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57 results for “Nanocomposites”
Characterization of TiO2/Fe2O3 nanocomposites prepared via impregnation-calcination method
<p>The link contains XRD, SEM-EDX, UV-DRS, PL, Electrochemical measurements of the prepared TiO<sub>2</sub>/Fe<sub>2</sub>O<sub>3</sub> photocatalyst</p>
A coupled MD-FE methodology to characterize mechanical interphases in polymeric nanocomposites: pseudo-experimental data
<p>readme.txt</p> <p><strong>Abstract:</strong><br> (from [1])</p> <blockquote> <p>This contribution introduces an unconventional procedure to characterize spatial profiles of elastic and inelastic properties inside polymer interphases around nanoparticles. Interphases denote those regions in the polymer matrix whose mechanical properties are influenced by the filler surfaces and thus deviate from the bulk properties. They are of particular relevance in case of nano-sized filler particles with a comparatively large surface-to-volume ratio and hence can explain the frequent observation that the overall properties of polymer nanocomposites cannot be determined by classical mixing rules, which only consider the behavior of the individual constituents.<br> <br> Interphase characterization for nanocomposites poses hardly solvable challengesto the experimenter and is still an unsolved problem in many cases. Instead of real experiments, we perform pseudo experiments using our recently developed Capriccio method, which is an MD-FE domain-decomposition tool specifically designed for amorphous polymers. These pseudo-experimental data then serve as input for a typical inverse parameter identification. With this procedure, spatially varying mechanical properties inside the polymer are, for the first time, translated into intuitively understandable profiles of continuum mechanical parameters.</p> <p><br> As a model material, we employ silica-enforced polystyrene, for which our procedure reveals exponential saturation profiles for Young’s modulus and the yield stress inside the interphase, where the former takes about seven times the bulk value at the particle surface and the latter roughly triples. Interestingly, hardening coefficient and Poisson’s ratio of the polymer remain nearly constant inside the interphase. Besides gaining insight into the constitutive influence of filler particles, these unexpected and intriguing results also offer interesting explanatory options for the failure behavior of polymer nanocomposites.</p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universiät Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p> </p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:<br> [1] Ries, M.; Possart, G.; Steinmann, P. & Pfaller, S., "A coupled MD-FE methodology to characterize mechanical interphases in polymeric nanocomposites," <em>International Journal of Mechanical Sciences, </em><em>Elsevier, </em><strong>2021</strong>, 106564.</p> <p>This dataset contains the results of a multiscale study on polystyrene-silica nanocomposites using an atomistic-continuum coupling approach. 120 polystyrene samples, each containing 2 nano-sized silica particles are subjected to uniaxial tension. Here we use coarse-grained molecular dynamics (MD) domain embedded into a larger finite element (FE) region. These two resolutions are coupled in a concurrent multiscale fashion using the so-called Capriccio method. We observe the deformation state of the MD and FE domain, as well as the relative displacement of the two nanoparticles with respect to each other. Based on this pseudo-experimental data, we derive the material properties (Young's modulus, Poisson's ratio, yield stress, hardening) of the interphase forming in the proximity of the nanoparticles in [1].</p> <p>A more detailed description of the used methods can be found in Ries et al. [1].</p> <p> </p> <p><strong>Content:</strong></p> <p>The attached text file contains the following quantities (columns) for all samples (rows):</p> <ul> <li>sample: [initial nanoparticle distance]-ID</li> <li>d0_NP: initial distance of nanoparticles in nm</li> <li>rot_x: rotation of nanoparticles with respect to x-axis in degree</li> <li>d_NP: distance of nanoparticles in nm (after equilibration)</li> <li>Elements: number of finite elements</li> <li>Element_warnings: number of element warnings by Abaqus</li> <li>LS: loadstep 1-6</li> <li>eps_NP(LS): tensile strain of nanoparticles in loadstep LS in %</li> <li>eps_MD(LS): tensile strain of MD domain in loadstep LS in %</li> <li>eps_NP_MD(LS): tensile strain of nanoparticles normalized to eps_MD(LS) in loadstep LS</li> <li>eps_FE(LS): tensile strain of FE domain in loadstep LS in %</li> <li>eps_NP_FE(LS): tensile strain of nanoparticles normalized to eps_FE(LS) in loadstep LS</li> <li>eps_NP_FE(LS): tensile strain of nanoparticles normalized to eps_FE(LS) in loadstep LS</li> <li>u_max(LS): maximum displacement of FE nodes in load step LS in nm</li> <li>F_ext(LS): external force in load step LS in E-11 N</li> </ul> <p> </p>
