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639 results for “spin”
Spin-mixing enhanced proximity effect in aluminum-based superconductor-semiconductor hybrids
<p>In superconducting quantum circuits, aluminum is one of the most widely used materials. It is currently also the superconductor of choice for the development of topological qubits. In this application, however, aluminum-based devices suffer from poor magnetic field compatibility. In this article, we resolve this limitation by showing that adatoms of heavy elements (e.g. platinum) increase the critical field of thin aluminum films by more than a factor of two. Using tunnel junctions, we show that the increased field resilience originates from spin-orbit scattering introduced by Pt. We exploit this property in the context of the superconducting proximity effect in semiconductor-superconductor hybrids, where we show that InSb nanowires strongly coupled to Al/Pt films can maintain superconductivity up to 7 T. The two-electron charging effect, a fundamental requirement for topo- logical quantum computation, is shown to be robust against the presence of heavy adatoms. Additionally, we use non-local spectroscopy in a three-terminal geom- etry to probe the bulk of hybrid devices, showing that it remains free of sub-gap states. Finally, we demonstrate that semiconductor states which are proximi- tized by Al/Pt films maintain their ability to Zeeman-split in an applied magnetic field. Combined with the chemical stability and well-known fabrication routes of aluminum, Al/Pt emerges as the natural successor to Al-based systems and is a compelling alternative to other superconductors, whenever high-field resilience is required. </p> <p> </p>
Supporting Data for "A hole spin qubit in a fin field-effect transistor above 4 kelvin"
<p>Supporting data for</p> <p><strong>"A hole spin qubit in a fin field-effect transistor above 4 kelvin"</strong>,<br> Leon C. Camenzind, Simon Geyer, Andreas Fuhrer, Richard J. Warburton, Dominik M. Zumbühl and Andreas V. Kuhlmann</p> <p>Nature Electronics (2022), <a href="https://doi.org/10.1038/s41928-022-00722-0">DOI:10.1038/s41928-022-00722-0</a></p>
Further Evidence of Modified Spin-down in Sun-like Stars: Pileups in the Temperature–Period Distribution
<p>Datasets for "Further Evidence of Modified Spin-down in Sun-like Stars: Pileups in the Temperature–Period Distribution," by David et al. 2022 (in review at The Astrophysical Journal).</p> <p>The article pre-print is available on <a href="https://arxiv.org/abs/2203.08920">arXiv</a>.The code required to reproduce this work is available on <a href="https://github.com/trevordavid/rossby-ridge">GitHub</a>.</p> <p> </p>
Supplementary information: The nuclear-spin-forbidden rovibrational transitions of water from first principles
<p><strong>Supplementary material to the manuscript <em>"The nuclear-spin-forbidden rovibrational transitions of water from first principles"</em> by Andrey Yachmenev, Guang Yang, Emil Zak, Sergei Yurchenko, and Jochen Küpper, <em>J. Chem. Phys., submitted. </em></strong><a href="https://arxiv.org/abs/2203.07945"> arXiv:2203.07945</a></p> <p>The data set contains hyperfine (spin-rovibrational) energies and dipole transition spectrum of water molecule (H<sub>2</sub><sup>16</sup>O), calculated using variational approach <a href="https://github.com/Trovemaster/TROVE">TROVE</a> and <a href="https://github.com/CFEL-CMI/richmol">RichMol</a>, and included spin-rotational and spin-spin hyperfine interactions.</p> <p>In addition, the data set includes HDF5-type richmol database file <strong><em>h2o_p48_j40_rovib.h5</em></strong> (see <a href="https://github.com/CFEL-CMI/richmol">https://github.com/CFEL-CMI/richmol</a>) containing rovibrational energies, matrix elements of nuclear spin-rotation, nuclear spin-spin, electric dipole, and electric quadrupole tensor operators of H<sub>2</sub><sup>16</sup>O, calculated using variational approach TROVE.