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112 results for “anode”
Dataset of "Characterization of Silicon-based Fibers Prepared by Electrospinning for Potential Li-ion Battery Anodes"
<p>The rapid growth of electric vehicles (EVs) is driven by advances in lithium-ion batteries (LIBs), particularly in anode materials. Graphite electrodes, widely used for their high porosity, conductivity, low weight, and cost-effectiveness, face competition from monocrystalline silicon. Silicon anodes offer higher capacity and energy density, and they are safer because of their nonflammable nature. However, silicon's tendency to expand and contract during cycling presents challenges. This study explores the use of silicon nano- and microfibers to enhance battery stability, addressing these issues effectively.<br>Monocrystalline silicon particles, obtained through milling and sieving, were used as the active component in the nanofibers. These particles, combined with organic precursors (PVP and TEOS), were processed using electrospinning to form fibers. The fibers were then annealed at 650 °C to remove the polymeric PVP component. <br>The results provide valuable insights into the properties and interactions of the silicon nanofibers, highlighting their potential in advanced energy storage devices. </p>
Dataset for Activation of Glassy Carbon Surfaces by Alkaline Anodization Enhances Dopamine Adsorption and Electron-Transfer Kinetics
<p>This dataset provides the raw data to the manuscript</p><p><strong>"Activation of Glassy Carbon Surfaces by Alkaline Anodization Enhances Dopamine Adsorption and Electron-Transfer Kinetics"</strong></p><p>published in ChemElectroChem</p><p>Specifically, the following measurements are provided:</p><ul><li>Scanning electrochemical cell microscopy (SECCM). Cyclic voltammetry (E, i) data for each location across the sample. 5 cycles.</li><li>Chronoamperometry (i, t) for the anodization process.</li><li>Atomic Force Microscopy (AFM) topography.</li><li>Raman microscopy</li><li>X-ray photoelectron spectroscopy (XPS)</li><li>Scanning electron microscopy (SEM)</li></ul>
Dataset of "Mn-doped WSe2 as an efficient electrocatalyst for hydrogen production and as anode material for lithium-ion batteries"
<p>The ongoing energy crisis has made it imperative to develop low-cost, easily fabricated, yet efficient materials. It is highly desirable for these nanomaterials to function effectively in multiple applications. Among transition metal dichalcogenides, tungsten diselenide (WSe2) shows great promise but remains understudied. In this work, we doped WSe2 with Mn using a simple hydrothermal method. The resulting material exhibited excellent electrocatalytic activity for the hydrogen evolution reaction, achieving a low overpotential of –0.28 V vs RHE at -10 mA/cm2, enhanced conductivity, and high stability and durability. Moreover, as an anode material in in lithium-ion batteries, the Mn-doped WSe2 outperformed pristine WSe2, reaching discharge and charge capacities of 1223 and 922 mAh g−1, respectively. Additionally, the Mn-doped material maintained a significantly higher discharge capacity of 201 mAh g−1 compared to intact WSe2, which had 68 mAh g−1 after 150 cycles. This work offers novel insights into designing efficient bifunctional nanomaterials using transition metal dichalcogenides.</p>
Influence of Anode Immersion Speed on Current and Power in Plasma Electrolytic Polishing
<p><span>Plasma electrolytic polishing (PeP) is mainly used to improve the surface quality and thus </span><span>the performance of electrically conductive parts. It is usually used as an anodic process, i.e., the </span><span>workpiece is positively charged. However, the process is susceptible to high current peaks during</span> <span>the formation of the vapour–gaseous envelope, especially when polishing workpieces with a large </span><span>surface area. In this study, the influence of the anode immersion speed on the current peaks and the</span> <span>average power during the initialisation of the PeP process is investigated for an anode the size of a </span><span>microreactor mould