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433 results for “FE”
Data set for the journal article: Tandem electrocatalytic CO2 reduction with Fe-porphyrins and Cu nanocubes enhances ethylene production
<p>Copper-based tandem schemes have emerged as promising strategies to promote the formation<br> of multi-carbon products of the electrocatalytic CO2 reduction reaction. In such approaches,<br> the CO-generating component of the tandem catalyst increases the local concentration of CO<br> and thereby enhances the intrinsic carbon-carbon (C-C) coupling on copper. However, the<br> optimal characteristics of the CO-generating catalyst for maximizing eventual C2 production<br> are currently unknown. In this work, we developed tunable tandem catalysts comprising iron<br> porphyrin (Fe-Por), as the CO-generating component, and Cu nanocubes (Cucub) to understand<br> how the turnover frequency for CO (TOFCO) of the molecular catalysts impacts C-C coupling<br> on the Cu surface. First, we tuned the TOFCO of the Fe-Por by varying the number of orbitals<br> involved in the π-system. Then, by coupling these molecular catalysts with the Cucub, we<br> assessed the current densities and faradaic efficiencies, discovering that all of the designed Fe-<br> Por boost ethylene production. The most efficient Cucub/Fe-Por tandem catalyst was the one<br> including the Fe-Por with the highest TOFCO and exhibited a nearly 22-fold increase in the<br> ethylene selectivity and 100 mV positive shift of the onset potential with respect to the pristine<br> Cucub. These results reveal that coupling the TOFCO tunability of molecular catalysts along with<br> copper nanocatalysts opens up new possibilities towards the development of Cu-based catalysts<br> with enhanced selectivity for multi-carbon product generation at low overpotential.</p>
Example input files and Fe foil data for FEFF EXAFS simulations of Fe
<p>Feff input file containing Fe atomic positions, needed to run FEFF simulations of the EXAFS of Fe. The Demeter XAS analysis program files are also included (free software) : http://bruceravel.github.io/demeter/#about. These files can be used to simulate EXAFS of Fe using the FEFF software. This simulation is a building block for a future enhancement of the SIMEX (Simulation of Experiments) platform : https://github.com/eucall-software/simex_platform</p>
The Mechanism of N2 Reduction Catalyzed by Fe-Nitrogenase Involves Reductive Elimination of H2
<p>Data sets for figures 2, 3, 4, 5 in comma delimited format from <em>Biochemistry </em>publication DOI: 10.1021/acs.biochem.7b01142. Headings are provided indicating the conditions and units.</p>
Research Data - Deformation Localisation in Ion-Irradiated Fe and Fe10Cr
<p>Research data and associated processing and plotting scripts for the article:</p> <p>Song <em>et al.,</em> 'Deformation localisation in ion-irradiated Fe and Fe10Cr', <em>Journal of Nuclear Materials</em>, 155104, 2024</p> <p><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jnucmat.2024.155104" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.jnucmat.2024.155104</span></a></p>
Compilation of Fe and ligand data along the West coast of the United States
<p>Compilation of Fe and ligand data along the West coast of the United States (Compiled by Anh Le-Duy Pham from current available literature as of April 2024)</p> <p> </p> <p><span>USWC_Iron_Ligand_Compilation.xlsx: an excel file containing all the combined dFe and ligand data</span></p> <p><span>USWC_IronLiganddata.mat: a MATLAB file containing all the combined dFe and ligand data</span></p>
Figure.3.Average accuracy of investigated FE methods-Single Trial Classification of Evoked EEG Signals Due to RGB Colors
