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96 results for “Material Properties”
Data and analysis scripts: Evidence supporting an evolutionary trade-off between material properties and architectural design in Anolis lizard long bones
<p>In biology, 'many-to-one mapping' occurs when multiple morphological forms can meet a particular functional demand. Knowledge of this mapping is crucial for understanding how selection on performance shapes the evolution of morphological diversity. Past research has focused primarily on the potential for geometrically alternative morphological designs to produce equivalent performance outcomes. Here we ask whether the material properties of biological tissues hold similar potential. Through phylogenetic comparative study of <em>Anolis</em> lizards, we show that the architectural design and mineral density of the femur trade off in a many-to-one functional system, yielding a morphospace featuring parallel isolines in size-relative bending strength. Anole femur evolution has largely tracked a narrow band of strength isolines over phylogenetic timescales, suggesting that geometry and mineral content shape the course of macroevolution through compensatory effects on performance. Despite this conserved evolutionary relationship, insular and continental species evolve strong bones differently, likely reflecting underlying ecological differences. Mainland anoles, which exhibit fast-paced life histories, typically have femora with lower mineralization and thinner walls than island species, which exhibit the opposite strategy. Together, our results reveal an overlooked dimension in the relationship between form and function, expanding our understanding of how many-to-one mapping can shape patterns of phenotypic diversity.</p>
Dataset for "Effect of benzothiadiazole-based π-spacers on fine-tuning of optoelectronic properties of oligothiophene-core donor materials for efficient organic solar cells: a DFT study"
<p># Data and code for "Effect of benzothiadiazole-based π-spacers on fine-tuning of optoelectronic properties of oligothiophene-core donor materials for efficient organic solar cells: a DFT study."</p><p>## Contents</p><p>* data-{type}/*: reproducible data</p><p>* job.job : example slurm script</p><p> </p><p>## Description of the data</p><p>The data are organized in subdirectories *data-{type}/{system}/* corresponding to the considered molecules and simulation type:</p><p>* data-gs: Ground state calculations</p><p>* data-td: TD-DFT calculations</p><p>The contents of each subdirectory are:</p><p>* data-gs/{system}/structure.xyz: physical atomic structure</p><p>* data-td/{system}/td-dft/td_uvvis.txt: photoabsorption spectrum</p><p>The spectrum plots in the article correspond to the first (x values) and second (y values) columns of the spectrum files.</p><p> </p><p>## Reproduction of the data</p><p>The data were produced using Gaussian version g16.A.01</p><p>The calculation of the data of a system consists of the following steps:</p><p>1. Ground-state (gs) calculation:</p><p> * Prepare the input file for the gs by adjusting the parameters of the ground state calculations:</p><p> * "# opt b3lyp/6-311+g(d,p) scrf=(smd,solvent=chloroform) geom=connectivity empiricaldispersion=gd3bj out=wfn"</p><p> * out = wfn keyword to create a wfn file of the ground state that will be used for EDD and RDG investigations</p><p> * Submit the job.job file for the gs calculation as appropriate for the particular input file of the system</p><p> * The optimized sturctures are visualised using GaussView</p><p>2. Time-propagation calculation:</p><p> * Requires finished ground-state calculation</p><p> * Set up the TD-DFT calculation parameters as necessary:</p><p> * "# td=(nstates=6) wb97xd/6-311+g(d,p) scrf=(smd,solvent=chloroform) guess=read density out=wfn"</p><p> * density out = wfn keywords to create a wfn file of the excited state that will be used for EDD investigation</p><p> * Submit the job.job file for TD-DFT calculation as appropriate for the particular system</p><p> * The photoabsoption specta are visualised using GaussView</p><p>3. RDG calculation:</p><p> * Put the .wfn file of the gs calculation in the command window of the open source Multiwfn software and follow the sturcture in Section 3.23.1 in the manual</p><p>4. DOS calculation:</p><p> * The dos curves are plotted starting from the .fchk of the ground state geometry, select the atoms index corresponding to the diffrents subpart of the studied molecules (donor, acceptor, pi-spacer)</p><p> * Put the .fchk file of the gs calculation in the command window of the open source Multiwfn software and follow the structure in Section 4.10.1 in the manual</p><p> * the output generates .chk file which is transformed to .fchk file : formchk .chk .fch</p><p>5. TDM calculations:</p><p> * Requires finished ground-state calculation</p><p> * Set up the TD-DFT calculaton parameters as necessary</p><p> * "# td=(nstates=6) wb97xd/6-311+g(d,p) scrf=(smd,solvent=chloroform) guess=read density transition=1 iop(6/8=3) out=wfn"</p><p> * Put the .fchk file of the gs calculation in the command window of the open source Multiwfn software and follow the sturcture in Section 4.18.8 in the manual</p><p>6. EDD calculation:</p><p> * Edd plots are plotted based on the es.wfn and gs.wfn following Section 4.18.1 in the manual</p><p> </p>
