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185 results for “Perovskites”
Dataset of "Perovskite QDs embedded in polymer as a wavelength-shifting layer for UV-sensitized silicon sensors"
<p>Detection of UV radiation is becoming increasingly important for many applications. Here we present novel UV sensor construction on the basis of standard Si detector modification. Wavelength shifting mechanism is achieved by the luminescence effect of perovskite quantum dots embedded in polymer layers. We comprehensively characterize these composite materials, various sensor modification routes and the optical properties of UV-enhanced visible sensors. Modified S1227-16 BG silicon photodiodes and S13360-1375 CS MPPC photodetectors with the enhanced UV response are successively manufactured.micrographs; cross sections of model simulation or prediction (MSP).</p>
GIXD data of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3), processed q-space maps
<p>This dataset contains grazing incidence x-ray diffraction (GIXD) maps projected in q-space and polar projection. The underlying raw data is published in <a href="https://doi.org/10.5281/zenodo.6683616">10.5281/zenodo.6683616</a> and processed with <a href="https://doi.org/10.5281/zenodo.6683658">10.5281/zenodo.6683658</a>. This data describes a time series of diffraction images acquired with 10 Hz.</p> <p> </p> <p>Parameters of the provided data:</p> <ul> <li> <p>Q-space-maps</p> </li> </ul> <p> </p> <ul> <li> <ul> <li> <p>Horizontal axis (Q<sub>xy</sub>) range: (0, 3.2) Å<sup>-1</sup></p> </li> <li> <p>Vertical axis (Q<sub>z</sub>) range: (0, 3.2) Å<sup>-1</sup></p> </li> <li> <p>Resolution: 1350x1350 pixels</p> </li> <li> <p>Origin (lower left coordinate in q): (0, 0)</p> </li> </ul> </li> <li> <p>Polar data</p> <ul> <li> <p>Horizontal axis (||<strong>q</strong>||) range: (0, 4.53) Å<sup>-1</sup></p> </li> <li> <p>Vertical axis (ф) range: (0, 90) deg</p> </li> <li> <p>Resolution: 512x1024 pixels</p> </li> <li> <p>Origin (lower left coordinate in q): (0, 0)</p> </li> </ul> </li> </ul>
In-situ grazing-incidence X-ray diffraction data of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) via employing an isopropanol antisolvent. Raw Data
<p>The dataset contains 400 diffraction images from a 40 second in-situ grazing-incidence wide-angle X-ray scattering measurement of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) on a glass substrate. The crystallization is initiated via employing an isopropanol antisolvent during the spin-coating of the perovskite precursor solution. 40 µL of MAPbBr3 solution (4:1 DMF/DMSO solvent mixture) was applied on plasma-cleaned glass substrate in a chamber with kapton windows. The two-phase spin-coating regime included 10 seconds at 1000 rpm followed by 30 seconds at 2000 rpm, 200 µL of antisolvent was dispensed at t = 30 s.</p> <p> </p> <p> </p> <p>The data was acquired at the P08 Beamline at PETRA III (DESY Hamburg). Acquisition parameters:</p> <p> </p> <ul> <li> <p>X-ray wavelength: 0.6888 nm</p> </li> <li> <p>Sample detector distance: 809 mm</p> </li> <li> <p>Incidence angle: 0.5 deg.</p> </li> <li> <p>Detector model: XRD 1621 CN3 EHS</p> </li> <li> <p>Acquisition rate : 10 frames per second (10 Hz)</p> </li> <li> <p>Direct beam position (pixels): 545, 222</p> </li> </ul>
Dataset of "Atomic-level description of thermal fluctuations in inorganic lead halide perovskites" publication
<p>The "Zenodo_22-02-2022.zip" file contains a "files" folder and a jupyter notebook to plot the figures reported in the publication. The "files" folder contains several subfolders with the txt files required to plot the figures.</p> <p>The "XAS_simulations.zip" file contains a README.txt and few subfolders with input and output files needed to reproduce the XAS simulations. The README.txt explains the structure of the archive and the content of each subfolders.</p>
Dataset of the publication "Halide Perovskites as Disposable Epitaxial Templates for the Phase-Selective Synthesis of Lead Sulfochloride Nanocrystals"