Brownian Relaxation Shakes and Breaks Magnetic Iron Oxide-Polymer Nanocomposites to Release Cargo
<p>Original data supporting the findings of the manuscript and supplementary materials sorted after Figures and their respective panels.</p>
Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite"
<p>Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite", by Paulina Szymoniak, Brian R. Pauw, Xintong Qu, and Andreas Schönhals.</p> <p>Datasets are in three-column ascii (processed and azimuthally averaged data) from a Xenocs NanoInXider SW instrument. Monte-Carlo analyses were performed using McSAS 1.3.1, other analyses are in the Python 3.7 worksheet. Graphics and result tables are output by the worksheet. </p>
Dataset for "Metallosupramolecular polymers as precursors for platinum nanocomposites"
<p>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.</p>
A quantitative interphase model for polymer nanocomposites: Verification, validation, and consequences regarding size effects: dataset
<p><strong>Abstract:</strong><br> (from [1])</p> <blockquote> <p>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.</p> </blockquote> <p><br> <br> <strong>Contact:</strong><br> Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong><br> Abaqus version R2018</p> <p><strong>License:</strong><br> Creative Commons Attribution 4.0 International<br> <br> <strong>Context:</strong><br> Data set supplementing journal paper:<br> [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.<br> This dataset contains the results presented in [1] and the necessary data to obtain those.</p> <p><br> <strong>Content:</strong></p> <p>simulation folder denotation (“-” used instead of decimal points):<br> distance_particles _ radius_particle _ thickness_ip _ num_ip _ length_box _ factor_el_length _ fraction_box_length _ switch_mat_ip</p> <p>with</p> <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> <p> <br> <br> each simulation folder contains the following file types:</p> <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> <p><strong>folder structure:</strong></p> <ul> <li>Standard_case:<br> 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:<br> 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:<br> 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:<br> 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>
Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites: supplementary information and dataset
<p><strong>Abstract:</strong><br> (from [1])</p> <blockquote> <p>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. </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], 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<strong>:</strong><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 [3]</p> <p>Post-processing Matlab R2019b</p> <p>Evaluation of polymer entanglements with Z1-Algorithm [4]</p> <p> </p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:</p> <p>[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, <strong>2022</strong>, 16.</p> <p>This dataset contains the results presented in [1] and the necessary data to obtain those as well as supplementary information.</p> <p><strong>Content:</strong></p> <p>supplementary material:</p> <p>supplementary_information.pdf</p> <p>data:<br> folder names vary depending on the context, explained in the following:</p> <p> </p> <p>01_matrix</p> <ul> <li> <p>01_equilibration<br> sample equilibration to different temperatures<br> nomenclature: equil_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>[-<batch_ID>]</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.1-1.0</p> </li> <li> <p>batch_ID: 2-5 </p> </li> </ul> </li> <li> <p>02_temperature_dependence<br> uniaxial tension simulations to identify temperature dependence<br> nomenclature: 01_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.1-1.0</p> </li> </ul> </li> <li> <p>03_directional_dependence<br> uniaxial tension simulations to prove isotropy in Y and Z direction; X direction in 04_rate_dependence<br> nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-<batchID></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-5</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>04_rate_dependence<br> uniaxial