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421 results for “Polymer”

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zenodo40/100

Dataset for "Metallosupramolecular polymers as precursors for platinum nanocomposites"

Source data of the study reported in the publication entitled "Metallosupramolecular polymers as precursors for platinum nanocomposites". The data should be considered together with the published manuscript and the supplementary information file.

opencc-by-4.0Mar 2022View details →

zenodo40/100

Supplementary material for 'In-situ full-field measurements for 3D printed polymers during mode I interface failure'

Additional raw data and correlation analysis output for 'In-situ full- field measurements for 3D printed polymers during mode I interface failure'. We provide the patterned images acquired by the stereo microscopic Correlated Solution system (tiff format) and the VIC3D analysis results (csv format) for one representative specimen with 0°- 0° stacking undergoing mode I interlayer failure.

opencc-by-4.0Dec 2021View details →

zenodo40/100

Compound Data for Robust Processes for Polymer Modification and Pharmaceutical Synthesis

Compound structural (IUPAC name, InChI, InChI Key, SMILES, .mol, .sdf) and spectral (NMR, MS) data included for compounds reported in the associated doctoral thesis. NMR data collected on Bruker Avance 400, 500, or 600 MHz spectrometers. Compound structure data were generated by ChemDraw v.20 (PerkinElmer). More details about the preparation and characterization of these compounds can be found in the associated thesis.

opencc-by-4.0May 2022View details →

zenodo40/100

FORECASTING MOLECULAR DYNAMICS SIMULATIONS OF POLYMER-LIPIDS IN SOLUTION WITH RNNs

Files and scripts pertaining to our work:  <ul> <li>GROMACS files for the topology (DSPE+PEG.top) and the initial structure of the aggregate (DSPE+PEG_EA_NPT.gro)</li> <li>GROMACS topology file for the ethyl acetate molecule: EA_SI.top</li> <li>Scripts to submit the GROMACS utilities for calculation of the interaction energies are described in README.txt (Subset_energy.sh , Interaction_energies.sh)</li> <li>Scripts pertaining to PyTorch use and access of methods are described in README.txt (Multiple-run.sh. Job.sh, Pytorch_train-model.py)</li> <li>Scripts pertaining to scikit learn access for the Expectation Maximization clustering are described in the README.txt (Job_EM.sh, EM_Clustering.py)</li> <li>Files with the time series of the potential energy (PE) and interaction energy (IE) of the DSPE-PEG aggregate with the ethyl acetate solvent. Series contain 500,000 snapshots taken every 10 fs along the NVT Molecular Dynamics trajectory at 300 K and 906.3 kg/m3 density. The molecular solution is in a cubic box of edge length 13.76 nm, containing 16,000 ethyl acetate molecules and one aggregate of 4 DSPE-PEG-amide macromolecules (224,000 atoms): Data_Andrews_etal_DSPE-PEG_2022.zip</li> <li>ArXiv preprint: https://doi.org/10.48550/arXiv.2203.00151 (JAndrews_etal_arXiv-doi.pdf)</li> </ul>

opencc-by-4.0May 2022View details →

zenodo40/100

In-depth characterization revealed polymer type and chemical content specific effects of microplastic on Dreissena bugensis

The files contain datasets that were generated during laboratory-based real-time valvometry, and laser doppler anemometry measurements. The article was published in Journal of Hazardous Materials (accepted June 8, 2022).

opencc-by-4.0Jun 2022View details →

zenodo40/100

The dataset for the mechanical parameters of the ultraviolet adhesive polymer-inorganic interfaces

We perform molecular dynamics (MD) simulation with full-atom representation to investigate the mechanical properties of interfaces between polymers, including seven ultraviolet (UV) adhesive polymers and other common polymers, and inorganic substrates (Si, SiO2, ZrO2). The interfacial mechanical parameters such as strength and energy release rate in the cohesive zone models (CZMs) are calculated from the MD simulations. The typical traction separation and shear deformation are applied to the polymer-inorganic interface. Different interfacial crosslink densities of the polymer-inorganic interfaces are also considered. The dataset provided here can be used as the input for failure prediction and design optimization by the finite element analysis (FEA), for example, layered polymer-inorganic composites used in electronic device packages.

opencc-by-4.0Jul 2022View details →

zenodo40/100

Supporting Information for "From Hofmeister to hydrotrope: Effect of anion hydrocarbon chain length on a polymer brush"

