Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
89
datasets available to search
ShareScore release 0.9.0
Dataset results
89 results for “Plasmonics”
Source Code and Simulation Results: Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna
<h3>Summary</h3> <p>This publication supplements the article "Chiral and directional optical emission from a dipole source coupled to a helical plasmonic antenna" with tabulated data and Matlab code that allows the reproduction of the results. Within the article, the chiral behavior of single and double plasmonic nano antennas made from silver is numerically investigated with a focus on the coupling of a linear polarized dipole as an excitation source to the helix.</p> <h3>Simulation Setup - FEM Simulations</h3> <p>The script "run_wavlengthscan.m" allows to reproduce all simulations of the article. It can be chosen between the single and double helices, by specifying the keys parameter "keys.doppelhelix" where 0 gives a single and 1 a double helix. The number of turns can be specified by choosing "keys.case". The dipol is located within a 20nm thick hBN substrate layer, on glass (BK7). Results of the Purcell enhancement can be plotted using the scripts "display_results_single_helix.m" and "display_results_doublehelix.m" in the folder "results". The far-field plots can be reproduced using the scripts "display_farfiel_polarization_single_helix.m" and "display_farfiel_polarization_double_helix.m" of the folder "FunctionsAndScripts".</p> <p>The template for the mesh is contained in the folder "generate_grid_file", where the parameters of the helix (for example: radius, tube radius, and pitch height) can be modified.</p> <p>Within the folder "project3D" all required .jcm files are stored. Copy the "grid.jcm" file with the geometry of interest to this folder to perform simulations.</p> <p>All required keys parameters for the JCM template files (.jcmt, jcmpt) are set within the functions "set_numerical_parameter.m" and "set_physical_parameters.m", contained in the folder "FunctionsAndScripts". Therein, the function "set_sources.m" specifies the parameters for the dipole excitation, such as the position, and the strength (equivalent to the polarization).</p> <h3>Semi-Analytical Model</h3> <p>The Jupyter notebook "Semi_Analytical_Plasmonic_Helix.ipynb" contains the commented Python script for the semi-analytical design tool used to obtain far-field radiation patterns of the single helix. This semi-analytical design tool is based on an analytical model developed in [4]. The script can be divided into three parts. First, the single helix is defined, and a linear wavelength scaling law [5] is used to determine the illuminating wavelengths at which Fabry-Pérot resonances occur. Second, the overlap integral between the mode current on the helix and the incident electric field is evaluated for a given direction of incident light. Thirdly, the direction of incidence is varied to obtain the far-field radiation patterns. The script allows for the radiation patterns to be exported as a .csv file. Alternatively, the radiation patterns can be plotted directly using the provided single_plot functions.</p> <h3>Material</h3> <p>The material data has been taken from the <a href="https://refractiveindex.info/" target="_blank" rel="noopener">refractiveindex.info</a> database. For silver the data is taken from tabulated data from Johnson and Christy [1] . The dispersion relation for hBN comes from [2] and tabulated data for glass (BK7) from [3]. The MATLAB script "material_properties_plot.m" plots the material fits above the wavelengths of interest. The required tabulated data is given in the folder "material_data".</p> <p>With 'material_properties_plot.m' the fits to the material data can be reproduced and plotted.</p> <h3>Usage</h3> <p>The .zip folder Helix_FEM contains all data and scripts to reproduce the plots from the 3D FEM simulations.</p> <p>The Jupyter Notebook Semi_Analytical_Plasmonic_Helix reprouces the results from the semi-analytical model.