<p>This dataset provides the raw data associated with the publication "Halide Perovskites as Disposable Epitaxial Templates for the Phase-Selective Synthesis of Lead Sulfochloride Nanocrystals".It contains:</p> <ul> <li>A readme file meant to help the user navigate the database</li> <li>The raw data associated with all the plots and charts found in the Main Text and in the Supplementary information.</li> <li>The raw data collected during the 3D electron diffraction experiments on Pb<sub>3</sub>S<sub>2</sub>Cl<sub>2</sub> Nanocrystals. </li> <li>The CIF files of all the crystal structures refined in the work</li> <li>An atomistic model of the Pb<sub>4</sub>S<sub>3</sub>Cl<sub>2</sub>/CsPbCl<sub>3</sub> interface, which can be visualized with the freeware software Vesta. </li> </ul>
Mixed ionic-electronic conduction in Ruddlesden-Popper and Dion-Jacobson layered hybrid perovskites with aromatic organic spacers
<p>Characterisation dataset for “Mixed ionic-electronic conduction in Ruddlesden-Popper and Dion-Jacobson layered hybrid perovskites with aromatic organic spacers”, DOI:10.1039/d4tc01010h. Data provided as *.xlsx, *.csv, *tif and *.png files.</p> <p> </p> <p> </p> <p> </p>
Dataset for "InGaN Nanohole Arrays Coated by Lead Halide Perovskite Nanocrystals for Solid-State Lighting"
<p>In this work, we demonstrate efficient light downconversion via FRET in InGaN/GaN multiple quantum well (MQW) nanohole arrays, coated with green-emitting CsPbBr3 and FAPbBr3 nanocrystals (NCs) and near-infrared (IR) FAPbI3 NC overlayers for solid-state lighting. Patterning the InGaN MQW into nanohole arrays allows a minimum nitride−NC separation while increasing the heterointerfacial area, thus improving simultaneously the nonradiative and radiative transfer efficiencies. Detailed spectroscopic studies of steady-state and time-resolved photoluminescence indicate a significant reduction in the quantum well photoluminescent decay time in the presence of NCs, accompanied by a significant concurrent increase of the NC integrated emission, providing evidence of efficient light down-conversion mediated by FRET with efficiencies as high as ∼83 ± 6% in the green and ∼74 ± 5% in the near-IR.</p>
Dataset for "Machine Learning Stability and Bandgaps of Lead-Free Perovskites for Photovoltaics"
<p>Datasets used in the publication "Machine Learning Stability and Bandgaps of Lead-Free Perovskites for Photovoltaics" [doi:10.1002/adts.201900178].</p> <p>All structures were relaxed with the following parameters using Quantumwise QATK 2017:</p> <p>- SG15-GGA norm-conserving (Vanderbilt) pseudopotentials employed in a LCAO-approach (200 Hartree cutoff)<br> - 2x1x2-cubic-perovskite-supercells, relaxed from cubic 11.4Åx5.7Åx11.4Å-structures (forces < 0.01eV/Å)<br> - 300K Fermi-Dirac-smearing<br> - a 6x12x6 k-point grid (Monkhorst-Pack)</p> <p><br> Specifically, the included files are:</p> <p><strong>db_2.data: </strong>the actual database used for model building (json-format)<br> <strong>lead_set.data:</strong> the "external" test set used to test predictive power with out of sample compounds (json-format)<br> <strong>load_stanley_c.py:</strong> a python script to parse the .json-files to a python-dictionary including the structures (relaxed and unrelaxed) as <a href="https://gitlab.com/ase/ase">ASE</a>-atoms</p> <p>The format of the datafiles is as follows (-1 generally denote values not parsed from the raw data):<br> {<br> "<idstring>" : {<br> "trajectory" : n/a,<br> "energy" : total DFT energy in eV,<br> "rstruc" : relaxed structure, 3-tuple: (cell-vectors, scaled_positions, elements),<br> "gaps" : { "opt_gap", "ind_gap } - both direct and indirect gap,<br> "effective_mass" : n/a,<br> "iterations" : number of relaxation steps,<br> "calc" : some calculation metadata,<br> "ustruc" : unrelaxed input structure,<br> <br> }<br> }<br> Missing ids relate to structures filtered out, because the calculation didn't converge.</p> <p>Some code which works with a different representation of this data can be found at https://github.com/jstanai/Machine-Learning-Perovskite-Properties-for-Photovoltaics</p> <p> </p> <p> </p>
Data and code for "Quantifying Dynamic Tilting in Halide Perovskites: Chemical Trends and Local Correlations"
