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421 results for “Polymer”
Input files for the MD simulations and free energy calculations for the article "Water Dissolved in a Variety of Polymers Studied by Molecular Dynamics Simulation and a Theory of Solutions"
<p>Article:<em> </em><a href="https://pubs.acs.org/doi/10.1021/acs.jpcb.1c04818">J. Phys. Chem. B. 125, 9357–9371 (2021) [DOI: 10.1021/acs.jpcb.1c04818]</a></p> <p>The structures of the homopolymers and copolymers simulated are shown in Figures 1 and S1 and Tables 2 and 3. All-atom MD simulation was carried out using GROMACS, and this repository provides the input files with the GAFF/RESP force and initial coordinate files. The free energy of water dissolution was obtained with <a href="https://sourceforge.net/projects/ermod/">ERmod</a>, and the input files for the free-energy calculations are also contained. See the README files for details.</p>
Data points for "Modelling sorption of hydrocarbons in polyethylene with the SAFT-γ Mie approach combined with a statistical-mechanical model to describe semi-crystalline polymers"
<p>A variety of thermodynamic calculations (VLE, sorption isotherms, etc.) performed with a combination of the SAFT-γ equation of state and a novel model to account for the constraints affecting the amorphous domains in semi-crystalline polyethylene (PE). Please refer to the original article (published in Macromolecules) for the bibliography and more details.</p>
Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites: data set
<p>Abstract:<br> from [1]</p> <blockquote> <p>Polymer nanocomposites are an important class of materials for engineering applications due to their high versatility and good mechanical properties combined with low density. By directly attaching the polymer chains to the nanofillers, the so-called grafting, a better load transfer between matrix and filler is achieved, and, in addition, a better dispersion of the fillers is obtained. Both result in enhanced mechanical properties. Since experimental investigations on the nanoscale are extremely challenging, complementary numerical studies are needed to unravel the mechanical behavior of polymer nanocomposites. To this end, molecular dynamics is ideally suited since it captures the microstructure, but is also numerically expensive. Therefore, this contribution presents a fast coarse-grained molecular dynamics model for the investigation of the mechanical behavior of grafted polymer nanocomposites. For this purpose, we extend an existing model by grafting bonds, which allows us to compare the effect of untreated and grafted fillers directly. In particular, we investigate the influence of filler content, grafting degree, and filler size on the stiffness and strength of the polymer (grafted) nanocomposites. We conclude that the grafting bonds have little effect on the stiffness, while the strength is significantly improved compared to the untreated fillers, which is in agreement with the literature. The presented molecular dynamics model for polymer grafted nanocomposites provides the basis for further investigations, particularly of the crucial matrix-filler interphase. In addition, this contribution translates molecular dynamics insights into mechanical properties, which bridges the gap to the engineering scale and thus represents a step towards exploiting the full potential of polymer (grafted) nanocomposites.</p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong></p> <p>All MD simulations were performed with LAMMPS [2,3], version: 29 Oct 2020 / 20201029</p> <p>Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) 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, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, S. Reber, P. Steinmann, & S. Pfaller, “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p><strong>Content:</strong></p> <p>structure of data set:</p> <ul> <li>04_Equilibration<br> folders containing the sample equilibration used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> <li>05_UT<br> folders containing the uniaxial tension simulations used in the presented parameter study <ul> <li>01_filler_content<br> variation of filler content</li> <li>02_grafting_density<br> variation of grafting density</li> <li>03_grafting_potential<br> variation of grafting potential</li> <li>04_filler_size<br> variation of filler size</li> <li>05_reference<br> reference samples without grafting</li> </ul> </li> </ul> <p>Each simulation directory contains:</p> <ul> <li> <p>lammps input file (*.in) of the specific simulation</p> </li> <li> <p>data file (*.data) containing the initial sample