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58 results for “Impurities”
Data for paper "Mathematical modelling of impurity deposition during evaporation of dirty liquid in a porous material"
<p>The attached files contain the data for the paper "Mathematical modelling of impurity deposition during evaporation of dirty liquid in a porous material" by Ellen Luckins, Chris Breward, Ian Griffiths and Colin Please, accepted for publication in JFM (April 2024). See the READ ME file for a description of the data.</p>
Impact of four inorganic impurities – iron, copper, nickel and zinc - on the quality attributes of a Fc-fusion protein upon incubation at different temperatures.
<p>Data regarding the impact of four inorganic impurities – iron, copper, nickel and zinc - on the quality attributes of a Fc-fusion protein upon incubation at different temperatures. </p>
Data for "Snow albedo feedbacks enhance snow impurity-induced radiative forcing in the Sierra Nevada"
<p>This is the data prepared for submission of "<strong>Snow albedo feedbacks enhance snow impurity-induced radiative forcing in the Sierra Nevada</strong>". Three folders are included: 1) The model output with aerosol deposition in snow (aero); 2) The model output without aerosol deposition in snow (noaero) 3) processed observations used to validate the model results (SPIReS)</p>
Can Caroli-de Gennes-Matricon and Majorana vortex states be distinguished in the presence of impurities?
<p>Code and data from the manuscript "Can Caroli-de Gennes-Matricon and Majorana vortex states be distinguished in the presence of impurities?"</p>
Datasets of the manuscript "Mixed-valence state in the dilute-impurity regime of La-substituted SmB6"
<p>You can find here the ARPES and XAS data used in the paper "Mixed-valence state in the dilute-impurity regime of La-substituted SmB6" by M. Zonno et al. . </p> <p>Data are in Igor Pro binary format.</p> <p>ARPESn5d_21eV and ARPESn5d_67eV are the ARPES n5d data for the two photon energies used. The corresponding Sm concentration scalings are found in ARPES_SmDoping_21eV and ARPES_SmDoping_67eV, respectively. </p> <p>XAS_07, XAS_13, XAS_20, XAS_30, XAS_70, XAS_90, XAS_97, XAS_100 are the XAS waves for the different Sm concentrations measured. The energy scaling is found in XAS_Energy. </p>
Systematic assessment of burst impurity in confocal-based single-molecule fluorescence detection using Brownian motion simulations - raw figures of all quantities from all simulations
<p>We performed many simulations in many conditions, calculated many quantities and reported them in the main paper. However, the journal manuscript did not place for so many figures. Nevertheless, the reported values of all simulations rely on the results that were also summarized in the figures. These figures were produced by the Jupyter notebooks analyzing the simulation results.</p> <p>Each figure file has the following nomenclature:</p> <p>TypeOfFigure_concentration_diffusionCoefficient_1or2pop_simulationDuration(long)_Type of PSF.</p> <p>For example:</p> <p><em>molecule_positions_pure_and_impure_bursts_varying_F_C62_D22_5_psfNumeric</em> - report on the molecular position histograms of pure and impure bursts for varying F values, in the simulation with C=62pM, D=22.5 um^2/s, a NUmerical PSF model and 60s duration of simulation (if nothing appears, it was 60s; if the word <em>long</em> appears, it was 180s).</p>
Impurity gas detection dataset on 2/3-scaled canister mock-up
<p>This repository contains datasets used for training Variational Autoencoders (VAE) and Wasserstein Autoencoders (WAE) in the detection of impurity gases on a 2/3-scaled sealed canister mock-up. The research focuses on identifying abnormal gas concentrations using acoustic sensing techniques. These datasets were utilized in the study titled "Acoustic Sensing and Auto-encoder Approach for Abnormal Gas Detection on a Spent Nuclear Fuel Canister Mock-up."</p> <p>Files</p> <p>1. X_train_helium(argon).npy: This file contains the training data with helium in day-1.<br>2. Argon_Percentage.npy: This file contains the different impurity levels of argon used in the experiments.<br>3. impurity_data.npz: This file is a dictionary containing ultrasonic response data for various argon impurity levels.<br>4. X_train_helium(air).npy: This file contains the training data with helium in day-2.<br>5. Air_Percentage.npy: This file contains the different impurity levels of argon used in the experiments.<br>6. impurity_data.npz: This file is a dictionary containing ultrasonic response data for various air impurity levels.</p> <p><br>Data Structure</p> <p>X_train_helium(argon).npy<br>- Shape: (1,991, 301)<br>- Description: This array contains the training data, where:<br> - Each of the 1,991 rows represents a unique sample.<br> - The 301 columns represent the truncated ultrasonic response for each sample.</p> <p>X_train_helium(air).npy<br>- Shape: (988, 301)<br>- Description: This array contains the training data, where:<br> - Each of the 988 rows represents a unique sample.