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421 results for “Polymer”
N-type molecular doping of a semicrystalline conjugated polymer through cation exchange
<p>N-type molecular doping of a semicrystalline conjugated polymer through cation exchange was conducted. UV-Vis absorption, photoelectron yield, and x-ray diffraction measurements were conducted to evaluate the resulting doping levels and stability.</p>
Correlation Between Segmental Order Parameter and Entanglement Length in a Monodisperse Comb Polymer
<p>This Dataset comprises the raw data contained in the figures of our journal article in <em>Macromolecules </em><strong>2024</strong><em> </em>(<a href="https://doi.org/10.1021/acs.macromol.4c02015" target="_blank" rel="noopener">DOI: 10.1021/acs.macromol.4c02015</a>) and its Supporting Information (SI)<em>. </em>We provide a <a href="https://zenodo.org/api/records/13342840/draft/files/meltdyn-combs-DTD+SI_sub.pdf/content" target="_blank" rel="noopener">preprint</a> of the initially submitted article including the SI for reference to the figures and their captions, necessary to use the data. Note that Fig. S1 was added upon revision, so Fig. S2 of the published SI is Fig. S1 of the SI that is part of the preprint. Please also check the published article in <a href="https://doi.org/10.1021/acs.macromol.4c02015" target="_blank" rel="noopener"><em>Macromolecules</em></a>. For copyright details and licensing we refer to the published article and the publisher. Here is the abstract of the article:</p> <blockquote> <p>The motion of a polymer chain within a hypothetical confining tube leads to arise a segmental order parameter. This parameter is assessed via multiple-quantum (MQ) NMR experiments. In both polymer networks and entangled melts, the order parameter is proportional to the inverse of the number of segments between two covalent crosslinks or physical entanglements. In entangled polymer networks, the entanglements have usually been considered as additional but temporary crosslinks and the contribution of the physical and chemical constraints are assumed additive. However, it was revealed by computer simulation results that this assumption is not valid for lowly crosslinked polymer networks; instead, the segmental order parameter was shown to scale with 1/√(<em>N_</em>e <em>N</em>_c ), <em>N</em>_c and <em>N</em>_e being the number of segments between crosslinks and entanglements, respectively [M. Lang and J.-U. Sommer, <em>Phys. Rev. Lett</em>. <strong>2010</strong>, <em>104</em>, 177801]. An experimental confirmation remains elusive due to challenges in distinguishing the contributions of entanglements and crosslinks, as well as the long averaging timescales involved. In this study, we assess this correlation by examining chain dynamics in a monodisperse polyisoprene comb, utilized as a model system. To model chain dynamics in this system, the dynamic dilution model, originally designed for predicting the rheological behavior of star and branched polymers, has been modified to facilitate its application in the analysis of MQ NMR signals. We also address some of its shortcomings.</p> </blockquote>
Data from: Extraction and identification of a wide range of microplastic polymers in soil and compost
<p>Microplastic (MP) pollution is globally widespread, however their presence in soil systems is poorly understood due to complexity of soil and lack of standardised extraction methods. Datasets provided contain data from optimisation (recoveries) of MPs extraction protocol from soil and compost based on olive oil and density separation using zinc chloride, in case of low-density polyethylene and polyethylene terephthalate. Density separation was further used to extract five microplastic polymers (PET, PS, PE, PP and PVC), added to soil and compost at different concentrations, which is also included in the dataset. Additionally, identification data of extracted MPs from spiked soil and compost samples.</p>
