Gloria L. Frontini

A novel methodology to estimate the particle size distribution of latex using relative measurements of elastic light scattering and turbidimetry

In a previous publication, turbidimetry and elastic light scattering (ELS) were shown to be two techniques that, combined, can be used to determine particle size distributions (PSDs) of suspended particles. The proposed method, consisting in the solution of a linear inverse problem, was based on the assumption that absolute measurements of both turbidimetry and ELS were available. However, such measurements are normally difficult to obtain in practice, whereas relative measurements can be easily made. The technique proposed in this paper relaxes the assumption of absolute measurements by including an unknown parameter in the combined model. In this form the inverse problem to be solved under the new assumptions becomes a non‐linear one. Since the problem can be solved semi‐analytically, in all cases the results are exact and not linked to issues such as lack of convergence or other similar types of complications sometimes encountered in purely numerical solutions. Three synthetic examples of PSD estimation of polymer latex are used to show how the proposed methodology works in practice. The approach proposed here avoids the use of any type of calibration of the instruments used to measure turbidity and ELS, and makes the technique based on the combination of these two measurements a more powerful alternative than it was before. Copyright

Inversion of turbidity measurements of polymer latex using wavelet functions

Abstract The determination of the particle size distribution (PSD) of latex from turbidimetry at several wavelength may be considered as an inverse problem. Classical regularization methods to solve ill conditioned linear inverse problems have been applied to this problem previously. Poor quality results were obtained when the range of the particle sizes is wide and the particles are small. The wavelet-based approach investigated in this work considers the expansion of the solution in wavelet series with selective regularization of each component of the expansion. The results achieved from simulated experiments show that the solutions obtained by classical regularization methods can be improved by means of wavelet-based regularization. The method is developed so that good initial values for the regularization parameters can be selected using different techniques.

Assessment of the modeling error of an approximate Light Scattering model by processing accurate simulated data

This paper presents the analysis of the modeling error of an approximate model in a Static Light Scattering (SLS) problem for the morphological characterization of particle systems through the estimation of the Particle Size Distribution (PSD). The modelling error of the employed approximate model called the Local Monodisperse Approximation (LMA) is obtained by means of processing simulated data generated by a theoretically accurate model called the Vrijs Finite Mixture Model (VFMM). As a simplification on the procedure, PSDs are supposed to be well-represented by a log-normal distribution. The data generated by the VFMM is processed by solving an inverse parametric problem using a Least-Squares approach. Bias on estimations is studied in function of all significant system parameters.