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2,118 results for “Metal”
Shedding Light on Metal-Based Nanoparticles in Zebrafish by Computed Tomography with Micrometer Resolution
<p>Supplementary 3D image stacks of microtomography data.</p> <p>100 layer xy, xz, and yz image stacks</p> <p>Publication included as PDF file (open access, DOI: 10.1002/smll.202000746)</p> <p>********************************************</p> <p>Metal-based nanoparticles are clinically used for diagnostic and therapeutic<br> applications. After parenteral administration, they will distribute throughout<br> different organs. Quantification of their distribution within tissues in the 3D<br> space, however, remains a challenge owing to the small particle diameter.<br> In this study, synchrotron radiation-based hard X-ray tomography (SRμCT)<br> in absorption and phase contrast modes is evaluated for the localization of<br> superparamagnetic iron oxide nanoparticles (SPIONs) in soft tissues based<br> on their electron density and X-ray attenuation. Biodistribution of SPIONs<br> is studied using zebrafish embryos as a vertebrate screening model. This<br> label-free approach gives rise to an isotropic, 3D, direct space visualization<br> of the entire 2.5 mm-long animal with a spatial resolution of around 2<br> μm. High resolution image stacks are available on a dedicated internet<br> page (http://zebrafish.pharma-te.ch). X-ray tomography is combined with<br> physico-chemical characterization and cellular uptake studies to confirm the<br> safety and effectiveness of protective SPION coatings. It is demonstrated<br> that SRμCT provides unprecedented insights into the zebrafish embryo<br> anatomy and tissue distribution of label-free metal oxide nanoparticles.</p>
Optical constants of several multilayer transition metal dichalcogenides measured by spectroscopic ellipsometry in the 300-1700 nm range: high-index, anisotropy, and hyperbolicity
<p># Data and plotting code for "Optical constants of several multilayer transition metal dichalcogenides measured by spectroscopic ellipsometry in the 300-1700 nm range: high-index, anisotropy, and hyperbolicity" by Battulga Munkhbat, Piotr Wróbel, Tomasz J. Antosiewicz, and Timur O. Shegai, ACS Photonics (2022); https://doi.org/10.1021/acsphotonics.2c00433</p> <p><br> ## Contents</p> <p>* <TMD-material>: directories with raw and derived data for all 10 TMDs<br> * f3_dataset_*_nm_ex1_ex2_ey1_ey2_ez1_ez2.txt: obtained permittivities<br> * plot_*_v1.m: Matlab scripts for plotting data</p> <p>## Description of the data</p> <p>The raw and derived data stored in directories <TMD> contain the following files:</p> <p>* <TMD>/<date>-<TMD>.SEsnap: binary data file with collected data, CompleteEASE format<br> * <TMD>/<date>-<TMD>-E*.mat: ascii text file with permittivity data separated into individual components as exported from CompleteEASE software<br> * <TMD>/<date>-<TMD>-full.mat: ascii text file with fitted model parameters as exported from CompleteEASE software<br> * <TMD>/<TMD>-data/*.txt: selected raw data and fits for all considered samples (Mueller Matrix or Delta/Psi/depolarization).</p> <p>The structure of the data file names is as follows:<br> <order-number-in-CompleteEASE>-s<sample-name>-<data-type>.txt for general ellipsometry (delta, psi, depolarization) or<br> <order-number-in-CompleteEASE>-s<sample-name>-o<in-plane-sample-rotation-number>-mm.txt for Mueller Matrix measurements.</p> <p>The following two scripts can be used to plot the raw measured data (solig lines) along with corresponding fits (black dotted lines):</p> <p>* plot_mm_v1.m: Matlab script for plotting Mueller Matrix data for WTe2 and ReS2<br> * plot_psi_delta_depol_v1.m: Matlab script for plotting psi, delta, and depolarization data for other TMDs</p> <p>The diagonal permittivity tensor data are saved in the f3_dataset_*_nm_ex1_ex2_ey1_ey2_ez1_ez2.txt files which can be plotted using the plot_permittivity_v1.m Matlab script. The format of this file is as follows:</p> <p>wavelength in nanometers; real part of epsilon_xx; imaginary part of epsilon_xx; real part of epsilon_yy; imaginary part of epsilon_yy; real part of epsilon_zz; imaginary part of epsilon_zz;</p> <p> </p> <p> </p>
Data set for the journal article Structural Analysis of Metal Coordination Sites in Single-Atom Catalysts Based on Carbon Nitrides
<p>The data is organized according to the figure in the manuscript. </p>