insert. Through systematic experimentation and analysis, this work provides</span> <span>insights into the control of the initialisation process by modulating the anode immersion speed. The </span><span>results clarify the relationship between immersion speed, peak current, and average power and</span> <span>provide a novel approach to improve process efficiency in PeP. The highest peak current and average </span><span>power occur when the electrolyte splashes over the top of the anode and not, as expected, when the </span><span>anode touches the electrolyte. By immersion of the anode while the voltage is applied to the anode</span> <span>and counterelectrode, the reduction of both parameters is over 80 %.</span></p>
Data associated with following publication: "In situ optical sub-wavelength thickness control of porous anodic aluminum oxide"
<p>Data associated with following publication: "In situ optical sub-wavelength thickness control of porous anodic aluminum oxide" (DOI: <a href="https://doi.org/10.3762/bjnano.15.12" target="_blank" rel="noopener">https://doi.org/10.3762/bjnano.15.12</a>)</p>
Supplementary files for "Influence of the Artificial Nanostructure on the LiF Formation at the Solid−Electrolyte Interphase of Carbon-Based Anodes"
<p>Databases containing DFT optimized structures used for the paper: ''Influence of the Artificial Nanostructure on the LiF Formation at the Solid−Electrolyte Interphase of Carbon-Based Anodes". For further details of the computational setup we refer to this paper.</p> <p>Each database contains structures for one carbon substrate. The structures can be retrieved using the Atomic Simulation Environment (ASE).</p>
Dataset for the publication: First-principles studies on the atomistic properties of metallic magnesium as anode material in magnesium-ion batteries
<p>This dataset contains the input and output files from the calculation of the atomistic properties of metallic magnesium, such as bulk, surface, adsorption, and diffusion properties.</p> <p>The discussion of the results were published in the ChemSusChem article: 'First-principles studies on the atomistic properties of metallic magnesium as anode material in magnesium-ion batteries' (<a href="https://doi.org/10.1002/cssc.202200414">https://doi.org/10.1002/cssc.202200414</a>). A preprint of the publication is further available under: <a href="http://doi.org/10.26434/chemrxiv-2022-qz055">https://doi.org/10.26434/chemrxiv-2022-qz055</a>.</p> <p>All calculations were performed using the density function theory code Vienna <em>ab initio</em> simulation package (VASP).</p> <p>The dataset contains all raw data for the performed convergence studies and calculated bulk-, surface-, adsorption-, and diffusion properties. An overview of the folder structure of the Zip archive, more precisely in which folders the data for the respective figures or tables of the underlying publication (<a href="https://doi.org/10.1002/cssc.202200414">https://doi.org/10.1002/cssc.202200414</a>) are stored, is provided in the following table:</p> <table> <tbody> <tr> <td>Convergence_study</td> <td>Figure S1</td> </tr> <tr> <td>Bulk_properties</td> <td>Table S3</td> </tr> <tr> <td>Surface_properties</td> <td>Table 1, Table 2, Figure 1, Table S5</td> </tr> <tr> <td>Adsorption_properties</td> <td>Monomer: Table S6; Dimer: Table 4, Table 5, Table 6; Islands: Figure S5, Table S9</td> </tr> <tr> <td>Diffusion_properties</td> <td>Table 3, Table 7, Table 8, Table 9, Table 10, Table 11, Table 12, Figure 13, Figure 14, Table S7, Table S8, Table S10 Table S11, Figure S4, Figure S7, Figure S9</td> </tr> </tbody> </table> <p> </p>
Research data supporting "Tin phosphide anodes for potassium-ion batteries: insights from crystal structure prediction"