Dataset for "Mechanically robust supramolecular polymer co-assemblies"
<p>Source data of the study reported in the publication entitled "Mechanically robust supramolecular polymer co-assemblies". The data should be considered together with the published manuscript and the supplementary information file.</p>
Research Data supporting "Controlling the length of porphyrin supramolecular polymers via coupled equilibria and dilution-induced supramolecular polymerization"
<p>Raw research data supporting the article E. Weyandt, L. Leanza, R. Capelli, G. M. Pavan, G. Vantomme, and E.W. Meijer, "Controlling the length of porphyrin supramolecular polymers via coupled equilibria and dilution-induced supramolecular polymerization".</p>
AFM-IR images and spectra of commercial polymers
<p>This data set contains photothermal IR images (AFM-IR) and spectra of a commercial polymers taken with a Bruker nanoIR3. A Daylight solutions MIRcat-QT external cavity quantum cascade laser. Fourier transform infrared (FTIR) spectra were recorded with Bruker Tensor 35 spectrometer using a "Platinum ATR" (Bruker) attenuated total reflection sampling accessory with a diamond ATR element.</p> <p> </p> <p>The AFM-IR raw data files (".axz") are gzipped XML files that can be opened with the <a href="https://github.com/GeorgRamer/anasys-python-tools">anasyspythontools</a> python library. FTIR files (".txt") are CSV files. The first column represents wavenumbers, the second column represents absorption.</p>
Upconverting Nanoparticles in Aqueous Media: Not a Dead-End Road. Avoiding Degradation by Using Hydrophobic Polymer Shells
<p>Dataset of https://zenodo.org/record/5793193#.YcCAcWjMJPY</p>
Materials for Design Open Repository. Polymer Derived Ceramics
<p>The current dataset is composed of a collection of Polymer Derived Ceramics (PDCs). It contains the number of chemical elements (NoE), PDC composition, used precursor, Pyrolysis temperature <em>T</em><sub>p</sub>, Pyrolysis time <em>t</em><sub>p</sub>, gas atmosphere, a set of columns containing the chemical elements and their corresponding fraction, and the references. This dataset was developed in the framework of the European project ACHIEF for the discovery of novel materials to be used in industrial processes.</p>
Towards bio-inspired polymer adhesives: Activation assisted via HOBt for grafting of dopamine onto poly(acrylic acid)
<p>The design of bio-inspired polymers has long been an area of intense study, however applications to the design of concrete admixtures for improved materials performance have been relatively unexplored. In this work we functionalized poly(acrylic acid) (PAA), a simple analogue to polycarboxylate ether admixtures in concrete, with dopamine to form a catechol-bearing polymer (PAA-g-DA). Synthetic routes utilizing hydroxybenzotriazole (HOBt) as an activating agent were examined for their ability in grafting dopamine to the PAA backbone. Previous literature using the traditional coupling reagent 1-Ethyl-3-(3-dimethylaminopropyl)-carbodiimide (EDC) to graft dopamine to PAA were found to be inconsistent and the sensitivity of EDC coupling reactions necessitated a search for an alternative. Additionally, HOBt allowed for greater control over percent functionalization of the backbone, is a simple, robust reaction, and showed potential for scalability. This finding also represents a novel synthetic pathway for amide bond formation between dopamine and PAA. Finally, we performed preliminary adhesion studies of our polymer on rose granite specimens and demonstrated a 56% improvement in the median adhesion strength over unfunctionalized PAA. These results demonstrate an early study on the potential of PAA-g-DA to be utilized for improving the bonds within concrete.</p>
Data and Code For: Closing the Loop Between Microstructure and Charge Transport in Conjugated Polymers by Combining Microscopy and Simulation.