Data for "Laplacian-level meta-GGA for the weakly-nonlocal solid and liquid metals"
<p>This dataset contains all VASP inputs and outputs for the paper "Improved Laplacian-level meta-GGA for the weakly-nonlocal solid and liquid metals." For the preprint, see <a href="https://arxiv.org/abs/2203.09403">arXiv:2203.09403</a>, and for the reference densities, fitting routines, and analysis scripts, see the <a href="https://gitlab.com/dhamil/laplacian-level-meta-gga">Gitlab code repository</a>.</p> <p>Description of individual tarballs:</p> <ul> <li>AE6: 6-molecule set of atomization energies</li> <li>ferro: relaxed geometries and magnetic moments for the ferromagnetic solids Fe, Ni, and Co</li> <li>intermetallics: formation energies of three intermetallic solids, HfOs, ScPt, and VPt<sub>2</sub></li> <li>LC20: relaxed geometries and equilibrium bulk moduli for the LC20 set of cubic solids. Equilibrium geometries by equation of state fit. Bandgaps for select insulators are included here.</li> <li>LC20_stress_tensor: same as LC20, but equilibrium geometries found by minimizing forces on unit cell computed with Laplacian-dependent stress tensor</li> <li>LC23: equilibrium geometries, bulk moduli, and cohesive energies for the LC23 set (LC20 + K, Rb, and Cs) found by equation of state fit. Bandgaps for select insulators are included here.</li> <li>Pt_monovac: monovacancy formation energies for Pt, computed in a few different ways described in the text</li> </ul>
Raw data for High temperature superconductivity arising in a metal sheet full of holes
<p>All raw data for the paper entitled "High temperature superconductivity arising in a metal sheet full of holes" are deposited here, together with the GDSII file used for the sample patterning. Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC BY 4.0). Addition of the author as a responsible author and/or an inventor in any publication, including electronic publications, is prohibited.</p>
Mining minerals and critical raw materials from bittern: Understanding metal ions fate in saltwork ponds
<p>Seawater represents a potential resource for raw materials extraction. Although NaCl is the most representative mineral<br> extracted other valuable compounds such as Mg, Li, Sr, Rb and B and elements at trace level (Cs, Co, In, Sc, Ga and<br> Ge) are also contained in this “liquid mine”. Most of them are considered as Critical Raw Materials by the European<br> Union. Solar saltworks, providing concentration factors of up-to 20 to 40, offer a perfect platform for the development<br> of minerals and metal recovery schemes taking benefit of the concentration and purification achieved along the evaporation<br> saltwork ponds.<br> However, the geochemistry of these elements in this environment has not been yet thoroughly evaluated. Their knowledge<br> could enable the deployment of technologies capable to achieve the recovery of valuable minerals. The high ionic<br> strengths expected (0.5–7 mol/kg) and the chemical complexity of the solutions imply that only numerical geochemical<br> codes, as PHREEQC, and the use of Pitzer model to estimate the activity coefficients of the different species in solution<br> can be adopted to provide valuable description of the systems.<br> In the present work, for the first time, PHREEQC Pitzer code database was extended to include the target minor and<br> trace elements using Trapani saltworks (Sicily, Italy) as a case study system. The model was able to predict: i) the purity<br> in halite and the major impurities contained, mainly Ca,Mgand sulphate species; ii) the fate of minor components as B,<br> Sr, Cs, Co, Ge and Ga along the evaporation ponds. The results obtained pose a fundamental step in critical raw materials<br> mining from seawater brine, for process intensification and combination with desalination.</p>
STEP CAD models of structural aerospace sheet metal parts
<p>This dataset consists of 26 STEP CAD models of aerospace sheet metal parts. The models were used to test a prototype (A) of an automated feature recognition method (B) for aerospace sheet metal part models. When models are used in a work, they can be credited by citing them directly or by citing whichever of the following papers that are relevant.</p> <p>A - A Prototype of an Automated Feature Recognition Algorithm for Aerospace Sheet Metal Parts</p> <p>B - Feature Recognition for Structural Aerospace Sheet Metal Parts</p>
