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105 results for “Material characterization”
Geochemical characterization and material properties of coastal permafrost near Drew Point, Alaska
Permafrost cores (4.5-7.5 m long) were collected April 10th-19th, 2018, along a geomorphic gradient near Drew Point, Alaska to characterize active layer and permafrost geochemistry and material properties. Cores were collected from a young drained lake basin, an ancient drained lake basin, and primary surface that has not been reworked by thaw lake cycles. Measurements of total organic carbon (TOC) and total nitrogen (TN) content, stable carbon isotope ratios (δ13C) and radiocarbon (14C) analyses of bulk soils/sediments were conducted on 45 samples from 3 permafrost cores. Porewaters were extracted from these same core sections and used to measure salinity, dissolved organic carbon (DOC), total dissolved nitrogen (TDN), anion (Cl-, Br-, SO4 2-, NO3 -), and trace metal (Ca, Mn, Al, Ba, Sr, Si, and Fe) concentrations. Radiogenic strontium (87Sr/86Sr) was measured on a subset of porewater samples. Cores were also sampled for material property measurements such as dry bulk density, water content, and grain size fractions.
Application and characterization of poly(vinyl alcohol) reinforced with cellulose nanofibrils as a coating for wood – Supplementary material
<p>Supplementary material to the article "Application and characterization of poly(vinyl alcohol) reinforced with cellulose nanofibrils as a coating for wood"</p>
Characterization of different surface materials and their antifouling activity
<p>The long-term performance of five surface materials - glass, perspex, polystyrene, epoxy-coated glass, and a silicone hydrogel coating - in inhibiting biofilm formation by cyanobacteria was evaluated concerning biofilm cell number, wet weight, chlorophyll a content, and biofilm thickness. In order to obtain more insight into the effect of surface properties on biofilm formation, they were characterized concerning their hydrophobicity and roughness.</p>
The dataset for publication "Characterization of scintillating materials in use for brachytherapy fiber based dosimeters" by S. Commeti, et al., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2022.
<p>This dataset is related to paper journal paper with DOI: <a href="http://dx.doi.org/10.1016/j.nima.2022.167083">10.1016/j.nima.2022.167083</a>.</p> <p>The dataset contains raw txt file and matlab files on the transmittance and the attenuation of Gadox and YVO specimens. </p> <p>Data files were prepared by agnieszka.gierej@vub.be</p>
Data on the material characterization of cast and additively manufactured IN939 subjected to room-temperature low-cycle fatigue load
<p>The original data to the research paper termed "Room-temperature low-cycle fatigue behaviour of cast and additively manufactured IN939 superalloy" are enclosed. Two specimen orientations of L-PBF IN939 - horizontal and vertical, and two thermodynamical states - without subsequent heat treatment (non-treated) and standard aged according to Delargy et al., 1986, were investigated. The paper concerns the low-cycle fatigue performance of cast and additively manufactured IN939 superalloy. It brings a comprehensive account on the damage and deformation behaviour of the tested alloy, combining the test analyses with high-resolution SEM and TEM observations.</p>
Supplementary Material to the PhD Thesis of Luz, Zoneibe (University of Lausanne): Characterizing conodont bioapatite from the Early-Triassic: an analytical and palaeoclimatological approach
<p>The present dataset contains the Supplements cited in the PhD Thesis of Zoneibe Augusto Silva Luz (University of Lausanne), entitled '<em>Characterizing conodont bioapatite from the Early-Triassic: an analytical and palaeoclimatological approach</em>', defended the 29th of June in Lausanne. Three table of contents (TOC) are provided for each of the three thesis chapters. The main thesis is deposited at the Bibliothèque cantonale et universitaire de Lausanne, Section des thèses imprimées et des échanges,and digitally at the SERveur Académique Lausannois (Serval) ().</p> <p>Le présent set de données contient les Suppléments cités dans la thèse de doctorat de Zoneibe Augusto Silva Luz (Université de Lausanne), intitulée 'Characterizing conodont bioapatite from the Early-Triassic : an analytical and palaeoclimatological approach', soutenue le 29 juin à Lausanne. Trois tables des matières (TOC) sont fournies pour chacun des trois chapitres de la thèse. La thèse principale est déposée dans la Bibliothèque cantonale et universitaire de Lausanne, Section des thèses imprimées et des échanges, et électroniquement dans le SERveur Académique Lausannois (Serval) ().</p>