<p>This repository contains the datasets and scripts used in the article "Closing the Loop Between Microstructure and Charge Transport in Conjugated Polymers by Combining Microscopy and Simulation." </p> <p>Contents:</p> <p>4D-STEM data of the polymer PBTTT, annealed at 180C for 2h, vapor doped with the molecule F4TCNQ</p> <ul> <li>3_80x80_ss=10nm_spot9_alpha=p48_cl=480_RT_300kV_33ms_bin=4.zip ("Scan 3", Main case study)</li> <li>05 CL480 alpha p48 300kV spot11 50ms 80x80 s10.zip</li> </ul> <p>4D-STEM data of the polymer PBTTT, annealed at 180C for 2h, undoped</p> <ul> <li>10 A1 CL480 alpha p48 300kV spot11 50ms 80x80 s10.zip</li> <li>15 A1 CL480 alpha p48 300kV spot11 50ms 80x80 s10.zip</li> </ul> <p>Preprocessed data from "Scan 3." Images are centered, artifacts are removed, and images are binned and converted to radial coordinates.</p> <ul> <li>4D-STEM Preprocessed.zip <ul> <li>sample_preprocessed_data_80x80_ss=10_cl=480__q 1.00 3.00 0.05 a 5.00.npy (medium resolution, wide range, used in paper)</li> <li>sample_data_cube_q_1.0_2.0_0.1_angle_5.npy (low resolution, narrow range)</li> </ul> </li> </ul> <p>Grazing-Incidence Wide-Angle X-ray scattering (GIWAXs) scan of annealed PBTTT, supporting our interpretation of the 4D-STEM data.</p> <ul> <li>3 raw images</li> <li>Calibration files</li> <li>Combined processed image</li> </ul> <p>Spatially resolved diffraction intensity mapped onto a spherical harmonic basis set, for use as an input to structural simulations. One for each of the four 4D-STEM datasets.</p> <ul> <li>spherical_harmonics.zip</li> </ul> <p>Simulated structures of polymer chains based on the processed 4D-STEM data, created using code at https://github.com/SpakowitzLab/wlcsim. Each simulation produced 15 structures using different levels of chain alignment.</p> <ul> <li>chain_geometries.zip <ul> <li>main_dataset_03 (main case study)</li> <li>hi_align_03 (3.3x higher alignment factors than main dataset)</li> <li>undoped_10</li> <li>undoped_15</li> </ul> </li> </ul> <p>Simulations of charge transport through the structures</p> <ul> <li>transport_simulations.zip <ul> <li>ct_16_n100 (explores structural modifications and their effects on mobility)</li> <li>ct_18_n1000 (explores transport in all 4 directions and is used for making GIFs of simulations)</li> <li>ct_20_n1000 (explores the effect of degree of chain alignment)</li> <li>ct_28_n1000 (explores the effect of electric field strength)</li> <li>ct_29_n100 (tracks distances of individual on-chain moves)</li> <li>ct_30_n10000 (uses a high sample size and varied start positions to sample both short-time and long-time mobility)</li> </ul> </li> </ul> <p>Code for 4D-STEM analysis and simulations of charge transport. (Code for structural simulations can be found at https://github.com/SpakowitzLab/wlcsim).</p> <ul> <li>code.zip <ul> <li>preprocess_4dstem.py: package for preprocessing 4D-STEM files</li> <li>flow_fields.py: package for visualizing 4D-STEM data using director maps</li> <li>autocorrelation.py: package for measuring correlation lengths in 4D-STEM data via multiple methods</li> <li>spherical_harmonics.py: package for mapping diffraction intensity onto a spherical harmonic basis set. The results can be used as an input for structural simulations. </li> <li>charge_transfer_assets.py: package for performing charge transfer simulation on simulated chain coordinates</li> </ul> </li> <li>examples.zip <ul> <li>preprocess_demo: sample usage for preprocess_4dstem.py</li> <li>visualization_demo: sample usage for flow_fields.py, autocorrelation.py, and spherical_harmonics.py</li> <li>charge_tranpsort_demo: sample usage for charge_transfer_assets.py</li> <li>paper_figures: shows the code for producing several figures from the paper, using the included simulation datasets. </li> </ul> </li> </ul>
Conjugated polymers for microwave applications: untethered sensing platforms and multifunctional devices
<p>Raw data for the manuscript "Conjugated polymers for microwave applications: untethered sensing platforms and multifunctional devices".</p> <p>In reference to the manuscript, the dataset is organized in three subsets, each related to one of the Figures in the main texts of the article:</p> <ol> <li>The return losses (S11 spectra) from the reconfigurable microwave resonators, in the form of .s1p files, realized with different tuning conjugated polymers;</li> <li>The electrochemical and microwave characterization of an enzymatic reaction cell / microwave resonator assembly;</li> <li>The microwave characterization of a amplitude- and frequency-tunable resonator.</li> </ol>