</p> <h3>Requirements</h3> <ul> <li>JCMsuite (at least 5.4.0)</li> <li>MATLAB (tested with version R2023b)</li> <li>Python (tested with Version 3.10.9)</li> <li>Jupyter Notebook (tested with 6.5.2) </li> </ul> <p>To run the simulations with JCMsuite you must replace corresponding placeholders with a path to your installation of JCMsuite. Free trial licenses are available, please refer to the homepage of <a href="https://jcmwave.com/">JCMwave</a>.</p> <h3>References</h3> <p>[1] P. B. Johnson and R.-W. Christy, “Optical constants of the noble metals,” Phys. Rev. B 6, 4370 (1972).</p> <p>[2] S.-Y. Lee, T.-Y. Jeong, S. Jung, and K.-J. Yee, “Refractive index dispersion of hexagonal boron nitride in the visible and near-infrared,” Phys. Status Solidi B 256, 1800417 (2019).</p> <p>[3] “SCHOTT Zemax catalogue 2017-01-20b,” (2017).</p> <div>[4] K. Höflich et al., "Resonant behavior of a single plasmonic helix." Optica 6, 1098(2019).</div> <div> </div> <div>[5]L. Novotny, "Effective wavelength scaling for optical antennas", Phys. Rev. Lett. 98,266802 (2007).</div>
Plasmon-Driven Chemical Transformation of a Secondary Amide Probed by Surface Enhanced Raman Scattering
<p>This data set complements the article "Plasmon-Driven Chemical Transformation of a Secondary Amide Probed by Surface Enhanced Raman Scattering" published at https://doi.org/10.1038/s42004-024-01276-2.</p>
Plasmonic Heating by Indium Tin Oxide Nanoparticles: Enabling Decoupled Near-Infrared Theranostics.
<p>Dataset corresponding to the experimental results shown in the figures 2 to 5 (four in total) within the original research work entitled "Plasmonic Heating by Indium Tin Oxide Nanoparticles: Enabling Decoupled Near-Infrared Theranostics".</p>
Original Data for Manuscript "Energy and Momentum Distribution of Surface Plasmon-induced Hot Carriers Isolated via Spatiotemporal Separation"
<p>Raw data of the time-dependent energy density calculation and time-resolved photoemission electron microscopy (TR-PEEM) measurements used in the manuscript.</p> <p>A preprint of the manuscript is available on arXiv: <a href="https://arxiv.org/abs/2107.14277">2107.14277</a></p> <p>The manuscript was published in <em>ACS Nano</em> 2021, 15, 12, 19559-19569 <a href="https://doi.org/10.1021/acsnano.1c06586">10.1021/acsnano.1c06586</a></p> <p>The data is provided in hdf5 files, which were produced using the snomtools python package (<a href="https://github.com/hartelt/snomtools">availale on github</a>) and can be read with any <a href="https://support.hdfgroup.org/HDF5/tools5desc.html">hdf5 compatible software</a>. The calculated data was produced as described in Ref.1 with the parameters given in the manuscript. For the experimental data, each zip file contains the raw data (hdf5 Files) as well as the PEEM settings used (sav Files as output from the experiment control software) for the respective measurement.</p> <p>Parts of the manuscript that are generated from the calculated data [calculation_energy_density.zip]:</p> <ul> <li>Figure 2 A</li> <li>Figure S2</li> </ul> <p>Parts of the manuscript that are evaluated from the PEEM real space dataset [PEEM_realspace.zip]:</p> <ul> <li>Figure 2 B</li> <li>Figure 3</li> <li>Figure S1</li> <li>Figure S3</li> <li>Figure S4</li> <li>Movie S2 [timeseries_binned_fermi.avi] in the supplementary material</li> </ul> <p>Parts of the manuscript that are evaluated from the PEEM momentum space (momentum microscopy) SPP dataset [PEEM_k-space_timesteps_SPP.zip]:</p> <ul> <li>Figure 4 (in combination with the pump pulse reference dataset)</li> <li>Figure S5 B</li> <li>Figure S6 B</li> <li>Figure S7 B</li> </ul> <p>Parts of the manuscript that are evaluated from the PEEM momentum space (momentum microscopy) pump pulse reference dataset [PEEM_k-space_timesteps_pump.zip]:</p> <ul> <li>Figure 4 (in combination with the SPP dataset)</li> <li>Figure S5 A</li> <li>Figure S6 A</li> <li>Figure S7 A</li> </ul> <p>Parts of the manuscript that are evaluated from the PEEM momentum space (momentum microscopy) data of the full timetrace, combining SPP dataset [PEEM_k-space_full-timetrace_SPP.zip] and pump pulse reference dataset [PEEM_k-space_full-timetrace_pump.zip]:</p> <ul> <li>Figure S8</li> </ul>
