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8 results for “diffuse interface”
Data for "Electroferrofluids with Non-Equilibrium Voltage-Controlled Magnetism, Diffuse Interfaces, and Patterns"
<p>This dataset contains the raw data used for the publication "Electroferrofluids with Non-Equilibrium Voltage-Controlled Magnetism, Diffuse Interfaces, and Patterns".</p>
Supporting information for "Illuminating the nanostructure of diffuse interfaces: Recent advances and future directions in reflectometry techniques"
<p>This deposition contains the data and analysis (Jupyter notebooks) detailed in Illuminating the nanostructure of diffuse interfaces: Recent advances and future directions in reflectometry techniques. All Jupyter notebooks have also been converted into PDF files for ease of viewing.</p><p> </p><p>All data and code (notebooks) required to reproduce the analysis can be found within the "supporting_data_analysis.zip" archive. This archive contains two sub-directories:</p><ul><li>insituAnalysis<ul><li>Jupyter notebook outlining how to perform the `on-the-fly' analysis.</li><li>Data directory containing all temporally sliced neutron reflectometry data.</li></ul></li><li>MaxEnt<ul><li>Jupyter notebook outlining how to perform the maximum entropy modelling approach for polymer volume fraction profiles.</li><li>Data directory containing relevant neutron reflectometry data and PCHIP spline modelling by Gresham et al. (<a href="www.doi.org/10.1107/S160057672100251X">10.1107/S160057672100251X</a>).</li><li>Code available on the <a href="https://github.com/refnx/refnx-models/tree/master/MaxEntVFP">refnx-models GitHub repo</a>.</li></ul></li></ul><p> </p>
(new version data) Probing the atomically diffuse interfaces in core-shell nanoparticles in three dimensions
<p><strong>Deciphering the three-dimensional atomic structure of solid-solid interfaces in core-shell nanomaterials is the key to understand their remarkable catalytical, optical and electronic properties. Here, we probe the three-dimensional atomic structures of palladium-platinum core-shell nanoparticles at the single-atom level using atomic resolution electron tomography. We successfully quantify the rich structural variety of core-shell nanoparticles with heteroepitaxy in 3D at atomic resolution. Instead of forming an atomically-sharp boundary, the core-shell interface is atomically diffuse with an average thickness of 4.2 Å, irrespective of the particle's morphology or crystallographic texture. We observed dissolved free Pd and Pt single atoms and sub-nanometer clusters using cryogenic electron microscopy. The high concentration of Pd in the diffusive interface is highly related to the free Pd atoms dissolved from the Pd seeds. These results advance our understanding of core-shell structures at the fundamental level, providing potential strategies into precise nanomaterial manipulation and chemical property regulation.</strong></p> <p> </p> <p>The data and source codes for the paper "Probing the atomically diffuse interfaces in core-shell nanoparticles in three dimensions" are posted below.</p> <p><strong># Repositary Contents</strong></p> <p><strong>### 1. Experiment Data</strong></p> <p>Folder: [Measured_data](./1_Measured_data)</p> <p>This folder contains denoised and aligned ADF-STEM projections and corresponding finalized tilt angles for three Pd@Pt core-shell nanoparticles. Three particles are named PB (pentagonal bipyramid shaped), EPB (elongated pentagonal bipyramid shaped) and TO (truncated octahedron shaped), respectively.</p> <p><strong>### 2. Reconstructed 3D Volume</strong></p> <p>Folder: [Final_reconstruction_volume](./2_Final_reconstruction_volume)</p> <p>This folder contains 3D tomographic reconstruction volumes of three particles. For the source code of RESIRE algorithm used in these reconstructions, please see the [source code](https://github.com/AET-MetallicGlass/Supplementary-Data-Codes/tree/master/2_RESIRE_package) of Yao Yang's paper on github.