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287 results for “alloy”
Data for "Insight into Ideal Shear Strength of Ni-based Dilute Alloys using First-Principles Calculations and Correlational Analysis"
<p><strong>Summary: </strong></p> <p>This dataset contains inputs, outputs, and analysis needed to replicate the results discussed in a published article. First-principles results were obtained through VASP and an external optimizer while the crystal plasticity finite element calculations were carried out by Abaqus and an existing subroutine. See the manuscript for more details.</p> <p><strong>Folders: </strong></p> <ul> <li>NiX_data-summary ("analysis" archive): a summary of the output of the DFT calculations, including plaintext and numpy binary versions of the elastic constants and ideal shear stresses.</li> <li>NiX_analysis ("analysis" archive): the data analysis scripts needed to reproduce correlation results (a separate calculation in Matlab) and plot figures.</li> <li>NiX_data-detailed ("data" archive): input files for three pre-strain conditions (0.00, 0.15, and 0.30) and all alloying elements, with common files being separated to a top-level folder of their own to limit repetition.</li> </ul> <p><strong>Article: </strong></p> <p>Published: <a href="https://doi.org/10.1016/j.commatsci.2022.111564">10.1016/j.commatsci.2022.111564</a></p> <p>@article{shimanek2022NiX, title = {Insight into ideal shear strength of Ni-based dilute alloys using first-principles calculations and correlational analysis}, journal = {Computational Materials Science}, volume = {212}, pages = {111564}, year = {2022}, issn = {0927-0256}, doi = {https://doi.org/10.1016/j.commatsci.2022.111564}, author = {John D. Shimanek and Shun-Li Shang and Allison M. Beese and Zi-Kui Liu}}</p> <p>Preprint: <a href="https://doi.org/10.48550/arXiv.2108.06412">10.48550/arXiv.2108.06412</a></p>
Raw Data for Evidence for Isotropic s-Wave Superconductivity in High-Entropy Alloys
<p>Raw Data for the paper "Evidence for Isotropic s-Wave Superconductivity in High-Entropy Alloys".</p>
Data for "Computational Design of Alloy Nanostructures for Optical Sensing of Hydrogen"
<p>This record contains data pertaining to the publication "Computational Design of Alloy Nanostructures for Optical Sensing of Hydrogen".</p>
Local structure and density of liquid Fe-C-S alloys at Moon's core conditions
<p>This file includes the raw CAESAR and absorption data measured in each P-T condition and the Python codes for diffraction and absorption data analysis.</p> <p>Matlab codes for P-T calibration are also enclosed.</p>
[Dataset] In situ laser-ultrasonic monitoring of elastic parameters during natural aging in an Al-Zn-Mg-Cu alloy (AA7075)
<p>Datasets generated and analyzed during the research study titled "In situ laser-ultrasonic monitoring of elastic parameters during natural aging in an Al-Zn-Mg-Cu alloy (AA7075)".<br> The files ending in ".xls" or ".xlsx" are stored in the Microsoft Excel table format, while the ".mat" files can be opened with Matlab.<br> Further questions about the data structure can be directed to the corresponding authors.</p>
Bonding analysis results for "Chemical ordering and magnetism in face-centered cubic CrCoNi alloy"
<p>This repository contains the code and data to produce the results of chapter <em>IIIC. Covalent bonding analysis for L12/L10 type configurations</em> of the publication <em>Chemical ordering and magnetism in face-centered cubic CrCoNi alloy</em> by Sheuly Ghosh et al.</p>
Digital image correlation displacements and strains around a growing fatigue crack in an AA2024-T3 aluminium alloy
