6Di9, Tet2Di4, Tet3Di3, Tet4-4Di8, Tet5Di10, and Tet6Di12. </p> <p>All.tar.bz2 contains all pore topologies, the total disk space after unzipping the bundle is 1.8 Gb. The total disk space for pore Tet6Di12 alone is 1.5Gb.</p> <p>The base structure of all pore topologies are constructed using a metal node of radius ~5 (represented by a Zirconium atom) and a benzene linker, while the functional group is represented by a Nitrogen atom.</p>
Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations
<p>The supporting data and utilities from the publication "Elasto-plastic residual stress analysis of selective laser sintered porous materials based on 3D-multilayer thermo-structural phase-field simulations" are recorded in this dataset. </p> <p>Non-isothermal phase-field simulations of SLS process on SS316L material and subsequent elasto-plastic calculations were performed to analyze the development of plastic deformation and residual stress in SLS produced components during the processing. The dependence of the fusion zone, residual stress and plastic strain on the processing parameters namely, Beam power (Unit: Watts) and Scan speed (Unit: mm/s) were investigated. </p> <p>To promote FAIR research data principles, the processed simulation data from the thermo-elasto-plastic calculations for all the process parameter sets (hereby refered as P-v sets) are curated in this dataset. The raw temporal data obtained from the processing simulations and the elasto-plastic could not be included in this dataset due to its high volume. However, the corresponding raw data can be requested by contacting the creators of this dataset (Yangyiwei Yang: <a href="mailto:yangyiwei.yang@mfm.tu-darmstadt.de">yangyiwei.yang@mfm.tu-darmstadt.de</a> and Somnath Bharech: <a href="mailto:somnath.bharech@tu-darmstadt.de">somnath.bharech@tu-darmstadt.de</a>).</p> <p>This dataset includes: </p> <ul> <li><code>average_value.csv</code>: Contains average values of mechanical properties (such as residual stress, plastic strain) for the powder bed and the fused strut of all the process parameter sets.</li> <li><code>mesostructures_tep_sls.zip</code> : Contains resampled mesostructures obtained at the last time step of the SLS processing simulations with thermo-elasto-plastic calculations for the P-v sets reported in the aforementioned investigation. Nomenclature of the sub-directories indicating the P-v sets follows: <code>tep_<beam power>-<scan speed></code>. Each of these sub-directories contain the mesostructures from last time step of the thermo-elasto-plastic analysis of each of the four layer scans and is named as: <code>TP_layer{1..4}_output_final.e</code>. These files can be opened using Paraview v.5.8.1 or higher. The nodal values are explained as follows:</li> </ul> <table> <tbody> <tr> <td><strong>Nodal value name</strong></td> <td><strong>Symbol</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> </tr> <tr> <td>T</td> <td>\(T\)</td> <td>Temperature field normalized by \(T_M\)</td> <td>-</td> </tr> <tr> <td>c</td> <td>\(\rho\)</td> <td>Substance order parameter</td> <td>-</td> </tr> <tr> <td>eps_ij </td> <td>\(\varepsilon\)</td> <td>Strain</td> <td>-</td> </tr> <tr> <td>epsp_ij</td> <td>\(\varepsilon^\text{pl}\)</td> <td>Plastic strain</td> <td>-</td> </tr> <tr> <td>peeq</td> <td>\(p_\text{e}\)</td> <td>Accumulated plastic strain</td> <td>-</td> </tr> <tr> <td>sigma_ij </td> <td>\(\sigma\)</td> <td>Stress</td> <td>MPa</td> </tr> <tr> <td>vonmises</td> <td>\(\sigma_\text{e}\)</td> <td>von Mises stress </td> <td>MPa</td> </tr> <tr> <td>u</td> <td>\(\mathbf{u}\)</td> <td>Displacement</td> <td>µm</td> </tr> </tbody> </table> <p> </p>
Fracture toughness of mixed-mode anticracks in highly porous materials dataset and data processing
