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1,175 results for “coating”
SEM image of SiO2 coated PyC tribocolloid
<p>This SEM image shows SiO2 spheres with 4.5 micron diameter coated by Pyrolytic carbon (PyC) at 950 C for 20 min</p>
Formation and cycling data for Na-ion batteries from high-throughput synthesis, coating, and assembly
<p>Formation and cycling data from a combinatorial/high-throughput upscaling process for the production and characterization of sodium-ion batteries. The process involves batch synthesis, screen printing of electrodes, robotic cell assembly, and battery cycling. The goal of this study was to test how fast a new chemistry (to the group) could be introduced into the workflow and if we are able to enhance efficiency, accuracy, and reproducibility. The cathode material, Na0.9[Cu0.22Fe0.30Mn0.48]O2, was synthesized through a solid-state reaction (Na2CO3 (purity 99.5 %), CuO (purity 99.7 %), Fe2O3 (purity 99.9 %) and Mn2O3 (purity 98 %) at 850°C for 15h) in a pressed pellet (10 MPa) that was ground up again to make a slurry. The electrodes were prepared using screen printing, which offers simplicity, low cost, and quick coating of large areas in a reproducible manner. The binder was sodium carboxymethyl cellulose to make the electrodes water processable in air. The assembled batteries utilized the synthesized cathode material and hard carbon as the anode, with a glass fiber separator and a 1M NaPF6 EC:EMC 3:7 with 2 wt% FEC electrolyte.</p>
dynamic water contact angle measurements of metallic glass coatings
<p>dynamic water contact angle measurements of Zr-Cu-Ag metallic glass coatings. The file named PBT is the non-coated sample. The other three samples were metallic glass coatings.</p>
Field Emission Scanning Electron microscopy from Zr-Cu-Ag metallic glass coatings after antibacterial test with E.Coli
<p>Field Emission Scanning Electron Microscopy Figures from metallic glass (Zr-Cu-Ag) antibacterial coatings. Coatings have the name SP in their file name. The non-coated comparison is PBT. This is after the antibacterial test with <em>E.coli</em> after 24 hours. </p>
small angle x-ray scattering from Zr-Cu-Ag metallic glass coatings
<p>small angle x-ray scattering from Zr-Cu-ag metallic glass coating to confirm whether they became amorphous or not. The coating is on PBT substrate. </p>
Electrodeposited hydroxyapatite coating of titanium after ultrashort-pulsed lasers processing
<p></p> <p class="MsoNormal">The dataset presents surfaces features of<span> electrodeposited hydroxyapatite coating on titanium </span><span>modify </span><span>with ultrashort-pulsed lasers. </span></p> <p class="MsoNormal"><span>Four different hydroxyapatite coatings are created (A-D). Every coating is conditioned with four different laser irradiations 1-4 to 4-4 carried out in different parameter settings with altered power, velocity, and frequency. The surface features of laser-irradiated coating are presented. </span></p> <p class="MsoNormal"> </p>
Dataset and Simulation Files for article "Alumina coating for dispersion management in ultra-high Q microresonators"
<p>Link to arXiv submission: <a href="https://arxiv.org/abs/2009.07826">https://arxiv.org/abs/2009.07826</a></p> <ul> <li>Figure 1 <ul> <li>Data, Jupyter scripts, and comsol simulations files for parts (d,e)</li> <li>Data, Jupyter scripts, and simulations files for parts (f,g,h)</li> </ul> </li> <li>Figure 2 <ul> <li>Raw AFM data for use in Gwydeon software</li> <li>Data, Jupyter scripts, and comsol simulations files for parts (d,e)</li> </ul> </li> <li>Figure 3 <ul> <li>Raw data and Jupyter processing scripts for transmission spectrum calibration (parts - b)</li> <li>Frequency calibrated transmission spectrum and GVD/ Quality factor Jupyter scripts (parts d-g)</li> </ul> </li> <li>Figure 4 <ul> <li>Experimental data for parts a-g</li> </ul> </li> <li>Supplementary data <ul> <li>Raw ellipsometer data</li> <li>1D COMSOL FEM solver and analytical validation (including Julia Jupyter script)</li> <li>Quality factor estimation based on scattering and water adsorption</li> </ul> </li> </ul>
Crack channelling mechanisms in brittle coating systems under moisture or temperature gradients.
