Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
287
datasets available to search
ShareScore release 0.9.0
Dataset results
287 results for “alloy”
Tailoring Ti Grade 2 and TNTZ alloy surfaces in a two-step mechanical-chemical modification
<p>This record contains all files generated in the preparation process of the following publication:</p> <p>Agnieszka Kowalczyk, Donata Kuczyńska-Zemła, Agata Sotniczuk, Klaudia Anuszewska and Halina Garbacz,<br> "Tailoring Ti Grade 2 and TNTZ alloy surfaces in a two-step mechanical-chemical modification", submitted to Surface Engineering.</p> <p><br> Designations:<br> Ti alloy - Ti-29Nb-13Ta-4,6Zr alloy (TNTZ)<br> G - sample grinded on #600 grit abrasive paper<br> S_1,2 - sample shot peened with 90-150 µm shots, under pressure of 0.2 MPa<br> S_1,3 - sample shot peened with 90-150 µm shots, under pressure of 0.3 MPa<br> S_1,4 - sample shot peened with 90-150 µm shots, under pressure of 0.4 MPa<br> S_1,5 - sample shot peened with 90-150 µm shots, under pressure of 0.5 MPa<br> S_2,4 - sample shot peened with 150-250 µm shots, under pressure of 0.4 MPa<br> S_2,5 - sample shot peened with 150-250 µm shots, under pressure of 0.5 MPa<br> SE_1,2 - sample shot peened with 90-150 µm shots, under pressure of 0.2 MPa and etched in a solution of 3% hydrofluoric acid (HF)<br> SE_1,3 - sample shot peened with 90-150 µm shots, under pressure of 0.3 MPa and etched in a solution of 3% hydrofluoric acid (HF)<br> SE_1,4 - sample shot peened with 90-150 µm shots, under pressure of 0.4 MPa and etched in a solution of 3% hydrofluoric acid (HF)<br> SE_1,5 - sample shot peened with 90-150 µm shots, under pressure of 0.5 MPa and etched in a solution of 3% hydrofluoric acid (HF)<br> SE_2,4 - sample shot peened with 150-250 µm shots, under pressure of 0.4 MPa and etched in a solution of 3% hydrofluoric acid (HF)<br> SE_2,5 - sample shot peened with 150-250 µm shots, under pressure of 0.5 MPa and etched in a solution of 3% hydrofluoric acid (HF)<br> HV - Vickers hardness<br> SEM - Scanning Electron Microscopy<br> R - roughness<br> W - wettability</p> <p><br> Folders content:<br> Hardness - contains files with data obtained from hardness tests using a Falcon 500 hardness tester with a Vickers indenter at a load of 1.96 N (HV0.2)<br> Roughness - contains files from topography analysis obtained using a Wyko NT9300 optical profilometer for various scan areas<br> SEM - contains images of samples surfaces from Hitachi SU8000 and Hitachi SU70 Scanning Electron Microscopes <br> Wettability - contains files with data obtained from wettability tests using a DataPhysics OCA 25 goniometer with the sessile drop method</p> <p>This research was funded in part by National Science Centre, Poland [Grant no. 2022/45/B/ST5/03398].</p> <p> </p>
Original data for "Unsupervised Analysis of Optical Imaging Data for the Discovery of Reactivity Patterns in Metal Alloy" article
<p>This upload includes both original and supplementary data for the publication "Unsupervised Analysis of Optical Imaging Data for the Discovery of Reactivity Patterns in Metal Alloy" by R. Li, A. Makogon, T. Galochkina, J-F. Lemineur, F. Kanoufi, and V. Shkirskiy, published in the Small Methods journal. The preprint version of the paper is available on ChemRxiv (<a href="https://doi.org/10.26434/chemrxiv-2023-sgvt0">https://doi.org/10.26434/chemrxiv-2023-sgvt0</a>).</p> <p>The file "Data_processing.zip" contains the original optical image of the interface, maps of film evolution rates in acidic and salt environments, COMSOL data output in acidic and salt environments, as well as a Jupyter Lab file that demonstrates how to process this data.</p> <p>The file "Comparison_of_SEM_images.zip" includes the original SEM images used in the current study and in our previous work (<a href="https://doi.org/10.26434/chemrxiv-2022-rn77b-v3">https://doi.org/10.26434/chemrxiv-2022-rn77b-v3</a>), along with a Jupyter Lab file that illustrates the data processing procedure.</p> <p>The file "Original_data_in_npy_format.zip" contains all original data from SEM/EDX and RM experiments.</p> <p>We recommend opening the Jupyter Lab files in a Python 3 environment. PDF files in the root directory provide outputs of all uploaded Jupyter Lab files.</p>
Modeling of pressure induced magnetic and magnetocaloric effects in dissipative magnetic shape memory alloy systems