Nonlinear bi-color holography using plasmonic metasurfaces
<p>Dataset of the publication “Nonlinear bi-color holography using plasmonic metasurfaces“, Daniel Frese, Qunshuo Wei, Yongtian Wang,<br> Mirko Cinchetti, Lingling Huang, and Thomas Zentgraf, ACS Photonics (2021), 8(4), pp. 1013-1019 ( <a href="https://doi.org/10.1021/acsphotonics.1c00028">https://doi.org/10.1021/acsphotonics.1c00028</a>). The files includes the data on which the plots shown in figure 2, 3, and 4 are based.</p>
Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses - Supporting Information for Trajectories
<p>We provide videos of trajectories for a test charge, bound by Lennard-Jones potential. First video, titled "PW Trajectory - Point 2 " refers to the particle under the effect of a plane wave pulse. The other video , titled "Emitter Trajectory - Point 2" refers to the particle affected by an orbital angular momentum carrying pulse.</p> <p>These videos are supplementary materials for the article titled "Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses".</p>
The source data for "Inductively shunted transmon: A superconducting qubit with flux noise insensitive plasmon states and a protected fluxon decay exceeding 3 hours"
<p>The following folder contains all the raw data, analysis Mathematica notebook and ScQubits python codes used to generate the results in “Inductively shunted transmon: A superconducting qubit with flux noise insensitive plasmon states and a protected fluxon decay exceeding 3 hours” in nature communications. Please follow the instruction below for proper navigation through the data:</p> <p>Fig. 1 folder:</p> <ol> <li>Run “Color map of Matrix element Vs energy parameters” to generate “x.dat”, “y.dat”, ”M.dat”(respectively EJ/EL, EJ/EC and the matrix element of the first flux transition). <strong>Make sure to correct the address where these file should be saved</strong>.</li> <li>The mathematica notebook plots the dispersion in IST limit and the matrix element gray scale color map contours separately and the full image was constructed in illustrator later. The green dots on the dispersion plot represent the position of other qubits on the color map. </li> </ol> <p>Fig. 2&3 folder:</p> <ol> <li>The python file “paper figures” uses ScQubits to generate different studies in IST limit presented in Fig. 2&3 and generates the following files:</li> </ol> <p>Fig. 2a:</p> <p>“fluxonium.hdf5”: The spectrum of a typical fluxonium</p> <p>“fluxoniumME.hdf5”: The matrix element of all transition in “fluxonium.hdf5”</p> <p> </p> <p>Fig. 2d:</p> <p>“case1.hdf5”: The spectrum of fluxonium with EJ/EC=6.6</p> <p>“ME1.hdf5”: The matrix element of transition in “case1.hdf5”</p> <p>“case2.hdf5”: The spectrum of fluxonium with EJ/EC=13</p> <p>“ME2.hdf5”: The matrix element of transition in “case2.hdf5”</p> <p>.</p> <p>.</p> <p>“case6.hdf5”: The spectrum of fluxonium with EJ/EC=200</p> <p>“ME6.hdf5”: The matrix element of transition in “case6.hdf5”</p> <p>“IST.hdf5”: The spectrum of the IST qubit</p> <p>“ISTME.hdf5”: The matrix element of transition in “IST.hdf5”</p> <p>“transmon.hdf5”: The spectrum of a transmon with the same EJ and EC as IST qubit</p> <p>Fig. 3a</p> <p>“Waveamp.hdf5”: The wave functions and eigenenergies of the IST qubit</p> <p>“WaveampT.hdf5”: The wave functions and eigenenergies of the transmon</p> <p> </p> <p>Fig. 3b:</p> <p>“ELcase1.hdf5”: The spectrum of IST qubit with EL=2 GHz</p> <p>“ELME1.hdf5”: The matrix element of transition in “ELcase1.hdf5”</p> <p>“ELcase2.hdf5”: The spectrum of IST qubit with EL=1.5 GHz</p> <p>“ELME2.hdf5”: The matrix element of transition in “ELcase2.hdf5”</p> <p>.</p> <p>.