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1,111 results for “nanoparticles”

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zenodo44/100

BAM reference data: EDS raw data of Al-coated titania nanoparticles (JRCNM62001a and JRCNM62002a)

<p>The EDS spectra are given in the EMSA/MAS format as defined by&nbsp;ISO 22029:2012 Microbeam analysis &mdash; EMSA/MAS standard file format for spectral-data exchange.&nbsp;The exact locations of the sample areas measured with&nbsp;EDS are indicated&nbsp;in the SEM images.</p> <p>For further information please look at:</p> <p>- Radnik, J. Kersting, R., Hagenhoff, B., Bennet, F., Ciornii, D.; Nymark, P., Grafstr&ouml;m R. and Hodoroaba, V.- D.&nbsp;<em>Nanomaterials&nbsp;</em><strong>2021</strong>,&nbsp;<em>11</em>, 639. https://doi.org/10.3390/nano11030639,&nbsp;and</p> <p>-&nbsp;Radnik, J&ouml;rg. (2021). BAM reference data: XPS raw data of Al-coated titania nanoparticles (JRCNM62001a and JRCNM62002a) [Data set]. Nanomaterials. Zenodo. http://doi.org/10.5281/zenodo.4986068</p> <p>Measurement conditions:</p> <p>The EDS analysis in the present study has been performed with a QUANTAX 400 EDS system (BRUKER, Berlin, Germany), which is equipped with an SDD (Silicon Drift-Detector) of the 10 mm2 nominal area. An excitation of 10 keV was applied for the analysis of the titania samples prepared as a thick dry powder layer on an aluminum stub, so that the substrate cannot be coexcited. The analysis&nbsp; reas were selected as large as 5 x&nbsp;5 &micro;m<sup>2</sup>&nbsp;on sample agglomerates of about 1eng0 &micro;m size.</p>

opencc-by-4.0Jun 2021View details →
zenodo44/100

Nanoparticle clustering in supraparticles to control magnetic long-range interactions

<p>This data publication is based on the metadata and datasets underlying the manuscript: Nanoparticle clustering in supraparticles to control magnetic long-range interactions</p> <p>To tailor superparamagnetic iron oxide nanoparticles (SPIONs) to the specific needs of diverse application fields, it is essential to understand not only their intrinsic properties but also their interactions with each other. Theoretical models predicting/explaining the magnetization behavior of macroscopic samples containing millions of SPIONs are intricate due to the complexity of the underlying relaxation mechanisms in alternating fields. This study introduces supraparticles (SPs) as model architectures to empirically investigate magnetic interactions within and between large SPION clusters (&gt; 100 nanoparticles). For this purpose, nanoparticle dispersions containing SPIONs and silica nanoparticles (SiO<sub>2</sub> NPs) as non‐magnetic building blocks are spray‐dried to form binary SPs. Selective salt‐induced agglomeration of the two building block types before spray‐drying is utilized to tailor SP architectures, including control over SPION cluster size, shape, and proximity. Magnetic particle spectroscopy (MPS), operating under ambient conditions, reveals altered magnetization behavior for different cluster structures. Not only the nearest SPION neighbors, but the whole cluster structure up to several micrometers is decisive for the magnetization behavior. This highlights the importance of long‐range magnetic interactions. This work presents a versatile approach for designing model architectures to advance empirical interaction studies between SPIONs in macroscopic samples.</p>

opencc-by-4.0Jul 2024View details →
zenodo44/100

In-Plane and Out-of-Plane MEMS Piezoresistive Cantilever Sensors for Nanoparticle Mass Detection (Data)

<p>Origin projects, figures and LabVIEW software used for the article &quot;In-Plane and Out-of-Plane MEMS&nbsp;Piezoresistive Cantilever Sensors for Nanoparticle Mass Detection&quot;, published in <em>Sensors </em>on 22 Jan 2020.</p>

opencc-by-4.0Nov 2021View details →
zenodo44/100

Supporting data set for: Simulations of the Electrochemical Oxidation of Shape-Selected Nanoparticle Catalysts

