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22 results for “Micromechanics”
Fracture Toughness of Off-Stoichiometric B2 NiAl as determined by micromechanical tests and atomistic simulations
<p>KQJ-Alconcentration-NiAl_experiment.csv : semicolon-separated ASCII file containing the fracture toughness (2nd column) and the contribution of the plastic deformation to the fracture toughness (3rd column) as function of Al concentration (1st column) for off-stoichiometric B2 NiAl as determined by micro mechanical tests on notched cantilever beams.</p> <p>KIc-Alconcentration-NiAl_static-simulations.dat : space-separated ASCII file containing the fracture toughness (K_Ic) of B2 NiAl (2nd column) for different Al concentrations (first column) as as determined by static atomistic calculations with the<br> # Potential by G. P. P. Pun, Y. Mishin, (Phil. Mag. 89 (34-36) (2009) 3245– 3267).</p> <p>NiAl_Pun_conc_0.40-0.65Ni_Esurf110_Cij.dat : space-separated ASCII file containing the energy of {110} surfaces (2nd column) and elastic constants (columns 3-5) of B2 NiAl for different Ni concentrations (f1st column) as determined by atomistic simulations using the the potential by G. P. P. Pun, Y. Mishin, (Phil. Mag. 89 (34-36) (2009) 3245– 3267)</p> <p>KIc-Alconcentration-NiAl_theory.dat : space-separated ASCII file containing the fracture toughness (K_Ic) of B2 NiAl (2nd column) for different Al concentrations (1st column) as calculated by the Griffith equation.</p> <p> </p>
Computational micromechanics of bioabsorbable magnesium stents: Supporting Data
<p>Data including UMATs, Abaqus input files and experimental measurements related to the paper 'Computational micromechanics of bioabsorbable magnesium stents' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jmbbm.2014.01.007" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.jmbbm.2014.01.007</span></a></p>
Micromechanical high-Q trampoline resonators from strained crystalline InGaP for integrated free-space optomechanics
<p>We share experimental data for Figs. 2,4,5,6,11,12,13 of arxiv manuscript arXiv:2211.12469 [physics.app-ph] entitled "Micromechanical high-Q trampoline resonators from strained crystalline InGaP for integrated free-space optomechanics".</p>
Micromechanics of Void Nucleation and Early Growth at Incoherent Precipitates: Lattice-trapped and Dislocation-mediated Delamination Modes
<p>This repository contains raw data analyzed in the referenced manuscript published in Crystals (<a href="https://doi.org/10.3390/cryst11010045">10.3390/cryst11010045</a>). See the included README file for detailed information on the contents.</p>
Data of " Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behavior"
<pre>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "Micromechanical characterization of the material response in a PA12-SLS fabricated lattice structure and its correlation with bulk behavior", journal = "Polymer Testing", pages = " ", year = "2022", issn = "####", doi = "<a href="https://doi.org/10.1016/j.polymertesting.2022.107556">https://doi.org/10.1016/j.polymertesting.2022.107556</a>", author = "L. Cobian, M. Rueda-Ruiz, J.P. Fernandez-Blazquez, V. Martinez, F. Galvez, F. Karayagiz, T. Lück, J. Segurado, M.A. Monclus"</pre> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 862015</p>
Data of "Ductile fracture of high entropy alloys: from the design of an experimental campaign to the development of a micromechanics-based modeling framework"
<p>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data):</p> <p>title = "Ductile fracture of high entropy alloys: from the design of an experimental campaign to the development of a micromechanics-based modeling framework",<br> journal = "Engineering Fracture Mechanics",<br> year = "2022",<br> volume = "275",<br> pages = "108844 ",<br> doi = "https://doi.org/10.1016/j.engfracmech.2022.108844",<br> author = "Antoine Hilhorst, Julien Leclerc, Thomas Pardoen, Pascal J. Jacques, Ludovic Noels, Van-Dung Nguyen"</p> <p>New version following review.</p> <p> </p> <p> </p>
Dataset for "Micromechanics and Strain Localization in Sand in the Ductile Regime"
<p>This dataset contains tomography images and stress-strain curves used in the publication titled "Micromechanics and Strain Localization in Sand in the Ductile Regime" in the Journal of Geophysical Research: Solid Earth</p>
Data of "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"
<p><strong>Id</strong><br>title = "A micromechanical Mean-Field Homogenization surrogate for the stochastic multiscale analysis of composite materials failure"<br>journal = International Journal for Numerical Methods in Engineering<br>year = 2023<br>volume = 124<br>pages = 5200-5262<br>doi = 10.1002/nme.7344<br>authors = "Calleja, Juan Manuel and Wu Ling, and Nguyen, Van-Dung and Noels, Ludovic"</p> <p>If you use these data or model, we would be grateful if you could cite this above paper</p> <p><strong>Software</strong><br>Requires GMSH and Python 3 with packages numpy, matplotlib, sklearn (scikit-learn), os, pickle, scipy, pandas, cvs, math, seaborn.<br>Each folder contains readme that will help the user to navigate through the data.