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94 results for “nanomaterials”
Rapid synthesis and characterization of silver-loaded graphene oxide nanomaterials and their antibacterial applications
<p>All original data used in figures and tables of the manuscript are available in the dataset, which includes the X-ray diffraction, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, transmission electron microscopy, scanning electron microscopy with energy dispersion spectroscopy, zeta potential analysis and antibacterial properties measurement. Ultrasound was practically used in large-scale production of water-soluble silver/graphene oxide (Ag/GO) by reducing the silver ions (Ag+) attached to graphene oxide into silver nanoparticles. The infrared and X-ray photoelectron spectra indicated that there is a strong interaction between Ag and GO. The morphology analysis showed that AgNPs having a monodisperse size was well dispersed on the surface of the GO nanosheet. The zeta potential analysis showed that the Ag/GO nanomaterials suspension has high stability. In addition, the Ag/GO nanomaterials exhibited an effective antibacterial activity against Escherichia coli and Staphylococcus aureus.</p>
Assessing corrosion resistance of 2D nanomaterial-based coatings on stainless steel substrates
<p><span>Two dimensional (2D) materials have elicited considerable interest in the past decade due to a diverse array of novel properties ranging from high surface to mass ratios, a wide range of band gaps (insulating boron nitride to semiconducting transition metal dichalcogenides), high mechanical strength and chemical stability. Given the superior chemo-thermo-mechanical properties, 2D materials may provide transformative solution to a familiar yet persistent problem of significant socio-economic burden: the corrosion of stainless steel (SS). With this broader perspective, we investigate corrosion resistance properties of stainless steel coated with 2D nanomaterials; molybdenum disulfide (MoS<sub>2</sub>), boron nitride (BN), bulk graphite in 3.5 wt. % aqueous NaCl solution. The nanosheets were prepared by a novel liquid phase exfoliation technique and the coatings were made by a paint brush to achieve uniformity. Open circuit potential (OCP) and potentiodynamic plots indicate the best corrosion resistance is provided by the MoS<sub>2</sub> and the BN coatings. Superior performance of the coating is attributed low electronic conductivity, large flake size, and uniform coverage of SS substrate, which likely impeded the corrosive ions from the solution to diffuse through the coating. </span></p>
Classifying Handedness in Chiral Nanomaterials Using Label Noise-Robust Deep Learning
<p>Images of individual Te chiral nanoparticles and labels of their handedness for classifier training.</p>
Mechanoluminescent nanomaterials for optogenetic neuromodulation-Part I
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MD data for Ionizable cationic lipids and helper lipids synergistically contribute to RNA packing and protection in lipid-based nanomaterials
<p>The data stored in this repository is part of the journal article: Zimmer, D. N., Schmid, F., & Settanni, G. (2024). Ionizable Cationic Lipids and Helper Lipids Synergistically Contribute to RNA Packing and Protection in Lipid-Based Nanomaterials. <em>The Journal of Physical Chemistry B</em> <a href="https://doi.org/10.1021/acs.jpcb.4c05057" target="_blank" rel="noopener">https://doi.org/10.1021/acs.jpcb.4c05057</a></p> <p> </p> <p>Data of multiscale simulations of DLinDMA:DOPE:Cholesterol, DLinDMA:DSPC:Cholesterol, DLinDAP:DOPE:Cholesterol and DLinDAP:DSPC:Cholesterol in the presence of RNA. For each formulation, data is provided with different coarse-grained parameterizations (generic, adapted) and differents treatments of the RNA (ELN, noELN). Provided are the first and the final frame of each run, the associated topologies, and the respective gromacs input files.