Light Scattering Reviews 3

Part of the series Springer Praxis Books pp 27-67

Statistical interpretation of light anomalous diffraction by small particles and its applications in bio-agent detection and monitoring

  • Min XuAffiliated withDepartment of Physics, Fairfield University
  • , A. KatzAffiliated withInstitute for Ultrafast Spectroscopy and Laser Department of Physics, City College of New York

* Final gross prices may vary according to local VAT.

Get Access


Light scattering by small particles is one of the most powerful techniques for probing the properties of particulate systems and has numerous applications in particle characterization and remote sensing of, for example, clouds and aerosols, interplanetary dust, marine environment, bacteria, biological cells and tissues. This subject, governed by Maxwell’s electromagnetic theory of light, developed in the later nineteenth century, was first summarized in van de Hulst’s classic 1957 work [1], since Lorentz [2], Mie [3], Rayleigh [4] and Tyndall [5] laid the foundations of light scattering. The field is yet vigorous and ever expanding, documented by the current interest and the increasing number of publications. Light scattering by small particles is actively being pursued, especially for non-spherical particles (see, for example, the review volume edited by Mishchenko, Hovenier and Travis [6]). Alongside the availability of computational capability and the advance of numerical methods based on an exact theory, approximate theories of light scattering are still attractive in providing both simpler alternatives and much more direct physical interpretations. Approximation theories are appealing in inverse problems such as remote sensing where the error introduced by the approximate theory can be negligible compared to that introduced by a priori assumptions. Approximation theories are sometimes also mandatory in cases (for example, computation of the optical efficiencies of particles of large size parameters and aspect ratios) where the exact numerical methods such as the T-matrix method [7] fail due to the limitation of current computational resources and floating point accuracy.