<p>This repository contains the data used in the research article:</p> <p>Strohmann, Melching, Paysan, Dietrich, Requena, Breitbarth. Next generation fatigue crack growth experiments of aerospace materials. <em>Scientific Reports</em>, 2024, <a href="https://doi.org/10.1038/s41598-024-63915-x" target="_blank" rel="noopener">https://doi.org/10.1038/s41598-024-63915-x</a>.</p> <p> </p> <p><strong>General Description</strong><br>The dataset contains digital image correlation (DIC) data of a growing fatigue crack in an AA2024-T3 alloy. For the experiment, two independent DIC measurement devices were used – a global full-field DIC and a local microscopic DIC. Thus, the dataset consists of two main directories. One for the 3D DIC data ("global_3d_dic") and a second one for the 2D microscopic DIC data ("local_2d_dic"). Both of these are described and connected by rich metadata.<br>The 3D DIC data directory contains two subdirectories (a "Nodemaps" directory and a "Connections" directory). Both of them contain 797 '.txt'-files. The "Nodemaps" give the DIC results (i.e. coordinates, displacement and strains) for every timestep throughout the experiment. These are usually maximum, minimum, and mean load of a certain load cycle. However, for few crack lengths, we obtained DIC data for a higher number (ca. 100) of images within one load cycle. The nodemaps' format and structure is optimized for data processing in the open-source Python package <a title="CrackPy" href="https://doi.org/10.5281/zenodo.10990494" target="_blank" rel="noopener">CrackPy</a>. The last integer number of each filename can be interpreted as a 'timestep' throughout the experiment. The "Connection" files represent the connections of the DIC facet center coordinates. These are necessary to export the "Nodemap" data to any mesh like dataset, e.g. for VTK. <br>The 2D microscopic DIC directory contains 3 subdirectories ("80", "90", "95") for predefined positions with respect to the specimen coordinate system. For each location, a number of DIC data are stored, again within two subdirectories "Nodemaps" and "Connections" as '.txt'-files. For the 2D DIC data, the last integer of each name cannot be correlated to a timestep. Instead, we provide a descriptive file "local_2d_microscopic_coordinates_by_nodemaps.csv" linking each and every "Nodemap"-file to its respective coordinates and timestep (i.e. the load cycles).</p> <p>To describe the data, we distinguish between<br>1. Higher-level metadata - these data contain information about the experiment and material. The data do not change between timesteps and are given within this description.<br>2. Timestep metadata - these data contain information about one timestep of the experiment and are stored in the header of each "Nodemap"-file.</p> <p> </p> <p><strong>Higher-level Metadata</strong><br>The experiment is described in detail in the reference publication by <a title="Strohmann et al. (2024)" href="https://www.researchsquare.com/article/rs-3128435/v1" target="_blank" rel="noopener">Strohmann et al. (2024)</a> and a summary is given below. Moreover, we provide a dictionary in javascript object notation explaining terms which are used in the higher-level metadata. We use such a dictionary since no standardized ontology is currently available. This dictionary is stored in the main directory as "higher_level_metadata_dictionary.json".</p> <p><em>Material </em><br>A commercially available AA2024-T3 aluminum alloy was tested in L-T orientation, i.e. rolling direction, L, parallel to the load axis. The specimen had a width W = 160 mm cut from a rolled sheet of 2 mm.</p> <p><em>Digital image correlation</em><br>For 3D DIC, we used a GOM Aramis 12M system with a facet size of 20 x 20 pixels and a 16 pixels facet distance. One facet, therefore, covers ~0.614 x 0.614 mm². For the 2D microscopic DIC we captured images using a Zeiss STEMI 206C light optical microscope (LOM), equipped with a Basler a2A5320-23µmPro global shutter CMOS camera. One image has a size of 10.2 x 5.7 mm², 5328 x 3040 Pixels and a facet size of 40x40 pixels (distance of facet center points 30 pixels). The LOM was mounted to a robotic arm, a KUKA lbr Iiwa Cobot.</p> <p><em>Fatigue crack growth</em><br>We used a standard uniaxial servo-hydraulic testing rig. We applied a cyclic load ranging from Fmin = 4.5 kN to Fmax = 15 kN, i.e. R=Fmin/Fmax = 0.3. Throughout the experiment, we measured the crack length using direct current potential drop (DCPD).