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58 results for “Impurities”
Supporting data for manuscript "Geochemical Characterization of Insoluble Particle Clusters in Ice Cores Using Two-dimensional Impurity Imaging"
<p>Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) offers micron-resolution 2D chemical imaging, which has been adapted recently to ice core analysis. The datasets are supporting information for the manuscript "Geochemical Characterization of Insoluble Particle Clusters in Ice Cores Using Two-dimensional Impurity Imaging" accepted for publication at Geochemistry, Geophysics, Geosystems (10.1029/2022GC010595). Measurements were performed at the Ca’Foscari University of Venice, considered as analytes are 23Na, 24Mg, 27Al, 29Si, 43Ca, 56Fe and 88Sr. Background and drift correction as well as image construction were performed using the software HDIP (Teledyne Photon Machines, Bozeman, MT, USA). Impurity maps are acquired as a pattern of lines, without overlap in the direction perpendicular to that of the scan, and without any further spatial interpolation. In a sample of the EGRIP Greenland ice core (from about 1256.95 m depth), maps were obtained over 3 adjacent areas. For each of the maps, for every chemical channel the intensities (in counts, after background and drift correction) are provided as a separate file, named as “ds01_Area1_Na.csv”, etc. These maps were obtained using a 20 µm square spot. This data can be used to obtain the images shown in the manuscript. For the additional map shown as Figure 9 in the manuscript, data were obtained using a LA-ICP-TOFMS for imaging a sample of the last glacial period in the EPICA Dome C (EDC) ice core, bag 1065. The maps were acquired using a 35 µm square spot, with 50% overlap between neighboring pixels to increase the spatial resolution horizontally.</p>
Data for: Bound impurities in a one-dimensional Bose lattice gas: low-energy properties and quench-induced dynamics
<p>Dataset for <em>Bound impurities in a one-dimensional Bose lattice gas: </em><em>low-energy properties and quench-induced dynamics</em> [<a href="https://scipost.org/SciPostPhysCore.7.3.049">SciPost Phys. Core 7, 049 (2024)</a>].</p>
Data for: Mobile impurities interacting with a few one-dimensional lattice bosons
<p>Dataset for <em>Mobile impurities interacting with a few one-dimensional lattice bosons</em> (<a href="https://iopscience.iop.org/article/10.1088/1361-6455/acb51b">10.1088/1361-6455/acb51b</a>). It corresponds to exact diagonalization results for energies and bipolaron sizes.</p> <p>The files correspond to the following figures in the preprint:</p> <p>Fig. 1a: Ep_UBB2.dat <br> Fig. 1b: Ep_UBB4.dat <br> Fig. 1c: Ep_UBB6.dat <br> Fig. 1d: Ep_UBB8.dat <br> Fig. 2: Ep_UBI50.dat<br> Fig. 5a: Ebp_UBB2.dat <br> Fig. 5b: Ebp_UBB4.dat <br> Fig. 5c: Ebp_UBB6.dat <br> Fig. 5d: Ebp_UBB8.dat <br> Fig. 6: Ebp_UBI50.dat <br> Fig. 7a: rbp_UBB2.dat <br> Fig. 7b: rbp_UBB4.dat <br> Fig. 7c: rbp_UBB6.dat <br> Fig. 7d: rbp_UBB8.dat <br> Fig. 8: rbp_UBI50.dat</p>
Evolution of FDA Guidelines on Control of Nitrosamine Impurities in Human Drugs – A Comparative Analysis of September 2024 Revisions
<p>Nitrosamine impurities have become a significant concern in the pharmaceutical industry due to their carcinogenic potential. In response, the U.S. Food and Drug Administration (FDA) has continuously updated its guidelines to ensure the safety and efficacy of drug products. This review article provides a comprehensive analysis of the evolution of FDA guidelines on the control of nitrosamine impurities, with a particular focus on the September 2024 revisions. By comparing the latest guidance with previous versions, this article highlights key changes, including the expanded focus on Nitrosamine Drug Substance-Related Impurities (NDSRIs), updated risk assessment strategies, and the introduction of new Acceptable Intake (AI) limits. The analysis underscores the FDA's commitment to enhancing drug safety through rigorous control measures and global harmonization efforts.</p>
PQRI Elemental Impurity Interlaboratory Study Raw Data
<p>The pharmaceutical industry recently implemented a new paradign in drug product elemental impurity (EI) analysis in ICH Q3D Guidelines and USP General Chapters <232> and <233>, which allow for EI analysis by inductively coupled plasma-mass spectrometry (ICP-MS) and similar techniques. To date, there have been few systematic evaluations of laboratory performance on EI analysis in drug products due to a lack of standardized samples and ongoing efforts to adopt best practices. We organized an interlaboratory study to provide a data-driven way to address key technical challenges faced by laboratories during the implementation of EI regulations, including laboratory equipment, interference correction strategies, and the method of calculating the final concentrations.</p> <p>The data contained in this file includes the analytical results and characteristics for all participant laboratories in a format readable and analyzable by R. A manuscript describing the analysis of this data is in preparation, with a target submission date of October 2021.</p>
