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17 results for “Dynamic light scattering”
Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Scanning dynamic light scattering optical coherence tomography for measurement of high omnidirectional flow velocities</em>”</strong> in Optics Express (<a href="https://doi.org/10.1364/OE.456139">doi.org/10.1364/OE.456139</a><em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 5-10 minutes.</p> <p>For ideal scanning alignment each dataset includes diffusion, focus (beam waist) calibration, and flow measurements (using both M-scan and B-scan methods) for all used sample lengths. The names “M-scan” and “A-scan” are used interchangeably. The analysis process is as follows: firstly, the diffusion coefficient is determined for every sample size (time series length) to be analyzed using the script ‘Diffusion.py’. Secondly, the beam waist (focus) calibration is performed using the script ‘Beam Waist.py’. Since the beam waist should be constant for each dataset, choose the value obtained from the file with a largest time series length for minimizing the statistical uncertainty and fix it for a given dataset. Beam scanning for our setup is not exactly perpendicular to the optical axis. Therefore, for B-scan Doppler flow measurements the calibration parameter v_d, quantifying the axial scan bias, must be used. This calibration parameter varies with time series length and needs to be obtained for each sample size. This is done with the same script as the beam waist calibration. Thirdly, the Doppler angle is determined using M-scan measurement with the lowest discharge rate using the script ‘Angle.py’. Finally, the flow profiles are obtained both for M-scan and B-scan methods with predetermined calibration parameters using the script ‘Flow.py’. All file names are sufficiently descriptive, showing sample size, scan mode, measurement type and discharge rate. The number on the file name represents the time series length.</p> <p>For arbitrary scanning alignment, the dataset includes one diffusion and one focus (beam waist) measurements for calibration purposes. The diffusion measurement is used only for the beam waist calibration and not for flow measurements. It also contains several B-scan flow measurements (with different scan speeds) for every discharge rate. The analysis process is same as before but without the angle calibration step. Use the script ‘Omnidirectional.py’ for this step.</p> <p>The table below summarizes all datasets and Python scripts uploaded to this repository.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Applicability</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Dataset, 12-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 0.39 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 16-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 0.94 deg and alignment angle of 0.94 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 20-04-2021.zip</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>Dataset for Doppler angle of 1.58 deg and alignment angle of 2.26 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 18-05-2021.zip</p> </td> <td> <p>Arbitrary alignment</p> </td> <td> <p>Dataset for alignment angle of 2.7 deg.</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>Both methods</p> </td> <td> <p>File containing k-interpolation data</p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> </tr> <tr> <td> <p>DataProcessing.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This module contains all analysis and processing routines.</p> </td> </tr> <tr> <td> <p>Diffusion.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This script determines diffusion coefficient from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Beam Waist.py</p> </td> <td> <p>Both methods</p> </td> <td> <p>This script determines focus beam waist from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Angle.py</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>This script determines Doppler angle from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Flow.py</p> </td> <td> <p>Ideal scan alignment</p> </td> <td> <p>This script determines M-scan and B-scan flow profiles from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Omnidirectional.py</p> </td> <td> <p>Arbitrary alignment</p> </td> <td> <p>This script determines flow profiles for arbitrary scan alignment.</p> </td> </tr> </tbody> </table>
Diffusion Coefficient Analysis by Dynamic Light Scattering Enables Determination of Critical Micelle Concentration