</p> <ul> <li><strong>h2o_exomol_F.states </strong>and<strong> h2o_exomol_F.trans</strong> - hyperfine linelist of water stored in the ExoMol format (see, e.g., <a href="https://doi.org/10.1016/j.jms.2016.05.002">J. Molec. Spectrosc., 327, 73-94 (2016)</a>). The two files contain a set of hyperfine states with assignments and a set of dipole transitions (Einstein A-coefficients), respectively. The states in <strong>h2o_exomol_F.states</strong> file are arranged by quantum number of total angular momentum F = I + J (spin + rotation) in ascending order.</li> <li><strong>h2o_exomol_J.states </strong>and<strong> h2o_exomol_J.trans</strong> - contain same data as <strong>h2o_exomol_F.states </strong>and<strong> h2o_exomol_F.trans</strong> files, except that the states in <strong>h2o_exomol_J.states</strong> file are arranged by rotational quantum number (J) in ascending order.</li> <li><strong>h2o_p48_j40_rovib.h5<em> - </em></strong>Richmol HDF5 database file for H<sub>2</sub><sup>16</sup>O containing rovibrational energies (in cm<sup>-1</sup>), matrix elements of nuclear spin-rotation (in kHz), spin-spin (in kHz), molecular electric dipole moment (in Debye), and molecular electric quadrupole moment (in a.u.) operators. For details on how to read this file, see <a href="https://github.com/CFEL-CMI/richmol">Richmol GitHub repository</a> and <a href="https://richmol.readthedocs.io/en/latest/">Richmol documentation</a> (<em>or contact Andrey Yachmenev at andrey.yachmenev@cfel.de</em>).</li> <li><strong>ortho_para_transitions.txt</strong> - table with strongest predicted ortho-para transitions in H<sub>2</sub><sup>16</sup>O at T = 296 K with the 10<sup>−36</sup> cm/molecule intensity cut-off.<br> <br> <strong><em>An example of hyperfine energies and hyperfine dipole spectrum calculation for water using h2o_p48_j40_rovib.h5 file from this repository may be found in the <a href="https://github.com/CFEL-CMI/richmol">Richmol GitHub repository's</a> examples folder: <a href="https://github.com/CFEL-CMI/richmol/tree/develop/examples/hyperfine">https://github.com/CFEL-CMI/richmol/tree/develop/examples/hyperfine</a></em></strong></li> </ul> <p>Structure of<strong> h2o_exomol_F.states </strong>and<strong> h2o_exomol_J.states </strong>files:</p> <table align="left"> <thead> <tr> <th scope="col">Column No.</th> <th scope="col">Kind </th> <th scope="col">Meaning </th> </tr> </thead> <tbody> <tr> <td>1</td> <td>int</td> <td>state ID number</td> </tr> <tr> <td>2</td> <td>float</td> <td>hyperfine state energy relative to the ZPE, in cm<sup>-1</sup></td> </tr> <tr> <td>3</td> <td>int</td> <td>state degeneracy</td> </tr> <tr> <td>4</td> <td>int</td> <td>value of F quantum number (total spin-rotational angular momentum)</td> </tr> <tr> <td>5</td> <td>str</td> <td>state symmetry in C<sub>2v</sub></td> </tr> <tr> <td>6</td> <td>int</td> <td>value of J quantum number (total rotational angular momentum)</td> </tr> <tr> <td>7</td> <td>str</td> <td>symmetry of state's rotational component in C<sub>2v</sub></td> </tr> <tr> <td>8</td> <td>int</td> <td>value of k<sub>a</sub> quantum number (a-axis projection of rotational angular momentum)</td> </tr> <tr> <td>9</td> <td>int</td> <td>value of k<sub>c</sub> quantum number (c-axis projection of rotational angular momentum)</td> </tr> <tr> <td>10</td> <td>int</td> <td>value of v<sub>1</sub> vibrational quantum number</td> </tr> <tr> <td>11</td> <td>int</td> <td>value of v<sub>2</sub> vibrational quantum number</td> </tr> <tr> <td>12</td> <td>int</td> <td>value of v<sub>3</sub> vibrational quantum number</td> </tr> <tr> <td>13</td> <td>int</td> <td>value of I quantum number (total nuclear spin)</td> </tr> <tr> <td>14</td> <td>float</td> <td>reference rovibrational state energy (i.e., without hyperfine effects) relative to the ZPE, in cm<sup>-1</sup></td> </tr> </tbody> </table> <p>Structure of<strong> h2o_exomol_F.trans </strong>and<strong> h2o_exomol_J.trans </strong>files:</p> <table> <thead> <tr> <th scope="col">Column No.</th> <th scope="col">Kind</th> <th scope="col">Meaning</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>int</td> <td>ID number of final transition state (col. no. 1 in <strong>h2o_exomol_F.states </strong>or<strong> h2o_exomol_J.states </strong>file)</td> </tr> <tr> <td>2</td> <td>int</td> <td>ID number of initial transition state (col. no. 1 in <strong>h2o_exomol_F.states </strong>or<strong> h2o_exomol_J.states </strong>file)</td> </tr> <tr> <td>3</td> <td>float</td> <td>Einstein A-coefficient, in s<sup>-1</sup></td> </tr> <tr> <td>4</td> <td>float</td> <td>Transition wavenumber, in cm<sup>-1</sup></td> </tr> </tbody> </table> <p> </p>