Josephson potentials for single impurity Anderson impurity in a junction between two superconductors
<p>This is a collection of the results from an extensive set of numerical renormalization group (NRG) calculations for the single-impurity Anderson model coupled to two superconducting leads described by the Bardeen-Cooper-Schrieffer (BCS) Hamiltonian, with a superconducting phase difference phi.</p> <p>All calculations are performed for the same BCS gap Delta=0.1. Each subdirectory contains the data sets for a given value of charge repulsion parameter U. In each case the hybridisation Gamma and the on-site potential delta=epsilon+U/2 are swept. For each of these, the phase difference phi is swept from 0 to pi in 50 steps.</p> <p>Contents<br> ========<br> U=0.01<br> U=0.05<br> U=0.1<br> U=0.125<br> U=0.25<br> U=0.4<br> U=0.5<br> U=0.5-asymmetry<br> U=0.5-B=0.001<br> U=0.5-B=0.01<br> U=0.5-B=0.02<br> U=0.5-B=0.03<br> U=0.5-B=0.04<br> U=0.5-Bsc=0.001<br> U=0.5-Bsc=0.01<br> U=0.5-Bsc=0.04<br> U=0.5-new<br> U=0.5-z=0.5<br> U=0.75<br> U=1<br> U=1.25-new<br> U=1.5-new<br> U=1.75-new<br> U=2<br> U=3</p> <p>Each directory contains the input file (param) and a set of perl scripts for running the calculations, as well as for post-processing the results. For most cases, there are also Mathematica notebooks for visulisation of the results, as well as a collection of plots (pdf files) of the key properties.</p> <p>The raw results are contained in the archive raw.tar. The Josephson potentials are contained in another archive, results-final.tar.gz. Finally, the results are also stored in tabulated files Vr_1.dat, Vr_2.dat, Va_1.dat, Va_2.dat. The parsing of these files is demonstrated in the Mathematica notebook files.</p> <p>Directories with -new in the name contain the results in an extended range of the hybridisation Gamma and on-site potential epsilon.</p> <p>Directories with -z=0.5 in the name contain the results of a calculation for a different discretization mesh in the NRG for testing discretization effects (those are small for the quantity of main interest, the Josephson potential).</p> <p>Directories with -B=value in the name contain the results for the case with a local magnetic field (Zeeman term) applied on the impurity site, while those with -Bsc=value in the name contain the results for the case with a magnetic field in the superconductor.</p> <p>The calculations have been performed with the NRG code "NRG Ljubljana", https://github.com/rokzitko/nrgljubljana, http://nrgljubljana.ijs.si/, commit number e6f73ff299d155c6ca9aefded45d67c48f34de0e.</p> <p>The computation has been performed on HPC VEGA, https://doc.vega.izum.si/.</p> <p>Note added at a later time: The U=2 data set contains some outliers due to premature termination of some calculations; the outliers are easy to spot, however.</p>
Suppression of 1/f noise in graphene due to non-scalar mobility fluctuations induced by impurity motion
<p>Experimental dataset for article ''Suppression of 1/f noise in graphene due to non-scalar mobility fluctuations induced by impurity motion'', <a href="https://doi.org/10.48550/arXiv.2112.11933">arXiv:2112.11933</a></p>
Annotation of the non-canonical translatome reveals that CHO cell microproteins are a new class of therapeutic antibody drug product impurity
<p>3,681 novel Chinese hamster proteoforms (derived from uORFs, ouORFs and ORFs encoded on NCBI annotated non-coding RNAs).</p>
Datasets for "Assessing satellite derived radiative forcing from snow impurities through inverse hydrologic modeling"
<p>This dataset contains observations and model output used in </p> <p>Matt, F. N., & Burkhart, J. F. (2018). Assessing satellite-derived radiative forcing from snow impurities through inverse hydrologic modeling. Geophysical Research Letters, 45. https://doi.org/10.1002/2018GL077133</p>
Systematic assessment of burst impurity in confocal-based single-molecule fluorescence detection using Brownian motion simulations - photon timetag simulation files
<p>Attached are the photon timestamp and channels simulated for different 3D diffusing molecules simulations at different conditions (simulation was performed by PyBroMo).