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312 results for “Confinement”
Data associated with the following publication: "Giant thermoelectric response of confined electrolytes with thermally activated charge carrier generation"
<p>Data associated with the following publication: "Giant thermoelectric response of confined electrolytes with thermally activated charge carrier generation" (DOI: <a title="" href="https://doi.org/10.48328/tudatalib-1376">https://doi.org/10.48328/tudatalib-1376</a>)</p>
Observation of confinement induced resonances in a 3D lattice
<p>Data set relative to the publication "Observation of confinement induced resonances in a 3D optical lattice", D.Capecchi, C.Cantillano, M.J. Mark, F. Meinert, A.Schindewolf, M. Landini, A. Saenz, F. Revuelta, H.-C. Naegerl (2022), arxiv:2209.12504</p>
Structure and dynamics of water confined in cylindrical nanopores with varying hydrophobicity
<p>Supporting data for Phil. Trans. R. Soc. A 379: 20200403 (2021)</p> <p><a href="https://doi.org/10.1098/rsta.2020.0403">http://doi.org/10.1098/rsta.2020.0403</a></p>
Data related to the article "Impedance of nanocapacitors from molecular simulations to understand the dynamics of confined electrolytes"
<p>Contains input files and data used to generate the figures of the article:</p> <p>Impedance of nanocapacitors from molecular simulations to understand the dynamics of confined electrolytes<br>(Giovanni Pireddu, Connie J. Fairchild, Samuel P. Niblett, Stephen J. Cox and Benjamin Rotenberg)</p> <p>ChemRxiv: https://doi.org/10.26434/chemrxiv-2023-2ccrw</p> <p>Published version: to be inserted upon publication</p> <p>The folder EXAMPLE_INPUT_FILES contains typical [MetalWalls](https://doi.org/10.21105/joss.02373) ([repository](https://gitlab.com/ampere2/metalwalls)) and [LAMMPS]([repository](https://github.com/lammps/lammps)) input files used to perform the molecular simulations.</p> <p>The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper (see below).</p> <p><br>Notes: <br>1) In the file names, the notation 'M01', 'M05', 'M10' and 'M15' refers to the salt concentration in each system (0.1, 0.5, 1.0 and 1.5, respectively). 'W' refers to pure water (0 M) systems.<br>2) In the file names, the notation 'd1', 'd2', 'd3', 'd4', refers to different interelectrode distances (d1= 2.56 nm; d2= 5.07 nm; d3= 9.80 nm; d4= 19.84 nm) <br>3) The files containing the polarization cross-correlation are marked with 'AxB' indicating the cross-correlation between the contributions A and B. Specifically A and B can be: <br> - T = total<br> - I = ion<br> - W = water</p> <p><br>Figure 1:<br>- Panel B<br> - 'Fig1_CapConcentration': Differential capacitance scaled by electrode area as a function of NaCl concentration<br>- Panel C<br> - 'Fig1_QACF_*': Electrode charge autocorrelation function<br>- Panel D<br> - 'Fig1_Norm_QACF_*': Normalized electrode charge autocorrelation function<br> - 'Fig1_NormChar_*': Normalized non-equilibrium charge response</p> <p>Figure 2:<br>- Panel A: <br> - 'Fig2_ReZ_*': Real part of impedance<br>- Panel B:<br> - 'Fig2_nImZ_*': Negative imaginary part of impedance<br>- Panel C:<br> - 'Fig2_ReZint_*': Real part of interfacial impedance<br> - 'Fig2_Resistivities.dat': Resistivity as a function of NaCl concentration (bulk, confined, Nernst-Einstein)<br>- Panel D:<br> - 'Fig2_nImZint_*': Negative imaginary part of interfacial impedance<br> - 'Fig2_ECM*': Capacitor contributions to the imaginary part of interfacial impedance (finite concentrations)<br> - 'Fig2_ECW1.dat': Capacitor contributions to the imaginary part of interfacial impedance (pure water). Full cell capacitance taken into account<br> - 'Fig2_ECW2.dat': Capacitor contributions to the imaginary part of interfacial impedance (pure water). Interfacial capacitance taken into account </p> <p>Figure 3:<br>- Panel A:<br> - 'Fig3_ReCond_Peyman_M10.dat': Real part of conductivity (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))<br> - 'Fig3_ReCond_Querry_M10.dat': Real part of conductivity (data from: MR Querry, RC Waring, WE Holland, GM Hale, W Nijm, Optical Constants in the Infrared for Aqueous Solutions of NaClt. J. Opt. Soc. Am. 62 (1972)) <br> - 'Fig3_ReCond_Vinh_M10.dat': Real part of conductivity (data from: NQ Vinh, et al., High-precision gigahertz-to-terahertz spectroscopy of aqueous salt solutions as a probe of the femtosecond-to-picosecond dynamics of liquid water. The J.