configuration</p> </li> <li> <p>input.prm: input parameters of the specific simulation (read by the input file)</p> </li> <li> <p>meta.info: meta data of the specific simulation run</p> </li> <li> <p>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <ul> <li> <p>thermo_out.Dat: raw output </p> </li> <li> <p>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> </li> <li> <p>thermo_out_STD.Dat: standard deviation of raw output</p> </li> </ul> </li> </ul> <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> <ul> <li> <p>Step: time step </p> </li> <li> <p>Time: time </p> </li> <li> <p>TotEng: total energy </p> </li> <li> <p>PotEng: potential energy</p> </li> <li> <p>KinEng: kinetic energy </p> </li> <li> <p>E_pair: pair energy </p> </li> <li> <p>E_bond: bond energy </p> </li> <li> <p>E_angle: angle energy </p> </li> <li> <p>E_dihed: dihedral energy </p> </li> <li> <p>Temp: temperature</p> </li> <li> <p>Press: hydrostatic pressure</p> </li> <li> <p>Pxx: xx component of pressure tensor </p> </li> <li> <p>Pyy: yy component of pressure tensor </p> </li> <li> <p>Pzz: zz component of pressure tensor </p> </li> <li> <p>Pxy: xy component of pressure tensor</p> </li> <li> <p>Pxz: xz component of pressure tensor</p> </li> <li> <p>Pyz: yz component of pressure tensor</p> </li> <li> <p>Volume: volume of simulation box </p> </li> <li> <p>Lx: box length in x direction </p> </li> <li> <p>Ly: box length in y direction </p> </li> <li> <p>Lz: box length in z direction </p> </li> <li> <p>Density: density </p> </li> <li> <p>c_RG: radius of gyration scalar </p> </li> <li> <p>c_RG[1]: squared radius of gyration tensor (xx component) </p> </li> <li> <p>c_RG[2]: squared radius of gyration tensor (yy component) </p> </li> <li> <p>c_RG[3]: squared radius of gyration tensor (zz component) </p> </li> <li> <p>c_RG[4]: squared radius of gyration tensor (xy component) </p> </li> <li> <p>c_RG[5]: squared radius of gyration tensor (xz component) </p> </li> <li> <p>c_RG[6]: squared radius of gyration tensor (yz component) </p> </li> <li> <p>c_bondave[1]: bond energy averaged over all atoms </p> </li> <li> <p>c_bondave[2]: bond distance averaged over all atoms </p> </li> <li> <p>c_bondave[3]: squared bond distance averaged over all atoms </p> </li> <li> <p>c_angleave[1]: angle energy averaged over all atoms </p> </li> <li> <p>c_angleave[2]: angle averaged over all atoms degree</p> </li> <li> <p>c_angleave[3]: cosine of angle </p> </li> <li> <p>c_angleave[4]: squared cosine of angle </p> </li> <li> <p>c_MSD[1]: mean squared displacement x-direction </p> </li> <li> <p>c_MSD[2]: mean squared displacement y-direction </p> </li> <li> <p>c_MSD[3]: mean squared displacement z-direction </p> </li> <li> <p>c_MSD[4]: total mean squared displacement </p> </li> <li> <p>c_COM[1]: x coordinate of center of mass </p> </li> <li> <p>c_COM[2]: y coordinate of center of mass </p> </li> <li> <p>c_COM[3]: z coordinate of center of mass </p> </li> <li> <p>v_strain_xx: xx component of engineering strain tensor </p> </li> <li> <p>v_strain_yy: yy component of engineering strain tensor </p> </li> <li> <p>v_strain_zz: zz component of engineering strain tensor </p> </li> <li> <p>v_vMisesequivstress: von Mises equivalent stress </p> </li> <li> <p>v_Cauchy_xx: xx component of stress tensor </p> </li> <li> <p>v_Cauchy_yy: yy component of stress tensor</p> </li> <li> <p>v_Cauchy_zz: zz component of stress tensor</p> </li> <li> <p>v_Cauchy_xy: xy component of stress tensor </p> </li> <li> <p>v_Cauchy_xz: xz component of stress tensor </p> </li> <li> <p>v_Cauchy_yz: yz component of stress tensor </p> </li> <li> <p>v_strain_xy: xy component of engineering strain tensor </p> </li> <li> <p>v_strain_xz: xz component of engineering strain tensor </p> </li> <li> <p>v_strain_yz: yz component of engineering strain tensor </p> </li> </ul> <p><strong>References</strong>:</p> <p>[1] M. Ries et al., “Extending a generic and fast coarse-grained molecular dynamics model to examine the mechanical behavior of grafted polymer nanocomposites,” <em>Forces in Mechanics</em>, vol. 12, p. 100 207, <strong>2023</strong>.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” <em>Journal of computational physics</em>, <strong>1995</strong>, 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,” <em>Computer Physics Communications</em>, vol. 271, p. 108171, <strong>2022</strong>.</p> <p>[4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, <em>Zenodo</em>, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a></p>
Data from: Reciprocating thermochemical mediator of pre-biotic polymer decomposition on mineral surfaces
Open the record for dataset details and reuse information.
