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647 results for “Silicon”
Рис. 3. А — гастроΛиты гуся беΛоΛобого (кварц и отΑеΛьные кристаΛΛы амфибоΛов), ХороΛьский район, сеΛо Сиваковка; Б — гастроΛиты из жеΛуΑка гуся беΛоΛобого (размерность кварцевых зерен), южный берег оз. Ханка Fig. 3. A — gastroliths of a white-fronted goose (quartz and individual crystals of amphiboles), Khorolsky District, Sivakovka village; Б — gastroliths from the stomach of a white-fronted goose (dimension of quartz grains), the southern shore of Lake Khanka in The mineral composition of gastroliths in the stomachs of Anatidae in Primorsky Region and the importance of silicon minerals in the physiology of birds
Рис. 3. А — гастроΛиты гуся беΛоΛобого (кварц и отΑеΛьные кристаΛΛы амфибоΛов), ХороΛьский район, сеΛо Сиваковка; Б — гастроΛиты из жеΛуΑка гуся беΛоΛобого (размерность кварцевых зерен), южный берег оз. Ханка Fig. 3. A — gastroliths of a white-fronted goose (quartz and individual crystals of amphiboles), Khorolsky District, Sivakovka village; Б — gastroliths from the stomach of a white-fronted goose (dimension of quartz grains), the southern shore of Lake Khanka
Рис. 1. А — среΑнее процентное соΑержание грануΛометрических фракций в составе гастроΛитов уток с Ханкайского (сΛева) и с Хасанского (справа) участков; Б — среΑнее процентное соΑержание минераΛов в гастроΛитах уток с Ханкайского (сΛева) и с Хасанского (справа) участков in The mineral composition of gastroliths in the stomachs of Anatidae in Primorsky Region and the importance of silicon minerals in the physiology of birds
Рис. 1. А — среΑнее процентное соΑержание грануΛометрических фракций в составе гастроΛитов уток с Ханкайского (сΛева) и с Хасанского (справа) участков; Б — среΑнее процентное соΑержание минераΛов в гастроΛитах уток с Ханкайского (сΛева) и с Хасанского (справа) участков
Data and code for article "Mid-infrared frequency comb via coherent dispersive wave generation in silicon nitride nano-photonic waveguides"
<p>This dataset contains the data presented in the figures of the article "Mid-infrared frequency comb via coherent dispersive wave generation in silicon nitride nano-photonic waveguides" (doi:10.1038/s41566-018-0144-1).</p> <p>The raw data in figures (curved plots) is packaged as an independent OriginLab project file (.opj). </p> <p>The layout of the design of the silicon nitride nano-photonic waveguide is presented. Fabrication process card (shown as a diagram) is provided as well.</p> <p>The source code for simulations presented in the article is also presented.</p>
An Ultra Low-Loss Silicon-Micromachined Waveguide Filter for D-Band Telecommunication Applications
<p>The dataset contains S-parameter measurements between 110-170 GHz, for a silicon micromachined filter. It also contains quality factor data for the filter and the complex propagation constant data.</p>
Data and code for figures in "Thermo-refractive noise in silicon nitride microresonators"
<p>Data and script used to produce the figures in "Thermo-refractive noise in silicon nitride microresonators".</p><p>Readout of some data files requires @MyTrace function from <a href="https://github.com/engelsen/Instrument-control">https://github.com/engelsen/Instrument-control</a>.</p><p>The Matlab live script is tested with Matlab_R2018a. The COMSOL file is tested with COMSOL Multiphysics 5.3a.</p>
Monolayer doping of silicon-germanium alloys: A balancing act between phosphorus incorporation and strain relaxation