BIOHARV project INTERREG V - Piezoelectric biobased polymers - Data set n°1
<p>The BIOHARV projet is financed by FEDER, Wallonia Region, West-Vlaanderen Region and Agentshap Innoveren & Ondernemen. BIOHARV project started on 2016, October 1st for 4 years. Six academic & technological partners are gathered (Institute Mines Telecom North Europe, Armines, University of Mons, Centexbel, University of Lille and Polytechnic University Hauts-De-France. The main objective of the BIOHARV project is demonstrate & investigate piezoelectric, electroactive & electromechanical properties of biobased polymers. </p> <p><strong>This dataset includes the evaluation of shear piezoelectric properties for various types of PLA processed by extrusion-MDO (machine-direction orientation) without high voltage poling. The technique used is based on a bimorph cantilver technique. The related article "Determination of Shear Piezoelectric Coefficients by a Bimorph Cantilever Technique for Extruded and Oriented Poly(L-Lactide) Films" will be published soon.</strong></p>
Supplemental Material to Journal Article "Determination of as-built properties of fiber reinforced polymers in a wind turbine blade using scanning electron and high-resolution X-ray microscopy"
<p>This set supplements the figure data to the article "Determination of as-built properties of fiber reinforced polymers in a wind turbine blade using scanning electron and high-resolution X-ray microscopy", DOI: <a href="https://doi.org/10.1016/j.jcomc.2022.100310">https://doi.org/10.1016/j.jcomc.2022.100310</a></p>
General database on O2/CO2 and H20 permeability for polymer-based nano composites
<p>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</p>
Solvent-triggered shape change in gradient-based 4D printed bilayers: case study on semi-crystalline polymer networks
<p>This dataset comes from the following paper:</p> <p>Lorenzo Bonetti, Aron Cobianchi, Daniele Natali, Stefano Pandini, Massimo Messori, Maurizio Toselli, Giulia Scalet, Solvent-triggered shape change in gradient-based 4D printed bilayers: case study on semi-crystalline polymer networks, Soft Matter, 2024. <a href="https://doi.org/10.1039/D4SM00304G" target="_blank" rel="noopener">https://doi.org/10.1039/D4SM00304G</a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>.xls files of the experimental data</li> <li>. m of the theoretical computations</li> </ul>
Dataset for "Optical Monitoring of Supramolecular Interactions in Polymers"
<p>This dataset contains the raw data for the open-access, peer-reviewed article “Optical Monitoring of Supramolecular Interactions in Polymers” (<a href="https://doi.org/10.1002/anie.202405922">https://doi.org/10.1002/anie.202405922</a>). The accepted version of the article was first published online on June 11, 2024 in Angewandte Chemie International Edition (Wiley).</p>
Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics: dataset
<div><strong>Abstract:</strong></div> <div>(from [1])</div> <div>Their great versatility makes polymer nanocomposites an important class of engineering materials. In order to gain detailed insights into the nanoscale mechanisms underlying their macroscopic mechanical properties, molecular dynamics (MD) simulations are a valuable tool to complement experimental studies. In this work, we modify the analytical potential functions of an efficient bead-spring model representing a generic polymer nanocomposite to account for the breaking of covalent bonds. We perform uniaxial tensile simulations of double-notched specimens and validate the model using experimental trends for overall stiffness, strength, and toughness. First, we study the effects of sample size, notch geometry, strain rate, temperature, and molar mass for the pure thermoplastic matrix material. Second, we analyze the influence of filler size and filler content on the mechanical behavior of the polymer nanocomposite. With this study, we show that in both the development of new materials and the optimization of established materials, it is possible to gain important preliminary insights into the effects of pertinent material characteristics with a simple MD setup, which can then be further refined by increasing the complexity of the material description and the boundary conditions. </div> <div> </div> <div> </div> <div><strong>Contact:</strong></div> <div>Felix Weber</div> <div>Institute of Applied Mechanics</div> <div>Friedrich-Alexander-Universität Erlangen-Nürnberg</div> <div>Egerlandstr. 