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51 results for “optical spectroscopy”
Raw spectra measurements of scattered sunlight collected using a MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) instrument in the austral summer of 2016/17 during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>To achieve the objectives of the project, we installed a MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) instrument on the vessel “Akademik Tryoshnikov”. This instrument is based on the DOAS technique, which is used to measure trace gas concentrations in the atmosphere. The method consists of the analysis of the spectral absorption lines that each trace gas produces in the solar spectra. The DOAS technique uses the narrowband features that every trace gas has in their spectral absorption coefficients. This differential cross section is unique and acts like a fingerprint for the trace gases, allowing to differentiate between them and to estimate their concentrations (for further details see Platt and Stutz, 2008).</p> <p>In the past decades, atmospheric chemists have come to realize that halogen species (like Cl, Br or I and their oxides ClO, BrO and IO) exert a powerful influence on the chemical composition of the troposphere and through that influence affect the evolution of pollutants, hence having a significant impact on climate. These reactive halogen species are potent oxidizers for organic and inorganic compounds throughout the troposphere. In particular, halogen cycles can act on several compounds (such as methane, ozone, particles…), all of which are climate forcing agents through direct and indirect radiative effects. Dynamic exchange of halogens between the ocean, sea ice, snowpack and atmosphere is the main driver for the frequent occurrence of Ozone Depletion Events (ODEs) and Atmospheric Mercury Depletion Events (AMDEs) (Saiz-Lopez and von Glasow, 2012).</p> <p>In this dataset we present the raw spectra measurements of scattered sunlight recorded by the MAX-DOAS onboard a research vessel in the Southern Ocean and Atlantic Ocean. Included are position and vessel inclination data. Data coverage is from December 2016 to April 2017.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_maxdoas_gps.zip</li> <li>GPS_JDDD.txt, data file, ASCII text</li> <li>ace_maxdoas_inclination.zip</li> <li>Inclination_JDDD.txt, data file, ASCII text</li> <li>ace_maxdoas_spectra-YYYY-MM.zip</li> <li>- MAXDOAS<br> - - WWW<br> - - - JDDD<br> - - - - LiveInfo_DDDhhmmss.WWW, data file, ASCII text<br> - - - - Atmos<br> - - - - - DDDhhmmss_90.WWW, data file, ASCII text<br> - ZENITH<br> - - WWW<br> - - - JDDD<br> - - - - LiveInfo_DDDhhmmss.WWW, data file, ASCII text<br> - - - - Atmos<br> - - - - - DDDhhmmss_90.WWW, data file, ASCII text</li> <li>README.txt, metadata, text</li> <li>data_file_header_gps.txt, metadata, text</li> <li>data_file_header_inclination.txt, metadata, text</li> <li>data_file_header_spectra_atmos.txt, metadata, text</li> <li>data_file_header_spectra_liveinfo.txt, metadata, text</li> </ul> <p>where YYYY is the year and MM is the month. JDDD is the day of the year (Julian day) YYYY in which the file was recorded. hhmmss is the time. WWW is the central wavelength of the measured spectrum in the UV or VIS region.</p> <p><strong>Dataset license</strong></p> <p>This dataset of raw spectra of scattered sunlight measurements from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Bromine monoxide (BrO) measurements made using a MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) instrument in the austral summer of 2016/17 during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>To achieve the objectives of the project, we installed a MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) instrument on the vessel “Akademik Tryoshnikov”. This instrument is based on the DOAS technique, which is used to measure trace gas concentrations in the atmosphere. The method consists of the analysis of the spectral absorption lines that each trace gas produces in the solar spectra. The DOAS technique uses the narrowband features that every trace gas has in their spectral absorption coefficients. This differential cross section is unique and acts like a fingerprint for the trace gases, allowing to differentiate between them and to estimate their concentrations (for further details see Platt and Stutz, 2008).