COMBINATION OF SEM AND LIGHT SCATTERING DATA FOR THE INVERSE ESTIMATION OF PARTICLE SIZE DISTRIBUTION USING A BAYESIAN APPROACH

Abstract

In this work, Static Light Scattering (SLS) measurements are used to estimate the Particle Size Distribution (PSD) of a polymeric particle system incorporating prior informa- tion obtained from measurements of Scanning Electron Microscopy (SEM). The inverse prob- lem is solved using a Bayesian approach following two different schemes for the representa- tion of the PSD. In the first one, the PSD is represented by a parameterized family of distribu- tions in a fixed-form scheme. In the second one, there is no assumption on the shape of the PSD, i.e. a free-form scheme is used. The Metropolis-Hastings algorithm is used to solve the inverse problem. The proposed objective is to obtain results from experimental data that are more consistent with respect to those obtained with SEM in a previous work, providing trust confidence intervals for the solution. The application of the Bayesian approach allows one to use, simultaneously, information pro- vided by two different experimental techniques. The main conclusion of this work is that the usage of a Bayesian approach is recommended for cases where only an approximate model is available and use of reliable additional information can be made.