5</div> <div>91058 Erlangen</div> <div>Germany</div> <div> </div> <div> </div> <div><strong>Software:</strong></div> <div>All simulations were performed with LAMMPS [2,3] (version 23 June 2022, patch_23Jun2022_update3) </div> <div> </div> <div>Compiler: GNU C++ 11.2.0 with OpenMP not enabled</div> <div>C++ standard: C++11</div> <div> </div> <div>Active compile time flags:</div> <div>-DLAMMPS_GZIP</div> <div>-DLAMMPS_SMALLBIG</div> <div> </div> <div>Installed packages:</div> <div>BPM CLASS2 DPD-BASIC EXTRA-DUMP EXTRA-FIX EXTRA-MOLECULE INTEL KSPACE MANYBODY </div> <div>MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT </div> <div> </div> <div>Moreover, we employ a self-avoiding random walker [4,5] implemented in MATLAB [6] for the initial positioning of the polymer chains and nanoparticles.</div> <div> </div> <div> </div> <div><strong>License:</strong></div> <div>Creative Commons Attribution 4.0 International</div> <div> </div> <div> </div> <div><strong>Context:</strong></div> <div>This dataset contains the results presented in [1] and the necessary data to obtain those.</div> <div> </div> <div> </div> <div><strong>Content:</strong></div> <div>Throughout this data set, LAMMPS lj units are used. The files to reproduce our simulations and their results are structured as follows:</div> <div>- 01_neat: Neat polymer systems</div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations, including the notch insertion (token "initcrack")</div> <div> - 1.1: Simulations for different sample sizes/numbers of chains (token "chains") at constant molar mass/number of beads per chain</div> <div> - 1.3: Simulations for different widths of the Dirichlet boundary (token "diri")</div> <div> - 2.1: Simulations for different critical bond lengths (token "bondcrit")</div> <div> - 2.2: Simulations for different bond breaking probabilities (token "bondcprob")</div> <div> - 3.1: Simulations for different crack widths (token "crackwidth")</div> <div> - 3.2: Simulations for different crack lengths (token "crackdepth")</div> <div> - 4: Simulations for different strain rates (token "strainrate")</div> <div> - 5: Simulations for different temperatures (token "tem")</div> <div> - 6: Simulations for different molar masses/numbers of beads per chain (token "chain-len")</div> <div>- 02_PNC: Polymer nanocomposite (PNC) systems </div> <div> - 01_EQU: Equilibration simulations</div> <div> - 02_UT: Uniaxial tensile simulations for different filler radii (token "rF") and filler contents/numbers (token "nF"), including the notch insertion (token "initcrack")</div> <div>- parameter_study: Postprocessing of the MD results </div> <div> - parameter_study.xlsx: Overview of the simulations with their respective parameters and statistical analysis of stiffness, strength, and toughness from filtered stress-strain curves (Savitzky-Golay filter applying a linear polynomial and frame length 21)</div> <div> - .csv files of the single sheets of parameter_study.xlsx:</div> <div> - samples.csv: Individual specimens</div> <div> - averages.csv: Statistical analysis of the different samples corresponding to one batch</div> <div> </div> <div>Each simulation directory contains:</div> <div>- LAMMPS input script (*.in) of the simulation</div> <div>- input.prm: Input parameters of the simulation (read by the input script)</div> <div>- LAMMPS data file (*.data, molecular style) of the investigated sample</div> <div>- LAMMPS_out: Resulting LAMMPS data files, log files and simulation results in tabulated form</div> <div> - additional files for the tensile tests: </div> <div> - brokenbonds.dat: Fix print output for fix brokenbondsprint (step time brokenbondsPerStep brokenbondsSum)</div> <div> - stressstrain.dat: Time-averaged data for fix dumpOpt (step v_strain_xx v_OBSstrain_xx v_Piola_xx) with