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57 results for “Nanocomposites”
Data from: Photothermal-assisted antibacterial application of GO-Ag nanocomposites against clinical isolated MDR E. coli
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Versatile Macroscale Concentration Gradients of Nanoparticles in Soft Nanocomposites
<p>Nanocomposite materials benefit from the diverse physicochemical properties featured by nanoparticles, and the presence of nanoparticle concentration gradients can lend functions to macroscopic materials beyond the realm of classical nanocomposites. It is shown here that linearity and time‐shift invariance obtained via the synergism of two independent physical phenomena—translational self‐diffusion and shear‐driven dispersion—may give access to an exceptionally high degree of flexibility in the design of scalable and programmable long‐range concentration gradients of nanoparticles in solidifiable liquid matrices.</p>
Enhancement of microwave absorption bandwidth of MXene nanocomposites through macroscopic design
<p><span>MXene, the new family of 2D materials having numerous nanoscale layers, is being considered as a novel microwave absorption material. However, MXene/functionalized MXene loaded polymer nanocomposites exhibit narrow reflection loss (RL) bandwidth (RL ≤ -10 dB). In order to enhance the microwave absorption bandwidth of <span>MXene hybrid-matrix materials</span>, for the first time, macroscopic design approach is carried out for TiO<sub>2</sub>-Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene and Fe<sub>3</sub>O<sub>4</sub>@TiO<sub>2</sub>-Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene hybrids through simulation. The simulated results indicate that use of pyramidal meta structure of MXene can significantly tune the RL bandwidth. For optimized <span>MXene hybrid-matrix materials</span> pyramid pattern, the bandwidth enhances to 3-18 GHz. Experimental RL value well matched with the simulated RL. On the other hand, for optimized Fe<sub>3</sub>O<sub>4</sub>@TiO<sub>2</sub>-Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> hybrid exhibits two specific absorption bandwidths viz. 3-18 GHz (minimum RL value - 47 dB). Compared to other 2D nanocomposites such as graphene or Fe<sub>3</sub>O<sub>4</sub>-graphene, <span>MXene hybrid-matrix materials</span> shows better microwave absorption bandwidth in macroscopic pattern. </span></p>
Data from: Insight into the aqueous Laponite nanodispersions for self-assembled poly(itaconic acid) nanocomposite hydrogels: The effect of multivalent phosphate dispersants
<p>The upload contains data associated with the publication, including raw data in the original file format whenever possible. Dataset content: SAXS, NMR reology, zeta potential.</p> <p>This work was financially supported by the Lead Agency bilateral a Czech-Polish project provided by the Czech Science Foundation (21-07004K) and National Science Center Poland (CEUS-UNISONO project grant no. 2020/02/Y/ST5/00021).</p>
Data for Rapid microwave-assisted hydrothermal in situ synthesis of nano-ZnS/kaolinite nanocomposite: a non-toxic photocatalyst active under UV and sunlight
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Data from: Effects of poly(vinyl alcohol) blending with Ag/alginate solutions to form nanocomposite fibers for potential use as antibacterial wound dressings
<p>The first set of data comprise FTIR spectra of Ca-alginate, Ag/Ca-alginate, pure PVA, PVA/Ca-alginate, and PVA/Ag/Ca-alginate fibers. The fibers were obtained by extrusion and gelation of the aqueous solutions with following compositions: for Ag/Ca-alginate fibers - 1.27 ± 0.08 w/v Na-alginate and AgNPs at 2.6 mM, for Ca-alginate fibers - 1.27 ± 0.08 w/v Na-alginate, for PVA fibers - 5.7 % w/v PVA, for PVA/Ca-alginate fibers - 5.7 % w/v PVA and 1.27 ± 0.08 w/v Na-alginate, and for PVA/Ag/Ca-alginate fibers - 5.7 % w/v PVA, 1.27 ± 0.08 w/v Na-alginate and AgNPs at 2.6 mM.</p> <p>The second set of data are UV-Visible absorption spectra of the initial PVA/Ag/Na-alginate colloid solution (5.7 % w/v PVA, 1.27 ± 0.08 w/v Na-alginate, 2.6 mM nominal silver concentration) and resulting fibers produced after gelling of alginate after dissolution in 2.28 % w/v sodium citrate solution.</p> <p>The third set of data are force and stroke values over time measured by using a Universal Testing Machine for Ca-alginate fibers obtained from the 1.27 ± 0.08 % w/v Na-alginate solution and PVA/Ca-alginate fibers, obtained from the solution containing 5.67 % w/v PVA and 1.27 ± 0.08 % w/v Na-alginate at the initial time, and after drying and rehydration for 24 h and 48 h in physiological saline solution. </p>