<p>Each data set is recorded with 60 trails for each color from four channels, each trail contains 768 frames per channel. In order to train all the data from all channels, the trail contained 3072 frames as one vector. Then, trail by trail passed to EMD to reduce the data into a collection of intrinsic mode functions (IMF) from which the features can be extracted. Each data set represents 9 IMFs, each IMF contains lower frequency components than the previous one. In this paper, we investigate some of feature extraction methods to find out which one can give us the most reliable features. In order to know that, we trained these features with the SVM classifier and the accurate results are placed in the below tables. The classification's accurate results of the investigated feature extraction methods are shown in Figure 3. According to the accuracy of the results, we found that the best method to extract features is through the EMD residual, where the average accuracy was of 88.5% within 14 seconds. This is due to the nature of the residue as it provides the frequency representation of the delta, alpha and beta rhythms, which are the main components of ERP that respond to different color stimuli. A flow chart is inserted in Figure 4 as a summary for the used methods in this study.</p>
Text-fig. 3. Geological map and schematic geological section of the discovery site of the Late Upper Palaeolithic skull from Moča (southern Slovakia). I. – Primary position (?), II. – The discovery site (secondary position), A – B – The schematic geological section of the discovery site 1. H – Fluvial clayey to sandy loams (subordinately humolites) – Holocene; secondary discovery site layer, 2. lm-pH – Loam – peat – Holocene, 3. e Wl – Eolian sands – Late Würm (Late glacial of Würm), 4. lm,sWl – Fluvial clayey (to humic) loams or fine sands – Late Würm (Late glas cial of Würm); original discovery site layer, now eroded, 4a. fe Wl – Fluvial – aeolian silty sands (calcareous) – Late Würm (Late glacial s-lm of Würm), 5. lmW3 – Fluvial loams, sandy loams – final Würm (W3), 5a. W3 – Fluvial sands – final (?) Würm (?W3), 6. gW2+3 – Fluvial gravs els, sandy gravels, sands with gravel – Pleniglacial of Würm (W2+3), 7. lW – Aeolian loess and loess loams – Würm (undivided) in A Late Upper Palaeolithic Skull From Moča (The Slovak Republic) In The Context Of Central Europe
Text-fig. 3. Geological map and schematic geological section of the discovery site of the Late Upper Palaeolithic skull from Moča (southern Slovakia). I. – Primary position (?), II. – The discovery site (secondary position), A – B – The schematic geological section of the discovery site 1. H – Fluvial clayey to sandy loams (subordinately humolites) – Holocene; secondary discovery site layer, 2. lm-pH – Loam – peat – Holocene, 3. e Wl – Eolian sands – Late Würm (Late glacial of Würm), 4. lm,sWl – Fluvial clayey (to humic) loams or fine sands – Late Würm (Late glas cial of Würm); original discovery site layer, now eroded, 4a. fe Wl – Fluvial – aeolian silty sands (calcareous) – Late Würm (Late glacial s-lm of Würm), 5. lmW3 – Fluvial loams, sandy loams – final Würm (W3), 5a. W3 – Fluvial sands – final (?) Würm (?W3), 6. gW2+3 – Fluvial gravs els, sandy gravels, sands with gravel – Pleniglacial of Würm (W2+3), 7. lW – Aeolian loess and loess loams – Würm (undivided)
Linked collectors and determiners for: Museo Provincial de Ciencias Naturales "Florentino Ameghino", Santa Fe, Argentina - Colección Squamata (no Serpentes) (MFA-ZV-Sq).
Natural history specimen data linked to collectors and determiners held within, "Museo Provincial de Ciencias Naturales "Florentino Ameghino", Santa Fe, Argentina - Colección Squamata (no Serpentes) (MFA-ZV-Sq)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/82a6608d-372b-4663-8ed3-dbe0c2bff883">https://bionomia.net/dataset/82a6608d-372b-4663-8ed3-dbe0c2bff883</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/82a6608d-372b-4663-8ed3-dbe0c2bff883">https://gbif.org/dataset/82a6608d-372b-4663-8ed3-dbe0c2bff883</a>. Formatted as a Frictionless Data package.