<br>Chem. Phys. 142, 164502 (2015).)<br> - 'Fig3_ReCond_M10.dat': Real part of conductivity from MD simulations<br>- Panel B:<br> - 'Fig3_ReCond_M*/W.dat': Real part of conductivity from MD simulations<br> - 'Fig3_ReCond_Peyman_M*': Real part of conductivity (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))<br>- Panel C:<br> - 'Fig3_Cond0.dat': Static conductivity as a function of concentration (MD data)<br> - 'Fig3_Cond0_Buchner.dat': Static conductivity as a function of concentration (data from: R Buchner, GT Hefter, PM May, Dielectric relaxation of aqueous nacl solutions. The J. Phys. Chem. A 103, 1–9 (1999))<br> - 'Fig3_Cond0_Peyman.dat': Static conductivity as a function of concentration (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))</p> <p>Figure 4:<br>- Panel A: <br> - 'Fig4_ReZ_d*': Real part of impedance (MD simulations)<br> - 'Fig4_ReZEC_d*': Real part of impedance (equivalent circuit model)<br>- Panel B:<br> - 'Fig4_nImZ_d*': Negative imaginary part of impedance (MD simulations)<br> - 'Fig4_nImZEC_d*': Negative imaginary part of impedance (equivalent circuit model)</p> <p>Figure 5:<br>- 'Fig5_TauQ.dat': timescales from the total charge autocorrelation functions<br>- 'Fig5_iontot.dat': timescales from the TxI autocorrelation function<br>- 'Fig5_RC.dat': timescales from the RC estimates<br>- 'Fig5_RbulkC.dat': timescales from the RbulkC estimates<br>- 'Fig5_Taudiff.dat': timescales from the difference between electrolyte and pure water QACFs<br>- 'Fig5_taud.dat': tau_d analytical timescales<br>- 'Fig5_tauDebye.dat': tau_Debye analytical timescales<br>- 'Fig5_taumix.dat': tau_mix analytical timescales</p> <p>Figure 6:<br>- Panel A:<br> - 'Fig6_Static_*: Static correlation between polarization contributions as a function of salt concentration<br>- Panel B:<br> - 'Fig6_Dynamic_EQ_*_M01' Dynamical correlations between polarization contributions (equilibrium MD results)<br> - 'Fig6_Dynamic_NEQ_*_M01' Dynamical correlations between polarization contributions (non-equilibrium MD results)<br>- Panel C:<br> - 'Fig6_Dynamic_EQ_*_M10' Dynamical correlations between polarization contributions (equilibrium MD results)<br> - 'Fig6_Dynamic_NEQ_*_M10' Dynamical correlations between polarization contributions (non-equilibrium MD results)</p> <p> </p> <p> </p>
Fluid Drag Reduction by Magnetic Confinement
<p>Drag reduction of viscous liquid (Honey) with ferrofluid APG314.</p> <p>Friction factors and Reynolds number.</p> <p>Numerical simulations</p> <p>Microfluidics drag reduction</p> <p> </p>
Supplementary data and codes for "Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures"
<p>This file contains the data and codes used in the paper</p> <p>“Confinement-induced accumulation accumulation and de-mixing of microscopic active-passive mixtures”, S. Williams et al, 2022.</p> <p>It includes the data used in all the figures and supplementary figures, as well as the codes used for simulations and escape rate estimation. The data files are in .mat format. The codes are Matlab codes with the exception of the analytical estimate of the escape rate which is a Mathematica worksheet.</p>
Earthquake Response of Reinforced Concrete Frames with Infill and Active External Confinement: Tests and Dataset
<p>One option to retrofit reinforced concrete (RC) frames is the construction of infill walls. Many studies have shown that infill increases lateral strength and stiffness but tends to reduce drift capacity relative to bare frames. Fewer studies have quantified reductions in drift demand attributed to infills prior to failure. This report summarizes experiments designed to compare drift demands of frames with and without infill. Included data comes from two theses completed at Purdue University which focused on the dynamic response of one-third scale, non-ductile RC frames to uniaxial simulated earthquake ground motions. Tests were conducted on bare frames, frames with masonry infill walls, and frames with timber infill walls. In 11 of 14 test series, active confinement was applied to columns using external post-tensioned reinforcement</p> <p> </p> <p>This dataset summarizes two experimental programs that studied the dynamic, in-plane response of one-third scale RC frames with full-height infills and active external column confinement. Theses summarized were written by Monical (2021) and Kerby (2022), and included data from 254 in-plane dynamic tests of non-ductile RC frames with various seismic retrofits.</p>