This deposition contains the data and analysis (Jupyter notebooks) detailed in “From Hofmeister to hydrotrope: Effect of anion hydrocarbon chain length on a polymer brush”. All Jupyter notebooks have also been converted into PDF files for ease of viewing. All data and code (notebooks) required to reproduce the analysis can be found within the “supporting_data_analysis.zip” archive. This archive contains four sub-directories: <ul> <li>Computional_data <ul> <li>Optimised geometry files (.xyz) for all short chain fatty acid (SCFA) anion-solvent and anion-NIPAM fragment structures.</li> <li>Summary of analysed data (“SAPT_computational_data.xlsx”) describing the different interaction energies between a SCFA anion-NIPAM fragment and anion-solvent: electrostatic, exchange, induction, dispersion and total interaction energy contributions.</li> </ul> </li> <li>Ellipsometry <ul> <li>Data directory containing all raw ellipsometry data.</li> <li>“refellips_Dry.ipynb” and “refellips_Spectroscopic_SL.ipynb” notebooks to reproduce the analysis of a dry (solid-air) and hydrated (solid-liquid) polymer brush, respectively. </li> <li>A spatial map of the polymer brush used for spectroscopic ellipsometry data analysis: “PNIPAM_brush_spatial_map.png”.</li> <li>“Ellipsometry_sigmoid_fitting.ipynb” notebook and “Water_data.csv” file for the demonstration of the extraction of a thermotransition temperature from an ellipsometry dataset. Relevant plotting tools can be found in the <a href="https://github.com/refnx/refellips">refellips repo</a>.</li> </ul> </li> <li>Neutron_reflectometry <ul> <li>Data directory containing all relevant reduced reflectivity profiles from the Platypus reflectometry at ANSTO.</li> <li>“refnx_dry.ipynb”, “refnx_D2O.ipynb” and “refnx_SCFA_electrolytes.ipynb notebooks required to reproduce the analysis pertaining to a dry, polymer brush and a brush exposed to pure D2O and various SCFA electrolytes, respectively.</li> <li>Additional code required to model the hydrated polymer brush and various plotting tools can be in the <a href="https://github.com/igresh/refnxtoolbox">refnxtoolbox repo</a>.</li> </ul> </li> <li>QCMD_data <ul> <li>Data directory containing all processed QCM-D data (.csv)</li> </ul> </li> </ul>

opencc-by-4.0Aug 2022View details →

zenodo40/100

A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects: dataset

Abstract: (from [1]) <blockquote> The enhanced mechanical behavior of polymer nanocomposites with spherical filler particles is attributed to the formation of matrix-filler interphases. The nano-scale leads to particularly high interphase volume fractions while rendering experimental investigations extremely difficult. Previously, we introduced a molecular dynamics-based interphase model capturing the crucial spatial profiles of elastic and inelastic properties inside the interphase. This contribution demonstrates that our model captures polymer nanocomposites’ essential characteristics reported from experiments. To this end, we thoroughly verify and validate the model before discussing the resulting local plastic strain distribution. Furthermore, we obtain a reinforcement in terms of the overall stiffness for smaller particles and higher filler contents, while the influence of particle spacing seems negligible, matching experimental observations in the literature. This paper proposes a methodology to unravel the underlying complex mechanical behavior of polymer nanocomposites and to translate the findings into engineering quantities accessible to a broader audience and technical applications. </blockquote>   Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: Abaqus version R2018 License: Creative Commons Attribution 4.0 International   Context: Data set supplementing  journal paper: [1] Ries, M.; Weber, F.; Possart, G.; Steinmann, P. & Pfaller, S., “A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects”, Composites Part A: Applied Science and Manufacturing, 2022, 107094. This dataset contains the results presented in [1] and the necessary data to obtain those. Content: simulation folder denotation (“-” used instead of decimal points): distance_particles _ radius_particle _ thickness_ip _ num_ip _ length_box _ factor_el_length _ fraction_box_length _ switch_mat_ip with <ul> <li>    distance_particles: center distance of the nanoparticles in nm</li> <li>    radius_particle: radius of the nanoparticles in nm</li> <li>    thickness_ip: thickness of the interphase layers in nm</li> <li>    num_ip: number of interphase layers</li> <li>    length_box: box edge length in nm</li> <li>    factor_el_length: factor scaling the element length on the arcs of the interphase layers (element length = factor_el_length * thickness_ip)</li> <li>    fraction_box_length: matrix element length = length_box / fraction_box_length</li> <li>    switch_mat_ip: if = 0: interphases are assigned their actual material properties, if = 1: interphases are assigned the material properties of the bulk</li> </ul>                                                           each simulation folder contains the following file types: <ul> <li>    .cae: Abaqus model database, containing parts, meshes, loads, etc.</li> <li>    .dat: Printed output from the analysis input file processor, as well as printed output of selected results written during the analysis</li> <li>    .inp: Analysis input file</li> <li>    .log: Log file, which contains start and end times for modules run by the current execution procedure</li> <li>    .msg: Diagnostic or informative messages about the progress of the solution</li> <li>    .odb: Output database containing all results data from an Abaqus analysis</li> <li>    .sta: Status file with increment summaries</li> </ul> folder structure: <ul> <li>Standard_case: simulation folders of the standard close (particle center distance: 5.1776 nm) and distant (particle center distance: 7.9481 nm) cases (particle radius: 2 nm, filler content 0.054 vol.%, number of interphase layers: 4, factor_el_length: 1.0) and further particle center distances</li> <li>Layers: simulation folders with different numbers of interphase layers, i.e., different values for num_ip,  based on the standard close and distant cases <ul> <li>Close_case</li> <li>Distant_case</li> </ul> </li> <li>Mesh: simulation folders with different mesh qualities, i.e., different values for factor_el_length, based on the standard close and distant cases <ul> <li>Close_case</li> <li>Distant_case</li> </ul> </li> <li>Particle_size: simulation folders with different particle sizes <ul> <li>2_nm: simulation folders with particle surface distance 2 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> <li>4_nm: simulation folders with particle surface distance 4 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> <li>8_nm: simulation folders with particle surface distance 8 nm <ul> <li>vol_ratio_0-00054: simulation folders with filler content 0.054 vol.%</li> <li>vol_ratio_0-0075: simulation folders with filler content 0.75 vol.%</li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Aug 2022View details →

zenodo40/100

Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites: supplementary information and dataset

