scholarly journals Resonant reflection by microsphere arrays with AR-quenched Mie scattering

2021 ◽  
Author(s):  
Nasrin Razmjooei ◽  
Yeong Ko ◽  
Fairooz Simlan ◽  
Robert Magnusson
Keyword(s):  
Mie Scattering ◽  
Resonant Reflection

Macrostructure and microphysics of Saturn's E-ring

1984 ◽  
Vol 75 ◽  
pp. 607-613 ◽  
Author(s):  
Kevin D. Pang ◽  
Charles C. Voge ◽  
Jack W. Rhoads
Keyword(s):  
Size Distribution ◽  
Spherical Shape ◽  
Mie Scattering ◽  
Narrow Size Distribution ◽  
Electrostatic Levitation ◽  
Volcanic Origin ◽  
Micron Diameter

Abstract.All observed optical and infrared properties of Saturn's E-ring can be explained in terms of Mie scattering by a narrow size distribution of ice spheres of 2 - 2.5 micron diameter. The spherical shape of the ring particles and their narrow size distribution imply a molten (possibly volcanic) origin on Enceladus. The E-ring consists of many layers, possibly stratified by electrostatic levitation.

Atmospheric Observation by a Laser Radar Using Raman Scattering and Mie Scattering

2003 ◽  
Vol 123 (10) ◽  
pp. 1714-1720 ◽  
Author(s):  
Tetsuo Fukuchi ◽  
Takuya Nayuki ◽  
Takashi Fujii ◽  
Koshichi Nemoto
Keyword(s):  
Raman Scattering ◽  
Mie Scattering ◽  
Laser Radar

Mathematical modelling of remote detection of molecular air pollutants

1987 ◽  
Vol 52 (6) ◽  
pp. 1397-1406
Author(s):  
František Zrcek ◽  
Milan Horák
Keyword(s):  
Atmospheric Aerosols ◽  
Air Pollutants ◽  
Complex Refractive Index ◽  
Mie Scattering ◽  
Heat Emission ◽  
Remote Detection ◽  
Radiation Absorption ◽  
Variable Concentration ◽  
Lidar Equation ◽  
Optical Pathway

A model of remote detection of molecular air pollutants is devised based on the lidar equation. The various kinds of interaction of radiation with matter, viz. absorption, induced fluorescence, and Raman scattering, are taken into account; detection of either scattered or reflected signal is considered. The reflection is assumed to be either axial, using a retroreflector, or omnidirectional from a field target. Based on this model, an algorithm was set up for simulation of the different variants of the experiment, making allowance for a generally variable concentration of the compound along the optical pathway of the light beam. The basic atmospheric processes, viz. radiation absorption by the backround, heat emission, turbulence, and the effect of atmospheric aerosols, are treated, and the last of them is found to play the major role. Aerosols are looked upon as a source of the Mie scattering and they are described by distribution equations with respect to the particle size and the complex refractive index. The variable concentration of the aerosol along the optical pathway and the simultaneous effect of a higher numberof aerosol types are included.

scholarly journals Three-component modelling of C-rich AGB star winds – V. Effects of frequency-dependent radiative transfer including drift★

2020 ◽  
Vol 499 (2) ◽  
pp. 1531-1560
Author(s):  
Christer Sandin ◽  
Lars Mattsson
Keyword(s):  
Radiative Transfer ◽  
Mass Loss ◽  
Density Ratio ◽  
Mie Scattering ◽  
Speed Ratio ◽  
Exponential Dependence ◽  
Frequency Dependent ◽  
Wind Velocities ◽  
Drift Models ◽  
Loss Rates

