Find research datasets worth reusing

opencc-by-4.0Aug 2024View details →

Dataset of "Balancing Activity and Stability through Compositional Engineering of Ternary PtNi–Au Alloy ORR Catalysts"

A systematic comparative analysis of the activity-stability relationship for compositionally tuned PtNi-Au model layers, prepared by magnetron co-sputtering, was conducted using a diverse range of complementary characterization techniques and electrochemistry, supported by density functional theory calculations. Our study reveals that progressively increasing the Au concentration in the Pt50Ni50 alloy from 3 to 15 at.% leads to opposing catalyst activity and stability trends. Specifically, we observe a decrease in ORR activity accompanied by an increase in catalyst stability, manifested in the suppression of both Pt and Ni dissolution. Despite the reduced activity compared to PtNi, the PtNi–Au alloy with 15 at.% Au still exhibits nearly three times the activity of monometallic Pt. It also demonstrates a significantly improved dissolution stability relative to the PtNi alloy and even monometallic Pt. These findings provide valuable insights into the intricate balance between activity and stability in multimetallic ORR catalysts, paving the way for the design of cost-effective and durable materials for PEMFCs.

opencc-by-4.0Jan 2024View details →

Dataset for Towards improved online dissolution evaluation of Pt-alloy PEMFC electrocatalysts via electrochemical flow cell - ICP-MS setup upgrades

Experimental data comprises raw data from ICP-MS (Inductively coupled plasma mass spectrometry) (i.e. time dependence of signal intensity for Co59 and Pt195) for different cell geometry and operating parameters.   Model data comprise of time- and space-dependent values of Pt ions concentration in the modelling cell and local velocity vectors.

opencc-by-4.0Apr 2019View details →

Data for "Nano-scale characterisation of sheared β'' precipitates in a deformed Al-Mg-Si alloy"

This dataset contains data used in the publication entitled "Nano-scale characterisation of sheared β'' precipitates in a deformed Al-Mg-Si alloy". This publication concerns how β'' precipitates are sheared by dislocations during deformation. The data contained in this repository are data acquired on various transmission electron microscopes of specimens of the aluminium alloy AA6060 in peak aged condition after uniaxial compression to 5%, 10%, and 20%, in addition to the undeformed reference alloy. There are five main types of data: <ul> <li>Transmission electron microscopy (TEM) images</li> <li>High-resolution TEM images</li> <li>High angle annular dark field (HAADF) scanning TEM (STEM) images</li> <li>Scanning precession electron diffraction (SPED) data.</li> <li>Cross-sectional data of precipitates in undeformed and 20% compressed conditions.</li> </ul> Data for the TEM, HRTEM, and STEM images are kept in zipped folders due to the large number of images (several hundreds for each compression condition). Folders are named following the format of "<alloy>_<compression>_<technique>", where technique refers to TEM, HRTEM, or STEM. Images are provided in both .hdf format and .jpg format (to aid in navigating the data). Please see <a href="https://www.hdfgroup.org/">HDF Group</a> for more information regarding the HDF file format, and <a href="https://www.hdfgroup.org/downloads/hdfview/">HDF View</a> for softaware to read and show HDF data. The Python package <a href="http://hyperspy.org/">HyperSpy</a>, is also useful for loading the HDF data for inspection, analysis, and presentation. For some STEM images, a stack of short-exposure STEM images acquired and analysed using the <a href="http://lewysjones.com/software/smart-align/">SmartAlign</a> plugin to <a href="http://www.gatan.com/products/tem-analysis/gatan-microscopy-suite-software">Gatan Digital Micrograph</a> is available. SmartAlign offers the possibility of rigidly and non-rigidly aligning the STEM images in the stack in order to reduce effect of specimen drift and scan noise during acquisition. The conventional STEM images are found in the zip archive labelled "STEM". When the filenames of the STEM images include "SAstack" and/or "SAimage", a STEM SmartAlign stack or the average through a non-rigidly aligned stack is available of the same field of view. In such cases, both the SmartAlign stack and the through-stack image is provided in the metadata in the .hdf file (note that not all stacks have been aligned, and in such cases no through-stack image is available). In addition, the SmartAlign stacks themselves are available in the subfolder "STEM\SmartAlign\" within each STEM folder. The through-stack images of the smart align stacks are also provided separately in the subfolder "STEM\SmartAlign\Aligned\". For the 20% compressed case, a lowloss electron energy loss spectroscopy (EELS) spectrum and thickness maps of the imaged areas are also provided, in the subfolder "STEM\EELS\". The SPED data, acquired using the ASTAR system of <a href="https://www.nanomegas.com/">NanoMegas</a>, is provided as .hdf5 files in the root directory of the repository. They should be read using and <a href="https://github.com/pyxem/pyxem">pyXem</a>. The attached Jupyter Notebook "SPED_data_inspection.ipynb" can be used to access the SPED datasets. These datasets are 4D datasets, with two spatial and two reciprocal dimensions. They have been decomposed using the non-negative matrix factorization algorithm (NMF) used in HyperSpy. These decomposition results are included in the .hdf5 files. In addition, parameters used in the preprocessing of the datasets are attached in the metadata in these files. The metadata of these files are also provided separately as .txt files. Finally, measurements of the precipitate cross-sectional area and circularity is available as .csv files with the first column being the row index, the second the cross-sectional areas of precipitates measured in nanometers squared, the third column is the perimeters of the precipitates measured in nanometers, and column four is the <a href="https://imagej.nih.gov/ij/plugins/circularity.html">circularity</a> of the precipitates.