All-inorganic micrometric CsPbBr3:Yb3+ powder as a multifunctional material for photovoltaics and optical thermometry: structural and optical characterization
<p>Halide perovskites have been studied very intensively by researchers during the last decade. Development of these materials has improved their unique optoelectrical properties reaching even higher standards making them promising candidates for photovoltaic applications. It should be noted that most inorganic halide perovskites obtained to date are synthesized using organic solvents in the form of nanosized colloids. Here, a low-temperature synthesis protocol for the preparation of microcrystalline CsPbBr<sub>3</sub> perovskite powder doped with Yb<sup>3+</sup> ions is proposed. The structural and photoluminescence features of the studied material have been thoroughly investigated and described. It turned out that the excitation of the CsPbBr<sub>3</sub>:Yb<sup>3+</sup> perovskite with a 375 nm wavelength leads to spontaneous luminescence of excitons and Yb<sup>3+ </sup>ions. Hence, the use of CsPbBr<sub>3</sub>:Yb<sup>3+</sup> as a luminescent thermometer or an additional absorbing layer on a solar cell surface is possible. The latter application may result in an increase in the conversion efficiency of the cell. In order to verify this, such a layer was prepared and installed on a commercial silicon solar cell. Its photovoltaic properties have been investigated by the measurements of current-voltage characteristics with 1-sun illumination and spectral characteristics of external quantum efficiency.</p>
Characterization of Functionalized Chromatographic Nanoporous Silica Materials by Coupling Water Adsorption and Intrusion with Nuclear Magnetic Resonance Relaxometry
<p>This data publication is based on the metadata and datasets underlying the manuscript "Characterization of Functionalized Chromatographic Nanoporous Silica Materials by Coupling Water Adsorption and Intrusion with Nuclear Magnetic Resonance Relaxometry" (<a href="https://doi.org/10.1021/acsanm.3c04330"><span>https://doi.org/10.1021/acsanm.3c04330</span></a>)</p> <p>Included are the datasets used, raw and processed data of Adsorption measurements (Water, Ar 87K, N2 77K), Water Intrusion measurements, NMR Relaxometry and solid state MAS NMR measurements. More information can be found in the Readme file.</p> <p> </p>
I-Seed_DS1 – CHARACTERIZATION OF BIOMECHANICS AND MATERIALS OF NATURAL SEEDS MODEL
<p>Dataset I-Seed_DS1 focus on bioengineering investigations of plant seed models, to define useful specifications for the design of the artificial systems in terms of multi-functional materials and morphological computation.</p> <p>Task 3.1. Erodium cicutarium seeds: from natural features to robotic specifics.</p> <p>Task 3.2. Samara seeds: from natural features to robotic specifics</p>
Supplementary material for the paper "Detection and Characterization of Multiple Discontinuities in Cables with Time Domain Reflectometry and Convolutional Neural Networks"
<p>This archive contains supplementary material for the paper "Detection and Characterization of Multiple Discontinuities in Cables with Time Domain Reflectometry and Convolutional Neural Networks".</p> <p>In particular, the following data is provided:</p> <ul> <li>dataset of labeled simulated TDR signals (1 million samples)</li> <li>dataset of labeled experimental signals (16 samples)</li> <li>trained convolutional neural network model</li> </ul>
Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica: dataset