Indicator values for the many-body localization of the Heisenberg spin chain at various energies, system sizes and disorder magnitude
<p>This data set accompanies the preprint <em>Scalable approach to many-body localization via quantum data </em>(arXiv: <a href="https://arxiv.org/abs/2202.08853">2202.08853</a>) and its code, available at <a href="https://github.com/GreschAl/MBLlearning">GitHub</a>.</p> <p>It consists of the numerically obtained indicator values for the many-body localization for the Heisenberg model (see preprint for details and background) for various values of the energy density <span class="math-tex">\(\epsilon = 0.05, 0.1, \dots, 0.9, 0.95\)</span> and for chain lengths <span class="math-tex">\(L = 10, 12, 14\)</span>. For each tuple <span class="math-tex">\((\epsilon,L)\)</span>, there exist two files, representing the training and the test set, respectively. Each such file contains various values of the disorder parameter <span class="math-tex">\(h = 0.5, 1, \dots, 14.5, 15\)</span> with <span class="math-tex">\(N = 1000\ (100)\)</span> sampled realizations of the disorder vector for each <span class="math-tex">\(h\)</span> for the training (test) set, followed by the three calculated values of the three indicators.</p> <p>The data is automatically processable by the code provided in the GitHub repository.</p>
Muon spin relaxation in Ce3Al
<p>We have investigated the dynamics of magnetic-field-driven antiferromagnetic-to-paramagnetic quantum phase transition in monocrystalline Ce3Al via transverse-field muon spin rotation experiments down to temperature of ~80 mK. The idea is to explore the magnetic transition at these temperatures.</p>
Tuning topological spin textures in size-tailored chiral magnet insulator particles
<p>The file contains the raw data and code used for the paper entitled "Tuning topological spin textures in size-tailored chiral magnet insulator particles" by Priya R. Baral et al. to be published in Journal of Physical Chemistry C. </p> <p>Requests for further information can be directed to the corresponding author: Arnaud Magrez (arnaud.magrez 'at' epfl.ch) </p>
Directional excitation of a high-density magnon gas using coherently driven spin waves
<p>Data corresponding to the figures of the main text of: "Directional excitation of a high-density magnon gas using coherently driven spin waves "</p>
Broadband microwave detection using electron spins in a hybrid diamond-magnet sensor chip
<p>Dataset accompanying "Broadband microwave detection using electron spins in a hybrid diamond-magnet sensor chip". </p>
Design Principles for the Development of Gd(III) Polarizing Agents for Magic Angle Spinning Dynamic Nuclear Polarization
<p>This is the raw dataset for publication </p> <p>Design Principles for the Development of Gd(III) Polarizing Agents for Magic Angle Spinning Dynamic Nuclear Polarization. with the DOI of 10.1021/acs.jpcc.2c01721. It contains all NMR, EPR raw data and the MATLAB codes that are used in this paper.</p> <p>For details, please refer to the readme file.</p>
Time-encoded pseudo-continuous arterial spin labeling: increasing SNR in ASL dynamic angiography
<p>This repository contains the raw K-space data and all MATLAB (The MathWorks, Natick, MA) code used for reconstruction, simulations, data analysis, and figure generation used in the article titled "Time-encoded pseudo-continuous arterial spin labeling: increasing SNR in ASL dynamic angiography".</p> <p>## Referencing</p> <p>If you use any of the data or the code provided here, please cite:</p> <p>1. Woods JG, Schauman SS, Chiew M, Chappell MA, Okell TW. Time-encoded pseudo-continuous arterial spin labeling: Increasing SNR in ASL dynamic angiography. Magnetic Resonance in Medicine. 