</p> <p>In the past decades, atmospheric chemists have come to realize that halogen species (like Cl, Br or I and their oxides ClO, BrO and IO) exert a powerful influence on the chemical composition of the troposphere and through that influence affect the evolution of pollutants, hence having a significant impact on climate. These reactive halogen species are potent oxidizers for organic and inorganic compounds throughout the troposphere. In particular, halogen cycles can act on several compounds (such as methane, ozone, particles…), all of which are climate forcing agents through direct and indirect radiative effects. Dynamic exchange of halogens between ocean, sea ice, snowpack and atmosphere is the main driver for the frequent occurrence of Ozone Depletion Events (ODEs) and Atmospheric Mercury Depletion Events (AMDEs) (Saiz-Lopez and von Glasow, 2012).</p> <p>In this dataset we present the mixing ratio and vertical column density of bromine monoxide (BrO) recorded in the austral summer of 2016/2017 in the Southern Ocean and Atlantic Ocean, averaged over one-hour time periods.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_bromine_monoxide_atmospheric_measurements.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.pdf, metadata, PDF/A-1a</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This dataset of atmospheric bromine monoxide measurements from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Iodine monoxide (IO) measurements made using a MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) instrument in the austral summer of 2016/17 during the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>To achieve the objectives of the project, we installed a MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) instrument on the vessel “Akademik Tryoshnikov”. This instrument is based on the DOAS technique, which is used to measure trace gas concentrations in the atmosphere. The method consists of the analysis of the spectral absorption lines that each trace gas produces in the solar spectra. The DOAS technique uses the narrowband features that every trace gas has in their spectral absorption coefficients. This differential cross section is unique and acts like a fingerprint for the trace gases, allowing to differentiate between them and to estimate their concentrations (for further details see Platt and Stutz, 2008).</p> <p>In the past decades, atmospheric chemists have come to realize that halogen species (like Cl, Br or I and their oxides ClO, BrO and IO) exert a powerful influence on the chemical composition of the troposphere and through that influence affect the evolution of pollutants, hence having a significant impact on climate. These reactive halogen species are potent oxidizers for organic and inorganic compounds throughout the troposphere. In particular, halogen cycles can act on several compounds (such as methane, ozone, particles…), all of which are climate forcing agents through direct and indirect radiative effects. Dynamic exchange of halogens between ocean, sea ice, snowpack and atmosphere is the main driver for the frequent occurrence of Ozone Depletion Events (ODEs) and Atmospheric Mercury Depletion Events (AMDEs) (Saiz-Lopez and von Glasow, 2012).</p> <p>In this dataset we present the mixing ratio and vertical column density of iodine monoxide (IO) recorded in the austral summer of 2016/2017 in the Southern Ocean and Atlantic Ocean, averaged over one-hour time periods.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_iodine_monoxide_atmospheric_measurements.csv, data file, comma-separated values</li> <li>data_file_header.txt, metadata, text</li> <li>README.pdf, metadata, PDF/A1-a</li> <li>README.txt, metadata, text</li> </ul> <p><strong>Dataset license</strong></p> <p>This dataset of atmospheric iodine monoxide measurements from ACE is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Interstitial null-distance time-domain diffuse optical spectroscopy using a superconducting nanowire detector
<p>We demonstrate a novel realization of Interstitial fiber, broadband, Time Domain Diffuse Optical Spectroscopy (TD-DOS) in Null Source-Detector separation (NSDS) approach without temporal gating, by using a Superconducting Nanowire single photon detector (SNSPD) for acquisition. As per the MEDPHOT protocol, we test experimentally, the absorption linearity of the system on tissue-equivalent liquid phantoms, and demonstrate the scattering-independent retrieval of the absorption spectrum of water using Intralipid phantoms in the wavelength range of 600-1100 nm.</p> <p>This work has been published in the Journal of Biomedical Optics - https://doi.org/10.1117/1.JBO.28.12.121202. Here, we present the dataset containing the acquired data pertaining to the aforementioned publication, including a brief overview, the tools to read it and the analysis corresponding to the figures in the article.</p>
