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3,963 results for “oxides”
Calculated O K-edge XAS Spectra of Niobium Oxide Phases using Bethe-Salpeter equation
<p>X-ray absorption spectra (XAS) were calculated for the oxygen K-edge for 20 different phases of Niobium oxides (NbO<sub>x</sub>) using the Bethe-Salpeter equation as implemented within the OCEAN code. Fifteen of the NbO<sub>x</sub> phases are amorphous, in which nine are stoichiometric Nb<sub>2</sub>O<sub>5</sub>, and slightly off-stoichiometric containing a variety of different defects. The other five structures are different crystalline phases (with spacegroup): NbO (Pm3m), NbO<sub>2</sub> (P4<sub>2</sub>/mnm), N-Nb<sub>2</sub>O<sub>5</sub> (C<sub>2</sub>/m), M-Nb<sub>2</sub>O<sub>5</sub> (I4/mmm), and B-Nb<sub>2</sub>O<sub>5</sub> (C<sub>2</sub>/c). The data is given in terms of four different folders: (1) VASP POSCARs for amorphous structures (called amorph_POSCARs), (2) VASP POSCARs for crystalline structures (called crystalline_nboxide_structures), (3) OCEAN outputs for amorphous structures (called amorph_U4), and (4) OCEAN outputs for crystalline structures (called cryst_U4). The 'amorph_U4' folder contains subfolders for each structure, and within each subfolder is a 'Results' folder containing the OCEAN input and output files, including individual XAS spectra for each individual oxygen atom in the structure. The organization of the 'cryst_U4' folder structure is the same as the 'amorph_U4' folder.</p> <p>To calculate the XAS spectra within the OCEAN code, we first performed DFT (using Quantum Espresso) with an energy cut-off of 92 Ryd., and a simplified Hubbard U of 4 eV was applied to the Nb <em>d</em> orbitals. The `O-high' and `Nb-sp' pseudopotentials from PseudoDojo were used. For the BSE, the electron orbitals were down-sampled onto real space grids chosen to match 1 grid point per 1 a.u., the k-point meshes were chosen to exceed 1 grid point per 0.16 a.u.<sup>-1</sup>, and the number of conduction bands was set to 0.126 times the unit cell volume (in a.u.<sup>3</sup>). <br> For the screening, electron orbitals were calculated on k-point meshes exceeding 1 grid point per 0.56 a.u.<sup>-1</sup>, and the number of conduction bands was set to 0.437 times the unit cell volume (in a.u.<sup>3</sup>).<br> A Lorentzian core-hole broadening of 0.07 eV was included, and additional Gaussian broadening of 0.5 eV was applied.</p>
Dataset: Experimental and Modelling Analysis of the Hyperthermia Properties of Iron Oxide Nanocubes
<p>This set of data complements the published article "Experimental and Modelling Analysis of the Hyperthermia Properties of Iron Oxide Nanocubes" published on Nanomaterials <a href="https://doi.org/10.3390/nano11092179">https://doi.org/10.3390/nano11092179</a></p> <p>Ferrero, R.; Barrera, G.; Celegato, F.; Vicentini, M.; Sözeri, H.; Yıldız, N.; Atila Dinçer, C.; Coïsson, M.; Manzin, A.; Tiberto, P. Experimental and Modelling Analysis of the Hyperthermia Properties of Iron Oxide Nanocubes. Nanomaterials 2021, 11, 2179. https://doi.org/10.3390/nano11092179</p>
Controllable temporal dynamics of titanium oxide memristor for analog time-based neuromorphic computing: Dataset
<p>Dataset used to produce graphs related to the temporal behavior of the Pt/TiO/Au memristors.</p>
Data part of the manuscript Anaerobic methanotrophy is stimulated by graphene oxide in a brackish urban canal sediment
<p>We surveyed three canals in the city of Amsterdam (Netherlands) for it methane emissions and potential to filter methane through anaerobic oxidation of methane in the canal sediment. To unravel the mechanisms involved we characterised the sediment geochemically. All data present in the manuscript is available in the Excel file.</p>
Brownian Relaxation Shakes and Breaks Magnetic Iron Oxide-Polymer Nanocomposites to Release Cargo
<p>Original data supporting the findings of the manuscript and supplementary materials sorted after Figures and their respective panels.</p>
Background optimization of powder electron diffraction to implement e-PDF technique and study the local structure of iron oxide nanocrystals
<p>The local structural characterization of iron oxide nanoparticles is explored using a total scattering analysis method known as Pair Distribution Function (PDF) (also known as Reduced Density Function) profiles derived from background corrected powder electron diffraction patterns. Due to the strong coulombic interaction between the electron beam and the sample, electron diffraction generally leads to multiple scattering, causing redistribution of intensities towards higher scattering angles and an increased background in the diffraction profile. In addition to this, the electron-specimen interaction gives rise to an undesirable inelastic scattering signal that contributes primarily to the background. The present work demonstrates the efficacy of a pre-treatment of the underlying complex background function, which is a combination of both incoherent multiple and inelastic scatterings that cannot be identical for different electron beam energies. Therefore, two different background subtraction approaches are proposed for the electron diffraction patterns acquired at 80 kV and 300 kV beam energies. From the least square refinement (small-box modelling), both approaches are found to be very promising, leading to a successful implementation of the e-PDF technique to study the local structure of the considered nanomaterial.</p>