Tailoring magnetic hysteresis of Fe-Ni additive manufactured permalloy via multiphysics-multiscale simulations: Temperature-dependent parameters, thermodynamic database, results, and utilities
<p>This dataset contains temperature-dependent parameters and thermodynamic database, supplementary data and utilities of the publication "Tailoring magnetic hysteresis of additive manufactured Fe-Ni permalloy via multiphysics-multiscale simulations of process-property relationships" (<a href="http://doi.org/10.1038/s41524-023-01058-9">Yang et al., 2023</a>).</p> <p>We performed non-isothermal phase-field simulations of SLS process of the Fe<sub>21.5</sub>Ni<sub>78.5</sub> permalloy and subsequential mesoscopic thermo-elasto-plastic calculations and nanoscopic chemical order-disorder (<span>\(\gamma/\gamma'\)</span>) transition simulations as well as micromagnetic hysteresis calculations on nanostructures. Temperature-dependent parameters are employed. We then investigate the dependence of the fusion zone size, the residual stress and plastic strain, and the magnetic hysteresis of AM-produced Fe<sub>21.5</sub>Ni<sub>78.5 </sub>on beam power and scan speed.</p> <p>This dataset contains:</p> <ul> <li><em>feni_cac.tdb</em>: Thermodynamic database of the Fe-Ni binary system based on <a href="https://doi.org/10.1016/j.intermet.2010.02.026">Cacciamani et al., 2010</a></li> <li><em>average_values.csv</em>: Average quantities for creating the contours in Fig. 6a, 6b, 7a, 7b, 8a, and Supp. Fig. 10a, 10b.</li> <li><em>mesostructures.zip</em>: Containing resampled mesostructures from SLS single scan simulations (final timestep) with associated temperature, stress, and strain evolution. Nodal values are explained in Table 1. Naming pattern is <ul> <li>SLS-TEP__<power>-<scan_speed>__.e</li> </ul> </li> <li><em>parameters.zip</em>: Containing temperature-dependent parameters for performing SLS simulations and thermo-elasto-plastic calculations with fine (1K) temperature increments. The same temperature-dependent parameters with coarse temperature increments are already listed as Supp. Table 1, 2.</li> <li><em>sampled_point_data.zip</em>: Containing mechanical quantities on sampled points and corresponding results of nanoscopic <span>\(\gamma'\)</span> phase fraction (<span>\(\Psi_{\gamma'}\)</span>) and magnetic coercivity <span>\(H_\mathrm{c}\)</span>. Naming pattern is <ul> <li>mech__<power>-<scan_speed>__.csv</li> <li>Psi__<power>-<scan_speed>__.csv</li> <li>Hc__<power>-<scan_speed>__.csv</li> </ul> </li> <li><em>utilities.zip</em>: Containing Python utilities to perform calculations of free energy density and related thermodynamic quantities, extracting parameters from <em>feni_cac.tdb. </em><br><strong>Notice: </strong><a href="https://pycalphad.org/docs/latest/">pyCALPHAD</a> (ver 0.8.4) is requested for performing the Python utilities.</li> </ul> <p>Table 1. Nodal values in an exodus file Nodal value name Symbol Meaning Unit T <span>\(T\)</span> Normalized Temperature by <span>\(T_\mathrm{M}\)</span> - c <span>\(\rho\)</span> Substance order parameter - pb <span>\(\xi\)</span> Fusion zone indicator - eps (eps_11, eps_12, eps_13, eps_22, eps_23, eps_33) <span>\({\varepsilon}\)</span> Strain - epsp (epsp_11, epsp_12, epsp_13, epsp_22, epsp_23, epsp_33) <span>\({\varepsilon}_\mathrm{pl}\)</span> Plastic Strain - peeq <span>\(p_\mathrm{e}\)</span> Accumulated plastic strain - sigma (sigma_11, sigma_12, sigma_13, sigma_22, sigma_23, sigma_33) <span>\({\sigma}\)</span> Stress MPa vonmises <span>\(\sigma_\mathrm{e}\)</span> von Mises stress MPa u (u_X, u_Y, u_Z) <span>\(\mathbf{u}\)</span> Displacement μm</p> <p> </p> <p><strong>Notice</strong>: The raw transient outputs are not cured in this dataset due to the vast file size. Please contact the authors to acquire related files/utilities.</p>
UVic2.9-MOBI2.0+Fe-15N_13C Isoscapes Hindcast