<p><strong>Abstract</strong>:<br> (from [1])</p> <blockquote> <p>The addition of fillers can significantly improve the mechanical behavior of polymers. The responsible mechanisms at the molecular level can be well assessed<br> by particle-based simulation techniques, such as molecular dynamics. However, the high computational cost of these simulations prevents the study of macroscopic<br> samples. Continuum-based approaches, particularly micromechanics, offer a more efficient alternative but require precise constitutive models for all<br> constituents, which are usually unavailable at these small length scales. In this contribution, we derive a molecular-dynamics-informed constitutive law by<br> employing a characterization strategy introduced in a previous publication. We choose silicon dioxide (silica) as an exemplary filler material used in polymer<br> composites and perform uniaxial and shear deformation tests with molecular dynamics. The material exhibits elastoplastic behavior with a pronounced anisotropy.<br> Based on the pseudo-experimental data, we calibrate an anisotropic elastic constitutive law and reproduce the material response for small strains accurately. <br> The study validates the characterization strategy that facilitates the calibration of constitutive laws from molecular dynamics simulations. Furthermore, the<br> obtained material model for coarse-grained silica forms the basis for future continuum-based investigations of polymer nanocomposites. In general, the presented<br> transition from a fine-scale particle model to a coarse and computationally efficient continuum description adds to the body of knowledge of molecular science<br> as well as the engineering community.<br> </p> </blockquote> <p><br> <strong>Contact</strong>:<br> Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><br> <strong>Software</strong>:<br> All simulations were performed with LAMMPS [3], version: 29 Oct 2020 / 20201029<br> Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11<br> Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p><strong>Installed packages:</strong><br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p><br> <strong>License:</strong><br> Creative Commons Attribution 4.0 International<br> <br> <strong>Context</strong>:<br> Data set supplementing journal paper:<br> [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080.</p> <p><br> This dataset contains the results presented in [1] and the necessary data to obtain those.</p> <p><br> <strong>Content</strong>:<br> The files to reproduce our simulations and their results are structured as follows:</p> <ul> <li>01_potentials<br> tabulated potentials calibrated via iterative Boltzmann inversion in [2] kindly provided by the Müller-Plathe group at Technische Universität Darmstadt <ul> <li>Angle_table<br> angular interactions</li> <li>Bond_table<br> bond interactions</li> <li>Nonbond_table<br> pair interactions</li> </ul> </li> <li>02_sample<br> Lammps data file (molecular style) of the investigated silica sample</li> <li>03_simulations<br> The condensed simulation directories with the naming convention given below are organized in the following subfolders: <ul> <li>01_time-proportional<br> time-proportional simulation data</li> <li>02_time-periodic<br> time-periodic simulation data</li> </ul> </li> </ul> <p>Each simulation directory contains:</p> <ul> <li>lammps input file (*.in) of the specific simulation</li> <li>input.prm: input parameters of the specific simulation (read by the input file)</li> <li>meta.info: meta data of the specific simulation run</li> <li>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li>thermo_out.Dat: raw output</li> <li>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</li> <li>thermo_out_STD.Dat: standard deviation of raw output</li> </ul> </li> </ul> <p><br> <strong>Naming convention</strong>:<br> Silica-[deformation]-[direction]_[deformation function]-[deformation magnitude]_[deformation rate]<br> ● [deformation]: uniaxial tension (UT), simple shear (SS)<br> ● [direction]: deformation carried out in X/Y/Z (UT) or XY/XZ/YZ (SS)<br> ● [deformation function]: time-proportional (strain), time-periodic (strain_ampl)<br> ● [deformation magnitude]: maximum strain (time-proportional), strain amplitude (time-periodic); unitless<br> ● [deformation rate]: rate-[strain rate] (only time-proportional): 0.001/ns-0.1/ns</p> <p><br> <strong>Output quantities</strong> (columns of *.Dat files):<br> ● Step: time step<br> ● Time: time in fs<br> ● TotEng: total energy in kcal/mol<br> ● PotEng: potential energy in kcal/mol<br> ● KinEng: kinetic energy in kcal/mol<br> ● E_pair: pair energy in kcal/mol<br> ● E_bond: bond energy in kcal/mol<br> ● E_angle: angle energy in kcal/mol<br> ● E_dihed: dihedral energy in kcal/mol<br> ● Temp: temperature in K<br> ● Press: hydrostatic pressure in atm<br> ● Pxx: xx component of pressure tensor in atm<br> ● Pyy: yy component of pressure tensor in atm<br> ● Pzz: zz component of pressure tensor in