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852 results for “grain”
Data from: Detecting sub-micron space weathering effects in lunar grains with synchrotron infrared nanospectroscopy
<p>Space weathering processes induce changes to the physical, chemical, and optical properties of space-exposed soil grains. For the Moon, space weathering causes reddening, darkening, and diminished contrast in reflectance spectra over visible and near-infrared wavelengths. The physical and chemical changes responsible for these optical effects occur on scales below the diffraction limit of traditional far-field spectroscopic techniques. Recently developed super-resolution spectroscopic techniques provide an opportunity to understand better the optical effects of space weathering on the sub-micrometer length scale. This paper uses synchrotron infrared nanospectroscopy to examine depth-profile samples from two mature lunar soils in the mid-infrared, 1500–700 cm<sup>-1</sup> (6.7–14.3 µm). Our findings are broadly consistent with prior bulk observations and theoretical models of space weathered spectra of lunar materials. These results provide a direct spatial link between the physical/chemical changes in space-exposed grain surfaces and spectral changes of space-weathered bodies.</p>
Grain Size Distribution of slickenlines from Plan de los Platanos and Big Piute Faults
<p>Grain Size Distribution (GSD) of Plan de Platanos (<strong>PP</strong>) and Big Piute (<strong>BP</strong>) Fault Slickenlines</p> <p>Grains were manually segmented from images of increasing magnifications obtained through optical and SEM microscopy. The size of the grains was then estimated from the grains' area by calculating the diameter (<strong>d<sub>equ</sub></strong>) of a circle of equivalent area.</p> <p><strong>Instruments:</strong></p> <ul> <li>ZEISS AX10 Petrographic microscope</li> <li>ZEISS Merlin HR-SEM (BSE)</li> </ul> <p><strong>PP</strong>---> sample <strong>PP_AU01a</strong>_--> Orientation <strong>XZ</strong>---->N=8617----->andesite</p> <p><strong>BP</strong>---> sample <strong>BP_07b</strong>--> Orientation <strong>YZ</strong>---->N=11425------>quartzite</p> <p><strong>Unit of length (1st colum): </strong>micro meters <strong>(µm</strong>)</p> <p>We use Tikoff et al (2019) naming convention for thin section orientation.</p> <p> </p> <p> </p> <p> </p> <p>Tikoff, B., Chatzaras, V., Newman, J., & Roberts, N. M. (2019). Big data in microstructure analysis: Building a universal orientation system for thin sections. <em>Journal of Structural Geology</em>, <em>125</em>, 226–234. https://doi.org/10.1016/j.jsg.2018.09.019</p>
Data from: Estimating maize harvest index and nitrogen concentrations in grain and residue using globally available data
<p class="MsoNormal">Estimates of crop nitrogen (N) uptake and offtake are critical in estimating N balances, N use efficiencies and potential losses to the environment. Calculation of crop N uptake and offtake requires estimates of crop product yield (e.g. grain or beans) and crop residue yield (e.g. straw or stover) and the N concentration of both components. Yields of crop products are often reasonably well known, but those of crop residues are not. While the harvest index (HI) can be used to interpolate the quantity of crop residue from available data on crop product yields, harvest indices are known to vary across locations, as do N concentrations of residues and crop products. The increasing availability of crop data and advanced statistical and machine learning methods present us with an opportunity to move towards more locally relevant estimates of crop harvest index and N concentrations using more readily available data. This dataset includes maize field experiment data. It is a culmination of summary statistic data collected from the literature as well as raw data requested from various researchers and organisations from around the world. These data will enable more locally relevant estimates of crop nutrient offtake, nutrient balances and nutrient use efficiency at national, regional or global levels, as part of strategies towards more sustainable nutrient management.</p>
Positional errors in species distribution modelling are not overcome by the coarser grains of analysis