Data set for publication "Effect of milling atmosphere on stability and surface properties of ZnO/vermiculite hybrid nanocomposite powders"
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Lysozyme-sensitive plasmonic hydrogel nanocomposite for colorimetric dry-eye inflammation biosensing_[Biosensing]
<p>Biosensing of a lysozyme-sensitive plasmonic hydrogel</p>
Lysozyme-sensitive plasmonic hydrogel nanocomposite for colorimetric dry-eye inflammation biosensing_[Photoresponsivity]
<p>Photoresponsivity of a lysozyme-sensitive plasmonic hydrogel</p>
Lysozyme-sensitive plasmonic hydrogel nanocomposite for colorimetric dry-eye inflammation biosensing_[Colorimetric Tests]
<p>Colorimetric tests for a lysozyme-sensitive plasmonic hydrogel.</p>
Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study - dataset
<p>Abstract:<br>from [1]</p> <p>Polymer nanocomposites (PNCs) have shown great potential to meet the ever-growing requirements of modern engineering applications. Nowadays, molecular dynamics (MD) simulations are increasingly employed to complement experimental work and thereby gain a deeper understanding of the complex structure–property relations of PNCs. However, with respect to the thermoplastic’s mechanical behavior, the role of its average molar mass is rarely addressed, and many MD studies only consider uniform (monodispersed) polymers. Therefore, this contribution investigates the impact that and the dispersity Đ have on the stiffness and strength of PNCs through coarse-grained MD. To this end, we employed a Kremer–Grest bead–spring model and observed the expected increase in the mechanical performance of the neat polymer for larger . Our results indicated that the unimodal molar mass distribution does not impact the mechanical behavior in the investigated dispersity range Đ. For the PNC, we obtained the same -dependence and Đ-independence of the mechanical properties over a wide range of filler sizes and contents. This contribution proves that even simple MD models can reproduce the experimentally well researched effect of the molar mass. Hence, this work is an important step in understanding the complex structure–property relations of PNCs, which is essential to unlock their full potential.</p> <p>Contact:</p> <p>Maximilian Ries<br>Institute of Applied Mechanics<br>Friedrich-Alexander-Universität Erlangen-Nürnberg<br>Egerlandstr. 5<br>91058 Erlangen</p> <p>Software:</p> <p>All MD simulations were performed with LAMMPS [2,3], version: 23 Oct 2022 / 20220623</p> <p>Compiled with<br>Compiler: GNU C++ 11.2.0 with OpenMP not enabled<br>C++ standard: C++11</p> <p>Active compile time flags:<br>-DLAMMPS_GZIP<br>-DLAMMPS_SMALLBIG</p> <p>Installed packages:<br>CLASS2 DPD-BASIC EXTRA-DUMP INTEL KSPACE MANYBODY MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT PERI</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p>License:</p> <p>Creative Commons Attribution 4.0 International</p> <p>Context:</p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>Content:</p> <p>structure of data set:</p> <p> -01_neat <br> containing the neat polymer simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -02_distributed<br> containing samples with distributed chain lengths<br> -100-dist<br> samples with mean molar mass 100<br> -200-dist<br> samples with mean molar mass 200<br> -02_PNC<br> containing the polymer nanocomposite simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> -02_distributed<br> containing samples with distributed chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> </p> <p>naming convention for simulation folders</p> <p> - neat polymer simulations<br> example: GTP_UT_num_chains-80_num_beads_per_chain-500-8<br> * num_chains: number of polymer chains<br> * num_beads_per_chain: molar mass (chain length)<br> * distribution: standard deviation of gauss distribution govering dispersity<br> * "trailing number": batch number of sample<br> <br> - polymer nanocomposite simulations<br> example: GTP_rF-5_nF-10_chainlen-5_7-T_0.2<br> * rF: nanofiller radius<br> * nF: number of nanofillers<br> * chainlen: molar mass (chain length)</p> <p> </p> <p>Each simulation directory contains:</p> <p> lammps input file (*.in) of the specific simulation</p> <p> data file (*.data) containing the initial sample configuration</p> <p> input.prm: input parameters of the specific simulation (read by the input file)</p> <p> meta.info: meta data of the specific simulation run</p> <p> LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <p> thermo_out.Dat: raw output </p> <p> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> <p> thermo_out_STD.Dat: standard deviation of raw output</p> <p>Output quantities (columns of *.Dat files):<br>Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <p> Step: time step </p> <p> Time: time </p> <p> TotEng: total energy </p> <p> PotEng: potential energy</p> <p> KinEng: kinetic energy </p> <p> E_pair: pair energy </p> <p> E_bond: bond energy </p> <p> E_angle: angle energy </p> <p> E_dihed: dihedral energy </p> <p> Temp: temperature</p> <p> Press: hydrostatic pressure</p> <p> Pxx: xx component of pressure tensor </p> <p> Pyy: yy component of pressure tensor </p> <p> Pzz: zz component of pressure tensor </p> <p> Pxy: xy component of pressure tensor</p> <p> Pxz: xz component of pressure tensor</p> <p> Pyz: yz component of pressure tensor</p> <p> Volume: volume of simulation box </p> <p> Lx: box length in x direction </p> <p> Ly: box length in y direction </p> <p> Lz: box length in z direction </p> <p> Density: density </p> <p> c_RG: radius of gyration scalar </p> <p> c_RG[1]: squared radius of gyration tensor (xx component) </p> <p> c_RG[2]: squared radius of gyration tensor (yy component) </p> <p> c_RG[3]: squared radius of gyration tensor (zz component) </p> <p> c_RG[4]: squared radius of gyration tensor (xy component) </p> <p> c_RG[5]: squared radius of gyration tensor (xz component) </p> <p> c_RG[6]: squared radius of gyration tensor (yz component) </p> <p> c_bondave[1]: bond energy averaged over all atoms </p> <p> c_bondave[2]: bond distance averaged over all atoms </p> <p> c_bondave[3]: squared bond distance averaged over all atoms </p> <p> c_angleave[1]: angle energy averaged over all atoms </p> <p> c_angleave[2]: angle averaged over all atoms degree</p> <p> c_angleave[3]: cosine of angle </p> <p> c_angleave[4]: squared cosine of angle </p> <p> c_MSD[1]: mean squared displacement x-direction </p> <p> c_MSD[2]: mean squared displacement y-direction </p> <p> c_MSD[3]: mean squared displacement z-direction </p> <p> c_MSD[4]: total mean squared displacement </p> <p> c_COM[1]: x coordinate of center of mass </p> <p> c_COM[2]: y coordinate of center of mass </p> <p> c_COM[3]: z coordinate of center of mass </p> <p> v_strain_xx: xx component of engineering strain tensor </p> <p> v_strain_yy: yy component of engineering strain tensor </p> <p> v_strain_zz: zz component of engineering strain tensor </p> <p> v_vMisesequivstress: von Mises equivalent stress </p> <p> v_Cauchy_xx: xx component of stress tensor </p> <p> v_Cauchy_yy: yy component of stress tensor</p> <p> v_Cauchy_zz: zz component of stress tensor</p> <p> v_Cauchy_xy: xy component of stress tensor </p> <p> v_Cauchy_xz: xz component of stress tensor </p> <p> v_Cauchy_yz: yz component of stress tensor </p> <p> v_strain_xy: xy component of engineering strain tensor </p> <p> v_strain_xz: xz component of engineering strain tensor </p> <p> v_strain_yz: yz component of engineering strain tensor </p> <p>References:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Computer Physics Communications, vol. 271, p. 108171, 2022.</p> <p>[4] J. Roksvaag, M.Ries . “A fast self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, manuscript in preparation</p>
Applications of clays on nanocomposites and ceramics - DATASET
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Movies of Crystallization of Nanocomposites
<p>Movie 1:</p> <p>Pure hexacontane (C<sub>60</sub>H<sub>122</sub>) is being crystallized at T=325K and P=1 atm, during ~56 ns of crystallization time. The movie is made from 600 individual frames, and the point of view is changed by 2 degrees at every frame, allowing for viewing the dynamic process of crystallisation from different angles. Individual molecules are shown with different colours for clarity to observe their conformation and formation of crystal lamella. The simulation includes 61400 united atoms.</p> <p>Movie 2:</p> <p>Hexacontane (C<sub>60</sub>H<sub>122</sub>) gold nanocomposite with a 4.5 nm cubic gold nanoparticle at the centre is crystallized at T=325K and under constant pressure (P=1 atm) for 56 ns. The volume fraction, in this case, is 3.2 %. The simulation includes 87480 hexacontane united atoms and 4794 gold atoms. Individual molecules are shown with different colours for clarity to observe their conformation and formation of crystal lamella. The movie is made from 600 individual frames, and the point of view is changed by 2 degrees at every frame, allowing for viewing the dynamic process of crystallisation from different angles.</p> <p>Movie 3:</p> <p>Slices of contours of the final degree of crystallinity (g<sub>2</sub>) after 56 ns of crystallization of pure hexacontane (C<sub>60</sub>H<sub>122</sub>). The results are shown at ~0.68 nm intervals in the XZ plane. The red regions have >90% crystallinity, whereas blue regions are amorphous.</p> <p> </p> <p>Movie 4:</p> <p>Slices of contours of the final degree of crystallinity (g<sub>2</sub>) after 56 ns of crystallization of composite gold hexacontane (C<sub>60</sub>H<sub>122</sub>). The cubic gold particle at the centre is 4.5 nm, and the volume fraction is 2.31%. The slices are shown at ~0.55 nm intervals in the XZ plane. The red regions have >90% crystallinity, whereas blue regions are amorphous.</p> <p> </p> <p> </p>