</p> <p><strong>### 3. Atom Tracing and Classification</strong></p> <p>Folder: [Tracing_and_classification](./3_Tracing_and_classification)</p> <p>This folder contains the source code to trace and classify atoms in the 3D volume.</p> <p><strong>### 4. Experimental Atomic Models</strong></p> <p>Folder: [Final_coordinates](./4_Final_coordinates)</p> <p>This folder contains the final coordinates of three nanoparticles.</p> <p><strong>### 5. Analysis of core-shell interface and others</strong></p> <p>Folder: [Analysis_of_interface](./5_Analysis_of_interface)</p> <p>This folder contains the codes to analyse the pair distribution function (PDF), the core-shell interface, the local coordination structure (PTM), the displacement and strain map of three nanoparticles.</p>
Data Files for PyGDSM: Python interface to the Global Diffuse Sky Model
<p>HDF5 data files for PyGDSM: Python interface to Global Diffuse Sky Models</p> <p>PyGDSM is a Python interface for the Global Diffuse Sky Models (GDSM ascl:1011.010). GDSM are models of diffuse galactic radio emission, constructed from a variety of all-sky surveys spanning the radio band (e.g. Haslam and WMAP). PyGDSM uses the Global Sky Model (GSM2008) of <a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2966.2008.13376.x/abstract">Oliveira-Costa et. al., (2008)</a>, <a href="http://arxiv.org/abs/1605.04920">Zheng et. al., (2016)</a> model GSM2016, and <a href="https://lda10g.alliance.unm.edu/LWA1LowFrequencySkySurvey/">LWA1 Low Frequency Sky Model</a> (LFSM). The PyGDSM module provides visualization utilities, file output in FITS format, and the ability to generate observed skies for a given location and date. PyGDSM requires <a href="https://healpy.readthedocs.org/en/latest/">Healpy</a>, PyEphem (ascl:1112.014), and AstroPy (ascl:1304.002).</p>
Homogenized anisotropic thermal conductivity on microstructure of binary composite with thermally imperfect diffuse interface
<p>This dataset contains supplementary data and utilities of the publication "A diffuse-interface model of anisotropic interface thermal conductivity and its application in thermal homogenization of composites" (<a href="https://doi.org/10.1016/j.scriptamat.2022.114537">Yang, 2022</a>).</p> <p>We performed thermal homogenization on microstructures with three types of inclusion geometries, in which ones with oval-shaped and fiber-shaped inclusions were read from characterized/generated digital microstructures, and ones with irregular-shaped inclusions were imported from the phase-field additive manufacturing simulations <a href="https://doi.org/10.1002/gamm.202100017">(Zhou, 2021)</a>. Si-Hf-N was chosen as the composite material system with β-Si<sub>3</sub>N<sub>4</sub> as the sole matrix phase and HfN as the sole inclusion phase. Direct homogenization method with the linear temperature BCs (see Supplementary Note 3 of the publication) was adopted.</p> <p>This dataset documents homogenized anisotropic thermal conductivity of corresponding microstructure as a tensor with normalized interface thermal resistance varying from 10<sup>-8</sup> to 10<sup>12</sup>. Voxelized digital microstructures and utilities for visualizing the overall thermal anisotropy are also attached.</p> <table> <tbody> <tr> <td>Properties</td> <td>Value</td> <td>Dimension</td> <td>Description</td> </tr> <tr> <td><span class="math-tex">\(k_\mathrm{(i)}\)</span></td> <td>90</td> <td><span class="math-tex">\(\mathrm{W~m^{-1}~K^{-1}}\)</span></td> <td>Thermal conductivity of HfN inclusion (<a href="https://10.1016/j.mtphys.2020.100256">Li, 2020</a>)</td> </tr> <tr> <td><span class="math-tex">\(k_\mathrm{(m)}\)</span></td> <td>180</td> <td><span class="math-tex">\(\mathrm{W~m^{-1}~K^{-1}}\)</span></td> <td>Thermal conductivity of β-Si<sub>3</sub>N<sub>4</sub> matrix (<a href="https://10.1016/s0955-2219(98)00258-1">Li, 1999</a>)</td> </tr> <tr> <td><span class="math-tex">\(\ell\)</span></td> <td>5</td> <td><span class="math-tex">\(\mathrm{nm}\)</span></td> <td>Diffuse interface width</td> </tr> <tr> <td><span class="math-tex">\((X,Y,Z)\)</span></td> <td>(500,500,500)</td> <td><span class="math-tex">\(\mathrm{nm}\)</span></td> <td>Simulation domain size</td> </tr> </tbody> </table> <p><strong>Notice:</strong> The digital microstructure has been voxelized, which can be loaded by default as a 200x200x200 numpy array (see utilities.ipynb). In order to perform the homogenization, interface smoothening is required, i.e., to generate diffuse interfaces. In this work, we smoothened the interface by operating transient Allen-Cahn calculation with finite timesteps. See Supplementary Note 4 of the publication for more information.</p>
