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287 results for “alloy”
Microstructure and mechanical properties of mechanically-alloyed CoCrFeNi high-entropy alloys using low ball-to-powder ratio
<p>The main issue of this work was to analyse the microstructural evolution and mechanical properties of FCC high entropy alloy (HEA) when BPR (ball-to-powder ratio) was limited to 5:1. The motivation of our work is to increase the amount of milled fraction without losing efficiency of the milling process. Nowadays many papers describe HEAs by using powder metallurgy processes, but higher BPR is used. In consequence less amount of powder is milled in one period and the process is not effective enough from the industrial point of view.</p> <p>In this work four equiatomic CoCrFeNi samples were made by Mechanical Alloying plus Spark Plasma Sintering using different milling times: 10, 20, 30, 40 hours. We used 200 Φ5 mm WC balls and milled with intervals 15:15 minutes. Milling speed was 250 rpm. After the mechanical alloying has been finished samples were sintered by using Spark Plasma Sintering technique. We chose 950 °C as a process temperature with heating rate 100 °C/min. Sintering pressure was 50 MPa. Samples were then homogenise in 1050 °C for 12 hours. Then samples were water quenched.</p> <p>The densification of samples during sintering was in satisfied level, what was confirmed by relative densities of samples (>90 %) The microstructure observation of sintered samples revealed Cr-rich particles evenly distributed in samples volume. The number of particles decreases with increasing the milling time. Elements are randomly distributed in the matrix phase except a small Cr-depletion. XRD technique shows multiple FCC structure. As the milling time exceeds, the main FCC structure is promoted. Microhardness increased as a function of milling time. After annealing microstructures were almost out of Cr-rich phase. Only the biggest particles remained. EBSD revealed the grain size decrement as a function of milling time. Also X-ray diffractograms presented significant homogenisation of manufactured samples. Despite the microhardness decrease after heat treatment, the longest milled sample still possess very promising properties. Moreover hardness of samples is not indent’s size dependent (micro- and nanohardness).</p> <p>During milling time the particles are joining and fracturing many times. As a consequence elements are mixing and promoting the new phase(s) growing. We deduced that Hall-Petch effect is the most important factor determining better mechanical properties in longer milled samples. However the milling process need to be improvement. Cr-rich phase observed in sintered samples is the effect of low efficiency of the process, which might be improved by either smaller fraction of Cr at the beginning (premilling process) or increase the other process parameters (milling speed, sintering time).</p>
MicroCT and CECT-based images of a Zn alloy explant after 84 days of implantation in the rat abdominal aorta.
<p><span>(A) MicroCT slices showing the wire and the corrosion products and (B) CECT slices after staining with Lugol for 17 h showing the wire, the corrosion products, and the surrounding soft tissue for a Zn alloy explant.</span></p>
CECT-based images a Zn alloy explant, after 84 days of implantation in the rat abdominal aorta.
<p><span>(A) The original grayscale slices, (B) the segmentation of the wire, the aorta, and the neointima, and (C) the segmentation of the wire, the aorta, the neointima, and the corrosion products.</span></p>
Tailoring the reaction pathway for control of size and composition of silver-gold alloy nanoparticles
<p>This is the raw data for the manuscript:</p> <p>Tailoring the reaction pathway for control of size and composition of silver-gold alloy nanoparticles<br><br>Abstract:<br><br>In this work, we focus on tuning both the particle size and chemical composition of bimetallic silver-gold alloy nanoparticles (NPs) by carefully controlling the reaction pathway. NP synthesis involves the control of the supersaturation profile in time and space. For the reaction-controlled case, this supersaturation profile is determined by a network of reactions leading to the build-up of the monomer concentration. Using a variety of characterization tools, we show how the process conditions influence the coupled reactions in the complex formation mechanism of silver-gold alloy NPs. Applying a simple mass balance allows for the independent control of size and chemical composition of these NPs, yielding particles with constant composition and varying size in the range of 20 to 40 nm. A series of constant sizes and varying chemical compositions in the range of 10 % to 100 % molar gold content is also possible. Our new methodology shows an example of how reaction networks can be tailored to achieve targeted NP properties and paves the way for better control in the synthesis of multicomponent NPs.