Dataset for "Skyrmion states in thin confined polygonal nanostructures"
<p>This dataset provides micromagnetic simulation data collected from a series of computational experiments on the effects of polygonal system shape on the energy of different magnetic states in FeGe. The data here form the results of the study ‘Skyrmion states in thin confined polygonal nanostructures.’</p> <p>The dataset is split into several directories:</p> <p><strong>Data</strong></p> <p><em>square-samples and triangle-samples</em></p> <p>These directories contain final state ‘relaxed’ magnetization fields for square and triangle samples respectively. The files within are organised into directories such that a sample of side length d = 40nm and which was subjected to an applied field of 500mT is labelled d40b500. Within each directory are twelve VTK unstructured grid format files (with file extension “.vtu”). These can be viewed in a variety of programmes; as of the time of writing we recommend either ParaView or MayaVi. The twelve files correspond to twelve simulations for each sample simulated, corresponding to twelve states from which each sample was relaxed - these are described in the paper which this dataset accompanies, but we note the labels are:</p> <p>‘0’, ‘1’, ‘2’, ‘3’, ‘4’, ‘h’, ‘u’, ‘r1’, ‘r2’, ‘r3’, ‘h2’, ‘h3’</p> <p>where:</p> <ul> <li>0 - 4 are incomplete to overcomplete skyrmions,</li> <li>h, h2 and h3 are helical states with different periodicities</li> <li>r1-r3 are different random states</li> <li>u is the uniform magnetisation</li> </ul> <p>The vtu files are labelled according to parameters used in the simulation. For<br> example, a file labelled ‘160_10_3_0_u_wd000000.vtu’ encodes that:</p> <ol> <li> <p>The simulation was of a sample with side length 160nm.</p> </li> <li> <p>The simulation was of a sample of thickness 10nm.</p> </li> <li> <p>The maximum length of an edge in the finite element mesh of the sample was 3nm.</p> </li> <li> <p>The system was relaxed from the ‘u’.</p> </li> <li> <p>‘wd’ encodes that the simulation was performed with a full demagnetizing<br> calculation.</p> </li> </ol> <p><em>square-npys and triangle-npys</em></p> <p>These directories contain computed information about each of the final states stored in square-samples and triangle-samples. This information is stored in NumPy npz files, and can be read in Python straightforwardly using the function numpy.load. Within each npz file, there are 8 arrays, each with 12 elements. These arrays are:</p> <ol> <li>‘E’ - corresponds to the total energy of the relaxed state.</li> <li>‘E_exchange’ - corresponds to the Exchange energy of the relaxed state.</li> <li>‘E_demag’ - corresponds to the Demagnetizing energy of the relaxed state.</li> <li>‘E_dmi’ - corresponds to the Dzyaloshinskii-Moriya energy of the relaxed state.</li> <li>‘E_zeeman’ - corresponds to the Zeeman energy of the relaxed state.</li> <li>‘S’ - Calculated Skyrmion number of the relaxed state.</li> <li>‘S_abs’ - Calculated absolute Skyrmion number - see paper for calculation details.</li> <li>‘m_av’ - Computed normalised average magnetisation in x, y, and z directions for relaxed state</li> </ol> <p>The twelve elements here correspond to the aforementioned twelve states relaxed from, and the ordering of the array is that of the order given above.</p> <p><em>square-classified and triangle-classified</em></p> <p>These directories contain a labelled dataset which gives details about what the final state in each simulation is. The files are stored as plain text, and are labelled with the following structure (the meanings of which are defined in the paper which this dataset accompanies):</p> <ol> <li>iSk - Incomplete Skyrmion</li> <li>Sk, or a number n followed by Sk - n Skyrmions in the state.</li> <li>He - A helical state</li> <li>Target - A target state.</li> </ol> <p>The files contain the names of png files which are generated from the vtu files in the format ‘d_165b_350_2.png’. This example, if found in the ‘Sk.txt’ file, means that the sample which was 165nm in side length and which was relaxed under a field of 350mT from initial state 2 was found at equilibrium in a Skyrmion state.