SIZING NANOPARTICLES FROM DYNAMIC LIGHT SCATTERING MEASUREMENTS I. A BAYESIAN APPROACH FOR SOLVING THE INVERSE PROBLEM

Joao Pessoa

Simposio; Inverse Problem, Desing and Optimization Symposium (IPDO 2010); 2010

Laboratorio de Energia Solar, Universidad Federal de Paraiba

A Bayesian method is used for solving the non-linear inverse problem of estimating the particle size distribution (PSD) in a colloidal system from multiangle dynamic light scattering (MDLS) measurements. The Markov Chain Monte Carlo sampling approach, implemented in the form of the Metropolis-Hasting algorithm, is used for solving the inverse problem. The method is tested through several simulated examples corresponding to polystyrene (PS) latexes of different PSD shapes, diameter ranges, and morphologies; and then experimentally validated through some PS samples. For comparison, all examples are also solved through a non-linear Tikhonov regularization technique. For PSDs exhibiting high asymmetries and/or bimodal shapes, the PSDs estimated through the Bayesian method result more accurate than those obtained with the regularization technique.