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151 results for “optical properties”
The data on the optical properties of kombucha and kombucha proteinoids, as well as chaos calculations are provided for the paper titled "Kombucha Mats as Responsive Materials."
<p>The data on the optical properties of kombucha and kombucha proteinoids, as well as chaos calculations are provided for the paper titled "Kombucha Mats as Responsive Materials."</p>
Figure 9 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 9. Means number of deposited eggs by females of tested mites after 4 days post-exposure to α- and γ-Al2O3 NPs at tested concentrations. Different letters denote to significant differences in means at tested concentrations (Duncan test, P ≤ 0.05).
Figure 12 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 12. SEM visualization of α- and γ-Al2O3 NPs aggregation on ventral side of C. mycophagus mite. A = treated female by α-Al2O3 NPs, B = treated female by γ-Al2O3 NPs, C = untreated female.
Figure 7 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 7. Females, nymphal and larval mortality (means ± SE) of C. mycophagus mites, subjected to synthesized α- and γ- Al2O3 NPs at different concentrations and exposure time – A. α-Al2O3 NPs; B. γ-Al2O3 NPs. Different letters within the same exposure time are significantly different (Duncan test, P ≤ 0.05).
Figure 5 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 5. Females, nymphal and larval mortality (means ± SE) of M. fungivorus mites, subjected to synthesized α- and γ-Al2O3 NPs at different concentrations and exposure time – A. α-Al2O3 NPs; B. γ-Al2O3 NPs. Different letters within the same exposure time are significantly different (Duncan test, P ≤ 0.05).
Figure 14 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 14. Mean growth Inhibition (A) and corresponding percentage (B), of F. oxysporum in response to different concentrations of α and γ-Al2O3 NPs after 5 days of growth at 30 °C and 180 rpm in PDB growth medium (Where R2: the relation coefficient and y: the predicted fungal inhibition value at "X" nanoparticles concentration).
Figure 6 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 6. Mortality (means ± SE) of M. fungivorus females, nymphs and larvae, concerning α- and γ-Al2O3 NPs at tested concentrations and exposure time.
Figure 10 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 10. Females' mortality (means ± SE) of (A) M. fungivorus and (B) C. mycophagus mites subjected to synthesized α and γ-Al2O3 NPs at different concentrations and exposure time. different letters within the same concentrations are significantly different, Duncan test (P ≤ 0.05).
Figure 13 in Morphological, optical and thermal properties of α- and γ-aluminum nanoparticles: Assessment of their biological activities against storage mites and mycotoxin producing fungi
Figure 13. Mean growth Inhibition (A) and corresponding percentage (B) of Aspergillus flavus in response to different concentrations of α- and γ-AL2O3 NPs after five days of growth at 30 ℃ and 180 rpm in PDB growth medium. (Where R2: the relation coefficient and y: the predicted fungal inhibition value at "X" nanoparticles concentration).
Data set of Ultrathin Eu- and Er-Doped Y2O3 Films with Optimized Optical Properties for Quantum Technologies
<p>Data corresponding to the figures of the publication " Ultrathin Eu- and Er-Doped Y2O3 Films with Optimized Optical<br> Properties for Quantum Technologies " by M. Scarafagio et al. J. Phys. Chem. C 2019, 123, 13354-13364<br> (<a href="https://doi.org/10.1021/acs.jpcc.9b02597">https://doi.org/10.1021/acs.jpcc.9b02597</a>). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </p>
AMT29 optical underway properties and estimates of chlorophyll-a concentration
<p>Dataset of inherent optical properties determined as described in https://doi.org/10.5194/essd-2024-267 and https://doi.org/10.1364/OE.25.0A1079</p>
Retrievals of aerosol optical, componential, and radiative properties from joint observations of sun photometer and Lidar using GRASP algorithm
<p>The site location is <span>114°21′E, 30°32′N (Central China). The time period is from 2021.07 to 2022.08.</span></p> <p><span>Data includes AOD (all sequences), SSA, ASY, ASD, CRI (only retrieved from sky irradiance), components (black carbon, brown carbon, dust, iron oxide, water-soluble inorganic salt and water), and vertical profiles of shapes of total extinction, fine-mode extinction, and coarse-mode extinction.</span></p>
Tailoring the optical and dynamic properties of iminothioindoxyl photoswitches through acidochromism