<p>Crack channelling is predicted in a brittle coating-substrate system that is subjected to a moisture or temperature gradient in the thickness direction. Competing failure scenarios are identified, and are distinguished by the degree to which the coating-substrate interface delaminates, and whether this delamination is finite or unlimited in nature. Failure mechanism maps are constructed, and illustrate the sensitivity of the active crack channelling mechanism and associated channelling stress to the ratio of coating toughness to interfacial toughness, to the mismatch in elastic modulus and to the mismatch in coefficient of hygral or thermal expansion. The effect of the ratio of coating to substrate thickness upon the failure mechanism and channelling stress is also explored. Closed-form expressions for the steady-state delamination stress are derived, and are used to determine the transition value of moisture state that leads to unlimited delamination. Although the results are applicable to coating-substrate systems in a wide range of applications, the study focusses on the prediction of cracking in historical paintings due to indoor climate fluctuations, with the objective of helping museums developing strategies for the preservation of art objects. For this specific application, crack channelling with delamination needs to be avoided under all circumstances, as it may induce flaking of paint material. In historical paintings, the substrate thickness is typically more than ten times larger than the thickness of the paint layer; for such a system, the failure maps constructed from the numerical simulations indicate that paint delamination is absent if the delamination toughness is larger than approximately half of the mode I toughness of the paint layer. Further, the transition between crack channelling with and without delamination appears to be relatively insensitive to the mismatch in the elastic modulus of the substrate and paint layer. The failure maps developed in this work may provide a useful tool for museum conservators to identify the allowable indoor humidity and temperature fluctuations for which crack channelling with delamination is prevented in historical paintings.</p>
Raw and analyzed data for manuscript: "Superhydrophilic coating of pine wood by plasma functionalization of self-assembled polystyrene spheres"
<p><strong>Abstract: </strong></p> <p>Self-assembling films typically used for colloidal lithography have been applied to pine wood substrates to change the surface wettability. Therefore, monodisperse polystyrene (PS) spheres have been deposited onto a rough pine wood substrate via dip coating. The resulting PS sphere film resembled a polycrystalline FCC-like structure with typical domain sizes of 5 – 15 single spheres. This self-assembled coating was further functionalized via an O<sub>2</sub> plasma. This plasma treatment strongly influenced the particle sizes in the outermost layer, and hydroxyl as well as carbonyl groups were introduced to the PS spheres’ surfaces, thus generating a superhydrophilic behaviour.</p>
BAM reference data: SEM raw data for the Particles Size Distribution of Al-coated titania nanoparticles (JRCNM62001a and JRCNM62002a)
<p>The SEM images are given in the TIF format.</p> <p>For further information please look at:</p> <p>- Radnik, J. Kersting, R., Hagenhoff, B., Bennet, F., Ciornii, D.; Nymark, P., Grafström R. and Hodoroaba, V.- D. <em>Nanomaterials </em><strong>2021</strong>, <em>11</em>, 639. https://doi.org/10.3390/nano11030639, and</p> <p>- Vasile-Dan Hodoroaba. (2021). BAM reference data: EDS raw data of Al-coated titania nanoparticles (JRCNM62001a and JRCNM62002a) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.4986420</p> <p>- Radnik, Jörg. (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>In the present work, a SEM of type Supra 40 (ZEISS, Oberkochen, Germany) with a Schottky field emitter and an InLens secondary electron detector was used at a 5 kV beam acceleration voltage.</p>
Ant handling changes myrmecochore seed coat microbiomes and alters diversity of seed-borne plant pathogenic fungi
<p>The putative benefits to seeds in myrmecochory (ant-mediated seed dispersal) are often cast in a reward context. However, microbes have been mostly overlooked as seed mortality agents in myrmecochory, as have potential treatments provided by ant-handling. We investigated the effects of ant handling on the diversity of seed coat fungal communities of three myrmecochorous plant species. Ant-handling altered measures of both alpha and beta diversity of fungal communities. Ant-handled seeds harbored different overall fungal communities and plant pathogen communities than non-ant-handled seeds. The myrmecochore pathogenic fungal community showed high dissimilarity (high pairwise community turnover) between ant-handled and control seeds, while beta diversity measures for ant-handled seeds and seeds with manually-removed elaiosomes were less dissimilar. Ant handling may offer an additional benefit to myrmecochorous seeds via the reduction of the seed coat pathogenic community, which may be driven by elaiosome removal or as a byproduct of ant cleaning behaviors and chemical secretions. </p>
Raw data for publication: Bioinspired Living Coating System for Wood Protection: Exploring Fungal Species on Wood Surfaces Coated with Biofinish during its Service Life
<p>Weather Data.xlsx</p> <p>This file contains hourly local weather conditions in Izola, Slovenia from October 2021- August 2022</p> <p>Number of colonies.xlsx</p> <p>This file contains the number of fungal colonies isolated from the InnoRenew CoE facade</p> <p>FUNGAL STRAINS_DNA sequence analysis.xlsx</p> <p>This file contains the Genomic DNA of the fungal strains detected on the InnoRenew CoE facade</p>
Supplementary data of article "Domestication has altered gene expression and secondary metabolites in pea seed coat".
