Estimation of the Particle Size Distribution of a Dilute Latex from Combined Elastic and Dynamic Light Scattering Measurements. A Method Based on Neural Networks

STEGMAYER, G. S.; GONZALEZ, V. D. G.; GUGLIOTTA, L. M.; CHIOTTI, O.; VEGA, J. R.

Río Gallegos

Congreso; XII Reunión de Trabajo en Procesamiento de la Información y Control RPIC 2007; 2007

Univ. Nac. de la Patagonia

A method for estimating the particle size distribution (PSD) of a dilute latex from light scattering measurements is presented. The method utilizes a general regression neural network (GRNN), that estimates the PSD from 2 independent sets of measurements carried out at several angles: (i) light intensity measurements, by elastic light scattering (ELS); and (ii) average diameters measurements, by dynamic light scattering (DLS). The GRNN was trained with a large set of measurements simulated on the basis of typical asymmetric PSDs represented by unimodal normal-logarithmic distributions (of variable mean diameters and variances). First, the ability of the method was tested on the basis of two synthetic examples. Then, the obtained GRNN was used for estimating the PSD of a polystyrene (PS) latex standard of narrow distribution and known nominal diameter (111 nm), that was also characterized by 2 independent techniques: capillary hydrodynamic fractionation (CHDF), and transmission electron microscopy (TEM). The PSD predicted by the GRNN resulted close to that obtained by TEM (the most accurate technique for measuring narrow PS distributions). The estimated PSDs were better than those obtained through standard numerical techniques for ‘ill-conditioned’ inverse problems (e.g., the PSD obtained by inverting the ELS measurement). Keywords— Elastic Light Scattering – Dynamic Light Scattering – Particle Size Distribution – Neural Network – Inverse Problems