</p> <p> </p> <p>All data are sorted according to their appearance in the figures of the main manuscript.</p>
[Data set] Alloy [FA,Cs]PbI3 Perovskite Surfaces, Stability and Tolerance to Defect Formation
<p>This is the data repository related to the simulations of alloy [FA,Cs]PbI<sub>3</sub> Perovskite Surfaces.</p> <p>The surfaces here (Slabs.tgz) included are the relaxed structure of the slab models obtained by making cuts in the (001) direction in the special quasi-random structure (SQS) bulk models of FA<sub>1-x</sub>Cs<sub>x</sub>PbI<sub>3</sub> with (x=0.25 and 0.5), and the supercell models of pure FAPbI<sub>3</sub> and CsPbI<sub>3</sub> perovskites.[1] Besides, we include the optimized structures of each neutral vacancy pair defects (Slabs-defects.tgz) of formamidinium iodide and/or cesium iodide created in the most stable alloy [FA,Cs]I-C-FA<sub>0.75</sub>Cs<sub>0.25</sub>PbI<sub>3</sub> surface (see details in the paper). The ionic relaxations were performed with VASP code (version 6.2.1), using the PBE exchange-correlation functional, including Van der Waals corrections using the Grimme method with zero-damping function.</p> <p>Finally, the data set includes the simulated ab initio molecular dynamics (AIMD) trajectories of the most stable alloy and pure slabs, [FA,Cs]I-C-FA<sub>0.75</sub>Cs<sub>0.25</sub>PbI<sub>3</sub> and FAI-PbI<sub>3</sub>), including neutral vacancy pair defects of formamidinium iodide on a surface (Slabs-defects-AIMD.tgz). The AIMD calculations were performed with the CP2K code (V7.1), evaluating the forces with the PBE functional with the Grimme correction scheme (DFT- D3, Zero–damped correction). The trajectory productions include up to 15 ps using the microcanonical ensemble with 0.5 fs of time-step, considering 5 ps of thermalization time. More details in the article support information. </p> <p> </p> <p>Reference:</p> <p>1. G. M. Dalpian, X. G. Zhao, L. Kazmerski, A. Zunger, <em>Chem. Mater.</em> <strong>31</strong>, 2497–2506 (2019).</p>
Dataset for the article entitled: "Revealing the strengthening contribution of stacking faults, dislocations and grain boundaries in severely deformed LPBF AlSi10Mg alloy"
<p>Dataset includes:</p> <p>EBSD data:</p> <p>AlSi10Mg_HT320.ang - Heat treated sample</p> <p>HT320E100.ang - Heat treated ECAP processed sample</p> <p>TKD data:</p> <p>HT320E100.ang - Heat treated ECAP processed sample</p> <p>XRD data:</p> <p>AlSi10Mg_HT320.ASC - Heat treated sample</p> <p>AlSi10Mg_HT320_ECAP100.ASC - Heat treated ECAP processed sample</p>
Underlying data for: The Fe addition as an effective treatment for improving the radiation resistance of fcc NixFe1-x single-crystal alloys
<div> <p>The set contains 5 folders (TEM, SRIM, Nanoindentation, MC/MD simulations and RBSc_MSDA) containing raw test results for a specific method.</p> <p><strong>→ TEM</strong></p> <p>In TEM folder there are 2 sub-folders named “2e14 (0.5 dpa)” and “1e15 (12 dpa). In each sub-folder there are 8 original images that make up Figure 7 and Figure 8 in the paper. Below please find the description:</p> <p><strong>Fig.7.</strong> A) Cross-sectional TEM images of the Ni, Ni<sub>0.77</sub>Fe<sub>0.23,</sub> Ni<sub>0.62</sub>Fe<sub>0.38</sub> and Ni<sub>0.38</sub>Fe<sub>0.62 </sub>irradiated with a fluence of 2×10<sup>14</sup> ions/cm<sup>2</sup> compared with SRIM calculations. B) Bright-field images of Ni, Ni<sub>0.77</sub>Fe<sub>0.23,</sub> Ni<sub>0.62</sub>Fe<sub>0.38</sub> and Ni<sub>0.38</sub>Fe<sub>0.62 </sub>irradiated with a fluence of 4×10<sup>15</sup> ions/cm<sup>2</sup>. The red arrow indicates dislocation loops, green – defect clusters and yellow – SFT.</p> <p><strong>Fig.8.