<p>The simulations provided here are based on the model version including a dynamic, prognostic iron cycle (Somes et al. 2021), and they use the previous formulations for nitrogen and carbon isotopes (Somes et al. 2017; Schmittner et al., 2013). The model code and simulation description can be found at <a href="https://hdl.handle.net/20.500.12085/9e490d1e-4873-4eec-905b-1c470f79b01b">https://hdl.handle.net/20.500.12085/9e490d1e-4873-4eec-905b-1c470f79b01b</a>. Here we provide euphotic zone results from the North Atlantic corresponding with the associated squid data locations.</p>
NEMARCO project: Dataset for the publication "Solidification Path, Strengthening Mechanisms and Hardness of Ni-Cr-Si-Fe-B Self-Fluxing Alloys Obtained by Laser-Directed Energy Deposition (LMD)"
<p><strong>LMD dataset</strong></p> <p>This dataset gathers data from different parts of the Laser Metal Deposition metal Additive Manufacturing process (DED-LB). The dataset covers not only the process development data for samples manufacturing and monitored data of the melt pool size during the process, but also the metrics associated to the powder feedstock consumption, energy consumption and process efficiency.</p> <p><strong>Motivation</strong></p> <p>Nickel-based Ni-Cr-Si-B self-fluxing alloys are excellent candidates to replace Cobalt-based alloys in aeronautical components. In this work, metal additive manufacturing by directed energy deposition using a laser beam (DED-LB, also known as LMD) and gas-atomized powders as a material feedstock is presented as a potential manufacturing route for the complex processing of these alloys. This research deals with the advanced material characterization of these alloys obtained by LMD and the study and understanding of their solidification paths and strengthening mechanisms.</p>
Marcell Experimental Forest peat core extraction chemical analysis data (DOC, Fe, Ca, Mg, K, P, Al)
This data set reports iron (Fe), dissolved organic carbon (DOC), calcium (Ca), magnesium (Mg), potassium (K), phosphorus (P), and aluminum (Al) measured in extractions of soil cores sampled from two boreal peatlands, the S1 and S2 bogs, in the Marcell Experimental Forest (MEF) in Itasca County, Minnesota. The soil cores were sampled on September 2, 2017. Elements were quantified in extractions with hydrochloric acid, sodium dithionite, sodium sulfate, and sodium dithionite plus hydrochloric acid to examine how iron influences carbon and nutrient cycling in peatlands. The S1 and S2 sites are research catchments instrumented for hydrologic monitoring. The S1 bog is also the location of the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. These data are used, analyzed, and reported in Curtinrich et al. (2021, Ecosystems).
Hydrogen storage properties of Mn and Cu for Fe substitution in TiFe0.9 intermetallic compound - Raw Dataset related to publication
<p>Data type: Experimental measurements and Rietveld Refinement. Date format: .opj, .pcr, .dat (Software FullProf package outputs). Origin of the data: Experimental x-ray diffraction patterns, and kinetic measurements of hydrogen absorption. Data generated by a Bruker D8 Advance Bragg Brentano diffractometer using Cu-Kα radiation (λ=1.5418 Å), and a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin. Software needed to analyse the data: FullProf package.</p>
Image analysis data for the study of the reactivity of the phases in Nd-Fe-B magnets etched with HCl-saturated Cyphos IL 101
<p>Scanning electronic microscopy (SEM), Energy dispersive X-rays Spectroscopy (EDS) and image analysis have been used as techniques to analyse the results of etching experiments carried on NdFeB permanent magnets by using the ionic liquid Cyphos IL 101 saturated with HCl. Image analysis is for the first time reported in the literature as a technique for corrosion studies.</p> <p>Operational conditions of the analysis equipment were the following:</p> <p>- The samples were analyzed via electron microscopy and image analysis. Scanning electron microscope (SEM) pictures and energy dispersed spectra (EDS) were collected with a JEOL JSM 5800 microscope, operating at 20 kV. The polished samples were made conductive by spraying a carbon layer on them using a Balzer SCD 050 sputter coater.</p> <p>- The EDS analysis were collected as average on 5 points per each SEM picture</p> <p>- A commercial software, ImageJ®, was used for the image analysis. Two data were analysed: the Feret diameter, d<sub>F</sub>, and the percentage of etched area, %area. The SEM images were converted to 8-bit grayscale, from 0 to 255 number of grey ranges. Simple linear scaling was applied</p>
