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The Interpretation of Diffuse Reflectance Spectra.

Harry G Hecht1

  • 1Department of Chemistry, South Dakota State University, Brookings, South Dakota 57006.

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|March 21, 2020
PubMed
Summary
This summary is machine-generated.

This study compares continuum and statistical models for diffuse reflection in scattering media. Statistical models offer more direct links between particle characteristics and optical properties like absorptivity and scattering coefficients.

Keywords:
Absolute absorptivitiescontinuum modelsdiffuse reflectanceradiative transferreflectance spectrascattering coefficientsstatistical models

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Area of Science:

  • Optics and Photonics
  • Materials Science
  • Radiative Transfer Theory

Background:

  • Numerous theories exist for diffuse reflection in scattering media, often tailored to specific experimental conditions.
  • Existing models vary in their generality and underlying assumptions regarding the medium's properties.

Purpose of the Study:

  • To provide a comparative analysis of general theories for diffuse reflection in scattering media.
  • To categorize and contrast continuum and statistical models based on their approach to scattering and absorption.

Main Methods:

  • Categorization of theories into continuum and statistical models.
  • Analysis of phenomenological constants in continuum models as approximations to radiative transfer equations.
  • Examination of statistical models based on summations of layer/particle transmittances and reflectances.

Main Results:

  • Continuum models use phenomenological constants, offering approximate solutions to radiative transfer.
  • Statistical models require assumptions about fundamental units but allow calculation of absolute absorptivity and scattering coefficients.
  • Comparison of model predictions for absorptivity and scattering coefficients with experimental data.

Conclusions:

  • Statistical models provide a more direct link between particle characteristics and optical properties (absorptivity, scattering).
  • The study facilitates understanding the relationship between different models and their applicability.
  • Consideration of appropriate model systems and standards for experimental validation is crucial.