Data of publication Coherent optical and spin spectroscopy of nanoscale Pr3+ : Y2O3
<p>Data corresponding to the figures of the publication " Coherent optical and spin spectroscopy of nanoscale Pr3+: Y2O3" by D. Serrano et al. (file:///C:/Users/diana.serrano/Zotero/storage/86IZ7H73/PhysRevB.100.html). A text file describes data in each compressed folder, please refer to the publication for more details. </p>
Research data supporting "Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy"
<p>Research data supporting the publication:</p> <p>M. Bergholt, 2017, Online quantitative monitoring of live cell engineered cartilage growth using diffuse fiber-optic Raman spectroscopy, Biomaterials, Volume 140, September 2017, Pages 128–137, DOI: 10.1016/j.biomaterials.2017.06.015</p>
Dataset related to the publication "Sub-Doppler optical-optical double-resonance spectroscopy using a cavity-enhanced frequency comb probe"
<p>The files contain </p><p>1. Binary files with normalized and interleaved double-resonance spectra recorded with three different pump transitions and two different relative pump-probe polarizations, indicated in the file name. These spectra are the results of 5 measurements.</p><p>2. Binary file with 45 normalized and interleaved double-resonance spectra recorded with pump on the R(2, <i>F2</i>) transition and parallel relative pump-probe polarization.</p><p>2. Data for Figures 4, S1 and S3 in the paper.</p><p> </p>
Optical spectroscopy supporting Chandra observations of Herbig AeBe stars
<p>This is optical data taken by AAVSO observers to support Chandra observations of Herbig AeBe stars. The purpose if this data is to check the accretion rate close in time to the Chandra observations.</p>
Dataset related to the publication "Measurement and assignment of J = 5 to 9 rotational energy levels in the 9070-9370 cm-1 range of methane using optical frequency comb double-resonance spectroscopy"
<p>The files contain the normalized interleaved double-resonance spectra recorded with four different pump transitions, indicated in the file name. The first column is the wavenumber, the second column is the transmission intensity.</p>
Dataset: Use of bioresorbable fibers for short-wave infrared spectroscopy using time-domain diffuse optics
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O2-O2, SO2, BrO, and IO differential slant column densities (dSCDs) measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Maido Observatory during April 29, 2018 and May 4, 2018
<p>Description: O<sub>2</sub>-O<sub>2</sub>, SO<sub>2</sub>, BrO, and IO differential slant column densities (dSCDs) measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Maido Observatory during April 29, 2018 and May 4, 2018.</p> <p>Instrument: University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS)<br> Instrument reference: Coburn et al. (2011); doi:10.5194/amt-4-2421-2011<br> Instrument contact: Christopher F. Lee (christopher.f.lee@colorado.edu)<br> Instrument PI: Rainer Volkamer (rainer.volkamer@colorado.edu)<br> <br> Measurement site: Maido Observatory, Reunion Island<br> Longitude: 55.384 degrees East<br> Latitude: 21.080 degrees South<br> Altitude: 2160 meters above sea level<br> Azimuth angle: Approximately 100 degrees clockwise from north<br> <br> The detection limit is defined as (2*Measured RMS) / (Maximum differential absorption cross section), where RMS = root-mean-square noise of spectral signal not accounted for by DOAS fit parameters [optical density units]. The maximum differential absorption cross sections used are 7.0e-21 [cm<sup>2</sup>] for SO<sub>2</sub>, 2.6e-17 [cm<sup>2</sup>] for BrO, and 3.5e-17 [cm<sup>2</sup>] for IO. Detection limits for SO<sub>2</sub> dSCDs, BrO dSCDs, and IO dSCDs are only reported during periods of significant SO<sub>2</sub> detection. BrO to SO<sub>2</sub> ratios are only reported during periods when both BrO dSCDs and SO<sub>2</sub> dSCDs are above the detection limit.</p> <p>Local time (RET) is UTC+4.