</p> <p>“ELcase6.hdf5”: The spectrum of IST qubit with EL=0.25 GHz</p> <p>“ELME6.hdf5”: The matrix element of transition in “ELcase6.hdf5”</p> <p> </p> <p>Fig. 3b inset:</p> <p>“WaveampEL.hdf5”contains the wave functions for El={2,1.5,1,0.75,0.5,0.25}GHz.</p> <p> </p> <p>Fig. 3c:</p> <p>“EC.hdf5” contains numerical simulation of an IST qubit with fixed EJ and Ec while EL is changing to calculate anharmonicity.</p> <ol> <li>The Mathematica notebook “Theory_figures” runs based on the files above and plot the result presented in the paper.</li> </ol> <p>Fig. 5 folder:</p> <ol> <li>Fig. 5a&b folder contains the raw data of spectroscopy of the IST qubit with different temperature and the Mathematica notebook “Tempsweeps_figa&b” simply plots the data. In the data set the I and Q quadrature as well as the amplitude and power of the signal coming back from cavity is provided.</li> <li>Fig. 5c folder contains several sweeps of both spectroscopy and resonator performed at fridge base temperature (7mK) labeled as “specge#.txt” and “Res_VNA_*.txt” respectively. The ScQubits python code “IST_Device” provides a fit for the data using the fit procedure explained in Supplementary Note 4 and generates the bare spectrum of the device saved in “Fit.h5”. The Mathematica notebook “spec_analysis” uses all spectroscopy data and the fit file to plot Fig. 5c.</li> </ol> <p>Fig. 6 folder: Contains all the raw data of T1 and T2 experiment at different flux positions across a flux quantum. The Mathematica notebook “T1&2” performs all the analysis presented in Fig. 6 for devices A, B and C.</p> <p>Fig. 7 folder:</p> <ol> <li>Fig. 7a: In this folder the we provide the raw data for fidelity experiment. The data is in the “*.mat” format and contains 40000 single shot I&Q bins collected with measurement band width of 2MHz and integration time of 500ns. The files names indicate whether the data was taken with qubit prepared in ground/excited state by having “_g_”/”_e_”. Following the state preparation condition, the measurement power at which the data was taken is indicated. The Mathematica notebook “fidelity_sweep” takes the data and extract the fidelities shown in Fig. 7a and the 2D histogram plots presented in Supplementary Figure 5d.</li> <li>Fig. 7b: The raw data for QND-ness experiment is presented in this folder. Each file contains 500 time traces of the two consecutive pulses applied to the resonator to study the non-QND effects of the IST qubit in high power. The Qubit preparation condition is apparent in the file name along with the power at which the measurement was performed. The Mathematica notebook “QND_ness” extracts the QND_ness and plots the results shown in Fig. 7b</li> </ol> <p> </p> <p>Fig. 8 folder:</p> <ol> <li>Fig. 8a: This folder contains the spectroscopy sweeps conditions by the fluxon state using a strong microwave pulse applied to the resonator. The Mathematica notebook “sweeps” plots the data.</li> <li>Fig. 8c: This folder contains the raw data for long fluxon decays collected using quantum machines (QM). In this experiment the fluxon excitation pulse was applied and repeated until a successful fluxon state is detected. Afterwards, the experiment enters monitoring stage where every 30s we check the fluxon state until a tunneling to fluxon ground state is detected. This event is logged and the QM repeats the fluxon excitation immediately followed by a monitoring stage and logging the time it took for tunneling to occur. The raw data of every 30 second monitoring stage is saved in files with “_raw_” in their labels. The files containing “_taus_” in their names have only the logged tunneling time events. The Mathematica notebook “qubit analysis” takes the data for three external flux bias and, by loading the “_taus_” files, reconstructs the quasi quantum jump traces and finally the decay traces presented in Fig. 8c.</li> </ol>
Data from: Physics-informed deep learning for plasmonic sensing of nanoscale protein dynamics in solution
Open the record for dataset details and reuse information.
