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287 results for “alloy”
Data and code for "A Library of Late Transition Metal Alloy Dielectric Functions for Nanophotonic Applications"
<p>This data set contains dielectric functions for the ten binary alloys comprised of the late transition metals most commonly employed in plasmonics (Ag, Au, Cu, Pd, Pt).</p>
Fundamental hydrogen storage properties of TiFe-alloy with partial substitution of Fe by Ti and Mn - Raw Dataset related to publication
<p>Data type: Experimental measurements and Rietveld Refinement. Date format: .xls, .xlsm,.opj, .pcr, .dat (Software FullProf package outputs). Origin of the data: Experimental EMPA, x-ray diffraction patterns, and kinetic measurements of hydrogen absorption. Data generated by electron probe micro-analysis (Cameca SX100), a Bruker D8 Advance Bragg Brentano diffractometer using Cu-Kα radiation (λ=1.5418 Å), and a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin and Excel. Software needed to analyse the data: FullProf package.</p>
Seal. Rectangular metal seal cast in a copper alloy; one-line inscription
<p>Seal. Rectangular metal seal cast in a copper alloy; one-line inscription. British Museum 1892,1103.95.</p> <p> </p>
Data for "Investigation of local strain rate sensitivity in dual-phase Ti alloys by nanoindentation"
<p>Data for "Investigation of local strain rate sensitivity in dual-phaes Ti alloys by nanoindentation"</p> <p>Tea-Sung Jun1, David E.J. Armstrong2 and T. Benjamin Britton1<br /> 1. Department of Materials, Imperial College London, Prince consort Road, London, SW7 2AZ, UK<br /> 2. Department of Materials, University of Oxford, Parks Road, Oxford, OX1 3PH, UK</p> <p>--</p> <p>This nanoindentation data folder contains 3 subfolders:<br /> (1) Raw indentation data obtained from TestWorks<br /> (2) Data for Figure 4<br /> (3) Data for Figure 5</p> <p>--</p> <p>In the subfolder (1), each file name indicates Material_Grain orientation_Indentation strain rate (e.g. 6242_H_0001). <br /> For the subfolders (2) and (3), readers can plot the graphs either using Excel, OriginPro or other software. <br /> Note that we used the OriginPro to generate all the graphs in this article.</p> <p>--</p> <p>If readers need further information, please feel free to contact: t.jun@imperial.ac.uk or terryjun83@gmail.com (Tea-Sung (Terry) Jun)</p>
Data for "Local strain rate sensitivity of single α phase within a dual-phase Ti alloy"
<p>Data for "Local strain rate sensitivity of single a phase within a dual-phase Ti alloy"</p> <p>Tea-Sung Jun, Zhen Zhang, Giorgio Sernicola, Fionn P.E. Dunne and T. Benjamin Britton<br /> Department of Materials, Imperial College London, Prince consort Road, London, SW7 2AZ, UK</p> <p>--</p> <p>This micropillar compression data folder contains 6 subfolders:<br /> (1) Micropillar compression videos<br /> (2) Data for Figure 6<br /> (3) Data for Figure 7<br /> (4) Data for Figure 10<br /> (5) Data for Figure 11<br /> (6) Data for Figure 12</p> <p>--</p> <p>In the subfolder (1), each file name indicates 'Pillar name (Slip system, Strain rate)', e.g. Pillar 1 (Basal, 0.01). <br /> For the subfolders (2) ~ (6), readers can plot the graphs either using Excel, OriginPro or other software. <br /> Note that we used the OriginPro to generate all the graphs in this article.</p> <p>--</p> <p>If readers need further information, please feel free to contact: t.jun@imperial.ac.uk or terryjun83@gmail.com (Tea-Sung (Terry) Jun)</p>
Data for "Using transmission Kikuchi diffraction to characterise alpha variants in an alpha+beta titanium alloy"
