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689 results for “crystals”
North Temperate Lakes LTER High Frequency Water Temperature Data, Dissolved Oxygen, Chlorophyll, pH - Crystal Lake 2011 - 2014
Data from the instrumented buoy on Crystal Lake include micrometeorological parameters, relative humidity, air temperature, wind velocity, wind driection (2 m height),and water temperatures, pH, chlorophyll, and dissolved oxygen measured by a sonde that is moving through the water column. Measurements were taken before, during, and after lake mixing experiment
High Frequency Under-Ice Water Temperature Buoy Data - Crystal Bog, Trout Bog, and Lake Mendota, Wisconsin, USA 2016-2020
Water temperature measurements from three Wisonsin lakes. Two bog lakes are in Northern Wisconsin, Lake Mendota is in Southern Wisconsin. Thermistor chains span the full depth of each lake. See freeze dates for periods of open or frozen lake (NTL 32, DOI 10.6073/pasta/1c1acdb5489a0355f6f8bb5c496fdf8b and NTL 33, DOI 10.6073/pasta/22a5b5f8bce193353e559918b0024f9d)
In-situ grazing-incidence X-ray diffraction data of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) via employing an isopropanol antisolvent. Raw Data
<p>The dataset contains 400 diffraction images from a 40 second in-situ grazing-incidence wide-angle X-ray scattering measurement of the crystallization process of organic-inorganic methylammonium lead bromide perovskite (MAPbBr3) on a glass substrate. The crystallization is initiated via employing an isopropanol antisolvent during the spin-coating of the perovskite precursor solution. 40 µL of MAPbBr3 solution (4:1 DMF/DMSO solvent mixture) was applied on plasma-cleaned glass substrate in a chamber with kapton windows. The two-phase spin-coating regime included 10 seconds at 1000 rpm followed by 30 seconds at 2000 rpm, 200 µL of antisolvent was dispensed at t = 30 s.</p> <p> </p> <p> </p> <p>The data was acquired at the P08 Beamline at PETRA III (DESY Hamburg). Acquisition parameters:</p> <p> </p> <ul> <li> <p>X-ray wavelength: 0.6888 nm</p> </li> <li> <p>Sample detector distance: 809 mm</p> </li> <li> <p>Incidence angle: 0.5 deg.</p> </li> <li> <p>Detector model: XRD 1621 CN3 EHS</p> </li> <li> <p>Acquisition rate : 10 frames per second (10 Hz)</p> </li> <li> <p>Direct beam position (pixels): 545, 222</p> </li> </ul>
Synchrotron diffraction images for the 2.9 Å crystal structure of L-Selenomethionine labeled human GDAP1
<p>Dataset collected at DLS, I04 beamline 16.5.2019. L-SeMet substituted crystals collected with SAD-method.</p> <ul> <li>Flux: 1.32e+11</li> <li>Ω Start: 0.0°</li> <li>Ω Osc: 0.10°</li> <li>Ω Overlap: 0°</li> <li>No. Images: 3600</li> <li>Resolution: 2.90Å</li> <li>Wavelength: 0.9790Å</li> <li>Exposure: 0.040s</li> <li>Transmission: 100.00%</li> <li>Beam size: 63x50μm</li> <li>Type: SAD</li> <li>Comment: X,Y,Z (-561,302,301), Aperture: Large</li> </ul> <p> </p>
Diffraction images of crystals of the first and second spectrin repeats (mutant C420A/C435A) of human plectin (PDB code 2ODV): 2-wavelength SeMet MAD dataset
<p>Diffraction images of SeMet labeled crystals of a fragment of human plectin that includes the first and second spectrin repeats (SR1-SR2) of the plakin domain. The two Cys in the wild type sequence were replaced by Ala.</p> <p>This Se-Met MAD dataset was used for the <em>de novo</em> phasing of the pdb entry 2ODV (http://www.rcsb.org/pdb/explore/explore.do?structureId=2ODV).</p> <p> </p> <p>Data was collected at the BM14 beamline of the European Synchrotron Radiation Facility (ESRF, Grenoble, France) using a Mar CCD detector. Data from the same crystal were collected at two wavelengths :</p> <ul> <li>Remote wavelength (0.9185 Å): 180 images (1 degree oscillation per image).</li> <li>Peak wavelength ( 0.9785 Å): 360 images (1 degree oscillation per image).</li> </ul>
Dataset Nucleation Patterns of Polymer Crystals Analyzed by Machine Learning Models