<p><strong>Table S1.</strong> Excel- GO_terms_MF_selected_WGCNA_modules.</p> <p><strong>Table S2.</strong> Excel- GO_terms_MF_DEGs_UP_and_DOWN.</p> <p><strong>Table S3.</strong> Excel- GO_terms_MF_DEGs_summary.</p> <p><strong>Table S4.</strong> Excel- List of DEGs involved in flavonoid pathway found in WILD gene set.</p> <p><strong>Table S5.</strong> Protein recoveries calculated for individual pea protein samples. Numbers 1, 2, 3 denote treatment groups corresponding to seed developmental stages (D1, D2 and mature seeds, respectively). Letters a–d denote biological replicates within the treatment groups.</p> <p><strong>Table S6.</strong> Excel- Annotation of proteins differentially expressed in wild and domesticated pea seed coat samples.</p> <p><strong>Table S7.</strong> Primary metabolites identified by spectral similarity library search and/or co-elution with authentic standards in pea seed coats aqua methanolic extracts. Metabolite analysis relied on GC-EI-Q-MS analysis after derivatization of the lyophilized extracts with methoxamine hydrochloride (MOA) and <em>N</em>-methyl-<em>N</em>-(trimethylsilyl)trifluoroacetamide (MSTFA).</p> <p><strong>Table S8.</strong> Primary metabolites detected in the aq. methanolic extracts of mature Cameor seed coats demonstrating statistically significant up- and down-regulation in comparison to those of wild JI261.</p> <p><strong>Table S9.</strong> Primary metabolites of mature JI92 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of wild JI261.</p> <p><strong>Table S10.</strong> Primary metabolites of mature JI1794 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261.</p> <p><strong>Table S11.</strong> Primary metabolites of mature JI64 seed coats demonstrating statistically significant up- and down-regulation in comparison with those of JI261.</p> <p><strong>Table S12.</strong> Mass analyzer settings applied for QqTOF-MS experiments in analysis of seed coat (cell wall) hydrolyzates and reference authentic standards.</p> <p><strong>Table S13.</strong> Cell wall-bound metabolites extracted from the seed coats of wild (JI64, JI1794, JI261) and domesticated (Cameor, JI92) peas upon alkali hydrolysis of corresponding isolated and purified cell wall material.</p> <p><strong>Table S14.</strong> Excel- Coordinates of markers in S-plot obtained from OPLS-DA analysis (FIA-ESI-HRTMS, negative ionization, lock mass uncorrected).</p> <p><strong>Table S15.</strong> List of identified significantly differential metabolites rising during seed coat development.</p> <p><strong>Table S16.</strong> List of identified significantly differential metabolites decreasing during seed coat development (positive ionization mode).</p> <p><strong>Table S17.</strong> List of identified metabolites with significantly higher content in wild compared cultivated genotypes in older developmental stages (D5-6).</p> <p><strong>Table S18.</strong> Excel- Expression of genes encoding enzymes of monolignol pathway in seed coats (SC) and embryos (E) of domesticated (Cameor, JI92 and <em>Pisum abyssinicum</em> PI358617) and wild (JI64, JI1794, JI261) peas over five seed developmental stages (13, 17, 20, 23, 28 DAP, labelled as 1-5). PAL: phenylalanine ammonia-lyase, C4H: cinnamate-4-hydroxylase, 4CL: 4-coumaroyl: CoA ligase, HCT: hydroxycinnamoyl CoA:shikimate hydroxycinnamoyltransferase, COMT: caffeic acid O-methyltransferase, CSE: caffeoyl shikimate esterase, CAD: cinnamyl alcohol dehydrogenase, CCR: cinnamoyl CoA reductase, CCoAMT: caffeoyl CoA-3-methyltransferase, F5H: ferulate-5-hydroxylase</p> <p><strong>Table S19.</strong> Studied metabolites of phenylpropanoid pathway.</p> <p><strong>Table S20.</strong> Instrument settings used in the proteomics LIT-Orbitrap-MS and -MS/MS experiments.</p> <p><strong>Table S21.</strong> Procedures and specific settings for data processing and post-processing of the proteomics data.</p> <p><strong>Table S22.