<p>The performance of species distribution models is known to be affected by the analysis grain and the positional error of species occurrences. Coarsening of the spatial analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine-scale environmental data in predictive models developed for conservation and climate change studies it is increasingly important to test this assumption. Species distribution models using fine-scale environmental data are more likely to be negatively affected by positional error as the inaccurate species occurrences might easier end up in unsuitable environment, which can result in inappropriate conservation actions.</p> <p>Here, we examine the trade-offs between positional error and analysis grain and provide recommendations for best practice. We generated virtual species using tree canopy height, topography wetness index, and altitude derived from LiDAR point clouds at 5 x 5 m fine-resolution. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grains in the range of 5 m to 500 m. In total, we assessed 49 combinations of positional accuracy and analysis grain. We used three common modelling techniques (MaxEnt, BRT and GLM) and four discrimination metrics to evaluate model performance (Sørensen index, overprediction and underprediction rate, AUC and TSS).</p> <p>We found that model performance decreased with increasing positional error in species occurrences and coarsening of the analysis grain. Most importantly, we showed that coarsening the analysis grain to compensate for positional error did not improve model performance. Our results reject coarsening of the analysis grain as a solution to address the negative effects of positional error on model performance.</p> <p>We recommend fitting models with the finest possible analysis grain (i.e., depending on data availablity) even when available species occurrences suffer from positional errors. If there are significant positional errors in species occurrence data, users are unlikely to benefit from making additional efforts to obtain higher resolution environmental data unless they also minimize the positional errors of species occurrences.</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Breed Classification Dataset (Tsinghua Dogs)
<p>Tsinghua Dogs Dataset with ground truth labels for breeds in YOLOv5 format.</p>
Code and experimental images - Stable and unstable capillary fingering in porous media with a gradient in grain size
<p>Matlab script. Invasion Percolation code in graded matrices and gravity.<br> Pictures: experimental invasion of air at constant flow rate in a 3D-printed graded porous medium wet by glycerol/water.<br> <br> Originally designed for the following article: https://arxiv.org/abs/2203.15524.<br> Accepted in Communications Physics (2022).<br> <br> Can easily be tweaked for other problems</p>
Scaling protein-water interactions in the Martini 3 coarse-grained force field to simulate transmembrane helix dimers in different lipid environments
<p>This dataset contains molecular dynamics (MD) trajectories used for preparation of the following manuscript: <br> "Scaling protein-water interactions in the Martini 3 coarse-grained force field to simulate transmembrane helix dimers in different lipid environments". </p>
ODP Site 1249, ODP Site 1252, and IODP Site U1325: X-ray fluoresence core scanning, laser diffraction grain size, CNS elemental/isotopic, environmental magnetism, and age model data
<p>We present data used as part of an integrative early diagenesis study (submitted September 2022 to Marine Geology) focused on identifying zones of magnetite dissolution and pyrite precipitation in which magnetic susceptibility records are altered in gas-hydrate bearing sediments on the Cascadia Margin using archived cores from the Ocean Drilling Program (ODP) and Integrated Ocean Drilling Program (IODP). We analyzed the upper 85 to 100 m below seafloor (mbsf) from ODP Sites 1249 and 1252, and IODP Site U1325. ODP 1249 is at the summit of Hydrate Ridge in an area of active methane seepage and massive gas hydrate accumulations and ODP 1252 is in a nearby slope basin with little occurrence of hydrate. IODP Site U1325 is on the northern Cascadia Margin in a slope basin, with turbidite-hosted gas hydrate. We also include XRF data from the upper sections of ODP Site 1251, IODP U1327, and U1328.