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183 results for “Nucleation”
Nucleation-Limited Kinetics of GaAs Nanostructures Grown by Selective Area Epitaxy: Implications for Shape Engineering in Optoelectronics Devices
<p>This dataset corresponds to the following manuscript: </p> <p>Zendrini, M., Dubrovskii, V., Rudra, A., Dede, D., Fontcuberta i Morral, A., Piazza, V. “Nucleation-Limited Kinetics of GaAs Nanostructures Grown by Selective Area Epitaxy: Implications for Shape Engineering in Optoelectronics Devices” <em>ACS Applied Nano Materials 7,16 (2024):</em> 19065–19074</p> <p>DOI: <a href="http://doi.org/10.1021/acsanm.4c02765">doi.org/10.1021/acsanm.4c02765</a></p> <p>The dataset contains raw SEM images in .tif format for all the arrays of nanowires and nanomembranes discussed in the paper. The dataset also contains the AFM scans in .xyz format for all the arrays of nanowires and nanomembranes. The data for the morphological analysis are extracted from the SEM images and the AFM scans and they are collected in two separate .txt files for NWs and NMs.</p>
Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase (supplementary data)
<p>This a dataset of scanning transmission electron microscopy data showing Pt clusters nucleating in an ionic liquid. For each of the 4 movies there is the raw data (uncompressed .tif and compressed as .avi) and denoised versions (uncompressed .tif and compressed as .avi).</p> <p>This data is for the article "Structure matters – Direct in-situ observation of cluster nucleation at atomic scale in a liquid phase" published in ChemNanoMat (2020), by Trond R. Henninen, Debora Keller and Rolf Erni. (https://onlinelibrary.wiley.com/doi/full/10.1002/cnma.202000503)</p> <p><strong>Movie 1:</strong> Homogeneous nucleations of two clusters in a suspended thin film of ionic liquid. </p> <p><strong>Movie 2: </strong>Heterogeneous nucleation of a ca 8-9 atom cluster near the edge of a nanodroplet supported on a carbon film.</p> <p><strong>Movie 3: </strong>Heterogeneous nucleation of multiple clusters in a nanodroplet. Shortly after nucleation, they coalesce to form disordered nanoclusters.</p> <p><strong>Movie 4:</strong> Heterogeneous nucleation and dissolution cycles of spherical particles in a nanodroplet.</p>
Ice Nucleating Particle number concentration from low-volume sampling over the Southern Ocean during the austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract </strong></p> <p>Ice nucleating particles (INP) are a subclass of atmospheric aerosol particles, which can force heterogeneous freezing of cloud droplets at temperatures above -38 degrees C. In contrast, ice particles form from cloud droplets at temperatures below -38 degrees C due to homogeneous freezing, without INP. Due to their abundance, these particles can affect micro-physical properties of clouds, while acting as INP. During the Antarctic Circumnavigation Expedition (ACE) around the Southern Ocean, off-line filter sampling was performed. Filters were stored on the ship and analysed after the cruise at Leibniz-Institute for Tropospheric Research (TROPOS) concerning INP abundance. Here, we give INP number concentrations for sampling of 8 hour periods.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_ice_nucleating_particles_frozen_fraction_from_lowvolume_filters.csv, data file, comma-separated values</li> <li>ACESPACE_ice_nucleating_particles_number_concentration_from_lowvolume_filters.csv, data file, comma-separated values</li> <li>data_file_header_frozen_fraction.txt, metadata, text format</li> <li>data_file_header_number_concentration.txt, metadata, text format</li> <li>README.txt, metadata, text format</li> <li>change_log.txt, metadata, text format</li> </ul> <p><strong>Change log</strong></p> <p>v1.1 - data files updated</p> <ul> <li>change dataset title to reflect low-volume sampling method</li> <li>addition of INP number concentration data from different temperatures</li> <li>addition of fraction of frozen droplets data</li> <li>addition of field blank filter data</li> <li>create separate data_file_header files</li> <li>add change log</li> </ul> <p>v1.0 - initial release of dataset</p> <p> </p>
Dataset Nucleation Patterns of Polymer Crystals Analyzed by Machine Learning Models