</p> <p><em>Image acquisition during fatigue crack growth</em><br>We acquired reference images for the DIC calculations before the experiment. For the global DIC, this is simply an image of the unloaded specimen. For the local microscopic DIC, the reference images are acquired in a checker board pattern with an overlap of 70 %. The depth of focus was calibrated for each image individually following (see <a title="Paysan et al. (2023)" href="https://doi.org/10.1007/s11340-023-00964-9" target="_blank" rel="noopener">Paysan et al. (2023)</a>). Images were acquired every 0.5 mm of crack extension at minimum, maximum and 0.5(Fmax- Fmin).</p> <p> </p> <p><strong>Timestep Metadata</strong><br>The timestep-wise metadata is stored in the individual DIC output files, "Nodemaps". We explain the terms used in a second dictionary, "timestep_level_metadata_dictionary.json". For all DIC data, we stored all data coming from the machine controller, i.e. number of cycles, force, displacement of the cylinder and also potential and crack length calculated from the potential as well as current values for back face strain gauges at both back faces of the MT specimen. In addition, for the local microscopic DIC data, we also store the current location of the center point of the image with respect to the global coordinate system provided by the current position of the robot carrying the LOM.</p>
Slip systems activity and grain boundary sliding investigation in zinc alloy
<p>Data obtained during the investigation of slip system activity and grain boundary sliding in Zn-0.5Cu alloy.</p> <p>Dataset contains RAW and processed EBSD data, SEM images, AFM data, and data analysis results in a form of Origin project data file.</p>
High resolution crystallographic and chemical characterisation of iodine induced stress corrosion crack tips formed in irradiated and non-irradiated zirconium alloys - Supporting data
<p>Data related to High resolution crystallographic and chemical characterisation of iodine induced stress corrosion crack tips formed in irradiated and non-irradiated zirconium alloys:</p> <p>Conor Gillen, Alistair Garner, Pia Tejland, Philipp Frankel,<br> High resolution crystallographic and chemical characterisation of iodine induced stress corrosion crack tips formed in irradiated and non-irradiated zirconium alloys, Journal of Nuclear Materials, Volume 519, 2019, Pages 166-172, ISSN 0022-3115,<br> https://doi.org/10.1016/j.jnucmat.2019.03.027.</p> <p>Datafiles for STEM-EDX analysis - STEM-EDX.zip</p> <p>Datafiles for TKD analysis - TKD.zip</p> <p>Datafiles for NanoSIMS analysis - 1: NanoSIMS raw data file (.im file) with all 160 planes and data for all ion signals acquired. Can be opened with the OpenMIMS plugin for ImageJ or other NanoSIMS software. 2: NanoSIMS processed data file (.nrrd file) which is drift corrected and can be opened with the OpenMIMS plugin for ImageJ</p>
Data for 'The effect of δ-hydride on the micromechanical deformation of a Zr alloy studied by in situ high angular resolution electron backscatter diffraction'
<p>This is the data bundle for <br> "The effect of delta-hydride on the micromechanical deformation of a Zr alloy studied by in situ high angular resolution electron backscatter diffraction" <br> published in Scripta Materialia in 2019</p> <p>Siyang Wang 1, Szilvia Kalácska 2, Xavier Maeder 2, Johann Michler 2, Finn Giuliani 1, T. Ben Britton 1</p> <p>1 Imperial College London, London, UK SW7 2AZ<br> 2 EMPA, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Mechanics of Materials and Nanostructures, Feuerwerkerstrasse 39, 3602, Thun, Switzerland</p> <p>Please refer to the newest version of this data bundle, if there are multiple versions.</p> <p>For more information email siyang.wang15@imperial.ac.uk (Mr. Siyang Wang).</p>
Unsupervised Machine Learning and Cepstral Analysis with 4D-STEM for Characterizing Complex Microstructures of Metallic Alloys