</strong> Gas chromatographic (GC) separation conditions and electron ionization-quadrupole-mass spectrometry (EI-Q-MS) settings for GC-EI-Q-MS analysis of the primary metabolites in pea seed coats.</p> <p><strong>Table S23.</strong> Chromatographic conditions used for UHPLC separation of seed coat (cell wall) hydrolyzates and reference authentic standards.</p> <p><strong>Table S24.</strong> Variable parameters of MS/cIMS/MS measurements.</p> <p><strong>Figure S1.</strong> The dynamics of gene expression between studied developmental stages within all genotypes (a) or among genotypes in particular developmental stages (b).</p> <p><strong>Figure S2.</strong> Twelve representative groups of transcription factors described within 20 gene modules of pea SC. Visualized by Cytoscape 3.9.0.</p> <p><strong>Figure S3.</strong> SDS-PAGE electropherograms of the total protein fractions isolated from the seed coats of JI92 (a, c, e) and JI64 (b, d, f) seeds before and after tryptic hydrolysis. Numbers 1, 2, 3 denote seed developmental stages: DS1, DS2 and mature seeds, respectively. Letters a-d denote biological replicates. The aliquots (10 μg) of samples before hydrolysis (a, b), the incompletely digested aliquots left on filter unit after peptide elution (c, d) and aliquots of tryptic hydrolysates (corresponding to 5 μg of protein), (e, f) were loaded on gels. Inter-gel normalization relied on the total density of the Protein Ladder (PageRuler™ Prestained Protein Ladder #26616, 10–180 kDa) lane (St); the ND (non-digested) sample represents a reference protein not subjected to hydrolysis.</p> <p><strong>Figure S4.</strong> The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups) (c) identified in domesticated JI92 seed coats at developmental stages D1, D2 and D6. The tryptic digests (<em>n</em> =&thinsp;3) obtained from seed coats were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode.</p> <p><strong>Figure S5.</strong> The numbers of tryptic peptides (a), possible proteins (b), and non-redundant proteins (protein groups, c) identified in wild pea JI64 seed coats at D1, D2 and D6 stages. The tryptic digests (<em>n</em> =&thinsp;3), obtained from pea seedlings, were analyzed by nano-high performance liquid chromatography-electrospray ionization linear ion trap-orbital trap mass spectrometry (nanoHPLC-ESI-LIT-Orbitrap-MS) operated in positive DDA mode.</p> <p><strong>Figure S6.</strong> Principal component analysis (PCA) with score plot representation (a) accomplished for seed coat proteins differentially expressed at developmental stages D1 and D2 and in the mature state (D6) and hierarchical clustering with a heatmap representation (b).</p> <p><strong>Figure S7.</strong> Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D1. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of domesticated JI92 and wild JI64, respectively.</p> <p><strong>Figure S8.</strong> Functional annotation (accomplished with the Mercator MapMan v3.6 tool) of the pea seed coat proteins isolated in stage D2. White and black columns denote the functional groups of the proteins, which were more expressed in the developing seeds of the domesticated JI92 and wild JI64, respectively.</p> <p><strong>Figure S9.</strong> Prediction of sub-cellular localization of the proteins more expressed in the developing seeds of JI92 and JI64 with the BUSCA prediction tool.</p> <p><strong>Figure S10.</strong> Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated Cameor by principal component analysis (PCA).</p> <p><strong>Figure S11.</strong> Representation of the differences in the metabolic profiles of the mature seed coats obtained from the wild JI261 and domesticated Cameor by the t-test with Volcano plot representation (a) and the top 30 differentially abundant metabolites demonstrating the most pronounced differences of corresponding GC-MS signals associated with seed dormancy (b).