opencc-by-4.0Dec 2022View details →

Titanium Alloys Database for Medical Applications

The new 2.0 version (12.7.2023) includes the following modifications: 247 biocompatible Ti alloys; the table shows only literature data; a Jupyter notebook provides the calculated data. In this database, 238 titanium alloys were collected, almost entirely of biocompatible alloying elements. The primary motivation behind creating such a database is to establish a foundation for designing new alloys using machine learning methods. The database can assist researchers, engineers, and biomedical professionals in developing titanium alloys for various medical applications, thereby improving health outcomes and driving advancements in biomaterials and biomedical engineering. For more information read the paper at: <a href="https://doi.org/10.30544/MMD5"> https://doi.org/10.30544/MMD5 </a> NOTE: To avoid misunderstandings, please cite both the database and the published article when citing this database. We invite other authors to contribute to the updating of this database (send at least 20 new alloys to appear as co-author)

opencc-by-4.0Dec 2023View details →

Data for a publication "Exploring the microstructure, mechanical properties, and corrosion resistance of innovative bioabsorbable Zn-Mg-(Si) alloys fabricated via powder metallurgy techniques"

These data are published as part of the paper: “Exploring the microstructure, mechanical properties, and corrosion resistance of innovative bioabsorbable Zn-Mg-(Si) alloys fabricated via powder metallurgy techniques” published in journal: “Journal of Materials Research and Technology”.

opencc-by-4.0Mar 2024View details →

Dataset for a publication: "A zinc phosphate layered biodegradable Zn-0.8Mg-0.2Sr alloy: Characterization and mechanism of hopeite formation"

These data are published as part of the paper: A zinc phosphate layered biodegradable Zn-0.8Mg-0.2Sr alloy: Characterization and mechanism of hopeite formation. The structure and organization of the data are outlined in the readme file.

opencc-by-4.0Jan 2022View details →

In situ Bragg Coherent X-ray Diffraction Imaging of Corrosion in a Co-Fe alloy microcrystal

Here we present the final crystal reconstructions and analysis scripts for the paper titled "In situ Bragg coherent X-ray diffraction imaging of corrosion in a Co–Fe alloy microcrystal" published in CrystEngComm, 24(7), 1334-1343, on 18/01/2021.  <a href="https://doi.org/10.1107/S1600577520016264">https://doi.org/10.1107/S1600577520016264</a>

opencc-by-4.0Jun 2021View details →

Dataset for: The Effect of Loading Direction on Slip and Twinning in an Irradiated Zirconium Alloy

