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38 results for “photoluminescence”
Dataset for the article "Influence of oxidative and consequential reductive annealing on the photoluminescence intensity, decay time and morphology of ZnO single-crystal faces".
<p>Dataset for the article "Influence of oxidative and consequential reductive annealing on the photoluminescence intensity, decay time and morphology of ZnO single-crystal facets".</p> <p>David John1,2, Zdeněk Remeš1, Radim Novák1, Štěpán Remeš1, Jakub Volf1,3,4, Oleg Babčenko1, Egor Ukraintsev5, Bohuslav Rezek5, and Maksym Buryi3</p> <p>1 Institute of Physics of the Czech Academy of Sciences, Cukrovarnická 10/112, 162 00, Prague, Czech Republic<br>2 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University, Břehová 7, 115019, Prague, Czech Republic<br>3 Institute of Plasma Physics of the Czech Academy of Sciences, U Slovanky 2525/1a, 182 00, Prague, Czech Republic <br>4 Department of Inorganic Chemistry, University of Chemistry and Technology, Technická 5, Prague 6, 166 28, Czech Republic<br>5 Faculty of Electrical Engineering of the Czech Technical University, Technická 2, 160 00, Prague, Czech Republic</p> <p> </p> <p>Dataset description:</p> <p>08_08_2024_ZnO_41a_700C_O_CF4_Multi75_10x10um.0_00000 AFM data<br>08_08_2024_ZnO_41a_700C_O_CF4_Multi75_10x10um.0_00003 AFM data<br>16_08_2024_ZnO_700C_O_CF4Multi_10x10um.0_00001 AFM data<br>16_08_2024_ZnO_700C_O_CF4Multi_10x10um.0_00003 AFM data<br>afm zn AFM data<br>data phase shift fit phase shift data<br>grafy phase shift fit phase shift data<br>ZnO faces optical images</p>
Data for "Low Barrier for Exciton Self-Trapping Enables High Photoluminescence Quantum Yield in Cs3Cu2I5"
<p>All structure files, including points along the configurational coordinate diagram, and data for optical spectra</p> <p> </p>
In Situ Photoluminescence Imaging Dataset of Blade-Coated Perovskite Photovoltaics
<p><strong>Content:</strong></p> <p>The dataset contains time-resolved in situ images acquired during the formation of the perovskite layer which is then built into a perovskite solar cell. The image time series in the dataset encompass the drying and crystallization of the blade-coated perovskite thin-films. An initial exploration of the data presented in the dataset is conducted in the paper <strong><a href="https://doi.org/10.1002/solr.202201114">Process Insights into Perovskite Thin-Film Photovoltaics from Machine Learning with In Situ Luminescence Data</a>.</strong></p> <p>A total of 1,129 solar cells were fabricated using the blade coating deposition method. To monitor the vacuum quenching process of the perovskite layer, a photoluminescence (PL) imaging setup was used to capture four channels of image data. These channels included time series images (2D+t) captured through various spectral filters, with one channel showing reflectance and the other three showing different parts of the PL spectrum. The three PL channels with different spectral transmissions were also used to compute a image time series of spatially resolved PL peak wavelengths. All images were cropped into smaller patches of 65x56 pixels each, which only included the active area of a single solar cell.</p> <p>Different metrics are available as target variables. For each solar cell in the dataset, the photovoltaic performance parameters, namely (1) power conversion efficiency (PCE), (2) open-circuit voltage (<em>V<sub>OC</sub></em>), (3) short-circuit current density (<em>J<sub>SC</sub></em>), and (4) fill factor (FF)), are available (measured backward and forward, as well as the average between forward and backward). Furthermore, information about the perovskite layer thickness of each solar cell’s active area is provided: mean thickness, root-mean-square thickness, and peak-2-valley thickness. Also, additional information like substrate ID and the position of each solar cell within its substrate is provided.</p> <p>All solar cells were fabricated using the same materials, methods, and experimental parameters. As a result, the dataset can be used to apply machine learning techniques to identify variations in the fabrication process between iterations, improve understanding of the process, and predict performance in-line before completing the half-stack into a functional solar cell.