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127 results for “Nanowires”
Kinetics of Guided Growth of Horizontal GaN Nanowires on Flat and Faceted Sapphire Surfaces_experimental dataset
<p>This dataset contains the raw experimental data for the particle Rothman et al., Kinetics of Guided Growth of Horizontal GaN Nanowires on Flat and Faceted Sapphire Surfaces, <em>Nanomaterials</em> <strong>2021</strong>, <em>11</em>(3), 624. </p>
Data and Code for Spin and Orbital Spectroscopy in the Absence of Coulomb Blockade in Lead Telluride Nanowire Quantum Dots
<p>This repository contains combined data for two papers. </p> <p>Growth of PbTe nanowires by Molecular Beam Epitaxy<br> Authors: Sander G. Schellingerhout, Eline J. de Jong, Maksim Gomanko, Xin Guan, Yifan Jiang, Max S.M. Hoskam,<br> Sebastian Koelling, Oussama Moutanabbir, Marcel A. Verheijen, Sergey M. Frolov, Erik P.A.M. Bakkers</p> <p><br> Spin and Orbital Spectroscopy in the Absence of Coulomb Blockade in Lead Telluride Nanowire Quantum Dots<br> Authors: M. Gomanko, E.J. de Jong, Y. Jiang, S.G. Schellingerhout, E.P.A.M. Bakkers, and S.M. Frolov</p> <p><br> Content of this repository: </p> <p>Readme file. </p> <p>/RawData/<br> Original data obtained at the time of measurement separated into 3 folders from different chips/cooldowns<br> Data from devices 1,3 and 4 can be found in "RawData/PbTe_chip1", device 2 in "RawData/PbTe_chip2" and devices 5-8 in "RawData/PbTe_GBg"</p> <p>/Measurement notebooks/<br> OneNote notebook with 4 different sections for 3 chips (two sections for backgate chip). Pages in these sections contain the device number in the name.<br> Also, the same notebook is exported in pdf format for convenience.</p> <p>/Data processing/<br> Readme file, Jupiter notebooks, data files, and pictures, that were used to extract g-factors for different field orientations in device2.</p> <p>/Data summaries/<br> Powerpoints, that were used to overview data during the measurement stage.<br> Not all data from these powerpoints are present in the repository or paper (specifically excluding early data used in "additional backgate devices.pptx" and "PbTe 3rd device.pptx").</p> <p>Data file types:</p> <p>data_NNN.dat - the original data file obtained at the time of the experiment<br> dataNNN.py - the original QTLab data acquisition script saved with data<br> data_NNN.set - settings of measurement instruments at the time of measurement<br> data_NNN.meta - auxillary file necessary for plotting data using SpyView (see below) <br> data_NNN.MTX - a simple 2D/3D matrix format developed for Spyview</p> <p>NNN stands for dataset number, automatically indexed by QTLab</p> <p><br> How to plot data:</p> <p>1) Spyview - a free data plotting program written by Gary Steele</p> <p>Data in this repository can be simply dropped into Spyview for plotting. </p> <p>Spyview also produces and can read .mtx files which are available for some of the data in this repository.</p> <p>https://nsweb.tn.tudelft.nl/~gsteele/spyview/</p> <p><br> 2) QTPlot - a Python plotter written by Ruben van Gulik</p> <p>Data in this repository can be directly opened with QTPlot, which will read axis labels.</p> <p>https://github.com/Rubenknex/qtplot</p> <p>Note: requires PyQT4</p>
Parametric exploration of zero-energy modes in three-terminal InSb-Al nanowire devices
<p>Files inlcude 1) Raw data for all figure 2) data process file 3) Generated figures</p>
Connectome of memristive nanowire networks through graph theory - Dataset
<p>This is the dataset of "Connectome of memristive nanowire networks through graph theory"</p>
Training dataset for "A deep learned nanowire segmentation model using synthetic data augmentation"
<p>This image dataset contains synthetic structure images used for training the deep-learning based nanowire segmentation model presented in our work "A deep learned nanowire segmentation model using synthetic data augmentation" to be published in <em>npj Computational materials. </em>Detailed information can be found in the corresponding article.</p>
Data and analysis for the paper "Nonlocal measurement of quasiparticle charge and energy relaxation in proximitized semiconductor nanowires using quantum dots"
<p>This repository contains the raw data and analysis code used to generate the figures in the manuscript <em>Nonlocal measurement of quasiparticle charge and energy relaxation in proximitized semiconductor nanowires using quantum dots</em>. </p> <p><a href="https://journals.aps.org/prb/abstract/10.1103/PhysRevB.106.064503">Link to publication</a></p> <p><a href="https://arxiv.org/abs/2110.05373">Link to arXiv</a></p>
Experimental and modeling study of metal-insulator interfaces to control the electronic transport in single nanowire memristive device - Dataset
<p>This is the dataset of "Experimental and modeling study of metal-insulator interfaces to control the electronic transport in single nanowire memristive device"</p>
Wired for Stability: Evaluating Electrical Performance in Zinc Oxide-Modified Silver Nanowire Solution-Processed Transparent Electrode
<p>Characterisation dataset for “<span>Wired for Stability: Evaluating Electrical Performance in Zinc Oxide-Modified Silver Nanowire Solution-Processed Transparent Electrode</span>”, DOI:xx. Data for main and supporting figures provided as *.xlsx and *.txt files.</p>
Figures code and data of "Coulomb blockade in open superconducting islands on InAs nanowires"