<p>This dataset contains input and output files for simulations of the oxidation of a set of shape-selected, 3 nm platinum nanoparticles associated with the manuscript found at https://arxiv.org/abs/2201.07605.</p> <p>The simulations are performed using a grand-canonical Monte-Carlo algorithm[1,2] in combination with the ReaxFF reactive force field method as implemented in the Amsterdam Density Functional (ADF) software package version 2017.106 by Software for Chemistry and Materials (SCM). The Pt/O ReaxFF force field parameterized by Fantauzzi et al. was used for the simulations.[3] Simulations were performed at oxygen chemical potential conditions corresponding to 200-1000 K at ultra-high vacuum (UHV, <em>p</em><sub>O2</sub> = 10<sup>-10</sup> mbar) and 400-1200 K at near-ambient pressure (NAP, <em>p</em><sub>O2</sub> = 1 mbar) conditions. The following nanoparticle shapes were used as input structures for the simulations: (111)-indexed octahedron, (100)-indexed cube, (110)-indexed dodecahedron, (111)- and (100)-indexed cuboctahedron, mixed-indexed sphere, and (730)-indexed tetrahexahedron.</p> <p>The folder structure is as follows:<br> Particle shape -&gt; pressure condition -&gt; temperature condition -&gt; simulation input and output files</p> <p>The simulation input and output files are of the following filetypes:<br> control: Input parameters for the ReaxFF software.<br> control_MC: Input parameters for the GCMC subroutine that interacts with the ReaxFF software.<br> geo: Atomic input coordinates in BGF file format.<br> geo_MCXXXXXX: Atomic output coordinates in BGF file format and ReaxFF total energy result for GCMC step XXXXXX.</p> <p>Simulations were performed for a total of 25,000 iterations. Only accepted GCMC steps result in the creation of a geo_XXXXXX output file. Therefore, the index XXXXXX is not continuous since output files are not written at every iteration. Other ReaxFF-specific output has been filtered in order to declutter the dataset.</p> <p>[1] T. P. Senftle, R. J. Meyer, M. J. Janik, A. C. T. van Duin, J. Chem. Phys. 2013, 139, 044109.<br> [2] T. P. Senftle, M. J. Janik, A. C. T. van Duin, J. Phys. Chem. C 2014, 118, 4967&ndash;4981.<br> [3] D. Fantauzzi, J. Bandlow, L. Sabo, J. E. Mueller, A. C. T. van Duin, T. Jacob, Phys. Chem. Chem. Phys. 2014, 16, 23118&ndash;23133.</p>

opencc-by-4.0Mar 2022View details →
zenodo44/100

Tilt-series 4DSTEM dataset of DNA origami with gold nanoparticles

<p>Raw TEM data for the manuscript "Three-dimensional Electron Ptychography of Organic-inorganic Hybrid Nanostructures".</p>

opencc-by-4.0Jul 2022View details →
zenodo44/100

Raw data for the article "Size-Dependent Structural Alterations in Ag Nanoparticles During CO2 Electrolysis in a Gas-Fed Zero-Gap Electrolyzer"

<p>In the article&nbsp;&quot;Size-Dependent Structural Alterations in Ag Nanoparticles During CO2 Electrolysis in a Gas-Fed Zero-Gap Electrolyzer&quot; we described our investigation on the size-dependent degradation behavior of Ag NPs (10, 40, and 100 nm in size) on GDE during CO<sub>2</sub> electrolysis. Here we present the dataset the work was based on. For each figure in the article and the supporting information we provide a set of raw and unprocessed data.</p>

opencc-by-4.0Aug 2022View details →
zenodo44/100

Raw data for the plot in the article entitled "On the electrophoretic deposition of Bi2Te3 nanoparticles through electrolyte optimization and substrate design"