<p>This record contains data, scripts, and models associated with the publication "Quantifying Dynamic Tilting in Halide Perovskites: Chemical Trends and Local Correlations".</p> <h3>Databases</h3> <p>The <code>*.db</code> files are databases with the results from density functional theory (DFT) calculations. These are sqlite databases in ase format, see <a href="https://wiki.fysik.dtu.dk/ase/tutorials/tut06_database/database.html">here</a> for more information. The <code>demo-database-access.py</code> script illustrates the most basic access.</p> <h3>Models</h3> <p>The neuroevolution potential (NEP) models described in the publication can be found in the <code>nep-*.txt</code> files. They can be used in conjunction with the <a href="https://gpumd.org">GPUMD package</a>. The <a href="https://calorine.materialsmodeling.org">calorine package</a> provides a Python interface to GPUMD.</p> <h3>Primitive structures</h3> <p>Several primitive structures in extended xyz format can be found in the <code>*.xyz</code> files. These structures have been relaxed using the NEP models included here. The <code>demo-for-using-structures-and-models.py</code> script illustrates how to access the structures and models.</p> <h3>Tools for analyzing tilt angles</h3> <p><strong><code>standardize-cell-orientation.py</code></strong><br>Converts each frame in `movie.xyz` to the standardized cell setting and writes the resulting trajectory to `movie-standardized.xyz`.<br>This is useful when working with triclinic cells.</p> <p><code><strong>analyze-tilt-angles-for-orthorhombic-cells.py</strong></code><br>Analyzes the distribution of tilt angles for each frame in a trajectory.<br>The analysis assumes that the B-B bonds are oriented along the Cartesian coordinate system either without further rotation or after a "simple" rotation of the entire cell.</p> <p><code><strong>angle-analysis.ovito</strong></code><br>Ovito script that approximately implements the procedure used in <code>analyze-tilt-angles-for-orthorhombic-cells.py</code>.</p>
Resistive switching in benzylammonium-based Ruddlesden–Popper layered hybrid perovskites for non-volatile memory and neuromorphic computing
<p><span>Structural, optoelectronic, and supplementary characterisation data for “</span><span>Resistive Switching in Benzylammonium-Based Ruddlesden-Popper Layered Hybrid Perovskites for Non-Volatile Memory and Neuromorphic Computing ”</span><span>, DOI:</span><span>10.1039/d3ma00618b</span><span>.</span></p>
Nanosegregation in arene-perfluoroarene π-systems for hybrid layered Dion-Jacobson perovskites
<p>Structural, optoelectronic, photovoltaic, and supplementary characterization data for “Nanosegregation in arene-perfluoroarene π-systems for hybrid layered Dion-Jacobson perovskites”, DOI:10.1039/d1nr08311b.</p> <ul> <li>Figure_2_XRD.opju: Data described in Figure 2 (XRD patterns) as Origin (.opj) software file.</li> <li>Figure 2_GIWAXS.zip: Data described in Figure 2 (GIWAXS images) as tiff files</li> <li>Figure_3_NMR.mnova: Data described in Figure 3 (NMR spectra) as MestReNova (.mnova) software file. </li> <li>Figure_4_spectra.opj: Data described in Figure 4 (UV-vis absorption and PL spectra) as Origin (.opj) software files.</li> <li>Figure_5_spectra.opj: Data described in Figure 5 (UV-vis absorption spectra and XRD patterns upon hydration) as Origin (.opj) software files.</li> <li>Figure_SI.zip: Data described in the Supporting Information Figures S1–S3 (NMR data as MestReNova (.mnova) software file) as well as Figures S4–S6 (XRD data, reciprocal space maps, radial profiles of q-maps, UV-vis absorption and PL spectra) as Origin (.opj) and image (.tif) files.</li> </ul>
Reversible Pressure-Dependent Mechanochromism of Dion–Jacobson and Ruddlesden–Popper Layered Hybrid Perovskites
<p>Structural, optoelectronic, and supplementary characterization data for "Reversible Pressure-Dependent Mechanochromism of Dion–Jacobson and Ruddlesden–Popper Layered Hybrid Perovskites" DOI: doi.org/10.1002/adma.202108720</p> <ul> <li>Dataset.zip: Data described in the main text and in the SI organised by figure number. Dataset are provided in .xy, .csv, .data and Excel (*.xlsx) file format.</li> <li>Figures.zip: figures described in the main text and in the SI in .png and .pdf</li> <li>readme.txt: replication packages information</li> </ul> <p> </p>
Reversible Pressure-Dependent Mechanochromism of Dion–Jacobson and Ruddlesden–Popper Layered Hybrid Perovskites