Ultimate strength assessment of stiffened panel using non-linear mechanical behavior of an equivalent single layer: grillage FE model used for analysis
<p>This example shows how the ESL can be applied in the ultimate strength structural analysis in Abaqus finite element software. In other words, ESL methodology is applied only in some parts of the structure while larger structural supporting components like girders and webframes are still modeled explicitly. FIles include also the Full_3D_FEM model used for validating the ESL model.</p> <p>Dataset includes following files:</p> <p>1. ESL_nonlinear_grillage.inp - this is Abaqus input file for running the ESL nonlinear grillage model.</p> <p>2. ugensFINALv_master.for - this defines the nonlinear stiffness or ABD matrix. This is called by input file (ESL_nonlinear_grillage.inp ).</p> <p>3. Full_3D_FEM.inp - Full_3D_FEM model used for validating the ESL model.</p> <p> </p>
CO as a Substrate and Inhibitor of H+ Reduction for Mo-, V-, and Fe-Nitrogenase Isozymes
<p>Alignment of α subunits of nitrogenase isozymes. Shown is<br> an alignment of α subunits of Mo-nitrogenase (PDB ID: 3U7Q), V-nitrogenase<br> (PDB ID: 5N6Y), and Fe-nitrogenase (sequence threaded<br> on to V-nitrogenase PDB ID: 5N6Y using Swiss-Model) highlighting<br> amino acid residues in the cofactor environment. The starting perspective is<br> looking down on the Fe2, 3, 6, 7 face of FeMoco and FeVco. S is in<br> yellow, Fe in orange, Mo in magenta, and V in pink with the homocitrate<br> to the right in grey. Side chains that are 100% conserved among the<br> isozymes are shown in green, whereas side chains where there is<br> variation in the residue in at least one isozyme are shown in red. The residue numbering refers<br> to Mo-nitrogenase. ChimeraX version 1.0 file.</p>
Fundamental hydrogen storage properties of TiFe-alloy with partial substitution of Fe by Ti and Mn - Raw Dataset related to publication
<p>Data type: Experimental measurements and Rietveld Refinement. Date format: .xls, .xlsm,.opj, .pcr, .dat (Software FullProf package outputs). Origin of the data: Experimental EMPA, x-ray diffraction patterns, and kinetic measurements of hydrogen absorption. Data generated by electron probe micro-analysis (Cameca SX100), a Bruker D8 Advance Bragg Brentano diffractometer using Cu-Kα radiation (λ=1.5418 Å), and a home-made Sieverts’ type apparatus from CNRS, ICMPE, Thiais, France. Software needed to plot the data: Origin and Excel. Software needed to analyse the data: FullProf package.</p>
Supplementary Data for "Influence of First And Second Coordination Environment on Structural Fe(II) Sites in MIL-101 for C-H Bond Activation in Methane"
<p>Cartesian coordinates for all the optimized geometries reported in "Influence of First And Second Coordination Environment on Structural Fe(II) Sites in MIL-101 for C-H Bond Activation in Methane" (acscatal.0c03906)</p>
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>
Refined Bathymetric Prediction based on Feature Extraction of Gravity Field Signals: BATHY-FE
<p>BATHY-FE is a refined global seafloor model derived from extraction of learnt bathymetric signatures inherent in gravity field signals. It spans longitudes -180 ~ 180, and latitudes -80 ~ 80. It contains more short-wavelength seafloor features, and is superior to existing bathymetric models in almost all marine regions, especially at regions close to the poles. It is a GMT readable grid file (i.e., a matrix of seafloor model, and vectors of longitudes and latitudes) with a spatial resolution of 15 arc-seconds.</p><p>Included in this repository are a MATLAB livescript and sample data in which a demonstration of the algorithm over a region north of Alaska and Canada is presented.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.