the local strain at the crack tip v_OBSstrain_xx</div> <div> - thermo_out.Dat: Thermodynamic output in condensed tabulated form</div> <div> - thermo_out_SG.Dat: Thermodynamic output in condensed tabulated form, filtered by a Savitzky-Golay filter (linear polynomial, frame length 21)</div> <div> - thermo_out_STD.Dat: Standard deviation between the filtered and unfiltered data</div> <div>- job.out: Simulation log file</div> <div>- meta.info: Meta data of the simulation run</div> <div> </div> <div>Naming convention:</div> <div>- 01_neat: GTPm-[number of chains]_chains-[number of beads per chain]_chain_len-[temperature]_tem-[parameter value]_[parameter]-[sample]</div> <div> - [parameter]: Parameter studied, i.e. diri/bondcrit/bondcprob/crackwidth/crackdepth/strainrate/tem (see above)</div> <div> - [parameter value]: Value of the parameter studied</div> <div> - [sample]: Sample ID</div> <div>- 02_PNC: GTPm_rF-[filler radius]_nF-[number of fillers]_[sample]</div> <div> - [sample]: Sample ID</div> <div> </div> <div>Output quantities (columns of *.Dat files):</div> <div>- Step: time step</div> <div>- Time: time</div> <div>- TotEng: total energy</div> <div>- PotEng: potential energy</div> <div>- KinEng: kinetic energy</div> <div>- E_pair: pair energy</div> <div>- E_bond: bond energy</div> <div>- E_angle: angle energy</div> <div>- E_dihed: dihedral energy</div> <div>- Temp: temperature</div> <div>- Press: hydrostatic pressure</div> <div>- Pxx: xx component of pressure tensor</div> <div>- Pyy: yy component of pressure tensor</div> <div>- Pzz: zz component of pressure tensor</div> <div>- Pxy: xy component of pressure tensor</div> <div>- Pxz: xz component of pressure tensor</div> <div>- Pyz: yz component of pressure tensor</div> <div>- Volume: volume of simulation box</div> <div>- Lx: box length in x direction</div> <div>- Ly: box length in y direction</div> <div>- Lz: box length in z direction</div> <div>- Density: mass density</div> <div>- c_RG: radius of gyration</div> <div>- c_RG[1]: squared radius of gyration tensor (xx component)</div> <div>- c_RG[2]: squared radius of gyration tensor (yy component)</div> <div>- c_RG[3]: squared radius of gyration tensor (zz component)</div> <div>- c_RG[4]: squared radius of gyration tensor (xy component)</div> <div>- c_RG[5]: squared radius of gyration tensor (xz component)</div> <div>- c_RG[6]: squared radius of gyration tensor (yz component)</div> <div>- c_bondave[1]: bond energy averaged over all atoms</div> <div>- c_bondave[2]: bond distance averaged over all atoms</div> <div>- c_bondave[3]: squared bond distance averaged over all atoms</div> <div>- c_angleave[1]: angle energy averaged over all atoms</div> <div>- c_angleave[2]: angle averaged over all atoms degree</div> <div>- c_angleave[3]: cosine of angle</div> <div>- c_angleave[4]: squared cosine of angle</div> <div>- c_MSD[1]: mean squared displacement x-direction</div> <div>- c_MSD[2]: mean squared displacement y-direction</div> <div>- c_MSD[3]: mean squared displacement z-direction</div> <div>- c_MSD[4]: total mean squared displacement</div> <div>- c_COM[1]: x coordinate of center of mass</div> <div>- c_COM[2]: y coordinate of center of mass</div> <div>- c_COM[3]: z coordinate of center of mass</div> <div>- v_strain_xx: xx component of engineering strain tensor </div> <div>- v_strain_yy: yy component of engineering strain tensor </div> <div>- v_strain_zz: zz component of engineering strain tensor </div> <div>- v_vMisesequivstress: von Mises equivalent stress</div> <div>- v_Piola_xx: xx component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yy: yy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_zz: zz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xy: xy component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_xz: xz component of the virial stress tensor normalized by the initial volume</div> <div>- v_Piola_yz: yz component of the virial stress tensor normalized by the initial volume</div> <div>- v_strain_xy: xy component of engineering strain tensor </div> <div>- v_strain_xz: xz component of engineering strain tensor </div> <div>- v_strain_yz: yz component of engineering strain tensor </div> <div> </div> <div> </div> <div><strong>References:</strong></div> <div>[1] F. Weber, V. Dötschel, P. Steinmann, S. Pfaller, M. Ries, "Evaluating the impact of filler size and filler content on the stiffness, strength, and toughness of polymer nanocomposites using coarse-grained molecular dynamics", Engineering Fracture Mechanics, vol. 307, p. 110270, 2024.