<br> <br> Column 1: UTC start datetime (yyyy-mm-dd HH:MM:SS)<br> Column 2: UTC center datetime (yyyy-mm-dd HH:MM:SS)<br> Column 3: UTC stop datetime (yyyy-mm-dd HH:MM:SS)<br> Column 4: Elevation angle above the horizon (degrees)<br> Column 5: O<sub>2</sub>-O<sub>2</sub> dSCD [molec<sup>2</sup> cm<sup>-5</sup>]<br> Column 6: Spectral fit error for O<sub>2</sub>-O<sub>2</sub> dSCD [molec<sup>-2</sup> cm<sup>-5</sup>]<br> Column 7: SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 8: Spectral fit error for SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 9: Detection limit for SO<sub>2</sub> dSCD [molec cm<sup>-2</sup>]<br> Column 10: BrO dSCD [molec cm<sup>-2</sup>]<br> Column 11: Spectral fit error for BrO dSCD [molec cm<sup>-2</sup>]<br> Column 12: Detection limit for BrO dSCD [molec cm<sup>-2</sup>]<br> Column 13: IO dSCD [molec cm<sup>-2</sup>]<br> Column 14: Spectral fit error for IO dSCD [molec cm<sup>-2</sup>]<br> Column 15: Detection limit for IO dSCD [molec cm<sup>-2</sup>]<br> Column 16: Ratio of BrO dSCDs to SO<sub>2</sub> dSCDs<br> Column 17: Error in ratio of BrO dSCDs to SO<sub>2</sub> dSCDs</p>
Classification and quantification of sucrose from sugar beetand sugarcane using optical spectroscopy and chemometrics
<p>Sucrose, obtained from either sugar beet or sugarcane, is one of the main ingredients used in the food industry. Due to the same molecular structure, chemical methods cannot distinguish sucrose from both sources. More practical and affordable methods would be valuable. Sucrose samples (cane and beet) were collected from nine countries, 25% (w/w) aqueous solutions were prepared and their absorbances recorded from 200 to 1380 nm. Spectral differences were observable in the ultraviolet–visible (UV–Vis) region from 200 to 600 nm due to impurities in sugar. Linear discriminant analysis (LDA), classification and regression trees, and soft independent modeling of class analogy were tested for the UV–Vis region. All methods showed high performance accuracies. LDA, after selection of five wavelengths, gave 100% correct classification with a simple interpretation. In addition, binary mixtures of the sugar samples were prepared for quantitative analysis by means of partial least squares regression and multiple linear regression (MLR). MLR with first derivative Savitzky–Golay were most accept- able with root mean square error of cross-validation, prediction, and the ratio of (standard error of) prediction to (standard) deviation values of 3.92%, 3.28%, and 9.46, respectively. Using UV–Vis spectra and chemometrics, the results show promise to distinguish between the two different sources of sucrose. An affordable and quick analysis method to differentiate between sugars, produced from either sugar beet or sugarcane, is suggested. This method does not involve complex chemical analysis or high-level experts and can be used in research or by industry to detect the source of the sugar which is important for some countries’ agricultural policies.</p>
Dataset of "Optical Signatures of Thermal Damage on ex-vivo Brain, Lung and Heart Tissues using Time-Domain Diffuse Optical Spectroscopy"
<p>Dataset for the article entitled "Optical Signatures of Thermal Damage on ex-vivo Brain, Lung and Heart Tissues using Time-Domain Diffuse Optical Spectroscopy" </p> <p> </p> <p>Abstract:</p> <p> </p> <p>Thermal Therapies treat tumors by means of heat, greatly reducing pain, post-operation complications, and cost as compared to traditional methods. Yet, effective tools to avoid under- or over-treatment are mostly needed, to guide surgeons in laparoscopic interventions.<br>In this work, we investigated the temperature-dependent optical signatures of ex-vivo calf brain, lung, and heart tissues, based on the reduced scattering and absorption coefficients in the near-infrared spectral range (657 to 1107 nm). These spectra were measured by time domain diffuse optics, applying a step-like spatially homogeneous thermal treatment at 43 °C, 60 °C, and 80 °C.<br>We found three main increases in scattering spectra, possibly due to the denaturation of collagen, myosin, and proteins secondary structure.<br>After 75 °C, we found the rise of two new peaks at 770 and 830 nm in the absorption spectra due to the formation of a new chromophore, possibly related to hemoglobin or myoglobin.<br>This research marks a significant step forward in controlling thermal therapies with diffuse optical techniques by identifying several key markers of thermal damage. This could enhance the ability to monitor and adjust treatment in real-time, promising improved outcomes in tumor therapy.</p> <p> </p> <p> </p> <p>Authors:</p> <p>ALESSANDRO BOSSI , LEONARDO BIANCHI , PAOLA SACCOMANDI , AND ANTONIO PIFFERI<br>Politecnico di Milano</p> <p> </p> <p> </p> <p><a href="https://opg.optica.org/boe/fulltext.cfm?uri=boe-15-4-2481&id=548108" target="_blank" rel="noopener">Link to the article</a></p>
