Precise analysis of frequency domain photon migration measurement for characterization of concentrated colloidal suspensions

Frequency domain photon migration (FDPM) is a new method for characterization of concentrated colloidal suspensions using multiply scattered light. The ability of FDPM to determine size and interaction characteristics of the particulate phase of colloidal suspensions depends largely upon the accurac...

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Veröffentlicht in:Review of scientific instruments 2002-02, Vol.73 (2), p.383-393
Hauptverfasser: Sun, Zhigang, Huang, Yingqing, Sevick-Muraca, Eva M.
Format: Artikel
Sprache:eng
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Zusammenfassung:Frequency domain photon migration (FDPM) is a new method for characterization of concentrated colloidal suspensions using multiply scattered light. The ability of FDPM to determine size and interaction characteristics of the particulate phase of colloidal suspensions depends largely upon the accuracy and precision of FDPM-measured optical properties. In this work, FDPM measurements at multiple modulation frequencies and multiple source-to-detector distances are systematically analyzed for obtaining accurate and precise scattering properties of colloidal suspensions. Two different data analysis methods, multifrequency (MF) nonlinear regression and multidistance (MD) linear regression, and corresponding various strategies for fitting to the optical diffusion equation are investigated. The accuracy and precision of estimated scattering coefficients by different approaches are compared. Results show that MD linear regression with simultaneous regression of average intensity and phase shift or amplitude and phase shift data provides the best data analysis method since it provides not only accurate estimated parameters but also an accurate estimation of their uncertainties. Finally, an important criterion, derived from the photon diffusion model, is proposed for checking the consistency of measurement data and optimizing experimental conditions for FDPM characterization of multiply scattering materials.
ISSN:0034-6748
1089-7623
DOI:10.1063/1.1427303