This is the dataset used in the following publication: R. Thomas, D. Lunt, M. D. Atkinson, J. Quinta da Fonseca, M. Preuss, F. Barton, J. O'Hanlon, and P. Frankel, "The Effect of Loading Direction on Slip and Twinning in an Irradiated Zirconium Alloy," in Zirconium in the Nuclear Industry: 19th International Symposium, ed. A. Motta and S. Yagnik (West Conshohocken, PA: ASTM International, 2021), 233-261. <a href="https://doi.org/10.1520/STP162220190027">https://doi.org/10.1520/STP162220190027</a>. Contained in this dataset are: A Jupyter notebook which uses the open-source DefDAP Python package (https://github.com/MechMicroMan/DefDAP) to open enclosed HRDIC, EBSD and image data for non-irradiated and 0.1 dpa proton irradiated Zircaloy-4 deformed to ~3% strain, along the rolling direction and transverse direction. Please use the 'develop' version of DefDAP: https://github.com/MechMicroMan/DefDAP/tree/f6b5d6ec33db9a45089fada17026432645044d2f Publication abstract: In this study, deformation experiments together with high-resolution digital image correlation were used to quantify the effect of proton irradiation on strain localization in Zircaloy-4 loaded along the rolling and transverse directions. Significant increases in strain heterogeneity were measured in the irradiated material compared to the nonirradiated material. This was a result of confinement of slip to channels in the irradiated material, which contain high effective shear strain values, with almost no strain in the regions between channels. The active slip systems in the material were also determined by comparing experimental slip trace angles from high-resolution digital image correlation with theoretical slip trace angles determined using grain orientation from electron backscatter diffraction. An increased amount of pyramidal and wavy basal slip, as well as tension twinning, were observed in the sample loaded along the transverse direction, compared to the sample loaded along the rolling direction, due to crystallographic texture. No significant change in slip system activity was observed as a result of 0.1 dpa proton irradiation, despite the dramatic change in slip pattern. The findings provide further insight into the role of irradiation on deformation behavior and provide quantitative data on slip system activation, for as-received and irradiated Zircaloy-4, against which to validate models.

opencc-by-4.0Dec 2019View details →

Fundamental hydrogen storage properties of TiFe-alloy with partial substitution of Fe by Ti and Mn - Dataset related to publication

Data type: Experimental measurements, correlations and Van't Hoff plot. Date format: .opj. Origin of the data: Experimental pressure composition isotherm measurements. Data generated by a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin.

opencc-by-4.0Dec 2020View details →

Data for "Atomic structure of solute clusters in Al-Zn-Mg alloys"

This dataset contains the data used in the publication entitled "<a href="https://www.sciencedirect.com/science/article/abs/pii/S1359645420310119">Atomic structure of solute clusters in Al-Zn-Mg alloys</a>", published in Acta Materialia 17. December 2020. The data contained herein are: <ul> <li>As-acquired transmission electron microscopy (TEM) images.</li> <li>Atom probe tomography data.</li> <li>All structural models used in density functional theory (DFT) calculations.</li> <li>Structures used for simulating scanning-TEM (STEM) images and nanobeam diffraction (NBD) patterns.</li> </ul>   The TEM images includes high angle annular dark field (HAADF) images and selected area diffraction patterns. These are given in .dm3/.dm4 files, and can be opened in e.g. the "<a href="https://www.gatan.com/products/tem-analysis/gatan-microscopy-suite-software">Gatan Microscopy Suite" </a>software. The images are also given as .tif images. The files are names after the "Figx_alloy_condition_xxx". "Figx" refers to the figure in the main article, "alloy" describes the alloy used and "condition" describes from what ageing condition. The uncorrected image series used for Fig. 6c (in the article) is included and requires the <a href="http://lewysjones.com/software/smart-align/">SmartAlign </a>plugin in the Gatan Microscopy Suite to analyse the dataset. SmartAlign allows for correcting rigid and non-rigid distortions in the STEM images in order to reduce effect of specimen drift and scan noise during acquisition.  The ATP data is given as a .xlsx file. The data here is the processed data after applying the maximum separation algorithm. The data here is used to produce Figs. 2b and 2c in the paper.  The structures used in the DFT calculations are given here as .cif files. These are separated into "Single_clusters" and "Stacked_clusters"  and named according to Tabs. 1 and 2 in the Supplementary material of the paper. The two structures used for simulating STEM-HAADF and NBD patterns are given in the folder "TEM_simulations". "Mg32Zn124D_94x94" was used for NBD and "Mg32Zn124D_X_Zn4" was used for HAADF-STEM. The stack used for Supplementary Fig. 7c is labeled "Mg32Zn124D_94x94_slab_1Allayerop.cif".

opencc-by-4.0Mar 2024View details →

Example of reverse Monte Carlo simulation for fcc high-entropy alloy CrMnFeCoNi

The data set contains the example of reverse Monte Carlo (RMC) simulation of EXAFS spectra collected for fcc high-entropy alloy CrMnFeCoNi. The simulation was performed by the EvAX code freely available from http://www.dragon.lv/evax/.