</p> <p><strong>> M_PE, M_PC, P_PE, P_PC</strong></p> <p>DLinDMA:DOPE:Cholesterol, DLinDMA:DSPC:Cholesterol, DLinDAP:DOPE:Cholesterol and DLinDAP:DSPC:Cholesterol in presence of a 40mer RNA fragment. </p> <ul> <li>cg_<strong>generic</strong>+aa: <ul> <li>cg: 2 microsecond production run based on a generic MARTINI parametrization <ul> <li>md_0.gro: first frame</li> <li>md_10.gro: final frame </li> <li>cg_rna_bilayer.top: Topology of the system</li> <li>cg_DLD{M/P}_lipid.itp: generic MARTINI topology of DLinDMA/DLinDAP</li> <li>martini_v2.0_CHOL_02.itp, martini_v2.0_DSPC_01.itp, martini_v2.0_ions, martini_v2.1.itp, martini_v2.1-dna.itp: Several MARTINI topology files for molecules not included in MARTINI</li> <li>Nucleic_A.itp or Nucleic_A_eln.itp: Topology of the RNA fragment for MARTINI</li> </ul> </li> <li>aa: 300/600 nanosecond production run based on CHARMM36 <ul> <li>md_0.gro: first frame</li> <li>md_60.gro: final frame </li> <li>backmapped.top: Topology of the system (including the parametrization of DLinDMA/DLinDAP)</li> <li>CHOL.itp, DOPE.itp, DSPC.itp, 40mer_autopsf.itp: topology files for Cholesterol, DOPE, DSPC and RNA fragment as they are not part of the standard molecules in CHARMM36.</li> </ul> </li> <li>ELN and noELN indicate presence or absence of an elastic network to fix the structure of the RNA during the cg runs. </li> <li>cgmdp: Gromacs input files for the cg runs</li> <li>aamdp: Gromacs input files for the aa runs</li> </ul> </li> <li>cg_<strong>adapted</strong>+aa: <ul> <li>cg: starting and ending frame of a 2 microsecond production run based on an adapted MARTINI parametrization <ul> <li>md_0.gro: first frame</li> <li>md_10.gro: final frame </li> <li>cg_rna_bilayer.top: Topology of the system</li> <li>martini_v2.0_DIDMA_20 or martini_v2.0_DIDAP_20: generic MARTINI topology of DLinDMA/DLinDAP</li> <li>martini_v2.0_CHOL_02.itp, martini_v2.0_DSPC_01.itp, martini_v2.0_ions, martini_v2.1-dna_cr1_POL_NACL.itp: Several MARTINI topology files for molecules not included in MARTINI</li> <li>Nucleic_A.itp or Nucleic_A_eln.itp: Topology of the RNA fragment for MARTINI</li> </ul> </li> <li>aa: 300/600 nanosecond production run based on CHARMM36 <ul> <li>md_0.gro: first frame</li> <li>md_60.gro: final frame </li> <li>backmapped.top: Topology of the system (including the parametrization of DLinDMA/DLinDAP)</li> <li>CHOL.itp, DOPE.itp, DSPC.itp, 40mer_autopsf.itp: topology files for Cholesterol, DOPE, DSPC and RNA fragment as they are not part of the standard molecules in CHARMM36.</li> </ul> </li> <li>ELN and noELN indicate presence or absence of an elastic network to fix the structure of the RNA during the cg runs. </li> <li>cgmdp: Gromacs input files for the cg runs</li> <li>aamdp: Gromacs input files for the aa runs</li> </ul> </li> </ul>
Dataset for "Supramolecular design of CO2-responsive lipid nanomaterials"
<p>This dataset provides the raw data supporting the paper: Supramolecular design of CO<sub>2</sub>-responsive lipid nanomaterials. It comprises SAXS data (Figure 4 on the manuscript), cryo-TEM images (Figure 5), Zeta-potential measurment (Table 1) and DLS data (Table1 and Figure S8).</p>
Dataset of paper "New trends on photoelectrocatalysis (PEC): nanomaterials, wastewater treatment and hydrogen generation"
<p>Dataset of paper "New trends on photoelectrocatalysis (PEC): nanomaterials, wastewater treatment and hydrogen generation"</p> <ul> <li>Revision of nanomaterials investigated for different applications of PEC for water treatment and hydrogen generation with summary of information on their performance, key advantages of the material and the reference of publication.</li> </ul>