</p> <p>We measured X-ray fluorescence using an Avaatech core scanner at the IODP Gulf Coast Repository at Texas A&M University. We measured total carbon, total organic carbon (TOC), total nitrogen, and total sulfur using a Perkin Elmer 2400 Series CHNS/O Analyzer at the university of New Hampshire (ODP Site 1249 and 1252 only). A subset was analyzed for δ<sup>13</sup>C-TOC using a Costech ECS 4010 elemental analyzer interfaced with a Thermo Finnegan Delta Plus XP continuous flow isotope ratio mass spectrometer at Washington State University. Grain size was measured with a Malvern Mastersizer 2000 laser diffraction particle size analyzer and Hydro 2000G dispersal unit at the University of New Hampshire. Mass frequency-dependent magnetic susceptibility was measured using a Bartington MS2 Magnetic Susceptibility Meter and Bartington MS2B dual frequency sensor (Site U1325 only). Isothermal remanent magnetization and thermal demagnetization curves were measured using a HSM2 SQUID-based Spinner Magnetometer with an ASC Scientific IM-10-30 Impulse Magnetizer and ASC Scientific TD-48SC magnetically-shielded oven (Site U1325 only). Radiocarbon was measured on mixed planktic foraminifers at the Radiocarbon was measured at National Ocean Sciences Accelerator Mass Spectrometry (NOSAMS) facility at Woods Hole Oceanographic Institution (ODP Site 1252 and IODP Site U1325).. Radiocarbon ages were calibrated to calendar ages using CALIB 8.2 and the Marine20 calibration curve. For ODP Site 1252 we used a reservoir correction of 230 ± 50 years (Yaquina Bay, Oregon, USA) and for IODP Site U1325 we used a reservoir correction of 202 ± 50 years (Amphitrite Point, British Columbia, Canada). δ<sup>18</sup>O was measured on benthic foraminifer <em>Uvigerina peregrina</em> tests using a Finnegan MAT 252 isotope ratio mass spectrometer with Kiel III device at the Oregon State University Stable Isotope Laboratory (ODP 1252) and a Finnegan MAT 253 isotope ratio mass spectrometer with Kiel IV device at the University of Michigan Stable Isotope Laboratory (ODP Site 1249 and IODP Site U1325).</p>
Data samples for Flow-matching -- efficient coarse-graining molecular dynamics without forces
<p>CG samples generated during the training and validation processes in the flow-matching project. Accompanying the preprint "Flow-matching -- efficient coarse-graining molecular dynamics without forces": https://arxiv.org/abs/2203.11167. Detailed descriptions can be found in the preprint as well as the included README.</p>
Dataset for: Identification of genomic regions of wheat associated with grain Fe and Zn content under drought and heat stress using genome-wide association study
<p>The study material in the GWAS panel with 282 advanced breeding lines of bread wheat genotypes from IARI stress breeding program was selected to map the genomic regions responsible for grain iron and Zinc content under drought and heat stress treatments.</p> <p>Phenotypic data:</p> <p>The GWAS panel was evaluated at IARI, New Delhi - DL (28.6550° N, 77.1888° E, MSL 228.61 m) under Irrigated (IR), Restricted Irrigated (RI) and Late sown (LS) treatment conditions with augmented RCBD design. Data was collected on Grain Iron and Grain zinc content along with thousand-grain weight. Around 20 g of grain sample from each of 282 genotypes from the GWAS panel under all three conditions were used for phenotyping GFeC and GZnC through high-throughput Energy Dispersive X-ray Fluorescence (ED-XRF) machine (model X-Supreme 8000; Oxford Instruments plc, Abingdon, United Kingdom) calibrated with glass beads-based values. To record TGW, manual counting of grains was followed and the weight of the grains was recorded in grams with an electronic balance.</p> <p>Genotypic data:</p> <p>Genomic DNA of the GWAS panel was extracted from the leaves of seedlings by Cetyl Trimethyl Ammonium Bromide (CTAB) method. The panel was genotyped using Axiom Wheat Breeder's Genotyping Array (Affymetrix, Santa Clara, CA, United States) having 35,143 genome-wide SNPs. The monomorphic, markers with minor allele frequency (MAF) of <5%, missing data of >20%, and heterozygote frequency >25% were removed from the analysis. The remaining set of 10546 high-quality SNPs was used in GWAS analysis.</p>