</div> <div>[2] S. Plimpton, "Fast parallel algorithms for short-range molecular dynamics", Journal of computational physics, vol. 117, no. 1, pp. 1-19, 1995.</div> <div>[3] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, S. J. Plimpton, "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.</div> <div>[4] V. Dötschel, S. Pfaller, and M. Ries, "Studying the mechanical behavior of a generic thermoplastic by means of a fast coarse-grained molecular dynamics model", Polymers and Polymer Composites, vol. 31, pp. 1–11, 2023.</div> <div>[5] M. Ries, V. Dötschel, J. Seibert, and S. Pfaller, A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites, Zenodo, 2022, https://doi.org/10.5281/zenodo.6245699.</div> <div>[6] The MathWorks, Inc., "Matlab. the language of technical computing", https://de.mathworks.com/help/matlab/.</div> <div> </div> <div> </div> <div><strong>Funding:</strong></div> <div>The authors gratefully acknowledge funding by various sources:</div> <div>The overall research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/2-2023. Sebastian Pfaller is furthermore funded by the DFG projects 396414850 (Individual Research Grant 'Identifikation von Interphaseneigenschaften in Nanokompositen') and 505866713 together with the Agence nationale de la recherché (ANR, French Research Agency) – ANR-22-CE92-0049 (Individuel Research Grant 'BIO ART'). In addition, scientific support and HPC resources have been provided by the Erlangen National High Performance Computing Center (NHR@FAU) of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) under the NHR project b136dc. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the DFG project 440719683.</div>
Dataset for a publication: "Silver-enriched Microdomain Patterns as Advanced Bactericidal Coatings for Polymer-based Medical Devices"
<p>The data set contains the data that were used within the article "Silver-enriched Microdomain Patterns as Advanced Bactericidal Coatings for Polymer-based Medical Devices".</p> <p> </p> <p> </p>
Benchmarking Study of Deep Generative Models for Inverse Polymer Design: Reinforcement Learning
<p>Well-trained models and generation results for reinforcement learning part of <a href="https://github.com/ytl0410/Polymer-Generative-Models-Benchmark">ytl0410/Polymer-Generative-Models-Benchmark: Well-trained models and generative outcomes for the paper "Benchmarking Study of Deep Generative Models for Inverse Polymer Design" (github.com)</a></p>
Data for a publication "Polymer-metal bilayer with alkoxy groups for antibacterial improvement"
<p><strong>Abstract:</strong></p> <p>Many bio‐applicable materials, medical devices, and prosthetics combine both polymer and metal components to benefit from their complementary properties. This goal is normally achieved by their mechanical bonding or casting only. Here, we report an alternative easy method for the chemical grafting of a polymer on the surfaces of a metal or metal alloys using alkoxy amine salt as a coupling agent. The surface morphology of the created composites was studied by various<br>microscopy methods, and their surface area and porosity were determined by adsorption/desorption nitrogen isotherms. The surface chemical composition was also examined by various spectroscopy techniques and electrokinetic analysis. The distribution of elements on the surface was determined, and the successful bonding of the metal/alloys on one side with the polymer on the other by alkoxy amine was confirmed. The composites show significantly increased hydrophilicity, reliable chemical stability of the bonding, even interaction with solvent for thirty cycles, and up to 95% less bacterial adhesion for the modified samples in comparison with pristine samples, i.e., characteristics that are promising for their application in the biomedical field, such as for implants, prosthetics, etc.<br>All this uses universal, two-step procedures with minimal use of energy and the possibility of production on a mass scale.</p> <p> </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.