</strong> A) Cross-sectional TEM images of the Ni, Ni<sub>0.77</sub>Fe<sub>0.23,</sub> Ni<sub>0.62</sub>Fe<sub>0.38</sub> and Ni<sub>0.38</sub>Fe<sub>0.62 </sub>irradiated with a fluence of 4×10<sup>15</sup> ions/cm<sup>2</sup> compared with SRIM calculations. B) Bright-field images of Ni, Ni<sub>0.77</sub>Fe<sub>0.23,</sub> Ni<sub>0.62</sub>Fe<sub>0.38</sub> and Ni<sub>0.38</sub>Fe<sub>0.62 </sub>irradiated with a fluence of 4×10<sup>15</sup> ions/cm<sup>2</sup>. The red arrow indicates dislocation loops, blue – dislocation lines, green – defect clusters and yellow – SFT.</p> <p> </p> <p>To be able to reproduce Fig.9 and Fig.10 one needs images taken at 500k (attached in the files) and follow the instruction given in the article:</p> “Moreover, in Fig.9 B defect densities have been calculated to better understand the defect configuration for various compositions. Calculations were made based on the TEM images taken at the peak damaged region (at the highest magnification of 500k). For this measurement, lamellae thickness was also measured at the peak damage region only. The densities were calculated by counting the defect sizes in a unit volume of crystalline material (based on the same image where an average defect size was calculated and presented in Fig.8 A).”</div> <p>The lamella size was as follows:</p> <table> <tbody> <tr> <td> </td> <td>0.5 dpa</td> <td>12 dpa</td> </tr> <tr> <td> </td> <td>Lamella thickness</td> </tr> <tr> <td>Ni</td> <td>54</td> <td>117</td> </tr> <tr> <td>Ni<sub>0.62</sub>Fe<sub>0.38</sub></td> <td>56</td> <td>119</td> </tr> <tr> <td>Ni<sub>0.38</sub>Fe<sub>0.62</sub></td> <td>82</td> <td>83</td> </tr> </tbody> </table> <p>Surface area for all the materials [m<sup>2</sup>] - 1,08138E-13</p> <p> </p> <p><strong>→ SRIM</strong></p> <p>In SRIM folder there are two subfolders named: “Ni” NiFe62”. In each folder there are 3 .txt files (RANGE.txt file, VACANCY.txt and NOVAC.txt) that makes up the Fig. 1 in the paper.</p> <p>“The corresponding displacement per atom (dpa) profiles were predicted by the SRIM code for all elements using the full cascade mode. The dpa has been calculated based on the following equation according to recommendations of [28,29]:</p> <p> </p> <p><em>dpa = [fluence (ions/cm<sup>2</sup>) × total vacancies/A-ion × 10<sup>8</sup>] /atomic density (atoms/cm<sup>3</sup>) (1)</em></p> <p>“</p> <p>“The ion distribution was estimated from the RANGE.txt file. The corresponding dpa profiles were calculated using two files, VACANCY.txt and NOVAC.txt, under an assumed displacement energy threshold of 40 eV for all elements. The dpa profile is the sum of the vacancy concentrations using the column of “Knock-Ons” for Ni ions and the columns of “Vacancies” from target elements (the sum of Ni vacancies and Fe vacancies in the case of Ni<sub>x</sub>Fe<sub>1</sub><sub>−</sub><sub>x</sub>) in VACANCY.txt, together with the replacement collisions in NOVAC.txt. [31].”</p> <p> </p> <p><strong>→ Nanoindentation</strong></p> <p>In “Nanoindentation” folder there are four subfolders (“Fig.5 A – virgin multicycle”, “Fig.5 B – hardness versus fluence”, “Fig.5 C – LD curve 0.1 dpa”,” Fig.5 D – LD curve 12 dpa”), which appropriately reproduces the figures 5A, B, C and D. In folder “Fig.5 A – virgin multicycle” there is an excel file with all the data needed to reproduce Fig. 5 A. In folder “Fig.5 B – hardness versus fluence” there is an excel file with all the data needed to reproduce Fig. 5 B. There are bookmarks in excel “Ni”, “NiFe12”,”NiFe23”, “NiFe38”, “NiFe62”, where are the data obtained for each material and each fluence. Hardness value is obtained as sum of an average hardness obtained in the multicycle mode (at each particular load). In folder “Fig.5 C – LD curve 0.1 dpa” there are five .txt files needed to reproduce each of Load-Displacement curve at the damage level of 0,1 dpa (“LD Ni 0,1 dpa.txt”, “LD NiFe12 0,1 dpa.txt”, “LD NiFe23 0,1 dpa.txt”, “LD NiFe38 0,1 dpa.txt”, “LD NiFe62 0,1 dpa.txt”). In folder, ”Fig.5 D – LD curve 12 dpa” there are five .txt files needed to reproduce each of Load-Displacement curve at the damage level of 12 dpa (“LD Ni 12 dpa.txt”, “LD NiFe12 12 dpa.txt”, “LD NiFe23 12 dpa.txt”, “LD NiFe38 12 dpa.txt”, “LD NiFe62 12 dpa.txt”).