atm<br> ● Pxy: xy component of pressure tensor in atm<br> ● Pxz: xz component of pressure tensor in atm<br> ● Pyz: yz component of pressure tensor in atm<br> ● Volume: volume of simulation box in (Angstroms)^3<br> ● Lx: box length in x direction in Angstroms<br> ● Ly: box length in y direction in Angstroms<br> ● Lz: box length in z direction in Angstroms<br> ● Density: density in g/(cm^3)<br> ● c_RG: radius of gyration in Angstroms<br> ● c_RG[1]: squared radius of gyration tensor (xx component) in (Angstroms)^2<br> ● c_RG[2]: squared radius of gyration tensor (yy component) in (Angstroms)^2<br> ● c_RG[3]: squared radius of gyration tensor (zz component) in (Angstroms)^2<br> ● c_RG[4]: squared radius of gyration tensor (xy component) in (Angstroms)^2<br> ● c_RG[5]: squared radius of gyration tensor (xz component) in (Angstroms)^2<br> ● c_RG[6]: squared radius of gyration tensor (yz component) in (Angstroms)^2<br> ● c_bondave[1]: bond energy averaged over all atoms in kcal/mol<br> ● c_bondave[2]: bond distance averaged over all atoms in Angstroms<br> ● c_bondave[3]: squared bond distance averaged over all atoms in (Angstroms)^2<br> ● c_angleave[1]: angle energy averaged over all atoms in kcal/mol<br> ● c_angleave[2]: angle averaged over all atoms degree<br> ● c_angleave[3]: cosine of angle (unitless)<br> ● c_angleave[4]: squared cosine of angle (unitless)<br> ● c_MSD[1]: mean squared displacement x-direction in (Angstroms)^2<br> ● c_MSD[2]: mean squared displacement y-direction in (Angstroms)^2<br> ● c_MSD[3]: mean squared displacement z-direction in (Angstroms)^2<br> ● c_MSD[4]: total mean squared displacement in (Angstroms)^2<br> ● c_COM[1]: x coordinate of center of mass in Angstroms<br> ● c_COM[2]: y coordinate of center of mass in Angstroms<br> ● c_COM[3]: z coordinate of center of mass in Angstroms<br> ● v_strain_xx: xx component of engineering strain tensor (unitless) <br> ● v_strain_yy: yy component of engineering strain tensor (unitless) <br> ● v_strain_zz: zz component of engineering strain tensor (unitless) <br> ● v_vMisesequivstress: von Mises equivalent stress in MPa<br> ● v_Cauchy_xx: xx component of stress tensor in MPa <br> ● v_Cauchy_yy: yy component of stress tensor in MPa<br> ● v_Cauchy_zz: zz component of stress tensor in MPa<br> ● v_Cauchy_xy: xy component of stress tensor in MPa<br> ● v_Cauchy_xz: xz component of stress tensor in MPa<br> ● v_Cauchy_yz: yz component of stress tensor in MPa<br> ● v_strain_xy: xy component of engineering strain tensor (unitless) <br> ● v_strain_xz: xz component of engineering strain tensor (unitless) <br> ● v_strain_yz: yz component of engineering strain tensor (unitless) </p> <p><strong>References</strong>:<br> [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080.<br> [2] Ghanbari, A.; Ndoro, T. V. M.; Leroy, F.; Rahimi, M.; Böhm, M. C. & Müller-Plathe, F., “Interphase Structure in Silica-Polystyrene<br> Nanocomposites: A Coarse-Grained Molecular Dynamics Study”, Macromolecules, 2012, 45, 572-584.<br> [3] Plimpton, S., “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.</p> <p> </p>
Dataset for "Applicability of Control Materials To Support Gene Promoter Characterization and Expression in Engineered Cells Using Digital PCR"
<p>This dataset constitutes the pre-processed data contained in the publication 'Applicability of Control Materials To Support Gene Promoter Characterization and Expression in Engineered Cells Using Digital PCR' Analytical Chemistry (2022) 94(14):5566-5574 (DOI: 10.1021/acs.analchem.1c05134).</p>
Figure 4: Spectro°uorimetric spectra of TiO2/CdS composites samples with a di®erent mass proportion-PREPARATION AND CHARACTERIZATION OF TIO2/CDS LAYERS AS POTENTIAL PHOTOELECTROCATALYTIC MATERIALS
<p>Fig. 4 illustrates the fluorescence emission spectra of TiO2/CdS nanostructured ¯lm at the exciting<br> wavelength of 340 nm. The curves for TC 1, TC 2, TC 3 and TC 4 exhibited<br> the clear photoemission peaks at about 375 nm, and compared with emission of<br> bulk CdS (520 nm), 145 nm of blue shift was observed. These features indicate<br> the quantum-con¯ned e®ect of the TiO2/CdS nanocomposite ¯lms [25].</p>
PREPARATION AND CHARACTERIZATION OF TIO2/CDS LAYERS AS POTENTIAL PHOTOELECTROCATALYTIC MATERIALS-Figure 2: The FT/IR microscopy images of the TiO2/CdS composites