</p> <p><strong>Figure S12.</strong> Evaluation of the differences in the metabolic profiles of the mature seeds obtained from the wild JI261 and domesticated JI92 by principal component analysis (PCA) with score plot representation (a) and hierarchical clustering with heatmap representation (b).</p> <p><strong>Figure S13.</strong> Principal component analysis (PCA) illustrating distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261.</p> <p><strong>Figure S14.</strong> Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild pea genotypes, JI64 and JI261(control).</p> <p><strong>Figure S15.</strong> Evaluation of the differences in the patterns of the cell wall-bound metabolites obtained from mature seed coats of wild JI261 and domesticated Cameor: principal component analysis (PCA) with score plot representation (a), hierarchical clustering with heatmap representation (b) and <em>t</em>-test analysis with the Volcano-plot representation (c).</p> <p><strong>Figure S16.</strong> Statistical analysis (<em>t</em>-test with Volcano plot representation) characterizing the differences between the levels of mature seed coat cell wall-bound metabolites of Cameor compared with those of wild JI261.</p> <p><strong>Figure S17.</strong> Principal component analysis (PCA) illustrates the distribution of mature seed coat metabolic profiles of domesticated JI92 and wild JI261.</p> <p><strong>Figure S18.</strong> Principal component analysis (PCA) shows the distribution of metabolic profiles of mature seed coats of two wild pea genotypes, JI1794 and JI261, control.</p> <p><strong>Figure S19.</strong> Principal component analysis (PCA) demonstrates the distribution of mature seed coat metabolic profiles of two wild genotypes, JI64 and control JI261.</p> <p><strong>Figure S20.</strong> Annotated cell wall-bound metabolites extracted from the seed coats of the dormant wild pea genotype JI261 and the seed coats from several pea genotypes varying in their dormancy (Cameor, JI92, JI64, and JI1794) upon alkali hydrolysis of corresponding isolated and purified cell wall material. </p> <p><strong>Figure S21.</strong> Ion mobility separation of <em>m/z</em> 299.0841.</p> <p><strong>Figure S22.</strong> Ion mobility separation of <em>m/z</em> 701.1907. </p> <p><strong>Figure S23.</strong> Ion mobility separation of <em>m/z</em> 619.1041.</p> <p><strong>Figure S24.</strong> Ion mobility separation of <em>m/z</em> 631.1017.</p> <p><strong>Figure S25.</strong> Ion mobility separation of <em>m/z</em> 641.1139.</p> <p><strong>Figure S26.</strong> Ion mobility separation of <em>m/z</em> 771.1346. </p> <p><strong>Figure S27.</strong> Reconstructed chromatograms of p-hydroxybenzoic and salicylic acids in DS5 of dormant JI64 and domesticated landraces JI92 (LC/HRTMS, negative ionization mode).</p> <p><strong>Figure S28.</strong> Module-trait relationship depiction showing the correlation between expression of the gene modules and the abundance of identified metabolites of the monolignol pathway.</p>
Thin Nickel Coatings on Stainless Steel for Enhanced Oxygen Evolution and Reduced Iron Leaching in Alkaline Water Electrolysis
<p>This is a dataset detailing the characterization of electrodeposited Ni-layers on 1 cm2 pure Ni- and stainless steel-plates. The data is available as text files (.txt), MS Excel files (.xls), Origin files (.opju), and Gamry raw data files (.DTA).</p>
Dataset for Diamond-coated quartz crystal microbalance sensors: Challenges in high yield production and enhanced detection of ethanol and sars-cov-2 proteins