<p>raw data of transport presented in Fig1a of the open access article with the following details:</p> <p>On the electrophoretic deposition of Bi2Te3nanoparticles through electrolyte optimization and substrate design</p> <p><a href="https://www.sciencedirect.com/journal/colloids-and-surfaces-a-physicochemical-and-engineering-aspects">Colloids and Surfaces A: Physicochemical and Engineering Aspects</a></p> <p><a href="https://www.sciencedirect.com/journal/colloids-and-surfaces-a-physicochemical-and-engineering-aspects/vol/649/suppl/C">Volume 649</a>,&nbsp;20 September 2022, 129537</p> <p><a href="https://doi.org/10.1016/j.colsurfa.2022.129537">https://doi.org/10.1016/j.colsurfa.2022.129537</a></p>

opencc-by-4.0Jun 2022View details →
zenodo44/100

Data for "Hot-carrier transfer across a nanoparticle-molecule junction: The importance of orbital hybridization and level alignment"

<p>This upload includes the data presented and analyzed in the article &quot;Hot-carrier transfer across a nanoparticle-molecule junction: The importance of orbital hybridization and level alignment&quot; by Jakub Fojt, Tuomas P. Rossi, Mikael Kuisma, and Paul Erhart.</p> <p>The codes for reproducing the data are provided at <a href="https://doi.org/10.5281/zenodo.7118376">doi:10.5281/zenodo.7118376</a>.</p> <p>See <em>README.md</em> in <em>data.zip</em> for a detailed description.</p>

opencc-by-4.0Sep 2022View details →
zenodo44/100

Data: Stability and biological response of PEGylated gold nanoparticles

<p><span>This dataset is focused on thermal stability of PEGylated Au NPs at 4 and 37 &deg;C and after sterilization in autoclave.</span></p>

opencc-by-4.0May 2024View details →
zenodo44/100

EXAFS spectra during formation of palladium oxides in alumina supported palladium nanoparticles

<p>This dataset contain extended X-ray absorption fine structure (EXAFS) spectra for 5%Pd/Al2O3 (named TO326) and 2%Pd/P4VP (named TO203) samples, measrured in situ with high time resolution during exposre of the samples to hydrogen and oxygen flows at different temperatures. The file Parameters.txt contain sample names, temperatures and times together with structural parameters obtained from EXAFS fitting. File Spectra.txt contain normalized EXAFS spectra (as rows) for each energy point (energies shown in the first row). Each row in Parameters.txt correspond to a row (spectrum) in Spectra.txt</p>

opencc-by-4.0Feb 2024View details →
zenodo44/100

Data of publication All-optical control of long-lived nuclear spins in rare-earth doped nanoparticles

<p>Data corresponding to the figures of the publication &quot;All-optical control of long-lived nuclear spins in rare-earth doped nanoparticles&quot; by D. Serrano et al. (https://www.nature.com/articles/s41467-018-04509-w). A text file&nbsp;describes data&nbsp;in each compressed folder, please refer to the publication for more details.&nbsp;</p>

opencc-by-4.0Jun 2018View details →
zenodo44/100

Data of publication Controlled size reduction of rare earth doped nanoparticles for optical quantum technologies

<p>Data corresponding to the figures of the publication &quot;&nbsp;Controlled size reduction of rare earth doped nanoparticles for optical quantum technologies&quot; by S. Liu et al. (https://pubs.rsc.org/en/content/articlelanding/2018/ra/c8ra07246a#!divAbstract). A text file&nbsp;describes data&nbsp;in each compressed folder, please refer to the publication for more details.&nbsp;</p>

opencc-by-4.0Nov 2018View details →
zenodo44/100

A set of typical relevant exposure scenarios for nanoparticles in semiconductor industry (dataset)

<p>This is an Excel database&nbsp; part of Deliverable 1.3 &quot;A set of typical relevant exposure scenarios for NP&rsquo;s in semiconductor industry&quot;</p> <p>https://www.zenodo.org/record/2538388</p> <p>&nbsp;</p>

opencc-by-4.0Nov 2018View details →
zenodo44/100

Dataset for the article "Capping agent control over the physicochemical and antibacterial properties of ZnO nanoparticles".

