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58 results for “Thermal properties”
Dataset exploring the use of quasi-harmonic approximation to understand the thermal properties of Bi2Se3
<p>This data set contains input and output files for DFT calculations on Bi<sub>2</sub>Se<sub>3</sub> for a number of different fixed unit cells with calculations performed using VASP and Phonopy. At each fixed volume, optimisations and phonon calculations have been performed. These have been used to understand the thermal properties of the material using the quasi-harmonic approximation. </p>
Soil hydraulic and thermal properties determined in surface organic and mineral soils in the region near Toolik Lake on the North Slope of Alaska, 2016-2019
Soil cores of 5 cm diameter down to frozen soil were taken from a subset of sample sites for laboratory analysis. Determinations of hydraulic conductivity, thermal conductivity, porosity, and bulk density were made for each core. For a further subset of sites we developed soil moisture retention curves.
Measurement data used in "Thermal and porosity properties of meteorites: A compilation of published data and new measurements".
<p>Measurement data used in “Thermal and porosity properties of meteorites: A compilation of published data and new measurements”. Includes the measurement data as a csv file, as well as 3D models and images of the measured meteorites as zip archives.</p>
Dataset for publication Cuevas K., Chougan M., Martin F., Ghaffar SH, Stephan D., Sikora P. 3D printable lightweight cementitious composites with incorporated waste glass aggregates and expanded microspheres – Rheological, thermal and mechanical properties. Journal of Building Engineering (2021) 44, 102718
<p>Dataset consisting of G-code for 3D mortar specimen's printing path and particle size distribution (Origin file) of materials used in the study Cuevas K., Chougan M., Martin F., Ghaffar SH, Stephan D., Sikora P. 3D printable lightweight cementitious composites with incorporated waste glass aggregates and expanded microspheres – Rheological, thermal and mechanical properties. Journal of Building Engineering (2021) 44, 102718. <a href="https://doi.org/10.1016/j.jobe.2021.102718">https://doi.org/10.1016/j.jobe.2021.102718</a></p>
Composition-based estimates of the thermal properties of New Zealand basement rocks: data used for calculations and figures
<p>This archive contains four files with compositional data (mineralogical and geochemical) used to estimate thermal conductivity and heat production in New Zealand basement terranes (Kirkby et al., 2024).</p> <p><br>HPR_source_data.csv - contains K, Th, and U concentrations and calculated heat production rates.</p> <p>Longitude - longitude (New Zealand Geodetic Datum)<br>Latitude - latitude(New Zealand Geodetic Datum)<br>K_wtpct - K concentration in weight percent<br>Th_ppm - Th concentration in ppm<br>U_ppm - U concentration in ppm<br>Heat_production_uW/m3 - heat production calculated from K, Th and U concentrations, in microWatts per meter cubed<br>Terrane - name of basement terrane that sample has been assigned to<br>Source - source of data, either PetLAB (Strong et al. 2016), PMAP (Turnbull ref) or separate compilation for this study<br>Reference - Reference citation for data as listed in PetLAB/PMAP or added for this study</p> <p> </p> <p>TC_from_majors_PMAP.csv - contains thermal conductivity estimated from major element geochemistry (Kirkby et al., 2024) using method of, Jennings et al. (2019).<br>Columns are as follows:</p> <p>Collection - collection that sample is contained in within the PetLAB database (Strong et al., 2016)<br>Collection_Number - sample number within the collection above<br>Sample_ID - unique sample ID in PetLAB<br>Analysis_ID - unique analysis ID in PetLAB<br>Longitude - longitude (New Zealand Geodetic Datum)<br>Latitude - latitude(New Zealand Geodetic Datum)<br>**_wtpct - major oxide concentration in weight percent (normalised to non-volatile component)<br>Terrane - name of basement terrane that sample has been assigned to<br>Analysis_Method - analysis method for major oxide concentrations<br>Thermal_conductivity_pred - calculated estimate of thermal conductivity based on major oxide composition, W/mK<br>Bib_ref - analysis source reference, direct copy of Bib_ref field in PetLAB<br>Reference - analysis source reference, direct copy of Reference field in PetLAB</p> <p><br>TC_from_modal_mineralogy.csv - contains thermal conductivity estimated from modal mineralogy (Kirkby et al., 2024).