<p>This dataset contains the raw data (01_raw_data), processed data (02_processed_data), and plotting scripts (03_figures) related to the paper:</p> <p>"Nucleation Patterns of Polymer Crystals Analyzed by Machine Learning Models"<br>Atmika Bhardwaj, Jens-Uwe Sommer, Marco Werner</p> <p>Macromolecules <strong>2024</strong>; DOI: <a href="10.1021/acs.macromol.4c00920">10.1021/acs.macromol.4c00920</a></p> <p>Please refer to the README.md files in their respective folders.</p>
Data from Portable Ice Nucleation Experiment during the Pallas Cloud Experiment 2022
<div> </div> <div> </div> <div>The data set contains data from the Portable Ice Nucleation Experiment during the Pallas Cloud Experiment 2022. The Level 1 data is given in its raw temporal resolution and the data is flagged. Invalid data should be removed before analysis.</div>
Data for the publication "Retrieving ice-nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations"
<p>This repository contains the data for the paper:</p> <p>Wieder, J., Ihn, N., Mignani, C., Haarig, M., Bühl, J., Seifert, P., Engelmann, R., Ramelli, F., Kanji, Z. A., Lohmann, U., and Henneberger, J.: Retrieving ice nucleating particle concentration and ice multiplication factors using active remote sensing validated by in situ observations, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-67, in review, 2022.</p> <p>More information can be found in the README files.</p> <p>Note that the scripts to reproduce the figures of the publication are available on request.</p>
Data package for "Fast event-driven simulations for soft spheres: from dynamics to Laves phase nucleation"
<p>This dataset contains supporting data for the publication:</p> <p><em>Fast event-driven simulations for soft spheres: from dynamics to Laves phase nucleation</em></p> <p>A. Castagnède, L. Filion, and F. Smallenburg, J. Chem. Phys. 160 (2024), doi:10.1063/5.0209178, arXiv:2403:12755</p> <p> </p> <p><strong>Contents:</strong></p> <p>The main folder <em>data_package</em> contains three subfolders: <em>figures</em>, <em>SLNN</em>, and <em>snapshots</em>. The <em>figures</em> subfolder contains supporting data for each of the figures found in the publication, accompanied by details on statepoints and methods in individual README files. The <em>SLNN</em> subfolder contains the trained neural network classifier used in this work for crystalline phase identification, alongside usage instructions and an exemple system to analyze. Finally, the <em>snapshots</em> subfolder contains supplementary snapshots of the crystalline clusters obtained in simulations. </p> <p> </p>
Measurements of Ice Nucleating Particles in Beijing, China - Data and processing code
<p>Dataset needed to replicate findings published in the journal article "Measurements of Ice Nucleating Particles in Beijing, China, published in the Journal of Geophysical Research. The dataset contains the following:</p> <p>1. Data files containing raw data from a Continuous Flow Diffusion Chamber - Ice Activation Spectrometer (CFDC-IAS), in comma-delimited format).</p> <p>2. Data and processing files for analysis of backward air trajectories as an Igor Pro 8 packed experiment package file. Igor Pro is available from www.wavemetrics.com and a free 30-day trial version can be used to export data to other formats.</p> <p>3. Data and processing files for analysis of CFDC, APS and meteorological data, including data in the form of waves, as part of an Igor Pro 8 packed experiment package.</p>
Lookup tables for H2SO4-H2O binary and H2SO4-H2O-NH3 ternary homogeneous and ion-mediated nucleation