<p>This dataset contains the raw data (01_raw_data), processed data (02_processed_data), and plotting scripts (03_figures) related to the paper:</p> <p>"Nucleation Patterns of Polymer Crystals Analyzed by Machine Learning Models"<br>Atmika Bhardwaj, Jens-Uwe Sommer, Marco Werner</p> <p>Macromolecules <strong>2024</strong>; DOI: <a href="10.1021/acs.macromol.4c00920">10.1021/acs.macromol.4c00920</a></p> <p>Please refer to the README.md files in their respective folders.</p>
Data From: Exploring Gelatin-A and Mouse Proline-Rich Protein 5 as Probes for Wine Polyphenols analysis by Quartz Crystal Microbalance with Dissipation Monitoring
<p>Polyphenols are essential in winemaking, affecting the wine's quality, color, astringency, bitterness, and chemical stability. Conventional methods for assessing polyphenolic content are both expensive and time-intensive, underscoring the need for new, efficient techniques.</p> <p>The Quartz Crystal Microbalance with Dissipation Monitoring (QCM-D) sensor is recognized for its speed and reliability as a label-free detection tool. This study applies QCM-D to evaluate Gelatin Type A (Gel-A) from porcine skin and Mouse Proline-Rich Protein 5 (MP5) for polyphenol analysis in red wines without pre-treatment. MP5 notably exhibited a linear dissipation signal response with both total polyphenol and hydroxybenzoic acid concentrations. These findings highlight the potential for creating a stand-alone sensor platform for real-time polyphenol monitoring in winemaking.</p>
Elasticity tensors of 10276 crystals from DFT computations
<h2>Paper introducing this dataset</h2> <p>Wen, M., Horton, M., Munro, J., Huck, P., & Persson, K. (2024). An equivariant graph neural network for the elasticity tensors of all seven crystal systems. <em>Digital Discovery</em>. DOI:<a title="Link to landing page via DOI" href="https://doi.org/10.1039/D3DD00233K">https://doi.org/10.1039/D3DD00233K</a></p> <p> </p> <p>This dataset consists of three data files in the json format. Each file is explained below.</p> <h2>crystal_elasticity_tensor.json</h2> <p>DFT computed elastic tensors of 10276 crystals used for developing the MatTen model.</p> <p>structure: crystal structure of the material<br>formula_pretty: chemical formula<br>crystal_system: crystal system<br>elastic_tensor: full fourth-rank elastic tensor<br>elastic_tensor_voigt: 6x6 Voigt matrix of the elastic tensor<br>split: split of the data into train, validation, and test subsets for model development</p> <h2><br>max_directional_E.json</h2> <p>New crystals with large maximum directional Young's modulus.</p> <p>material_id: Materials Project identifier<br>formula_pretty: chemical formula</p> <p>structure_original: crystal structure from the Materials Project database<br>elastic_tensor_matten_original: MatTen predicted elastic tensor using `structure_original`<br>max_directional_E_matten_original: MatTen predicted maximum directional Young's modulus using `structure_original`</p> <p>structure: further DFT optimized structure with a tigher criterion<br>elastic_tensor: DFT elastic tensor corresponding to `structure`<br>max_directional_E: DFT maximum directional Young's modulus using `structure`<br>elastic_tensor_matten: MatTen predicted elastic tensor using `structure`<br>max_directional_E_matten: MatTen predicted maximum directional Young's modulus using `structure`</p> <h2><br>elemental_cubic_metal_max_E_along_100_direction.json</h2> <p>New crystals with its maximum directional Young's modulus along the [100] direction.</p> <p>material_id: Materials Project identifier<br>formula_pretty: chemical formula</p> <p>structure_original: crystal structure from the Materials Project database<br>elastic_tensor_matten: MatTen predicted elastic tensor using `structure_original`<br>Delta_S_matten: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor_matten`</p> <p>structure: further DFT optimized structure with a tigher criterion<br>elastic_tensor: DFT elastic tensor corresponding to `structure`<br>Delta_S: value of $S_{1111} - S_{1122} - 2*S_{2323}$ using `elastic_tensor`</p>
Data for manuscript: Functional Protein Dynamics in a Crystal
