A Bayesian Method for Estimating the Particle Size Distribution of Polymer Dispersions from Multiangle Dynamic Light Scattering Measurements

CLEMENTI, L.A.; ORLANDE, H.R.; GUGLIOTTA, L.M.; VEGA, J.R.

Los Cocos (Córdoba)

Simposio; V Simposio Argentino – Chileno de Polímeros - ARCHIPOL ‘09; 2009

Dynamic light scattering (DLS) is frequently used for measuring particle diameters of dilute latexes. A single-angle DLS measurement only provides a small amount of information on the PSD, and consequently the estimated PSD exhibits a poor resolution. In contrast, multi-angle DLS (MDLS) measurements allow an increase in the information content, thus improving the PSD estimate (Vega et al., 2003). Typically, the scattering phenomenon is modeled on the basis of Mie theory (Bohren and Huffman, 1983). The PSD estimation requires solving a linear ill-conditioned inverse problem (ICIP); and to this effect regularization methods must be used (Tikhonov and Arsenin, 1977). Recently, the MDLS problem was stated as a formally simpler and more robust (but non-linear) ICIP, and a technique based on artificial neural networks (ANN) was developed and successfully validated when applied to several latexes (Gugliotta et al., 2009). In this work, we propose a statistical method for solving the non-linear ICIP of Gugliotta et al. (2009), based on a Bayesian inference approach.