Deciphering the impacts of main inflowing rivers on dissolved organic matter in Lake Daye using isotopes, optical spectroscopy, and FT-ICR-MS during non-flood season
<p>The uploaded data include water quality data, isotopes, DOM fluorescence index and FT ICR MS data of Daye Lake and its inflowing rivers.</p>
FAIRmat Tutorial 15: Use of pynxtools with Examples from Optical Spectroscopy
<p>The FAIRmat Tutorial 15 will address the necessity of FAIR research data management when working with experimental data in materials science. FAIRmat provides NOMAD (https://nomad-lab.eu/nomad-lab/) to the scientific community as a platform specifically developed for this purpose. </p> <p>NOMAD integrates the NeXus Ontology based on the NeXus community standard (https://www.nexusformat.org/). The NeXus standard has been significantly expanded over the years and now includes a comprehensive range of metadata definitions, making it applicable to various experimental techniques used in materials science. </p> <p>FAIRmat, in collaboration with the scientific community and technology partners, has developed pynxtools. These software tools simplify the conversion of experimental data and metadata according to the community standard, making it easy to integrate experimental data into NOMAD. </p> <p>This tutorial will cover using the pynxtools and how such datasets are managed within NOMAD. To demonstrate the functionality of pynxtools in combination with NOMAD, we will use ellipsometry and Raman spectroscopy data as examples.</p> <p>The main topics to be covered are: <br>• FAIR research data management <br>• NeXus data modelling <br>• Data conversion and verification using pynxtools<br>• Data management with NOMAD </p> <p>Disclaimer: NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation https://nomad-lab.eu/prod/v1/docs/</p>
Accurate and Rapid Molecular Subgrouping of High-Grade Glioma via Deep Learning-assisted Label-free Fiber-optic Raman Spectroscopy
<p>Dataset for the manuscript "<span>Accurate and Rapid Molecular Subgrouping of High-Grade Glioma via Deep Learning-assisted Label-free </span><span>F</span><span>iber-optic Raman Spectroscopy"</span></p>
Dataset for magneto-optical spectroscopy of CsPbBr3-based perovskite nanoplatelets
<p>This dataset includes raw photoluminescence and transmission spectra acquired in a pulsed magnetic field. The values of the magnetic field in each acquisition frame of a given pulse are reported in the corresponding *.dat file. In this file, the three columns relate to the value of the field at the beginning of the acquisition, at the end of the acquisition, and to the magnetic field value averaged over the duration of the acquisition frame. For each pulse, identified as sxx, where xx is an integer from, 30 frames are acquired. The PL spectra are identified explicitly by writing PL in the file name. The transmission spectra do not have any explicit mention. The acquisition time of each frame is 2ms. Measurements carried out in the Voigt configuration are performed in the linear polarization basis. 0d indicates that the polarizer is oriented parallel to the magnetic field vector. 90d indicates that the polarizer is oriented perpendicular to the magnetic field vector. The dataset is organized in three main folders, each conteining data for a given thickness of the colloidal nanoplatelets investigated.</p>
Data for "Optically Enhanced Solid-State 1H NMR Spectroscopy"
<p>Raw 1H NMR and photo-CIDNP-enhanced NMR data for "Optically Enhanced Solid-State 1H NMR Spectroscopy". A Mathematica notebook for data processing is also included.</p>
DATA SET: In vivo characterization of the optical and hemodynamic properties of the human sternocleidomastoid muscle through ultrasound-guided hybrid near-infrared spectroscopies.
<p>This repository contains the data sets of the article:</p><p>L. Cortese et al., "In vivo characterization of the optical and hemodynamic properties of the human sternocleidomastoid muscle through ultrasound-guided hybrid near-infrared spectroscopies."</p>
Data from: Coherent evolution of superexchange interaction in seconds long optical clock spectroscopy
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