</p> <p>Further information on the experimental acquisition procedure can be found in the paper <a href="https://doi.org/10.1002/solr.202201114"><strong>Process Insights into Perovskite Thin-Film Photovoltaics from Machine Learning with In Situ Luminescence Data</strong>.</a></p> <p> </p> <p><strong>Usage:</strong></p> <p>The dataset is made available as a single hdf5-file. The npy-data can be extracted using the notebook “00_extract_data_from_hdf5_file.ipynb” which is provided in the GitHub repository <a href="https://github.com/AI-InSu-Pero/ML-PerovskitePV-InSituLuminescene">https://github.com/AI-InSu-Pero/ML-PerovskitePV-InSituLuminescene</a> </p> <p>The structure of the dataset after extraction from the hdf5-file is depicted below. The dataset (1,129 solar cells) is split into two subfolders, containing train (780 solar cells) and test data (349 solar cell), respectively. For training and test data, the corresponding labels are listed in csv files. In the train and test folders, there are subfolders for each of the substrate assigned to either of the two sets. In the substrate folders, the data for all the patches of a substrate is saved in npy-format with the shape (719, 5, 65, 56), representing (time step, channel, image height, image width). It can be loaded using numpy.load(path_to_file). The order of the five channels is as follows: (0) reflectance, (1) entire PL spectrum, (2) filtered PL spectrum – longer wavelengths remaining, (3) filtered PL spectrum – shorter wavelengths remaining, (4) computed peak wavelength of PL spectrum.</p> <p>In the train folder, an additional folder “cv_splits_5fold” gives the train and validation splits for the 5-fold cross-validation used in the dataset exploration paper. For each fold, the labels are given as csv-files for train and validation split.</p> <p> </p> <pre><code>dataset ├── train │ ├── ACA │ │ ├── 11.npy │ │ ├── 12.npy │ │ ├── 13.npy │ │ ├── 14.npy │ │ ├── 21.npy │ │ └── ... (all other patches of this substrate) │ ├── ACA │ │ ├── 11.npy │ │ ├── 12.npy │ │ ├── 13.npy │ │ ├── 14.npy │ │ ├── 21.npy │ │ └── ... (all other patches of this substrate) │ ├── ... (all other train substrates) │ ├── cv_splits_5fold │ │ ├── fold0 │ │ │ ├── train.csv │ │ │ └── val.csv │ │ └── ... (all other folds) │ └─── labels.csv └── test ├── ACE │ ├── 11.npy │ ├── 12.npy │ ├── 13.npy │ ├── 14.npy │ ├── 21.npy │ └── ... (all other patches of this substrate) ├── ... (all other test substrates) └── labels.csv </code></pre> <p> </p>
Hyperspectral photoluminescence and reflectance microscopy of 2D materials
<h2>Description of Uploaded Raw Data and Programs for Recreating Figures</h2><h3>Raw Data</h3><p>The raw data in this dataset is primarily in ".sif" binary format, which is used in the creation of Figures 2, 3, 4, and Supplementary Information (SI) Figure 2 in the paper. The ".sif" files contain spectrum data. The data for Figure 3 also includes focal data provided as .png and intensity line-cuts in .csv files.</p><p>A Python program, "load_sif.py", is included in the dataset to read and process these ".sif" files.</p><h3>Software and Programs</h3><p>The figures in the paper were generated using Python programs, which are included in the dataset. These programs are:</p><p>for Figure 2:<i> RClf_calibration.py </i></p><p>for Figure 3: <i>knife_edge_measurement.py </i>and <i>plot_intensity_profile_imageJ.py </i></p><p>for Figure 4 as well as SI Figure 1: <i>PL_linefocus_2color.py, PL_fit_image.py, PL_line_fit.py, RC_linefocus_2color.py </i>and<i> RC_line.py </i></p><p>for SI Figure 2: <i>BG_spectum_PL.py </i>and<i> Ref_spectum_RC.py </i></p><h3>Steps to Recreate Figures</h3><p>Download the zipped folder for each figure. The Python programs are using the ".sif", ".png", and ".csv" files from the downloaded folder.</p><p>Please ensure you have the appropriate software to run these Python programs and handle the provided file formats.</p>
Replication Data for Coloring Tetrahedral Semiconductors: Synthesis and Photoluminescence Enhancement of Ternary II-III2‑VI4 Colloidal Nanocrystals