</p> <p><strong>Figures</strong></p> <p><strong><em>square-pngs and triangle-pngs</em></strong></p> <p>These directories contain generated pngs from the vtu files. These are included for convenience as they take several hours to generate. Each directory contains three subdirectories:</p> <p><em>all-states</em></p> <p>This directory contains the simulation results from all samples, in the format ‘d_165b_350_2.png’, which means that the image contained here is that of the 165nm side length sample relaxed under a 350mT field from initial state 2.</p> <p><em>ground-state</em></p> <p>This directory contains the images which correspond to the lowest energy state found from all of the initial states. These are labelled as ‘d_180b_50.png’, such that the image contained in this file is the the lowest energy state found from all twelve simulations of the 180nm sidelength under a 50mT field.</p> <p><em>uniform-state</em></p> <p>This directory contains the images which correspond to the states relaxed only from the uniform state. These are labelled such that an image labelled ‘d_55b_100.png’ is the state found from relaxing a 180nm sample under a 100mT applied field.</p> <p><em><strong>phase-diagrams</strong></em></p> <p>These are the generated phase diagrams which are found in the paper.</p> <p><strong>scripts</strong></p> <p>This folder contains Python scripts which generate the png files mentioned above, and also the phase diagram figures for the paper this dataset accompanies. The scripts are labelled descriptively with what they do - for e.g. ’triangle-generate-png-all-states.py’ contains the script which loads vtu files and generates the png files. The exception here is ’render.py’ which provides functions used across multiple scripts. These scripts can be modified - for example; the function 'export_vector_field' has many options which can be adjusted to, for example, plot different components of the magnetization.</p> <p>In order to run the scripts reproducibly, in the root directory we have provided a Makefile which builds each component. In order to reproduce the figures yourself, on a Linux system, ParaView must be installed. The Makefile has been tested on Ubuntu 16.04 with ParaView 5.0.1. In addition, a number of Python dependencies must also be installed. These are:</p> <ul> <li>scipy >=0.19.1</li> <li>numpy >= 1.11.0</li> <li>matplotlib == 1.5.2</li> <li>pillow>=3.1.2</li> </ul> <p>We have included a requirements.txt file which specifies these dependencies; they can be installed by running 'pip install -r requirements.txt' from the directory.</p> <p>Once all dependencies are installed, simply run the command ‘make’ from the shell to build the Docker image and generate the figures. Note the scripts will take a long time to run - at the time of writing the runtime will be on the order of several hours on a high-specification desktop machine. For convenience, we have therefore included the generated figures within the repository (as noted above). It should be noted that for the versions used in the paper, adjustments have been made after the generation of the figures, (for e.g. to add images of states within the metastability figure, and overlaying boundaries in the phase diagrams).</p> <p>If you want to reproduce only the phase diagrams, and not the pngs, the command ‘make phase-diagrams’ will do so. This is the smallest part of the figure reproduction, and takes around 5 minutes on a high-specification desktop.</p>
Data for "Diversity of Non-Equilibrium Patterns and Emergence of Activity in Confined Electrohydrodynamically Driven Liquids"
<p>Raw data (microscopy videos and image sequences) and scripts used for the analysis for the publication "Diversity of Non-Equilibrium Patterns and Emergence of Activity in Confined Electrohydrodynamically Driven Liquids", Science Advances 7 (38), eabh1642</p>
Distinct States in an Active Liquid Crystal of Flagella and Kinesin-Powered Microtubules under Varying Confinement