<p>This dataset contains the output files of crystal structure prediction calculations (density-functional theory relaxations, bandstructures, phonon calculations, GIPAW-NMR calculations) on the ternary K-Sn-P phase diagram. All calculations were performed with the CASTEP DFT package (https://www.castep.org/) and the "matador" Python library (https://github.com/ml-evs/matador).</p> <p><strong>Contents:</strong></p> <ul> <li>"convergence_tests.zip": contains the results of convergence tests on the K-P system at two levels of accuracy "polish" and "searches" on the corresponding edge of the K-Sn-P ternary system</li> <li>"phonons.zip": contains CASTEP output files for phonon calculations on the predicted low-lying phases on the corresponding edge of the K-Sn-P phase diagram</li> <li>"polish.zip": contains CASTEP output files of relaxations on the corresponding edge of the K-Sn-P system at the "polish" level of accuracy using various different xc-functionals or external pressures.</li> <li>"searches.zip": contains ".res" files that provide the relaxed structure from each different crystal structure prediction method on the corresponding edge of the K-Sn-P system.</li> <li>"bulk_modulus.zip" contains CASTEP output files for calculation of E(V) curves for low-lying KP phases with different xc-functionals.</li> <li>"nmr.zip" contains CASTEP output files for GIPAW-NMR calculations of chemical shifts for low-lying K-Sn-P phases.</li> <li>"spectral.zip" contains CASTEP and OptaDOS output files for projected bandstructure and DOS calculations of low-lying K-Sn-P phases.</li> <li>"digests.zip" contains JSON representations of all the structures from polish and searches, broken down into K-P and K-Sn-P specific digests.</li> </ul>
Bio‐derived Carbon Nanofibres from Lignin as High‐Performance Li‐Ion Anode Materials
<p>Development of cost‐effective and increasingly efficient sustainable materials for energy‐storage devices, such Li‐ion batteries, is of crucial future importance. Herein, the preparation of carbon nanofibres from biopolymer blends of lignin (byproduct from the paper and pulp industry) and polylactic acid (PLA) or a thermoplastic elastomeric polyurethane (TPU) is described. SEM analysis shows the evolving microstructural morphology after each processing step (electrospinning, stabilisation and carbonisation). Importantly, it is possible to tailor the nanofibre porosity by utilising miscibility/immiscibility rules between lignin and the polymer additive (PLA/TPU). PLA blends (immiscible) generate porous structures whereas miscible lignin/TPU blends are solid when carbonised. Electrodes produced from 50 % PLA blends have capacity values of 611 mAh g<sup>−1</sup> after 500 charge/discharge cycles, the highest reported to date for sustainable electrodes for Li‐ion batteries. Thus, this work will promote the development of lignocellulose waste materials as high‐performance energy‐storage materials.</p>
Solvent Engineered Synthesis of SnO Nanoparticles for High-Performance Anodes
<p>Batteries are the most abundant form of electrochemical energy storage. Lithium and sodium ion batteries account for a significant portion of the battery market, but high-performance electrochemically active materials still need to be discovered and optimized for these technologies. Recently, tin(II) oxide (SnO) has emerged as a highly-promising battery electrode. In this work, we present a facile synthesis method to produce SnO nanoparticles whose size and shape can be tailored by changing the solvent nature. We study the complex relationship between wet chemistry synthesis conditions and resulting nanoparticle morphology. Furthermore, high-level electronic structure theory, including dispersion corrections to account for van der Waals forces, are employed to augment our understanding of the underlying chemical mechanisms. The electronic vacuum alignment and surface energies are determined, allowing the prediction of the thermodynamically-favoured crystal shape (Wulff construction) and surface-weighted work function. Finally, the synthesized nanomaterials were tested as Li-ion battery anodes, demonstrating significantly enhanced electrochemical performance for morphologies obtained from specific synthesis conditions. </p> <p>Open-access publication in npj 2D Mater & Appl <a href="https://rdcu.be/cgdWZ">here</a>.</p>
Light intensity in reflection mode of PAAO (AJ-5-04-27 sample, 2nd anodization)