<p>The data is provided as a part of the manuscript "<strong>Functional Protein Dynamics in a Crystal</strong>". This repository includes an archive with folders:<br> <br> <strong>md_data</strong></p> <ul> <li>contains various simulation systems (crystal supercell, apo and ligand-bound solution) built from the crystal structure of the PDZ domain (PDB ID: 5E11) and carried out using three force fields: Amber ff14SB, CHARMM36m, Amber ff94. <em>The details of the simulations are provided in the Methods and Supplementary methods sections of the manuscript. </em></li> </ul> <p><strong>fig_data</strong></p> <ul> <li>contains the data sets underlying Figures 1-5 of the manuscript's main text. </li> </ul> <p> </p>
Multiscale assessment of the effect of a stearic-palmitic sucrose ester on the crystallization of anhydrous milk fat
<p>Dataset belonging to publication 'Multiscale assessment of the effect of a stearic-palmitic sucrose ester on the crystallization of anhydrous milk fat'.</p> <p>Available via: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.foodres.2024.115243" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.foodres.2024.115243</a>.</p> <p> </p> <p>PLM = polarized light microscopy</p> <p>CryoSEM = cryo-scanning electron microscopy</p> <p>> data obtained after de-oiling fat samples with isobutanol (4x) and aceton (1x), see publication</p> <p>SAXS = small-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of empty capillary, see publication</p> <p>WAXS = wide-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of empty capillary, see publication</p> <p>USAXS = ultra-small-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of the capillary at 70°C, see publication</p> <p>DSC = differential scanning calorimetry</p> <p>> Samples are heated at 70°C for 10 min, and then crystallized following a certain protocol (see publication).</p> <p>> Samples are maintained one hour at their respective isothermal crystallization temperature.</p> <p>> Samples are rehaeted at 5°C/min to 70°C.</p> <p>SE = sucrose ester (SP30, HLB6)</p> <p>AMF = anhydrous milk fat</p> <p>AMFE = anhydrous milk fat + 0.5 wt% SE</p> <p>FC = fast cooling (20°C/min)</p> <p>SC = slow cooling (1°C/min)</p> <p> </p> <p>Project funding agency: Fonds Wetenschappelijk Onderzoek (FWO). Grant number: 1128923N. </p>
Data of publication Ultra-narrow Optical Linewidths in Rare-Earth Molecular Crystals
<p>Data corresponding to main text Figures, Extended Data figures, and Supplementary Figures in publication 'Ultra-narrow Optical Linewidths in Rare-Earth Molecular Crystals, by D. Serrano, S. Kumar Kuppusamy, B. Heinrich, O. Fuhr, M. Ruben and P. Goldner.</p>
Chiral Conglomerate Crystals in the CSD (v5.41; Nov 2019)
<p>The list of chiral conglomerate crystals found by manual search of the CSD (Version 5.41; November 2019) and literature search.<br> Dataset is associated with the following manuscript: <a href="https://doi.org/10.26434/chemrxiv-2022-3g59b">https://doi.org/10.26434/chemrxiv-2022-3g59b</a></p>
Nonlinear two-level dynamics of quantum time crystals
<p>Dataset for the manuscript titled "AC Josephson effect between two superfluid time crystals" by the same authors.</p>
data for Tunable magnonic crystal in a hybrid superconductor–ferrimagnet nanostructure
<h3><strong>General description</strong></h3> <p>The data set designed for the reconstruction of the graphs identified in the manuscript as Fig. 2, Fig. 4 and Fig. 6 has been compiled. The data were respectively described as <strong>calculations</strong> relating to the method of obtaining the data (Brandt's method then plane wave method - PWM) and <strong>simulations</strong> (finite element method - FEM). </p> <h3><strong>Fig. 2</strong></h3> <p>In the case of Fig. 2(a-f), the data marked with a black solid line have been included. The data set includes information on the (x,y) components of the magnetic field induction generated by the superconductor (expressed in millitesla) in the x-direction (expressed in metres). </p> <h3><strong>Fig. 4</strong></h3> <p>The data set for each subsection of Fig. 4 comprises the results of the simulations and calculations. The resulting data from the simulations are classified according to their respective modes. For each mod, the wave vector (expressed as part of the first Brillouin zone) and frequency (GHz) are determined. The data sets resulting from the semi-analytical calculations comprise the common axis (1st column) of the wave vector (expressed as part of the Brillouin zone) followed by columns containing frequency for each mode (GHz).