<p>2019/10/10</p> <p>This directory contains fortran code and lookup tables for calculating<br> nucleation rate for the following four nucleation mechanims:<br> 1. H2SO4-H2O binary homogenous nucleation (BHN),<br> 2. H2SO4-H2O-NH3 ternary homogeneous nucleation (THN),<br> 3. H2SO4-H2O-Ions binary ion-mediated nucleation (BIMN), and<br> 4. H2SO4-H2O-NH3-Ions ternary ion-mediated nucleation (TIMN)</p> <p>References:</p> <p>1. Yu, F., Nadykto, A. B., Herb, J., Luo, G., Nazarenko, K. M., and<br> Uvarova, L. A.: H2SO4-H2O-NH3 ternary ion-mediated nucleation (BHN):<br> Kinetic-based model and comparison with CLOUD measurements, Atmos.<br> Chem. Phys.,18, 17451-17474,<br> https://doi.org/10.5194/acp-18-17451-2018, 2018.<br> 2. Yu, F., Nadykto, A. B., Herb, and J., Luo, H2SO4-H2O binary and<br> H2SO4-H2O-NH3 ternary homogeneous and ion-mediated nucleation: Lookup tables,<br> in review, GMD, 2019.</p> <p><br> Files included in the package:</p> <p>YBHN/: BHN code and lookup table<br> YTHN/: THN code and lookup table<br> YBIMN/: BIMN code and lookup table<br> YTIMN/: TIMN code and lookup table</p> <p>nucl_XXX_mod.f (XXX = BHN, THN, BIMN, TIMN): Stand alone module for XXX rate calculation.</p> <p>mainXXX.f: Test program to demonstrate how the lookup table is used.</p> <p>inputXXX.dat: Example input data for testing the code and lookup table.<br> outputXXX.dat: Example output after running the test code</p> <p>Use "f95 *.f" to compile and get executable program a.out</p> <p>To run the test program:</p> <p>>a.out</p> <p>For your reference, output of running the program on my workstation has been saved to outputXXX.dat.</p> <p>I hope that the comments provided in the subroutines make<br> the code self-explanatory. Contact me if you have questions.</p> <p>Enjoy and have fun!</p> <p>Fangqun Yu<br> SUNY-Albany<br> (fyu@albany.edu)</p>
3-D model data used to investigate the role of K-feldspar and quartz in global ice nucleation by mineral dust in mixed-phase clouds
<p>These simulations were run by Chemical Transport Model TM4-ECPL covering the years 2009-01 to 2016-12 and are used for the bellow publication:</p> <p>Chatziparaschos, M., Daskalakis, N., Myriokefalitakis, S., Kalivitis, N., Nenes, A.,<br> Gonçalves Ageitos, M., Costa-Surós, M., Pérez García-Pando, C., Zanoli, M., Vrekoussis,<br> M., and Kanakidou, M.: Role of K-feldspar and quartz in global ice nucleation by mineral dust in mixed-phase clouds,<br> Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-551, in press 2023.</p> <p>Laboratory: Environmental Chemical Processes Laboratory (EPCL), Department of Chemistry, University of Crete, Heraklion.<br> contact: Kanakidou Maria <mariak@uoc.gr></p> <p>Model resolution: 2x3<br> Model Levels: 25</p> <p>Data info:</p> <p>DU_m2m(time, lev, lat, lon)<br> short_name :DU_m2m<br> long_name : Dust mode 2 mass accumulation</p> <p>DU_m3m(time, lev, lat, lon)<br> short_name : DU_m3m<br> long_name : Dust mode 3 mass coarse</p> <p>qua2_acc(time, lev, lat, lon)<br> short_name :qua2_acc<br> long_name :Quartz – accumulation mode</p> <p>qua2_coa(time, lev, lat, lon)<br> short_name :qua2_coa<br> long_name :Quartz – coarse mode</p> <p>FEL_acc(time, lev, lat, lon)<br> short_name :FEL_acc<br> long_name : K-Feldspar – accumulation mode</p> <p>FEL_coa(time, lev, lat, lon)<br> short_name :FEL_coa<br> long_name : K-Feldspar – coarse mode</p> <p>INP_QUA(time, lev, lat, lon)<br> short_name :INP_QUA<br> long_name :Ice Nucleating Particles derived form Quartz</p> <p>INP_FELD(time, lev, lat, lon)<br> short_name :INP_FELD<br> long_name :Ice Nucleating Particles derived form K-Feldpsar</p> <p> </p>
Data presented in figure 2 of "Evidence of nitrate based nighttime atmospheric nucleation driven by marine microorganisms in the South Pacific"
<p>Data collected at the Maïdo observatory between April 24th and April 29th 2018 used in the calculation of statistics presented in Figure 2 of "Evidence of nitrate based nighttime atmospheric nucleation driven by marine microorganisms in the South Pacific". Data were obtained by an API-ToF-MS; molecular clusters are grouped by family as described in Chamba et al. 2023. Data were first filtered based on SO2 mixing ratios to exclude the periods when the station was under the influence of the volcanic plume of the Piton de la Fournaise. Hourly averages of the signals of interest were then calculated and only the data corresponding to the periods during which the station was in the free troposphere were considered.</p>
Data for: Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid-ammonia nucleation