<p>Raw 4D-STEM data of Ni50Ti26Hf20Al4 used for analysis in the publication "Unsupervised Machine Learning and Cepstral Analysis with 4D-STEM for Characterizing Complex Microstructures of Metallic Alloys". Datasets were collected using the electron microscope pixel array detector (EMPAD) with a Themis Z STEM. Custom python scripts used for data analysis are available upon request to one of the corresponding authors.</p>
Supporting Data for "Potassium Alloy Reference Electrodes for Potassium-Ion Batteries: The K-In and K-Bi Systems"
<p>This is the dataset of the data for our publication "Potassium Alloy Reference Electrodes for Potassium-Ion Batteries: The K-In and K-Bi Systems". This archive contains all the raw data gathered and used to produce the results presented in this manuscript.</p> <p>Abstract of manuscript:</p> <div> <div> <div> <div> <p>Potassium-ion batteries (KIBs) are a promising alternative to conventional lithium- ion batteries with reduced critical mineral dependency, but accurate three-electrode characterization is hindered by the lack of a suitable reference electrode. Potassium metal is frequently used as a reference electrode out of necessity, but its high reactivity and unstable potential limit its reliability. Here we investigate the K-In and K-Bi alloy systems, synthesize two-phase In-In4K and Bi-Bi2K alloys, and identify Bi-Bi2K as a promising material owing to its stable potential of 1.07 V vs. K+/K. We prove the use of Bi-Bi2K as a reference electrode by cycling graphite in three-electrode cells and demonstrate that it results in significantly less electrolyte reduction than potassium metal, facilitating the accurate electrochemical characterization necessary to accelerate KIB development.</p> </div> </div> </div> </div>
Experimental data used in the article entitled "Exploring microstructure refinement and deformation mechanisms in severely deformed LPBF AlSi10Mg alloy"
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Experimental data used in the article entitled "Tuning the defects density in additively manufactured fcc aluminium alloy via modifying the cellular structure and post-processing deformation"
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Experimental data used in the article entitled "Electron Microscopy Study of Structural Defects Formed in Additively Manufactured AlSi10Mg Alloy Processed by Equal Channel Angular Pressing"
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"Fruity" Dye-based Fluorescent Nanoparticles (dFONs): A Fully Organic Counterpart of Alloy and Core-Shell Metallic Nanoparticles. Tuning Topology to Maximize Nano-interfacial Promoted Fluorescence Enhancement
<p>Data set related to the production of the figures in the article "“Fruity” Dye-based Fluorescent Nanoparticles (dFONs): A Fully Organic Counterpart of Alloy and Core-Shell Metallic Nanoparticles. Tuning Topology to Maximize Nano-interfacial Promoted Fluorescence Enhancement" by Kurek et al.</p> <p> </p> <p>The data are in txt, lif and opju format, organised by figure and sub-figures and compressed. </p>
Dataset: Enhancing homogenous precipitation and strengthening effectiveness in AlCuMg alloy
<p>Set of data used for publication in Journal of Materials Research and Technology entitled 'Enhancing homogenous precipitation and strengthening effectiveness in AlCuMg alloy'</p> <p><strong>Research Funders</strong></p> <div> <div>National Science Centre Poland <div> </div> <span>Grant numbers: 2020/39/D/ST5/01375</span></div> </div>
Datasheet of 3D printed bonded magnets from rare-earth micropowder alloys
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Demonstrator of 3D printed bonded magnets from rare-earth micropowder alloys
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EBSD Kikuchi Patterns from Identification, classification and characterisation of hydrides in Zr alloys
<p>Data from paper <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.scriptamat.2023.115768" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.scriptamat.2023.115768</span></span></a></p>
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DANDI Archive for NWB datasets
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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.