tension simulations to identify strain rate dependence<br> nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>[-<batchID>]</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>05_cyclic_loading<br> sinusoidal uniaxial deformation<br> 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></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4</p> </li> <li> <p>strain_amplitude: 0.01, 0.05, 0.15, 0.2</p> </li> </ul> </li> <li> <p>06_relaxation<br> relaxation subsequent to time-proportional deformation<br> 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</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4</p> </li> <li> <p>strain_amplitude: 0.01, 0.05, 0.15, 0.2</p> </li> </ul> </li> <li> <p>07_simple_shear<br> time-proportional simple shear deformation with different strain rates<br> nomenclature: SS_P2VPSi-rate_<strain_rate>-<batchID></p> <ul> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>08_large_deformation<br> uniaxial deformation up to 100% strain<br> nomenclature: 02_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperatur>-strain_<max_strain></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>max_strain: 1</p> </li> </ul> </li> </ul> <p>02_filler</p> <ul> <li> <p>01_Silica_equilibration<br> sample equilibration</p> </li> <li> <p>02_time_proportional<br> time-proportional uniaxial and simple shear tests<br> nomenclature: Silica_BV-<loadcase>_<direction>-strain_<max_strain>-rate_<strain_rate></p> <ul> <li> <p>loadcase: uniaxial tension (UT), simple shear (SS)</p> </li> <li> <p>max_strain: 0.1</p> </li> <li> <p>direction: X, Y, Z (UT); XY, XZ, YZ (SS)</p> </li> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> </ul> </li> <li> <p>03_time_periodic<br> time-periodic uniaxial and simple shear tests<br> nomenclature: Silica_BV-<loadcase>_<direction>_sin-ampl_<strain_amplitude>-rate_<max_strain_rate></p> <ul> <li> <p>loadcase: uniaxial tension (UT), simple shear (SS)</p> </li> <li> <p>direction: X, Y, Z (UT); XY, XZ, YZ (SS)</p> </li> <li> <p>strain_amplitude: 0.025</p> </li> </ul> </li> </ul> <p>03_composite</p> <ul> <li> <p>01_equilibration<br> sample equilibration<br> nomenclature: equil_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID></p> <ul> <li> <p>filler_radius: 2.5-10.0</p> </li> <li> <p>filler_number: 1-160 (depending on filler_radius)</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>02_uniaxial-tension<br> uniaxial tension simulations<br> nomenclature: UT_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID></p> <ul> <li> <p>filler_radius: 2.5-10.0</p> </li> <li> <p>filler_number: 1-160 (depending on filler_radius)</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>03_filler-maxtrix-adhesion<br> equilibration and uniaxial deformation of samples with mid and weak filler-matrix adhesion (for strong adhesion see 01_equilibration and 02_uniaxial-tension<br> nomenclature: see above</p> </li> <li> <p>04_IP_equilibration<br> equilibration of samples to evaluate the microstructure for neat polymer and composites with filler radius 2.5-7.5<br> nomenclature: P2VPSi-<chains>x<chain_atoms>_rNP_<filler_radius>-nNP_<filler_number>_pos_<filler_pos>-<batchID></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>filler_radius: 0 (neat), 2.5, 5.0, 7.5</p> </li> <li> <p>filler_number: 0 (neat), 1</p> </li> <li> <p>batchID: 1-20</p> </li> </ul> </li> </ul> <p> </p> <p> </p> <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> </p> <p>Output quantities (columns of *.Dat files):<br> Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <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><br> </p> <p><strong>References</strong>:</p> <p>[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”, <em>Express Polymer Letters</em>, <strong>2022</strong>, 16.</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>
X-ray scattering Datasets of gold and silver nanoparticle composites, relating to the publication "Gold and silver dichroic nanocomposite in the quest for 3D printing the Lycurgus cup"
<p>Wide-range X-ray scattering datasets and analyses for all samples described in the 2020 publication "Gold and silver dichroic nanocomposite in the quest for 3D printing the Lycurgus cup". These datasets are composed by combining multiple small-angle x-ray scattering and wide-angle x-ray scattering curves into a single dataset. They have been analyzed using McSAS to extract polydispersities and volume fractions. They have been collected using the MOUSE project (instrument and methodology). </p> <p> </p>