Abstract: (from [1]) <blockquote> The addition of nano-sized filler particles enhances the mechanical performance of polymers. The resulting properties of the polymer nanocomposite depend on a complex interplay of influence factors such as material pairing, filler size, and content as well as filler-matrix adhesion. As a complement to experimental studies, numerical methods, such as molecular dynamics (MD), facilitate an isolated examination of the individual factors in order to understand their interaction better. However, particle-based simulations are, in general, computationally very expensive, rendering a thorough investigation of nanocomposites’ mechanical behavior both expensive and time-consuming. Therefore, this paper presents a fast coarse-grained MD model for a generic nanoparticle-reinforced thermoplastic. First, we examine the matrix and filler phase individually, which exhibit isotropic elasto-viscoplastic and anisotropic elastic behavior, respectively. Based on this, we demonstrate that the effect of filler size, filler content, and filler-matrix adhesion on the stiffness and strength of the nanocomposite corresponds very well with experimental findings in the literature. Consequently, the presented computationally efficient MD model enables the analysis of a generic polymer nanocomposite. In addition to the obtained insights into the mechanical behavior, the material characterization provides the basis for a future continuum mechanical description, which bridges the gap to the engineering scale.  </blockquote>   Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All MD simulations were performed with LAMMPS [2], version: 29 Oct 2020 / 20201029 Compiled with Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled C++ standard: C++11 Active compile time flags: -DLAMMPS_GZIP -DLAMMPS_SMALLBIG Installed packages: CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [3] Post-processing Matlab R2019b Evaluation of polymer entanglements with Z1-Algorithm [4]   License: Creative Commons Attribution 4.0 International   Context: Data set supplementing  journal paper: [1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, 2022, 16. This dataset contains the results presented in [1] and the necessary data to obtain those as well as supplementary information. Content: supplementary material: supplementary_information.pdf data:     folder names vary depending on the context, explained in the following:   01_matrix <ul> <li> 01_equilibration sample equilibration to different temperatures nomenclature: equil_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>[-<batch_ID>] <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.1-1.0 </li> <li> batch_ID: 2-5  </li> </ul> </li> <li> 02_temperature_dependence uniaxial tension simulations to identify temperature dependence nomenclature: 01_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.1-1.0 </li> </ul> </li> <li> 03_directional_dependence uniaxial tension simulations to prove isotropy in Y and Z direction; X direction in 04_rate_dependence nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-<batchID> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-5 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 04_rate_dependence uniaxial tension simulations to identify strain rate dependence nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>[-<batchID>] <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 05_cyclic_loading sinusoidal uniaxial deformation nomenclature: 05_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4 </li> <li> strain_amplitude: 0.01, 0.05, 0.15, 0.2 </li> </ul> </li> <li> 06_relaxation relaxation subsequent to time-proportional deformation nomenclature: 07_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude>_relax <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> strain_rate: 5E-4 </li> <li> strain_amplitude: 0.01, 0.05, 0.15, 0.2 </li> </ul> </li> <li> 07_simple_shear time-proportional simple shear deformation with different strain rates nomenclature: SS_P2VPSi-rate_<strain_rate>-<batchID> <ul> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> <li> batchID: 1-5 </li> </ul> </li> <li> 08_large_deformation uniaxial deformation up to 100% strain nomenclature: 02_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperatur>-strain_<max_strain> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> initial_box_length: 100 </li> <li> SARW_distance: 0.9 </li> <li> SARW_angle: 50 </li> <li> final_temperature: 0.3 </li> <li> max_strain: 1 </li> </ul> </li> </ul> 02_filler <ul> <li> 01_Silica_equilibration sample equilibration </li> <li> 02_time_proportional time-proportional uniaxial and simple shear tests nomenclature: Silica_BV-<loadcase>_<direction>-strain_<max_strain>-rate_<strain_rate> <ul> <li> loadcase: uniaxial tension (UT), simple shear (SS) </li> <li> max_strain: 0.1 </li> <li> direction: X, Y, Z (UT); XY, XZ, YZ (SS) </li> <li> strain_rate: 5E-4, 5E-5, 5E-6 </li> </ul> </li> <li> 03_time_periodic time-periodic uniaxial and simple shear tests nomenclature: Silica_BV-<loadcase>_<direction>_sin-ampl_<strain_amplitude>-rate_<max_strain_rate> <ul> <li> loadcase: uniaxial tension (UT), simple shear (SS) </li> <li> direction: X, Y, Z (UT); XY, XZ, YZ (SS) </li> <li> strain_amplitude: 0.025 </li> </ul> </li> </ul> 03_composite <ul> <li> 01_equilibration sample equilibration nomenclature: equil_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID> <ul> <li> filler_radius: 2.5-10.0 </li> <li> filler_number: 1-160 (depending on filler_radius) </li> <li> batchID: 1-5 </li> </ul> </li> <li> 02_uniaxial-tension uniaxial tension simulations nomenclature: UT_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID> <ul> <li> filler_radius: 2.5-10.0 </li> <li> filler_number: 1-160 (depending on filler_radius) </li> <li> batchID: 1-5 </li> </ul> </li> <li> 03_filler-maxtrix-adhesion equilibration and uniaxial deformation of samples with mid and weak filler-matrix adhesion (for strong adhesion see 01_equilibration and 02_uniaxial-tension nomenclature: see above </li> <li> 04_IP_equilibration equilibration of samples to evaluate the microstructure for neat polymer and composites with filler radius 2.5-7.5 nomenclature: P2VPSi-<chains>x<chain_atoms>_rNP_<filler_radius>-nNP_<filler_number>_pos_<filler_pos>-<batchID> <ul> <li> chains: 200 </li> <li> chain_atoms: 200 </li> <li> filler_radius: 0 (neat), 2.5, 5.0, 7.5 </li> <li> filler_number: 0 (neat), 1 </li> <li> batchID: 1-20 </li> </ul> </li> </ul>       Each simulation directory contains: <ul> <li> lammps input file (*.in) of the specific simulation </li> <li> data file (*.data) containing the initial sample configuration </li> <li> input.prm: input parameters of the specific simulation (read by the input file) </li> <li> meta.info: meta data of the specific simulation run </li> <li> LAMMPS_out: simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li> thermo_out.Dat: raw output  </li> <li> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter) </li> <li> thermo_out_STD.Dat: standard deviation of raw output </li> </ul> </li> </ul>   Output quantities (columns of *.Dat files): 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]. <ul> <li> Step: time step  </li> <li> Time: time  </li> <li> TotEng: total energy  </li> <li> PotEng: potential energy </li> <li> KinEng: kinetic energy  </li> <li> E_pair: pair energy  </li> <li> E_bond: bond energy  </li> <li> E_angle: angle energy  </li> <li> E_dihed: dihedral energy  </li> <li> Temp: temperature </li> <li> Press: hydrostatic pressure </li> <li> Pxx: xx component of pressure tensor  </li> <li> Pyy: yy component of pressure tensor  </li> <li> Pzz: zz component of pressure tensor  </li> <li> Pxy: xy component of pressure tensor </li> <li> Pxz: xz component of pressure tensor </li> <li> Pyz: yz component of pressure tensor </li> <li> Volume: volume of simulation box  </li> <li> Lx: box length in x direction   </li> <li> Ly: box length in y direction   </li> <li> Lz: box length in z direction   </li> <li> Density: density   </li> <li> c_RG: radius of gyration scalar  </li> <li> c_RG[1]: squared radius of gyration tensor (xx component)   </li> <li> c_RG[2]: squared radius of gyration tensor (yy component)   </li> <li> c_RG[3]: squared radius of gyration tensor (zz component)   </li> <li> c_RG[4]: squared radius of gyration tensor (xy component)   </li> <li> c_RG[5]: squared radius of gyration tensor (xz component)   </li> <li> c_RG[6]: squared radius of gyration tensor (yz component)   </li> <li> c_bondave[1]: bond energy averaged over all atoms   </li> <li> c_bondave[2]: bond distance averaged over all atoms   </li> <li> c_bondave[3]: squared bond distance averaged over all atoms   </li> <li> c_angleave[1]: angle energy averaged over all atoms   </li> <li> c_angleave[2]: angle averaged over all atoms degree </li> <li> c_angleave[3]: cosine of angle  </li> <li> c_angleave[4]: squared cosine of angle  </li> <li> c_MSD[1]: mean squared displacement x-direction   </li> <li> c_MSD[2]: mean squared displacement y-direction   </li> <li> c_MSD[3]: mean squared displacement z-direction   </li> <li> c_MSD[4]: total mean squared displacement   </li> <li> c_COM[1]: x coordinate of center of mass   </li> <li> c_COM[2]: y coordinate of center of mass   </li> <li> c_COM[3]: z coordinate of center of mass   </li> <li> v_strain_xx: xx component of engineering strain tensor    </li> <li> v_strain_yy: yy component of engineering strain tensor     </li> <li> v_strain_zz: zz component of engineering strain tensor     </li> <li> v_vMisesequivstress: von Mises equivalent stress  </li> <li> v_Cauchy_xx: xx component of stress tensor   </li> <li> v_Cauchy_yy: yy component of stress tensor </li> <li> v_Cauchy_zz: zz component of stress tensor </li> <li> v_Cauchy_xy: xy component of stress tensor  </li> <li> v_Cauchy_xz: xz component of stress tensor  </li> <li> v_Cauchy_yz: yz component of stress tensor  </li> <li> v_strain_xy: xy component of engineering strain tensor    </li> <li> v_strain_xz: xz component of engineering strain tensor    </li> <li> v_strain_yz: yz component of engineering strain tensor    </li> </ul>   References: [1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, 2022, 16. [2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19. [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. [4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, Zenodo, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a>