Quantitative three-dimensional local order analysis of nanomaterials through electron diffraction
<p>Diffraction data for Yttrium Stabilized Zirconia Zr<sub>0.82</sub>Y<sub>0.18</sub>O<sub>1.91</sub></p> <p><strong>Electron diffraction data:</strong></p> <ul> <li>FEI Tecnai G2 microscope</li> <li>200 keV electrons</li> <li>Cheetah detector (ASI, 512 x 512 pixel)</li> <li>Condenser Aperature 10 micrometer, spot size 9</li> <li>Exposure 1.512 s / frame, rotation 0.4 deg/frame</li> <li>raw data was processed with PETS2 (http://pets.fzu.cz/)</li> </ul> <p><strong>X-ray diffraction data:</strong></p> <ul> <li>Rigaku Syneergy S diffractometer</li> <li>Eiger 1M Detector, energy discriminating threshold 17.4 keV</li> <li>Mo K-alpha radiation</li> <li>120 s exposure time, 0.5 deg/frame</li> <li>Raw data can be processed with CrysAlisPro (https://www.rigaku.com/products/crystallography/crysalis)</li> </ul> <p><strong>Neutron diffraction data:</strong></p> <ul> <li>D19 ILL, Grenoble</li> <li>0.95 A wavelength</li> <li>80 s exposure time, 0.1 deg/frame</li> <li>Raw data can be accessed at <a href="http://dx.doi.org/10.5291/ILL-DATA.5-13-277">doi:10.5291/ILL-DATA.5-13-277</a> and processed with the Instrument software Int3D (https://www.ill.eu/users/instruments/instruments-list/d19/software)</li> </ul> <p><strong>Reciprocal space reconstructions and 3D-DeltaPDF maps</strong></p> <p>Provided in .h5 format and can be vizualized e.g. with DISCUS (https://github.com/tproffen/DiffuseCode/releases) or DensityViewer (https://github.com/aglie/DensityViewer/releases/)</p>
Assessing corrosion resistance of 2D nanomaterial-based coatings on stainless steel substrates
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Rapid synthesis and characterization of silver-loaded graphene oxide nanomaterials and their antibacterial applications
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Comparative study on photocatalytic activity of transition metals (Ag and Ni doped ZnO) nanomaterials synthesised via sol-gel method
Ag and Ni/ZnO photocatalyst nanostructures were successfully synthesized by a sol-gel method. In this work, the photocatalyst sample was systematically studied based on several factors affecting the performance of photocatalyst which are size, morphology, band gap, textural properties and the number of active sites presence on the surface of the nanocatalyst. X-ray Diffraction (XRD) revealed that Ag/ZnO nanomaterials experienced multiple phases, meanwhile for Ni/ZnO the phase of nanomaterials were pure and single phase for stochiometry less than 5%. Field emission scanning electron microscope (FESEM) showed almost all of the synthesised nanomaterials possessed a mixture of nanorods and spherical-like shape morphology. The Ag/ZnO showed high photocatalytic activity, producing at least 14th trials of nanocatalyst reusability on the degradation of Methyl Orange under UV irradiation. Interestingly this phenomenon was not observed in larger surface area of Ni/ZnO nanomaterials which supposedly favour photocatalytic activity, but instead producing poor photocatalytic performance. The main reason were studied and exposed by temperature-programmed desorption of carbon dioxide (TPD–CO2) which showed that incorporation of Ag into ZnO lattice has enhanced the number of active sites on the surface of the nanocatalyst. Whereas incorporation of Ni in ZnO has lowered the number of active sites with respect to undoped ZnO. Active sites measurement is effective and significant, providing opportunities in developing an intensive study as an additional factor.