</p> <p>Each of the files has, in its name, a code. The meaning of the codes are as following:</p> <pre>f32445 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model </pre> <pre>a01f8f - 15 molecules at a concentration of 31 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>9ff667 - 15 molecules at a concentration of 15.5 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>71154a - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 22.5 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>ad926d - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 60 second simulation using a numerical PSF model</pre> <pre>1ab235 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 180 second simulation using a numerical PSF model</pre> <pre>d00978 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 180 second simulation using a numerical PSF model</pre> <p> </p> <pre>2469bb - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model </pre> <pre>4be121 - 15 molecules at a concentration of 31 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>a7088f - 15 molecules at a concentration of 15.5 pM, with a diffusion coefficient of 90 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>023983 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 22.5 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>653f61 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 60 second simulation using a Gaussian PSF model</pre> <pre>4f06ee - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s - 180 second simulation using a Gaussian PSF model</pre> <pre>dec32c - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 5.625 micron^2/s - 180 second simulation using a Gaussian PSF model</pre> <p> </p> <pre>85b0a1 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 60 second simulation using a Numerical PSF model</pre> <pre>964ef3 - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 180 second simulation using a Numerical PSF model</pre> <p> </p> <pre>f28f6e - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 60 second simulation using a Gaussian PSF model</pre> <pre>c311dd - 15 molecules at a concentration of 62 pM, with a diffusion coefficient of 90 micron^2/s , 10 of which belong to a sub-population with a mean FRET efficiency of 0.75, and the leftover 5 belong to another sub-populations with a mean FRET efficiency of 0.50 - 180 second simulation using a Gaussian PSF model</pre>
Dataset for the publication: Metallic Impurities in Electrolysis: Catalytic Effect of Pb Traces in Reductive Amination and Acetone Reduction
<p>This dataset contains the experimental data of the publication "Metallic Impurities in Electrolysis: Catalytic Effect of Pb Traces in Reductive Amination and Acetone Reduction" that is published in Angewandte Chemie International Edition. </p>
Analytical model for collisional impurity transport in tokamaks at arbitrary collisionality
<p>Database of NEO simulations used to develop a set of formulae for an analytical model for collisional impurity transport in tokamaks.</p>
ImPure Injection Molding Sensor Data - Trial 12th May
<p>ImPure project, open access data from PASCOE IM line. </p>
ImPure Injection Molding Sensor Data - Trial 17th May
<p>ImPure project, open access data from PASCOE IM line. </p>
ImPure Injection Molding Sensor Data - Trial 16th May
<p>ImPure project, open access data from PASCOE IM line. </p>
Impurity Knight shift in quantum dot Josephson junctions
<p>Supplemental material for the publication "Impurity Knight shift in quantum dot Josephson junctions", arXiv:2212.07185. Includes input files for NRG calculations and a Wolfram Mathematica notebook with perturbative calculations.</p>
Dataset for Determination of Inorganic Impurities and Ash Content from Biofuels
<p>The complete data set that was the basis of the article:</p> <p>Stratulat, C.; Ginghina, R.E.; Bratu, A.E.; Isleyen, A.; Tunc, M.; Hafner-Vuk, K.; Frey, A.M.; Kjeldsen, H.; Vogl, J. Development- and Validation-Improved Metrological Methods for the Determination of Inorganic Impurities and Ash Content from Biofuels. <em>Energies</em> <strong>2023</strong>, <em>16</em>, 5221. https://doi.org/10.3390/en16135221</p>
Weak probe readout of coherent impurity orbital superpositions in silicon
<p>Each file contains data which is similar to the one shown in Fig.3.b for different laser bandwidths: 001 and 002 - 0.078THz; 004 and 005 - 0.127THz; 007 and 009 - 0.223THz. The data files 001, 004, and 007 are the probe transmission changes as functions of the delay between one of the pumps and the probe in picoseconds (The other pump arrives a fixed time of 50ps before the probe). The data files 002, 005, and 009 are the same as 001, 004, and 007 but with the probe beam blocked. Please, see the paper for details. </p>
Data for: Light-absorbing impurities in glacial environments over western Himalaya from reanalysis data and in situ observations
<p>The repository contains information about 6 aerosol variables extracted from MERRA-2 reanalysis over western Himalayan region of Jammu and Kashmir. It also contains data about glacier ice chemistry based on 9 physicochemical characteristics of 3 glaciers nestled in the Greater Himalayan Mountain range of Kashmir. Air mass back trajectories as simulated from HYSPLIT model on a seasonal basis are also provided.</p>
Data for paper "The role of temperature and drying cycles on impurity deposition in drying porous media"
<p>The attached files contain the data for the paper "The role of temperature and drying cycles on impurity deposition in drying porous media" by Ellen Luckins, Chris Breward, Ian Griffiths and Colin Please, accepted for publication in Europhysics Letters (April 2024). See the READ ME file for a description of the data.</p>
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