Platinum-Iron(II) Oxide Sites Directly Responsible for Preferential Carbon Monoxide Oxidation at Ambient Temperature: An Operando X-ray Absorption Spectroscopy Study
<p>Open data for "Platinum-Iron(II) Oxide Sites Directly Responsible for Preferential Carbon Monoxide Oxidation at Ambient Temperature: An Operando X-ray Absorption Spectroscopy Study" Angew. Chem.Int. Ed. 2023,62, e202214032(1 of 11) <a href="https://doi.org/10.1002/anie.202214032">https://doi.org/10.1002/anie.202214032</a></p>
Out-of-equilibrium charge redistribution data in a copper-oxide based superconductor by time-resolved X-ray photoelectron spectroscopy
<p>This dataset was measured using a momentum microscope by time-resolved X-ray photoelectron spectroscopy (XPS) on the prototypical high-temperature superconductor: optimally doped BSCCO at FEL FLASH, DESY in Hamburg. With time-resolved XPS, unique access to the dynamics of individual atoms in the unit cell is granted by means of chemical shifts of the core levels. Though the induced changes are small, with a rigorous fitting procedure, it is possible to extract significant changes observed mainly at the oxygen atoms in the copper oxide planes, while other oxygen atoms as well as strontium remain largely unaffected. Although it was acquired not in the superconducting phase, the observed dynamics point to a significant coupling of energy scales involving charge-transfer processes and optical excitations. Such findings can thus provide another puzzle piece for a better understanding of high-temperature superconductivity.</p>
Global OCpetro Oxidation: data, code and environments
<p>A repository of data files, code and python/R environments for the manuscript "Rock organic carbon oxidation CO<sub>2</sub> release offsets silicate weathering sink" by Jesse R. Zondervan, Robert G. Hilton, Mathieu Dellinger, Fiona J. Clubb, Tobias Roylands, Mateja Ogrič.</p> <p>This repository contains an Excel file and several zip files.</p> <p><em>Zip files containing code, data and python environment to run a simulation of the Global OCpetro Oxidation model:</em></p> <ul> <li><strong>River rhenium (Re) and OC<sub>petro</sub> oxidation data: </strong>Supplementary Tables.xlsx</li> <li><strong>Code only:</strong> Global_OCpetro_Oxidation-v1.1.0.zip (uploaded Github repository)</li> <li><strong>Geospatial data files only:</strong> input_global_(data files only).zip</li> <li><strong>Python environment only:</strong> ocpetro_oxidation_env.zip</li> <li><strong>Code, data and environment:</strong> ocpetro_oxidation_code_data_env.zip</li> </ul> <p><em>Outputs of the model presented in the manuscript:</em></p> <ul> <li>Geospatial raster files of Fig 1: <ul> <li>Re sample locations shapefile (panel A): Re_sample_locations.zip</li> <li>Re sample catchment shapefile (panel A): Re_sample_catchments.zip</li> <li>Median OC<sub>petro</sub> stocks model (panel B): Output_OCpetro_stock_median.zip</li> <li>Median denudation model (panel C): Output_denudation_median.zip</li> <li>Best-fit OC<sub>petro</sub> oxidation extrapolation (panel D): Output_OC_petro_oxidation.zip</li> </ul> </li> </ul> <p><em>Denudation and OCpetro stock subroutines (for transparency only, not needed to run OCpetro oxidation simulation):</em></p> <ul> <li><strong>Code, data and R environment (python environment from ocpetro_oxidation_env.zip, see above):</strong> Submodels_code_data_Renv.zip</li> </ul> <p>The easiest way to run the code is by downloading the zip file containing code, data and environment, and then unpacking using packages provided by the OS, or by running the 'anaconda-project unarchive' command. Instructions for this can be found by searching for anaconda-project online, or directly via <a href="https://anaconda-project.readthedocs.io/en/latest/user-guide/tasks/create-project-archive.html?highlight=unarchive#extracting-the-archive-file">https://anaconda-project.readthedocs.io/en/latest/user-guide/tasks/create-project-archive.html?highlight=unarchive#extracting-the-archive-file</a> [last accessed 26/01/2023]</p> <p>The code was developed in a python environment detailed in anaconda-project.yml, with every recursive dependency down to the individual build in anaconda-project-locked.yml. The code file is "Glob_newmethod_parr_globalresidual.py" in this repository.