opencc-by-4.0Sep 2022View details →

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Structure of single-walled carbon nanotube reinforced polymer matrix composites

Structure of single-walled carbon nanotube reinforced polymer matrix composites Junjie Chen Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com   A composite material, also called a composite, is a solid material that results when two or more different substances, each with its own characteristics, are combined to create a new substance whose properties are superior to those of the original components in a specific application. The term composite more specifically refers to a structural material within which a fibrous material is embedded. The remarkable properties of composites are achieved by embedding fibers of one substance in a host matrix of another. In materials science, a polymer matrix composite is a composite material composed of a variety of short or continuous fibers bound together by a matrix of organic polymers. Polymer matrix composites are designed to transfer loads between fibers of a matrix. Some of the advantages with polymer matrix composites include their light weight, high resistance to abrasion and corrosion, and high stiffness and strength along the direction of their reinforcements. The function of the matrix in polymer matrix composites is to bond the fibers together and transfer loads between them. Polymer matrix composites matrices are typically either thermosets or thermoplastics. Thermosets are by far the predominant type in use today. Thermosets are subdivided into several resin systems including epoxies, phenolics, polyurethanes, and polyimides. Of these, epoxy systems currently dominate the advanced composite industry. Unlike fiber-reinforced polymer matrix composites, nanomaterials reinforced polymer matrix composites are able to achieve significant improvements in mechanical properties at much lower loadings. Carbon nanotubes in particular have been intensely studied due to their exceptional intrinsic mechanical properties and low densities. In particular carbon nanotubes have some of the highest measured tensile stiffnesses and strengths of any material due to the strong covalent bonds between carbon atoms. However, in order to take advantage of the exceptional mechanical properties of the nanotubes, the load transfer between the nanotubes and matrix must be very large. Like in fiber-reinforced composites, the size dispersion of the carbon nanotubes significantly affects the final properties of the composite. Long carbon nanotubes lead to an increase in tensile stiffness and strength due to the large-distance stress transfer and crack propagation prevention. On the other hand, short carbon nanotubes do not lead to any enhancement of properties without any interfacial adhesion. However once modified, short carbon nanotubes are able to further improve the stiffness of the composite, however there is still very little crack propagation countering. In general, long and high aspect ratio carbon nanotubes lead to greater enhancement of mechanical properties, but are more difficult to process. Aside from size, the interface between the carbon nanotubes and the polymer matrix is of exceptional importance. In order to achieve better load transfer, a number of different methods have been used to better bond the carbon nanotubes to the matrix by functionalizing the surface of the carbon nanotube with various polymers. These methods can be divided into non-covalent and covalent strategies. Non-covalent carbon nanotube modification involves the adsorption or wrapping of polymers to the carbon nanotube surface, usually via van der Waal's or π-stacking interactions. In contrast, covalent functionalization involves direct bonding onto the carbon nanotube. This can be achieved in a number of ways, such as oxidizing the surface of the carbon nanotube and reacting with the oxygenated site, or using a free radical to directly react with the carbon nanotube lattice. Covalent functionalization can be used to directly attach the polymer to the carbon nanotube, or to add an initiator molecule which can then be used for further reactions.

opencc-by-4.0Oct 2022View details →

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Fluid mechanics of carbon nanotube reinforced polymer composites

Fluid mechanics of carbon nanotube reinforced polymer composites Junjie Chen Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com   Fluid mechanics is the science concerned with the response of fluids to forces exerted upon them. It is a branch of classical physics with applications of great importance in hydraulic and aeronautical engineering and chemical engineering. Fluid mechanics is a subject with almost endless ramifications, and the account that follows is necessarily incomplete. Some knowledge of the basic properties of fluids will be needed. Fluids are not strictly continuous media in the way that all the successors of Euler and Bernoulli have assumed, for they are composed of discrete molecules. The molecules, however, are so small and, except in gases at very low pressures, the number of molecules per milliliter is so enormous that they need not be viewed as individual entities. There are a few liquids, known as liquid crystals, in which the molecules are packed together in such a way as to make the properties of the medium locally anisotropic, but the vast majority of fluids are isotropic. In fluid mechanics, the state of an isotropic fluid may be completely described by defining its mean mass per unit volume, or density, its temperature, and its velocity at every point in space, and just what the connection is between these macroscopic properties and the positions and velocities of individual molecules is of no direct relevance. A number of phenomena of considerable physical interest can be discussed using little more than the law of conservation of energy. However, the argument has so far been restricted to cases of steady flow. To discuss cases in which the flow is not steady, an equation of motion for fluids is needed, and one cannot write down a realistic equation of motion without facing up to the problems presented by viscosity, which have so far been deliberately set aside. Thermodynamics is the science of the relationship between heat, work, temperature, and energy. In broad terms, thermodynamics deals with the transfer of energy from one place to another and from one form to another. The key concept is that heat is a form of energy corresponding to a definite amount of mechanical work. Although thermodynamics developed rapidly during the 19th century in response to the need to optimize the performance of steam engines, the sweeping generality of the laws of thermodynamics makes them applicable to all physical systems. In particular, the laws of thermodynamics give a complete description of all changes in the energy state of any system and its ability to perform useful work on its surroundings. Classical thermodynamics does not involve the consideration of individual atoms or molecules. Such concerns are the focus of the branch of thermodynamics known as statistical thermodynamics, or statistical mechanics, which expresses macroscopic thermodynamic properties in terms of the behavior of individual particles and their interactions. It has its roots in the latter part of the 19th century, when atomic and molecular theories of matter began to be generally accepted. The application of thermodynamic principles begins by defining a system that is in some sense distinct from its surroundings. In general, systems are free to exchange heat, work, and other forms of energy with their surroundings. A particularly important concept is thermodynamic equilibrium, in which there is no tendency for the state of a system to change spontaneously. For example, the gas in a cylinder with a movable piston will be at equilibrium if the temperature and pressure inside are uniform and if the restraining force on the piston is just sufficient to keep it from moving. The system can then be made to change to a new state only by an externally imposed change in one of the state functions, such as the temperature by adding heat or the volume by moving the piston. A sequence of one or more such steps connecting different states of the system is called a process.

opencc-by-4.0Oct 2022View details →

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Fundamental interactions between carbon nanotubes and polymers

