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293 results for “cuing”
Data for PhD Thesis: "Chemical and electronic structure of Cu$_2$O, NiO, and Cu$_2$O-NiO combinatorial material libraries as hole-transport material for halide perovskite solar cells"
<p>Here, the whole data measured during the PhD time of L. CW. Bodenstein-Dresler + Labbook is uploaded. T data in the "data"-folder was measured at HZB with XPS, UPS and IPES. </p> <p> </p> <p>The PEYS and CPD and XRD data was measured by A. Kama at BIU. </p> <p> </p>
Cu-C-O - Model and Dataset
<p>This dataset supports our publication <strong>Reactant-Induced Dynamic Active Sites on Cu Catalysts During the Water-Gas Shift Reaction</strong> .<br>It includes the Cu-C-O model, a machine learning force field (MLFF) developed for simulating CO-induced surface reconstruction of Cu(111), along with the data and files used in the training and simulation processes.</p> <ul> <li><strong>Freezed and compressed Cu-C-O model (graph-compress.pb)</strong>: The fully trained MLFF model used for production simulations in our study.</li> <li><strong>Cu-C-O dataset (Dataset.tgz)</strong>: Contains the complete datasets of various structures appearing in the process of CO-induced Cu (111) surface reconstruction, in DeePMD-kit format (Dataset.tgz). Each image is labeled with coordinations (coord.npy) in Å, total energies (energy.npy) in eV, force (force.npy) in eV/Å, and cell parameters (box.npy) in Å.</li> <li><strong>DPMD trajectories (DPMD_trajectory.tgz)</strong>: The full trajectories of Cu(111) surface with/without CO adsorption; evolution of adatoms with/without CO adsorption at different adatom coverages and temperatures.</li> <li><strong>Input_files (Input_Files.tgz)</strong>: Examples of input files used for AIMD simulations and SCF calculations with VASP, for training with DeePMD-kit, and for NVT molecular dynamics simulations with LAMMPS.</li> </ul>
Scanning tunneling microscopy of pentacene on Cu(111)
<p>STM images and spectroscopy of pentacene molecules on Cu(111). Data were taken at 5K. Data format is CreaTec ca 2007. (unpublished)</p>
Accuracy in Rietveld quantitative phase analysis with Mo and Cu strictly monochromatic radiations
<p><strong>Abstract </strong>This chapter is mainly based in the article entitled “Accuracy in Rietveld quantitative phase analysis: A comparative study of Mo and Cu strictly monochromatic radiations” by León-Reina et al., Journal of Applied Crystallography (2016). It reports Rietveld quantitative phase analyses using laboratory based Mo and Cu radiations where synchrotron powder diffraction (λ = 0.77439(2) Å) has been used to validate the most challenging analyses. From the results for three series with increasing contents of an analyte (inorganic crystalline phases, organic crystalline phases and a glass), it is inferred that Rietveld analyses from high-energy Mo-Kα<sub>1</sub> patterns have slightly better accuracies than those obtained from Cu-Kα<sub>1</sub> diffraction data. This behaviour has been established from the results of the calibration graphics obtained through the spiking method and also from Kullback-Leibler distance statistic studies. We explain this outcome, in spite of the lower diffraction power for Mo-radiation when compared to Cu-radiation, due to the larger volume (and hence a larger number of crystallites) tested with Mo and also because higher energy allows the recording of patterns with fewer systematic errors. Limits of detection (LoD) and limits of quantification (LoQ) have also been established for the studied series. For similar recording times, LoDs in Cu-patterns, ~0.2 wt%, are slightly lower than those derived from Mo-patterns, ~0.3 wt%. LoQ for a well crystallised inorganic phase using laboratory powder diffraction was established close to 0.10 wt%, as stable fits were obtained. However, the accuracy of these analyses was very poor, with relative errors close to 100%. Only contents higher than 1.0 wt% yielded analyses with relative errors lower than 20%.</p>