<p>This zipped folder contains data for "Using transmission Kikuchi diffraction to characterise alpha variants in an alpha+beta titanium alloy" published in Journal of Microscopy.</p> <p>The zipped folder contains:<br> - Phase map, where green regions are indexed as alpha Ti and red regions are indexed as beta Ti.<br> - IPF orientation maps along the X, Y and Z directions (with both phases combined).<br> - IPF colour keys for the alpha and beta phases.<br> - FSD micrographs at lower and higher magnification (not resized: one pixel per acquisition data point).<br> - TKD_Trial.ctf - text file describing orientations according to Bruker software conventions [1], as-exported from Bruker Esprit 2.1.<br> - TKD_BOR_MTEXplotter.m, an MTEX plotting tool which will plot orientations for this dataset (using MTEX version 4.3.2 installed on MATLAB 9.2 (R2017a)).</p> <p>[1] T.B. Britton, J. Jiang, Y. Guo, A. Vilalta-Clemente, D. Wallis, L.N. Hansen, A. Winkelmann, A.J. Wilkinson, Tutorial: Crystal orientations and EBSD — Or which way is up?, Materials Characterization, Volume 117, July 2016, Pages 113-126, ISSN 1044-5803, http://dx.doi.org/10.1016/j.matchar.2016.04.008.<br> (http://www.sciencedirect.com/science/article/pii/S1044580316300924)</p>
Thermal and dynamo evolution of the lunar core based on transport properties of Fe-S-P alloys
<p>These data are our original measured resistivity data and calculation data. </p>
Hybrid dynamic model for shape memory alloy linear and unimorph actuators
<p>Shape memory alloy morphing actuators are a type of soft actuator with many attractive properties. These actuators exhibit large deformation, small form factor, self-sense ability, and physical reservoir computing potential, while also being inexpensive. These morphing actuators are composed of active shape memory alloy wires and a passive base layer that is used to magnify the overall deflection. Although morphing actuators have great potential, the modeling of shape memory alloy actuators is difficult due to both shape memory alloy characteristics and the nonlinearity of the passive layer. Here, a hybrid dynamical model is proposed that couples the phase kinetics & thermal modeling for the shape memory alloy with a dynamic Cosserat nonlinear beam model. This hybrid model is benchmarked against linear and morphing experimental actuators. The model resulted in a root mean squared error of 1.48 mm and 1.63 mm for the morphing actuator configuration for two different actuators. This model can expand the capability and design of novel morphing actuators for a designed deformation profile for use in soft robotics.</p>
Kinoson data for FCC random and high entropy alloys
<p>This database contains the single KMC-step datasets for the "Ordered" and "Random" rate lattice gas systems, as well the "Complex" high-entropy alloy to accompany our main publication along with our code repository mentioned therein. The datasets are given as HDF5 files, readable with the `h5py` module in python 3. These files are listed below:</p><p>* CrystalData: `CrystData.h5` and `CrystData_ortho_5_cube.h5` contain all the necessary FCC crystal structure data for primitive 8x8x8 and orthogonal 5x5x5 FCC supercells.</p><p>* Lattice_gas/2-component/Ordered_rate: `singleStep_FCC_SR2_c0_X_Run2.h5` are the datasets for the 2-component "Ordered" rate lattice gas systems</p><p>* Lattice_gas/2-component/Random_rate: `singleStepFCC_CR2_c0_X_Run_3.h5` are the datasets for the 2-component "Random" rate lattice gas systems, with X = 60, 70, 75, 80, 85 for the slow species concentration. In these datasets, the slow and fast species have integer labels of 0 and 1, and the vacancy has an integer label of 2.</p><p>* Lattice_gas/5-component/Ordered_rate: `singleStep_FCC_5comp_LG_EquiComp_T.h5` are the datasets for the 5-component "Ordered" rate lattice gases</p><p>* Lattice_gas/5-component/Random_rate: `singleStep_HEA_dist_EquiComp_T.h5` are the datasets for the 5-component "Random" rate lattice gases, with T = 1073, 1173, 1273, 1373 for the simulated temperatures. In these datasets, the four slow species have integer labels 0, 1, 2 and 3, while the fast species has an integer label of 4, and the vacancy has an integer label of 5.</p><p>* HEA_MEAM: `singleStep_HEA_MEAM_T_ftol_1e-3.h5` (T = 773, 1073, 1173, 1273, 1373) contain the single KMC step samples for the complex high-entropy alloy simulated with the MEAM potential. In these datasets, the vacancy has an integer label of 0, while Co, Ni, Cr, Fe and Mn have integer labels 1, 2, 3, 4 and 5.