<blockquote> <div>This repository contains the code and datasets used in the data analysis for "Fracture toughness of mixed-mode anticracks in highly porous materials". The analysis is implemented in Python, using Jupyter Notebooks.</div> </blockquote> <h2>Contents</h2> <ul> <li><code>main.ipynb</code>: Jupyter notebook with the main data analysis workflow.</li> <li><code>energy.py</code>: Methods for the calculation of energy release rates.</li> <li><code>regression.py</code>: Methods for the regression analyses.</li> <li><code>visualization.py</code>: Methods for generating visualizations.</li> <li><code>df_mmft.pkl</code>: Pickled DataFrame with experimental data gathered in the present work.</li> <li><code>df_legacy.pkl</code>: Pickled DataFrame with literature data.</li> </ul> <h2>Prerequisites</h2> <ul> <li>To run the scripts and notebooks, you need:</li> <li>Python 3.12 or higher</li> <li>Jupyter Notebook or JupyterLab</li> <li>Libraries: <code>pandas</code>, <code>matplotlib</code>, <code>numpy</code>, <code>scipy</code>, <code>tqdm</code>, <code>uncertainties</code>, <code>weac</code></li> </ul> <h2>Setup</h2> <ol> <li>Download the zip file or clone this repository to your local machine.</li> <li>Ensure that Python and Jupyter are installed.</li> <li>Install required Python libraries using <code>pip install -r requirements.txt</code>.</li> </ol> <h2>Running the Analysis</h2> <ol> <li>Open the <code>main.ipynb</code> notebook in Jupyter Notebook or JupyterLab.</li> <li>Execute the cells in sequence to reproduce the analysis.</li> </ol> <h2>Data Description</h2> <div>The data included in this repository is encapsulated in two pickled DataFrame files, <code>df_mmft.pkl</code> and <code>df_legacy.pkl</code>, which contain experimental measurements and corresponding parameters. Below are the descriptions for each column in these DataFrames:</div> <h3><code>df_mmft.pkl</code></h3> <div>Includes data such as experiment identifiers, datetime, and physical measurements like slope inclination and critical cut lengths.</div> <ul> <li><code>exp_id</code>: Unique identifier for each experiment.</li> <li><code>datestring</code>: Date of the experiment as a string.</li> <li><code>datetime</code>: Timestamp of the experiment.</li> <li><code>bunker</code>: Field site of the experiment. Bunker IDs 1 and 2 correspond to field sites A and B, respectively.</li> <li><code>slope_incl</code>: Inclination of the slope in degrees.</li> <li><code>h_sledge_top</code>: Distance from sample top surface to the sled in mm.</li> <li><code>h_wl_top</code>: Distance from sample top surface to weak layer in mm.</li> <li><code>h_wl_notch</code>: Distance from the notch root to the weak layer in mm.</li> <li><code>rc_right</code>: Critical cut length in mm, measured on the front side of the sample.</li> <li><code>rc_left</code>: Critical cut length in mm, measured on the back side of the sample.</li> <li><code>rc</code>: Mean of <code>rc_right</code> and <code>rc_left</code>.</li> <li><code>densities</code>: List of density measurements in kg/m^3 for each distinct slab layer of each sample.</li> <li><code>densities_mean</code>: Daily mean of <code>densities</code>.</li> <li><code>layers</code>: 2D array with layer density (kg/m^3) and layer thickness (mm) pairs for each distinct slab layer.</li> <li><code>layers_mean</code>: Daily mean of <code>layers</code>.</li> <li><code>surface_lineload</code>: Surface line load of added surface weights in N/mm.</li> <li><code>wl_thickness</code>: Weak-layer thickness in mm.</li> <li><code>notes</code>: Additional notes regarding the experiment or observations.</li> <li><code>L</code>: Length of the slab–weak-layer assembly in mm.</li> </ul> <h3><code>df_legacy.pkl</code></h3> <div>Contains robustness data such as radii of curvature, slope inclination, and various geometrical measurements.</div> <ul> <li><code>#</code>: Record number.</li> <li><code>rc</code>: Critical cut length in mm.</li> <li><code>slope_incl</code>: Inclination of the slope in degrees.</li> <li><code>h</code>: Slab height in mm.</li> <li><code>density</code>: Mean slab density in kg/m^3.</li> <li><code>L</code>: Lenght of the slab–weak-layer assembly in mm.</li> <li><code>collapse_height</code>: Weak-layer height reduction through collapse.</li> <li><code>layers_mean</code>: 2D array with layer density (kg/m^3) and layer thickness (mm) pairs for each distinct slab layer.