Fundamental interactions between carbon nanotubes and polymers Junjie Chen Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com   Carbon nanotubes, also called buckytubes, are nanoscale hollow tubes composed of carbon atoms. The cylindrical carbon molecules feature high aspect ratios typically above one thousand, with diameters from about one nanometer up to tens of nanometers and lengths up to millimeters. This unique one-dimensional structure and concomitant properties endow carbon nanotubes with special natures, rendering them with unlimited potential in nanotechnology-associated applications. Carbon nanotubes are members of the fullerene family. According to the number of graphic shells, they are mainly categorized as single-walled and multi-walled carbon nanotubes. Novel chemical, electrical, and mechanical properties absent in other materials have been discovered in carbon nanotubes. Pristine carbon nanotubes are inert to most chemicals and need to be grafted with surface functional groups to increase their chemical reactivity and add new properties. Along the longitude directions, carbon nanotubes show superior mechanical strength, with the highest known tensile strength and elastic modulus among known materials. As for thermal properties, carbon nanotubes outperform diamond as the best thermal conductor. Applications of carbon nanotubes are aimed to make use of their unique properties to solve problems at the nanoscale. Their high surface area, together with the unique ability to carry any chemical compounds after surface modification, offers carbon nanotubes the potential to be used as nanoscale catalyst supports with high catalytic reactivity and chemical sensors. A polymer, is any of a class of natural or synthetic substances composed of very large molecules, called macromolecules, that are multiples of simpler chemical units called monomers. The word polymer designates an unspecified number of monomer units. When the number of monomers is very large, the compound is sometimes called a high polymer. Polymers are not restricted to monomers of the same chemical composition or molecular weight and structure. Some natural polymers are composed of one kind of monomer. Most natural and synthetic polymers, however, are made up of two or more different types of monomers; such polymers are known as copolymers. Synthetic polymers are produced in different types of reactions. Many simple hydrocarbons, such as ethylene and propylene, can be transformed into polymers by adding one monomer after another to the growing chain. Polyethylene, composed of repeating ethylene monomers, is an addition polymer. Polyethylene is crystalline, translucent, and thermoplastic. It is used for coatings, packaging, molded parts, and the manufacture of bottles and containers. Polypropylene is also crystalline and thermoplastic but is harder than polyethylene. Other addition polymers include polybutadiene, polyisoprene, and polychloroprene, which are all important in the manufacture of synthetic rubbers. Some polymers, such as polystyrene, are glassy and transparent at room temperature, as well as being thermoplastic. Polystyrene can be colored any shade and is used in the manufacture of toys and other plastic objects. Many important polymers have oxygen or nitrogen atoms, along with those of carbon, in the backbone chain. Among such macromolecular materials with oxygen atoms are polyacetals. The simplest polyacetal is polyformaldehyde. It has a high melting point and is crystalline and resistant to abrasion and the action of solvents. Acetal resins are more like metal than are any other plastics and are used in the manufacture of machine parts such as gears and bearings. A linear polymer characterized by a repetition of ester groups along the backbone chain is called a polyester. Open-chain polyesters are colorless, crystalline, thermoplastic materials.

opencc-by-4.0Oct 2022View details →

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General database of O2/CO2 and H20 permeability for polymer-based nano composites

More than 1000 values (i.e. about 170 articles) of the 1995-2015 period containing measured values of O2, CO2 and H2O permeability in polymer-based nanocomposites were collected from the available literature and capitalized in this dedicated on-line database. These data were assorted and compared in order to decipher the role of particle shape (either iso-dimensional, elongated or platelets nanoparticles) on the reduction of the relative permeability of the nano composite. The proposed on-line database consists in the first and unprecedented compilation of permeability values for nanocomposite based materials.

opencc-by-4.0Jan 2018View details →

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In vivo polymer mechanochemistry with polynucleotides

Original data underpinning Figures, Schemes, Videos, and Tables of the manuscript and supplementary information.

opencc-by-4.0May 2024View details →

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Revealing the percolation–agglomeration transition in polymer nanocomposites via MD-informed continuum RVEs with elastoplastic interphases - dataset

Abstract: from [1] This contribution builds the concluding step of a multiscale approach to effectively capture the mechanical  behavior of polymer nanocomposites (PNCs), in this case, silica-modified polystyrene. By introducing  continuum-based representative volume elements (RVEs) that employ previously identified elastoplastic property  gradients for the interphases surrounding the fillers, the effects of particle size, particle volume fraction,  and agglomeration on the mechanical performance are investigated. Uniaxial tension tests are simulated with  the respective finite-element RVEs, and stress–strain curves are derived. The elastic and plastic material  properties of the RVE can then be extracted and analyzed quantitatively by fitting the stress–strain curves  with a Voce-type elastoplasticity formulation.  At small degrees of agglomeration, i.e., good particle dispersion, in combination with sufficiently large  particle volume fraction, percolation bands form, leading to improved elastic and plastic properties. Higher  degrees of agglomeration or particle clusters behave like large single particles, which has an adverse effect, i.e.,  the nanoscale size effect is thereby neutralized. Therefore, the precise MD-informed elastoplastic interphase  representation of our RVEs enables the investigation of the transition from beneficial percolation to unfavorable  agglomeration. Ultimately, this contribution establishes a link between the effects of particle size, particle  volume fraction, agglomeration, and percolation, which have so far only been discussed separately in the  literature.  Our methodology offers new insights into the structure–property relations of PNCs and their resulting  mechanical behavior. The underlying multiscale approach with a systematic transition from molecular to  microscopic scales is required to complement experimental observations and exploit the full potential of PNCs.  Contact: Maximilian Ries Institute of Applied Mechanics Friedrich-Alexander-Universität Erlangen-Nürnberg Egerlandstr. 5 91058 Erlangen Software: All finite element simulations were performed with Simulia Abaqus/CAE2018  License: Creative Commons Attribution Non Commercial 4.0 International Context: Data set supplementing  journal paper: [1] E.-M. Richter, G. Possart, P. Steinmann, S. Pfaller, & M. Ries, “Revealing the percolation–agglomeration transition in polymer nanocomposites via MD-informed continuum RVEs with elastoplastic interphases,” Composites Part B: Engineering, vol. 281, p. 111477, 2024. Content: - excel sheet summarizing all RVE simulations in combination with the elastoplastic constitutive model calibration: elastoplastic_constitutive_model_calibration.xlsx - input data for each RVE FE simulation in *.inp format following the naming convention:     agg_<degree of agglomeration>-fillercont_<filler content>Percent-fillerrad_<filler radius>nm     - degree of agglomeration is defined in [1]     - filler content is given in volume percent     - filler radius is given in nanometer

opencc-by-nc-4.0May 2024View details →

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Microscopic images of screen-printed conductive layers on polymer fabrics