<br>Columns are as follows:</p> <p>Longitude - longitude (New Zealand Geodetic Datum)<br>Latitude - latitude(New Zealand Geodetic Datum)<br>Analysis_ID - unique analysis ID in PetLAB<br>Collection - collection that sample is contained in within the PetLAB database (Strong et al., 2016)<br>Collection_ID - sample number within the collection above<br>Subsample - in the case of thermal conductivity, sometimes two samples were measured. This column distinguishes the two measurements<br>Mineralogy_method - method of determining mineralogy, either point count or QEMSCAN<br>Quartz - percentage of quartz in sample<br>Olivine - percentage of olivine in sample<br>Pyroxene - percentage of pyroxene in sample<br>Other - percentage of other minerals in sample<br>Thermal_conductivity_grain_pred - estimated thermal conductivity (W/mK) from mineralogy (Kirkby et al, 2024), W/mK<br>Thermal_conductivity_dry_measured - measured dry thermal conductivity (W/mK) for samples reported by Sanders et al (2024), W/mK<br>Porosity_measured_pct - measured porosity (percent) for samples reported by Sanders et al (2024)<br>Thermal_conductivity_grain_measured - grain thermal conductivity (W/mK) calculated from measured dry thermal conductivity and porosity<br>Terrane - name of basement terrane that sample has been assigned to<br>Bib_ref - analysis source reference, direct copy of Bib_ref field in PetLAB<br>Reference - analysis source reference, direct copy of Reference field in PetLAB</p> <p><br>TC_from_normative_mineralogy.csv</p> <p>Longitude - longitude (New Zealand Geodetic Datum)<br>Latitude - latitude(New Zealand Geodetic Datum)<br>Analysis_ID - unique analysis ID in PetLAB<br>Collection - collection that sample is contained in within the PetLAB database (Strong et al., 2016)<br>Collection_ID - sample number within the collection above<br>Mineralogy_method - method of determining mineralogy, either Mesonorm or CIPWnormhb (CIPW norm with hornblende)<br>Quartz - percentage of quartz in sample<br>Olivine - percentage of olivine in sample<br>Pyroxene - percentage of pyroxene in sample<br>Other - percentage of other minerals in sample<br>Thermal_conductivity_grain_pred - estimated thermal conductivity (W/mK) from mineralogy (Kirkby et al, 2024)<br>Thermal_conductivity_dry_measured - measured dry thermal conductivity (W/mK) for samples reported by Sagar et al (2022)<br>Porosity_measured_pct - measured porosity (percent) for samples reported by Sagar et al (2024)<br>Thermal_conductivity_grain_measured - grain thermal conductivity (W/mK) calculated from measured dry thermal conductivity and porosity<br>Terrane - name of basement terrane that sample has been assigned to<br>Bib_ref - analysis source reference, direct copy of Bib_ref field in PetLAB<br>Reference - analysis source reference, direct copy of Reference field in PetLAB</p> <p> </p> <p>tc_by_terrane.csv</p> <p>Contains thermal conductivity, standard deviation, and standard error of thermal conductivity estimates by terrane as shown in Figure 7 of Kirkby et al (2024)</p> <p> </p> <p>hpr_by_terrane.csv</p> <p>Contains thermal conductivity, standard deviation, and standard error of thermal conductivity estimates by terrane as shown in Figure 7 of Kirkby et al (2024).</p> <p> </p> <p><br>References</p> <p>Jennings, S., Hasterok, D., & Payne, J. (2019). A new compositionally based thermal conductivity model for plutonic rocks. Geophysical Journal International, 219(2), 1377-1394. https://doi.org/10.1093/gji/ggz376<br>Kirkby, A., N. Mortimer, R. Funnell, M. Sagar, A. Seward, K. Faure and F. Sanders (2024). Composition-based estimates of the thermal properties of New Zealand basement rocks, New Zealand Journal of Geology and Geophysics.<br>Sagar, M. W., Funnell, R., Randell, K., Faure, K., Seward, A., Sanders, F., & Stratford, W. R. (2022). Physical properties of Te Riu-a-Māui / Zealandia crustal rocks: Reconnaissance study and future research. (GNS Science Internal Report 2022/05. <br>Sanders, F., Seward, A., Sagar, M., & Faure, K. (2024). Thermal Properties of Zealandia Basement Rocks: results of Thermal Conductivity Scanner measurements 2023. Lower Hutt. (GNS Science Report. <br>Strong, D. T., Turnbull, R. E., Haubrock, S., & Mortimer, N. (2016). Petlab: New Zealand’s national rock catalogue and geoanalytical database. New Zealand Journal of Geology and Geophysics, 59(3), 475-481. https://doi.org/10.1080/00288306.2016.1157086 </p>
Figure 9 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 9. Means number of deposited eggs by females of tested mites after 4 days post-exposure to α- and γ-Al2O3 NPs at tested concentrations. Different letters denote to significant differences in means at tested concentrations (Duncan test, P ≤ 0.05).