Dual-band Reflectarray Antennas Using Integrated Resonant and Non-Resonant Natures of Metallic Waveguide Elements at Millimeter Wave Frequencies
<p>Pure metallic reflectarray antennas have good power efficiency and low fabrication cost, which were limited to single band operation. A dual-band reflectarray antenna is developed for two directional beams by two feeds. The reflecting elements are metal waveguides, and have novel properties of both resonant and non-resonant modes at the two frequency bands. The low and high frequency bands can be easily separated by the cutoff frequency of waveguide modes. The phase changing mechanisms via ray optics and fundamental waveguide modes, respectively provide simple formulations for elemental structure design of directional beams. Radiation characteristics of the linear and circular polarizations are cross-examined with good performance by full-wave simulations using HFSS, FEKO and CST at 28 and 60 GHz bands for 5G front-haul network applications.</p>
Reproduction package for the paper "The effects of surface fossil magnetic fields on massive star evolution: IV. Grids of models at Solar, LMC, and SMC metallicities"
<p>This is a reproduction package for the paper "The effects of surface fossil magnetic fields on massive star evolution - IV. Grids of models at Solar, LMC, and SMC metallicities" by <a href="https://doi.org/10.1093/mnras/stac2598">Keszthelyi et al. (2022).</a></p>
Neural network for Lithium Metal Battery
<p>Electric cars are an integral part of our clean energy future – every time one replaces a gas-powered vehicle, it can save 1.5 tons of carbon dioxide per year. But to truly expand the population and reach of electric cars, new energy storage solutions must be developed to produce lighter vehicles with longer ranges and more powerful batteries.</p> <p>A team of researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Irvine recently moved this effort forward with the development of deep-learning algorithms to automate the quality control and assessment of new battery designs.</p> <p>The research team, led by Berkeley Lab’s Daniela Ushizima, a staff scientist in the Applied Mathematics and Computational Research Division and a Berkeley Institute for Data Science Research Affiliate, includes scientists from the National Fuel Cell Research Center (NFRC) at UC Irvine and collaborators from UC Berkeley’s Department of Electrical Engineering and Computer Sciences and School of Information. Together, they created these deep-learning algorithms to automate the inspection of batteries with data acquired using advanced instruments, including those at Berkeley Lab’s Advanced Light Source (ALS). By using X-ray tomography as the input data, as well as prototypes defined by battery experts, the research team developed automated methods to detect battery defects in rechargeable lithium metal batteries and measure their growth during battery cycling.</p> <p>The researchers focused on solid-state lithium metal batteries (LMB), which are different from traditional lithium-ion batteries in that they use solid electrodes and electrolytes, providing superior electrochemical performance and high energy density. Some of the challenges of this new technology are predicting battery cycling stability and preventing the formation of lithium dendrite growth, Ushizima noted. This harmful phenomenon may occur during LMB charge and discharge, when lithium can deposit irregularly, building up dendrites (lithium plating) that lead to failures, such as short-circuiting. These morphologies are key to the LMB quality, and they can be captured and analyzed using X-ray microtomography (XRT) scans. Machine learning algorithms and multiscale representation of XRT from LMB samples enable the quantification of LMB defects.</p>
Transfer Learning Dataset for Metal Oxide Semiconductor Gas Sensors