<p>This article presents a coupled magneto-thermo-mechanical model of pressure-dependent Magneto-caloric Effect (MCE) and magnetization responses for polycrystalline Magnetic Shape Memory Alloys (MSMA). Coupled constitutive equations are derived from a Helmholtz free energy function in a consistent thermodynamic way. The hysteretic and dissipative characteristics of phase transformations in MSMAs are captured by the internal state variables approach with their evolution equations. The model is calibrated and validated with the existing experimental data. The validated constitutive model is then exploited to predict MCEs at different pressures and magnetic field levels. Some predicted results are compared with the available experimental data.</p>
Automatic Featurization Aided Data-Driven Method for Estimating the Presence of Intermetallic Phase in Multi-Principal Element Alloys
<p><strong>Description:</strong></p> <p>There are two folders in the dataset:</p> <p><strong>(A. ) AutomaticFeaturizerMPEA.zip</strong> : </p> <p>AutomaticFeaturizerMPEA.pynb is an interactive computational environment developed in python programming language to compute the properties of Multi Principal-component Element Alloy (MPEA) system .</p> <p>The code present in the .ipynb file is capable of firstly extracting composition feature from the name of MPEA and then compute thermodynamic/physical and chemical properties using these composition features. The properties that can be automatically calculated and featurized by the code are: <br> 1. Valence Electron Concentration<br> 2. Average Melting Temperature<br> 3. Enthalpy of Mixing<br> 4. Entropy of Mixing<br> 5. Electronegativity<br> 6. Atomic size difference<br> 7. Omega parameter <br> 8. 41 elemental composition</p> <p>The primary source used for the calculation of these alloy properties are taken from two python libraries: "pymatgen" and "matminer". </p> <p>The AutomaticFeaturizerMPEA.pynb is not only capable of calculating an alloy's properties but also featurizes them along with the composition and export it as a .csv file for further machine learning training process. The AutomaticFeaturizerMPEA.pynb file consists of two parts:</p> <p>1. "Part 1": A sample set of 25 MPEAs is used to demonstarte how to calculate & featurize the porperties and composition of alloy, save the calculated values in a .csv file.<br> 2. "Part 2": The alloy property of a single MPEA "Ti0.8CoCrFeNiCu" is calculated using the same code, if one needs to calculate them for a single MPEA at a time (as in our GUI application IMCATHEA https://github.com/subediupadesh/IMCATHEA) </p> <p>The repository also consists two "CSV" files:</p> <p>1. "MPEA_data.csv" file is the sample set of 25 collected MEPA observations with their source and phase information in one hot encoding method for which we want to calculate their properties and featurize them.<br> 2. "Featurized_MPEA.csv" file is the processed ready to train file which consists of MPEA, their properties and composition featurized in special order. It is the final file generated by the AutomaticFeaturizerMPEA.pynb python file.</p> <p> </p> <p><strong>(B.) IMCATHEA.zip:</strong> </p> <p>IMCATHEA is a python based Graphical User Interface application capable of detecting the IM phase in a High Entropy Alloy or Multi Principal Element Alloy system using a Neural Network Algorithm.</p> <p>## For Linux User:<br> Python with following libraries are needed to run the GUI application: pymatgen (v2022.0.16), matminer (v0.7.4), tensorflow (v2.2.0), pandas (v1.3.4), numpy (v1.21.2), tkinter (v8.6), sklearn (v1.0.1).</p> <p>"IMCATHEA_GUI.py" also needs 4 extra supporitng files (icon, standardizations, and model) to operate, which will be made available from the author upon reasonable request. </p> <p><br> ## For Windows User:<br> Executable (.exe) file: </p> <p>User can run "IMCATHEA_GUI.exe" wihout any softwares to be installed.</p> <p><br> ## Procedure for using "IMCATHEA" GUI Application:</p> <p>Step 0. Open / Run "IMCATHEA_GUI.py" (for Linux users) & "IMCATHEA_GUI.exe" (for Windows users) file to open the GUI application</p> <p>Step 1. User need to select the No. of Elements/Components in the HEA where IM phase is to be detected, from the dropdown menu at the top left corner.<br> After selection of element size/number (from 2 to 10),<br> <br> Step 2. User can select each element of HEA, one at a time from the drop down menu generated just below "No. of Component" tab</p> <p>Step 3. After selection of each element a blank space is provided just at the right side of selected element tab where user need to enter the corresponding composition/elemental fraction of the element.