<p>Ternary tetrahedral II-III2-VI4 semiconductors, where II is Zn or Cd, III In or Ga, and VI S, Se, or Te, are of interest in UV radiation detectors in medicine and space physics as well as CO2 photoreduction under visible light. We synthesize colloidal II-III2-VI4 semiconductor nanocrystals from readily available precursors and<br>ascertain their ternary nature by structural and spectroscopic methods, including 77Se solid-state NMR spectroscopy. The pyramidally shaped nanocrystals range between 2 and 12 nm and exhibit optical gaps of 2−3.9 eV. In the presence of excess anions on the particle surface, treatment with Lewis acidic, Z-type ligands results in better passivation and enhanced photoluminescence. Electronic structure calculations reveal<br>the most stable, lowest energy polymorphs and coloring patterns. This work will pave the way toward more environmentally friendly, ternary semiconductors for optoelectronics and electrocatalysis.</p>
Raman and photoluminescence of MoS2 flakes and pyramids
<p>Raman and photoluminescence spectra of MoS2 flakes and pyramids.</p> <p>Data used for article entitled "Excitonic absorption and defect-related emission in three-dimensional MoS2 pyramids" <a href="https://doi.org/10.1039/D1NR06041D">https://doi.org/10.1039/D1NR06041D</a></p>
Data underlying the paper titled "Integrating multimodal Raman and photoluminescence microscopy with enhanced insights through multivariate analysis"
<p>The folder includes Raman and Photoluminescence surface maps of microsamples from Cultural Heritage materials. The maps were obtained using a multimodal optical microscope that integrates Raman and Photoluminescence optical techniques to perform a raster scanning of microsample surface. </p> <p>Data refer to the publication: https://doi.org/10.1088/2515-7647/ad5773</p> <p> </p>
Effect of Organic Cation Size on Structural, Thermochromic, Dielectric and Photoluminescence Properties of Two-Dimensional Lead Iodide Perovskites with Extremally Reduced Dielectric Confinement
<p>Dataset for scientific publication entitled Effect of Organic Cation Size on Structural, Thermochromic, Dielectric and Photoluminescence Properties of Two-Dimensional Lead Iodide Perovskites with Extremally Reduced Dielectric Confinement. </p> <p>This research was supported by the National Science Center (Narodowe Centrum Nauki) in Poland under project No. 2020/38/A/ST3/00214. JKZ acknowledges support from Academia Iuvenum, Wroclaw University of Science and Technology.</p>
CdSe Quantum Dots Sonochemically Synthesized in the Presence of Oleic Acid & Oleylamine at different concentrations- UV-Vis Spectra, Photoluminescence, SAXS
<p>Conntent Smmary: </p> <ul> <li>Data from experiments in which CdSe quantum dots and magic sized clusters were sonochemically synthesized in the presence of Oleic Acid and Oleylamine at different concentration. A total of 625 unique sample conditions were tested, in triplicates using an open-hardware sonochemical materials acceleration platform (Jubilee) and a liquid handling robot ( Opentrons)</li> <li>Notebooks for loading and plotting the data</li> </ul> <p>README: </p> <p><strong>/Spectral_Data</strong></p> <p>The primary portion of te experimental dataset. UV-Vis spectroscopy was collected on a Biotek Epoch 2 micropate spectrometer. The Photoluminescence data was collected on a Biotek Synergy H1 microplate spectrometer. Notebook to visualize the data can also be found in this folder. </p> <p>The<strong> "CdSe_sample_info.csv" </strong>contains all the sample information, including:</p> <ol> <li>Metal precursors and ligands concentration (M)</li> <li>Labware information- name, OT2 deck location, plate number</li> <li>Sample code- this is a combination of plate # and well position within the plate</li> </ol> <p>The "<strong>CdSe_Summary_Final.csv"</strong> contains all the sample information, along with the key parameters extracted from the UV-Vis and Photoluminescence data. These include, first peak position & peak intensity from both spectroscopic tecniques, particle diameter ( based on the UV-Vis peak position) for all 3 replicates. </p> <p>Finally,there are 3 notebooks to visualize the data in their spectral form, for both pre and post processing of the samples, as well as the notebook to recreate the scatterplot visualization of the summary parameters from the spectroscopic techniques implemented. </p> <p><strong>/Spectral_Data/Spectral_Data_Files</strong></p> <p>Folder containing all the raw data for the pre and post processing of the CdSe samples for UV-Vis spectroscopy and Photoluminescence spectroscopy. </p> <p><strong>/SAXS_Data</strong></p> <p>Folder containing all the data small-angle