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1,013 results for “glass”
Blaschka Glass Model of a Sand Mason Worm
Micro CT scan of a Blaschka model of a sand mason worm (Lanice conchilega) from the D'Arcy Thompson Zoology Museum at the University of Dundee. DUNUC 9322 - glass model of Polychaete worm - Terebella conchilega (sand mason worm, aka Lanice conchilega). Length=13cm. D'Arcy Thompson acquired a set of Blaschka's exquistely delicate glass models in 1888. In Growth and Form, D'Arcy refers repeatedly to the art of the glass-blower, perhaps thinking of these very models. Scanned with a Nikon XT H 225ST micro CT scanner. The resulting file is exported as an stl. and imported in to Zbrush. We'd love to hear from you if you download or print our models, especially if you use them in education and outreach. Source: Objaverse 1.0 / Sketchfab
Blaschka Glass Model of Sponge Development
Micro CT scan of a Blaschka model of the sponge Sycandra Raphanus from the D'Arcy Thompson Zoology Museum at the University of Dundee. DUNUC 9319 - glass model (incomplete) of the development stage of a sponge (Sycandra raphanus). Width=9cm. D'Arcy Thompson acquired a set of Blaschka's exquistely delicate glass models in 1888. In Growth and Form, D'Arcy refers repeatedly to the art of the glass-blower, perhaps thinking of these very models. Scanned with a Nikon XT H 225ST micro CT scanner. The resulting file is exported as an stl. and imported in to Zbrush. We'd love to hear from you if you download or print our models, especially if you use them in education and outreach. Source: Objaverse 1.0 / Sketchfab
Blaschka Glass Model of a Sand Mason Worm
Micro CT scan of a Blaschka model of a sand mason worm (Lanice conchilega) from the D'Arcy Thompson Zoology Museum at the University of Dundee. DUNUC 9325 - glass model of Polychaete worm in its developmental stage - Terebella conchilega (sand mason worm, aka Lanice conchilega). Length=15cm. D'Arcy Thompson acquired a set of Blaschka's exquistely delicate glass models in 1888. In Growth and Form, D'Arcy refers repeatedly to the art of the glass-blower, perhaps thinking of these very models. Scanned with a Nikon XT H 225ST micro CT scanner. The resulting file is exported as an stl. and imported in to Zbrush. We'd love to hear from you if you download or print our models, especially if you use them in education and outreach. Source: Objaverse 1.0 / Sketchfab
Glass_cloth
3d model bow Source: Objaverse 1.0 / Sketchfab
Roman Glass Jar
Roman glass jar made in Maya 3D for university class. Source: Objaverse 1.0 / Sketchfab
Glass Vessel, Dibba Al Hisn, Sharjah, 1st C. CE
Opaque Glass Vessel, Dibba Al Hisn, Sharjah, 1st C. CE Glass was an important export product of the Mediterranean Roman world from the middle of the first century BC onwards. Large quantities reached the Oman peninsula. This specimen was discovered in a communal tomb at Dibba al-Hisn (Sharjah Emirate) on the East coast of the Oman peninsula, together with glass unguentaria, Indian ivory combs, a Roman intaglio-- luxury products that illustrate the importance of Dibba as a trading port (Jasim 2006). Sabah A. Jasim. 2006. Trade centres and commercial routes in the Arabian Gulf: Post-Hellenistic discoveries at Dibba, Sharjah, United Arab Emirates. *Arabian Archaeology and Epigraphy* 2006: 17: 214–237. Source: Objaverse 1.0 / Sketchfab
Chemical and Cl isotopic composition data for "Extremely Large Cl Isotopic Fractionation in Chang'e-5 Impact Glass Beads"
<p>Data for "Extremely Large Cl Isotopic Fractionation in Chang'e-5 Impact Glass Beads".</p>
Dataset for "A micro-opto-mechanical glass interferometer for megahertz modulation of optical signals"
<p>This dataset contains the raw data at the basis of the graphs reported in the paper:<br>"A micro-opto-mechanical glass interferometer for megahertz modulation of optical signals"<br>by Roberto Memeo, Andrea Crespi and Roberto Osellame.</p> <p>The preprint is uploaded in the arXiv.org repository: <br> arXiv:2402.01374 https://doi.org/10.48550/arXiv.2402.01374<br>This paper is published as:<br> Optica 11, 178-183 (2024). https://doi.org/10.1364/OPTICA.506669</p> <p>It contains one CSV file per graph. Figure numbering of the arXiv preprint is followed in naming the folders.</p>
Influence of oxygen distribution on the Li-ion conductivity in oxy-sulfide glasses - taking a closer look.