Revealing the percolation–agglomeration transition in polymer nanocomposites via MD-informed continuum RVEs with elastoplastic interphases - dataset
<p><strong>Abstract</strong>:<br>from [1]</p> <p>This contribution builds the concluding step of a multiscale approach to effectively capture the mechanical <br>behavior of polymer nanocomposites (PNCs), in this case, silica-modified polystyrene. By introducing <br>continuum-based representative volume elements (RVEs) that employ previously identified elastoplastic property <br>gradients for the interphases surrounding the fillers, the effects of particle size, particle volume fraction, <br>and agglomeration on the mechanical performance are investigated. Uniaxial tension tests are simulated with <br>the respective finite-element RVEs, and stress–strain curves are derived. The elastic and plastic material <br>properties of the RVE can then be extracted and analyzed quantitatively by fitting the stress–strain curves <br>with a Voce-type elastoplasticity formulation. <br>At small degrees of agglomeration, i.e., good particle dispersion, in combination with sufficiently large <br>particle volume fraction, percolation bands form, leading to improved elastic and plastic properties. Higher <br>degrees of agglomeration or particle clusters behave like large single particles, which has an adverse effect, i.e., <br>the nanoscale size effect is thereby neutralized. Therefore, the precise MD-informed elastoplastic interphase <br>representation of our RVEs enables the investigation of the transition from beneficial percolation to unfavorable <br>agglomeration. Ultimately, this contribution establishes a link between the effects of particle size, particle <br>volume fraction, agglomeration, and percolation, which have so far only been discussed separately in the <br>literature. <br>Our methodology offers new insights into the structure–property relations of PNCs and their resulting <br>mechanical behavior. The underlying multiscale approach with a systematic transition from molecular to <br>microscopic scales is required to complement experimental observations and exploit the full potential of PNCs. </p> <p><br><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 finite element simulations were performed with Simulia Abaqus/CAE2018 </p> <p><strong>License</strong>:</p> <p>Creative Commons Attribution Non Commercial 4.0 International</p> <p><strong>Context</strong>:</p> <p>Data set supplementing journal paper:</p> <p>[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.</p> <p><strong>Content</strong>:</p> <p>- excel sheet summarizing all RVE simulations in combination with the elastoplastic constitutive model calibration: elastoplastic_constitutive_model_calibration.xlsx<br>- input data for each RVE FE simulation in *.inp format following the naming convention:<br> agg_<degree of agglomeration>-fillercont_<filler content>Percent-fillerrad_<filler radius>nm<br> - degree of agglomeration is defined in [1]<br> - filler content is given in volume percent<br> - filler radius is given in nanometer </p> <p> </p>
Raw data for Investigation of Combined Aging and Mullins Stress Softening of Rubber Nanocomposites
<p><strong>Specification of affiliations:</strong></p> <ul> <li>Barbora Hanulikova - Centre of Polymer Systems</li> <li>Milan Masar - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript Investigation of Combined Aging and Mullins Stress Softening of Rubber Nanocomposites.</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>
Comprehensive study of antimicrobial polycaprolactone/clay nanocomposite films: preparation, characterization, properties and degradation in simulated body fluid
<p>Even though the biodegradability of polycaprolactone (PCL) and its nanocomposites is lower compared to other biodegradable polyesters, this property and good biocompatibility are used for development of materials for drug delivery with a long-term effect. We prepared novel PCL/clay nanocomposite films with antimicrobials chlorhexidine (CH) or octenidine (OCT) combined with ZnO anchored on vermiculite (VER). The intercalation of CH and OCT into the interlayer of VER/ZnOVER was confirmed by XRD, FTIR and SEM. The organically modified nanofillers compared to VER (−46.0 mV) or ZnOVER (−34.9 mV) showed a positive ζ-potential (+30.7 mV (VER_CH), +21.9 mV (VER_OCT), +24.6 mV (ZnOVER_CH)) indicating a relatively stable materials, except ZnOVER_OCT (+8.6 mV), which strongly agglomerated.</p> <p>Thin PCL/clay films were prepared by solvent casting method and the effect of used nanofillers on structural, thermal, mechanical and antimicrobial properties followed by degradation under hydrolytic conditions was studied. The results showed that presence of ZnO significantly decreases thermal and mechanical stability. The nanofillers with the higher hydrophilic character are responsible for the fastest degradation of PCL matrix. Films possessed high antimicrobial efficiency in long time intervals, hence these nanocomposites open new avenues for the possible application of such materials for the drug delivery with a long-term effect.</p>