Sub-picosecond thermalization dynamics in condensation of strongly coupled lattice plasmons
<p>Raw data used in manuscript Figures 1-7 of the publication.</p> <p>The data are in the form of .csv as read from a spectrometer. </p>
Neutrino emission by plasmon decay in a strong magnetic field: Data material
<p>This data reference paper summarizes the fundamental calculated results obtained analytically for the case of a neutron star (NS) crust: it’s cooling rate, time-scale evolution and neutrino luminosity, based on two different theories- Photo-Neutrino (PN) interaction and conventional weak interaction. Considering the NS environment as a degenerate plasmon composition the calculation tables are given. </p>
Fundamental Limit of Plasmonic Cathodoluminescence_experimental dataset
<p>This file contains the raw unprocessed experimental data for the results published in Schmidt et al., Fundamental Limit of Plasmonic Cathodoluminescence, Nano Letters, Volume 21, 2021, 590-596. </p>
Engineering surgical face masks with photothermal and photodynamic plasmonic nanostructures for enhancing filtration and on-demand pathogen eradication_[photothermal properties]
<p>Engineering surgical face masks with photothermal and photodynamic plasmonic nanostructures for enhancing filtration and on-demand pathogen eradication: photothermal properties</p>
Surface plasmons-phonons for mid-infrared hyperspectral imaging
<p>Dataset for the hyperspectral imaging of spike proteins of the severe acute respiratory syndrome coronavirus (SARS-CoV) using the synergistic plasmon-phonon hyperspectral bioimaging system.</p>
Inverse Design of Octagonal Plasmonic Structure for Switching Using Deep Learning
<p>OctagonalRR_AOPS: Datasets for "<strong>Inverse Design of Octagonal Plasmonic Structure for Switching Using Deep Learning</strong>," (2024).</p>
Plasmon-enhanced fluorescence (bio)sensors and other bioanalytical technologies
<pre>DATASET DESCRIPTION This Dataset contains the raw data from the following publication: ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ Plasmon-enhanced fluorescence (bio)sensors and other bioanalytical technologies Dario Cattozzo Mor, Gizem Aktug, Katharina Schmidt, Prasanth Asokan, Naoto Asai, Chun Jen Huang, Jakub Dostalek* *corresponding author: dostalek@fzu.cz (J. Dostalek) Trends in Analytical Chemistry </pre>
Plasmonic Copper Sulfide Nanoparticles Enable Dark Contrast in Optical Coherence Tomography
<p>Dataset of https://onlinelibrary.wiley.com/doi/10.1002/adhm.201901627</p>
Dataset for "Ultra-strong coupling of a single molecule to a plasmonic nanocavity: A first-principles study"
<p># Data and code for "Ultra-strong coupling of a single molecule to a plasmonic nanocavity: A first-principles study," M. Kuisma, B. Rousseaux, K.M. Czajkowski, T.P. Rossi, T. Shegai, P. Erhart, T.J. Antosiewicz, ACS Photonics, doi:10.1021/acsphotonics.2c00066 (2022).</p> <p><br> ## Contents</p> <p>* *data-{type}/*: reproducible data<br> * *src/*: input scripts</p> <p><br> ## Description of the data</p> <p>The data are stored in directories *data-{type}/*. The contents of the directories<br> can be reproduced with the included input scripts.</p> <p>The data are organized in subdirectories *data-{type}/{system}/* corresponding to<br> the considered nanoparticle-molecule systems and simulation type:</p> <p>* data-fd: free energy calculations done with the finite difference mode<br> * data-lcao: strong coupling calculations done with LCAO mode<br> * data-d3: DFT-D3 calculations</p> <p>The contents of each subdirectory are:</p> <p>* *data-{fd,lcao,d3}/{system}/structure.xyz*: physical atomic structure<br> * *data-lcao/{system}/td-x/dm.dat*: delta-kick-induced time-dependent dipole moment<br> * *data-lcao/{system}/td-x/dm_abs_Lorentz_0.100.dat*: photoabsorption spectrum</p> <p>The spectrum plots in the article correspond to the first (x values) and<br> second (y values) columns of the spectrum files.