Moth proboscis morphology material properties raw data
<p>Insects have evolved unique structures that host a diversity of material and mechanical properties, and the mouthparts (proboscis) of butterflies and moths (Lepidoptera) are no exception. Here, we examined proboscis morphology and material properties from several previously unstudied moth lineages to determine if they relate to flower-visiting and non-flower-visiting feeding habits. Scanning electron microscopy and 3D imaging were used to study proboscis morphology and assess surface roughness patterns on the galeal surface, respectively. Confocal laser scanning microscopy was used to study patterns of cuticular autofluorescence, which was quantified with color analysis software. We found that moth proboscises display similar color and morphological patterns in relation to these feeding habits as those previously described for flower and non-flower-visiting butterflies. The distal region of proboscises of non-flower visitors is brush-like for augmented capillarity and exhibited blue autofluorescence, indicating the possible presence of resilin and increased flexibility. Flower visitors have smoother proboscises and show red autofluorescence, an indicator of high sclerotization, which is adaptive for floral tube entry. We propose the lepidopteran proboscis as a model structure for understanding how insects have evolved a suite of morphological and material adaptations to overcome the challenges of acquiring fluids from diverse sources.</p>
Supplementary material to 'Exotic Symmetry Breaking Properties of Self-Dual Fracton Spin Models'
<p>I. GENERAL INFORMATION</p> <p>1. Title<br>Dataset of "Degeneracy and Scaling Properties of Self-Dual Fracton Spin Models"</p> <p>2. Author Information<br> <br>Giovanni Canossa [1,2], Lode Pollet [1,2], Miguel A. Martin-Delgado [3,4], Hao Song [5], and Ke Liu [1,2,6,7]<br>1. Arnold Sommerfeld Center for Theoretical Physics, University of Munich</p> <p>2. Munich Center for Quantum Science and Technology (MCQST)</p> <p>3. Departamento de Física Teórica, Universidad Complutense, 28040 Madrid, Spain</p> <p>4. CCS-Center for Computational Simulation, Universidad Politécnica de Madrid, Spain</p> <p>5. CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, China</p> <p>6. Hefei National Research Center for Physical Sciences at the Microscale, University of technology of China</p> <p>7. Shanghai Research Center for Quantum Science and CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China</p> <p>Links to publications that cite or use the data:<br>TBA</p> <p>II. Files</p> <p>1. Convention</p> <p>Datas from the multicanonical MC simulations are stored in "Tetra-Ising" and "Fractal-Ising" folders.</p> <p>Lattice size: Each subfolder is named "L=value" where value denotes the linear system size.</p> <p>Multicanonical weights: Files "g_init_T=value.data" contains the set of log(weights) at a given temperature and lattice size, derived from the iterative weight-learning procedure.</p> <p>Datas: HDF5 files "name.out.h5" contain the results of the multicanonical MC simulation at a given lattice size.</p> <p><br>III. Data in HDF5 file</p> <p>1. Convention</p> <p>The results obtained at each temperature point is stored in a separate subdirectory of the .out.h5 file. Each of these subdirectories contains:</p> <p>Energy_Hist: normalized energy histograms obtained from the multicanonical MC simulation (unweighted)</p> <p>Energy_Hist_rw: normalized reweighted energy histograms. For each bin, Energy_Hist_rw[i] = Energy_Hist[i] * e**g[i] / norm, where norm = sum( Energy_Hist[i] * e**g[i] ).</p> <p>c: 1/norm. Gives an estimate of the ratio Z_muca/Z_ca.</p> <p>g: vector containing the weights used for the multicanonical MC simulation at that specific temperature. These are derived from reweighting the weights in "g_init_T=value.data" file.</p> <p>Energy, Energy_Susc, Energy_Kurt Q_x, Q_x_Susc, Q_x_Kurt: canonical expectation value of each relevant observables, along with their susceptibilities and Kurtosis, obtained by reweighting each measurement taken during the simulation by the appropriate weight. (NB: Energy and Q_x need to be multiplied by C in order to give the correct canonical expectation value.)</p> <p><br>2. Relevance</p> <p>These data reproduce Figs. 5 & 6 in the manuscript.</p> <p><br>III. Finite size scaling</p> <p>1. Convention</p> <p>All estimated transition temperatures with their respective uncertainties are stored in "fitting_Tetra" and "fitting_Fractal" folders in the fittemps.txt file.</p> <p>2. Relevance</p> <p>These data reproduce Figs. 3 & 4 in the manuscript.<br> </p> <p> </p>