</p><p>Along with the datasets, in each directory, the optimal neural network models for each system have also been provided as PyTorch "state dictionaries", along with the optimal neural network-predicted relaxation vectors. To illustrate the use of these datasets, neural networks and their relaxation vectors, python 3 Jupyter notebooks have also been provided to show the calculation of the transport coefficients using the Scaled Bias Basis (SBB) Method for all systems, as well as the NN+SRBC method for the "Complex" high-entropy alloy. In each directory, these notebooks have been numbered in their file names so as to make it easier to navigate through them. Along with such examples and the datasets, source codes for our neural network models and cluster expansion models are available in the code repository mentioned in our main text. Modules in this repository are also required to run these example notebooks.</p><p> </p>
Dataset for "Mo-Si alloys studied by atomistic computer simulations using a novel machine-learning interatomic potential: Thermodynamics and interface phenomena"
<p>This dataset was used to fit a general purpose machine-learning interatomic potential for Mo-Si alloys based on the Atomic Cluster Expansion (ACE) formalism. It supports the paper "Mo-Si alloys studied by atomistic computer simulations using a novel machine-learning interatomic potential: Thermodynamics and interface phenomena".</p>
Room temperature and elevated temperature tensile test and elastic properties data of Al-alloy EN AW-2618A after different aging times and temperatures
<p><span>The dataset contains two types of data: elastic properties (Young's and shear modulus, Poisson's ratio) between room temperature and 250 °C and a set of tensile tests at different aging times, aging temperatures, and test temperatures. </span></p>
Data from: Ultra-uniform, strong and ductile 3D printed titanium alloy through bifunctional alloy design
<p>Coarse columnar grains and heterogeneously distributed phases commonly form in metallic alloys produced by three-dimensional (3D) printing and are often considered undesirable because they can impart non-uniform and inferior mechanical properties. We demonstrate a design strategy to unlock consistent and enhanced properties directly from 3D printing. Using Ti−5Al−5Mo−5V−3Cr as a model alloy, we show that adding molybdenum (Mo) nanoparticles promotes grain refinement during solidification and suppresses the formation of phase heterogeneities during solid-state thermal cycling. The microstructural change due to the bifunctional additive results in uniform mechanical properties and simultaneous enhancement of both strength and ductility. We demonstrate how this alloy can be modified by a single component to address unfavourable microstructures, providing a pathway to achieve desirable mechanical characteristics directly from 3D printing.</p>
Impact of Catalysis-Relevant Oxidation and Annealing Treatments on Nanostructured GaRh Alloys
<p>Dataset of "Impact of Catalysis-Relevant Oxidation and Annealing Treatments on Nanostructured GaRh Alloys"</p>
Datasets for Insights into prismatic loop formation in irradiated Fe-Cr alloys from hypothesis-driven active learning and causal analysis
<p>Datasets for irradiated Fe-Cr alloys are collected from the experimental reports on dislocation loop type and dislocation density. We have constructed a data set from experimental literature containing 182 data points. To address such challenges to predict dislocation density, we have implemented a three-step ML approach as listed in the following:</p> <div> <div> <div> <ul> <li> <p>impute dataset to fill in the missing data to construct a predictive model using the RF regression algorithm. </p> </li> <li> <p>generate functionalized features and evaluate feature importance using the predictive model</p> </li> <li> <p>use the physics-based important functionalized features as hypotheses (physics-augmented GP models) in a hypothesis-driven active learning scheme to learn and predict dislocation density for all alloys. </p> </li> </ul> </div> </div> </div>
Data and Codes for Experimentally Validated Inverse design of Multi Property Fe-Co-Ni alloys: Data and codes release v1.0.1