X-ray scattering data. This was collected on a Xenocs Xeuss 3.0 SAXS instrument. </p> <p>The "<strong>SAXS_Sample_Composition.csv" </strong> file contains the composition of precursors and ligands for the subset of samples tested using small-angle scattering. </p> <p>Notebooks to visualized the SAXS profiles obtained for all samples characterized, as well as their comparison with UV-Vis data can also be found in this folder. </p> <p><strong>/SAXS_Data/Reduced_Raw_Data</strong></p> <p>Folder containing the 1D data obtained from the reduction of the 2D dector data for each sample tested at three different dector distances ( 50, 370, and 900 nm). </p> <p><strong>/SAXS_Data/Processed_Data</strong></p> <p>Folder containing the final process SAXS data which includes merging of the data in the 3 tested instrumental configuration and subsequent background subtraction. </p> <p><strong>/SAXS_Data/McSAS_Fit_Data</strong></p> <p>Folder containing the results obtained form fitting the SAXS data from the diluted samples using the McSAS Python software. The fitting parameters used for the samples can be found in the <strong>"SAXS_Sample_McSAS_Fitting_Parameters.csv" </strong>file.</p> <p><strong>/SAXS_Data/UV-Vis_Data/ </strong></p> <p>Folder containing the UV-Vis data of the subset of samples characterized using SAXS. </p>
Dataset for publication: "Spectroscopic properties and photoluminescence of the Li2B4O7:Mn,Sm glass"
<p>Dataset for the article: B.V. Padlyak, I.I. Kindrat, V.T. Adamiv, A. Drzewiecki, I. Stefaniuk, Spectroscopic properties and photoluminescence of the Li2B4O7:Mn,Sm glass, Materials Research Bulletin 175 (2024) 112788, <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.materresbull.2024.112788" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.materresbull.2024.112788</a></p> <p> </p>
Vocal range and ultraviolet-induced photoluminescence in gliding mammals and their relatives
<p>Gliding is only present in six extant groups of mammals – interestingly, despite divergent evolutionary histories, all mammalian gliders are strictly nocturnal. Gliding mammals also seem to have relatively high rates of ultrasound use and ultraviolet-induced photoluminescence (UVP) in contrast with their close relatives. Therefore, we hypothesized that, despite diverging lineages, gliding mammals use similar modes of cryptic communication compared to their non-gliding counterparts. We developed two datasets containing the vocal range (minimum-maximum of the dominant harmonic; kHz) and UVP of 73 and 82 species, respectively; we report five novel vocal repertoires and 57 novel observations of the presence or absence of UVP. We complemented these datasets with information about body size, diel activity patterns, habitat openness, and sociality to explore possible covariates related to vocal production and UVP. We found that the maximum of the dominant harmonic was significant higher in gliding mammals when vocalizing than their non-gliding relatives. Additionally, we found that nocturnality was the only significant predictor of UVP, consistent with the previous hypothesis that luminophores primarily drive UVP in mammal fur. In contrast, however, we did not find UVP ubiquitous in nocturnal mammals, suggesting that some unknown process may contribute to variation in this trait.</p>
data and codes for paper "Deriving mobility-lifetime products in halide perovskite films from spectrally- and time-resolved photoluminescence"
<p>These are the data and Matlab codes used in the paper "Deriving mobility-lifetime products in halide perovskite films from spectrally- and time-resolved photoluminescence".</p>
Effect of Dimensionality on Photoluminescence and Dielectric Properties of Imidazolium Lead Bromides
<p>single-crystal, powder XRD data, Raman, absorption, emission spectra, DSC, dielectric measurements and SHG data</p>
Persistent Photoluminescence and Mechanoluminescence of a Highly Sensitive Pressure and Temperature Gauge in Combination with a 3D-printable Optical Coding Platform