<p>Model dataset variables produced from the IFS and TM5 modules in EC-Earth3 version 3.3.4. which contains the control case and three experiments with the NPF lookup table. This paper is published at EGUshpere by journal: Geoscientific Model Development.</p> <p>The files contain:</p> <p>Compressed tar file of NetCDF data from IFS output for all four simulations. All IFS data have been averaged to monthly means from 6-hourly grib datasets. The post-process bash script which contains the function for the CDN and cloud effective radius weighted average towards cloud_time is found in the supplemented zendo link.</p> <p>NetCDF files from TM5 general output for each simulation. </p>
Research Data - Nucleation and Arrangement of Abrikosov Vortices in Hybrid Superconductor-Ferromagnetic Nanostructure
<p>Source data from micromagnetic simulations performed in COMSOL Multiphysics and Python codes for data post-processing utilized in the paper "Nucleation and Arrangement of Abrikosov Vortices in Hybrid Superconductor-Ferromagnetic Nanostructure."</p> <p><strong>Square 250-250-205 (nm 3).gif<br></strong>The time evolution of normal-phase indentations and vortex structures in 3D superconducting prism with dimensions \(250 \times 250 \times 205\) nm\(^3\) is analyzed under an inhomogeneous magnetic field generated by a nearby ferromagnetic nanodot with dimensions \(250 \times 250 \times 700\) nm\(^3\), positioned at a distance of \(d\) = 10 nm.</p> <p><strong>Square 250-250-205 (nm 3)- B(H).gif</strong><br>The time evolution of normal-phase indentations and vortex structures in 3D superconducting prism with dimensions \(250 \times 250 \times 205\) nm\(^3\) is analyzed under a homogeneous magnetic field of 315 mT.</p> <p><strong>Sphere radius 200 nm.gif</strong><br>The temporal evolution of vortex structures in a 3D superconducting sphere with a radius of 200 nm is visualized under the effect of a spatially varying magnetic field generated by a ferromagnetic nanodot with dimensions \(350 \times 350 \times 700\) nm\(^3\), positioned 10 nm away.</p> <p><strong>2D_empty Comsol file<br></strong>The TDGL (Time-Dependent Ginzburg-Landau) model is implemented in COMSOL Multiphysics to simulate 2D superconducting systems, a long wire with a square cross-section and a side length of \(a = 250\) nm, under the influence of homogeneous magnetic fields.</p> <p><strong>3D-B(H)-dynamic_empty Comsol file<br></strong>The TDGL model is utilized in COMSOL to simulate a 3D superconducting prism with a square cross-section, where the side length is \(a = 250\) nm and the height is either 205 nm or 185 nm, subjected to homogeneous magnetic fields.</p> <p><strong>3D-350 nm-B(FM)_empty Comsol file</strong><br>The TDGL model is implemented in COMSOL to simulate a 3D superconducting prism with a square cross-section, where the side length is \(a = 350\) nm and the height is 320 nm. The prism is exposed to inhomogeneous magnetic fields produced by a ferromagnetic nanodot with dimensions \(350 \times 350 \times 700\) nm\(^3\), located at varying distances \(d\) from the superconducting prism.</p> <p><strong>3D-250 nm-B(FM)_empty Comsol file</strong><br>The TDGL model is implemented in COMSOL to simulate a 3D superconducting prism with a square cross-section, where the side length is \(a = 250\) nm and the height is 320 nm. The prism is exposed to inhomogeneous magnetic fields generated by a ferromagnetic nanodot with dimensions \(250 \times 250 \times 700\) nm\(^3\), positioned at varying distances \(d\) from the superconducting prism.</p> <p>The files from Comsol (.mph) are without simulation solutions due to their large size - please contact us if needed.</p>
Support Data to "Interface flexibility controls the nucleation and growth of supramolecular networks"
<p><strong>Overview</strong></p> <p>This repository contains the inputs and support data for the publication "Interface Flexibility Controls the Nucleation and Growth of Supramolecular Networks," which is currently under review in Nature Chemistry.</p> <p><strong>Folder Structure</strong><br>Each subfolder is named after the primary method used to obtain the data. The internal structure may vary depending on whether it was more convenient to organize the data by the figure they were used for or by the structures analyzed. Each folder includes a detailed README.md file providing further information.<br><br><strong>Notes<br></strong>In the second version we added the data for Figure S27, which was not uploaded before due oversight.</p>