Supporting data tables and Python scripts for the paper: "Multi Grain-Size Total Sediment Load Model Based on the Disequilibrium Length"
<p>This repository contains all the data tables and Python scripts necessary to generate the results presented in Le Minor et al. (2022): "Multi Grain-Size Total Sediment Load Model Based on the Disequilibrium Length".</p>
Supplementary Data: Kapitza thermal resistance across individual grain boundaries in graphene
<p>The data here are the numerical bi-crystalline graphene samples used in Ref. [1].</p> <p>There are 13 folders named "1", "2", ..., "13". Each folder contains a file named "xyz.in". They are in the format of the "xyz.in" file used in the GPUMD package [2]. From folder "1" to "13", the tilt angle increases monotonically. See Table 1 of Ref. [1] for the values of the tilt angles.</p> <p>[1] Khatereh Azizi, Petri Hirvonen, Zheyong Fan, Ari Harju, Ken R Elder, Tapio Ala-Nissila, and S Mehdi Vaez Allaei,<br> Kapitza thermal resistance across individual grain boundaries in graphene,<br> Carbon 125, 384 (2017).<br> https://doi.org/10.1016/j.carbon.2017.09.059<br> https://arxiv.org/abs/1709.09529</p> <p>[2] Zheyong Fan, GPUMD,<br> https://github.com/brucefan1983/GPUMD<br> http://doi.org/10.5281/zenodo.995667</p> <p> </p>
Maize grains and maize grain-based diets dataset
<p>The MS and NMR datasets is composed of:</p> <ul> <li>a ReadMe file </li> <li>one Excel file named "Sample Rosetta Stone" containing the MS and NMR dataset sample description,</li> <li>the MS dataset:</li> </ul> <p>- a "Grains" directory containing LCMS raw files in mzML format. They are organized according to the following groups: Blank, MON810_I_woR, MON810_T_woR, Nk603_I_woR, Nk603_T_woR, Nk603_T_wR and QC,</p> <p>- a "Pellets" directory containing LCMS raw files in mzML format. They are organized according to the following groups: Blank, QC and T0-1 to T0-9,</p> <ul> <li>the NMR dataset:</li> </ul> <p>- 21 NMR spectra directories (containing <sup>1</sup>H single-pulse (zg) NMR spectrum for each maize grain extract sample and 1 directory GMO-11-2D containing 2D (COSY and HSQC) spectra for “NK603 transgenic without roundup” extract sample) in Bruker format,</p> <p>- 18 NMR spectra directories (containing <sup>1</sup>H single-pulse (zg) NMR spectrum and <sup>1</sup>H CPMG spectrum for each pellet extract sample) in Bruker format.</p>
Data for manuscript "Estimating grain stress and distinguishing between mobility and transportability improves bedload transport estimates in coarse-bedded mountain rivers"
<p>This repository contains data collected that was used in the manuscript:</p> <p><span>Estimating grain stress and distinguishing between mobility and transportability improves bedload transport estimates in coarse-bedded mountain rivers</span></p> <p> </p>
BaTiO3 coarse-grained molecular dynamics simulations
<p>This repository contains the simulation results for BaTiO3 using coarse-grained molecular dynamics package <a title="Feram" href="https://loto.sourceforge.net/feram/" target="_blank" rel="noopener">Feram</a>.</p> <p>The files (1: data.avg, 2: *.csv) use the space-separated format or comma-separated format.</p> <p>(1) data.avg columns:<br>T: temperature in Kelvin<br>Ex Ey Ez: external_E_field along x,y,z in V/Angstrom.<br>exx eyy ezz eyz ezx exy: strain tensor<br>ux uy uz: dipole displacements in Angstrom<br>uxux uyuy uzuz uyuz uzux uxuy: cross-terms of dipole displacements in Angstrom^2<br>dk: dipo_kinetic in eV/u.c.<br>lr: long_range in eV/u.c.<br>dEf: dipole_E_field in V/Angstrom<br>unhar: unharmonic in eV/u.c.<br>s_ho: homo_strain in eV/u.c.<br>c_ho: homo_coupling in eV/u.c.<br>s_inho: inho_strain in eV/u.c.<br>c_inho: inho_coupling in eV/u.c.<br>etot: total energy in eV/u.c.<br>HNP: H_Nose_Poincare in eV/u.c.<br>e2: e2<br>dkt: dipo_kinetic_true in eV/u.c.<br>ak: acuou_kinetic in eV/u.c.<br>sr: short_range in eV/u.c.<br>mod: inho_modulation in eV/u.c.<br>px py pz: px py pz<br>ppx ppy ppz ppyz ppzx ppxy: ppx ppy ppz ppyz ppzx ppxy<br>mx my mz: <ux>, <uy>, <uz> in Angstrom<br>amx amy amz: <|ux|>, <|uy|>, <|uz|> in Angstrom</p> <p>(2) *.csv contains header in each file.</p> <p>(3) avg2csv.ipynb contains script to convert files.</p>
Dataset on the effects of mineral grain size and seawater salinity on Mg(OH)2 dissolution and CaCO3 precipitation kinetics.