</p> <p> </p> <h3><strong>Fig. 6</strong></h3> <p>The data pertaining to the subsections of Fig. 6(a) and (b) are stored in a separate set of files. The data set for Fig. 6a comprises frequencies that indicate the boundaries of bands in relation to the external magnetic field (B_{0}). Each column has been assigned a number. The numbering is from the lowest frequencies to the highest for k_{x}=0. Each line is comprised of two columns, the first of which describes the magnetic field induction (expressed in millitesla) and the second of which describes the frequency (expressed in GHz). <br>Subsection (b) contains the dependence of the band boundaries (frequencies) on the separation between superconductors (d). The lines are also numbered from lowest frequency to highest for k_{x}=0. Each line is described by two columns, named by a number. The first column is the separation (expressed in nanometres), and the second is the frequency (expressed in GHz). </p>
Entropy-Driven Crystallization of Hard Colloidal Mixtures of Polymers and Monomers
<p>Data archive corresponding to the publication "Entropy-Driven Crystallization of Hard Colloidal Mixtures of Polymers and Monomers " by O. Bouzid <em>et al</em>., Polymers <strong>16</strong>, 2311 (2024). </p> <p>Preprint available at: 10.20944/preprints202407.0786.v1</p> <p>Please see README.txt for instructions on how to access and read the files from the crystallographic analysis based on the CCE norm descriptor.</p> <p>All system configurations have been generated and successively analyzed by the Simu-D software.</p> <p> </p> <p>This research was funded by MICINN/FEDER (Ministerio de Ciencia, Innovación y Universidades, Fondo Europeo de Desarrollo Regional), grant number “PID2021-127533NB-I00”, by the scholarship program from the Algerian Ministry of Higher Education and Scientific Research and by UPM and Santander Bank, “Programa Propio UPM Santander”.</p>
Geometric Frustration Directs the Self-assembly of Nanoparticles with Crystallized Ligand Bundles
<p>This is the supporting dataset of the publication "Geometric Frustration Directs the Self-assembly of Nanoparticles with Crystallized Ligand Bundles".</p> <p><a href="https://doi.org/10.1021/acs.jpcb.4c04562">https://doi.org/10.1021/acs.jpcb.4c04562</a></p> <p>The description of the dataset can be found in the file README.txt</p>
Data for Figure 8 of Publication "Additional global climate cooling by clouds due to ice crystal complexity"
<p>This repository contains the data to produce Figure 8 in the paper:</p> <p>"Järvinen, E., Jourdan, O., Neubauer, D., Yao, B., Liu, C., Andreae, M. O., Lohmann, U., Wendisch, M., McFarquhar, G. M., Leisner, T., and Schnaiter, M.: Additional global climate cooling by clouds due to ice crystal complexity, Atmos. Chem. Phys., 18, 15767–15781, https://doi.org/10.5194/acp-18-15767-2018, 2018."</p> <p>Note that the scripts are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.8095469)</p>
North Temperate Lakes LTER: High Frequency Meteorological and Metabolism Data - Crystal Bog Buoy 2005 - present
The instrumented buoy on Crystal Bog is equipped with a dissolved oxygen sensor, a thermistor chain, light sensor, and in the past, meteorological sensors that provide fundamental information on lake thermal structure and lake metabolism. A surface buoy was used from 2005 - 2014, which included a met station beginning 2006. Since then, only a subsurface buoy as been used (no met station), and includes frequent over-winter deployments under the ice. The thermistor chain data is included in a separate dataset (knb-lter-ntl.119). Hourly and daily averages are provided for the met data, while hourly averages are provided for the dissolved oxygen and light data. To accommodate the under-ice deployments, sensor depths vary somewhat year-to-year. Light data is collected with HOBO pendant light and temperature sensors. The make of the dissolved oxygen sensor has changed over the years: YSI (2005), D-Opto (2006-2014), and PME miniDOT (2015-present).