The data collection includes data from SEM and optical microscope of screen-printed conductive layers on polymer fabrics (PET and cotton). The results of the work related to the attached data and detailed description of the layer production procedure were published in Rac-Rumijowska, O., Pokryszka, P., Rybicki, T., Suchorska-Woźniak, P., Woźniak, M., Kaczkowska, K., & Karbownik, I. (2024). Influence of Flexible and Textile Substrates on Frequency-Selective Surfaces (FSS). Sensors, 24(5), 1704.   Description of the included files: PET_Ag_PE672_cross_section_1 – microscopic image of a cross-section of PET fabric covered with DuPoint PE672 silver paste – sample 1 PET_Ag_PE672_cross_section_2 – microscopic image of a cross-section of PET fabric covered with DuPoint PE672 silver paste – sample 2 PET_Ag_PE674_cross_section_1 – microscopic image of a cross-section of PET fabric covered with DuPoint PE674 silver paste – sample 1 PET_Ag_PE674_cross_section_2 – microscopic image of a cross-section of PET fabric covered with DuPoint PE674 silver paste – sample 2  SEM_PET_Ag_1W (1-9) – microscopic SEM image of a PET fabric covered with 1 layer of DuPoint PE672 silver paste – image 1-9 SEM_PET_Ag_2W (1-6) – microscopic SEM image of a PET fabric covered with 2 layers of DuPoint PE672 silver paste – image 1-6 SEM_PET_Ag_2W (1-6) – microscopic SEM image of a PET fabric covered with 3 layers of DuPoint PE672 silver paste – image 1-6 SEM_BAWELNA_Ag_2W (1-6) – microscopic SEM image of a cotton fabric covered with 2 layers of DuPoint PE672 silver paste – image 1-6 SEM_BAWELNA_Ag_3W (1-6) – microscopic SEM image of a cotton fabric covered with 3 layers of DuPoint PE672 silver paste – image 1-6 SEM_BAWELNA_Ag_4W (1-6) – microscopic SEM image of a cotton fabric covered with 4 layers of DuPoint PE672 silver paste – image 1-6 BAWELNA  – microscopic image of a cotton fabric BAWELNA_Ag_4w (1-3)  – microscopic image of  cotton fabric covered with 4 layers DuPoint PE674 silver paste – image 1-3 PET  – microscopic image of a PET fabric PET_Ag_1W (1-2) – microscopic image of a PET fabric covered with 1 layer of DuPoint PE672 silver paste – image 1-2 PET_Ag_2W (1-3) – microscopic image of a PET fabric covered with 2 layers of DuPoint PE672 silver paste – image 1-3 PET_Ag_2W (1-2) – microscopic image of a PET fabric covered with 3 layers of DuPoint PE672 silver paste – image 1-2

opencc-by-4.0Jun 2024View details →

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Data - Effect of electrolytes as adjuvants in GFP and LPS partitioning on aqueous two-phase systems: 1. Polymer-polymer systems