<p>This upload contains dynamic light scattering data files obtained from the work described in the manuscript that is published by Lena Nielinger and co-workers in ChemPlusChem (<a href="https://doi.org/10.1002/cplu.202400645">https://doi.org/10.1002/cplu.202400645</a>) (WILEY). The files in this repository contain dynamic light scattering data obtained from the analysis of different detergents series and can be downloaded and analysed with a Zetasizer software according to the instructions procied in the manuscript. For information on how to obtain the the Zetasizer software, we refer to the customer support and/or website of the company Malvern Panalytical.</p>
Dynamic light scattering datasets used to assess the Raynals software
<p>Updated - 23/04/2023<br> This folder contains the associated data from Burastero et al., 'Raynals, an online tool for the analysis of dynamic light scattering'</p> <p>The experimental datasets can be found at ./experimentalData/<br> The artificially generated datasets at ./SimulatedDataDLS_case1 and ./SimulatedDataDLS_case2</p> <p>To produce the experimental datasets, we performed measurements on Carbonic Anhydrase, Bovine Serum Albumin, Gold Nanoparticles, and three in-house samples: A protein with a beta-Propeller domain, a coiled-coil like protein, and an intrinsically disordered protein.</p> <p>Additionally, you'll find R scripts to generate Fig. 3, Fig. 5, and Fig. S1 to S4.<br> </p>
Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Precision and bias in dynamic light scattering optical coherence tomography measurements of diffusion and flow</em>”</strong> in Biomedical Optics Express (doi.org/10.1364/BOE.505847<em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 5-10 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails.</p> <p>For the diffusion measurement under static conditions, there is only one file. However, for experiments involving both flowing and diffusing particles, the dataset comprises diffusion calibration, focus (beam shape) calibration, and flow measurement files. Due to the upload size limitations of the Zenodo repository, only the flow measurements corresponding to one discharge rate have been uploaded. Furthermore, only the non-dilute flow dataset has been uploaded for the same reason. However, for the dilute flow, the analysis logic remains the same, but users will need to utilize the complete g2 formula outlined in Section 2.2 of our article. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement. To conduct the analysis, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate.</p> <p>The results are plotted at the end of our analysis routines. The parameters are displayed as a function of depth. Users can readily compute the Signal-to-Noise Ratio (SNR) at each depth by utilizing the fitted autocorrelation amplitudes. Occasionally, the fitted amplitudes may surpass unity. In such instances, users can assume an extremely high (even infinite) SNR.</p> <div> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td> <p>Diffusion_03032023.oct</p> </td> <td> <p>Diffusion measurement file.</p> </td> <td> <p>Na=4096, Nb=1100, 5.5 kHz</p> </td> </tr> <tr> <td> <p>Diffusion_07032023.oct</p> </td> <td> <p>Diffusion calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=10, 36 kHz</p> </td> </tr> <tr> <td> <p>Waist_07032023.oct</p> </td> <td> <p>Beam waist calibration file for flow measurement.</p> </td> <td> <p>Na=4096, Nb=40, 36 kHz</p> </td> </tr> <tr> <td> <p>Q=2_07032023.oct</p> </td> <td> <p>Flow measurement file for a discharge rate of 2 ml/min.</p> </td> <td> <p>Na=4096, Nb=1000, 36 kHz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis, simulation and processing routines.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_diffusion.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Simulation_flow.py</p> </td> <td> <p>This script is for simulating and fitting g1 and g2 from flowing and diffusive particles.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Diffusion_parallel.py</p> </td> <td> <p>This script is for analyzing static diffusion measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Flow_parallel.py</p> </td> <td> <p>This script is for analyzing flow measurements performed using Thorlabs Ganymede OCT system.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </p>
Dynamic light scattering differentiate parameters of blood flow
<p>This dataset demonstrates blood perfusion recordings measurements on the 3rd fingers and wrists simultaneously (sitting position) in volunteers of three groups: healthy volunteers younger group (20 years old), healthy volunteers younger group (~55 years old), patients with Diabetes Type 2 (~55 years old).</p>
Supporting experimental data for "Z-average and Polydispersity Index of Dynamic Light Scattering" by Bazzoni et al.