Dataset to: Terrestrial runoff is an important source of biological ice-nucleating particles in Arctic marine systems
<p>The dataset contains supplementary information to the manuscript "Terrestrial runoff is an important source of biological ice-nucleating particles in Arctic marine systems"</p> <p>The file <a href="https://zenodo.org/api/records/14988900/draft/files/INP_data_all_samples.csv/content" target="_blank" rel="noopener noreferrer">INP_data_all_samples.csv</a> contains information on the ice nucleation measurements for all samples presented.</p> <p>The file "<a href="https://zenodo.org/api/records/14044414/draft/files/Significant_taxa_list_16S.xlsx/content" target="_blank" rel="noopener noreferrer">Significant_taxa_list_16S.xlsx</a>" contains a list of the bacterial taxa that significantly correlated with the concentration of INPs observed in the samples, while the file <a href="https://zenodo.org/api/records/14044414/draft/files/Significant_taxa_list_18S.xlsx/content" target="_blank" rel="noopener noreferrer">Significant_taxa_list_18S.xlsx</a> contains the same information for the microalgae. </p> <p>The relative abundance of the taxa in each sample is indicated in the columns "F" to "T". </p>
Colloidal-ALD Grown Hybrid Shells Nucleate via a Ligand–Precursor Complex
<p>Colloidal atomic layer deposition (c-ALD) enables the growth of hybrid organic–inorganic oxide shells with tunable thickness<br> at the nanometer scale around ligand-functionalized inorganic nanoparticles (NPs). This recently developed method has demonstrated improved stability of NPs and of their dispersions, a key requirement for their application.<br> Nevertheless, the mechanism by which the inorganic shells form is still unknown, as is the nature of multiple complex interfaces between the NPs, the organic ligands functionalizing the surface, and the shell. Here, we demonstrate that carboxylate ligands are the key element that enables the synthesis of these core–shell structures. Dynamic nuclear polarization surface-enhanced nuclear magnetic resonance spectroscopy (DNP SENS) in combination with density functional theory (DFT) structure calculations shows that the addition of the aluminum organometallic precursor forms a ligand–precursor complex that interacts with the NP surface. This ligand–precursor complex is the first step for the nucleation of the shell and enables its further growth.</p>
Supplementary data for Atomistic Mechanism of the Nucleation of Methylammonium Lead Iodide Perovskite from Solution
<p>Supplementary data for "Atomistic Mechanism of the Nucleation of Methylammonium Lead Iodide Perovskite from Solution"</p>
Dataset for "Bioaerosol diversity and Ice nucleating particles in the North-Western Himalayan Region"
<p>Dataset for "Bioaerosol diversity and Ice nucleating particles in the North-Western Himalayan Region". Contains data and scripts to reproduce the figures in the manuscript. See README.md for more information.</p>
Dataset for "Experimental determination of the relationship between organic aerosol viscosity and ice nucleation at upper free tropospheric conditions"
<p><strong>Data and scripts used to create the figures in the manuscript titled: "Experimental determination of the relationship between organic aerosol viscosity and ice nucleation at upper free tropospheric conditions"</strong></p>
Real-time observation of alpha nucleation in Ti-6Al-4V
<p>This video was captured during an in-situ heating stage SEM experiment using a secondary electron camera at the University of Manchester, UK. The Ti-6Al-4V sample was heated to 1000°C to the full β-phase field and then slowly cooled (0.3°C/s) through the β transus, and the α-phase nucleation was recorded by the contrast change in the secondary electron camera from topography development on the sample surface due to surface relief. Microscope operated by Dr Alec E Davis and Dr Jack Donoghue, sample preparation by Nick Byres. These results were published in an Acta Materialia paper in 2021:</p> <p>Acta Materialia paper: https://doi.org/10.1016/j.actamat.2021.117315</p> <p>Researchgate (free peer reviewed preprint): https://bit.ly/3EHs7Na</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.