2023; doi:10.1002/mrm.29491<br> 2. This Zenodo repository: Joseph G. Woods, S. Sophie Schauman, Mark Chiew, Michael A. Chappell & Thomas W. Okell. (2022). Time-encoded pseudo-continuous arterial spin labeling: increasing SNR in ASL dynamic angiography [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6791097</p> <p> </p> <p>If you use the reconstruction code provided here, please also cite:</p> <p>1. Fessler JA, Sutton BP. Nonuniform fast fourier transforms using min-max interpolation. IEEE Transactions on Signal Processing. 2003;51(2):560-574. doi:10.1109/TSP.2002.807005<br> 2. Fessler JA. Michigan Image Reconstruction Toolbox. https://web.eecs.umich.edu/~fessler/code/. Accessed February 26, 2018.<br> 3. Schauman SS. Accelerated_TEASL. https://github.com/SophieSchau/Accelerated_TEASL. Accessed October 19, 2020.<br> 4. Chiew M. MR Linear Encoding Operators. https://users.fmrib.ox.ac.uk/~mchiew/Tools.html. Accessed March 30, 2020.</p> <p>## Notes for use:</p> <p>All data processing is performed in MATLAB (tested with 2021a and 2021b on macOS 10.14 (Mojave) and 12 (Monterey))</p> <p>The "code/" and "data/" folders should be placed within the same folder. The necessary file paths within each script in "code/" will be set up automatically.</p> <p>**Note**: the file paths use "/", so would need to be manually changed for use on Windows, which uses "\" in file paths.</p> <p>- For performing full reconstruction of in vivo K-space data, **run reconalldata.m**.<br> - All code provided<br> - Uses:<br> 1. Jeff Fessler's MIRT: http://web.eecs.umich.edu/~fessler/irt/fessler.tgz<br> 2. Mark Chiew's: xfm_NUFFT.m, etc: https://users.fmrib.ox.ac.uk/~mchiew/Tools.html<br> 3. Sophie Schauman's Accelerated_TEASL: https://github.com/SophieSchau/Accelerated_TEASL</p> <p>- For plotting in-vivo data, **run figures_invivo.m**.<br> - FSL's read_avw to read in NIFTIs (but can replace this with MATLAB's in-built niftiread instead).<br> - (Install FSL from here: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation)<br> - All other code provided</p> <p>- For performing simulation and plotting results, **run figures_simulation.m**.<br> - All code provided</p> <p>- Location of specific Figure generation code:<br> - Figure 1: no code - manually made.<br> - Figure 2: figures_invivo.m<br> - Figure 3: figures_simulation.m<br> - Figure 4: figures_invivo.m<br> - Figure 5: figures_invivo.m<br> - Figure 6: figures_invivo.m<br> - Figure 7: figures_invivo.m<br> - Figure 8: figures_invivo.m<br> - Figure 9: figures_simulation.m<br> - Figure 10: figures_invivo.m<br> - Supporting Information Figure S1: figures_simulation.m<br> - Supporting Information Figure S2: figures_invivo.m<br> - Supporting Information Figure S3: figures_invivo.m<br> - Supporting Information Figure S4: figures_invivo.m<br> - Supporting Information Figure S5: figures_invivo.m<br> - Supporting Information Figure S6: figures_invivo.m<br> - Supporting Information Figure S7: figures_invivo.m<br> - Supporting Information Figure S8: figures_invivo.m<br> - Supporting Information Figure S9: figures_invivo.m<br> - Supporting Information Figure S10: figures_simulation.m</p>
Supporting data for the article "Charge-Induced Artifacts in Nonlocal Spin-Transport Measurements: How to Prevent Spurious Voltage Signals"
<p>Supporting data for the article "Charge-Induced Artifacts in Nonlocal Spin-Transport Measurements: How to Prevent Spurious Voltage Signals"</p> <p>If the design files are used to reproduce the current source, we ask to cite the peer-reviewed publication of our work in any publication in which the adjustable virtual ground feature is used for measurements.</p> <p>We provide the following data and design files:</p> <p>1.) Data used to create each figure both in the main manuscript and the supplementary material in the zip-folder "Data presented in figures".<br> The data is provided in two formats:<br> I.) Raw data in freely accessible file formats such as .dat, .txt, or .csv.<br> II.) Graphically processed data (figures as shown in the publication) in the proprietary file format .opju. Used program: OriginPro 2019</p> <p>2.) LTspice models in the zip-folder "LTspice models". The simulations were conducted with LTspice version XVII(x64).</p> <p>3.) Altium Designer files of the current source in the zip-folder "Altium Designer files".</p> <p>4.) Gerber X2 and NC drill files (dimensions in millimeters) for the manufacturing of the PCB that is used in our project in the zip-folder "Fabrication files". See the readme file in the directory for more information on the fabrication process.</p> <p>5.) A bill of materials.</p>