<p>Light intensity data recorded during the anodization of aluminum monocrystal.</p> <p>Light source: SLS201L/M (ThorLabs).</p> <p>Spectrometer: USB4000 (OceanOptics).</p> <p>Spectra acquisition software: SpectraSuite (OceanOptics). Integration time: 380 µs. Scans to average: 10. Spectrum is recorded every 500 ms during anodization. ref.txt includes reference spectra just before the start of anodization process. All measurements data is also included in a single "AJ-5-04-27.zip" file.</p> <p>Anodization was performed in 0.3 mol/L oxalic acid at 40 V for 4 min 57 s.</p>
Light intensity in reflection mode of PAAO (AJ-3-04-20 sample, 2nd anodization)
<p>Light intensity data recorded during the anodization of aluminum monocrystal.</p> <p>Light source: SLS201L/M (ThorLabs).</p> <p>Spectrometer: USB4000 (OceanOptics).</p> <p>Spectra acquisition software: SpectraSuite (OceanOptics). Integration time: 380 µs. Scans to average: 10. Spectrum is recorded every 500 ms during anodization. ref.txt includes reference spectra just before the start of anodization process. All measurements data is also included in a single "AJ-3-04-20.zip" file.</p> <p>Anodization was performed in 0.3 mol/L oxalic acid at 40 V for 4 min 8 s.</p>
Light intensity in reflection mode of PAAO (AJ-4-04-20 sample, 2nd anodization)
<p>Light intensity data recorded during the anodization of aluminum monocrystal.</p> <p>Light source: SLS201L/M (ThorLabs).</p> <p>Spectrometer: USB4000 (OceanOptics).</p> <p>Spectra acquisition software: SpectraSuite (OceanOptics). Integration time: 360 µs. Scans to average: 10. Spectrum is recorded every 500 ms during anodization. ref.txt includes reference spectra just before the start of anodization process. All measurements data is also included in a single "AJ-1-04-20.zip" file.</p> <p>Anodization was performed in 0.3 mol/L oxalic acid at 40 V for 4 min 30 s.</p>
Light intensity in reflection mode of PAAO (AJ-2-04-20 sample, 2nd anodization)
<p>Light intensity data recorded during the anodization of aluminum monocrystal.</p> <p>Light source: SLS201L/M (ThorLabs).</p> <p>Spectrometer: USB4000 (OceanOptics).</p> <p>Spectra acquisition software: SpectraSuite (OceanOptics). Integration time: 360 µs. Scans to average: 10. Spectrum is recorded every 500 ms during anodization. ref.txt includes reference spectra just before the start of anodization process. All measurements data is also included in a single "AJ-2-04-20.zip" file.</p> <p>Anodization was performed in 0.3 mol/L oxalic acid at 40 V for 3 min 37 s.</p>
Light intensity in reflection mode of PAAO (AJ-1-04-20 sample, 2nd anodization)
<p>Light intensity data recorded during the anodization of aluminum monocrystal.</p> <p>Light source: SLS201L/M (ThorLabs).</p> <p>Spectrometer: USB4000 (OceanOptics).</p> <p>Spectra acquisition software: SpectraSuite (OceanOptics). Integration time: 330 µs. Scans to average: 10. Spectrum is recorded every 500 ms during anodization. ref.txt includes reference spectra just before the start of anodization process. All measurements data is also included in a single "AJ-1-04-20.zip" file.</p> <p>Anodization was performed in 0.3 mol/L oxalic acid at 40 V for 3 min 16 s.</p>
Light intensity in reflection mode of PAAO (AJ-3-04-20 sample, 1st anodization)
<p>Light intensity data recorded during the anodization of aluminum monocrystal.</p> <p>Light source: SLS201L/M (ThorLabs).</p> <p>Spectrometer: USB4000 (OceanOptics).</p> <p>Spectra acquisition software: SpectraSuite (OceanOptics). Integration time: 360 us. Scans to average: 10. Spectrum is recorded every 2 s during anodization. ref.txt includes reference spectra just before the start of anodization process. All measurements data is also included in a single "AJ-3-04-20.zip" file.</p> <p>Anodization was performed in 0.3 mol/L oxalic acid at 40 V for 1 hour.</p>
Resistive switching and role of interfaces in memristive devices based on amorphous NbOx grown by anodic oxidation - Dataset
<p>This is the dataset of "Resistive switching and role of interfaces in memristive devices based on amorphous NbOx grown by anodic oxidation"</p>
Original data for "Deciphering the Interplay Between Local and Global Dynamics of Anodic Metal Oxidation"