<p>The data set to paper: </p> <p>Name: Diamond-coated quartz crystal microbalance challenges in mass production and enhanced detection of ethanol and sars-cov-2 proteins</p> <p>Authors: Tibor Izsák1*, Marian Varga1, Michal Kočí2,3, Ondrej Szabó2, Katarína Aubrechtová Dragounová2, Gabriel Vanko2, Miroslav Gál4, Jana Korčeková5, Michaela Hornychová 4, Alexandra Poturnayová5, Alexander Kromka2*</p> <p>Affiliations: 1 Department of Microelectronics and Sensors, Institute of Electrical Engineering, Slovak Academy of Sciences, Dúbravská Cesta 9, Bratislava, 841 04, Slovak Republic<br> 2 Department of Semiconductors, Institute of Physics of the Czech Academy of Sciences, Cukrovarnicka 10/112, Prague 6 162 00, Czech Republic<br> 3 Department of Microelectronics, Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, Prague 6, 166 27, Czech Republic<br> 4 Faculty of Chemical and Food Technology, Slovak University of Technology, Bratislava, Slovak Republic<br> 5 Center of Biosciences, Institute of Molecular Physiology and Genetics, Slovak Academy of Sciences, Bratislava, Slovak Republic<br> *corresponding author: tibor.izsak@savba.sk</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of collection: 1. 5. 2023 - 31. 7. 2024</p> <p>Description: Figure 1: Photos of QCM substrates oriented horizontally or vertically on the substrate holder in the deposition chamber (left) and during the diamond CVD process with ignited plasma (right).<br> Figure 2: a) 3D model of the measurement setup and b) photograph of the open gas chamber with embedded QCM sample.<br> Figure 3: Photo of the a) measurement setup and b) disassembled flow cell with V-Dia-QCM. c) Side view photo of the assembled flow cell in the measurement setup.<br> Figure 4: a) SEM images revealing surface morphology and b) corresponding Raman spectra of Dia-QCM and Dia-Si substrates horizontally or vertically oriented on the substrate holder and corresponding optical photos. There is also the Raman spectrum of the bare QCM (Au-QCM) sample before the diamond deposition.<br> Figure 5: a) Raman spectra and b) SEM images depicting surface morphology of porous diamond film grown on Si (H-PorDia-Si) and QCM (H-PorDia-QCM) substrate. The inset in Fig. 5a represents the optical photo of diamond-coated QCM. Note: ‘H-’ in sample names means horizontally loaded samples.<br> Figure 6: The response delta fR of diamond-coated QCM sensors horizontally and vertically oriented, i.e., single-sided and double-sided diamond-coated QCMs, when applying periodic switching (at 3-minute intervals) of ethanol vapour (E) with various concentrations (from 10 ppm to 100 ppm) and synthetic air (Air).<br> Figure 7: a) First resonant frequency shift (delta fR) of individual QCM sensors and b) mean values of delta fR with corresponding error bars for each QCM sensor group dependent on ethanol concentration.<br> Figure 8: a) The changes of the resonant frequency, delta fR, after the addition of neutravidin (NA) dissolved in water, biotinylated 1C aptamers (1C APT) dissolved in PBS with MgCl2, and 50 pg/mL S-RBD protein in PBS. The addition of neutravidin, aptamers, proteins, and surface washings by water (H2O) or buffer (PBS) are highlighted by arrows. b) Zoom in on the highlighted area in Fig. 8a.<br> Figure 9: Decrease of the resonant frequency, fR, at various S-RBD protein concentrations. The comparison of the sensitivity of diamond and gold QCM surfaces on which S-RBD was determined is indicated in the graph legend.</p>
Dataset for "Coating thickness prediction for a viscous film on a rough plate"
<p>This dataset supports the publication 'Coating thickness prediction for a viscous film on a rough plate' by Lebo Molefe, Giuseppe A. Zampogna, John M. Kolinski, and François Gallaire, <em>Journal of Fluid Mechanics,</em> <strong>1001</strong>(A59), 2024. The data are film thicknesses measured for silicone oil films coated on rough plates. Please refer to the publication and its supplemental material for details.</p> <p><a href="https://doi.org/10.1017/jfm.2024.1015">https://doi.org/10.1017/jfm.2024.1015</a></p> <p><strong>Contents</strong></p> <p>Three folders containing the code and data required to produce the figures in the journal article and supplemental material.