opencc-by-4.0Sep 2024View details →

Negative Muon Spectroscopy Data for Ag-Al-Au Alloys

Negative muon spectroscopy data for Ag/Al/Au alloys. The data is generated by mixing elemental spectra of each of the species in randomly selected ratios. The underlying physical data was collected at the ISIS Neutron and Muon Source. The data is assocaited with the manuscript 'Enhancing Performance of Multilayer Perceptrons by Knot-Gathering Initialization'.

opencc-by-4.0Nov 2021View details →

A compilation of experimental data on the mechanical properties and microstructural features of Ti-alloys

A compilation of mechanical properties of 282 distinct multicomponent Ti-based alloys. The majority of the data was published in high-quality journals after 2010 (≈84%) and concerns alloys produced via an ingot metallurgy route, followed by solubilization and water quench (≈58%), considered a standard condition for β-Ti alloys. The dataset includes the chemical composition (in at.%), phase constituents, Young modulus, hardness, yield strength, ultimate strength, and elongation, among other relevant features. The authors established a blind-review procedure for 1/3 of the dataset to mitigate human error during data extraction. Files: <ul> <li>dax-ti-static.csv: static version of the dataset; can be easily imported into your preferred data processing software.</li> <li>table1-static.md: detailed description of properties and additional fields included in the database; requires *markdown extra* syntax;</li> <li>utils.py: a helper script to load and filter desired entries; dependencies are matplotlib (3.4.3+), numpy (1.21.2+), and pymatgen (2022.0.16+).</li> </ul> For more information, please visit https://gitlab.com/comari/dax-ti.

opencc-by-4.0Aug 2021View details →

Materials for Design Open Repository. High Entropy Alloys

The current dataset is composed of a collection of High Entropy Alloys (HEAs). It contains the alloy composition, the number of chemical elements (No), the phase in a simple form (S_Phase), where 4 classes of phases were considered, namely amorphous (AM), intermetallic (IM), solid solution (SS), and solid solution + intermetallic (SS+IM). It contains also a second phase column (Phase), where we added the type of phase present in alloys with SS and repeated the S_Phase entry for the other cases. We have calculated 13 design parameters (see their definition below) used to design HEAs, known as the parametric approach. Finally, a set of columns containing the chemical elements and their corresponding fraction in the alloy is included. This dataset was developed in the framework of the European project ACHIEF for the discovery of novel materials to be used in industrial processes. <ol> <li>Mean atomic radius a (Å) <ul> <li>\(a = \displaystyle\sum_{i=1}^{n} c_i r_i\)</li> </ul> </li> <li>Atomic size difference δ <ul> <li>\(\delta = \sqrt{\displaystyle\sum_{i=1}^{n} c_i \bigg(1 - \dfrac{r_i}{a} \bigg)^2}\)</li> </ul> </li> <li>Average melting temperature Tm (K) <ul> <li>\(T_m = \displaystyle\sum_{i=1}^{n} c_i T_{mi}\)</li> </ul> </li> <li>Average melting temperature standard deviation (K) <ul> <li>\(\sigma_{T_m} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i \bigg(1 - \dfrac{T_{mi}}{T_m} \bigg)^2}\)</li> </ul> </li> <li>Mixing enthalpy ΔHmix (kJ/mol) <ul> <li>\(\Delta H_{mix} = 4 \displaystyle\sum_{i \neq j} c_i c_j H_{ij}\)</li> </ul> </li> <li>Mixing enthalpy standard deviation (kJ/mol) <ul> <li>\(\sigma_{\Delta H_{mix}} = \sqrt{\displaystyle\sum_{i \neq j} c_i c_j (H_{ij} - \Delta H_{mix})^2}\)</li> </ul> </li> <li>Ideal mixing entropy Sid (R)* <ul> <li>\(S_{id} = \Delta S_{mix} = -R \displaystyle\sum_{i=1}^{n} c_i \ln c_i\)</li> </ul> </li> <li>Electronegativity χ <ul> <li>\(\chi = \displaystyle\sum_{i=1}^{n} c_i \chi_i\)</li> </ul> </li> <li>Electronegativity difference in a multi-component alloy system <ul> <li>\(\Delta\chi = \displaystyle\sqrt{\sum_{i=1}^{n} c_i(\chi_i - \chi)^2}\)</li> </ul> </li> <li>Valence electron concentration VEC <ul> <li>\(VEC = \displaystyle\sum_{i=1}^{n} c_i \cdot VEC_i\)</li> </ul> </li> <li>Valence electron concentration standard deviation <ul> <li>\(\sigma_{VEC} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i (VEC_i - VEC)^2}\)</li> </ul> </li> <li>Mean bulk modulus K (GPa) <ul> <li>\(K = \displaystyle\sum_{i=1}^{n} c_i K_i\)</li> </ul> </li> <li>Bulk modulus standard deviation (GPa) <ul> <li>\(\sigma_{K} = \sqrt{\displaystyle\sum_{i=1}^{n} c_i (K_i - K)^2}\)</li> </ul> </li> <li>Young's modulus E (GPa) <ul> <li>\(E = \displaystyle\sum_{i=1}^{n} c_i E_i\)</li> </ul> </li> <li>Shear modulus G (GPa) <ul> <li>\(G = \displaystyle\sum_{i=1}^{n} c_i G_i\)</li> </ul> </li> </ol> where n is the number of components in the alloy system, ci is the stoichiometric ratio, ri is the atomic radius, Tmi is the melting temperature, χi is the Pauli electronegativity, VECi is the valence electron concentration, and Ki is the bulk modulus, Ei is the Young's modulus, and Gi is shear modulus for the i-th component of the alloy. Hij is the binary mixing enthalpy in the liquid phase, and R is the gas constant. *Note: the ideal mixing entropy Sid units in the first version of the dataset appear as kJ/mol, but they should be written in terms of the gas constant R, e.g., the compound Ag2Al has Sid = 0.636 R, where R = 8.314 J · K−1 · mol−1. The second version the Sid units are corrected and two new features are included.