<p>At significant confinement levels, the potent active stress disrupts the alignment of the flagella, leading to a turbulent state in the composite liquid crystal. Conversely, minimal confinement enables the dominant nematic alignment to counteract the active stress, inducing the phase separation of microtubules within a uniform flagella background. The confocal micrograph unveils uniform flagella distribution across the medium, while the microtubules segregate into a distinct lane-like structure within the channel's narrowest confinement.</p>
Dati di supporto al volume Enrica Salvatori, Il fegato del vescovo. Studi di confine sui confini della Lunigiana medievale
<p>Dati di supporto al volume Enrica Salvatori, Il fegato del vescovo. Studi di confine sui confini della Lunigiana medievale </p>
Data set for: Real-space Imaging of Confined Magnetic Skyrmion Tubes
<p>This repository contains the scripts and notebooks to reproduce the figures, simulations and numerical data shown in <strong>Real-space Imaging of Confined Magnetic Skyrmion Tubes</strong> by <em>M. T. Birch, D. Cortés-Ortuño, L. A. Turnbull, M. N. Wilson, F. Groß, N. Träger, A. Laurenson, N. Bukin, S. H. Moody, M. Weigand, G. Schütz, H. Popescu, R. Fan, P. Steadman, J. A. T. Verezhak, G. Balakrishnan, J. C. Loudon, A. C. Twitchett-Harrison, O. Hovorka, H. Fangohr, F. Ogrin, J. Gräfe and P. D. Hatton.</em></p> <p>Both simulation and experimental data analysis are performed using Python with the Matplotlib, Jupyter, Scipy, Numpy and h5py libraries.</p> <p>Jupyter notebooks are provided to process the experimental data and reproduce the STXM, X-Ray Holography and LTEM images, which are shown as Figures 2, 3, 4 and 5 in the paper.</p> <p>Simulation scripts are based on the finite difference micromagnetic code OOMMF with the extension to simulate DMI for materials with symmetry class <em>T</em>: [oommf-extension-dmi-t](https://github.com/joommf/oommf-extension-dmi-t)</p> <p>The analysis of OOMMF's output files, which are in the `OMF` format, are processed using the [OOMMFPy](https://github.com/davidcortesortuno/oommfpy) library, which can calculate the topological charge in a 2D slice.</p> <p>Three-dimensional visualisations of the magnetic states are performed using Paraview. In order to get VTK files for visualisation, convert the `OMF` files into `.vtk` using the `OOMMFPy` library.</p> <p> </p> <p>Latest version of this Data Set can be found at the Github repository:</p> <p><a href="https://github.com/davidcortesortuno/paper-2020_real-space_imaging_of_confined_magnetic_skyrmion_tubes">https://github.com/davidcortesortuno/paper-2020_real-space_imaging_of_confined_magnetic_skyrmion_tubes</a></p>
Fig. 7 in Reproduction of two loricariid species in a confined river and implications for environmental impacts of dams
Fig. 7. Monthly changes in mean (± SE) gonadosomatic index (GSI; dots represent individual values) of Hemiancistrus fuliginosus (a. females; c. males) and Hypostomus isbrueckeri (b. females; d. males). Results of the Kruskal-Wallis tests: Hem. fuliginosus females, H = 47.5, df = 7, p <0.001; males, H = 6.9, df = 7, p <0.44; Hyp. isbrueckeri females, H = 14.4, df = 7, p <0.045, males H = 20.4, df = 7, p <0.005.
Stealth dark matter confinement transition and gravitational waves --- data release
<p>This HDF5 file collects data and analysis results for non-perturbative lattice field theory calculations investigating the confinement transition of SU(4) stealth dark matter and the possibility that this early-universe transition may produce an observable stochastic background of gravitational waves. See the README for further information.</p> <p> </p>
Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus
<p>Description for Zenodo Data Set DOI:10.5281/zenodo.45507</p> <p>This dataset accompanies the article, submitted to Physics of Plasmas, titled "Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus," by Garnier, Mauel, Roberts, Kesner, and Woskov. </p> <p>---------------------------------------------</p> <p>Data is presented as HDF5 datafiles <br /> (see https://www.hdfgroup.org/HDF5/) <br /> as HDF4 datafiles<br /> (see https://www.hdfgroup.org/release4/doc/index.html)<br /> and as CSV datafiles <br /> (see http://www.digitalpreservation.gov/formats/fdd/fdd000323.shtml).