<p>All Data conneted to the publication: "Influence of oxygen distribution on the Li-ion conductivity in oxy-sulfide glasses - taking a closer look."</p>
NMR Data - Densification of Sodium and Magnesium Aluminosilicate Glasses at Ambient Temperature
Open the record for dataset details and reuse information.
Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study - dataset
<p>Abstract:<br>from [1]</p> <p>Polymer nanocomposites (PNCs) have shown great potential to meet the ever-growing requirements of modern engineering applications. Nowadays, molecular dynamics (MD) simulations are increasingly employed to complement experimental work and thereby gain a deeper understanding of the complex structure–property relations of PNCs. However, with respect to the thermoplastic’s mechanical behavior, the role of its average molar mass is rarely addressed, and many MD studies only consider uniform (monodispersed) polymers. Therefore, this contribution investigates the impact that and the dispersity Đ have on the stiffness and strength of PNCs through coarse-grained MD. To this end, we employed a Kremer–Grest bead–spring model and observed the expected increase in the mechanical performance of the neat polymer for larger . Our results indicated that the unimodal molar mass distribution does not impact the mechanical behavior in the investigated dispersity range Đ. For the PNC, we obtained the same -dependence and Đ-independence of the mechanical properties over a wide range of filler sizes and contents. This contribution proves that even simple MD models can reproduce the experimentally well researched effect of the molar mass. Hence, this work is an important step in understanding the complex structure–property relations of PNCs, which is essential to unlock their full potential.</p> <p>Contact:</p> <p>Maximilian Ries<br>Institute of Applied Mechanics<br>Friedrich-Alexander-Universität Erlangen-Nürnberg<br>Egerlandstr. 5<br>91058 Erlangen</p> <p>Software:</p> <p>All MD simulations were performed with LAMMPS [2,3], version: 23 Oct 2022 / 20220623</p> <p>Compiled with<br>Compiler: GNU C++ 11.2.0 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 DPD-BASIC EXTRA-DUMP INTEL KSPACE MANYBODY MC MISC MOLECULE MOLFILE MPIIO NETCDF OPT PERI</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [4]</p> <p>Post-processing Matlab R2019b</p> <p>License:</p> <p>Creative Commons Attribution 4.0 International</p> <p>Context:</p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>Content:</p> <p>structure of data set:</p> <p> -01_neat <br> containing the neat polymer simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -02_distributed<br> containing samples with distributed chain lengths<br> -100-dist<br> samples with mean molar mass 100<br> -200-dist<br> samples with mean molar mass 200<br> -02_PNC<br> containing the polymer nanocomposite simulations<br> -01_uniform<br> containing samples with uniform chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> -02_distributed<br> containing samples with distributed chain lengths<br> -T_0.2<br> simulations at temperature 0.2<br> -T_0.3<br> simulations at temperature 0.3<br> -T_0.4<br> simulations at temperature 0.4<br> </p> <p>naming convention for simulation folders</p> <p> - neat polymer simulations<br> example: GTP_UT_num_chains-80_num_beads_per_chain-500-8<br> * num_chains: number of polymer chains<br> * num_beads_per_chain: molar mass (chain length)<br> * distribution: standard deviation of gauss distribution govering dispersity<br> * "trailing number": batch number of sample<br> <br> - polymer nanocomposite simulations<br> example: GTP_rF-5_nF-10_chainlen-5_7-T_0.2<br> * rF: nanofiller radius<br> * nF: number of nanofillers<br> * chainlen: molar mass (chain length)</p> <p> </p> <p>Each simulation directory contains:</p> <p> lammps input file (*.in) of the specific simulation</p> <p> data file (*.data) containing the initial sample configuration</p> <p> input.prm: input parameters of the specific simulation (read by the input file)</p> <p> meta.info: meta data of the specific simulation run</p> <p> LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <p> thermo_out.Dat: raw output </p> <p> thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> <p> thermo_out_STD.Dat: standard deviation of raw output</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> <p> Step: time step </p> <p> Time: time </p> <p> TotEng: total energy </p> <p> PotEng: potential energy</p> <p> KinEng: kinetic energy </p> <p> E_pair: pair energy </p> <p> E_bond: bond energy </p> <p> E_angle: angle energy </p> <p> E_dihed: dihedral energy </p> <p> Temp: temperature</p> <p> Press: hydrostatic pressure</p> <p> Pxx: xx component of pressure tensor </p> <p> Pyy: yy component of pressure tensor </p> <p> Pzz: zz component of pressure tensor </p> <p> Pxy: xy component of pressure tensor</p> <p> Pxz: xz component of pressure tensor</p> <p> Pyz: yz component of pressure tensor</p> <p> Volume: volume of simulation box </p> <p> Lx: box length in x direction </p> <p> Ly: box length in y direction </p> <p> Lz: box length in z direction </p> <p> Density: density </p> <p> c_RG: radius of gyration scalar </p> <p> c_RG[1]: squared radius of gyration tensor (xx component) </p> <p> c_RG[2]: squared radius of gyration tensor (yy component) </p> <p> c_RG[3]: squared radius of gyration tensor (zz component) </p> <p> c_RG[4]: squared radius of gyration tensor (xy component) </p> <p> c_RG[5]: squared radius of gyration tensor (xz component) </p> <p> c_RG[6]: squared radius of gyration tensor (yz component) </p> <p> c_bondave[1]: bond energy averaged over all atoms </p> <p> c_bondave[2]: bond distance averaged over all atoms </p> <p> c_bondave[3]: squared bond distance averaged over all atoms </p> <p> c_angleave[1]: angle energy averaged over all atoms </p> <p> c_angleave[2]: angle averaged over all atoms degree</p> <p> c_angleave[3]: cosine of angle </p> <p> c_angleave[4]: squared cosine of angle </p> <p> c_MSD[1]: mean squared displacement x-direction </p> <p> c_MSD[2]: mean squared displacement y-direction </p> <p> c_MSD[3]: mean squared displacement z-direction </p> <p> c_MSD[4]: total mean squared displacement </p> <p> c_COM[1]: x coordinate of center of mass </p> <p> c_COM[2]: y coordinate of center of mass </p> <p> c_COM[3]: z coordinate of center of mass </p> <p> v_strain_xx: xx component of engineering strain tensor </p> <p> v_strain_yy: yy component of engineering strain tensor </p> <p> v_strain_zz: zz component of engineering strain tensor </p> <p> v_vMisesequivstress: von Mises equivalent stress </p> <p> v_Cauchy_xx: xx component of stress tensor </p> <p> v_Cauchy_yy: yy component of stress tensor</p> <p> v_Cauchy_zz: zz component of stress tensor</p> <p> v_Cauchy_xy: xy component of stress tensor </p> <p> v_Cauchy_xz: xz component of stress tensor </p> <p> v_Cauchy_yz: yz component of stress tensor </p> <p> v_strain_xy: xy component of engineering strain tensor </p> <p> v_strain_xz: xz component of engineering strain tensor </p> <p> v_strain_yz: yz component of engineering strain tensor </p> <p>References:</p> <p>[1] M. Ries, L. Laubert, P. Steinmann, & S. Pfaller, “Impact of the unimodal molar mass distribution on the mechanical behavior of polymer nanocomposites below the glass transition temperature: A generic, coarse-grained molecular dynamics study,” European Journal of Mechanics - A/Solids, vol. 107, p. 105 379, 2024.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 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,” Computer Physics Communications, vol. 271, p. 108171, 2022.</p> <p>[4] J. Roksvaag, M.Ries . “A fast self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, manuscript in preparation</p>
FIGURE 7 in New species of glass knifefish Eigenmannia loretana (Gymnotiformes: Sternopygidae) from the Western Amazon
FIGURE 7. Type localities of species in the E. trilineata species group. Watershed map of South America displaying the geographic distribution of type localities of all 15 species in the Eigenmannia trilineata species group. Inset represents collection localities of E. loretana; red star indicates holotype, black dots indicate paratypes, and black triangles indicate nontypes. Inset scale bar = 200 km.
FIGURE 6 in New species of glass knifefish Eigenmannia loretana (Gymnotiformes: Sternopygidae) from the Western Amazon
FIGURE 6. Photograph of a live specimen collected near Iquitos, Peru. Uncatalogued. Scale bar = 10 mm.