<p>Data (auto-correlation functions used for preparing Figure 5-8) accompanying manuscript "Z-average and Polydispersity Index of Dynamic Light Scattering" by Bazzoni et al.</p> <p> </p> <p>1. “<strong>Figure 5_ACFs.zip</strong>” Large sets of autocorrelation functions (ASCII format) recorded to demonstrate heteroscedastic and angle-dependent uncertainties. Each file may list the basic parameters, instrument’s estimation of Z-average (not evaluated and not used by us), the auto-correlation function, and a low temporal-resolution trace of the scattering intensity. Figure 5 is based on the analyses of subsets of this data set.</p> <p>2. “<strong>Figure 6 & 7_ACFs.zip</strong>” Large sets of autocorrelation functions (ASCII format) recorded to demonstrate the impact of uncertainty on precision, and the impact of duration and photon counting rate on the signal-to noise ratio. Each file may list the basic parameters, instrument’s estimation of Z-average (not evaluated and not used by us), the auto-correlation function, and a low temporal-resolution trace of the scattering intensity. Figure 6 and 7 are based on the analyses of subsets of this data set.</p> <p>3. “<strong>Figure 8_ACFs.zip</strong>” Large sets of autocorrelation functions (ASCII format) recorded to demonstrate the positive impact of data averaging on precisions. Each file may list the basic parameters, instrument’s estimation of Z-average (not evaluated and not used by us), the auto-correlation function, and a low temporal-resolution trace of the scattering intensity. Figure 8 is based on the analyses of subsets of this data set.</p>
Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Wavenumber-dependent dynamic light scattering optical coherence tomography measurements of collective and self-diffusion</em>”</strong> in Optics Express (doi.org/10.1364/OE.521702)<em>. </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.11 was used for programming. Kindly note that simulating autocorrelation functions from extensive time series data, especially with a high repetition rate, can be time-consuming, often requiring more than 20-30 minutes. Despite parallelized processing routines for the measurement data, the full analysis may still take up to an hour. Please restart the kernel and run the code again if the parallelization fails. Also, keep in mind the significant RAM usage.</p> <p>We've conducted measurements using both a custom-built OCT system and the Thorlabs OCT system. The custom setup specifically focused on measuring diffusion in concentrated suspensions, while the Thorlabs OCT system was used to analyze both concentrated and dilute suspensions. To analyze the data from the custom setup, we require an additional dark measurement file. Conversely, analyzing the Thorlabs measurements necessitates a chirp interpolation file. All filenames, whether for raw data or analysis files, are sufficiently descriptive. Files obtained with the Thorlabs OCT system are easily identifiable as they contain “Thorlabs” in their names. To conduct the analysis of Thorlabs measurements, it's essential to have information regarding the time series length (number of A-scans), the number of repeats (B-scans), and the acquisition rate. The results are plotted at the end of our analysis routines, with the parameters displayed as a function of depth or wavenumber. Raw measurement files and analysis routines are described below.</p> <div> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Parameters</strong></p> </td> </tr> <tr> <td> <p>10050, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated Kostrosöl 10050 sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>CS50-28, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated Levasil CS50-28 sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Mix, 10 us.mat</p> </td> <td> <p>Interference intensity from the custom setup for the concentrated mixed sample.</p> </td> <td> <p>Na=8192, Nb=20, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Dark, 10 us.mat</p> </td> <td> <p>Background interference intensity from a custom setup.</p> </td> <td> <p>Na=2048, Nb=5, 4.5 kHz</p> </td> </tr> <tr> <td> <p>Concentrated 8050, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 8050 sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Concentrated 9550, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated Kostrosöl 9550 sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Concentrated mix, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the concentrated mixed sample.</p> </td> <td> <p>Na=65536, Nb=10, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute 8050, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 8050 sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute 9550, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute Kostrosöl 9550 sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Dilute mix, Thorlabs.oct</p> </td> <td> <p>Interference intensity from the Thorlabs OCT for the dilute mixed sample.