<p>Structural, optoelectronic, and supplementary characterization data for “Reversible Pressure-Dependent Mechanochromism of Dion–Jacobson and Ruddlesden–Popper Layered Hybrid Perovskites”, DOI:10.1002/adma.202108720.</p> <ul> <li>Dataset.zip: Data described in the Figures of the main text and Supporting Information as .xy, .data, .csv, .xlsx, and .asc files</li> <li>Figures.zip: Figures provided in the main text and Supporting Information as .pdf and .png files.</li> </ul>
DFT-computed datasets for cation ordering in double perovskites
<p>This repository has datasets on cation ordering of double perovskites, computed using density functional theory. The README.md file gives descriptions of each of the datasets available via this repository.</p>
Crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3), GIXD analysis results: diffraction features and crystal structure
<p>Analysis result of an <em>in-situ</em> measurement of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) on a glass substrate.</p> <p>This dataset contains the positions, sizes, and integrated intensities of extracted diffraction peaks with 0.1s time resolution.</p> <p>For crystal structure matching, the provided CIF file (CCDC 1446529) was used.</p>
Resistive switching memories with enhanced durability enabled by mixed-dimensional perfluoroarene perovskite heterostructures
<p><span>Characterisation dataset for “</span><span>Resistive switching memories with enhanced durability enabled by mixed-dimensional perfluoroarene perovskite heterostructures”</span><span>, DOI:</span><span>10.1039/d4nh00104d</span><span>. Data for main and supporting figures provided as *.xlsx and *.txt files.</span></p> <p> </p> <p> </p> <p> </p>
Data set of "Capacitive and Inductive Characteristics of Volatile Perovskite Resistive Switching Devices with Analog Memory"
<p>The dataset of all data presented in the article published in the virtual special issue of the Journal of Physical Chemistry Letters:</p> <p>"Capacitive and Inductive Characteristics of Volatile Perovskite Resistive Switching Devices with Analog Memory"</p> <p>DOI: <a title="DOI URL" href="https://doi.org/10.1021/acs.jpclett.4c00945">https://doi.org/10.1021/acs.jpclett.4c00945</a></p> <p> </p> <p>The dataset contains the following raw data:</p> <p>## FILE DESCRIPTION<br>--------------<br>### Figure 2<br>- Fig2a.txt : Representative characteristic _I-V_ response of memristor (5 cycles)<br>- Fig2b.txt : Upper vertex-dependent multilevel/multistate analog resistive switching<br>- Fig2c.txt : Characteristic _I-V_ response of 20 distinct devices<br>- Fig2d.txt : Endurance measurements for 1000 cycles of the LRS (ON state) and HRS (OFF state)</p> <p>### Figure 3<br>- Fig3a.txt : Characteristic _I-V_ response with an upper vertex of 0.25 V<br>- Fig3b.txt : Characteristic _I-V_ response with an upper vertex of 0.75 V<br>- Fig3c.txt : Characteristic _I-V_ response with an upper vertex of 1.25 V</p> <p>### Figure 4<br>- Fig4a.txt : IS spectrum under dark conditions at 0 V<br>- Fig4b.txt : IS spectrum under dark conditions at 0.2 V<br>- Fig4c.txt : IS spectrum under dark conditions at 0.3 V<br>- Fig4d.txt : IS spectrum under dark conditions at 0.4 V<br>- Fig4e.txt : IS spectrum under dark conditions at 0.6 V<br>- Fig4f.txt : IS spectrum under dark conditions at 1.0 V</p> <p>### Figure 5<br>- Fig5a.txt : Voltage-dependent transient current response of the perovskite memristor<br>- Fig5b.txt : Magnified view of the transient current response of a single voltage pulse at representative applied voltages<br>- Fig5c.txt : Pulse width-dependent transient current response<br>- Fig5d.txt : Corresponding magnified view of the first and last transient responses<br>- Fig5e.txt : Synaptic potentiation and depression characteristic response of the memristor</p> <p>### Figure 6<br>- Fig6a.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 0.4 V<br>- Fig6b.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 0.4 V<br>- Fig6c.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 0.8 V<br>- Fig6d.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 0.8 V<br>- Fig6e.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 1.2 V<br>- Fig6f.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 1.2 V</p>