Transformer-based graphical neural network with expert experience multimodal learning (TGEML) framework: a nanocomposite performance predictor
<p>TGEML is a novel multimodal nanocomposite processing framework consists of a polymer multimodal featurizer called TGEML-polymer and a nanoparticle expert experience featurizer called TGEML-nano.</p>
Bifunctional upconverting luminescent-magnetic FeS2@NaYF4:Yb3+,Er3+ core@shell nanocomposites with tunable luminescence for temperature sensing†
<p>Advanced optically active materials have experienced significant development in recent years. Light-emitting materials combined with materials that exhibited magnetic properties result in bifunctional materials with an expanded range of capabilities and applications. New functionalities as a result of the core@shell structure enable interactions with the light and the external magnetic field as well. Continuous progress in materials science leads to innovations that enhance data storage and transmission, bioimaging, sensing, optical thermometry, and other applications crucial to advanced technology. In this research, we have focused on the optimization of the synthesis of a nano-sized FeS<span>2</span> material as an optically active, magnetic component of the core, and NaYF<span>4</span>:Yb<span>3+</span>,Er<span>3+</span> nanoparticles (NPs) as a temperature sensitive, up-conversion (UC) luminescence part of the shell. The synthesized core@shell type nanocomposite (NC) material FeS<span>2</span>@NaYF<span>4</span>:Yb<span>3+</span>,Er<span>3+</span> exhibits simultaneously the unique features of the core and shell components. The recorded UC emission spectra under 975 nm laser excitation show the possibility of tuning the UC luminescence color with the application of a highly absorbing FeS<span>2</span> component in the composite material. Moreover, the magnetic properties of the FeS<span>2</span> core nanoparticles and the synthesized NC were compared to confirm the potential application of the NC as a novel bifunctional luminescent-magnetic sensing platform. For both materials the luminescence color changed with the increasing laser power density, allowing color-tunable light generation. Additionally, optical temperature sensing properties of the NPs and NC were compared, resulting in a very high relative temperature sensitivity of <span>∼</span>2.0% K<span><span>−</span>1</span> for both materials.</p>
Data from: CoZr nanocomposites in a ceramic-metal AlOx(OH)y/Al matrix with different Co/Zr ratio and its potential for syngas processing
<p>Data from article in Dalton Transaction</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>
RAW data - Boric acid modified polydopamine and nanocolumnar hydrogenated TiO2 nanocomposite with improved photocatalytic performance. Appl. Surf. Sci. 2025, 686, 162118. DOI: 10.1016/j.apsusc.2024.162118 - AFM - ICONBruker - CNBM UAM - BAPDA/H:TiO2
<p>This repository contains RAW data for the experiment described in the publication: Jakub Szewczyk, Tim Tjardts, Fabian Symalla, Igor Iatsunskyi, Franz Faupel, Cenk Aktas, Emerson Coy, Salih Veziroglu, Boric acid modified polydopamine and nanocolumnar hydrogenated TiO2 nanocomposite with improved photocatalytic performance. Applied Surface Science 2025, 686, 162118.</p> <p>The remaining data supporting this study's findings are available from the corresponding author upon reasonable request. The raw AFM data is posted here in the open-access model.</p> <p> </p> <p> </p>
SYNTHESIS, CHARACTERIZATION AND ECOTOXICOLOGICAL EVALUATION OF Ag3VO4/Ag NANOCOMPOSITE