</p> <p>## Reproduction of the data</p> <p>The data was produced using the Python scripts in *src/*,<br> Python version 3.7.3, GPAW version 20.1.0, libxc version 4.3.4,<br> ASE version 3.20.0, NumPy version 1.16.2, and SciPy version 1.2.1.</p> <p>The calculation of the data of a system consists of<br> the following steps (in *bash* shell with, e.g., system=rlx-ico-Al147`):</p> <p>1. Ground-state calculation:<br> * Copy the gs folder to a data-lcao/{system} folder<br> * Set up parellel calculaton parameters as necessary for the computing infrastructure (parallel.py)<br> * Select the Poisson Solver in the settings.py file by commenting out / uncommenting:<br> * for single particles or molecules use poissonsolver = PoissonSolver(eps=eps, remove_moment=9)<br> * otherwise comment out the above line and uncomment the last 8 lines<br> * Submit the gs.py calculation as appropriate for the particular system<br> 2. Time-propagation calculation:<br> * Requires finished ground-state calculation<br> * Set up parellel calculaton parameters as necessary for the computing infrastructure (parallel.py)<br> * Submit the td.py calculation as appropriate for the particular system<br> 3. Spectrum calculation:<br> * Requires finished time-propagation calculation (30 fs propagation)<br> * Run the `$ python spec.py` script</p> <p>Note that the example python scripts use variables STARTTIME and WALLTIME to define<br> allocated compuing time in HPC environments. The `WALLTIME` and `STARTTIME` environment<br> variables defined in *submit.sbatch* are required for a clean exit of the calculation<br> within the allocated time.</p> <p>If the ground-state or time-propagation calculations do not finish within the<br> allocated time, the same *gsc.py* or *tdc.py* scripts can be (re)run to continue<br> the calculation.</p>
Dataset for the Manuscript: Demonstration of optically-driven plasmonic nanomotors designed by deep learning networks
<p>This repository contains the data corresponding to the manuscript "Demonstration of the optically-driven plasmonic nanomotor designed by deep learning networks." It consists of 5 parts: Machine learning, Numerical analysis, Rotation measurement, Scattering measurement, and Supplementary information. The code for the machine learning algorithm is available at "https://github.com/mintaechung/Nanomotor_Predictor_Generator."</p> <p> </p> <ul> <li><strong>'Machine_learning.zip'</strong>: Correlation between optical torques calculated by SIE and predicted by trained CNN, Objective loss functions at the 1st iteration, and the torque distribution of the initial randomset and the output of the nanorotor generator after the 3rd iteration.</li> <li><strong>'Numerical_analysis.zip'</strong>: MATLAB codes to retrieve 'Moments', 'Field intensity distribution', 'Poynting vectors', and 'Torques'. </li> <li><strong>'Rotation_measurement.zip'</strong>: Raw videos, Intensity profiles of ROI, Rotation measurement results.</li> <li><strong>'Scattering_measurement.zip'</strong>: Scattering intensity measurement with reference light.</li> <li><strong>'Supplementary_Info.zip'</strong>: Random geometry generation, Optical torques of 6 blades, Expanded structure, Shrinkage, Polarization independence, Angular momentum, and Machine learning progress.</li> </ul>
Development of Rotaxanes as E-Field Sensitive Superstructures in Plasmonic Nano-Antennas
<p>NMR, HR-MS, UV-VIS, Fluorescence, and Photoluminescence data of the graphs and supplementary data provided with the manuscript.</p>
Surface Plasmon Resonance Biosensor with Anti-Crossing Modulation Readout
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.