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Dataset results
13 results for “Photodetector”
Dataset of "Photodetector Based on the Non-Centrosymmetric 2D Pseudo-Binary Chalcogenide MnIn2Se4"
<p>Due to their attractive band gap properties and Van de Waals structure 2D binary chalcogenides materials have been widely investigated in the last decade, finding applications in several fields such as catalysis, spintronic, and optoelectronic. Ternary 2D chalcogenide materials are a subject of growing interest in material science due to their superior chemical tunability which endows tailored properties to the devices prepared thereof. In the family of AIIBIII2XVI4, ordered ZnIn2S4-like based photocatalytic systems have been studied meticulously. In contrast, reports on disordered phases appear to a minor extent. Herein, a photo-electrochemical (PEC) detector based on the pseudo-binary MnIn2Se4 system is presented. A combination of optical measurements and DFT calculations confirmed that the nature of the bandgap in MnIn2Se4 is indirect. Its performances outclass parent compounds, reaching responsivity values in the order of 8.41 mA W-1. The role of the non-centrosymmetric crystal structure is briefly discussed as a possible cause of the improved charge separation of the photogenerated charge carriers. </p>
Current-voltage characteristics of Si/SnO/Ag NPs/ZnO photodetectors
<p>The text files contain data on the current-voltage characteristics of photodetectors based on a silicon substrate, tin oxide (SnO), silver nanoparticles (NPs), and Zinc Oxide (ZnO). The bottom electrode of the device was aluminum, and the top contact was tin indium oxide (ITO). NPs were grown using Ion Beam Sputter Deposition (IBSD). There were five samples measured with different NPs growth times: 10 s, 20 s, 40 s, 80 s, and 160 s, which corresponds to the sizes of the NPs from 37 nm to 69 nm.</p>
Time-current characteristics of Si/SnO/Ag NPs/ZnO photodetectors
<p>The text files contain data regarding the time-current characteristics of photodetectors fabricated on a silicon substrate, incorporating tin oxide (SnO), silver nanoparticles (NPs), and Zinc Oxide (ZnO). The bottom electrode of the device was made of aluminum, while the top contact consisted of tin indium oxide (ITO). The silver nanoparticles were synthesized using Ion Beam Sputter Deposition (IBSD). The experiment involved measuring five samples with varying growth times for the nanoparticles: 10 s, 20 s, 40 s, 80 s, and 160 s, corresponding to nanoparticle sizes ranging from 37 nm to 69 nm. Additionally, a reference sample without nanoparticles was measured for comparison purposes.</p> <p>During the measurement, the samples were illuminated with laser pulses, with the wavelength and frequency specified in the filename. The filenames include 'PD1' and 'PD2,' denoting power density, with the respective values being PD1 = 317 mW/cm² and PD2 = 887 mW/cm².</p>
Current-voltage characteristics of ZnO/Co3O4/Si photodetector
<p>The text files contain data regarding the current-voltage characteristics of photodetectors comprised of a zinc oxide (ZnO) layer, a cobalt oxide (Co3O4) layer, and a silicon (Si) substrate. The thickness of the layers is as follows: 70 nm for ZnO, 15 nm for Co3O4, and 200 µm for Si. The sample was annealed at 400°C for 30 minutes. The bottom electrode of the device was made of aluminum, while the top contact consisted of tin indium oxide (ITO).</p> <p>During the measurement, the samples were illuminated with a laser, with the wavelength specified in the filename. Additionally, measurements without illumination were conducted for reference. The filenames include 'PD1' to 'PD5,' denoting power density, with the respective values being PD1 = 251 mW/cm², PD2 = 89 mW/cm², PD4 = 21.5 mW/cm², and PD5 = 9.35 mW/cm².</p>
Time-current characteristics of ZnO/Co3O4/Si photodetector
<p>The text files contain data regarding the time-current characteristics of photodetectors consisting of a zinc oxide (ZnO) layer, a cobalt oxide (Co3O4) layer, and a silicon (Si) substrate. The thickness of the layers is as follows: 70 nm, 15 nm, and 200 µm. The sample was annealed at 400°C for 30 minutes.The bottom electrode of the device was made of aluminum, while the top contact consisted of tin indium oxide (ITO). </p> <p>During the measurement, the samples were illuminated with laser pulses, with the wavelength and frequency specified in the filename. The filenames include 'PD1' to 'PD5' denoting power density, with the respective values being PD1 = 251 mW/cm², PD2 = 89 mW/cm², PD3 = 38 mW/cm², PD4 = 21.5 mW/cm², and PD5 = 9.35 mW/cm². If stated in the filename, the sample was biased with a given value of voltage during the measurement.</p>
Dataset for Next-Generation Self-Powered Photodetectors using 2D Bismuth Oxide Selenide Crystals
<p>The dataset contains relevant data and figures regarding the manuscript "Next-Generation Self-Powered Photodetectors using 2D Bismuth Oxide Selenide Crystals".</p> <p>All Figures are in jpg/tiff format and all relevant data are in csv formats. </p> <p>The data in csv format are labelled as specified in the corresping images (e.g. Figure 1a csv file corresponds to data used to plot graphs from Figure 1a etc.). </p> <p>Axis labeling and units are always specified at the beginning of individual columns. If more than one curve was plotted from the csv file, the conditions can also be found at the beginning of corresponding columns.</p>
Covalent Organic Framework Thin-Film Photodetectors from Solution-Processable Porous Nanospheres
<p>This repository contains the source data for the manuscript <strong>"Covalent Organic Framework Thin-Film Photodetectors from Solution-Processable Porous Nanospheres" </strong></p> <p>The statistical model of 50 layers, 4-6 layers for TpDPP, TpTta, and TpTab.</p> <p>A code for building the statistical structure for layered materials by stacking layers following the Maxwell-Boltzmann energy distribution of their stacking modes. Full Changelog: <a href="https://github.com/Ying-Ying-Zhang/COFpiler/commits/v1.1">https://github.com/Ying-Ying-Zhang/COFpiler/commits/v1.1</a></p>
Dataset for all plots in journal 100Gbps PAM4 photodetector by Hemalatha-Tyndall
<p>The excel file contains all the dataset for the plots included in journal article, '100 Gbps PAM4 ultra-thin photodetectors integrated on SOI platform by micro transfer printing'. </p>
Spectral kernel machines with electrically tunable photodetectors
Open the record for dataset details and reuse information.
Quantum Tunneling Photodetector - concept movie
<p>simple illustration of the idea of Quantum Tunneling Photodetector</p>
Squaraine dyes for single-component shortwave infrared-sensitive photodiodes and upconversion photodetectors
Open the record for dataset details and reuse information.
Self-Powered Photodetectors for Smarter Healthcare
ClinicalTrials.gov study NCT06973317. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
SnowEx17 UM Quantum Well Infrared Photodetector (QWIP) Surface Temperature V001
This data set contains infrared camera images collected during the 2017 SnowEx campaign in Grand Mesa and Senator Beck Basin, Colorado. Images were taken using a Quantum Well Infrared Photodetector (QWIP) camera system manufactured by QmagiQ and mounted to the P-3 aircraft, which was flown over the study areas. The QWIP camera records images at a rate of 60 Hz and has a field of view of 11x9 degrees. The QWIP camera can distinguish temperature variations of approximately 0.02°C. This data set contains raw QWIP data, presented as "counts." Data are contained in TIFF image files with dimensions of 320x256 counts.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.