<p>Historically, the development of new methods of synthesizing materials has been linked to the development of societies, mainly due to the ability to promote changes in materials and enable new types of technological applications. Although there is quite sophisticated content about pulsed lasers in femtoseconds, there is still a need to understand the mechanisms of formation and transformation of semiconductor materials, as well as to verify the changes provided, in a more appropriate way, when synthesized by this technique. However, such development must always be combined with the need to minimize and verify environmental effects. This study had as its main objectives to synthesize Ag<sub>3</sub>VO<sub>4</sub> via the microwave-assisted hydrothermal method, in different synthesis times (t: 2, 4, 8, 16, 32, 64 min); irradiate the Ag3VO4 samples using a pulsed laser in femtoseconds to form the composite; characterize particulate composites; as well as to evaluate the ecotoxicity of the composite on the floating aquatic macrophyte species <em>Ricciocarpus natans</em>. The powders obtained from the composites were structurally characterized by the techniques of X-ray diffraction, Raman scattering spectroscopy and UV-Vis electronic spectroscopy, and had the surface characterized by scanning and transmission electron microscopy techniques, allowing the identification of structural alterations and surface. Theoretical calculations were used to understand the geometric, vibrational, electronic and charge distribution structures at the atomic level and the nature of chemical interactions present in semiconductor structures. In addition, toxicity bioassays were carried out until the 42nd day in order to obtain an analysis of the ecotoxicological effects, with the aid of mathematical modeling, of these particles on the bioindicator <em>Ricciocarpus natans</em>.</p> <p><strong>Electron Charge Density: 2D Map</strong></p> <p>Raw theoretical electron charge density files which are used to create 2D difference maps for Ag<sub>3</sub>VO<sub>4</sub> and AgVO<sub>3</sub> used in the main manuscript</p> <p><strong>Bader Charges</strong></p> <p>Raw theoretical Bader charges files for Ag<sub>3</sub>VO<sub>4</sub> and AgVO<sub>3</sub> used in the main manuscript</p> <p><strong>Band Structure and Density of States</strong></p> <p>Raw band structure and density of states files for Ag<sub>3</sub>VO<sub>4</sub> and AgVO<sub>3</sub> used in the main manuscript</p> <p><strong>Bioassays</strong></p> <p>Raw bioassays files for composite used in the main manuscript</p> <p><strong>Raman</strong></p> <p>Raw Raman files for Ag<sub>3</sub>VO<sub>4</sub> and composite and theoretical raw Raman files for Ag<sub>3</sub>VO<sub>4</sub> and AgVO<sub>3</sub> used in the manuscript</p> <p><strong>Rietveld Refinement</strong></p> <p>Raw Rietveld refinement files for Ag<sub>3</sub>VO<sub>4</sub> used in the manuscript</p> <p><strong>SEM</strong></p> <p>SEM images for Ag<sub>3</sub>VO<sub>4</sub> and composite used in the manuscript</p> <p><strong>TEM</strong></p> <p>TEM and EDX spectres and maps images for Ag<sub>3</sub>VO<sub>4</sub> and composite and nanodiffraction image for composite used in the manuscript</p> <p><strong>Optimized Structure</strong></p> <p>Raw optimized structure files for Ag<sub>3</sub>VO<sub>4</sub> and AgVO<sub>3</sub> used in the manuscript</p> <p><strong>TOPOND</strong></p> <p>Raw TOPOND files for Ag<sub>3</sub>VO<sub>4</sub> and AgVO<sub>3</sub> used in the manuscript</p> <p><strong>UV-Vis Spectroscopy</strong></p> <p>Raw UV-Vis files for Ag<sub>3</sub>VO<sub>4</sub> and AgVO<sub>3</sub> used in the manuscript</p> <p><strong>XRD</strong></p> <p>Raw XRD files for Ag<sub>3</sub>VO<sub>4</sub> and composite used in the manuscript</p> <p>Note: The words "composite" and "2min_irra or 64 min_irra" are used for the same kind of sample. They are synonymous in this work.</p> <p>Funding provided by: grant #2020/11232-3, São Paulo Research Foundation (FAPESP).</p> <p><em>This</em><em> </em><em>study</em><em> </em><em>was</em><em> </em><em>financed in part</em><em> </em><em>by</em><em> </em><em>the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance</em><em> Code 001.</em></p>
MCNP Results for Nanocomposite Shielding in High-Energy Proton Fields
<p>Monte Carlo radiation transport results for various physics schemes (neutron + proton, neutron + proton + delta ray, and neutron + proton + delta ray + light recoil ions) and nanocomposite structural models (bulk homogenous material, hollow carbon cylinders suspended in polymer matrix, and carbon spheres in nanotube structure suspended in polymer matrix) of a polymer-carbon-nanotube nanocomposite shielding material in high-energy proton beams of 63 MeV and 105 MeV. </p>
Photocatalytic degradation of rhodamine B using zinc oxide/silver nanowire nanocomposite films under UV irradiation
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