</p> </td> <td> <p>Na=32768, Nb=20, 36 Khz</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>File containing k-interpolation data for the Thorlabs OCT measurements.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw Thorlabs OCT files.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Data_processing.py</p> </td> <td> <p>This module contains all analysis functions.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Custom_concentrated.py</p> </td> <td> <p>The script is for analyzing raw concentrated measurement files from the custom setup.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Thorlabs_concentrated.py</p> </td> <td> <p>The script is for analyzing raw concentrated measurement files from the Thorlabs setup.</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>Thorlabs_dilute.py</p> </td> <td> <p>The script is for running analysis of raw dilute measurement files from the Thorlabs setup.</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> </div> <p> </p>
Dynamic light scattering data
<p>The upload archive consists of all the obtained results based on DLS measurements ( commonly known as dynamic light scattering) . More detain you could find in the cited article above.</p>
Raw data on dynamic light scattering assessment of small cellular particles isolated from conditioned culture media of Dunaliella tertiolecta and Phaeodactylum tricornutum. Effect of Triton X-100 and temperature
<p>Raw data on dynamic light scattering assessment of small cellular particles isolated from conditioned culture media of microalgae <em>Dunaliella tertiolecta</em> (<em>D. tertiolecta</em>)<em> </em>and <em>Phaeodactylum tricurnutum</em> (<em>P. tricornutum</em>)<em> </em>by dynamic light scattering are presented. The project contains spreadsheet files with the measured dependencies of g2 function on time. We collected several g2 functions for each setting (3 for <em>D. tertiolecta</em> samples, 5 for <em>D. tertiolecta</em> with added TX100, 3 for <em>P. tricornutum</em> samples, 3 for <em>P. tricornutum</em> with added TX100). Curves were analyzed independently and compared with the respective averaged curve fitted by the inverse Laplace transform program CONTIN (freely available at: <a href="http://s-provencher.com/index.shtml">http://s-provencher.com/index.shtml</a>, the code was accessed 25. 1. 2011). The correlation curves were fitted with up to 50 exponents.</p> <p>For analysis of stability of small cellular particles with respect to temperature change, we have overall reports for each microalgae type and reports on the size distribution function, data for the g2 function and dependence of scattered light intensity on time for each temperature measured. There were 14 temperatures chosen for each type of microalgae. The files are marked with respective temperatures.</p> <p>The samples were prepared as described below:</p> <p><strong>Cultivation of the algae:</strong> Cultures of <em>D. tertiolecta</em> CCAP 19/22 and <em>P. tricornutum</em> CCAP 1052/1A from the Culture Collection of Algae and Protozoa (CCAP) of SAMS (Oban, Scotland) were grown in artificial seawater (Reef Crystals, Aquarium Systems, France). 22 g of salt was dissolved in one litre of distilled water, sterile filtered (0.2-micron cellulose filters, ref. 11107-47-CAN, Sartorius Stedim Biotech GmbH, Germany), autoclaved, and supplemented with Guillard’s (F/2) Marine Water Enrichment Solution (ref. G0154, Sigma Aldrich, USA). Cultures were grown in a respirometer (Echo, Slovenia) in 0.5-L borosilicate bottles, at 20 °C and 20 % illumination (approximately 250 μmol/m2s) with a 14-hour light / 10-hour dark cycle, with aeration of 0.2 L/min.</p> <p><strong>Isolation of small cellular nanoparticles:</strong> Small cellular particles were isolated by differential centrifugation, using a protocol widely used for the isolation of extracellular vesicles (Théry C, Amigorena S, Raposo G, Clayton A. Isolation and Characterization of Exosomes from Cell Culture Supernatants and Biological Fluids. Current Protocols in Cell Biology. 2006;30(1). doi:10.1002/0471143030.cb0322s30). Microalgal cells were removed by low-speed centrifugation (300 g, 10 min, 4°C, centrifuge Centric 260R with rotor RA 6/50 (Domel, Slovenia)), using 50 mL conical centrifuge tubes (ref. S.078.02.008.050, Isolab Laborgeräte GmbH, Germany); and 2000 g, 10 min, 4°C (Centric 400R centrifuge with rotor RS4/100 (Domel, Slovenia)), using 15 mL conical centrifuge tubes (ref. S.078.02.001.050, Isolab Laborgeräte GmbH, Germany). Each step was repeated twice. Then, the cell-depleted medium was centrifuged twice at 10 000g and 4°C for 30 min (Beckman L8-70M ultracentrifuge, rotor SW55Ti (Beckman Coulter, USA)), using thin-wall polypropylene centrifuge tubes (ref. 326819, Beckman Coulter, USA) to remove larger cell debris. Finally, small cellular particles were pelleted by centrifugation at 118 000 g and 4°C, for 70 min in the same type of ultracentrifuge and ultracentrifuge tubes. The isolate obtained from about 30 mL of conditioned media was not visible to the eye.</p> <p>For treatment with Triton X-100, the sample was incubated with Triton X-100 at concentration of 0.1%.