</p> <p><strong>→ </strong><strong>MC/MD Simulations</strong></p> <p>In “MC/MD Simulations” folder there are two subfolders: “Fig. 6a” and “Fig. 6b”. In subfolder “Fig. 6a” there are 4 .txt files which make up Fig. 6a – “Ni38Fe62-swaps-energy.txt”, “Ni62Fe38-swaps-energy.txt”, “Ni77Fe23-swaps-energy.txt”, “Ni88Fe12-swaps-energy”. In subfolder “Fig. 6b” there are 4 .txt files which make up Fig. 6b – “Ni38Fe62-swaps-l12.txt”, “Ni62Fe38-swaps-l12.txt”, “Ni77Fe23-swaps-l12.txt”, “Ni88Fe12-swaps-l12.txt”. Moreover, in the main “MC/MD Simulations” folder one can find 4 movies (namely: “Ni38Fe62”, “Ni62Fe38”, “Ni77Fe23”, “Ni88Fe12“), which shows nanoprecipitation during hybrid MD-MC.</p> <p><strong>→ RBS/C_MSDA</strong></p> <p>In “RBS/C, MSDA” folder there is one origin .opj file “NiFe_implanted_Ni_2e14-2e15_rbs_1.62He_165degr”, in which one can find all the <u>experimentally obtained spectra</u>. “<em>These spectra </em><em>for pure Ni and Ni<sub>x</sub>Fe<sub>1-x</sub> alloys irradiated with different fluences were simulated using the Monte Carlo McChasy code developed at the NCBJ [30,32]. The energy of the backscattered particle can be directly related to the depth at which the close encounter scattering event occurred. The bulk scattering arises from particles that have been deflected atomic rows and have crossed over to another row, where they undergo a close-encounter event. To reveal the damage kinetics for investigated alloys the Multi-Step Damage Accumulation (MSDA) analysis was performed [33,34]. This model is based on the equation assuming that the damage accumulation occurs through a series of structural transformations caused by the destabilization of the present crystal structure</em>.”</p> <p>“<em>Points in the MSDA figure are corresponding to maximal values of extended defects formed in irradiated materials. Solid lines are the fits made following the MSDA equation [30,33,34]:</em></p> <p>f_{d} = \sum_{i=1}^{n}(f_{d, i}^{sat} - f_{d, i-1}^{sat})G[1-exp(\sigma_{i}(\Phi - \Phi_{i-1})))]</p> <p>where:</p> <p>\sigma_{i}<em> </em> - <em>cross-section for the formation of a given kind of defect</em></p> <p>f_{d, i}^{sat} <em>- level of damage at saturation for i-th kind of defects</em></p> <p>\Phi{i} <em>- fluence threshold for triggering the formation of i-th kind of defects “</em></p> <p> </p> <p> </p> <p> </p> <p><span>“Financial support from the National Science Centre, Poland through the </span><a href="https://www.sciencedirect.com/science/article/pii/S0169433224017045#gp010" target="_blank" rel="noopener noreferrer">PRELUDIUM 21</a><span> program in the frame of grant no. </span><a href="https://www.sciencedirect.com/science/article/pii/S0169433224017045#gp010" target="_blank" rel="noopener noreferrer">2022/45/N/ST5/02980</a><span> is gratefully acknowledged.”</span></p> <p> </p>
Supplementary material for "Shock-induced spallation in a nanocrystalline high-entropy alloy: An atomistic study"
<p>Data provided in this upload:</p> <ul> <li>Pictures and tables from "Shock-induced spallation in a nanocrystalline high-entropy alloy: An atomistic study" paper</li> <li>Excel-Charts used to create the graphs and tables</li> <li>Vzz data files from our dump files extracted using OVITO used to calculate the SWVs (position datapoints are the crucial part)</li> <li>The potential used for our simulations</li> <li>Readme with the following information: <ul> <li>Crucial parameters used in our simulations</li> <li>Captions of all the figures and tables used in order</li> <li>Additional information in regards to shock wave velocities (SWV) and their calculation, plus the necessary values</li> </ul> </li> </ul>
Research data supporting 'Machine-Learned Interatomic Potentials for Transition Metal Dichalcogenide Mo1−xWxS2−2ySe2y Alloys'
<p>Research data supporting 'Machine-Learned Interatomic Potentials for Transition Metal Dichalcogenide Mo1−xWxS2−2ySe2y Alloys'</p>
Dataset for Atomistic simulations on liquid Mg-Sr alloys assisted with deep learning potential
<p>This Dataset is for the paper "Atomistic simulations on liquid Mg-Sr alloys assisted with deep learning potential," including an example script, interatomic potentials, and training data.</p>
Rational design of optimal bimetallic and trimetallic nickel-based single-atom alloys for bio-oil upgrading toward hydrogen production
<p>Supplementary Data for the "Rational design of optimal <em>bimetallic</em> and <em>trimetallic</em> nickel-based single-atom alloys for bio-oil upgrading toward hydrogen production".</p>
Al-Si alloy dataset
Open the record for dataset details and reuse information.