<p>The microstructural properties of the deposited TiO2/CdS layers depend<br> on the mass proportions of TiO2:CdS. The proportion of CdS phase is di-<br> rect proportional with the CdS powder used in the preparation process. The<br> FT/IR microscopy images of the TiO2/CdS composites upon annealing at high<br> temperatures, are shown in Fig.2<br> The micrograph shows agglomerates with irregular morphology and size<br> ranging from few micrometers down to hundreds of nanometer.</p>
Figure 1: X-ray di®raction pattern for TiO2/CdS composites samples-PREPARATION AND CHARACTERIZATION OF TIO2/CDS LAYERS AS POTENTIAL PHOTOELECTROCATALYTIC MATERIALS
<p>Fig. 1 shows the X-ray di®raction pattern for TiO2/CdS<br> composites (sample TC 1, TC 2, TC 3 and TC 4). The di®raction pattern of<br> the TiO2/CdS composites exhibits the di®raction peaks owning to CdS phase<br> (hexagonal and cubic). TiO2 (rutile and anatase) phases were also detected.</p>
Figure 3: Absorption spectra of TiO2/CdS composites samples with a di®erent mass proportion-PREPARATION AND CHARACTERIZATION OF TIO2/CDS LAYERS AS POTENTIAL PHOTOELECTROCATALYTIC MATERIALS
<p>Optical properties of TiO2/CdS semi-<br> conductor composites have been investigated using optical absorption and pho-<br> toluminescence spectroscopy. UV-VIS absorption spectra of the as-prepared<br> photocatalysts were taken on a JASCO V-550 spectrometer. The °uorescence<br> spectra were obtained using an ABL&Jasco V 6500 spectro°uorimeter with<br> xenon lamp. The optical studies of the TiO2/CdS composites were performed<br> using the absorption spectra observed in the wavelength range 200-900 nm.<br> Fig. 3 shows the UV-Vis spectra of TiO2/CdS samples.</p>
Supplementary Material from: Spatial characterization of the rotifer Community in a reservoir in the Northeast
<p>Supplementary Material for the paper: <strong>Spatial characterization of the rotifer Community in a reservoir in the Northeast </strong>submitted to Acta Liminologica Brasiliensia.</p>
BeMAGIC_Structural characterization of magnetoelectric materials
<p>BeMAGIC ITN (GA861145) Structural characterization of magnetoelectric materials. Results from UAB, UCAM, TUC, AALTO and KIT</p>
Low-cost test rig for characterization of photocatalytic two dimensional materials using photonically sized UV-A LED light sources
<p>In the presented studies, a system for the characterization of two-dimensional photocatalysts was developed and tested. In the system, reference substances can be studied online with regard to their degradability and adsorption on photocatalytic materials. In order to perform accurate calculations of the quantum and photon efficiency of the catalysts, the LED arrays used were adjusted in their spacing by simulations so that a homogeneous light field is imaged on the catalysts. The system was tested with respect to measurement accuracy and reproducibility and the photocatalytic degradation of methylene blue, methyl orange and rhodamine B was investigated. Exemplarily, the reaction kinetics, photolysis and adsorption on the tested photocatalysts were determined for these compounds and the calculation was presented in detail. The exact construction plans and circuits as well as the sensors and their programming are presented in detail and should encourage other scientists to replicate the experimental setup, since especially in the field of photocatalysis research, often the results of publications cannot be compared with each other.</p>
Dataset - Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurements
<p>With the trend towards multinary materials and the associated increase in measurement time, there is a clear need for increasing the efficiency of measurement procedures. In systems requiring long materials characterization times, the implementation of active learning can help decreasing the measurement duration significantly. This dataset is part of the publication in Digital Discovery under the same title and holds the algorithm as well as the data used to test its performance. The algorithm leverages an active learning approach with a Gaussian process model capable of selecting the next measurement area of a library of materials based on the highest uncertainty. Ten materials libraries were manufactured by magnetron sputtering, the composition was measured with EDX and the electrical resistance was measured using the described test stand. The code can also be found on <a href="https://gitlab.ruhr-uni-bochum.de/fthelen/auto-resist-meas">Gitlab</a>.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.