<p>Multi-responsive functional molecules are key for obtaining user-defined control of the properties and functions of chemical and biological systems. In this respect, pH-responsive photochromes, whose switching can be directed with light and acid–base equilibria, have emerged as highly attractive molecular units. The challenge in their design comes from the need to accommodate application-defined boundary conditions for both light- and protonation-responsivity. Here we combine time-resolved spectroscopic studies, on time scales ranging from femtoseconds to seconds, with density functional theory (DFT) calculations to elucidate and apply the acidochromism of a recently designed iminothioindoxyl (ITI) photoswitch. We show that protonation of the thermally stable <em>Z</em> isomer leads to a strong batochromically-shifted absorption band, allowing for fast isomerization to the metastable <em>E</em> isomer with light in the 500–600 nm region. Theoretical studies of the reaction mechanism reveal the crucial role of the acid–base equilibrium which controls the populations of the protonated and neutral forms of the <em>E</em> isomer. Since the former is thermally stable, while the latter re-isomerizes on a millisecond time scale, we are able to modulate the half-life of ITIs over three orders of magnitude by shifting this equilibrium. Finally, stable bidirectional switching of protonated ITI with green and red light is demonstrated with a half-life in the range of tens of seconds. Altogether, we designed a new type of multi-responsive molecular switch in which protonation red-shifts the activation wavelength by over 100 nm and enables efficient tuning of the half-life in the millisecond–second range.</p> <p> </p> <p>Article information: <a href="https://doi.org/10.1039/D0SC07000A">https://doi.org/10.1039/D0SC07000A</a></p>
Centroid values of aerosol optical properties for 8 sub-types based in AERONET inversion data (1993–2018)
<p>In this project, we adapted our previously defined 5 aerosol optical typology scheme (Hamill et al. 2016) to result in a more discriminating 8 aerosol typology scheme (Giordano 2019). Previously we presented an aerosol classification based upon AERONET level 2.0 almucantar retrieval products from the period 1993 to 2012. In the initial phases of this research, we opto-physically identified five major types of <strong>Bulk Columnar Aerosol</strong> (BCA) based solely upon intensive optical properties of spectral Single Scattering Albedo (<strong>SSA</strong>), spectral Indices of Refraction (real – <strong>RRI</strong> and imaginary – <strong>IRI</strong>), and two Angstrom Exponents (extinction – <strong>EAE</strong> and absorption – <strong>AAE</strong>). These BCA were classified as Maritime Aerosol, Dust Aerosol, Urban Industrial Aerosol, Biomass Burning Aerosol, and Mixed Aerosol. The classification of a particular observation as one of these aerosol types is determined by its five-dimensional Mahalanobis distance (MD) to the centroid of each reference cluster (itself a 5-D hyperellipsoid). To retain a greater number of AERONET sites in the study (200+), we kept the variable space to 5-D. To generate reference clusters, we only retained data points that were found to lie within 2 MD from the data centroid. Our typology is based on AERONET retrieved quantities, which do not include low optical depth values (AOD440nm < 0.4 as per AERONET criteria for almucantar scan inversion). </p> <p>The classifications obtained are made available to be used in interpreting aerosol retrievals from satellite-borne instruments and as input for regional climate models. A major result of this aerosol typology is a dataset describing the types of aerosol particles that are distinct from one another in optical properties and a geographic distribution of those aerosol types. We used the typology scheme upon the qualifying AERONET data archive and produced seasonal aerosol climatologies by aerosol type for each of the AERONET sites included in the study, regional aerosol climatology maps, and a time-integrated global aerosol climatology map based entirely upon ground-based photometric data (Giordano 2022). An internally hyperlinked compendium of the individual AERONET site aerosol climatologies was produced to contain the results of the first phase of this work [available at <a href="https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf">https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf</a>]. Each of these original five aerosol types (Hamill et al. 2016, Giordano 2019) was further discriminated into specific sub-types by this same scheme to achieve an 8-aerosol typology (Giordano 2019 Chapter 2). For example, optical discrimination into specific sub-types of Biomass Burning aerosol may provide insight into sources exhibiting spectrally