Supplementary data and code for "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes"
<p>This repository contains the relevant data and code supporting the study "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes". Files are password protected during the revision process. A completely public version of the repository will be availble after the revision process is completed. </p> <p>In detail, the uploaded archive folder contains the following data sources:</p> <ul> <li>the relevant code and supporting data (code_to_upload and supporting_data);</li> <li>supplementary materials of the paper, including: <ul> <li>individual enrichment results of the 93 exposures to the 31 ENMs (enrichments_results);</li> <li>comparison between the mechanism of action retrieved from differentially expressed genes and network modelling (network_comparison_results);</li> <li>overrepresented network edges in categories of networks (overrepresented_structures)</li> </ul> </li> </ul>
Electronic structure properties of the SmartNanoTox data set (nanomaterials) for the use of meta models assesing cytotoxicity
<p>Important set of electronic structure properties data on the SmartNanoTox dataset consisting of large molecular systems representing coated materials. The data were used to study lung inflammation within a service offered through EU Horizon 2020 NanoCommons project.</p>
A Multi-Omics Approach Reveals Mechanisms of Nanomaterial Toxicity and Structure-Activity-Relationships in Alveolar Macrophages
<p>This deposit contains the proteomics and metabolomics data belonging to the publication with the title "A Multi-Omics Approach Reveals Mechanisms of Nanomaterial Toxicity and Structure-Activity-Relationships in Alveolar Macrophages".</p>
GIS data for the maps in publication Spatial perspectives enhance modeling of nanomaterial risks
<p>These files include the datasets utilized to perform geospatial modeling in the publication: Spatial perspectives enhance modeling of nanomaterial risks in the Journal of Industrial Ecology. </p> <p>The following data sources were used in this modeling effort:</p> <p><strong>National Hydrography Dataset (NHD): United States Geological Survey (USGS)</strong></p> <p>Upstate NY Lakes, ponds, streams, rivers, springs, and wells</p> <p><strong>Critical Environmental Areas in New York State: New York State Department of Environmental Conservation </strong></p> <p>Areas designated as critical under 6 NYCRR Part 617: “ecological, geological, or hydrological sensitivity that may be adversely affected by any change” (NY DEC)</p> <p><strong>National Land Cover Dataset (NLCD): United States Geological Survey (USGS)</strong></p> <p>National Land Cover Database classification schemes based primarily on Landsat data (2011)</p> <p><strong>Elevation Data: United States Geological Survey (USGS)</strong></p> <p>Digital Elevation Models (10-meter) for New York, elevation values were derived from USGS contour lines mapped at a scale of 1:24,000. </p> <p><strong>Interstate Highway: Federal Highway Administration’s National Transportation Atlas Database</strong></p> <p>Rural and urban highways for New York</p> <p> </p> <p><strong>Other references</strong></p> <p>Bureau, U.S. Census., American community survey 5-year estimates. 2017.</p> <p>EPA, Toxics Resource Inventory. 2019</p> <p> </p> <p>.</p>
Assessing the antimicrobial Capacity of metal oxide nanomaterials using ZOI and MIC measurements by employing Machine learning tools.
<p>Assessing the antimicrobial Capacity of metal and metal oxide nanomaterials (IONPs, AgNPs, ZnONPs) using ZOI and MIC measurements by employing Machine learning tools.</p>
Raw Data for manuscript published at Nanomaterials, entitled: Asymmetrical Plasmon Distribution in Hybrid AuAg Hollow/Solid Coded Nanotubes
<p>.dm3 TEM, STEM and EELS raw data</p>
Sublethal effects of photoactive engineered nanomaterials on filamentous bacteriophage infection and <em>E. coli</em> gene expression in freshwater
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Comparative study on photocatalytic activity of transition metals (Ag and Ni doped ZnO) nanomaterials synthesised via sol-gel method
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Fate and Toxicity of Engineered Nanomaterials in the Environment: a Meta-analysis
Engineered nanomaterials (ENMs, particles less than 100 nanometers) are being manufactured at increasing levels for a variety of reasons including cosmetics, food packaging and preservation, fertilizers, and medical technology. Thousands of metric tons of ENMs are released to soils, water bodies, air, and landfills each year. These particles have distinct properties owing to their small size and relatively large surface area to volume ratio. These characteristics can result in these materials having higher reactivity and toxicity in biological systems - especially because ENMs are small enough to enter cells. However, studies on environmental and ecological effects of ENMs have shown mixed results. The goal of this project is to address three primary research questions using meta-analysis of existing literature: 1) Which ENMs have been studied and in what context? 2) How do particle identity, size, concentration, and study duration influence toxicity (as estimated by response ratios, lethal concentration (LC50), and effective concentration (EC50)) in different organisms related to the role they play in the environment? 3) What are bioaccumulation, biomagnification, and bioconcentration factors of ENMs as a function of ecosystem role and trophic level in different organisms? We collected data from 191 published scientific papers and extracted 2102 unique observations that are used to address the questions outlined above. We calculated response ratios related to biological responses of biomass, diversity, growth, metabolism, and survival to evaluate how these are influenced by ENM exposure. Values of LC50 (concentration at which 50% of test organisms died) and EC50 (concentration at which 50% of test organisms showed an effect) were collected to estimate concentrations at which toxicity occurs. Bioaccumulation (BAF), bioconcentration (BCF), and biomagnification (BMF) factors were recorded to estimate environmental accumulation and trophic transfer. This work is a resu
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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.