</p> <p>To run the model you need the open source code <a href="http://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries </a>but you need to request access to cm3MFH as well</p> <p><strong>Directories</strong></p> <ol> <li>Main: Contains fast and easy access to the plots presented in the paper. The readme contained in this plot specifies the plots that are run with each code.</li> <li>1_SVE_Generator:Contains the files needed for the generation of the SVE, the statistical properties of the microstructure, and PLY samples for the full-field simulations, as well as the used samples</li> <li>2_Full_Field: contains the extracted data from the FF composite realizations, as well as the used random SVE geometries.</li> <li>3_Identification: Contains the identification code to find the effective parameters for each SVE realization as well as the obtained identification results.</li> <li>4_Generator: Contains the generated set of parameters for the 25 and 45 micrometer squared SVEs as well as the codes for the new data generation, the file with the generated data and the plots related with the MF-ROM random parameters and their cross-relations shown in Sections 2.5.2, 3.2.3 and 4.</li> <li>5_Tests: Contains all the information concerning the tests used for the verification of the MF-ROM and the ply and experimental compression results.</li> <li>MFH_vs_FF: Allows to easily test the inverse identification process through the use of random SVEs and verify the performance of the identified MFH parameters against its full-field counterpart.</li> </ol> <p><strong>Plot of figures</strong></p> <p>Figure 9 : Run "python plot_Gc.py" which can be found in folder Main/Full_Field_Energy<br>Figure 10: Run "python3 PDF_HIST_Gc.py", which can be found in folder Main/Histograms<br>Figure 23: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 24: Run "python3 plot.py" which can be found in folder Main/MFH_FF_Comparison<br>Figure 27: Run "python3 Correlation_Graphs_25.py contained in folder Main/Distributions_25_Micrometer_SVE<br>Figure 29: Run "python3 PDF_HIST.py" which can be found in folder Main/Histograms<br>Figure 30: To obtain the data used in this figure, run "python3 DistanceCorrelation_25.py" which can be found in folder /4_Generator<br>Figure 31: To obtain the data used in this figure, run "python3 DistanceCorrelation_45.py" which can be found in folder /4_Generator<br>Figure 32: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 33: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 34: Run "python3 Correlation_Graphs_25.py" which can be found in folder Main/Distributions_25_Micrometer_SVE<br>Figure 36: Run "python3 plot_New.py" which can be found in folder Main/PlyTests<br>Figure 46: Run "python3 plot_Test.py" which can be found in folder Main/CompressionExperiment<br>Figure B3: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure B4: Run "python3 MicroStrAna.py" which can be found in folder Main/MicroStructStatistics<br>Figure D5: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D6: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D7: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D8: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D9: Run "python3 PDF_HIST_B.py" which can be found n folder Main/Histograms<br>Figure D10: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D11: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D12: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D13: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure D14: Run "python3 PDF_HIST_B.py" which can be found in folder Main/Histograms<br>Figure E15: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E16: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure E17: Run "python3 Correlation_Graphs_45.py" which can be found in folder Main/Distributions_45_Micrometer_SVE<br>Figure F18: Run "python3 plot_Convergence_25.py" which can be found in folder Main/Convergence<br>Figure F19: Run "python3 plot_Convergence_45.py" which can be found in folder Main/Convergence<br> </p> <p> </p> <p> </p> <p> </p>
Data for paper "Micromechanical modeling of MXene-polymer composites"
<p>Data for paper “Micromechanical modeling of MXene-polymer composites” <a href="https://doi.org/10.1016/j.carbon.2020.02.070">https://doi.org/10.1016/j.carbon.2020.02.070</a></p> <p>M_M_MX_P_C_data.xlsx is the data represented in the paper.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 777810.</p>
Micromechanics of dental cement paste
<p>These zipped folders contains underlying raw data, figures, and codes used for analyses in Dohnalik et al, Journal of the Mechanical Behavior of Biomedical Materials, 124, (2021) 104863, <a href="https://doi.org/10.1016/j.jmbbm.2021.104863">https://doi.org/10.1016/j.jmbbm.2021.104863</a>. The contents of the individual folders are described in the "README.txt" files; they are located in each zipped folder.</p> <p>The data were produced as part of the work carried out at the Vienna University of Technology (TU Wien), AT, under the EC H2020-MSCA-ITN project ‘ERICA: Engineered Calcium-Silicate-Hydrates for Applications’, Grant Agreement No. 764691 (November 2017 – February 2022).</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>
Multi-porous extension of anisotropic poroelasticity: linkage with micromechanics