<p>This paper presents the application of monolayer doping (MLD) to silicon-germanium (SiGe). This study was carried out for phosphorus dopants on wafers of epitaxially grown thin films of strained SiGe on silicon with varying concentrations of Ge (18%, 30%, and 60%). The challenge presented here is achieving dopant incorporation while minimizing strain relaxation. The impact of high temperature annealing on the formation of defects due to strain relaxation of these layers was qualitatively monitored by cross-sectional transmission electron microscopy and atomic force microscopy prior to choosing an anneal temperature for the MLD drive-in. Though the bulk SiGe wafers provided are stated to have 18%, 30%, and 60% Ge in the epitaxial SiGe layers, it does not necessarily mean that the surface stoichiometry is the same, and this may impact the reaction conditions. X-ray photoelectron spectroscopy (XPS) and angle-resolved XPS were carried out to compare the bulk and surface stoichiometry of SiGe to allow tailoring of the reaction conditions for chemical functionalization. Finally, dopant profiling was carried out by secondary ion mass spectrometry to determine the impurity concentrations achieved by MLD. It is evident from the results that phosphorus incorporation decreases for increasing mole fraction of Ge, when the rapid thermal annealing temperature is a fixed amount below the melting temperature of each alloy.</p>
Fig. 1 in Resistance in rice to Tibraca limbativentris (Hemiptera: Pentatomidae) influenced by plant silicon content
Fig. 1. Percentage of damaged stems (PDS) in 3 rice cultivars treated with different sources of silicon. Urutaí, Goiás, Brazil. 2016. Means followed by the same capital letter (inducers) and lower case letters (cultivars) in the columns do not differ statistically from each other according to the Tukey test at 0.05% probability.
Figure 4 in The role of silicon in the mitigation of water stress in Eugenia myrcianthes Nied. seedlings
Figure 4. Hierarchical groups based on the Euclidean distance of the characteristics evaluated in Eugenia myrcianthes Nied. seedlings grown under water fluctuations (deficit – 1st P0 and flooding – 2nd P0) and silicon doses (0, 2, and 4 mmol). I: continuous irrigation; S: stress; P0: photosynthesis close to zero; R: recovery.
Figure 3 in The role of silicon in the mitigation of water stress in Eugenia myrcianthes Nied. seedlings
Figure 3. Pearson's linear correlation (r) of the characteristics evaluated in Eugenia myrcianthes Nied. seedlings grown under water regimes (continuous irrigation, deficit – 1st P0, and flooding – 2nd P0) and silicon doses (0, 2, and 4 mmol).
Figure 2 in The role of silicon in the mitigation of water stress in Eugenia myrcianthes Nied. seedlings
Figure 2. Principal component analysis (PCA) of the characteristics evaluated in Eugenia myrcianthes Nied. seedlings grown under water regimes (continuous irrigation, deficit – 1st P0, and flooding – 2nd P0) and silicon doses (0, 2, and 4 mmol). I: continuous irrigation; S: stress; P0: photosynthesis close to zero; R: recovery.
Figure 1 in The role of silicon in the mitigation of water stress in Eugenia myrcianthes Nied. seedlings
Figure 1. Photosynthetic rate (A) of Eugenia myrcianthes Nied. seedlings grown under water regimes (continuous irrigation, deficit – 1st P0, and flooding – 2nd P0), with silicon doses (0, 2, and 4 mmol). I: continuous irrigation; S: stress; P0: photosynthesis close to zero; R: recovery.