Evaluation of the impact of imprinted polymer particles on morphology and motility of breast cancer cells by using digital holographic cytometry
<p>Supplemented Videos used in "Evaluation of the impact of imprinted polymer particles on morphology and motility of breast cancer cells by using digital holographic cytometry"</p>
Data supplement for "Gradient dynamics model for drops spreading on polymer brushes"
<p>This dataset contains the data and source files for the diagrams of the following publication:</p> <p><em>Thiele, U. & Hartmann, S.<br> Gradient dynamics model for drops spreading on polymer brushes<br> arXiv preprint arXiv:1910.10582, 2019 </em></p> <p>We provide the data and sources necessary to generate the figures 3 & 4 of the manuscript.</p> <p>For more information, please see the included README.md</p>
Data from: Spirobifluorene-based polymers of intrinsic microporosity for the adsorption of methylene blue from wastewater: effect of surfactants
<p>Owing to their high surface area and superior adsorption properties, spirobifluorene PIMs namely, PIM-SBF-Me (methyl) and PIM-SBF-tBu (<i>tert</i>-butyl) were used for the first time for the removal of methylene blue (MB) dye from wastewater. Spirobifluorene PIMs are known to have large surface area (can be up to 1100 m2/g) and have been previously used mainly for gas storage applications. Dispersion of the polymers in aqueous solution was challenging due to their extreme hydrophobic nature leading to poor adsorption efficiency of MB. For this reason, cationic (CPC), anionic (SDS) and nonionic (Brij-35) surfactants were utilized and tested with the aim of enhancing the dispersion of the hydrophobic polymers in water and hence improving the adsorption efficiencies of the polymers. The effect of surfactant type and concentration was investigated. All surfactants offered a homogenous dispersion of the polymers in the aqueous dye solution, however, the highest adsorption efficiency was obtained using an anionic surfactant (SDS) and this seems due to the predominance of electrostatic interaction between its molecules and the positively charges dye molecules. Furthermore, the effect of polymer dosage and initial dye concentration on MB adsorption were also considered. The kinetic data for both polymers were well described by pseudo-second-order model, while Langumir model better simulated the adsorption process of MB dye on PIM-SBF-Me and Freundlich model was more suitable for PIM-SBF-tBu. Moreover, the maximum adsorption capacities recorded were 84.0 and 101.0 mg/g for PIM-SBF-Me and PIM-SBF-tBu, respectively. Reusability of both polymers was tested by performing three adsorption cycles and the results substantiate that both polymers can be effectively reused with insignificant loss of their adsorption efficiency (%AE). These preliminary results suggested that incorporation of a surfactant to enhance the dispersion of hydrophobic polymers and adsorption of organic contaminants from wastewater is a simple and cost-effective approach that can be adapted for many other environmental applications.</p>
Three‐dimensional reconstruction of porous polymer films from FIB‐SEM nanotomography data using random forests
<p>Dataset and code used in M. Röding, et al, "Three-dimensional reconstruction of porous polymer films from FIB-SEM nanotomography data using random forests", published in Journal of Microscopy, 2020. In this work, we develop a segmentation method for focused ion beam scanning electron microscopy (FIB-SEM) data acquired by volumetric imaging of porous polymer films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. This type of polymer films are used for controlled release applications. Based on manual segmentation of a fraction of the data, a random forest classifier is trained and applied to the full data set. Here, raw data, manual segmentations, and the Matlab code used for all steps in the analysis are supplied.</p>