<p>Data and Codes for Experimentally Validated Inverse design of Multi-Property Fe-Co-Ni alloys</p>
Data for "Competition between phase ordering and phase segregation in the Ti$_x$NbMoTaW and Ti$_x$VNbMoTaW refractory high-entropy alloys"
<p>Data associated with the arXiv preprint: "Competition between phase ordering and phase segregation in the Ti$_x$NbMoTaW and Ti$_x$VNbMoTaW refractory high-entropy alloys". Version 2 corrects an error in the files associated with fitted atom-atom interaction energies.</p>
The results of thermodynamic calculations for alloys presented in a paper published in Journal of Materials Research and Technology, 2023, 27, pp. 6182–6191
<p>Thermophysical data for the alloys developed within the project. The results have been published in ournal of Materials Research and Technology, 2023, 27, pp. 6182–6191.</p>
Photophysical and Spectroscopic dataset for Ag-In-Zn-S alloyed nanocrystals as photocatalysts of controlled light-mediated radical polymerization
<p>(1) Energy-dispersive spectra of alloyed Ag<sub>1.0</sub>In<sub>1.5</sub>Zn<sub>0.3</sub>S<sub>3.3</sub> (R) and Ag<sub>1.0</sub>In<sub>10.3</sub>Zn<sub>12.4</sub>S<sub>11.8</sub> (G) nanocrystals.</p> <p>(2) HR-TEM images of alloyed Ag<sub>1.0</sub>In<sub>1.5</sub>Zn<sub>0.3</sub>S<sub>3.3</sub> (R) and Ag<sub>1.0</sub>In<sub>10.3</sub>Zn<sub>12.4</sub>S<sub>11.8</sub> (G) nanocrystals.</p> <p>(3) X-ray powder diffractograms of alloyed Ag<sub>1.0</sub>In<sub>1.5</sub>Zn<sub>0.3</sub>S<sub>3.3</sub> (R) and Ag<sub>1.0</sub>In<sub>10.3</sub>Zn<sub>12.4</sub>S<sub>11.8</sub> (G) nanocrystals.</p> <p>(4) UV-vis-NIR spectra of toluene dispersion of Ag<sub>1.0</sub>In<sub>1.5</sub>Zn<sub>0.3</sub>S<sub>3.3</sub> (R) Ag<sub>1.0</sub>In<sub>10.3</sub>Zn<sub>12.4</sub>S<sub>11.8</sub> (G) nanocrystals.</p> <p>(5) Photoluminescence excitation and emission spectra of toluene dispersion of Ag<sub>1.0</sub>In<sub>1.5</sub>Zn<sub>0.3</sub>S<sub>3.3</sub> (R) and Ag<sub>1.0</sub>In<sub>10.3</sub>Zn<sub>12.4</sub>S<sub>11.8</sub> (G) nanocrystals.</p> <p>(6) <sup>1</sup>H and <sup>13</sup>C NMR spectra (in benzene-<em>d<sub>6</sub></em>) of the reaction mixture used for the photocatalytic bulk and solution polymerization of methyl methacrylate (MMA) with <a name="_Hlk161909311"></a>Ag<sub>1.0</sub>In<sub>1.5</sub>Zn<sub>0.3</sub>S<sub>3.3</sub> (R) and Ag<sub>1.0</sub>In<sub>10.3</sub>Zn<sub>12.4</sub>S<sub>11.8</sub> (G) nanocrystals as a photocatalyst.</p> <p>(7) SEC profiles of PMMA prepared in bulk and solution polymerization.</p> <p>(8) MALDI-TOF spectra in the low and high molecular weight ranges of PMMA synthesized in the presence of Ag<sub>1.0</sub>In<sub>1.5</sub>Zn<sub>0.3</sub>S<sub>3.3</sub> (R) and Ag<sub>1.0</sub>In<sub>10.3</sub>Zn<sub>12.4</sub>S<sub>11.8</sub> (G) nanocrystals as photocatalysts.</p> <p>(9) EPR spectra of adducts generated for Ag<sub>1.0</sub>In<sub>1.5</sub>Zn<sub>0.3</sub>S<sub>3.3</sub> (R) and Ag<sub>1.0</sub>In<sub>10.3</sub>Zn<sub>12.4</sub>S<sub>11.8</sub> (G) nanocrystals + 5,5-dimethyl-1-pyrroline-<em>N</em>-oxide (DMPO) under illumination by a green LED (l = 523 nm).</p> <p>This work was supported by the National Science Centre of Poland, Grant No. 2022/45/B/ST5/02120</p>
Dataset for publication Reaction Mechanism and Performance of Innovative 2D Germanane-Silicane Alloys SixGe1−xH Electrodes in Lithium-Ion Batteries
<p>A dataset for publication Datase for publication Reaction Mechanism and Performance of Innovative 2D Germanane-Silicane Alloys SixGe1−xH Electrodes in Lithium-Ion Batteries including all relevant data used in the manuscript. Information on how to use the dataset are included in the readme file.</p>
Long Cycle-Life Ca Batteries with Poly(anthraquinonylsulfide) Cathodes and Ca-Sn Alloy Anodes
<p>This is a collection featuring the data generated and used within the paper: "Long Cycle-Life Ca Batteries with Poly(anthraquinonylsulfide) Cathodes and Ca-Sn Alloy Anodes"</p>
ScienceDex guides
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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.