</p> <p><strong>Readme</strong></p> <p>A .txt file describing contents of code and datasets.</p> <p><strong>Code</strong></p> <p>Contains:</p> <ul> <li>Python code to produce Figures 5-10 and 12 in the main text, as well as supplementary Figures 1-4.</li> <li>Source code: COMSOL files for solving microscopic problem for effective parameters (L, Kitf) describing the rough surface; Python files for solving the macroscopic model equations once effective parameters are known.</li> </ul> <p><strong>Data</strong></p> <p>Contains data needed to plot the figures mentioned above, as well as supporting data.</p> <p>The data includes density, surface tension, and viscosity measurements for silicone oil, with rheometry measurements performed on an Anton Paar MCR 302 rheometer, as detailed in the Supplementary Material.</p> <p><strong>Plots</strong></p> <p>Contains output of plotting code corresponding to the figures mentioned above.</p>
Supporting data and software for: Low-temperature open-air synthesis of PVP-coated NaYF4:Yb,Er,Mn upconversion nanoparticles with strong red emission
<p>Upconversion nanoparticles (UCNPs) have unique photonic properties that make them ideally suited for many applications. They are excited by low-energy near-infrared photons and emit at higher energy (typically visible) wavebands. However, synthesis of UCNPs requires either high pressure reaction chambers or inert atmospheres. Combined with the requirements for high-temperatures (200 to 400 °C) and long reaction times (e.g. up to 24 hours), these place barriers to entry for UCNP research, in terms of both financial barriers and knowledge/"know how". These constraints may also limit the scale of UCNP production for end-user applications.</p> <p>We adapted and further developed a method for producing UCNPs with simple laboratory equipment, i.e. a hot-plate and beakers. No pressure vessel or inert atmosphere is required. The UCNPs produced have a<span> polyvinylpyrrolidone (PVP) polymer coating, with strong red emission due to Mn<sup>2+</sup> co-doping within the UCNP crystal lattice. It was found that UCNPs of composition NaYF<sub>4</sub>:Yb,Er,Mn (Yb = 20 mol %, Er = 2 mol%, Mn = 35 mol%) maximised the red emission whilst also minimising the diameter of the UCNPs to </span> 36 ± 15 nm. These combination of optical and physical properties should make these UCNPs ideal for further development and exploitation, particularly for biological applications where red emission can penetrate over a centimetre of tissue.</p> <p>This dataset and software accompanies the manuscript <em>'Low-temperature open-air synthesis of PVP-coated NaYF<sub>4:</sub>Yb,Er,Mn upconversion nanoparticles with strong red emission</em>', which was published in Royal Society Open Science on 19th January 2022. https://doi.org/10.1098/rsos.211508</p>
A Comparison of Different Textured and Non-Textured Anti-Reflective-Coatings for Planar Monolithic Silicon-Perovskite Tandem Solar Cells
<p>Figure data for the paper: A Comparison of Different Textured and Non-Textured Anti-Reflective-Coatings for Planar Monolithic Silicon-Perovskite Tandem Solar Cells. Submitted to ACS Applied Energy Materials.</p>
Simulation file for "Titanium dioxide and silver nanoparticles air emissions risk assessment for spray coating processes in Witek, Italy – A case study"
<p>Underlying data for “Nanosized titanium dioxide particle emission potential from a commercial indoor air purifier photocatalytic surface – A case study”</p>
Electrical measurement of coated steels
<p>Electrical measurements of AISI441 coated with Cu-Mn spinel and Crofer 22 APU coated with Co-Mn spinel.</p> <p>Measurement were carried out up to 400 hours, at 750°C and 850°C.</p> <p>At different times were also measured the electrical properties versus temperature to measure the activation energy of conduction.</p>
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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.