Dataset for paper "Towards "Matter matters" in spin foam quantum gravity"
<p>This is a dataset and Julia tools for evaluation / presentation corresponding to the article: https://arxiv.org/abs/2206.04076</p> <p>The data correspond to samples of a coupled spin foam / matter system (via a Markov Chain Monte Carlo algorithm) for different lattice sizes. We also add an IJulia file and to generate the plots from the data and eventually modify them to study also other aspects.</p> <p>The code to generate these samples can be found at: https://github.com/amoosam/CuboidSpinfoamScalarField</p>
VESPA ASL: VElocity and SPAtially Selective Arterial Spin Labeling
<p>This repository contains the simulation code, in vivo data, preprocessing code, and analysis code used in the Magnetic Resonance in Medicine article titled "VESPA ASL: VElocity and SPAtially Selective Arterial Spin Labeling" (<a href="https://doi.org/10.1002/mrm.29159">https://doi.org/10.1002/mrm.29159</a>). Please cite this article and this repository (<a href="https://doi.org/10.5281/zenodo.6870683">https://doi.org/10.5281/zenodo.6870683</a>) if you use any of this code or data in your work.</p> <p>Software requirements: BASH, FSL6, and MATLAB (tested with MATLAB versions 2021a and 2021b; requires the Optimization Toolbox and the Statistics and Machine Learning Toolbox). Preprocessing steps tested on macOS 12.4 (Monterey) and 10.14 (Mojave).</p> <p>This updated version (V2.0) simplifies and streamlines the setup of command line and MATLAB paths, making it easier to re-run the preprocessing and analysis steps.</p>
Data for the article "Tuning Spin-Orbit Torques Across the Phase Transition in VO2/NiFe Heterostructure"
<p>Data for the article "Tuning Spin-Orbit Torques Across the Phase Transition in VO2/NiFe Heterostructure" (<a href="https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.202111555">https://onlinelibrary.wiley.com/doi/full/10.1002/adfm.202111555</a> and <a href="http://arxiv.org/abs/2201.12984">http://arxiv.org/abs/2201.12984</a>)</p>
Figure data to "Quantitative description of long-range order in the spin-1/2 XXZ antiferromagnet on the square lattice"
<p>This collection contains the data of the figures shown in the publication "Quantitative description of long-range order in the spin-1/2 XXZ antiferromagnet on the square lattice" as txt files.</p> <p>The CST datasets "Fig1_Gap_CST.txt", "Fig1_Energy_CST.txt" and "Fig3_CST.txt" are already published in https://doi.org/10.5281/zenodo.7528316 and included here for the sake of completeness.</p>
Microscopic understanding of NMR signals by dynamic mean-field theory for spins
<p>Data collection for several plots of the article <a href="https://doi.org/10.1016/j.ssnmr.2024.101936">Microscopic understanding of NMR signals by dynamic mean-field theory for spins</a>. Each hdf5-file contains one or several datasets of a specific figure. The datasets are described by the attribute "description".</p>
Many-body quantum dynamics of spin-orbit coupled Andreev states in a Zeeman field
<p>We provide the raw data used to produce Figs.3-6-7-8-9-10-11 of our paper "Many-body quantum dynamics of spin-orbit coupled Andreev states in a Zeeman field"</p>
Long-lived magnetization in an atomic spin chain tuned to a diabolic point
<p>This is the OpenData Folder for the paper under the same name.</p>
Electron Spin Resonance dataset
<p>Dataset of size [3001x2463], the last row is labels, which are 0 for noise, 1 for single peak, 3 for triple peak</p>
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International Brain Laboratory public data
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OpenNeuro
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