Lattice configurations for Cu/ Zn order-disorder transitions in Cu2ZSnS4
<p>Data on thermodynamic Cu/ Zn disorder in Cu2ZnSnS4 generated using ERIS (10.5281/zenodo.1471439).</p>
Lattice thermal conductivity of Cu-based sulvanites
<p>Input ShengBTE files (CONTROL file and IFC files) and output file with main results.</p>
Dataset: Data of microstructural and magnetic characterization of HPT-deformed Fe-Cu
<p>This dataset contains the raw data of the microstructural and magnetic characterization of Fe and Fe-Cu samples with a Cu-content ranging from 5 at.% to 30 at.% processed by HPT-deformation. </p> <p>The dataset contains data from:</p> <ul> <li>Hardness measurements of all samples</li> <li>SQUID measurements of all samples</li> <li>Magnetostriction measurements of all samples</li> <li>EDS measurements of all Fe-Cu samples</li> <li>Synchrotron X-ray diffraction measurements of selected Fe-Cu samples</li> </ul>
A combined experimental and theoretical approach to the electronic and magnetic properties of Cu-doped LaMnO3 perovskites
<p>Collected data in Origin file format of magnetic and cw-EPR measurements of LaMnO3, La2CuO4 and Cu-doped LaMnO3. Temperature and magnetic field dependent magnetization measurements have been carried out on a Quantum Design Physical Properties Measurement System (PPMS). Room temperature continuous wave EPR (cw-EPR) measurements at X-band (9.86 GHz) frequencies have been conducted with a Bruker B-ER420 spectrometer upgraded with Bruker ECS 041XG microwave bridge and a lock-in amplifier (Bruker ER023M) using Bruker TE102 resonator applying a modulation amplitude of 5 G, a modulation frequency of 100 kHz and an attenuation of 20 dB for the microwave bridge. The samples have been measured in quartz tubes of 2.9 mm outer diameter with a filling height of approx. 9 mm containing approx. 7 mg to 20 mg of the powdered sample. All spectra are background-corrected taking an empty quartz tube as reference.</p>
Data from: 'Venus trapped, Mars transits': Cu and Fe redox chemistry, cellular topography and in situ ligand binding in terrestrial isopod hepatopancreas
Woodlice efficiently sequester copper (Cu) in 'cuprosomes' within hepatopancreatic 'S' cells. Binuclear 'B' cells in the hepatopancreas form iron (Fe) deposits; these cells apparently undergo an apocrine secretory diurnal cycle linked to nocturnal feeding. Synchrotron-based m-focus X-ray spectroscopy undertaken on thin sections was used to characterize the ligands binding Cu and Fe in S and B cells of Oniscus asellus (Isopoda). Main findings were: (i) morphometry confirmed a diurnal B-cell apocrine cycle; (ii) X-ray fluorescence (XRF) mapping indicated that Cu was co-distributed with sulfur (mainly in S cells), and Fe was co-distributed with phosphate (mainly in B cells); (iii) XRF mapping revealed an intimate morphological relationship between the basal regions of adjacent S and B cells; (iv) molecular modelling and Fourier transform analyses indicated that Cu in the reduced Cuþ state is mainly coordinated to thiol-rich ligands (Cu–S bond length 2.3 A˚ ) in both cell types, while Fe in the oxidized Fe3þ state is predominantly oxygen coordinated (estimated Fe–O bond length of approx. 2 A˚ ), with an outer shell of Fe scatterers at approximately 3.05 A˚ ; and (v) no significant differences occur in Cu or Fe speciation at key nodes in the apocrine cycle. Findings imply that S and B cells form integrated unit-pairs; a functional role for secretions from these cellular units in the digestion of recalcitrant dietary components is hypothesized.
