A Neural Network Approach for Estimating the PSD of a Polymer Latex from Combined Optical Measurements

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

Graz (Austria)

Congreso; 8th Int. Congress on Optical Particle Characterization (OPC 2007); 2007

OPC 2007

This work aims at developing an artificial neural network for estimating the PSD of a dilute latex, from combined ELS and DLS measurements, and without solving any inverse problem[2]. The network is fed with: (i) the ELS measurement; i.e. the light intensity scattered at different detection angles (qr); and (ii) the average diameter at each qr, obtained from the DLS measurement. A general regression neural network was trained with measurements simulated on the basis of the Mie scattering theory. As a first approximation, only log-normal PSDs with mean diameters in the range [140–800] nm, and standard deviations in the range [0.01–0.20] nm were simulated. The measurements were assumed to be taken each Dq = 10°, in the range [10°–170°]. The model was tested through synthetic and experimental examples. The synthetic examples considered (unimodal) log-normal and exponentially-modified Gaussian PSDs. The experimental PSD was a narrow standard latex, that was also measured by electron microscopy and capillary hydrodynamic fractionation. At present, the model is restricted to deal with unimodal PSDs; but in the future, a larger spectrum of PSD shapes (including bimodal PSDs) will be included in the training set.