Humidity dependence of the atmospheric absorption coefficient in the transparency windows centered at 0.88 and 0.73 MM

1985 ◽  
Vol 6 (8) ◽  
pp. 751-764 ◽  
Author(s):  
N. I. Furashov ◽  
V. Yu. Katkov
Keyword(s):  
Absorption Coefficient ◽  
Atmospheric Absorption ◽  
Humidity Dependence

EFFECT OF HIGH PRESSURES ON THE INFRARED AND RED ATMOSPHERIC ABSORPTION BAND SYSTEMS OF OXYGEN

1963 ◽  
Vol 41 (12) ◽  
pp. 1991-2002 ◽  
Author(s):  
C. W. Cho ◽  
E. J. Allin ◽  
H. L. Welsh
Keyword(s):  
Absorption Band ◽  
Power Series ◽  
Absorption Coefficient ◽  
Magnetic Dipole ◽  
Total Pressure ◽  
High Pressures ◽  
Linear Term ◽  
Quadratic Term ◽  
Atmospheric Absorption ◽  
Induced Absorption

Absorption intensities in the 0–0 and 1–0 bands of the infrared [Formula: see text] and the 0–0 band of the red [Formula: see text] band systems of oxygen were measured in the pure gas in the range 50 to 150 atm and in O2–N2, O2–A, and O2–He mixtures up to 3000 atm total pressure at 25 °C. The integrated absorption coefficient of each band is expressed as a power series in ρO2 and ρf, the Amagat densities of oxygen and the foreign gas, respectively. The coefficient of the linear term AρO2 is a measure of the intrinsic (magnetic dipole) absorption of the O2 molecule; it is much greater in the red than in the infrared system. The quadratic term, BρO22, gives the induced absorption in O2–O2 pairs; B is about ten times greater in the infrared than in the red system. These results explain intensity anomalies which have been observed in condensed oxygen. Induction effects in O2–N2 and O2–A pairs are much smaller than in O2–O2 pairs, and scarcely observable in O2–He pairs. The significance of these results is discussed.

Is there a “universal” MAC equation?

1990 ◽  
Vol 48 (2) ◽  
pp. 228-229
Author(s):  
Robert E. Ogilvie
Keyword(s):  
Absorption Coefficient ◽  
Atomic Absorption ◽  
Atomic Number ◽  
X Ray ◽  
Time Period ◽  
Image Position

The search for an empirical absorption equation begins with the work of Siegbahn (1) in 1914. At that time Siegbahn showed that the value of (μ/ρ) for a given element could be expressed as a function of the wavelength (λ) of the x-ray photon by the following equationwhere C is a constant for a given material, which will have sudden jumps in value at critial absorption limits. Siegbahn found that n varied from 2.66 to 2.71 for various solids, and from 2.66 to 2.94 for various gases.Bragg and Pierce (2) , at this same time period, showed that their results on materials ranging from Al(13) to Au(79) could be represented by the followingwhere μa is the atomic absorption coefficient, Z the atomic number. Today equation (2) is known as the “Bragg-Pierce” Law. The exponent of 5/2(n) was questioned by many investigators, and that n should be closer to 3. The work of Wingardh (3) showed that the exponent of Z should be much lower, p = 2.95, however, this is much lower than that found by most investigators.

Linear and Nonlinear Optical Properties of Natural Dyes Freestanding Films and Application in Optical Limiting

2020 ◽  
pp. 139-143
Keyword(s):  
Absorption Coefficient ◽  
Water Solution ◽  
Optical Limiting ◽  
Refraction Index ◽  
Laser Wavelength ◽  
Solid State Laser ◽  
Natural Dyes ◽  
Linear And Nonlinear ◽  
532 Nm ◽  
Absorbance Spectra

Natural dyes were followed and prepared from a pomegranate, purple carrot, and eggplant peel. The absorbance spectra was measured in the wavelength range 300-800 nm. The linear properties measurements of the prepared natural dye freestanding films were determined include absorption coefficient (α0), extinction coefficient (κ), and linear refraction index (n). The nonlinear refractive index n2 and nonlinear absorption coefficient β2 of the natural dyes in the water solution were measured by the optical z-scan technique under a pumped solid state laser at a laser wavelength of 532 nm. The results indicated that the pomegranate dye can be promising candidates for optical limiting applications with significantly low optical limiting of 3.5 mW.

Estimating Pigment Concentrations from Spectral Images Using an Encoder‐Decoder Neural Network

2020 ◽  
Vol 64 (3) ◽  
pp. 30502-1-30502-15
Author(s):  
Kensuke Fukumoto ◽  
Norimichi Tsumura ◽  
Roy Berns
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Absorption Coefficient ◽  
Spectral Data ◽  
High Accuracy ◽  
Pigment Concentration ◽  
Scattering Coefficient ◽  
A Value ◽  
Input And Output ◽  
Pigment Concentrations

Abstract A method is proposed to estimate the concentration of pigments mixed in a painting, using the encoder‐decoder model of neural networks. The model is trained to output a value that is the same as its input, and its middle output extracts a certain feature as compressed information about the input. In this instance, the input and output are spectral data of a painting. The model is trained with pigment concentration as the middle output. A dataset containing the scattering coefficient and absorption coefficient of each of 19 pigments was used. The Kubelka‐Munk theory was applied to the coefficients to obtain many patterns of synthetic spectral data, which were used for training. The proposed method was tested using spectral images of 33 paintings, which showed that the method estimates, with high accuracy, the concentrations that have a similar spectrum of the target pigments.

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