Nanocomposite formulation for a sustained release of free drug and drug-loaded responsive nanoparticles: an approach for a local therapy of glioblastoma multiforme
<p>Malignant gliomas are a type of primary brain tumour that originates in glial cells. Among them, glioblastoma multiforme (GBM) is the most common and the most aggressive brain tumour in adults, classified as grade IV by the World Health Organization. The standard care for GBM, known as the Stupp protocol includes surgical resection followed by oral chemotherapy with temozolomide (TMZ). This treatment option provides a median survival prognosis of only 16–18 months to patients mainly due to tumour recurrence. Therefore, enhanced treatment options are urgently needed for this disease. Here we show the development, characterization, and in vitro and in vivo evaluation of a new composite material for local therapy of GBM post-surgery. We developed responsive nanoparticles that were loaded with paclitaxel (PTX), and that showed penetration in 3D spheroids and cell internalization. These nanoparticles were found to be cytotoxic in 2D (U-87 cells) and 3D (U-87 spheroids) models of GBM. The incorporation of these nanoparticles into a hydrogel facilitates their sustained release in time. Moreover, the formulation of this hydrogel containing PTX-loaded responsive nanoparticles and free TMZ was able to delay tumour recurrence in vivo after resection surgery. Therefore, our formulation represents a promising approach to develop combined local therapies against GBM using injectable hydrogels containing nanoparticles.</p>
Figs. 1―4 in Effects of AFM tip wear on frictional images of laser-patterned diamond-like nanocomposite films
Figs. 1―4. Arixyleborus nudulus, holotype, female. 1) Dorsal habitus; 2) Lateral habitus; 3) Head; 4) Elytral declivity.
Supplementary Information and Raw Data for 'Low-dose 4D-STEM Tomography for Beam-Sensitive Nanocomposites'
<p>Supplementary information containing TEM data and analysis for the article </p> <p>"<strong>Low-dose 4D-STEM Tomography for Beam-Sensitive Nanocomposites</strong>"</p> <p>Link to paper: <a title="DOI URL" href="https://doi.org/10.1021/acsmaterialslett.3c01042">https://doi.org/10.1021/acsmaterialslett.3c01042</a></p> <p>The archive provides a documentation of the evaluation routine as a .pdf file and two scripts that are needed for evaluations. In addition, 4 folders are present after unzipping the archive, which contain</p> <ul> <li>the raw 4D-STEM datasets,</li> <li>vSTEM images,</li> <li>an example of the denoised vSTEM images,</li> <li>the denoised and aligned image stack, and the reconstructed volumes.</li> </ul> <p>If there are any questions/bugs, feel free to contact Milena Hugenschmidt (https://orcid.org/0000-0001-5020-9302).</p>
Data from: Effects of poly(vinyl alcohol) blending with Ag/alginate solutions to form nanocomposite fibers for potential use as antibacterial wound dressings
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Launaea cornuta (wild lettuce) leaf extract: Phytochemical analysis and synthesis of silver-zinc oxide nanocomposite
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Enhancement of microwave absorption bandwidth of MXene nanocomposites through macroscopic design
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Migration Values measured from LLDPE-based nanocomposite materials in contact with different food simulating liquids
<p>Experimental values of overall migration (mg/dm<sup>2</sup>), migration of Si, Al and Mg (constitutive element of Montmorillonite) (mg/kg of food) and Normalized migration (%) of the selected additives measured in food simulating liquids after 10 days of contact (at 40°C) according standard testing condition recommended by the EU/10/2011 regulation with LLDPE-Based nanocomposite packaging including 5% w/w of organo-modified Montmorillonite (Cloisite 20).</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.