</li> <li><code>wl_thickness</code>: Weak-layer thickness in mm.</li> <li><code>surface_lineload</code>: Surface line load from added weights in N/mm.</li> </ul> <p>For more detailed information on the datasets, refer to the paper or the documentation provided within the Jupyter notebook.</p> <h2>License</h2> <div>This work is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</div> <p> </p> <div>You are free to:</div> <ul> <li><strong>Share</strong> — copy and redistribute the material in any medium or format</li> <li><strong>Adapt</strong> — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <div>Under the following terms:</div> <div> <ul> <li><strong>Attribution</strong> — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</li> </ul> </div> <h2>Citation</h2> <div>Please cite the following paper if you use this analysis or the accompanying datasets:</div> <div> <ul> <li>Adam, V., Bergfeld, B., Weißgraeber, P. van Herwijnen, A., Rosendahl, P.L., Fracture toughness of mixed-mode anticracks in highly porous materials. <em>Nature Communincations</em> <strong>15</strong>, 7379 (2024). https://doi.org/10.1038/s41467-024-51491-7</li> </ul> </div>
Data of "Interaction-based material network: a general framework for (porous) microstructured materials"
<pre>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "Interaction-based material network: a general framework for (porous) microstructured materials", journal = "Computer Methods in Applied Mechanics and Engineering", pages = " ", year = "202?", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.", author = "Nguyen, Van Dung and Noels, Ludovic"</pre>
Ultrahigh-throughput cross-flow filtration of solution-processed 2D materials enabled by porous ceramic membranes
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Data from: Nanoindentation methods for viscoelastic characterization of stiff porous materials
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Data for paper "Mathematical modelling of impurity deposition during evaporation of dirty liquid in a porous material"
<p>The attached files contain the data for the paper "Mathematical modelling of impurity deposition during evaporation of dirty liquid in a porous material" by Ellen Luckins, Chris Breward, Ian Griffiths and Colin Please, accepted for publication in JFM (April 2024). See the READ ME file for a description of the data.</p>
Data for: Separation of oil vapor by polyether block amide composite membrane modified with porous materials
<p>The ability of membranes to separate oil vapor is affected by their permeance and selectivity. This study modifies polyether block amide (PEBA) composite membranes with a microporous zeolite, Silicalite-1, or a mesoporous zeolite, MCM-41. The results show that when PEBA composite membranes are modified with these zeolites, the selective layer of the composite membrane is coated more thinly, resulting in a higher flux of organic gas. Silicalite-1 increases the hydrophobicity of the membrane, which facilitates the adsorption of organic vapor on the membrane surface, thus improving the membrane selectivity. In the separation of oil vapor, both modified membranes can effectively increase the gas permeabilities and selectivities. The main mechanism governing gas transport in the MCM-41-modified membrane is Knudsen diffusion, so the selectivity for small molecules is improved more significantly. By contrast, the dissolution–diffusion mechanism is dominant in the Silicalite-1-modified membranes, which considerably increases the selectivity for large molecules.</p>
Data for Functional Group Pair Distance Based Descriptor for Isomerisation in Porous Molecular Framework Materials
<p>This is a dataset of isomer structure files for pore topology: Tri2Di3, Tri4Di6, Tri4-2Di6,Tri6Di9, Tet2Di4, Tet3Di3, Tet4-4Di8, Tet5Di10, and Tet6Di12. </p> <p>All.tar.bz2 contains all pore topologies, the total disk space after unzipping the bundle is 1.8 Gb.</p> <p>The total disk space for pore Tet6Di12 alone is 1.5Gb.</p>