</p> <p>This code should be reproducible indefinitely, without depending on online package repositories. Both the commands and the environment have been captured into a fully locked anaconda project with all conda packages unpacked and included using anaconda-project --pack-envs. Note that only packages for running the code on Linux can be unpacked in this way; building on other platforms (Windows, Mac) will still require access to repositories. </p> <p> </p> <p>Notes:</p> <p>This code was run on an HPC environment with a job submitter called SLURM. As such, the code will run according to a slurm job array with numbers from 1-100 (10,000 monte carlo simulations). The command to run the Monte Carlo simulation as 10,000 seperate jobs is done like this: "sbatch --array=1-10000:1 job_script_file_name.sh". Note that the version of code uploaded here is set to run 100 simulations ("sbatch --array=1-100:1 job_script_file_name.sh"). When running 10,000 simulations, please change line 46 to "quantile = float(os.getenv('SLURM_ARRAY_TASK_ID'))/10000."</p> <p>Whilst it is possible to run this code on a single machine such as a personal computer, the user is warned that it takes 24 core hours per simulation to run. For example, a typical 4-core laptop would need 6 hours to run one simulation. Now calculate how many 10,000 would take...</p> <p>To run one simulation, line 46 ("quantile = float(os.getenv('SLURM_ARRAY_TASK_ID'))/100.") can be replaced with ("quantile = float(number between 0 and 1)"). Outputs will be saved for each simulation, which can rack up a lot of space, unless you specifically put in lines to delete these from the disk, or, in the case of the example job script for HPC usage, exclude the files when moving data from the node that ran the job.</p> <p> </p> <p>Example:</p> <p>sbatch --array=1-100:1 run_Glob_OCpetro_model.sh #note that this runs 100 simulations.</p> <p>An example of a job script file has been appended. Please note that the details of this job script depend on your machine or HPC system. Please consult your HPC support or platform's (Linux, Mac, Windows) command prompt instructions.</p>
Influence of protein corona on cytotoxicity of metal oxide nanoparticles against human keratinocyte cell line (HaCaT)
<p>The model identified, among the factors determining the cytotoxic properties of metal oxide nanoparticles against HaCaT cell lines, a number of variables related to the processes occurring on the surface of nanoparticles in a biological medium, including the ability to form protein corona.</p> <p>The selected descriptors describe both the electronic structure of the metal oxides that are the components of the nanoparticles, i.e. the ionization potential (IP_ActivM_SM_#1, IP_ActivM_SM_#2) and the initial nanoforms, i.e. the particle size (Primary size) and the percentage content of the metal oxide which is the main component of the nanoparticle (Purity_#1) ; characterize nanoparticles in the medium, i.e. the isoelectric point (PZZP_#2), stability (Stability), potential for dissolution (Dissolution), generation of reactive oxygen species (ROS production) and protein adsorption (Protein adsorption). The listed descriptors reflect the features that are discussed in the literature as potentially related to the toxic effect of nanoparticles.</p>
Identification of factors determining the process of aggregation/agglomeration of metal oxide nanoparticles in a biological medium
<p>The model allows to identify factors determining the process of aggregation/agglomeration of metal oxide nanoparticles in a biological medium and to verify the importance of ion adsorption and protein adsorption in this process. </p> <p>Model confirms the significant effect of protein adsorption on the hydrodynamic diameter of metal oxide particles in the biological medium, and does not confirm the significant effect of ion adsorption in this process. It’s an example of modeling the properties of nanoparticles, where apart from the descriptors describing the structure of nanoparticles, there are also parameters characterizing the medium.</p>
Datasets supporting the publication "Technical Note: in-situ measurements and modelling of the oxidation kinetics in films of a cooking aerosol proxy using a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D)" by Milsom et al.
<p>Supporting experimental and modelling data for the manuscript entitled "Technical Note: Modelling and in-situ measurements of the oxidation kinetics in films of a cooking aerosol proxy using a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D)" by Adam Milsom et al. 2023. </p> <p>Includes raw QCM-D data with the numbers at the beginning of the files corresponding to the experiment numbers in the manuscript. </p> <p>Normalised Raman peak area data for modelling and model ensemble outputs, including uptake coefficients. </p>
Dataset of the paper "Improving the Stability of Photodoped Metal Oxide Nanocrystals with Electron Donating Graphene Quantum Dots"
<p>The dataset provides the data for the publication: "Improving the Stability of Photodoped Metal Oxide Nanocrystals with Electron Donating Graphene Quantum Dots"</p>
Data in Support of Effects of Urbanization and Forest Fragmentation on Atmospheric Nitrogen Inputs and Ambient Nitrogen Oxide and Ozone Concentrations in Mixed Temperate Forests.