</p> <p><strong>Dynamic light scattering (DLS): </strong>The average hydrodynamic radius (<em>R</em><sub>h</sub>) of NPs and the average intensity of scattered light (<em>I</em>) were assessed for characterization of small cellular particles by DLS. The value of <em>I</em> was interpreted as a measure of small cellular particles concentration (in the case of preserved particle size distribution) or as a topological change (in the case of altered particle size distribution)(Paterna A, Rao E, Adamo G, et al. Isolation of Extracellular Vesicles From Microalgae: A Renewable and Scalable Bioprocess. <em>Front Bioeng Biotechnol</em>. 2022;10:836747. doi:10.3389/fbioe.2022.836747; Brown W, ed. <em>Dynamic Light Scattering: The Method and Some Applications</em>. Clarendon Press ; Oxford University Press; 1993). For analysis of the samples we used Instrument 3D-DLS-SLS cross-correlation spectrometer from LS Instruments GmbH (Fribourg, Switzerla nd) with a 100 mW DPSS laser (Cobolt Flamenco, Cobolt AB, Sweden) having a wavelength λ<sub>0</sub> = 660 nm. Before measurements, samples were equilibrated in a decalin bath at 25 °C for 15 min. The scattered light was measured at an angle <em>θ</em> = 90° for 120 s. The correlation functions and integral time-averaged intensities <em>I</em>(<em>θ</em>)≡ <em>I</em>(<em>q</em>) (where <em>q</em> is the scattering vector, defined as <em>q</em> =(4π<em>n</em><sub>0</sub>/λ<sub>0</sub>)sin(<em>θ</em>/2), with <em>n</em><sub>0</sub> the refractive index of the medium, in our case estimated by the corresponding value for water, i.e. <em>n</em><sub>0</sub> = 1.33 at 25°C), were recorded simultaneously. The <em>R</em><sub>h</sub> values of small cellular particles were obtained from the diffusion coefficients (<em>D</em>) that were assessed from the correlation function of the scattered electric field (<em>g</em><sub>1</sub>(<em>t</em>)). The <em>g</em><sub>1</sub>(<em>t</em>) function was calculated from the measured correlation function of the scattered light intensity <em>g</em><sub>2</sub>(<em>t</em>) by applying Siegert’s relation (Schärtl W. <em>Light Scattering from Polymer Solutions and Nanoparticle Dispersions</em>. Springer; 2007; Shurer CR, Kuo JCH, Roberts LM, et al. Physical Principles of Membrane Shape Regulation by the Glycocalyx. <em>Cell</em>. 2019;177(7):1757-1770.e21. doi:10.1016/j.cell.2019.04.017).</p> <p>To convert <em>D</em> to <em>R</em><sub>h</sub>, the Stokes-Einstein equation was used (<em>R</em><sub>h</sub> = <em>kT</em>6π<em>ηD</em>, where <em>k</em> is the Boltzmann constant, <em>T</em> is the absolute temperature, and <em>η</em> is the viscosity of the medium in which the particles diffuse). It was assumed that particles have a spherical shape. The viscosity of the medium was not known. We approximated the viscosity value to that of of water at 25°C.To test the effect of Triton X-100 on the samples, 0.1% (V/V) of Triton X-100 was added to the sample before the measurement. The change in <em>R</em><sub>h</sub> distribution and the change of scattered light intensity (Δ<em>I = I</em><sub>sample </sub><em>- I</em><sub>sample<em>+</em>0.1%.TX100</sub>) was determined.</p> <p>The analysis was made with an in-house created software based on the inverse Laplace transform program CONTIN (freely available at: <a href="http://s-provencher.com/index.shtml">http://s-provencher.com/index.shtml</a>, the code was accessed 25. 1. 2011). We collected several intensity correlation functions for each setting. Curves were analyzed independently and compared with the averaged curve. The correlation curves were fitted with up to 50 exponents.</p> <p>To test the effect of Triton X-100 on NPs, 0.1% (V/V) of Triton X-100 was added to the sample before the measurement. The change in <em>R</em><sub>h</sub> distribution and the change of scattered light intensity (Δ<em>I = I</em><sub>sample </sub><em>- I</em><sub>sample<em>+</em>0.1%.TritonX-100</sub>) was determined.</p> <p>Thermal stability analysis was performed using the LitesizerTM 500 instrument (Anton Paar GmbH). Samples were heated from 15 °C to 80 °C in 5 °C steps. When the target temperature was reached, the samples were equilibrated for another 5 minutes before 10 measurements of 20 s duration were performed. The size distributions were determined from the mean correlation function using the Anton Paar Kalliope Professional; Version 2.16.0. (Anton Paar GmbH), <a href="https://www.anton-paar.com/corp-en/products/details/software-for-particle-analysis-kalliopetm/">https://www.anton-paar.com/corp-en/products/details/software-for-particle-analysis-kalliopetm/</a>, applying the CONTIN approach. A new version of Kalliope<sup>TM </sup>4.12.0 <a href="https://www.kalliope.com/2021/05/03/versione-firmware-4-12-0/?lang=en">https://www.kalliope.com/2021/05/03/versione-firmware-4-12-0/?lang=en</a> is freely available online.</p>
Dynamic light scattering size distribution, ζ-potential data, and fluorescence spectra
Open the record for dataset details and reuse information.