<p>Dataset for the article "Capping agent control over the physicochemical and antibacterial properties of ZnO nanoparticles".</p> <p>David Rutherford1, Mark&eacute;ta &Scaron;lapal Bařinkov&aacute;1, Thaiskang Jamatia2, Pavol &Scaron;uly2, Martin Cvek2, Bohuslav Rezek1</p> <p><br>1 Faculty of Electrical Engineering, Czech Technical University in Prague, Technick&aacute; 2, 16227 Prague, Czech Republic<br>2 Centre of Polymer systems, Tomas Bata University in Zlin, Trida T. Bati 5678, 760 01 Zl&iacute;n, Czech Republic</p> <p><br>Dataset description:</p> <p>240909 UV-vis_capped_ZnO.xlsx &nbsp; &nbsp; &nbsp; &nbsp;UV-vis spectroscopy<br>220623 ZnO Zlin Zn ion.xlsx &nbsp; &nbsp; &nbsp; &nbsp;Zinc ion measurement<br>230511 dls_zeta_data.xlsx &nbsp; &nbsp; &nbsp; &nbsp;DLS &amp; zeta potential measurement<br>230221 ZnO_Zlin_MIC_MASTER.xlsx &nbsp; &nbsp; &nbsp; &nbsp;Minimum inhibitory concentration</p>

opencc-by-4.0Sep 2024View details →
zenodo44/100

Raw Data for "Vanadium incorporation in ferrite nanoparticles serves as electron buffer and anisotropy tuner in catalytic and hyperthermia applications"

<p>Raw data for VxFe2-XO4 magnetic nanoparticles: characterization,magnetic hyperthermia experiments in polyacrylamide gels and EPR reactive oxygen species quantification after exposure to H2O2.</p>

opencc-by-4.0Oct 2024View details →
zenodo44/100

Customizable induction heating profiles: from tailored colloidally stable nanoparticles towards multi-stage heatable supraparticles

<p>This data publication is based on the metadata and datasets underlying the manuscript: "Inductively heatable nano- and supraparticles: from colloidally stable hot nanoparticles to supraparticles with customizable multi-stage heating profiles"</p> <p>Magnetic nanoparticles (NPs) are efficient heat mediators in induction heating. Originally explored for hyperthermia, their applications have broadened to industrial processes where temperature control is crucial. By adjusting the NP composition or morphology, magnetic characteristics such as Curie temperatures can be tailored, allowing control over maximum heating thresholds. These NPs are, however, usually designed for maximum heating rates at specific magnetic fields. In this work, the synthesis is presented for colloidally stable Co and ZnCo ferrite NPs with customizable maximum heating temperatures, and their combination within micron-scaled supraparticles (SPs). Maximum induction heating temperatures of ZnCo ferrite NPs are tuned between 150 and 220 &deg;C, while customization of Co ferrite species yields temperatures between 200 and 350 &deg;C. These distinct magnetic properties are exploited in the selective multi-stage heating of SPs consisting of both species. Here, ZnCo ferrite components heat up to a first temperature plateau at low alternating magnetic fields (AMF), while Co ferrite NPs reach higher temperatures at increased AMF. The precise control of induction heating thresholds through the adaptability of NPs offers a high degree of customizability which makes induction heating particularly attractive for applications requiring sequential or spatial heating, such as catalysis or debonding on demand.</p>

opencc-by-4.0Jul 2024View details →
zenodo44/100

RDF version of the data from Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019)