Urban ecosystems around the globe experience greater atmospheric nitrogen (N) deposition compared to rural areas and are particularly vulnerable to fragmentation due to land-use change. However, while the influences of urbanization and forest fragmentation on atmospheric inputs to temperate forests have been determined separately, the combined effects of the two changes on temperate forest ecosystems have yet to be assessed. To investigate these combined effects, we deployed throughfall collectors to measure atmospheric N inputs and passive samplers to measure nitrogen oxides (NOx) and ozone (O3) throughout the 2018 and 2019 growing seasons in seven temperate forest sites along an urbanization gradient from Boston to central Massachusetts. We found a positive relationship between the amount of impervious surface area surrounding each site (% ISA) and throughfall nitrate (NO3-) inputs at the forest edge, with urban edge NO3- inputs nearly double the rate at rural edge sites. There were higher rates of NO3- inputs in the rural forest interior than edge sites. Urban sites experienced significantly higher concentrations of NOx and O3 both in the interior and at the edge compared to rural sites. Atmospheric N inputs were significantly elevated in the early (May-July) compared to the late (August-November) growing season and concentrations of NOx and O3 were also elevated in the mid-growing season (June-September). Our results demonstrate that together, urbanization and forest fragmentation lead to greater rates of atmospheric N inputs and ambient pollutant concentrations of NOx and O3 in temperate forests of the northeastern U.S.
Association of Organic Carbon with Reactive Iron Oxides
We used soils provided by the National Ecological Observatory Network (NEON), along with additional samples and data collected from published studies, to characterize Fe-associated C and its relationships with climate and soil physicochemical factors across global mineral soils
Photo-oxidation and photomineralization apparent quantum yield dataset for dissolved organic carbon leached from permafrost soils collected from the North Slope of Alaska, July 2018.
Dissolved organic carbon (DOC) was leached from permafrost soils near the Toolik Field Station in the Alaskan Arctic and then characterized for its photochemical properties. Oxygen (O2) consumed from photo-oxidation of permafrost DOC was measured as a function of sunlight wavelength, defined as the apparent quantum yield spectrum of photo-oxidation (O2 consumed per mol photon absorbed by DOC). Carbon dioxide (CO2) produced from photomineralization of permafrost DOC was measured as a function of sunlight wavelength, defined as the apparent quantum yield spectrum of photomineralization (CO2 produced per mol photon absorbed by DOC).
Fig. 3 in Water pH and hardness alter ATPases and oxidative stress in the gills and kidney of pacu (Piaractus mesopotamicus)
Fig. 3. Thiobarbituric acid reactive substances (TBARS) content (nmol TMP mg wet tissue-1) in a. gills and b. kidney of pacu (Piaractus mesopotamicus) juveniles under different water hardness and pH at different times. LWH = low water hardness (50 mg CaCO L-1); HWH = high water hardness (120 mg CaCO L-1). Data are presented as the means ± SEM (n = 3 3 9 fish treatment–1). Different uppercase letters indicate statistically differences between pH at the same hardness (P <0.05). Different lowercase letters indicate statistically differences between hardness at the same pH (P <0.05).
Fig. 2 in Water pH and hardness alter ATPases and oxidative stress in the gills and kidney of pacu (Piaractus mesopotamicus)
Fig. 2. Total antioxidant capacity against peroxyl radicals (ACAP) (relative area) in a. gills and b. kidney of pacu (Piaractus mesopotamicus) juveniles under different water hardness and pH at different times. LWH = low water hardness (50 mg CaCO L-1); HWH = high water hardness (120 mg CaCO L-1). Data are presented as the means ± SEM (n = 9 fish treatment–1). 3 3 Different uppercase letters indicate statistically differences between pH at the same hardness (P <0.05).
Molecular dynamics trajectories for "Structure and chemistry of graphene oxide in liquid water from first principles"
<p>This dataset contains molecular dynamics (MD) trajectories from the paper <a href="https://doi.org/10.1038/s41467-020-15381-y">“Structure and chemistry of graphene oxide in liquid water from first principles”, F. Mouhat, F.-X. Coudert and M.-L. Bocquet, <em>Nature Commun.</em>, <strong>2020</strong>, <em>11</em>, 1566, 10.1038/s41467-020-15381-y</a></p> <p> </p>
Arsenic(III) photocatalytic oxidation kinetics (using TiO2 and composite TiO2/Fe2O3 photocatalysts)
<p>Data sets on the photocatalytic oxidation of arsenic(III) using TiO<sub>2</sub> and composite TiO<sub>2</sub>/Fe<sub>2</sub>O<sub>3</sub> photocatalysts.</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.