opencc-by-4.0Jun 2022View details →

In-situ neutron diffraction during reversible deuterium loading in Ti-rich and Mn-substituted Ti(Fe,Mn)0.90 alloys - Dataset related to publication

Data type: resume of Rietveld refinement outputs and original refinements Date format: .zip, .opj;  .xlsm, .dat, .pcr (Software FullProf package outputs), .inp (Software Topas package outputs) Origin of the data: neutron diffraction patterns from ILL and ISIS, and manual Sievert measurements (PCI curves from home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France) Software needed to plot the data: folders need to be unzipped, Origin, FullProf package and Topas package.

opencc-by-4.0Jul 2022View details →

Data for: Comparison of Friction Extrusion Processing from Bulk and Chips of Aluminum-Copper Alloys

This dataset contains measurement data, machine logs as well as microstructure and overview images for the publication "Comparison of Friction Extrusion Processing from Bulk and Chips of Aluminum-Copper Alloys".

opencc-by-4.0Aug 2022View details →

TiFe0.85Mn0.05 alloy produced at industrial level for a hydrogen storage plant

Data type: XRD patterns; SEM and EDX results, hydrogen sorption data (pcT-curves, absorption/desoprtion curves).  Data format: *.opj; *.tif.; *docx; *jpg Software needed: Origin.

opencc-by-4.0Sep 2022View details →

Data for paper entitled, "Discontinuous Precipitation in Mg-Al Alloy Studied in 3-Dimensions"

Data for paper entitled, "Discontinuous Precipitation in Mg-Al Alloy Studied in 3-Dimensions" including: -Raw SEM imaging and EBSD data -Processed SEM images -3D slices -Supplementary summary figure -Supplementary video

opencc-by-4.0May 2019View details →

EBSD Dataset of the Alpha and Beta Phase Orientations for a Hot-Rolled Zr-2.5Nb Alloy

A set of ex-situ electron backscatter diffraction (EBSD) datafiles following rolling of a Zr-2.5Nb alloy at different temperatures (700C, 750C, 775C, 800C, 825C, 850C, 900C) and rolling reductions (50%, 75%, 87.5%). Data for annealing of the material (750C for 2 hours) and following rolling at 800C from a different ‘as-forged’ starting texture is also included. Note, phases in the ctf files marked as Titanium Cubic refer to measurement of the Zirconium Cubic phase.  The data in the 'Beta ctf' folder includes a reconstruction of the high temperature beta-phase orientations where possible, reconstructed from the alpha phase orientations using a software based on the Burgers relationship. Please see the accompanying paper for the interpretation of crystallographic texture changes; C.S. Daniel, P.D. Honniball, L. Bradley, M. Preuss, J. Quinta da Fonseca, A detailed study of texture changes during alpha–beta processing of a zirconium alloy, J. Alloys Compd. 804 (2019) 65–83, <a href="https://doi.org/10.1016/j.jallcom.2019.06.338">10.1016/j.jallcom.2019.06.338</a>