North Temperate Lakes LTER: High Frequency Water Temperature Data - Crystal Bog Buoy 2005 - current
The instrumented buoy on Crystal Bog is equipped with a thermistor chain that measures water temperature from depths ranging from the surface to 2.25 m placed every 0.25m throughout the water column. From the initial deployment in 2005 through 2014, a surface buoy was used in the open water season. Since 2017, a subsurface buoy has been used, including under-ice deployments during the winter. Recorded depths can vary slightly depending on specifics of the year's deployment hardware. The sampling frequency:varies for instantaneous samples. Prior to mid-season 2009, the sample frequency was every 10 minutes. Since then the sampling frequency has been one minute Hourly and daily averages are also provided. Number of sites: 1.
Single-Photon Emitters in Lead-Implanted Single-Crystal Diamond
<p>Single-Photon Emitters in Lead-Implanted Single-Crystal Diamond<br> We report on the creation and characterization of Pb-related color centers in diamond upon ion implantation and subsequent thermal annealing. Their optical emission in the photoluminescence (PL) regime consists of an articulated spectrum with intense emission peaks at 552.1 and 556.8 nm, accompanied by a set of additional lines in the 535−700 nm range. The attribution of the PL emission to stable Pb-based defects is corroborated by the correlation of its intensity with the implantation fluence of Pb ions. PL measurements performed as a function of sample temperature (in the 143−300 K range) and under different excitation wavelengths (i.e., 532, 514, 405 nm) suggest that the complex spectral features observed in Pb-implanted diamond might be related to a variety of different defects and/or charge states. The emission of the 552.1 and 556.8 nm lines is reported at the single-photon emitter level, demonstrating that they originate from the same individual defect. This work follows from previous reports on optically active centers in diamond based on group-IV impurities, such as Si, Ge, and Sn. In perspective, a comprehensive study of this set of defect complexes could bring significant insight on the common features involved in their formation and opto-physical properties, thus offering a basis for the development of a new generation of quantum-optical devices.We report on the creation and characterization of Pb-related color centers in diamond upon ion implantation and subsequent thermal annealing. Their optical emission in the photoluminescence (PL) regime consists of an articulated spectrum with intense emission peaks at 552.1 and 556.8 nm, accompanied by a set of additional lines in the 535−700 nm range. The attribution of the PL emission to stable Pb-based defects is corroborated by the correlation of its intensity with the implantation fluence of Pb ions. PL measurements performed as a function of sample temperature (in the 143−300 K range) and under different excitation wavelengths (i.e., 532, 514, 405 nm) suggest that the complex spectral features observed in Pb-implanted diamond might be related to a variety of different defects and/or charge states. The emission of the 552.1 and 556.8 nm lines is reported at the single-photon emitter level, demonstrating that they originate from the same individual defect. This work follows from previous reports on optically active centers in diamond based on group-IV impurities, such as Si, Ge, and Sn. In perspective, a comprehensive study of this set of defect complexes could bring significant insight on the common features involved in their formation and opto-physical properties, thus offering a basis for the development of a new generation of quantum-optical devices.</p>
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