<p>Electrons in closed systems can exhibit Coulomb blockade (CB) oscillations due to charge quantization. Here, we report CB oscillations in aluminum superconducting islands on InAs nanowires in the open regime. The Al island is connected to the source/drain leads through two contacts: One is fully transmitting while the other is tuned into the tunneling regime. This device configuration is typical for tunneling spectroscopy where charging energy is generally considered negligible. The oscillation periods are 2e or 1e, depending on the gate settings. A magnetic field can induce the 2e to 1e transition. Our result is reminiscent of the ``mesoscopic Coulomb blockade'' in open quantum dots caused by electron interference. </p>
Figure data of 'Epitaxy of advanced nanowire quantum devices'
<p>Data belonging to 'Epitaxy of advanced nanowire quantum devices' including metadata files are in this ZIP folder</p>
In Situ Epitaxy of Pure Phase Ultra-Thin InAs-Al Nanowires for Quantum Devices
<p>This repository contains the raw data and processing codes of the paper "<em>In Situ</em> Epitaxy of Pure Phase Ultra-Thin InAs-Al Nanowires for Quantum Devices".</p>
In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks - Dataset
<p>This is the dataset of "<em>In materia</em> reservoir computing with a fully memristive architecture based on self-organizing nanowire networks"</p>
Dataset: Long Range Electron Transport Rates Depend on Wire Dimensions in Cytochrome Nanowires
<p>The ability to redirect electron transport to new reactions in living systems opens possibilities to store energy, generate new products, or probe physiological processes. Recent work by Huang et al. showed that 3D crystals of small tetraheme cytochromes (STC) could transport electrons over nanoscopic to mesoscopic distances by an electron hopping mechanism. Such protein-based structures with multiple localized electron carriers are promising materials for nanowires. A potential barrier to protein nanowire adoption for handling long-range electron transport is that fluctuations at room temperature may distort the nanostructure, hindering efficient electron transport. To study these fluctuations at the nano- and mesoscopic scales, we carry out classical molecular dynamics simulations for a small fragment of a STC nanowire and measure the effective distance distribution for electron tunneling. From distance distributions, we develop a graph network representation for electron transport along nanowires with varying dimensions, and through stochastic methods determine the maximum electron flow that can be driven through these STC wires. Longer nanowires were capable of carrying less electron flow than shorter nanowires with the same diameter, as long electron transfer distances that occasionally arise reduce the efficiency for electron transport.<br> Thicker nanowires permit more alternative transport pathways, increasing electron transport beyond the increase in cross-section. Thus, this model implies that the design of protein-based nanowires that depend on electron hopping between charge carriers must consider control of the inherent protein flexibility, as more flexible protein-protein interfaces impose a limit on the required minimum diameter to carry currents commensurate with conventional electronics.</p>
In-plane growth of topological crystalline insulator Pb1−xSnxTe nanowires
<p>data of '<em>In-plane growth of topological crystalline insulator Pb1−x</em><em>Sn</em><em>x</em><em>Te nanowires'</em></p>
Subgap spectroscopy along hybrid nanowires by nm-thick tunnel barriers
<p>These are datasets and codes used for the manuscript with a title : Subgap spectroscopy along hybrid nanowires by nm-thick tunnel barriers</p>
Tomography of memory engrams in self-organizing nanowire connectomes - Dataset
<p>This is the dataset of "Tomography of memory engrams in self-organizing nanowire connectomes"</p>
Photocatalytic degradation of rhodamine B using zinc oxide/silver nanowire nanocomposite films under UV irradiation
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Growth of carbon nanofibres on molybdenum carbide nanowires and their self-decoration with noble-metal nanoparticles
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Data from: Anomalous thermal transport in Eshelby twisted van der Waals nanowires
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Germanium tin alloy nanowires as anode materials for high performance Li-ion batteries
<p><strong>Abstract</strong><br> The combination of two active Li-ion materials (Ge and Sn) can result in improved conduction paths and higher capacity retention. Here we report for the first time, the implementation of Ge<sub>1–x</sub>Sn<sub>x</sub> alloy nanowires as anode materials for Li-ion batteries. Ge<sub>1−x</sub>Sn<sub>x</sub> alloy nanowires have been successfully grown via vapor–liquid–solid technique directly on stainless steel current collectors. Ge<sub>1−x</sub>Sn<sub>x</sub> (x = 0.048) nanowires were predominantly seeded from the Au<sub>0.80</sub>Ag<sub>0.20</sub> catalysts with negligible amount of growth was also directly catalyzed from stainless steel substrate. The electrochemical performance of the the Ge<sub>1−x</sub>Sn<sub>x</sub> nanowires as an anode material for Li-ion batteries was investigated via galvanostatic cycling and detailed analysis of differential capacity plots (DCPs). The nanowire electrodes demonstrated an exceptional capacity retention of 93.4% from the 2nd to the 100th charge at a C/5 rate, while maintaining a specific capacity value of ∼921 mAh g−1 after 100 cycles. Voltage profiles and DCPs revealed that the Ge<sub>1−x</sub>Sn<sub>x</sub> nanowires behave as an alloying mode anode material, as reduction/oxidation peaks for both Ge and Sn were observed, however it is clear that the reversible lithiation of Ge is responsible for the majority of the charge stored.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.