</p> <p>Step 4. Repeat Step 2-3 until the last element of the HEA and it's composition/elemental fraction is entered.</p> <p>Step 5. Press "Detect IMC" tab to get the prediction if IM is present in the provided HEA or not along with the physical properties of the entered HEA displayed in the right side of the GUI application.</p> <p>Step 6. If user wants to detect the presence or absence of IM phase for another HEA, user can click on "Restart" tab at the right top side of GUI application to restart the application instantly.</p> <p><br> If the user makes errors while selecting any options from dropdown menu as suggested in Step 1,2,3,4 user can click on "Restart" tab at the right top side of GUI application to restart the application instantly and start the process for prediction again from Step 1.</p> <p>## Video (YouTube) Tutorial for using IMCATHEA:<br> The stepwise process to predict the phase for an example case of "CrNbTiZr" MPEA is shown in the YouTube video tutorial at: https://youtu.be/G3aHE1Wsmgk</p>
Dataset for: The role of hydrides and precipitates on the strain localisation behaviour in a zirconium alloy
<p><strong>This is the dataset used in the following publication: </strong></p> <p> </p> <p>R. Thomas, D. Lunt, M.D. Atkinson, J. Quinta da Fonseca, M. Preuss, P. Honniball, P. Frankel, The role of hydrides and precipitates on the strain localisation behaviour in a zirconium alloy, Acta Materialia, 2023, 119327, ISSN 1359-6454, https://doi.org/10.1016/j.actamat.2023.119327.</p> <p><strong>Contained in this dataset are:</strong></p> <p>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 hydrides Zircaloy-4 deformed to ~3% strain, along the rolling direction.</p> <p>Please use the 'master' version of DefDAP: <a href="https://github.com/MechMicroMan/DefDAP/tree/51074e158b0131c69358ddf7eee319e41cf582ca">https://github.com/MechMicroMan/DefDAP/tree/51074e158b0131c69358ddf7eee319e41cf582ca</a></p> <p><strong>Publication abstract:</strong></p> <p>In service, zirconium alloys undergo aqueous corrosion and hydrogen is absorbed, which can lead to the formation of hydrides. Hydrides, as well as the precipitates in zirconium alloys, affect the mechanical performance, though their contribution to plasticity is not yet understood. This study uses a combination of high-resolution digital image correlation and electron backscatter diffraction to quantify the strain partitioning between the different phases. Following uniaxial tensile deformation, it was found that the average strain within the δ-ZrH and Zr(Fe,Cr)2 was lower than in the α-Zr. Shear bands in the α-Zr matrix were observed to interact with small and medium sized hydride in multiple ways, such as terminating at the interface, cracking and shear around the interface or causing plastic slip in the hydride. Large hydrides showed a particularly detrimental effect on deformation behaviour, with large strain localisations and cracking observed at interfaces, which are typically precursors to failure. In contrast, the Zr(Fe,Cr)2precipitates remained undeformed and forced the expected metal-matrix lattice rotation. The importance of these observations in context of fuel cladding integrity is discussed.</p>
Dataset_How Halides Alloying Influences the Optoelectronic Quality in Tin-Halide Perovskite Solar Absorbers_BOLLA_v1
<p>The dataset contains the raw data used in the publications: How Halides Alloying Influences the Optoelectronic Quality in Tin-Halide Perovskite solar Absorbers. More specifically it contains the main photovoltaic parameters used to plot the statistic of solar cells, the photothermal deflection spectroscopy data with respective linear fits and the transient absorption spectra. </p>
Dataset for "Scanning precession electron diffraction data analysis approaches for phase mapping of precipitates in aluminium alloys"
<p>Data needed to reproduce the results in "Scanning precession electron diffraction data analysis approaches for phase mapping of precipitates in aluminium alloys" published in Ultramicroscopy. The codes and notebooks can be found at 10.5281/zenodo.8321258.</p>
Modeling of pressure induced magnetic and magnetocaloric effects in dissipative magnetic shape memory alloy systems
Open the record for dataset details and reuse information.
Hybrid dynamic model for shape memory alloy linear and unimorph actuators
Open the record for dataset details and reuse information.