<p>This upload includes both original and supplementary data for the publication titled "Deciphering the Interplay Between Local and Global Dynamics of Anodic Metal Oxidation" by A. Makogon, J.-M. Noël, F. Kanoufi, and V. Shkirskiy. The preprint version of the paper can be found on ChemRxiv at <a href="https://doi.org/10.26434/chemrxiv-2023-stbm1">https://doi.org/10.26434/chemrxiv-2023-stbm1</a>.</p> <p>The files "DATA_PROCESSING.pdf" and "DATA_PROCESSING.ipynb" contain the developed Python script routine used for data mining and extraction. All figures in the article were generated using this script. For the best experience, we recommend opening the Jupyter Lab files in a Python 3 environment.</p> <p>The "Videos.zip" file houses all original data from the optics experiment, showcasing the evolution of current, averaged normalized reflectivities, and optical movies.</p> <p>The "OriginalData.zip" file contains all raw data from the opto-electrochemical experiments. Refer to the Python script as a guide if you wish to open and process this data.</p> <p>Lastly, "Drafts_of_scripts.zip" includes all the unedited drafts of Python scripts that were developed during the data processing phase.</p>
FIB-tomography data of Ni-YSZ anodes for Solid Oxide Fuel Cells (SOFC): Comparison of pristine and degraded materials (before/after redox cycling)
<p><em>Contents: </em></p> <p>This dataset contains 3D image stacks acquired with FIB-tomography from Ni-YSZ cermet anodes for Solid Oxide Fuel Cells (SOFC).</p> <p>The data was collected from three different Ni-YSZ anodes (fine-, medium- and coarse-grained). Each of these anodes was investigated first in pristine state (after sintering and reduction) and then also in degraded state (after exposure to 8 redox cycles).</p> <p>The 6 tomographs are then presented as stacks of 2D-tiff-images in 2 different versions: as gray-scale images (raw data) and as segmented images (Ni=white, YSZ=gray and pores=black). In total this gives 12 image stacks.</p> <p><strong>Further details</strong>, such as the voxel resolutions and image window sizes are listed in the downloadable excel file (<strong>2_3D_Data_Info.xlsx</strong>).</p> <p> </p> <p><em>Scientific Context: </em></p> <p>The microstructures of the cermet anodes were investigated for the purpose of optimizing the anode performance, which depends on effective transport properties (i.e. conductivity of ions in YSZ and of electrons in Ni, as well as diffusivity of fuel/gas in the pores). Furthermore the anode performance also depends on the catalytic/electrochemical activity (i.e. Ni-surface area and three phase boundary length TPBL). The microstructure characteristics have a strong influence on effective properties, electrochemical activity and associated anode performance. Furthermore, microstructure degradation (e.g. by Ni-coarsening) may lead to performance loss over time.</p> <p>Hence, the investigations focus on a fundamental, quentitative understanding of the relationships between microstructure characteristics and effective properties. The study reveals quantitative descriptions of all relevant microstructure characteristics (porosity, tortuosity, constrictivity, surface/interface areas, TPBL) and of the corresponding effective transport porperties (electric and ionic.conductivities). The corresponding anode performance was characterized by impedance spectroscopy.</p> <p>The quantitative <strong>results of the microstructure investigation were published</strong> in:</p> <p><strong>Pecho et al</strong> 2015a (doi:10.3390/ma8095265),</p> <p><strong>Pecho et al</strong> 2015b (doi:10.3390/ma8105370),</p> <p><strong>Holzer et al</strong> 2013 (doi: 10.1016/j.jpowsour.2013.05.047),</p> <p><strong>Holzer et al</strong> 2011a (doi: 10.1016/j.jpowsour.2010.08.017) and</p> <p><strong>Holzer et al</strong> 2011b (doi: 10.1016/j.jpowsour.2010.08.006).</p>
NiFe-NO3 layered double hydroxide as a novel anode for sodium ion batteries
<p>raw data of the scheme present in the paper entitled: <span>NiFe-NO<sub>3</sub> layered double hydroxide as a novel anode for sodium ion batteries</span></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.