Polymer-assisted modification of metal-organic framework MIL-96 (Al): influence on particle size, crystal morphology and perfluorooctanoic acid (PFOA) removal
<p>Dataset supporting publication.</p> <p><strong>Polymer-assisted modification of metal-organic framework MIL-96 (Al): influence of HPAM concentration on particle size, crystal morphology and removal of harmful environmental pollutant PFOA</strong></p> <p>Chemosphere, <a href="https://doi.org/10.1016/j.chemosphere.2020.128072">https://doi.org/10.1016/j.chemosphere.2020.128072</a></p> <p>Preprint available from ChemRxiv, <a href="https://doi.org/10.26434/chemrxiv.12262010.v2">https://doi.org/10.26434/chemrxiv.12262010.v2</a></p> <p><strong>Abstract</strong></p> <p>A new synthesis method was developed to prepare an aluminum-based metal organic framework (MIL-96) with a larger particle size and different crystal habits. A low cost and water-soluble polymer, hydrolyzed polyacrylamide (HPAM), was added in varying quantities into the synthesis reaction to achieve >200% particle size enlargement with controlled crystal morphology. The modified adsorbent, MIL-96-RHPAM2, was systematically characterized by SEM, XRD, FTIR, BET and TGA-MS. Using activated carbon (AC) as a reference adsorbent, the effectiveness of MIL-96-RHPAM2 for perfluorooctanoic acid (PFOA) removal from water was examined. The study confirms stable morphology of hydrated MIL-96-RHPAM2 particles as well as a superior PFOA adsorption capacity (340 mg/g) despite its lower surface area, relative to standard MIL-96. MIL-96-RHPAM2 suffers from slow adsorption kinetics as the modification significantly blocks pore access. The strong adsorption of PFOA by MIL-96-RHPAM2 was associated with the formation of electrostatic bonds between the anionic carboxylate of PFOA and the amine functionality present in the HPAM backbone. Thus, the strongly held PFOA molecules in the pores of MIL-96-RHPAM2 were not easily desorbed even after eluted with a high ionic strength solvent (500 mM NaCl). Nevertheless, this simple HPAM addition strategy can still chart promising pathways to impart judicious control over adsorbent particle size and crystal shapes while the introduction of amine functionality onto the surface chemistry is simultaneously useful for enhanced PFOA removal from contaminated aqueous systems.</p>
Reversible H2 Oxidation and Evolution by Hydrogenase Embedded in a Redox Polymer Film - Original Data
<p>Original data files of the Nature catalysis manuscript submission NATCATAL-20033655A. The data is named according to the corresponding figure captions of teh manuscript main text and the supporting information. A text document lists the figure titles that corresponds to the figure numbers. Fig1, Scheme S1, Scheme S2, FigS 51 are general schemes without any underlying data and are, therefore, not listed here.</p>
pH-Responsive, Lysine-Based, Hyperbranched Polymers Mimicking Endosomolytic Cell-Penetrating Peptides for Efficient Intracellular Delivery-DATA
<p>Original data and supporting data for the paper entitled "pH-Responsive, Lysine-Based, Hyperbranched Polymers Mimicking Endosomolytic Cell-Penetrating Peptides for Efficient Intracellular Delivery".</p>
Research data supporting: "Non-trivial stimuli-responsive collective behaviours emerging from microscopic dynamic complexity in supramolecular polymer systems"
<p>Contains the relevant simulation data and input files. See "readme.txt" for information.</p>
Files for MD simulation of the interaction between LCC-ICCG cutinase and PET polymer
<p>500 ns MD simulation of Cutinase adsorption onto the PET surface and cutinase in water.</p> <p>MD simulations were done using Gromacs package</p> <p>Force field:Charmm36</p>
NFFA-Europe|Pilot proporsal "Production and characterization of highly controlled silicon oxide nanoparticles for solid polymer electrolytes" (PID: 444).