Dataset for "Liquid Structure of Iron and Iron-Nitrogen-Carbon Alloys within the Cores of Small Terrestrial Bodies"
<p>The following is a copy of the processed data files used in the submitted manuscript: "Liquid Structure of Iron and Iron-Nitrogen-Carbon Alloys within the Cores of Small Terrestrial Bodies"</p> <p><br>In the text the six experiments are denoted at #-##. For example, 7-17, this notation means cell 7 in the year 2017. In this data repository, the file names follow the notation of year_loaded composition_cell#. So in the case of 7-17, that experimental dataset corresponded to 2017_Fe_cell7. </p>
Dataset for "The effect of the unique microstructure of additively manufactured Fe-Mn-Si-Cr shape memory alloys on recovery stress"
<p>The uploaded dataset contains all relevant primary data for the publication titled "The effect of the unique microstructure of additively manufactured Fe-Mn-Si-Cr shape memory alloys on recovery stress". </p> <p>The data is structured in subfolders:</p> <p>01_Recovery stress:</p> <p>Primary data of thermo-mechanical experiments including force, strain and temperature measurements and a data description file.</p> <p>02_Micrographs:</p> <p>Image files of etched and unetched sample cross-sections of additively manufactured samples with different Mn content.</p> <p>03_EBSD:</p> <p>OIM files of electron backscatter diffraction measurements for all samples. </p> <p>04_Chemical analysis</p> <p>Analysis report of externally perfomed chemical analysis of the used metal powders and the resulting additively manufactured parts.</p> <p> </p> <p>For further information see the publication as soon as published.</p> <p> </p>
Automatic exhaustive calculations of large material space by Korringa-Kohn-Rostoker coherent approximation method --- Applied to equiatomic quaternary high entropy alloys
<p>Calculated data of equiatomic quaternary solid solution phase (high-entropy alloys) on local magnetic moment, total magnetization, magnetic phase transition temperature and residual resistivity.</p> <p>The data was added on October 28.</p>
Data and code for "Hydrogen-driven Surface Segregation in Pd-alloys from Atomic Scale Simulations"
<p>This record contains data and scripts pertaining to the publication "Hydrogen-driven Surface Segregation in Pd-alloys from Atomic Scale Simulations".</p>
Alloy CsCdxPb1–xBr3 Perovskite Nanocrystals: The Role of Surface Passivation in Preserving Composition and Blue Emission
<p>Raw data generated for the publication titled "Alloy CsCd<em><sub>x</sub></em>Pb<sub>1–<em>x</em></sub>Br<sub>3</sub> Perovskite Nanocrystals: The Role of Surface Passivation in Preserving Composition and Blue Emission".</p> <p>The *.zip file contains two main folders that group the data for the main document and for the supporting information. Each folder is devided in sub-folders with the files for the figures in the reported work. </p>
Cast Al-Si-Mg alloy TEM data
<p>Data from publication.</p> <p>Hardness, conductivity, EBSD and TEM.</p>
RAW Data - Mechanical characterization for the severely processed FSPed WE54 magnesium alloy
<p>The present data set present the raw data of the mechanical characterization of a WE54 magnesium alloy, processed by friction stir processing (FSP), using a refrigerated backing anvil. File names describe the kind and characteristics of each test as follows: </p> <ol> <li>All file names start by the initial temper of the WE54 magnesium alloy (T6 or TT) followed by the FSP processing conditions: first two digits are the rotation speed while the second two digits correspond to the advancing speed, both divided by a factor of 100.</li> <li>Files tagged at the end as _iUMI.opj correspond to the characterization by instrumented ultra-microindentation and provide a data matrix including prosition and hardness value (readable in Origin).</li> <li>Files tagged at the end as _CSRtt.opj correspond to the characterization by constant strain rate tensile test.</li> </ol>
Dataser from paper "Proliferation of osteoblast precursor cells on the surface of TiO2 nanowires anodically grown on a -type biomedical titanium alloy"
<p>Dataser from paper "Proliferation of osteoblast precursor cells on the surface of TiO2 nanowires anodically grown on a -type biomedical titanium alloy":</p> <p>- <strong>Contact Angle: </strong>Images and measurements.</p> <p><strong>- Fluorescence Microscopy Images:</strong> Images.</p> <p><strong>- MTT and pixel counting:</strong> Measurements.</p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
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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.