Electrochemical removal of Cu, Fe and Mn from molten ZnCl2:KCl:NaCl
<p>The dataset contains raw data and experimental results of electrochemical removal of CuCl, FeCl2, Fecl3 and MnO2 from ZnCl2-KCl-NaCl molten salt. </p>
FIGURE. Results of discriminant function an alysis (DFA) for C. brizoides (br), C. curvata (cu) and C. praecox (pr). Characters abbreviated as in Table 2. A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): LB = 0.63, LN = -0.93, WN = 0.94, LN/WN = 1.13, FGL = -0.62. Loadings for the second axis: WW = -0.76, LB = 1.51, LN = 2.21, WN = -1.79, LN/WN = -1.48, FGL = 0.55, FGW = -0.51. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): CLL = -0.58. Loadings for the second axis: CW = -0.72, IL = -0.78. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Results of discriminant function an alysis (DFA) for C. brizoides (br), C. curvata (cu) and C. praecox (pr). Characters abbreviated as in Table 2. A. Reproductive characters. Loadings for the first axis (only absolute values>0.50 are given): LB = 0.63, LN = -0.93, WN = 0.94, LN/WN = 1.13, FGL = -0.62. Loadings for the second axis: WW = -0.76, LB = 1.51, LN = 2.21, WN = -1.79, LN/WN = -1.48, FGL = 0.55, FGW = -0.51. B. Vegetative characters. Loadings for the first axis (only absolute values>0.50 are given): CLL = -0.58. Loadings for the second axis: CW = -0.72, IL = -0.78.
FIGURE. Results of discriminant function analysis (DFA) for the reproductive characters of the Ammoglochin taxa. A—along axes DF1 and DF2; B—along axes DF1 and DF3. Characters abbreviated as in Table 2). Loadings for the first axis (only absolute values>0.50 are given: LN = 1.09, WN = -1.57, LN/WN = -1.59, FGL = 0.74. Loadings for the second axis: UL/UW = 0.57, LW = 0.71, WW = 0.52. Loadings for the third axis: UL = -0.73, UW = 1.56, UL/UW = 0.92, LN = -2.76, WN = 2.53, LN/WN = 2.14. ar—C. arenaria, br—C. brizoides, co—C. colchica, cu—C. curvata, pr—C. praecox, ps—C. pseudobrizoides. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Results of discriminant function analysis (DFA) for the reproductive characters of the Ammoglochin taxa. A—along axes DF1 and DF2; B—along axes DF1 and DF3. Characters abbreviated as in Table 2). Loadings for the first axis (only absolute values>0.50 are given: LN = 1.09, WN = -1.57, LN/WN = -1.59, FGL = 0.74. Loadings for the second axis: UL/UW = 0.57, LW = 0.71, WW = 0.52. Loadings for the third axis: UL = -0.73, UW = 1.56, UL/UW = 0.92, LN = -2.76, WN = 2.53, LN/WN = 2.14. ar—C. arenaria, br—C. brizoides, co—C. colchica, cu—C. curvata, pr—C. praecox, ps—C. pseudobrizoides.
FIGURE. Results of discriminant function analysis (DFA) for the vegetative characters of the Ammoglochin taxa. A—along axes DF1 and DF2; B—along axes DF1 and DF3. Characters abbreviated as in Table 2. Loadings for the first axis (only absolute values>0.50 are given): SN = 0.57. Loadings for the second axis: CL = -0.91, IL = 0.54. Loadings for the third axis: CW = 0.62, CLL = -0.52, CLW = -0.89, IL = 0.51. ar—C. arenaria, br—C. brizoides, co—C. colchica, cu—C. curvata, pr—C. praecox, ps—C. pseudobrizoides, re—C. repens. in Carex section Ammoglochin (Cyperaceae) in Poland
FIGURE. Results of discriminant function analysis (DFA) for the vegetative characters of the Ammoglochin taxa. A—along axes DF1 and DF2; B—along axes DF1 and DF3. Characters abbreviated as in Table 2. Loadings for the first axis (only absolute values>0.50 are given): SN = 0.57. Loadings for the second axis: CL = -0.91, IL = 0.54. Loadings for the third axis: CW = 0.62, CLL = -0.52, CLW = -0.89, IL = 0.51. ar—C. arenaria, br—C. brizoides, co—C. colchica, cu—C. curvata, pr—C. praecox, ps—C. pseudobrizoides, re—C. repens.