<p>XPS, IR, and QMS data of the nanoparticles synthesized within the NFFA-Europe|Pilot proporsal PID 444</p>
Dynamics of leaching of POPs and additives from plastic in a Procellariiform gastric model: Diet and polymer dependent effects and implications for long-term exposure
<p>Procellariiform seabirds are known to have high rates of plastic ingestion. We investigated the bioaccessibility of plastic-associated chemicals [plastic additives and sorbed persistent organic pollutants (POPs)] leached from plastic over time using an in vitro Procellariiform gastric model. High-density polyethylene (HDPE) and polyvinyl chloride (PVC), commonly ingested by Procellariiform seabirds, were manufactured with one additive [decabrominated diphenyl ether (PBDE-209) or bisphenol S (BPS)]. HDPE and PVC added with PBDE-209 were additionally incubated in salt water with 2,4,4'-trichloro-1,1'-biphenyl (PCB-28) and 2,2',3,4,4',5'-hexachlorobiphenyl (PCB-138) to simulate sorption of POPs on plastic in the marine environment. Our results indicate that the type of plastic (nature of polymer and additive), presence of food (i.e., lipids and proteins) and gastric secretions (i.e., pepsin) influence the leaching of chemicals in a seabird. In addition, 100% of the sorbed POPs were leached from the plastic within 100 hours, while only 2-5% of the additives were leached from the matrix within 100 hours, suggesting that the remaining 95% of the additives could continue to be leached. Overall, our study illustrates how plastic type, diet and plastic retention time can influence a Procellariform's exposure risk to plastic-associated chemicals.</p>
Data supplement for "Drops on polymer brushes – advances in thin-film modelling of adaptive substrates"
<p>This dataset contains supplementary data for the following publication:</p> <p>Hartmann, S., Diekmann, J., Greve, D., and & Thiele, U.<br>Drops on polymer brushes – advances in thin-film modelling of adaptive substrates<br><span><em>Langmuir</em></span> <span>2024</span><span>, 40</span><span>, 8</span><span>, 4001–4021</span></p> <p>We provide the source files and data for figures 3-15.</p>
Tribological tests - block-on-ring - polymers vs. AISI 4130 - IL+CNTs, IL+Cu_CNTs
<p>Polymers: UHMWPE (Tivar 1000), POM-C (Ertacetal-C), PA (Nylon-6);</p> <p>Ionic liquids: 1-Ethyl-3-methylimidazolium dicyanamide; 1-Butyl-3-methylimidazolium bis (trifluoromethylsulfonyl)imide; Trihexyltetradecylphosphonium bis(2-ethylhexyl) phosphate;</p> <p>Test conditions: 1kN (load); 30 min (time); 200 rpm; 0.1 mL (lubricants vol.);</p>
Thermal conductivity analysis of polymer-derived nano-composite via image-base structure reconstruction, computational homogenization and machine learning
<p>This dataset includes supplementary data and utilities for validating simulation results and training machine learning models as outlined in the publication titled "Thermal Conductivity Analysis of Polymer-Derived Nanocomposite via Image-Based Structure Reconstruction, Computational Homogenization, and Machine Learning" (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>).</p> <p>This dataset containes the microstructure images (identified by particle diameters size \(D_1\) and \(D_2\) volume fraction \(V_\mathrm{f}\) and aspect ratio \(A_\mathrm{r}\)) (see Table 1) and their corresponding homogenized thermal conductivity. these images resemble the microstructure of the monolithic \(\mathrm{(Hf,Ta)C/SiC}\) ceramic following FAST sintering, the material system of this work (<a href="https://doi.org/10.1002/adem.202302021">Fathidoost, 2024</a>). White and black colors within the images represent distinct regions of the material system, respectively referring to former powder particles (FPPs) and sinter necks (SNs), which is explained in this work.