AN INFORMATION-THEORETIC FILTER SINGLE-STEP SEQUENTIAL MONTE CARLO METHOD IN PRACTICE. AN EXPERIMENTAL CASE STUDY FOR THE PARTICLE SIZE DISTRIBUTION ESTIMATION

Abstract

In this article, we implement an extended version of a Monte Carlo methodology making use of a Sampling/Mapping/Filtering (SMF) strategy, to estimate the Particle Size Distribution (PSD) of a particle system. The methodological difference is shown by the use of a filter, based on information theory, which computes a cut-off level using an optimization scheme following the principle of relevant information (PRI). The practical application of the full strategy considers the fusion between scanning electron microscope (SEM) data and static light scattering (SLS) measurements. The two models are utilized, assuming spherical particles represented as hard spheres: the so-called local monodisperse approximation (LMA) and the Vrij’s finite mixtures model (VFMM). We analyze the resulting estimates for different configurations of both prior information and SLS models, where final results are compared to those obtained in previous studies for a polymeric particle system embedded in a solid polymer matrix.