<p>The "Transfer Learning Dataset for Metal Oxide Semiconductor Gas Sensors" can be used to test machine learning approaches on their capability of interpreting sensor patterns of commercially available MOS gas sensors, i.e., SGP40 (Sensirion AG, Stäfa, Switzerland), to predict multiple different gas concentrations and the relative humidity. Furthermore, the dataset can be used to test the transferability between sensors. <br> The dataset was recorded with the help of a custom-built gas mixing apparatus (GMA). The GMA allows applying well-known gas mixtures to multiple gas sensors. For this experiment, three SGP40 with four sub-sensors each were exposed to 900 different unique gas mixtures (UGMs) consisting of ten different gases. In detail, the dataset consists of eight volatile organic compounds (VOCs) (acetic acid, acetone, ethanol, ethyl acetate, formaldehyde, isopropanol, toluene, and xylene), two background gases (carbon monoxide and hydrogen), and the relative humidity at 20 °C. During exposure, the sensors are operated in a temperature-cycled operation. The temperature cycle consists of alternating high and low-temperature phases. The high-temperature phases are set at 400 °C and have a duration of 5 seconds, while the low-temperature steps increase in 25 °C steps from 100 °C-375 °C, where each step has a duration of 7 seconds. The only exception is sub-sensor 4, where the temperature is only alternated between 250 °C and 300 °C. The total duration of the temperature cycle is 144 seconds, and during this time, the logarithmic sensor resistance is read out at 10 Hz. Each gas mixture was recorded for ten temperature cycles to ensure that stable gas mixtures were applied to the sensor. Only stable samples 6 (not always),7,8, and 9 were used for further evaluation. The 900 UGMs can be separated into three parts, and for each part, the mixtures were generated based on Latin hypercube sampling and the ranges specified in Table 1.</p> <table> <caption>Tabel 1: Uniform distributed ranges for all gasses within the gas mixtures</caption> <tbody> <tr> <td> </td> <td>UGM 1-200</td> <td>UGM 201-500</td> <td>UGM501-900</td> </tr> <tr> <td>Carbon monoxide</td> <td>100 - 2000 ppb</td> <td>100 - 2000 ppb</td> <td>100 - 2000 ppb</td> </tr> <tr> <td>Hydrogen</td> <td>400 - 2000 ppb</td> <td>400 - 2000 ppb</td> <td>400 - 2000 ppb</td> </tr> <tr> <td>Relative humidity</td> <td>25 - 80 %</td> <td>25 - 80 %</td> <td>25 - 80 %</td> </tr> <tr> <td>Acetic acid</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Acetone</td> <td>3 - 50 ppb</td> <td>3 - 150 ppb</td> <td>3 - 500 ppb</td> </tr> <tr> <td>Ethanol</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Ethyl acetate</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Formaldehyde</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 300 ppb</td> </tr> <tr> <td>Isopropanol</td> <td>1 - 50 ppb</td> <td>1 - 150 ppb</td> <td>1 - 500 ppb</td> </tr> <tr> <td>Toluene</td> <td>1 - 75 ppb</td> <td>1 - 75 ppb</td> <td>1 - 250 ppb</td> </tr> <tr> <td>Xylene</td> <td>2 - 150 ppb</td> <td>2 - 150 ppb</td> <td>2 - 500 ppb</td> </tr> </tbody> </table> <p>To be able to use this dataset for transfer learning, the dataset consists of three different SPG40; two are from the same batch (sensor A and sensor B), and sensor C is from a different batch. <br> The dataset consists of the sensors' data and a target for evaluation. The data is already split into training and Validation and is stored in cells for each sensor: <br> sensorA_train<br> sensorA_test<br> sensorB_train<br> sensorB_test<br> sensorC_train<br> sensorC_test</p> <p> Each sensor cell contains four arrays, one for each sub-sensor within one SGP40. The number of rows in the arrays represents the number of observations (693 for test and 2401 for training), and the number of columns represents the number of samples per observation (1440).<br> The targets, i.e., the concentrations of each gas, are given in the target_train and targe_test structs. Since the data were recorded simultaneously, those structs can be used as targets for all sensors. The ten different gases, relative humidity, and TVOCsens are actual targets, while the range parameter represents the specific unique gas mixture ID.</p> <p>Although this is a mat file, it can be opened as an hdf5 file.</p>
Data associated to the manuscript "Co-Ion Desorption as the Main Charging Mechanism in Metallic 1T-MoS2 Supercapacitors"
<p>Supporting data for the article:</p> <p>Co-Ion Desorption as the Main Charging Mechanism in Metallic 1T-MoS<sub>2</sub> Supercapacitors</p> <p>Sheng Bi, Salanne Mathieu, <em>ACS Nano</em>, 2022</p> <p>https://pubs.acs.org/doi/10.1021/acsnano.2c07272</p> <p>The folders <em>slab </em>and <em>slit</em> contain typical MetalWalls input files used to perform the simulations for two electrode geometries.</p>
Data used in article 'Tuning Charge Carrier Dynamics and Surface Passivation in Organolead Halide Perovskites with Capping Ligands and Metal Oxide Interfaces'
<p>Data underlying the article 'Tuning Charge Carrier Dynamics and Surface Passivation in Organolead Halide Perovskites with Capping Ligands and Metal Oxide Interfaces' published in Advanced Optical Materials.</p>