<br> - The 301 columns represent the truncated ultrasonic response for each sample.</p> <p>Argon_Percentage.npy or Air_Percentage.npy<br>- Shape: (41, 1)<br>- Description: This array contains the impurity levels of argon and air used in the experiments. There are 41 different impurity levels, each represented by a percentage in this file.</p> <p>impurity_data.npz<br>- Structure: This file is a dictionary containing 40 items, each representing data for a specific argon impurity concentration level.<br>- Description: Each item in the dictionary is structured as follows:<br> - Shape: (51, 301)<br> - Details:<br> - The 51 rows represent repeated ultrasonic response measurements at each impurity concentration level.<br> - The 301 columns represent the truncated ultrasonic response corresponding to each measurement.</p> <p>Contact<br>For any questions or issues related to this dataset, please contact the repository owner via bozhouzh@usc.edu.</p>
molybdenum and sulfur incorporation as oxyanion substitutional impurities in calcium carbonate minerals: density functional theory data
<p>Provided here are density functional theory research datasets generated using the Vienna Ab Initio Simulation Package. Data relates to publication in Chemical Geology, entitled, Molybdenum and sulfur incorporation as oxyanion substitutional impurities in calcium carbonate minerals: A computational investigation. By Scott D Midgley, James O Taylor, Dominik Fleitmann, Ricardo Grau-Crespo. </p> <p>We include here final geometries in .cif format, as well as full OUTCAR files generated in VASP. Using this file, readers can access all details of the DFT simulations reported. </p>
Supplementary tables for "Impurity resistivity of the Earth's inner core"
<p>Supplementary tables for "Impurity resistivity of the Earth's inner core" contain the results of first-principles calculations on the electrical resistivity of 4- and 6-component hcp Fe alloys at the Earth's inner core pressure.</p>
Figure 4 from: Drapak I, Zimenkovsky B, Ivanauskas L, Bezruk I, Perekhoda L, Muzychenko V, Logoyda L, Demchuk I (2020) HPLC method for simultaneous determination of impurities and degradation products in Cardiazol. Pharmacia 67(1): 29-37. https://doi.org/10.3897/pharmacia.67.e37004
Figure 4 Chromatogram of a resolution mixture (impurity B (12.04; 0.65); impurity B (18.5; 0.98); Cardiazol (18.87; 1.00).
Repository - Ganymede's far-ultraviolet reflectance: constraining impurities in the surface ice
<p>This repository contains the modeled spectra and material optical properties used to produce Figures 3-11 in the main text, and the supplementary figures.</p>
Figure 6 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Quality by design approach for simultaneous determination of original active pharmaceutical ingredient quinabut and its impurities by using HPLC. Message 1. Pharmacia 68(1): 79-87. https://doi.org/10.3897/pharmacia.68.e50704
Figure 6 Linear dependence of the theoretical concentration of quinabut on the found concentration in normalized coordinates.
Supplementary material 1 from: Golembiovska О, Voskoboinik O, Berest G, Kovalenko S, Logoyda L (2021) Quality by design approach for simultaneous determination of original active pharmaceutical ingredient quinabut and its impurities by using HPLC. Message 1. Pharmacia 68(1): 79-87. https://doi.org/10.3897/pharmacia.68.e50704
Shimadzu LCsolution Analysis Report
Raw data of article "Substantial organic impurities at the surface of synthetic ammonium sulfate particles"
<p>Raw data of figures shown in the article. </p>
STEM dataset (Si impurities in graphene hole)
<p>Scanning transmission electron microscopy movie (300 frames) represented as a numpy array on Si atoms moving along the edges of graphene nanohole.</p>
In Vivo and in Vitro Assessment of Residual DNA Impurity-Derived Transcriptions [II]
GEO Series GSE280789. synthetic construct. 1 samples. Type: Expression profiling by high throughput sequencing.
A rapid assessment model for liver toxicity of macrolides and an integrative evaluation for azithromycin impurities
GEO Series GSE195903. Danio rerio. 9 samples. Type: Expression profiling by high throughput sequencing.
Gene expression profiles in rat lung following intratracheal instillation with impurity-free single-wall carbon nanotubes
GEO Series GSE61483. Rattus norvegicus. 15 samples. Type: Expression profiling by array.
Gene expression profiles in human alveolar epithelial cell line exposure to impurity-free single-wall carbon nanotubes
GEO Series GSE41101. Homo sapiens. 12 samples. Type: Expression profiling by array.
Figure 9 from: Drapak I, Zimenkovsky B, Ivanauskas L, Bezruk I, Perekhoda L, Muzychenko V, Logoyda L, Demchuk I (2020) HPLC method for simultaneous determination of impurities and degradation products in Cardiazol. Pharmacia 67(1): 29-37. https://doi.org/10.3897/pharmacia.67.e37004
Figure 9 Chromatogram of cardiazol degraded under oxidative conditions.
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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.