<p><span>Distinct types of luminescence that are activated by various stimuli in a single material offer exciting developmental opportunities for functional materials. In this study, we introduce a versatile sensing platform that exhibits three types of luminescence: photoluminescence (PL), persistent luminescence (PersL), and mechanoluminescence (ML), which enables the sensitive detection of temperature, pressure, and force/stress. The developed Sr<sub>2</sub>MgSi<sub>2</sub>O<sub>7</sub>:Eu<sup>2+</sup>/Dy<sup>3+</sup> material exhibits a linear relationship between ML intensity and force, and can be used as an ML stress sensor within the 3–30 N range. Additionally, the full width at half maximum (FWHM) value of the PL emission band and the PL lifetime of this material are remarkably sensitive to changes in temperature, with values of approximately 0.05 nm/K and 1.29 %/K, respectively. This study demonstrated the use of PersL for sensing pressure for the first time, along with its long-lasting (seconds) lifetime as a manometric parameter. The developed material functions as an exceptionally sensitive triple-mode visual pressure sensor; specifically it exhibits: i) a sensitivity of approximately −297.4 cm<sup>−1</sup>/GPa (8.11 nm/GPa) in bandshift mode, ii) a sensitivity of ~272.7 cm<sup>−1</sup>/GPa (14.8 nm/GPa) in bandwidth mode, and iii) a sensitivity of 42 %GPa<sup>−1</sup> in PL-lifetime mode, which is the highest value reported to date. Notably, anti-counterfeiting, night-vision safety-sign, 8-bit optical-coding, and QR-code applications that exhibit intense PersL were demonstrated by 3D-printing the studied material in combination with a polymer.</span></p>
Data for "Photoluminescence tells us about voltages, recombination and diode factors in solar cells"
<p>Detailed description in "Photoluminescence tells us about voltages, recombination and diode factors in solar cells"</p> <p>by</p> <p>Susanne Siebentritt, Thomas Paul Weiss, Mohit Sood, Max Hilaire Wolter, Alberto Lomuscio, Omar Ramirez</p> <p>submitted to J. Phys. Materials</p>
Photoluminescence study of interband transitions in few-layer, pseudomorphic, and strain-unbalanced Ge/GeSi multiple quantum wells - Dataset
<p>Relevant PL data (as txt files) for the publication </p> <p><a href="https://arxiv.org/ct?url=https%3A%2F%2Fdx.doi.org%2F10.1103%252FPhysRevB.98.195310&v=88200607">10.1103/PhysRevB.98.195310</a></p>
Research data supporting "Surface dynamics and ligand-core interactions of quantum size photoluminescent gold nanoclusters"
<p>Experimental research raw data supporting the publication by Lin, Y. et al, 2018, Surface dynamics and ligand-core interactions of quantum sized photoluminescent gold nanoclusters, Journal of the American Chemical Society. DOI: 10.1021/jacs.8b04436</p> <p>Molecular simulation data is available upon reasonable request from irene.yarovsky@rmit.edu.au.</p>
DATASET_Photoluminescence Intensity Enhancement in Tin Halide Perovskites_BOLLA_v1
<p>This Dataset contains the data that have been collected and added to the publication titled: Photoluminescence Intensity Enhancement in Tin Halide Perovskites by Poli et al., published in Advanced Science (<a href="https://doi.org/10.1002/advs.202202795">https://doi.org/10.1002/advs.202202795</a>)</p> <p>A readme.txt file describes the files contained in each folder of the dataset. Detailed characterization methods can be found directly in the publication (open access). </p>
Circularly polarized photoluminescence from nanostructured arrays of light emitters
<p>Strategically designed metamaterials can influence the properties of light emitters in several ways, including shaping of the directionality and polarization of luminescence. These properties, however, are limited in systems where the luminophores uniformly coat the metamaterial. Here, we study and design metamaterials comprised of both Au nanobars and nanopatterned light emitters. We systematically investigate the role of spatial averaging, dipole orientation, chirality, near-field effects, and other factors for these multi-material systems. Finally, we discuss multiple design routes to create metasurfaces that can emit photoluminescence of any circular polarization at any arbitrary angle. These systems simultaneously exhibit high photoluminescence intensity and tailored, directional, and polarized photoluminescence.</p>
Photoluminescence of Dy3+:Y2SiO5 and luminescence thermometry in the 300-900 K temperature range
<p>Data from Dy3+:Y2SiO5 photoluminescence and luminescence thermometry measurements in the 300-900 K temperature range. All data are in Origin files.</p>
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