</p> <p>Table 1. Parameterized descriptors extracted from the mesoscale SEM image analysis</p> <table> <tbody> <tr> <td>Param.</td> <td>Mean [unit]</td> <td>Std.</td> </tr> <tr> <td>\(D_{1}\)</td> <td>40, 50, 60 [μm]</td> <td>20%</td> </tr> <tr> <td>\(D_{2}\)</td> <td>20, 25, 26, 30, 33, 40 [μm]</td> <td>30%</td> </tr> <tr> <td>\(V_\mathrm{f}\)</td> <td>1.5, 2.0</td> <td>-</td> </tr> <tr> <td>\(A_\mathrm{r}\)</td> <td>35, 40, 45, 55, 60 [%]</td> <td>-</td> </tr> </tbody> </table> <p>This dataset contains:</p> <ul> <li><em>dataset.csv: </em>containing a summary of data including the names of microstructure images, their corresponding geometric details, as well as the first and third principal components of two-point statistics for all images, along with the effective thermal conductivity of the corresponding microstructures. Further details can be found in the associated publication.</li> <li><em>microstructures_images.zip</em>: containing binary cross-section images of the RVEs from synthetic microstructures。</li> <li><em>results.zip:</em> contains all the simulation results based on digitized diffuse-interface microstructures, which can be opened by the post-processing software, such as ParaView.</li> </ul>
The effects of microplastics on crop variation depend on polymer types and their interactions with soil nutrient availability and weed competition
<p>Microplastics pollution of agricultural soil is a global environmental concern because of its potential risk to food security and human health. Although many studies have tested the direct effects of microplastics on growth of <em>Eruca sativa</em> Mill., little is known about whether these effects are regulated by fertilization and weed competition in field management practices.</p> <p>Here, we performed a greenhouse experiment growing <em>E. sativa</em> as target species in a three-factorial design with two levels of fertilization (low versus. high), two levels of weed competition treatments (weed competition versus no weed competition) and five levels of microplastic treatments (no microplastics, Polybutylene adipateco-terephthalate [PBAT], Polybutylene succinate [PBS], Polycaprolactone [PCL] or Polypropylene [PP]).</p> <p>Compared to the soil without microplastics, PBS and PCL reduced aboveground biomass and leaf number of the <em>E. sativa</em>. PBS also resulted in increased root allocation and thicker roots in <em>E. sativa</em>. In addition, fertilization significantly mitigated the negative effects of PBS and PCL on aboveground biomass of <em>E. sativa</em>, but weed competition significantly promoted these effects. Although fertilization alleviated the negative effect of PBS on aboveground biomass, such alleviation became weaker under weed competition than when <em>E. sativa</em> grew alone.</p> <p>The results indicate that the effects of specific polymer types on <em>E. sativa</em> growth could be regulated by fertilization, weed management, and even their interactions. Therefore, reasonable on-farm management practices may help in mitigating the negative effects of microplastics pollution on <em>E. sativa</em> growth in agricultural fields.</p>
Wear topography measurements: selected polymers after friction tests with steel – lubrication with ionic liquids containing CNTs
<p>Measurement files of the topography of worn surfaces of polymers cooperating with steel and lubricated with hybrids of ionic liquids and carbon nanotubes. The files correspond to the tests presented in:</p> <p><span>Tribological tests - block-on-ring - polymers vs. AISI 4130 - IL+CNTs, IL+Cu_CNTs https://doi.org/10.5281/zenodo.10817199. </span></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.