Properties of different biomass materials and their higher heating values (% Dry basis)
<p>Properties of biomass materials and their higher heating values (% Dry basis) from literature. Aggregated for BSE 619: Mathematical Modeling for Engineers course for Fall 2021 semester at the University of Tennessee, Knoxville.</p>
The dataset for an article - An Evaluation of 3D-Printed Materials' Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data
<p>Dataset used in the research presented in the article:</p> <p>Szymanik, Barbara. 2022. "An Evaluation of 3D-Printed Materials’ Structural Properties Using Active Infrared Thermography and Deep Neural Networks Trained on the Numerical Data" <em>Materials</em> 15, no. 10: 3727. https://doi.org/10.3390/ma15103727</p> <p>The database in the .mat (matlab) format contains arrays of double type related to: A - original thermograms obtained for the plate made with the 3D printing technique Ar - thermograms with ROI included FITorg - approximation of original thermograms ImDiff, ImInt, ImProp - data obtained after subtracting the approximation.</p>
Supplemental Material to Journal Article "Determination of as-built properties of fiber reinforced polymers in a wind turbine blade using scanning electron and high-resolution X-ray microscopy"
<p>This set supplements the figure data to the article "Determination of as-built properties of fiber reinforced polymers in a wind turbine blade using scanning electron and high-resolution X-ray microscopy", DOI: <a href="https://doi.org/10.1016/j.jcomc.2022.100310">https://doi.org/10.1016/j.jcomc.2022.100310</a></p>
Supplementary material 1 from: Baum S, Weih M, Bolte A (2012) Stand age characteristics and soil properties affect species composition of vascular plants in short rotation coppice plantations. BioRisk 7: 51-71. https://doi.org/10.3897/biorisk.7.2699
Number of plots containing the respective species is stated.
Supplementary material from: Modeling biodiesel properties by preference learning: case study of cetane number
<p><span>Data obtained from the available bibliography between the year 2002 and 2022. This document contains the FAME distribution and cetane number of 543 biodiesels.</span></p> <p><span> </span><span>The dataset is divided into three sections within the worksheet.</span></p> <ul> <li><span>The first section contains the definition of the data's feedstock and its source reference. The reference includes the year, DOI (if available, as some are collected from books), publication journal, article title, and authors.</span></li> <li><span>The second section describes the FAME distribution, starting from C4:0 up to C24:0, including a column of unidentified FAMEs.</span></li> <li><span>The third and final section describes the measured property Cetane Number.</span></li> </ul>
Research Data - Microstructural and material property changes in severely deformed Eurofer-97
<p>Research data and associated processing and plotting scripts for the article:</p> <p>Song <em>et al</em>., 'Microstructural and material property changes in severely deformed Eurofer-97', <em>Materials Characterization</em>, 114144, 2024</p> <p><a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.matchar.2024.114144" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.matchar.2024.114144</span></span></a></p>
Deliverable D2.1 - Supplementary material : Datasets on the physicochemical properties of PFAS compounds
<p>Additional information on deliverable D2.1. Data sets for Machine Learning models on physicochemical properties of PFAS compounds for endpoints: logP (n-octanol/water), Sw (solubility in water), VP (vapor pressure), BCF (bioconcentration factor), MP (melting point) developped within the project H2020 PROMISCES. </p>
The role of chemical properties of the material deposited in nests of white stork in shaping enzymatic activity and fungal diversity - dataset