</p> <p>Data files are associated with FIGURES 2, 3, 4, 5, 6</p> <p>---------------------------------------------<br /> start of figure list<br /> ---------------------------------------------<br /> FIGURE 1: No data set</p> <p>---------------------------------------------<br /> FIGURE 2: (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br /> S140529016_DensityData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 8 micro-sec<br /> samples: 250,000 x 4 channels + 250,000 (total)<br /> Unit: radian "Interferometer"<br /> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br /> S140529016_PDAData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 20 micro-sec<br /> samples: 100,000 x 16 channels<br /> Unit: A.U. "PDA-1"</p> <p>TOTAL ECRH Injected Heating Power<br /> S140529016_ECRHData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 20 micro-sec<br /> samples: 100,000<br /> Unit: kW "Microwave-Power"</p> <p>S140529016_LoopVoltage.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 80 micro-sec<br /> samples: 25,000 <br /> Unit: milli-Volt x sec "Loop-Voltage"</p> <p>---------------------------------------------<br /> FIGURE 3(a): (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br /> S140529016_DensityData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 8 micro-sec<br /> samples: 250,000 x 4 channels + 250,000 (total)<br /> Unit: radian "Interferometer"<br /> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br /> S140529016_PDAData.hdf <br /> time range: 5.00 sec - 7.00 sec<br /> time sample: 20 micro-sec<br /> samples: 100,000 x 16 channels<br /> Unit: A.U. "PDA-1"</p> <p>---------------------------------------------<br /> FIGURE 4 (a), (b), (c): (CSV Files)</p> <p>time period: 5.0 - 6.0 sec<br /> Ensemble Window: 8 msec<br /> sample period: 8 micro-sec<br /> Nyquist Freq: 62.475 kHz</p> <p>Line-Density-Coherence.csv<br /> Frequency (Hz) Hz<br /> d(nl-1)^2 dimensionless<br /> d(nl-2)^2 dimensionless <br /> d(nl-3)^2 dimensionless<br /> d(nl-4)^2 dimensionless<br /> Kappa 1-2 dimensionless<br /> Kappa 1-3 dimensionless<br /> Kappa 1-4 dimensionless</p> <p>Isat-Coherence.csv <br /> Frequency (Hz) Hz<br /> d(I)^2 dimensionless<br /> Kappa 8deg dimensionless<br /> Kappa 16deg dimensionless<br /> Kappa 24deg dimensionless</p> <p>Float-Potential-Coherence.csv<br /> Frequency (Hz) Hz<br /> d(Pot)^2/Te^ dimensionless<br /> Kappa 8deg dimensionless<br /> Kappa 16deg dimensionless<br /> Kappa 24deg dimensionless</p> <p>---------------------------------------------<br /> FIGURE 5 (a), (b): (CSV Files)</p> <p>Figure 5(a)<br /> time period: 5.0 - 6.0 sec<br /> Ensemble Window: 8 msec<br /> sample period: 8 micro-sec<br /> Nyquist Freq: 62.475 kHz</p> <p>Float-Isat-CrossPhase.csv <br /> Frequency (Hz) Hz<br /> alpha-Float degree<br /> alpha-Isat degree</p> <p>Figure 5(b)<br /> time period: 6.02 - 6.05 sec<br /> Ensemble Window: 1.6 msec<br /> sample period: 8 micro-sec<br /> Nyquist Freq: 62.475 kHz</p> <p>Float-Isat-DuringCrossPhase.csv <br /> Frequency (Hz) Hz<br /> alpha-Float degree<br /> alpha-Isat degree<br /> kappa-Float dimensionless<br /> kappa-Isat dimensionless<br /> ---------------------------------------------<br /> FIGURE 6: No data set</p> <p>---------------------------------------------<br /> FIGURE 7: No data set</p> <p>---------------------------------------------<br /> FIGURE 8: No data set</p> <p>---------------------------------------------<br /> FIGURE 9: No data set</p> <p>---------------------------------------------<br /> FIGURE 10: No data set</p> <p>---------------------------------------------<br /> end of figure list<br /> ---------------------------------------------<br /> ---------------------------------------------<br /> start of file list<br /> ---------------------------------------------<br /> Filename Size <br /> ----------------------------------------------<br /> Potential-Time-Angle-Data.h5 254.68 KB <br /> All-Probe-Data.h5 9.81 MB <br /> Average-Probe-Data.h5 1.06 MB <br /> Average-Isat-Data.h5 422.22 KB <br /> S140529016_PDAData.hdf 6.80 MB <br /> S140529016_DensityData.hdf 6.00 MB <br /> S140529016_ECRHData.hdf 803.39 KB <br /> S140529016_LoopVoltage.hdf 203.39 KB <br /> Line-Density-Coherence.csv 420.59 KB <br /> Isat-Coherence.csv 258.51 KB <br /> Float-Potential-Coherence.csv 257.90 KB<br /> Float-Isat-DuringCrossPhase.csv 48.87 KB <br /> Float-Isat-CrossPhase.csv 138.47 KB <br /> LDX-Pellet-Supplementary.pdf 1.84 MB <br /> S140529016_frPlots.mp4 3.24 MB <br /> ---------------------------------------------<br /> end of file list<br /> ---------------------------------------------</p>
Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus
<p><strong>Description for Zenodo Data Set DOI:10.5281/zenodo.220992</strong></p> <p>This dataset accompanies the article, to appear in Physics of Plasmas, titled "Turbulent Fluctuations During Pellet Injection into a Dipole Confined Plasma Torus," by Garnier, Mauel, Roberts, Kesner, and Woskov. </p> <p>---------------------------------------------</p> <p>Data is presented as HDF5 datafiles <br> (see https://www.hdfgroup.org/HDF5/) <br> as HDF4 datafiles<br> (see https://www.hdfgroup.org/release4/doc/index.html)<br> and as CSV datafiles <br> (see http://www.digitalpreservation.gov/formats/fdd/fdd000323.shtml).</p> <p>Data files are associated with FIGURES 2, 3, 4, 5</p> <p>Data plotted in other figures are derived from the dataset as described<br> in the paper.</p> <p>---------------------------------------------<br> start of figure list<br> ---------------------------------------------<br> <strong>FIGURE 1: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 2:</strong> (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br> S140529016_DensityData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 8 micro-sec<br> samples: 250,000 x 4 channels + 250,000 (total)<br> Unit: radian "Interferometer"<br> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br> S140529016_PDAData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 20 micro-sec<br> samples: 100,000 x 16 channels<br> Unit: A.U. "PDA-1"</p> <p>TOTAL ECRH Injected Heating Power<br> S140529016_ECRHData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 20 micro-sec<br> samples: 100,000<br> Unit: kW "Microwave-Power"</p> <p>S140529016_LoopVoltage.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 80 micro-sec<br> samples: 25,000 <br> Unit: milli-Volt x sec "Loop-Voltage"</p> <p>---------------------------------------------<br> <strong>FIGURE 3(a)</strong>: (HDF4 Files)</p> <p>Four-channel Microwave (60 GHz) Interferometer <br> S140529016_DensityData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 8 micro-sec<br> samples: 250,000 x 4 channels + 250,000 (total)<br> Unit: radian "Interferometer"<br> Unit: 1.0E18 particles "Total-Particles"</p> <p>16-channel Photodiode Array 1 <br> S140529016_PDAData.hdf <br> time range: 5.00 sec - 7.00 sec<br> time sample: 20 micro-sec<br> samples: 100,000 x 16 channels<br> Unit: A.U. "PDA-1"</p> <p>---------------------------------------------<br> <strong>FIGURE 4 (a), (b), (c), (d), (e), (f): </strong> (CSV Files)</p> <p>time period: 5.0 - 6.0 sec<br> Ensemble Window: 8 msec<br> sample period: 8 micro-sec<br> Nyquist Freq: 62.475 kHz</p> <p>(a) Fig4a-Line-Density-Coherence.csv<br> Frequency (Hz) Hz<br> d(nl-1)^2 dimensionless<br> d(nl-2)^2 dimensionless <br> d(nl-3)^2 dimensionless<br> d(nl-4)^2 dimensionless<br> Lambda 1-2 dimensionless<br> Lambda 1-3 dimensionless<br> Lambda 1-4 dimensionless</p> <p>(b) Fig4b-Isat-Coherence.csv <br> Frequency (Hz) Hz<br> d(I)^2 dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>(c) Fig4c-Float-Potential-Coherence.csv<br> Frequency (Hz) Hz<br> d(Pot)^2/Te^ dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>(d) Fig4d-Line-Density-Coherence.csv<br> Frequency (Hz) Hz<br> d(nl-1)^2 dimensionless<br> d(nl-2)^2 dimensionless <br> d(nl-3)^2 dimensionless<br> d(nl-4)^2 dimensionless<br> Lambda 1-2 dimensionless<br> Lambda 1-3 dimensionless<br> Lambda 1-4 dimensionless</p> <p>(e) Fig4e-Isat-Coherence.csv <br> Frequency (Hz) Hz<br> d(I)^2 dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>(f) Fig4f-Float-Potential-Coherence.csv<br> Frequency (Hz) Hz<br> d(Pot)^2/Te^ dimensionless<br> Lambda 8deg dimensionless<br> Lambda 16deg dimensionless<br> Lambda 24deg dimensionless</p> <p>---------------------------------------------<br> <strong>FIGURE 5 (a), (b):</strong> (CSV Files)</p> <p>Figure 5(a)<br> time period: 5.0 - 6.0 sec<br> Ensemble Window: 8 msec<br> sample period: 8 micro-sec<br> Nyquist Freq: 62.475 kHz</p> <p>(a) Fig5a-Float-Isat-CrossPhase.csv <br> Frequency (Hz) Hz<br> alpha-Float degree<br> alpha-Isat degree</p> <p>Figure 5(b)<br> time period: 6.02 - 6.05 sec<br> Ensemble Window: 1.6 msec<br> sample period: 8 micro-sec<br> Nyquist Freq: 62.475 