Data from: Ultra-uniform, strong and ductile 3D printed titanium alloy through bifunctional alloy design
Open the record for dataset details and reuse information.
Data from: High-throughput computational screening of bioinspired dual atom alloys for CO2 activation
Open the record for dataset details and reuse information.
Influence of Ce content on the microstructure and thermal expansion properties of suction cast Al-Cu-Fe alloys
Open the record for dataset details and reuse information.
Germanium tin alloy nanowires as anode materials for high performance Li-ion batteries
<p><strong>Abstract</strong><br> The combination of two active Li-ion materials (Ge and Sn) can result in improved conduction paths and higher capacity retention. Here we report for the first time, the implementation of Ge<sub>1–x</sub>Sn<sub>x</sub> alloy nanowires as anode materials for Li-ion batteries. Ge<sub>1−x</sub>Sn<sub>x</sub> alloy nanowires have been successfully grown via vapor–liquid–solid technique directly on stainless steel current collectors. Ge<sub>1−x</sub>Sn<sub>x</sub> (x = 0.048) nanowires were predominantly seeded from the Au<sub>0.80</sub>Ag<sub>0.20</sub> catalysts with negligible amount of growth was also directly catalyzed from stainless steel substrate. The electrochemical performance of the the Ge<sub>1−x</sub>Sn<sub>x</sub> nanowires as an anode material for Li-ion batteries was investigated via galvanostatic cycling and detailed analysis of differential capacity plots (DCPs). The nanowire electrodes demonstrated an exceptional capacity retention of 93.4% from the 2nd to the 100th charge at a C/5 rate, while maintaining a specific capacity value of ∼921 mAh g−1 after 100 cycles. Voltage profiles and DCPs revealed that the Ge<sub>1−x</sub>Sn<sub>x</sub> nanowires behave as an alloying mode anode material, as reduction/oxidation peaks for both Ge and Sn were observed, however it is clear that the reversible lithiation of Ge is responsible for the majority of the charge stored.</p>
3D EBSD Dataset of the Alpha and Beta Phase Orientations for a Hot-Rolled Model Zircaloy-4 with 7 wt.% Nb Alloy
<p>A set of serial-section electron backscatter diffraction (EBSD) data files and a 3D reconstruction of a model Zircaloy-4 with 7 wt.% Nb addition alloy following hot-rolling.</p> <p>The 3D data set contains measurements of the material rolled at 725C to 75% reduction. The measurements include indexing of both the alpha and the beta phases in 441 sequential slices, each of 0.1 μm, through a small section of the material, taken using the dual beam Thermo Scientific Helios Xe<sup>+</sup> plasma focused ion-beam scanning electron microscope (PFIB-SEM). The 3D EBSD data set includes EBSD measurements in the form of ctf files, binary data files, an Aztec project file, and accompanying images for each slice.</p> <p>A 3D volume was reconstructed from the 3D EBSD data set using a customised pipeline within the DREAM.3D software. The results of this analysis are included in the 'dream3d' folder, which includes a description of the pipeline, the final fully reconstructed dream3d data file, an xdmf file used for visualising the data in ParaView, a h5ebsd file containing results for the reconstruction, and grain averaged data for each of the identified features.</p> <p>Please see our accompanying paper for analysis of the 3D reconstruction - as well as analysis of the 2D measurements from <a href="https://doi.org/10.5281/zenodo.3784460">10.5281/zenodo.3784460</a> - and for interpretation of the coupled crystallographic texture evolution;</p> <p>C.S. Daniel, A. Garner, P.D. Honniball, L. Bradley, M. Preuss, P.B. Prangnell, J. Quinta da Fonseca, Co-deformation and dynamic annealing effects on the texture development during alpha–beta processing of a model Zr-Nb alloy, Acta Materialia 205 (2021) 116538. <a href="https://doi.org/10.1016/j.actamat.2020.116538">10.1016/j.actamat.2020.116538</a></p>
Deformation Enhanced Diffusion in Aluminium Alloys - Supporting data
<p>Supporting data and manuscript for the paper "Deformation Enhanced Diffusion in Aluminium Alloys" published in Metallurgical and Materials Transactions A.</p>
Ni-W Based Alloy 4D STEM Data