Data for Microsaccades are directed toward the midpoint between targets in a variably cued attention task
<p>Matlab data in table format and readme text to accompany "Microsaccades are directed toward the midpoint between targets in a variably cued attention task"</p>
Composition variations in Cu(In,Ga)(S,Se)2 solar cells: not a gradient, but an interlaced network of two phases
<p><strong>Abstract</strong></p> <p>Record efficiency in chalcopyrite-based solar cells Cu(In,Ga)(S,Se)<sub>2</sub> is achieved using a gallium gradient to increase the band gap of the absorber towards the back side. Although this structure has successfully reduced recombination at the back contact, we demonstrate that in industrial absorbers grown in the pilot line of Avancis, the back part is a source of non-radiative recombination. Depth-resolved photoluminescence (PL) measurements reveal two main radiative recombination paths at 1.04 eV and 1.5-1.6 eV, attributed to two phases of low and high band gap material, respectively. Instead of a continuous change in the band gap throughout the thickness of the absorber, we propose a model where discrete band gap phases interlace, creating an apparent gradient. Cathodoluminescence and Raman scattering spectroscopy confirm this result.</p> <p>Additionally, deep defects associated to the high gap phase reduce the absorber performance. Etching away the back part of the absorber leads to an increase of one order of magnitude in the PL intensity, i.e., 60 meV in quasi Fermi level splitting. Non-radiative voltage losses correlate linearly with the relative contribution of the high energy PL peak, suggesting that reducing the high gap phase could increase the open circuit voltage by up to 180 mV.</p>
Effect of cyclic ageing on the early-stage clustering in Al-Zn-Mg(-Cu) alloys
<p>Raw data of all tensile tests has been put up along with the pos and range files for all the datasets that have been used in the paper. Further analysis can be done using IVAS software (CAMECA) for the results presented.</p>
Data from: Cu+ transient species mediate Cu catalyst reconstruction during CO2 electroreduction
<p>Understanding metal surface reconstruction is of the uttermost importance in heterogeneous<br> catalysis as this phenomenon directly affects the nature of available active sites. However,<br> surface reconstruction is notoriously difficult to study because of the dynamic nature of the<br> phenomena behind it, particularly when solid/liquid interfaces are involved. Here, we report<br> on the intermediates which drive the rearrangement of copper catalysts for the electrochemical<br> CO2 reduction reaction (CO2RR). Online mass spectrometry and UV-Vis absorption<br> spectroscopy data are consistent with a dissolution–redeposition process, previously<br> demonstrated by in-situ electron microscopy. The data indicate that the soluble transient<br> species contain copper in +1 oxidation state. Density functional theory identifies copper adsorbate<br> complexes which can exist in solution under operating conditions. Copper carbonyls and oxalates are suggested as the major reaction-specific species driving copper reconstruction during CO2RR. This work motivates future methodological studies to enable the direct detection of these compounds and strategies which specifically target them to improve the catalyst operational stability.</p>
Cupă cu toarte
Source: Objaverse 1.0 / Sketchfab
Effect of Gut-Cued Eating on BMI and Efficacy of Open-Label Placebo to Augment Weight Loss
ClinicalTrials.gov study NCT03850990. IPD Sharing: NO. Countries: 1. Publications: 1.
Psychometric Testing and Cue Utilization During Cued Visual Search
ClinicalTrials.gov study NCT04964674. IPD Sharing: YES. Countries: 1. Publications: 2.
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
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.