Figure 12 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 12. SEM visualization of α- and γ-Al2O3 NPs aggregation on ventral side of C. mycophagus mite. A = treated female by α-Al2O3 NPs, B = treated female by γ-Al2O3 NPs, C = untreated female.
Figure 7 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 7. Females, nymphal and larval mortality (means ± SE) of C. mycophagus mites, subjected to synthesized α- and γ- Al2O3 NPs at different concentrations and exposure time – A. α-Al2O3 NPs; B. γ-Al2O3 NPs. Different letters within the same exposure time are significantly different (Duncan test, P ≤ 0.05).
Figure 5 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 5. Females, nymphal and larval mortality (means ± SE) of M. fungivorus mites, subjected to synthesized α- and γ-Al2O3 NPs at different concentrations and exposure time – A. α-Al2O3 NPs; B. γ-Al2O3 NPs. Different letters within the same exposure time are significantly different (Duncan test, P ≤ 0.05).
Figure 14 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 14. Mean growth Inhibition (A) and corresponding percentage (B), of F. oxysporum in response to different concentrations of α and γ-Al2O3 NPs after 5 days of growth at 30 °C and 180 rpm in PDB growth medium (Where R2: the relation coefficient and y: the predicted fungal inhibition value at "X" nanoparticles concentration).
Figure 6 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 6. Mortality (means ± SE) of M. fungivorus females, nymphs and larvae, concerning α- and γ-Al2O3 NPs at tested concentrations and exposure time.
Figure 10 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 10. Females' mortality (means ± SE) of (A) M. fungivorus and (B) C. mycophagus mites subjected to synthesized α and γ-Al2O3 NPs at different concentrations and exposure time. different letters within the same concentrations are significantly different, Duncan test (P ≤ 0.05).
Figure 13 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 13. Mean growth Inhibition (A) and corresponding percentage (B) of Aspergillus flavus in response to different concentrations of α- and γ-AL2O3 NPs after five days of growth at 30 ℃ and 180 rpm in PDB growth medium. (Where R2: the relation coefficient and y: the predicted fungal inhibition value at "X" nanoparticles concentration).
Results of inter-laboratory testing on COSC-1 cores and reference samples (density, porosity, thermal properties)
<p>The data files contain the results of comparative measurements of thermal properties of four widely used rock references and nine core samples originating from the ICDP COSC-1 borehole, using a steady-state and a transient divided-bar device, a transient plane source device, a modified Angstrom device, as well as two optical thermal conductivity scanners. In addition, the Dewar method provided benchmark values for specific heat capacity. </p> <p>participating institutions and methods (acronyms used in file headers)</p> <p>Chalmers University (CU) transient plane source (TPS)<br>Geological Survey of Sweden (SGU) optical scanning (TCS) <br>Ruhr-Universit ̈at Bochum (RUB) Dewar method, modified Angstrom (mAng), optical scanning (TCS)<br>Aarhus University (AU) transient divided bar (TDB)<br>University College Dublin (UCD) divided bar (DB)</p>
Simulated top-of-atmosphere (120 km) downward and upward solar and thermal-infrared irradiances and ice cloud optical thickness; calculated solar, TIR and net cloud radiative effect. Simulated with ice crystal properties for aggregates, droxtals, and plates based on Yang (2013).
