A Neural Network Model for Estimating the Particle Size Distribution of a Dilute Latex from Multiangle Dynamic Light Scattering Measurements

GUGLIOTTA, LUIS M.; STEGMAYER, GEORGINA S.; CLEMENTI, LUIS; GONZALEZ, VERÓNICA D.G.; MINARI, ROQUE J.; LEIZA, JOSÉ R.; VEGA, JORGE R.

Particle & Particle Systems Characterization

Wiley InterScience

Lugar: Weinheim; Año: 2009 vol. 26 p. 41 - 52

0934-0866

The particle size distribution (PSD) of a dilute latex is estimated through a general regression neural network (GRNN), that is fed with the PSD average diameters derived from multiangle dynamic light scattering (MDLS) measurements. The GRNN was trained with a large set of measurements that were simulated from unimodal normallogarithmic distributions representing the PSDs of polystyrene (PS) latexes. The proposed method was first tested through three simulated examples involving different PSD shapes, widths, and diameter ranges. Then, the GRNN was employed for estimating the PSD of two PS samples: a latex standard of narrow PSD and known nominal diameter; and a latex synthesized in our laboratory. Both samples were also characterized through independent techniques (capillary hydrodynamic fractionation, transmission electron microscopy, and disc centrifugation). For comparison, all examples were solved by numerical inversion of the MDLS measurements through a Tikhonov regularization technique. The PSDs estimated by the GRNN resulted more accurate than those obtained though other conventional techniques. The proposed method is a simple, effective, and robust tool for characterizing unimodal PSDs.