Effect of low-temperature compression on crystal structure and superconductivity in strontium metal
<p>The superconducting and structural properties of elemental strontium metal were studies as a function of pressure to 60 GPa, while maintaining cryogenic conditions during the pressure application.</p> <p>This data set includes raw and analyzed electrical resistivity and xray diffraction data.</p>
A corrosion model for bioabsorbable metallic stents: Supporting Data
<p>Data including UMATs, Abaqus input files and experimental measurements related to the paper 'A corrosion model for bioabsorbable metallic stents' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.actbio.2011.05.032" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.actbio.2011.05.032</span></a></p> <p> </p> <p> </p>
A physical corrosion model for bioabsorbable metal stents: Supporting Data
<p>Data including UMATs and Abaqus input files related to the paper 'A physical corrosion model for bioabsorbable metal stents' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.actbio.2013.12.059" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.actbio.2013.12.059</span></a></p>
Optimizing the design of a bioabsorbable metal stent using computer simulation methods: Supporting Data
<p>Data including UMATs and Abaqus input files related to the paper 'Optimizing the design of a bioabsorbable metal stent using computer simulation methods' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.biomaterials.2013.07.010" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.biomaterials.2013.07.010</span></a></p> <p> </p>
The Mechanical Performance of Permanent and Bioabsorbable Metal Stents: Supporting Data
<p>Materials including scripts, finite element models and experimental data that were created during the Phd work toward 'The Mechanical Performance of Permanent and Bioabsorbable Metal Stents' http://hdl.handle.net/10379/3744 but wasn't ultimately used in the thesis or publications.</p>
Catalogue of metal urns discovered in Europe (14th - 2nd centuries BC)
<p>This dataset provides a list of archaeological sites that have provided metal cinerary urns. These occurrences were used for the doctoral thesis defended in December 2020, entitled "L'usage des textiles dans les pratiques funéraires : le cas des incinérations en urns métallique en Europe au Ier millénaire av. J.-C." (The use of textiles in funerary practices: the case of cremation in metal urns in<br>Europe in the 1st millennium BC) (Paris, Sorbonne University). It represents a current state of art and needs to be supplemented and expanded by future work and research.</p> <p>This dataset also constitutes the basis of the following two articles:<br>- Desplanques E. (2022). Protohistoric metal-urn cremation burials (1400–100 BC): a pan-European phenomenon. Antiquity 96(389):1162-1178. https://doi.org/10.15184/aqy.2022.109<br>- Desplanques E. (2022). Les textiles dans les tombes gauloises à dépôt de crémation en vase métallique : usages pratiques, mises en scène et perspectives anthropologiques (seconde moitié du VIe s.-V e s. av. J.-C.). Gallia, 79(2):1-25. https://doi.org/10.4000/gallia.6684</p>
Predicting metal-protein interactions using cofolding methods: Status quo
<p>Metals play important roles for enzyme function and many therapeutically relevant proteins. Despite the fact that the first drugs developed via computer aided drug design were metalloprotein inhibitors, many computational pipelines still discard metalloproteins due to the difficulties of modelling them computationally. New "cofolding" methods such as AlphaFold3 (AF3) and RoseTTAfold-AllAtom (RFAA) promise to improve this issue by being able to dock small molecules in presence of multiple complex cofactors including metals or covalent modifications. Here, we analyze the current status for metal ion prediction using these methods. We find that currently only AF3 provides realistic predictions for metal ions, RFAA in contrast does perform worse than more specialized models such as AllMetal3D in predicting the location of metal ions accurately. We find that AF3 predictions are consistent with expected physico-chemical trends/intuition whereas RFAA often also predicts unrealistic metal ion locations.</p>
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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.