Thermal and percolative analysis of 3D diffuse-interface composite microstructure
<p>This dataset contains supplementary data and utilities of the publication "Data-driven thermal and percolative analysis of 3D diffuse-interface composite microstructure" (<a href="https://doi.org/10.1016/j.matdes.2023.111746">Fathidoost, 2023</a>).</p> <p>This dataset documents homogenized anisotropic thermal conductivity of the corresponding microstructure (identified by volume fraction (Vf) and aspect ratio (Ar)) as a tensor with normalized interface thermal resistance (see Table 1). Voxelized digital microstructures and utilities are also attached for visualizing the overall thermal anisotropy.</p> <p><em>Table 1: The geometrical and thermal parameters employed in the generated microstructures.</em></p> <table> <thead> <tr> <th>Parameters</th> <th>Value (Unit)</th> <th>Type</th> <th>Increment</th> </tr> </thead> <tbody> <tr> <td>Minor principal axes length</td> <td>5 (nm)</td> <td>Constant</td> <td>-</td> </tr> <tr> <td>Aspect ratio, <span class="math-tex">\(A_\mathrm{r}\)</span></td> <td>[1 ,6]</td> <td>Linear</td> <td>1</td> </tr> <tr> <td>Inclusion volume fraction, <span class="math-tex">\(V_\mathrm{f}\)</span></td> <td>[5, 60]</td> <td>Linear</td> <td>5</td> </tr> <tr> <td>Thermal conductivity ratio, <span class="math-tex">\(K_\mathrm{r}\)</span></td> <td>[15, 100]</td> <td>Linear</td> <td>15</td> </tr> <tr> <td>Normalized interface resistance, <span class="math-tex">\(\tilde{R}_\mathrm{s} \)</span></td> <td>[1e-6, 1e10]</td> <td>Logarithmic</td> <td>1e2</td> </tr> </tbody> </table> <p><strong>Notice:</strong> The digital microstructure has been voxelized and stored in the ExodusII format, which can be loaded and visualized by the post-processing software, such as ParaView. In order to perform the homogenization, interface smoothening is required, i.e., to generate diffuse interfaces. In this work, we smoothened the interface by operating transient Allen-Cahn calculation with finite timesteps. Sec. 2.1 of the publication for more information).</p>
Intra-tumour heterogeneity of Diffuse Large B-cell Lymphoma involves the induction of diversified stroma-tumour interfaces
GEO Series GSE157920. Mus musculus. 30 samples. Type: Expression profiling by high throughput sequencing.
Multi-scale coupling during magnetopause reconnection: interface between the electron and ion diffusion regions
<p>Output in IDL-save format from four frames of the two particle-in-cell simulations used in the manuscript. See manuscript (Appendix C) for simulation set-up. Unless otherwise specified, each of the following variables are given as NxM matrices, where N is the number of grid cells per row (X axis) and M is the number per column (Z axis). Among other variables, the data files named "frame#.sav" contain the following items, used in the paper:</p> <ul> <li>E[i]: the i (X, Y, or Z) component of the electric field vector</li> <li>B[i]: the i (X, Y, or Z) component of the magnetic field vector</li> <li>Ay: the Y component of the magnetic vector potential</li> <li>V[s][i]: the i (X, Y, or Z) component of the bulk velocity of species s (ions or electrons)</li> <li>den[s]: the number density of species s</li> <li>xx: N-element vector of the x location of each grid cell</li> <li>zz: M-element vector of the z location of each grid cell</li> </ul> <p>One other file, named "frame38_P.sav", gives each of the 6 unique elements of the ion and electron pressure tensor with the variables named as P[s][i][j], where i and j are X, Y, or Z and s is either i for ions or e for electrons. </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.