Dynamic Light Scattering for Studying Lens Aging and Cataract Formation
ClinicalTrials.gov study NCT00081991. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Dynamic Light Scattering Ocular Measurement in the Detection of Dementia
ClinicalTrials.gov study NCT06613971. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Dynamic Light Scattering Device (DLS) Study of Age-Related Changes in the Lens and Cataracts
ClinicalTrials.gov study NCT00007215. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Dynamic Light Scattering to Study Crystalline Proteins in Young Normal Lenses
ClinicalTrials.gov study NCT00304967. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Dynamic Light Scattering and Keratoscopy for Corneal Examination
ClinicalTrials.gov study NCT00050466. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Pilot Study: Dynamic Light Scattering Device for Studying Early Changes in Cataract
ClinicalTrials.gov study NCT00067483. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Refractive index determination of dynamic droplets in a flow by analyzing light scattering signals with a machine learning approach
<p>This container includes the measurement data, python script and weights of trained machine learning model associated with the scientific work, which will be presented in 2025 at the <em><strong>Turbulence, Heat and Mass Transfer 11</strong> </em>conference in Tokyo.</p> <p><strong>Title:</strong> Refractive Index Determination of Dynamic Droplets in Flow by Analyzing Light Scattering Signals with a Machine Learning Approach <br><strong>Authors:</strong> W. Schaefer<br><strong>Affiliation:</strong> ai-quanton GmbH, Dr.-Werner-Freyberg-Str. 7, 69514 Laudenbach, Germany <br><strong>Contact:</strong> info@ai-quanton.com </p> <p>The following data files are provided:</p> <ul> <li><strong>Dataset_40_4ch1234.rar (unpacked: Dataset_40_4ch1234.pth)</strong></li> <li><strong>M1_SegmentsTHR40.csv</strong></li> <li><strong>SegmentsTHR40.rar (unpacked: M1_SegmentsTHR40.csv ... M55_SegmentsTHR40.csv)</strong></li> <li><strong>Model_weights_4ch1234.pth</strong></li> </ul> <p> </p> <p><strong>Dataset_40_4ch1234.pth</strong> is a file, containing a ready-to-use dataset of 4-channel signals prepared for use in Python scripts.</p> <p><strong>M1_SegmentsTHR40.csv </strong>is an example of a file used for storing and loading light scattering signals of individual droplets with corresponding additional data. The meaning of each column is:</p> <p>'MID' – measurement ID</p> <p>'FID' – frame ID</p> <p>'SID' – signal ID</p> <p>'CID' – channel ID</p> <p>'NOP' – number of parts</p> <p>'PNM' – part number</p> <p>'TCH' – trigger channel</p> <p>'TLE' – trigger level</p> <p>'TID' – trigger ID</p> <p>'CON' – label used for training</p> <p><strong>SegmentsTHR40.rar</strong> is an archived folder containing .csv files, the same format as M1_SegmentsTHR40.csv.</p> <p><strong>Model_weights_4ch1234.pth </strong>contains weights for a model trained on data from all 4 channels.</p> <p> </p> <p><strong>External files:</strong></p> <p>The correcponding repository to this dataset is published on Azure Dev Ops: <a href="https://dev.azure.com/ai-quanton/PBa202">https://dev.azure.com/ai-quanton/PBa202</a><br>This repository contains the Python script developed for a neural network that determines the refractive index of single droplets by analyzing light scattering signals generated as they pass through a Gaussian beam. </p> <p>The script is designed to build and test a machine learning model capable of accurately predicting refractive indices from light scattering data in dynamic spray environments.</p>
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