kHz</p> <p>(b) Fig5b-Float-Isat-DuringCrossPhase.csv <br> Frequency (Hz) Hz<br> alpha-Float degree<br> alpha-Isat degree<br> kappa-Float dimensionless<br> kappa-Isat dimensionless<br> ---------------------------------------------<br> <strong>FIGURE 6: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 7: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 8:</strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 9: </strong> No data set</p> <p>---------------------------------------------<br> <strong>FIGURE 10:</strong> No data set</p> <p>---------------------------------------------<br> end of figure list<br> ---------------------------------------------<br> ---------------------------------------------<br> <strong>start of file list</strong><br> ---------------------------------------------<br> Filename Size <br> ----------------------------------------------<br> All-Probe-Data.h5 9.8 MB<br> Average-Isat-Data.h5 422 KB<br> Average-Probe-Data.h5 1.1 MB<br> Fig4a-Line-Density-Coherence.csv 413 KB<br> Fig4b-Isat-Coherence.csv 251 KB<br> Fig4c-Float-Potential-Coherence.csv 250 KB<br> Fig4d-Line-Density-Coherence.csv 103 KB<br> Fig4e-Isat-Coherence.csv 62 KB<br> Fig4f-Float-Potential-Coherence.csv 62 KB<br> Fig5a-Float-Isat-CrossPhase.csv 138 KB<br> Fig5b-Float-Isat-DuringCrossPhase.csv 36 KB<br> Potential-Time-Angle-Data.h5 255 KB<br> S140529016_DensityData.hdf 6 MB<br> S140529016_ECRHData.hdf 803 KB<br> S140529016_LoopVoltage.hdf 203 KB<br> S140529016_PDAData.hdf 6.8 MB</p> <p><br> LDX-Pellet-Supplementary.pdf 1.8 MB<br> S140529016_frPlots.mp4 3.2 MB<br> ---------------------------------------------<br> <strong>end of file list</strong><br> ---------------------------------------------</p> <p> </p> <p> </p>
Skyrmions at the edge: Confinement effects in Fe/Ir(111)
<p>We have employed spin-polarized scanning tunneling microscopy and Monte-Carlo simulations to investigate the effect of lateral confinement onto the nanoskyrmion lattice in Fe/Ir(111). We find a strong coupling of one diagonal of the square magnetic unit cell to the close-packed edges of Fe nanostructures. In triangular islands this coupling in combination with the mismatching symmetries of the islands and of the square nanoskyrmion lattice leads to frustration and triple-domain states. In direct vicinity to ferromagnetic NiFe islands, the surrounding skyrmion lattice forms additional domains. In this case a side of the square magnetic unit cell prefers a parallel orientation to the ferromagnetic edge. These experimental findings can be reproduced and explained by Monte-Carlo simulations. Here, the single-domain state of a triangular island is lower in energy, but nevertheless multi-domain states occur due to the combined effect of entropy and an intrinsic domain wall pinning arising from the skyrmionic character of the spin texture.</p>
In-plane dielectric constant and conductivity of confined water
<p>This dataset contains source data from the article "In-plane dielectric constant and conductivity of confined water". AFM data are provided in .txt format and plots are provided in .xlsx format, with filenames corresponding to the figure labels as in the article.</p>
Data and figure reproduction for paper titled 'Lateral quantum-confined Stark effect for integrated quantum dot electroabsorption modulators'
<p>The dataset contains the data behind, and instructions to reproduce, the figures in the paper titled 'Lateral quantum-confined Stark effect for integrated quantum dot electroabsorption modulators' written by the named dataset creators.</p>
Data for "Mechanochemistry of Phosphate Esters Confined between Sliding Iron Surfaces"
<p>Data from nonequilibrium Molecular Dynamics simulation of tri(n-butyl)phosphate confinid between sliding iron surfaces at 400K and 2GPa conditions, from Mechanochemistry of Phosphate Esters Confined between Sliding Iron Surfaces (10.21203/rs.3.rs-608818). Other conditions, and simulations of tri(s-butyl)phosphate simulations, are available on request.</p> <p> </p> <p>The data contains the reaxff bonding information from reaxff (bonds_*.txt), the trajectories (dump_*.lammpstrj) and the vertical and horizontal forces on the wall (fc_ave.dump), from which the shear and friction are calculated.</p>
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