Stimulated Emission from Hexagonal Silicon-Germanium Nanowires
<p>Data repository in companion with the publication "Stimulated Emission from Hexagonal Silicon-Germanium Nanowires"</p>
Magnetic-Free Silicon Nitride Integrated Optical Isolator (Original Data)
<p>This is the raw dataset for the paper titled "Magnetic-Free Silicon Nitride Integrated Optical Isolator", which includes the original data and theoretical code. </p>
Data for "Two-phase mixture of iron-nickel-silicon alloys in the Earth's inner core"
<p>This file is the dataset used in the article "Two-phase mixture of iron-nickel-silicon alloys in the Earth's inner core", <em>Commun. Earth Environ.</em> <strong>2</strong>, 225 (2021). https://doi.org/10.1038/s43247-021-00298-1</p>
Open data source for "Optically reconfigurable quasi-phase-matching in silicon nitride microresonators"
<p>The folder includes includes the raw data as well as codes that were used for generation of all Figures in the paper "Optically reconfigurable quasi-phase-matching in silicon nitride microresonators".</p>
Data from: "Lithium-ion battery degradation: measuring rapid loss of active silicon in silicon-graphite composite electrodes"
<p>Dataset from the publication "Lithium-ion battery degradation: measuring rapid loss of active silicon in silicon-graphite composite electrodes". Full experimental details can be found in the related publication in ACS Applied Energy Materials: <a href="https://doi.org/10.1021/acsaem.2c02047">https://doi.org/10.1021/acsaem.2c02047</a></p> <p>Commercial 21700 cylindrical cells (LG M50T, LG GBM50T2170) were cycle aged under 3 different temperatures [10, 25, 40] °C and 2 SoC ranges [0-30, 0-100]%, with multiple cells tested under each condition. Cells were base-cooled at set temperatures using bespoke test rigs (see pubilcation for details). All electrochemical data were recorded using a Biologic BCS-815 battery cycler.</p> <p> </p> <p><strong>Break-in cycles:</strong></p> <p>Prior to any ageing or performance checks, all cells were subject to 5 full charge-discharge cycles as part of the break-in procedure. This consisted of a 0.2C charge to 4.2 V with CV-hold till C/100, and 0.2C discharge to 2.5 V (repeated for 5 cycles). Cells were rested under open circuit conditions for 2 hours after each charge and 4 hours after each discharge. These break-in cycles were performed at 25°C for all cells.</p> <p> </p> <p><strong>Ageing Conditions:</strong></p> <table align="center"> <caption>Ageing Conditions</caption> <thead> <tr> <th scope="col">Expt</th> <th scope="col">SoC Range</th> <th scope="col">C-rate</th> <th scope="col">Temperature</th> <th scope="col"># of cells</th> <th scope="col">Cell IDs</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>10°C</td> <td>3</td> <td>A, B, J</td> </tr> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>25°C</td> <td>3</td> <td>D, E, F</td> </tr> <tr> <td>1</td> <td>0-30%</td> <td>0.3C / 1D</td> <td>40°C</td> <td>3</td> <td>K, L, M</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>10°C</td> <td>3</td> <td>A, B, C</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>25°C</td> <td>2</td> <td>D, E</td> </tr> <tr> <td>5</td> <td>0-100%</td> <td>0.3C / 1D</td> <td>40°C</td> <td>3</td> <td>F, G, H</td> </tr> </tbody> </table> <p>For cells aged in the 0-30% SoC range, each ageing set consisted of 256 cycles over the 0-30% SoC range (discharge to 2.5 V, charge by passing 1500 mA h (== 0.3*nominal capacity)). C-rates were 0.3C for charge, and 1C for discharge.</p> <p>For cells aged in the 0-100% SoC range, each ageing set consisted of 78 cycles over the full SoC range (discharge to 2.5 V, charge to 4.2 V with CV hold till C/100). C-rates were 0.3C for charge, and 1C for discharge.</p> <p> </p> <p><strong>Reference Performance Tests (RPTs):</strong></p> <p>All cells were characterised at beginning of life (BoL) and after each ageing set using a reference performance test (RPT). The RPT was always performed at 25°C. Two different RPT procedures were used: a longer procedure which was performed after each even-numbered ageing set, and a shorter procedure which was used after each odd-numbered ageing set. Both procedures are detailed below. A CC-CV charge at 0.3C to 4.2 V, 4.2 V till C/100 was performed between each step of the procedures.