Overview The production of recombinant biopharmaceuticals is highly dependent of a proper choice of the downstream processing stages. Particularly, the purification that must ensure that all the endotoxins (lipopolysaccharide - LPS) are efficiently removed from the final product. This dataset contains the raw data and statistical analysis for the research entitled - "Effect of electrolytes as adjuvants in GFP and LPS partitioning on aqueous two-phase systems: 1. Polymer-polymer systems".  Info ANOVA_Turkey_Sub.R <- code for ANOVA analysis in R statistic 3.3.3     glm.R <- code for GLM analysis in R statistic 3.3.3 K&REC_LPS_PEG_NaPA.xlsx <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) and recover (REC) for ANOVA analysis K&REC_LPS_PEG_NaPA_K.docx <- File with ANOVA result of partition coefficient (K) for GFP K&REC_LPS_PEG_NaPA_REC.docx <- File with ANOVA result of recover (REC) for GFP          K_GFP_Pol_005.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.05M salt assays     K_GFP_Pol_005.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.05M salt assays   K_GFP_Pol_005_QQ.png <- Residual quantile plot of GLM analysis for partition coefficient (K) in 0.05M salt assays   K_GFP_Pol_025.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.25M salt assays   K_GFP_Pol_025.doc  <- File with GLM analysis of GFP partition coefficient (K) in 0.25M salt assays   K_GFP_Pol_025_QQ.png <- Residual quantile plot of GLM analysis for partition coefficient (K) in 0.25M salt assays          REC_GFP_Pol_005.csv <- File with raw values organized in a spreadsheet of GFP recover (REC) for GLM analysis in 0.05M salt assays          REC_GFP_Pol_005.doc  <- File with GLM analysis of GFP recover (REC) in 0.05M salt assays     REC_GFP_Pol_005_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.05M salt assays    REC_GFP_Pol_025.csv <- File with raw values organized in a spreadsheet of GFP recover (REC) for GLM analysis in 0.25M salt assays           REC_GFP_Pol_025.doc  <- File with GLM analysis of GFP recover (REC) in 0.25M salt assays  REC_GFP_Pol_025_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.25M salt assays         REM_LPS_PEG_NaPA.docx  <- File with ANOVA result of LPS removal           REM_LPS_PEG_NaPA.xlsx <- File with raw values organized in a spreadsheet of LPS removal for ANOVA analysis Stability_GFP_PEG_NaPA.docx <- File with ANOVA result of GFP stability Stability_GFP_PEG_NaPA.xlsx <- File with raw values organized in a spreadsheet of GFP stability results for ANOVA analysis REM_LPS_Pol_005.csv  <- File with raw values organized in a spreadsheet of LPS removal (REM) for GLM analysis in 0.05M salt assays          REM_LPS_Pol_005.doc  <- File with GLM analysis of LPS removal (REM) in 0.05M salt assays     REM_LPS_Pol_005_QQ.png <- Residual quantile plot of GLM analysis of LPS removal (REM) in 0.05M salt assays   REM_LPS_Pol_025.csv  <- File with raw values organized in a spreadsheet of LPS removal (REM) for GLM analysis in 0.25M salt assays          REM_LPS_Pol_025.doc   <- File with GLM analysis of LPS removal (REM) in 0.25M salt assays     REM_LPS_Pol_025_QQ.png <- Residual quantile plot of GLM analysis of LPS removal (REM) in 0.25M salt assays K_GFP_Pol_025_NaCl_Li2SO4.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.25M salt assays comparing NaCl and Li2SO4 effect  K_GFP_Pol_025_NaCl_Li2SO4.doc  <- File with GLM analysis of GFP partition coefficient (K) in 0.25M salt assays comparing NaCl and Li2SO4 effect  K_GFP_Pol_025_NaCl_Li2SO4_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.25M salt assays comparing NaCl and Li2SO4 effect    REM_LPS_Pol_KI_0.05_vs_0.25.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in KI assays comparing salt concentration effect   REM_LPS_Pol_KI_0.05_vs_0.25.doc <- File with GLM analysis of GFP partition coefficient (K) in KI assays comparing salt concentration effect REM_LPS_Pol_KI_0.05_vs_0.25_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in KI assays comparing salt concentration effect REM_LPS_Pol_KNO3_0.05_vs_0.25.csv  <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in KNO3 assays comparing salt concentration effect   REM_LPS_Pol_KNO3_0.05_vs_0.25.doc <- File with GLM analysis of GFP partition coefficient (K) in KNO3 assays comparing salt concentration effect REM_LPS_Pol_KNO3_0.05_vs_0.25_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in KNO3 assays comparing salt concentration effect REM_LPS_Pol_Li2SO4_0.05_vs_0.25.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in Li2SO4 assays comparing salt concentration effect   REM_LPS_Pol_Li2SO4_0.05_vs_0.25.doc <- File with GLM analysis of GFP partition coefficient (K) in Li2SO4 assays comparing salt concentration effect REM_LPS_Pol_Li2SO4_0.05_vs_0.25_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in Li2SO4 assays comparing salt concentration effect REM_LPS_Pol_NaCl_0.05_vs_0.25.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in NaCl assays comparing salt concentration effect   REM_LPS_Pol_NaCl_0.05_vs_0.25.doc <- File with GLM analysis of GFP partition coefficient (K) in NaCl assays comparing salt concentration effect  REM_LPS_Pol_NaCl_0.05_vs_0.25_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in NaCl assays comparing salt concentration effect   Annotation 12/12 - Concentration of 12% of each polymer PEG/NaPA 16/16 - Concentration of 16% of each polymer PEG/NaPA P/N -  PEG/NaPA 10e4, 10e5, 10e6 - Concentration of LPS in scientific notation - 10000, 100000, 100000 EU/mL poly - Polymer salt - Salt concentration in the assay tsalt - Type of salt in the assay (NaCl, KNO3, KI and Li2SO4) lps - lipopolysaccharide K - GFP partition coefficient REM - LPS removal REC - GFP recover wo_salt - Assay without salt addition Acknowledgements The authors are grateful for financial support from FAPESP (São Paulo Research Foundation, Brazil) through the following projects: 2005/60159-7; 2007/51978-0; 2014/16424-7; and 2014/19793-3. The authors also acknowledge the support from CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil) through the process #0366/09-9 and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil). Consider citing our work.  1. Work in progress...

opencc-by-4.0Mar 2018View details →

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Research data supporting "Post-polymerisation functionalisation of conjugated polymer backbones and its application in multi-functional emissive nanoparticles"

Research data supporting the publication: Creamer A. et al., "Post-polymerisation functionalisation of conjugated polymer backbones and its application in multi-functional emissive nanoparticles", Nature Communications, 9:3237 (2018).

opencc-by-4.0Aug 2018View details →

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Microfluidic solvent extraction of poly(vinyl alcohol) droplets: effect of polymer structure on particle and capsule formation

Raw data from the majority of figures of our 2018 Soft Matter Paper: Selected datasets from figures are excluded, owing to them being transformations of the raw data provided in the same figure.

opencc-by-nc-sa-3.0Apr 2018View details →

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Supporting Data for "Refractive index matched, nearly hard polymer colloids" (Proc. R. Soc. A, doi:10.1098/rspa.2018.0763)

SAXS data [Q / Å^{-1}, I(Q) / Arb. unit, error I(Q) / Arb. unit] as *.dat files Data for Figures 1, 2, and 5 [description and units in column headers] as *.csv files

opencc-by-4.0Dec 2018View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record