<p>The codes used for numerical simulations in Adamus et al. (2023). ''Multi-porous extension of anisotropic poroelasticity: linkage with micromechanics''. Codes are written as Matlab scripts.</p>
Data from: Variation in setal micromechanics and performance of two gecko
Biomechanical models of the gecko adhesive system typically focus on setal mechanics from a single gecko species, Gekko gecko. In this study, we compared the predictions from three mathematical models with experimental observations considering an additional gecko species Phelsuma grandis, to quantify interspecific variation in setal micromechanics. We also considered the accuracy of our three focal models: the frictional adhesion model, work of detachment model, and the effective modulus model. Lastly, we report a novel approach to quantify the angle of toe detachment using the Weibull distribution. Our results suggested the coupling of frictional and adhesive forces in isolated setal arrays, first observed in G. gecko is also present in P. grandis although P. grandis displayed a higher toe detachment angle, suggesting they produce more adhesion relative to friction than G. gecko. We also found the angle of toe detachment accurately predicts a species' maximum performance limit when fit to a Weibull distribution. When considering the energy stored during setal attachment, we observed less work to remove P. grandis arrays when compared with G. gecko, suggesting P. grandis arrays may store less energy during attachment, a conclusion supported by our model estimates of stored elastic energy. Our predictions of the effective elastic modulus model suggested P. grandis arrays to have a lower modulus, E eff, but our experimental assays did not show differences in moduli between the species. The considered mathematical models successfully estimated most of our experimentally measured performance values, validating our three focal models as template models of gecko adhesion (see Full and Koditschek in J Exp Biol 202(23):3325–3332, 1999), and suggesting common setal mechanics for our focal species and possibly for all fibular adhesives. Future anchored models, built upon the above templates, may more accurately predict performance by incorporating additional parameters, such as variation in setal length and diameter. Variation in adhesive performance may affect gecko locomotion and as a result, future ecological observations will help to determine how species with different performance capabilities use their habitat.
Data from: Nanoindentation analysis of the micromechanical anisotropy in mouse cortical bone
Studies investigating micromechanical properties in mouse cortical bone often solely focus on the mechanical behaviour along the long axis of the bone. Therefore, data on the anisotropy of mouse cortical bone is scarce. The aim of this study is the first-time evaluation of the anisotropy ratio between the longitudinal and transverse directions of reduced modulus and hardness in mouse femurs by using the nanoindentation technique. For this purpose, nine 22-week-old mice (C57BL/6) were sacrificed and all femurs extracted. A total of 648 indentations were performed with a Berkovich tip in the proximal (P), central (C) and distal (D) regions of the femoral shaft in the longitudinal and transverse directions. Higher values for reduced modulus are obtained for indentations in the longitudinal direction, with anisotropy ratios of 1.72 ± 0.40 (P), 1.75 ± 0.69 (C) and 1.34 ± 0.30 (D). Hardness is also higher in the longitudinal direction, with anisotropic ratios of 1.35 ± 0.27 (P), 1.35 ± 0.47 (C) and 1.17 ± 0.19 (D). We observed a significant anisotropy in the micromechanical properties of the mouse femur, but the correlation for reduced modulus and hardness between the two directions is low (r2 < 0.3) and not significant. Therefore, we highly recommend performing independent indentation testing in both the longitudinal and transverse directions when knowledge of the tissue mechanical behaviour along multiple directions is required.
Clogging and Unclogging of Fine Particles in Porous Media: Micromechanical Insights from an Analog Pore System
<p>Contain data for supporting and replicating the article results</p>
Micromechanical Modeling Using Low Magnitude Mechanical Stimulation
ClinicalTrials.gov study NCT01921517. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Variation in setal micromechanics and performance of two gecko
Open the record for dataset details and reuse information.
Data from: Nanoindentation analysis of the micromechanical anisotropy in mouse cortical bone
Open the record for dataset details and reuse information.
Transcriptome of CD4+ T cells from mice conditionally lacking YAP1 versus WT in different synthetic micromechanical environments
GEO Series GSE146643. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Supporting video for "Temperature Modulated Micromechanical Thermal Analysis with Microstring Resonators Detects Multiple Coherent Features of Small Molecule Glass Transition"
<p>Supporting video for "Temperature Modulated Micromechanical Thermal Analysis with Microstring Resonators Detects Multiple Coherent Features of Small Molecule Glass Transition" . 720p Version. </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.