</p> <p>Long RPT procedure:</p> <ol> <li>C/10 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>C/2 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>GITT discharge at 0.5C; 25 pulses with each pulse passing 200 mA h of charge, with 1 hour rest between pulses; lower cut-off voltage of 2.5 V (but continued test for all pulses).</li> <li>GITT discharge at 0.5C; 5 pulses with each pulse passing 1000 mA h of charge, with 1 hour rest between pulses; lower cut-off voltage of 2.5 V (but continued test for all pulses).</li> </ol> <p>Short RPT procedure:</p> <ol> <li>C/10 discharge-charge cycle between the voltage limits (2.5 V and 4.2 V).</li> <li>Hybrid CC-pulse test with average current of C/2. A baseline DC current of C/2 was applied with an HPPC-type profile superimposed on top. This was done for discharge and charge (with voltage limits of 2.5 V and 4.2 V).</li> <li>Hybrid CC-pulse test with average current of 1C. A baseline DC current of 1C was applied with an HPPC-type profile superimposed on top. This was done for discharge only (with a voltage limit of 2.5 V).</li> </ol> <p> </p> <p><strong>Extracted Data - Main </strong></p> <p>One csv file exists for each cell being tested, summarising the important data extracted from the ageing cycles and the RPTs. This includes:</p> <p>Ageing Set: numbered 0 (BoL) to x, where x is the number of ageing sets the cell has been subject to.</p> <p>Ageing Cycles: number of ageing cycles the cell has been subject to. *this is <strong>not </strong>equivalent full cycles.</p> <p>Ageing Set Start Date/ End date: The date that each ageing set began/ ended.</p> <p>Days of Degradation: Number of days between the date of the first ageing set beginning and the current ageing set ending.</p> <p>Age Set Average Temperature: average recorded surface temperature of the cell during cycle ageing. Temperature was recorded approximately 1/2 way up the length of the cell (i.e. between positive and negative caps) using a K-type thermocouple. Units: °C.</p> <p>Charge Throughput: total accumulated charge recorded during all cycles during ageing (i.e. sum of charge and discharge). This is the cummulative total since BoL (not including RPTs). Units: Ah.</p> <p>Energy Throughput: as with "charge throughput", but for energy. Units: Wh.</p> <p>C/10 Capacity: the capacity recorded during the C/10 discharge test of each RPT. Units: mAh.</p> <p>C/2 Capacity: the capacity recorded during the C/2 discharge test of each even-numbered RPT. Units: mAh.</p> <p>0.1s Resistance: The resistance calculated from the 25-pulse GITT test of each even-numbered RPT. This value is taken from the 12th pulse of the procedure (which corresponds to ~52% SoC at BoL). The resistance is calculated by dividing the voltage drop by the current at a timecale of 0.1 seconds after the current pulse is applied (the fastest timescale possible under the 10 Hz recording condition). Units: Ohms.</p> <p> </p> <p><strong>Extracted Data - Degradation Modes:</strong></p> <p>Degradation Mode Analysis (DMA) was also performed on the C/10 discharge data at each RPT. This analysis uses an optimisation function to determine the capacities and offset of the positive and negative electrodes by calculating a full cell voltage vs capacity curve using 1/2 cell data and comparing against the experimentally measured voltage vs capacity data from the C/10 discharge.</p> <p>The results of this analysis are saved in the DMA folder, with 4 csv files for each cell, which contain data for all RPTs. The 4 files contain:</p> <p>Fitting parameters: output from the DMA optimisation function; 5 parameters which detail the upper/lower lithitation fractions of each electrode and the capacity fraction of graphite in the negative electrode.</p> <p>Capacity and offset data: calculated based on the fitting parameters above alongside the measured C/10 discharge capacity.</p> <p>DM data: Quantities of LLI, LAM-PE, LAM-NE, LAM-NE-Gr, and LAM-NE-Si calculated from the change in capacities/offset of each electrode since BoL.</p> <p>RMSE data: the root-mean-square error of the optimisation function calculated from the residual between the measured and calculated voltage vs capacity profiles.