<p>Ni-W based alloys have been extensively employed in the nuclear energy and national defense industries due to their excellent static/dynamic mechanical properties and high densities. The mechanical performance of these alloys can be further tailored through the addition of a variety of impurities with varying compositions. To investigate the structure of this material and identify any potential precipitates and/or second phases, a 4D-STEM data set was acquired using a Gatan K3 IS camera and a STEMx system in electron counting mode on a JEOL ARM 300F. Data was binned 8x in diffraction space for faster analysis. This data was analyzed for the NUANCE/Gatan Virtual Workshop on 4D STEM: Theory, Acquisition, and Analysis on December 9th, 2020. The python analysis code "DIY: Using Python to Process 4D STEM Data" is available on github at <a href="https://github.com/smribet/DIY_4DSTEM?fbclid=IwAR0xpnlYnnFA3K3hUBSHgizB3MMuDuOHP_kutFoz_UurX9xqDPbStGeeDYk">https://github.com/smribet/DIY_4DSTEM</a>.</p>
Fe 0.70 Ni 0.11 Cr 0.19 alloy - FCC
<p>Dislocation data extracted from MD simulations of Fe 0.70 Ni 0.11 Cr 0.19 alloys.</p> <p>The data was interpreted in the following article:<br> Péterffy, G., Ispánovity, P.D., Foster, M.E., Zhou, X., and Sills, R.B. Length scales and scale-free dynamics of dislocations in dense solid solutions. Mater Theory 4, 6 (2020). <a href="https://doi.org/10.1186/s41313-020-00023-z">https://doi.org/10.1186/s41313-020-00023-z</a></p> <p> </p>
Effects of Cd1-xZnxS alloy composition and post-deposition air anneal on ultra-thin CdTe solar cells produced by MOCVD
<p>Ultra-thin CdTe:As/Cd<sub>1-x</sub>Zn<sub>x</sub>S photovoltaic solar cells with an absorber thickness of 0.5 µm were deposited by metal-organic chemical vapour deposition on indium tin oxide coated boro-aluminosilicate substrates. The Zn precursor concentration was varied to compensate for Zn leaching effects after CdCl<sub>2</sub> activation treatment. Analysis of the solar cell composition and structure by X-ray photoelectron spectroscopy depth profiling and X-ray diffraction showed that higher concentrations of Zn in the Cd<sub>1‑x</sub>Zn<sub>x</sub>S window layer resulted in suppression of S diffusion across the CdTe/Cd<sub>1‑x</sub>Zn<sub>x</sub>S interface after CdCl<sub>2 </sub>activation treatment. Excessive Zn content in the Cd<sub>1-x</sub>Zn<sub>x</sub>S alloy preserved the spectral response in the blue region of the solar spectrum, but increased series resistance for the solar cells. A modest increase in the Zn content of the Cd<sub>1-x</sub>Zn<sub>x</sub>S alloy together with a post-deposition air anneal resulted in an improved blue response and an enhanced open circuit voltage and fill factor. This device yielded a mean efficiency of 8.3% over 8 cells (0.25 cm<sup>2</sup> cell area) and best cell efficiency of 8.8%.</p>
Controlled Li Alloying by Post-Synthesis Electrochemical Treatment of Cu2ZnSn(S, Se)4 Absorbers for Solar Cells
<p>...</p>
Targeted color design of silver-gold alloy nanoparticles
<p>This is the raw data for the manuscript:</p><p>Targeted color design of silver-gold alloy nanoparticles<br><br>Abstract:<br><br>This research article focuses on the targeted color design of silver-gold alloy nanoparticles (NPs), employing a multivariate optimization approach. NP synthesis involves interconnected process parameters, making independent variation challenging. Data-based property-process relationships are established to optimize optical properties effectively. We define a color target, employ a green chemical co-reduction method at room temperature and optimize process parameters accordingly. The CIEL*a*b* color space and Euclidean distances facilitate accurate color matching to establish the property-process relationship. Concurrently, theoretical Mie calculations explore the structure-property relationship across particle sizes, concentrations, and molar gold contents. The theoretically optimal structure agrees very well with experimental particle structures at the property-process relationship's optimum. The data-driven property-process relationship provides valuable insights into the formation mechanism of a complex particle system, sheds light on the role of relevant process parameters and allows to evaluate the practically available property space. Model validation beyond the original grid demonstrates its robustness, yielding colors close to the target. Additionally, Design of Experiments (DoE) methods reduce experimental work by threefold with slight accuracy trade-offs. Our novel methodology for targeted color design demonstrates how data-based methods can be utilized alongside structure-property relationships to unravel property-process relationships in the design of complex nanoparticle systems and paves the way for future developments in targeted property design.</p><p>All data are sorted according to their appearance in the figures of the main manuscript.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.