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A Neural Network Model for Estimating the Particle Size Distribution of Dilute Latex from Multiangle Dynamic Light Scattering Measurements
Author(s) -
Gugliotta Luis M.,
Stegmayer Georgina S.,
Clementi Luis A.,
Gonzalez Verónica D. G.,
Minari Roque J.,
Leiza José R.,
Vega Jorge R.
Publication year - 2009
Publication title -
particle and particle systems characterization
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 56
eISSN - 1521-4117
pISSN - 0934-0866
DOI - 10.1002/ppsc.200800010
Subject(s) - dynamic light scattering , materials science , biological system , logarithm , tikhonov regularization , particle size distribution , particle size , multiangle light scattering , light scattering , scattering , analytical chemistry (journal) , optics , mathematics , inverse problem , chromatography , physics , chemistry , mathematical analysis , nanotechnology , nanoparticle , biology
The particle size distribution (PSD) of dilute latex was estimated through a general regression neural network (GRNN) that was supplied with 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 normal‐logarithmic 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 to estimate 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 MDLS measurements through a Tikhonov regularization technique. The PSDs estimated by the GRNN gave more accurate results than those obtained through other conventional techniques. The proposed method is a simple, effective, and robust tool for characterizing unimodal PSDs.