Supplementary data for "Heterometallic perovskite-type metal-organic framework with an ammonium cation: structure, phonons, and optical response"
<p>Optimised structures of [NH<sub>4</sub>][Na<sub>0.5</sub>M<sub>0.5</sub>(COOH)<sub>3</sub>] (M = Al, Cr)</p> <p>Phonon output for [NH<sub>4</sub>][Na<sub>0.5</sub>Cr<sub>0.5</sub>(COOH)<sub>3</sub>]</p> <p>Gif of the T’(NH<sub>4</sub><sup>+</sup>) mode (no. 23). The c-axis is the vertical direction.</p> <p>For further information please see the associated publication.</p>
Supplementary files for "Pressure-enhanced ferroelectric polarisation in polar perovskite-like [C2H5NH3]Na0.5Cr0.5(HCOO)3 metal-organic framework"
<p>DFT optimised structures for the paper: Pressure-enhanced ferroelectric polarisation in polar perovskite-like [C2H5NH3]Na0.5Cr0.5(HCOO)3 metal-organic framework. See the paper for additional information on the computational setup.</p> <p>Files are named HP or LP for high-pressure and low-pressure phases, followed by the pressure as calculated by DFT. The HP structure was optimized in two different space groups and are labelled accordingly. The DFT optimized structure without volume restrictions is found in opt-vol.POSCAR</p> <p> </p>
Three-dimensional subnanoscale imaging of unit cell doubling due to octahedral tilting and cation modulation in strained perovskite thin films
<p>Transmission electron microscopy data used in the journal publication <a href="https://doi.org/10.1103/PhysRevMaterials.3.063605">"Three-dimensional subnanoscale imaging of unit cell doubling due to octahedraltilting and cation modulation in strained perovskite thin films"</a></p> <p><strong>Data files</strong></p> <p>There are two data types:</p> <ul> <li>Scanning TEM (STEM) diffraction patterns acquired with a Medipix3 detector (Merlin): m004_LSMO_LFO_STO_medipix.hdf5 <ul> <li>Acquired on a probe corrected Jeol ARM200CF</li> <li>Acceleration voltage: 200 kV</li> <li>Convergence semi-angle: 20.4 mrad (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring)</li> <li>Detector calibration: 1.357 mrad per pixel (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring)</li> </ul> </li> <li>Atomic resolution STEM data, both annular dark field (ADF) and annular bright field (ABF), which were acquired simultaneously: s007_ADF.hdf5, s007_ABF.hdf5</li> </ul> <p>The data can be loaded in python using h5py.</p> <p>For the Medipix3 data:</p> <pre><code class="language-python">import h5py f = h5py.File('m004_LSMO_LFO_STO_medipix.hdf5', mode='r') data = f['fpd_expt/fpd_data/data'] data_subset = data[0:16, 0:16, :, :]</code></pre> <p>For the STEM-ADF or STEM-ABF data:</p> <pre><code class="language-python">import h5py f = h5py.File('s007_ADF.hdf5', mode='r') data = f['Experiments/__unnamed__/data']</code></pre> <p>Exploring the Medipix3 dataset lazily, i.e. without loading the whole dataset into memory at the same time. Using pixStem:</p> <pre><code class="language-python">import pixstem.api as ps s = ps.load_ps_signal("003_stripe1.hdf5", lazy=True) s.plot()</code></pre> <p>Loading the STEM-ADF or STEM-ABF data using HyperSpy, which automatically loads the probe scaling:</p> <pre><code class="language-python">import hyperspy.api as hs s = hs.load("s007_ADF.hdf5") s.plot()</code></pre> <p><br> <strong>Processing files</strong></p> <p>All the TEM data has been processed using python scripts, which is named based on the type of processing:</p> <ul> <li>d00N_...: Medipix3 data processing</li> <li>a00N_...: Atomic resolution STEM-ADF and STEM-ABF processing using Atomap</li> </ul> <p>The scripts generate intermediate files, which are saved in folders with the same prefix as the scripts. So the d001_... script makes a folder named d001_... . These intermediate files are included here as zip-files, since Zenodo doesn't support folder structures.</p> <p>The python libraries required to run the scripts are listed in requirements.txt. Newer versions of the libraries will most likely also work.</p> <p>To setup the python environment with the required libraries, and run all the scripts:</p> <pre><code class="language-bash">pip3 install -r requirements.txt python3 run_all_scripts.py</code></pre> <p> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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