distinct smoke properties. Here we segmented the Biomass Burning Aerosol type into the sub-types of <em>Flaming</em> (<strong>BMF</strong>) and <em>Smoldering</em> (<strong>BMS</strong>) using the centroid separation method and the MD criteria for in-class inclusion was adjusted to 1.5 MD. Similarly, we found great confidence in discriminating the MIXED aerosol type into two distinct regimes which we simply labeled as <em>MIXEDtype1</em> (<strong>MIXED1</strong>) and <em>MIXEDtype2</em> (<strong>MIXED2</strong>). These can be visually verified by examining any one of many possible renditions of 3-D optical spaces noting their 5-D centroids are separated by a distance of 3.47-3.85 MD [Giordano 2019 Chapter 2]. Likewise, the Urban Industrial Aerosol class was further discriminated into European Urban Industrial (<strong>EURO UI</strong>) and North American (<strong>NA UI</strong>), whose 5-D centroids are separated by a distance of 2.60–3.08 MD. We then used the previously employed mathematical strategies to sort the global AERONET data retrievals into the aerosol types classified against their reference standards. We believe the strategies regarding aerosol differentiation using polarization data (Hamill, Piedra and Giordano 2020) are an additional method useful for analysis of the newer AERONET version 3 data retrievals, and data collected from the deployment of newer CIMEL sun-photometers (with enhanced polarization measurement capabilities) to the network. The resulting AERONET-based 8-aerosol optical typology, in a 5-D basis is useful for applications in aerosol optics, including direct forward modeling of radiative transfer to determine the effects of aerosol absorption and/or scattering on vertical heating profiles and ground received irradiance quantities, for input into more complicated remote sensing algorithms, used as calibration/validation values for in-situ and laboratory experimental studies, and evaluating radiative forcing calculations in atmospheric models.</p> <p>[Work related to an 8-aerosol typology in 6-D, 8-D, 9-D and 10-D optical property bases, and their files, are to be published subsequently as a different database project in 2023.]</p>
Data set. Optical properties. J-aggregate:PVA polaritonic films.
<p>Optical properties (real and imaginary part of permittivity) of J-aggregate:PVA materials analysed in manuscript entitled "Bio-inspired building blocks for all-organic metamaterials from visible to near-infrared". </p> <p>arXiv preprint arXiv:2210.02315</p> <p> </p> <p> </p> <p> </p>
Investigating the geometrical and optical properties of the persistent stratospheric aerosol layer observed over a Southern European lidar station during 2019
<p>Simulted aerosol extinction at 550nm profiles over Thessaloniki by the IFS-CB05-BASCOE-GLOMAP system.</p>
Tailoring optical properties of 2D semiconductors in van der Waals heterostructures
<p>Dataset for the publication 'Tailoring the dielectric screening in WS<sub>2</sub>-graphene heterostructures'</p>
Rapid decline of aerosol absorption coefficient and aerosol optical properties effects on radiative forcing in urban areas of Beijing from 2018 to 2021
<p>data for Rapid decline of aerosol absorption coefficient and aerosol optical properties effects on radiative forcing in urban areas of Beijing from 2018 to 2021</p>
Simulated top-of-atmosphere (120 km) downward and upward solar and thermal-infrared irradiances and ice cloud optical thickness; calculated solar, TIR and net cloud radiative effect. Simulated with ice crystal properties for aggregates, droxtals, and plates based on Yang (2013).
<p>This dataset consists of three .nc files for ice crystal shapes of aggregates, plates, and droxtals. The files include ice cloud optical thickness <span class="math-tex">\(\tau\)</span> (550nm), the simulated upward and downward irradiances <span class="math-tex">\(F\)</span> at the top-of-atmosphere (with and without the presence of the ice cloud), and the calculated ice cloud radiative effect <span class="math-tex">\(\Delta F\)</span> (solar [0.3-3.5 <span class="math-tex">\(\mu\)</span>m], thermal-infrared [3.5-75 <span class="math-tex">\(\mu\)</span>m], and net). The data set allows the user to extract <span class="math-tex">\(\Delta F\)</span> values for their parameter combinations. The available cloudy and cloud-free irradiances further allow to calculate the cirrus radiative effect (RE) by scaling the 'cloudy' RE with the required cloud cover. This serves as a first-approximation because, as 3D effects are neglected.</p>
Data and original code for: A generalized approach to characterise optical properties of natural objects
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