<p>Dataset to paper: Błońska E., Jankowiak R., Lasota J., Krzemińska N., Zbyryt A., Ciach M. 2024. The role of chemical properties of the material deposited in nests of white stork in shaping enzymatic activity and fungal diversity. Environmental Science and Pollution Research 31, 2: 2583-2594. https://doi.org/10.1007/s11356-023-31383-x</p> <p>This study was financially supported by the National Science Centre, Poland (grant no. 2021/41/B/NZ8/03456).</p>
Properties of selected alkali-activated materials for sustainable development
<p>The presented research focuses on three selected variants of alkali-activated materials, where the goal is to compare key properties from the point of view of material engineering and structural design. Tests of the mechanical properties of the examined materials are carried out and their durability is compared, namely frost resistance, resistance to chemical and de-icing substances and resistance to elevated temperature.</p>
Data for "Nanoscale modification of MOC-based composites: The influence of alumina nanosheets on microstructure and material properties"
Open the record for dataset details and reuse information.
Guided wave representations dataset for material property estimation and generation
<p>Material property identification in composite materials is necessary for material degradation as well as non-destructive characterization. The inverse problem needs a forward simulator. Ultrasonic-guided waves are sensitive to material properties and can be used for the purpose. The stiffness matrix method and group velocity calculation routine are used as the forward solver. The solver outputs polar group velocity curves of two fundamental Lamb wave modes for different material properties and ply layup sequences. The curves can be converted into binary images (black and white) named polar representations for image processing algorithms. The datasets contain polar representations corresponding to different material properties and ply layup sequences of a transversely isotropic laminate.</p>
Determined Material Properties within the framework of the NCN project (OPUS) No. 2021/41/B/ST8/00148
<p>This file presents information on the determination of material properties, of the composite material undertaken for the study. The file presents both the data from which the material properties were determined and also summarises and averages the extracted data in tabular form. The data relates to the composite material manufactured for the NCN project (OPUS) No. 2021/41/B/ST8/00148.</p>
Supporting data for: Optical properties of electrochemically gated La1-xSrxCoO3-δ as a topotactic phase-change material
<p>The data included here contain the information necessary to recreate the figures in a manuscript titled "<em>Optical Properties of Electrochemically Gated La<sub>1-x</sub>Sr<sub>x</sub>CoO<sub>3-δ</sub> as a Topotactic Phase-Change Material</em>". The data files include scanning transmission electron microscopy (STEM) images of electrochemically gated La<sub>1-x</sub>Sr<sub>x</sub>CoO<sub>3-δ</sub> (LSCO) films, finite-difference time-domain (FDTD)-simulated electric field and reflectance data for LSCO-based metasurfaces, transfer-matrix model reflectance data for LSCO films on gold substrates, complex refractive index data for LSCO films before and after electrochemical gating, electronic resistivity data for LSCO films before and after electrochemical gating, source-drain current measurements of LSCO films during electrochemical gating, and X-ray diffraction data for LSCO films before and after electrochemical gating.</p>
Data & scripts - The effect of temperature-dependent material properties on simple thermal models of subduction zones
<p>Data and scripts used in Van Zelst et al. (2023, Solid Earth): 'The effect of temperature-dependent material properties on simple thermal models of subduction zones'. Includes the data and figures for the benchmark of Van Keken et al. (2008) plus the results from our code xFieldstone; processing and visualisation scripts; raw and final figures; and all results for all model runs used in the publication (as listed in Table 1 in the paper). </p>
Characterisation of electro- and thermophysical properties of materials used in 3D printing
<p>A complex of materials’ properties was measured: electrical conductivity, thermal expansion, thermal conductivity, the density of the materials, and the technical density of construction with porosity.</p>
Data sets of measured cross-sectional area coordinates and material properties of an old Vindeby blade
<p>Data sets of measured cross-sectional area coordinates and material properties of an old Vindeby blade </p>
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OpenNeuro
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