ABSTRACT Stellar winds of cool carbon stars enrich the interstellar medium with significant amounts of carbon and dust. We present a study of the influence of two-fluid flow on winds where we add descriptions of frequency-dependent radiative transfer (RT). Our radiation hydrodynamic models in addition include stellar pulsations, grain growth and ablation, gas-to-dust drift using one mean grain size, dust extinction based on both the small particle limit (SPL) and Mie scattering, and an accurate numerical scheme. We calculate models at high spatial resolution using 1024 gridpoints and solar metallicities at 319 frequencies, and we discern effects of drift by comparing drift models to non-drift models. Our results show differences of up to 1000 per cent in comparison to extant results. Mass-loss rates and wind velocities of drift models are typically, but not always, lower than in non-drift models. Differences are larger when Mie scattering is used instead of the SPL. Amongst other properties, the mass-loss rates of the gas and dust, dust-to-gas density ratio, and wind velocity show an exponential dependence on the dust-to-gas speed ratio. Yields of dust in the least massive winds increase by a factor 4 when drift is used. We find drift velocities in the range $10\!-\!67\, \mbox{km}\, \mbox{s}^{-1}$, which is drastically higher than in our earlier works that use grey RT. It is necessary to include an estimate of drift velocities to reproduce high yields of dust and low wind velocities.

Output Power Increase of Cladding Pumped 7-core EDFA by Using Mie Scattering

2020 ◽  
Author(s):  
Shigehiro Takasaka ◽  
Koichi Maeda ◽  
Ryuichi Sugizaki ◽  
Yoshihiro Arashitani
Keyword(s):  
Output Power ◽  
Mie Scattering ◽  
Power Increase

Nearly Perfect Transmissive Subtractive Coloration through the Spectral Amplification of Mie Scattering and Lattice Resonance

2021 ◽  
Author(s):  
Taejun Lee ◽  
Joohoon Kim ◽  
Ishwor Koirala ◽  
Younghwan Yang ◽  
Trevon Badloe ◽  
...  
Keyword(s):  
Mie Scattering

In Situ Visualizing Oxidase-Mimicking Activity of Single MnOOH Nanotubes with Mie Scattering-Based Absorption Microscopy

2021 ◽  
Vol 60 (7) ◽  
pp. 5264-5270
Author(s):  
Ling Yu ◽  
Hua Li ◽  
Wei Huang ◽  
Haili Yu ◽  
Yi He
Keyword(s):  
Mie Scattering

scholarly journals Mie scattering and microparticle-based characterization of heavy metal ions and classification by statistical inference methods

2019 ◽  
Vol 6 (5) ◽  
pp. 190001 ◽  
Author(s):  
Katherine E. Klug ◽  
Christian M. Jennings ◽  
Nicholas Lytal ◽  
Lingling An ◽  
Jeong-Yeol Yoon
Keyword(s):  
Heavy Metal ◽  
Support Vector Machine ◽  
Metal Ions ◽  
Heavy Metal Ions ◽  
Mie Scattering ◽  
Training Data ◽  
Scattering Data ◽  
Support Vector ◽  
Linear Discriminant ◽  
Machine Analysis

A straightforward method for classifying heavy metal ions in water is proposed using statistical classification and clustering techniques from non-specific microparticle scattering data. A set of carboxylated polystyrene microparticles of sizes 0.91, 0.75 and 0.40 µm was mixed with the solutions of nine heavy metal ions and two control cations, and scattering measurements were collected at two angles optimized for scattering from non-aggregated and aggregated particles. Classification of these observations was conducted and compared among several machine learning techniques, including linear discriminant analysis, support vector machine analysis, K-means clustering and K-medians clustering. This study found the highest classification accuracy using the linear discriminant and support vector machine analysis, each reporting high classification rates for heavy metal ions with respect to the model. This may be attributed to moderate correlation between detection angle and particle size. These classification models provide reasonable discrimination between most ion species, with the highest distinction seen for Pb(II), Cd(II), Ni(II) and Co(II), followed by Fe(II) and Fe(III), potentially due to its known sorption with carboxyl groups. The support vector machine analysis was also applied to three different mixture solutions representing leaching from pipes and mine tailings, and showed good correlation with single-species data, specifically with Pb(II) and Ni(II). With more expansive training data and further processing, this method shows promise for low-cost and portable heavy metal identification and sensing.

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