<p>Dataset from the manuscript "Effects of grain size and seawater salinity on magnesium hydroxide dissolution and secondary calcium carbonate precipitation kinetics: implications for ocean alkalinity enhancement" from Moras et al., 2024 (https://doi.org/10.5194/egusphere-2024-645). The dataset compiles all data used in the manuscript. The manuscript covers Mg(OH)2 dissoluton and CaCO3 precipitation kinetics for Ocean Alkalinity Enhancement. These kinetics are reported under different conditions, such as varying grain size and seawater salinity.</p>
Research data supporting: "A millisecond coarse-grained simulation approach to decipher allosteric cannabinoid binding at the glycine receptor α1"
<p>This repository contains the set of coarse-grained (CG) m<span>olecular snapshots representative </span><span>of the most populated binding modes for glycine receptor (GlyR) in complex with the ligands, along with atomistic backmapped representations. In addition, it contains also CG Martini 3 parameters, charmm36-FF backmapping library file and GROMACS files (itp and gro files) for both ligands.</span></p>
Supplementing a grain diet with insects instead of fruits sustains the body condition of an omnivorous bird
<p>Omnivores utilize dietary sources that differ in nutrients, hence dietary limitations due to environmental change or habitat alteration could cause nutrient limitations, and thus deterioration of body condition if omnivory is obligate. We investigated how the body condition of the omnivorous Village Weaver <em>Ploceus cucullatus</em>, which forages predominantly on grains, responds to the supplementation of its grain diet with insects instead of fruits. Forty wild‐caught weavers held in aviaries were fed a combination of grains and fruits, or grains and insects ad libitum for 8 weeks. We determined diet preference by recording the number of birds on each diet option per minute for 1 h and the amount of food left over after 3 hours of foraging. Fortnightly, we assessed indices of body condition including body mass, pectoral muscle, and fat scores, packed cell volume (PCV), and hemoglobin concentration (HBC). We modeled the number of foragers, food left over, and body condition indices as functions of diet while accounting for time (weeks) and sex effects. Grains were the preferred diet, but males ate more fruits and insects than females. Weavers fed on grains and fruits lost body and pectoral muscle mass and accumulated less fat than those fed on grains and insects. This effect was sex‐dependent: females supplemented with fruits lost more pectoral muscle mass than males of the same group and males but not females, supplemented with insects accumulated more fat reserve than those supplemented with fruits. PCV and HBC did not differ between diets but increased over the 8 weeks. Village Weavers are likely obligate rather than facultative omnivores, with insects being a more nutritive supplement than fruits. Nutrient limitations arising from environmental change or habitat alteration could impair body condition and affect physiological function to environmental seasonality in obligate omnivores like the weavers.</p>
Supplementary Dataset for "Grain Size Measurements of the Eolian Stimson Formation, Gale Crater, Mars and Implications for Sand Provenance and Paleoatmospheric Conditions"
<p>Grain size measurements and results in support of "Grain Size Measurements of the Eolian Stimson Formation, Gale Crater, Mars and Implications for Sand Provenance and Paleoatmospheric Conditions".</p> <p>The subfolder "MAHLI images" consists of images taken by the Mars Science Laboratory <em>Curiosity</em> Mars Hand Lens Imager; these images are accesible via the MSL Analyst's Notebook at an.rsl.wustl.edu.</p> <p>The subfolder "ImageJ ROIs" contains .zip files that can be opened with the ImageJ software, available at imagej.nih.gov/ij.</p> <p>The subfolder "GRADISTAT results" contains PDFs with grain size statistics that were created from the data in "Grain size measurements" using the GRADISTAT software, available at https://doi.org/10.1002/esp.261. </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>
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