<p>This is an RDFied version of the dataset published by&nbsp;Hagar I. Labouta et al. Meta-Analysis of Nanoparticle Cytotoxicity via Data-Mining the Literature. NanoImpact (2019).</p> <p>The original dataset publication DOI:&nbsp;<a href="https://doi.org/10.1021/acsnano.8b07562">https://doi.org/10.1021/acsnano.8b07562</a></p> <p>The Original publication authors:&nbsp;Hagar I. Labouta, Nasimeh Asgarian, Kristina Rinker, and David T. Cramb</p>

opencc-by-4.0Nov 2021View details →
zenodo44/100

RDF version of the data from Anastasios G. Papadiamantis et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform (2020)

<p>This is an RDFied version of the dataset published in&nbsp;Papadiamantis, A.G. et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform.&nbsp;<em>Nanomaterials</em>&nbsp;<strong>2020</strong>,&nbsp;<em>10</em>, 2017.</p> <p>The original dataset publication DOI:&nbsp;<a href="https://doi.org/10.3390/nano10102017">https://doi.org/10.3390/nano10102017</a></p> <p>The Original publication authors:&nbsp;Papadiamantis, A.G.; J&auml;nes, J.; Voyiatzis, E.; Sikk, L.; Burk, J.; Burk, P.; Tsoumanis, A.; Ha, M.K.; Yoon, T.H.; Valsami-Jones, E.; Lynch, I.; Melagraki, G.; T&auml;mm, K.; Afantitis, A.</p>

opencc-by-4.0Nov 2021View details →
zenodo44/100

Electrochemical and Spectroscopic Data supported by Computational Models for Exploring the Metal- and Ligand-Based Oxidation of Mackinawite Nanoparticles

<p>Supporting information to our study, where under anaerobic conditions, ferrous iron reacts with sulfide producing FeS&nbsp;precipitate, which can then undergo a temperature, redox potential, and pH dependent maturation process resulting in the formation of oxidized mineral phases such as gregite or pyrite. The dataset&nbsp;provide information about&nbsp;the chemical speciation of iron-sulfide by cyclic voltammetry, Raman and X-ray absorption spectroscopic techniques. Nanoparticulate FeS&nbsp;was found to get oxidized&nbsp;to a Fe<sup>3+</sup> containing FeS phase at -0.5 V vs. Ag/AgCl (pH = 7) and&nbsp;in a concomitant oxidation step, polysulfides are proposed to give a material described as Fe<sup>2+</sup><sub>(1&minus;3x)</sub>Fe<sup>3+</sup><sub>(2x)</sub>S<sup>2-</sup><sub>(1-y)</sub>(S<sub>n</sub><sup>2-</sup>)<sub>y</sub>. The thermodynamic differences between ligand- and metal-based oxidation processes from&nbsp;density functional theory can be used to describe one- and two-electron&nbsp;electronic and structural transformations. These findings together point to the existence of a previously unknown, metastable FeS phase located between FeS and greigite (Fe<sup>2+</sup>Fe<sup>3+</sup><sub>2</sub>S<sup>2-</sup><sub>4</sub>) along a metal oxidation path, and Fe<sup>2+</sup>S<sup>2-</sup> and pyrite (Fe<sup>2+</sup>S<sub>2</sub><sup>2-</sup>)&nbsp;along a ligand oxidation path, respectively.</p>

opencc-by-4.0Jun 2021View details →
zenodo44/100

DATASET: Predicting Protein Function and Orientation on a Gold Nanoparticle Surface Using a Residue-Based Affinity Scale

<p>This upload contains data for the manuscript &quot;<strong>Predicting Protein Function and Orientation on a Gold Nanoparticle Surface Using a Residue-Based Affinity Scale</strong>.&quot; It contains kinetics data, UV-vis data, surface calculations, and activity assays for the systems described in the manuscript.</p>

opencc-by-4.0Oct 2022View details →

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Allen Brain Atlas

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allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record