<p>This dataset consists of three .nc files for ice crystal shapes of aggregates, plates, and droxtals. The files include ice cloud optical thickness <span class="math-tex">\(\tau\)</span> (550nm), the simulated upward and downward irradiances <span class="math-tex">\(F\)</span> at the top-of-atmosphere (with and without the presence of the ice cloud), and the calculated ice cloud radiative effect <span class="math-tex">\(\Delta F\)</span> (solar [0.3-3.5 <span class="math-tex">\(\mu\)</span>m], thermal-infrared [3.5-75 <span class="math-tex">\(\mu\)</span>m], and net). The data set allows the user to extract <span class="math-tex">\(\Delta F\)</span> values for their parameter combinations. The available cloudy and cloud-free irradiances further allow to calculate the cirrus radiative effect (RE) by scaling the 'cloudy' RE with the required cloud cover. This serves as a first-approximation because, as 3D effects are neglected.</p>
Data from: Physical, chemical, and functional properties of neuronal membranes vary between species of Antarctic notothenioids differing in thermal tolerance
Disruption of neuronal function is likely to influence limits to thermal tolerance. We hypothesized that with acute warming the structure and function of neuronal membranes in the Antarctic notothenioid fish Chaenocephalus aceratus are more vulnerable to perturbation than membranes in the more thermotolerant notothenioid Notothenia coriiceps. Fluidity was quantified in synaptic membranes, mitochondrial membranes, and myelin from brains of both species of Antarctic fishes. Polar lipid compositions and cholesterol contents were analyzed in myelin; cholesterol was measured in synaptic membranes. Thermal profiles were determined for activities of two membrane-associated proteins, acetylcholinesterase (AChE) and Na+/K+-ATPase (NKA), from brains of animals maintained at ambient temperature or exposed to their critical thermal maxima (CTMAX). Synaptic membranes of C. aceratus were consistently more fluid than those of N. coriiceps (P < 0.0001). Although the fluidities of both myelin and mitochondrial membranes were similar among species, sensitivity of myelin fluidity to in vitro warming was greater in N. coriiceps than in C. aceratus (P < 0.001), which can be explained by lower cholesterol contents in myelin of N. coriiceps (P < 0.05). Activities of both enzymes, AChE and NKA, declined upon CTMAX exposure in C. aceratus, but not in N. coriiceps. We suggest that hyper-fluidization of synaptic membranes with warming in C. aceratus may explain the greater stenothermy in this species, and that thermal limits in notothenioids are more likely to be influenced by perturbations in synaptic membranes than in other membranes of the nervous system.
Thermal and dynamo evolution of the lunar core based on transport properties of Fe-S-P alloys
<p>These data are our original measured resistivity data and calculation data. </p>
Effect of experimental flour preparation and thermal treatment on the volatile properties of aqueous chickpea flour suspensions
<p>Final data used for figures in the paper: Noordraven, L. E., Buvé, C., Grauwet, T., & Van Loey, A. M. (2022). Effect of experimental flour preparation and thermal treatment on the volatile properties of aqueous chickpea flour suspensions. <em>LWT</em>, 113171.</p>
Potential profile, design parameters and thermal properties of asymmetric double-barrier heterostructures based on AlGaAs simulated with NEGF+H
<p>This is a repository for assymmetric double barrier heterostructures that were simulated using the NEGF coupled with the heat equation described in <a href="https://doi.org/10.1103/PhysRevApplied.14.064022">BESCOND:Phys. Rev. Applied:2020</a>.</p> <ul> <li>Relation between the csv columns and variables:</li> </ul> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Variable</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td>1</td> <td>Lb1 [nm] </td> <td>First barrier length</td> </tr> <tr> <td>2</td> <td>LQW [nm] </td> <td>Quantum well length</td> </tr> <tr> <td>3</td> <td>Lb2 [nm] </td> <td>Second barrier length</td> </tr> <tr> <td>4</td> <td>γ</td> <td>Fraction on Al in AlGaAs alloy</td> </tr> <tr> <td>5</td> <td>V [V] </td> <td>Bias between emitter and collector</td> </tr> <tr> <td>6</td> <td>CP [W/m²] </td> <td>Cooling power</td> </tr> <tr> <td>7</td> <td>Te [K] </td> <td>Electron temperature in the Quantum well</td> </tr> <tr> <td>8</td> <td>W1 [eV] </td> <td>First activation energy</td> </tr> <tr> <td>9</td> <td>W2 [eV] </td> <td>Second activation energy</td> </tr> <tr> <td>10-1525</td> <td>PP [eV]</td> <td>Potential profile</td> </tr> </tbody> </table> <p>These data was used to feed the machine learning workflow shared in <a href="https://gitlab.citius.usc.es/modev/coolML">https://gitlab.citius.usc.es/modev/coolML</a>.</p> <p><br>This work was supported by the Spanish MICINN/AEI, Xunta de Galicia, and FEDER Funds under Grant RYC-2017-23312, Grant PID2019-104834GB-I00, Grant PID2022-141623NB-I00, Grant PID2022-142709OB-C21/PID2022-142709OA-C22, Grant ED431F 2020/008, Grant ED431C 2022/16 and GELATO ANR project (ANR-21-CE50-0017).</p>
High temperature properties incuding creep, creep crack growth rate and thermal fatigue linked with chemical composition of alloys derived from 1.4848 refractory stainless steels
<p>The following set of data results from cast alloys modifying chemical composition taking as reference 1.4848 alloy and getting sound samples that have been tested to calculate creep, creep crack growth rate and thermal fatigue data. </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.