</p> <p> </p> <p><strong>Timeseries data from RPTs:</strong></p> <p>Timeseries datafiles from the Biologic battery cycler which have been exported to csv and sliced for each step of each RPT procedure to help with future use of the data. Files contain [time, voltage, current, charge, temperature] data.</p> <p> </p> <p><strong>Jupyter Notebook:</strong></p> <p>A jupyter notebook has been included to aid futher use of this data. The notebook shows how to load the data into pandas DataFrame objects and provides a couple of example plots to view the datasets.</p> <p> </p> <p><strong>Notes:</strong></p> <p>A faulty electrical connection to cell A of Expt 5 (i.e. one of the cells being aged at 0-100% SoC at 10°C) during RPT4 led to erroneous results for that performance check (as evidenced in the 0.1s resistance value). The faulty electrical connection was fixed prior to subsequent cycling but the RPT was not repeated. We have kept the data collected during this RPT as part of the dataset, so caution should be used when using this specific portion.</p>
Silicon bearing molecules in eta Carina's Homunculus
<p>Open access to data published by Bordiu et al. (2022).<br> The article reports the detection and study of silicon-bearing and other molecules in the equatorial ring of the Homunculus around eta Carina.<br> Data are hosted as a catalog at the Spanish Virtual Observatory site (see related identifier below).<br> A total of seven rotational lines from 13CO, 13CN, SiO, SiS, and SiN are included, observed toward twelve clumps within the equatorial ring of the Homunculus.<br> The catalog contains: (1) spectra as FITS files; (2) preview as png files; and (3) physical parameters derived from modeling as VOTables.</p> <p>Link:<br> <a href="http://svocats.cab.inta-csic.es/etacar_si/">http://svocats.cab.inta-csic.es/etacar_si/</a></p> <p>References:<br> Bordiu, C., Rizzo, J.R., Bufano, F., et al. 2022, Astrophysical Journal Letters, 939, L30<br> </p>
Figure 2 in Silicon derivatives induced host plant resistance against Tetranychus urticae (Acari: Tetranychidae) in eggplants farms
Figure 2. (A) Silicon leaf, total protein and phenol contents, (B) Activity of POD, CAT, and PPO of S. melongena- treated plants. Means followed by the same letter are not significantly different using Tukey's HSD Test at P <0.05. T1 = Control, T2 = OSAB 2 mL L−1, T3= OSAB 4 mL L−1, T4= Silica K 2 mL L−1, and T5 = Silica K 4 mL L−1.
Figure 1 in Silicon derivatives induced host plant resistance against Tetranychus urticae (Acari: Tetranychidae) in eggplants farms
Figure 1. Mean number ± SE of the different stages of T. urticae on S. melongena leaves 10, 30 and 50 days after spraying (DAS). Means followed by the same letter are not significantly different using Tukey's HSD at P <0.05. T1 = Control, T2 = OSAB 2 mL L−1, T3 = OSAB 4 mL L−1, T4 = Silica K 2 mL L−1, and T5 = Silica K 4 mL L−1.
Dynamical simulation of EBSD master pattern of silicon
<p>Dynamical simulation of an electron backscatter diffraction (EBSD) master pattern of silicon (<em>Fd<span class="math-tex">\(\bar{3}\)</span>m</em>, <em>a</em> = 5.4307 Å). The master pattern was simulated with EMsoft v5.0. The HDF5 file includes master patterns of the upper and lower hemispheres, in both the stereographic projection and the square Lambert projection, of accelerating voltages from 5 to 20 kV with an increment of 1 kV.</p> <p>The HDF5 file can be opened with any HDF5 reader, e.g. the applications HDFView and HDFCompass or the Python library h5py. The file can also be read and plotted with the Python library kikuchipy (https://kikuchipy.org). Assuming Python 3.7 or higher and the library is installed, the stereographic projection of the master pattern with all energies can be read and plotted with the following commands:</p> <pre><code class="language-python">import kikuchipy as kp s = kp.load("/path/to/si_mc_mp_20kv.h5") s.plot()</code></pre> <p>The PNG file shows the stereographic projection of the upper hemisphere of the master pattern from 20 kV. The remaining files are input and output files to the EMsoft programs EMmkxtal (output: si.xtal), EMMCOpenCL (input: si.xtal, mcopencl.nml; output: si_mc_mp_20kv.h5) and EMEBSDmaster (input: BetheParameters.nml, ebsdmaster.nml, si_mc_mp_20kv.h5; output: added to existing si_mc_mp_20kv.h5).</p>
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