Effects of disorder on deformation and failure of brittle porous materials: dataset
<p><strong>Data from:</strong></p> <p>[1] Ritter, J.; Shegufta, S. & Zaiser, M. "Effects of disorder on deformation and failure of brittle porous materials" <em>arXiv, </em><strong>2023</strong></p> <p> </p> <p><strong>Abstract:</strong></p> <p>from [1]</p> <p>The mechanical behavior of porous materials depends strongly on porosity and pore geometry, but also on morphological parameters characterizing the spatial arrangement of pores. Here we use bond-based peridynamics to study effects of disorder on the deformation and failure behavior of brittle porous solids both in the quasi-static limit and in case of dynamic loading scenarios. We show that structural disorder, which has a strong influence on stiffness, strength and toughness in the quasi-static limit, becomes less relevant under dynamic loading conditions. </p> <p> </p> <p><strong>Contact:</strong></p> <p>Jonas Ritter<br> Institute of Materials Simulation<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Dr.-Mack-Str. 77<br> 90762 Fürth<br> Germany</p> <p> </p> <p><strong>Software:</strong><br> <a href="https://github.com/peridigm/peridigm">Peridigm</a></p> <p> </p> <p><strong>Content:</strong></p> <p>The dataset consists of the input geometries as well as the combined results (values for each simulation and mean values and standard deviation of each parameter set). Each folder contains a README.md that lists the content and provides description of the data and further metadata.</p>
Data for: Separation of oil vapor by polyether block amide composite membrane modified with porous materials
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Large-scale statistical learning for mass transport prediction in porous materials using 90,000 artificially generated microstructures
<p>Dataset and code used in B Prifling, et al, "Large-scale statistical learning for mass transport prediction in porous materials using 90,000 artificially generated microstructures", published in Frontiers in Materials. In this work, we investigate relationships between 3D microstructure and effective diffusivity and permeability, based on a dataset of 90,000 structures and using analytical formulas, artificial neural networks (ANNs), and convolutional neural networks (CNNs). Herein, the codes in Matlab and Python/Tensorflow necessary to investigate the prediction models and reproduce the results of the paper are supplied. Also, microstructures together with their computed geometrical descriptors and effective properties are included.</p>
Dataset for "An approach for the pore-centred description of adsorption in hierarchical porous materials"
<p>Supporting dataset for: “An approach for the pore-centred description of adsorption in hierarchical porous materials”, J. D. Evans, <strong>2022</strong></p> <p><strong>Contains:</strong></p> <ul> <li>structures: Structural models used in YAFF .chk format.</li> <li>code: All the code used to generate the pore distributions, adsorption simulations and analysis.</li> <li>geometricporosity: Output from zeo++ used in this analysis.</li> <li>porestructure: Pore size distribution probes labeled by Pr, Nd, Pm.</li> <li>clustering: Metadata produced from the clustering analysis.</li> <li>porecenters: Locations of the pore centres combined with the </li> <li>structure in CIF format, where each pore is labeled by Pr, Nd, Pm.</li> <li>adsorption: Results of Ar simulation at 87K.</li> </ul>
Supplementary material for "A large-scale demonstration and sustainability evaluation of ductile-porous vascular networks for self-healing concrete"
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Study on the Construction Mechanism of Porous Tita-nia-Bisphosphonates Hybrid Materials's supporting information
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Novel Porous Bioceramic Material as a Bone Substitute
ClinicalTrials.gov study NCT04719624. IPD Sharing: NO. Countries: 1. Publications: 0.
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OpenNeuro
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