FIGURE 5 in New species of glass knifefish Eigenmannia loretana (Gymnotiformes: Sternopygidae) from the Western Amazon
FIGURE 5. High resolution computed tomography renderings of Eigenmannia loretana ANSP 202364, 130 mm, 94 mm LEA, Peru, Loreto, Cocha Santa Thomas, affluent to Río Nanay, Iquitos. A) dorsal view of the head, B) lateral view of the head. bao = basioccipital, den = dentary, end = endopterygoid, epo = epioccipital, exo = exoccipital, exs = extrascapular, fro = frontal, hyo = hyomandibula, let = lateral ethmoid, max = maxilla, mes = mesethmoid, met = metapterygoid, obs = orbitosphenoid, ope = opercle, par = parietal, pas = parasphenoid, pmx = premaxilla, pre = preopercle, pro = prootic, pto = pterotic, pts = pterosphenoid, qua = quadrate, soc = supraoccipital, spo = sphenotic, sub = subopercle, vet = ventral ethmoid, vom = vomer. Scale bar = 5 mm.
FIGURE 1 in New species of glass knifefish Eigenmannia loretana (Gymnotiformes: Sternopygidae) from the Western Amazon
FIGURE 1. Lateral view of full body (top) and head (bottom) of Eigenmannia loretana, holotype. MUSM 61210, 135 mm, 120 mm LEA, Peru, Loreto, Río Pacaya, Lago Tomana, southwest of Iquitos, 5°20'40"S, 74°30'00"W. Scale bar = 10 mm.
FIGURE 3 in New species of glass knifefish Eigenmannia loretana (Gymnotiformes: Sternopygidae) from the Western Amazon
FIGURE 3. Line drawing of suspensorium, opercular series, and maxilla of Eigenmannia loretana (UF 116557, 141 mm). Abbreviations: m, maxilla; d, dentary; ang, anguloarticular; q, quadrate; mt, metapterygoid; end, endopterygoid; sym, symplectic; pre, preopercle; int, interopercle; op, opercle; hy, hyomandibula. Scale bar = 5 mm.
FIGURE 4 in New species of glass knifefish Eigenmannia loretana (Gymnotiformes: Sternopygidae) from the Western Amazon
FIGURE 4. Line drawing of medial view of mandible (UF 116557, 141 mm) of Eigenmannia loretana. Abbreviations: d, dentary; ang, anguloarticular; cb, coronomeckelian bone; mc, Meckel's cartilage; r, retroarticular. Dark grey represents fully mineralized cartilage; light grey represents poorly or non-mineralized cartilage. Scale bar = 1 mm.
FIGURE 2 in New species of glass knifefish Eigenmannia loretana (Gymnotiformes: Sternopygidae) from the Western Amazon
FIGURE 2. Line drawing of infraorbital series (UF 116557, 141 mm) of Eigenmannia loretana. Lighter stippling represents laminar bone; darker stippling represents laterosensory canal arches and tubes. Arabic numerals label the corresponding infraorbital bone numbers; abbreviations: ant = antorbital; IO = infraorbital. Scale bar = 1 mm.
FIGURE 3 in A new glass sponge genus (Hexactinellida: Euplectellidae) from abyssal depth of the Yap Trench, northwestern Pacific Ocean
FIGURE 3. Rhizophyta yapensis gen. et sp. nov. Spicules of holotype (SEM). A. Four types of pinular dermal hexactins. B. Four types of pinular atrial hexactins, whole and enlarged ray end. C. Pinular dermal hexactin from the transition of main body and peduncle. D. Two types of atrial pentactins, similar to dermal pentactins of the main body. E. Atrial pentactin with longer proximal ray. F. Dermal pentactin from the transition of main body and peduncle, whole and enlarged proximal ray end. G. Discohexaster and enlarged secondary ray end. H. Three types of choanosomal hexactins, whole and enlarged ray end. I. Two types of choanosomal diactins, whole and enlarged tip and center. J. Dermal pentactin of the peduncle, whole and enlargements of ray ends and coarsely spined central area.
FIGURE 4 in A new glass sponge genus (Hexactinellida: Euplectellidae) from abyssal depth of the Yap Trench, northwestern Pacific Ocean
FIGURE 4. Rhizophyta yapensis gen. et sp. nov. Differing spicules of paratypes. A. Five types of diactins from paratype A. B. Discohexaster from paratype C. C. Enlarged ray ends of discohexaster from paratype C.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.