0000000000147603
AUTHOR
Silviu Mihai Rei
Dynamic Light Scattering Signal Conditioning for Data Processing
Abstract When performing data acquisition for a Dynamic Light Scattering experiment, one of the most important aspect is the filtering and conditioning of the electrical signal. The signal is amplified first and then fed as input for the analog digital convertor. As a result a digital time series is obtained. The frequency spectrum is computed by the logical unit offering the basis for further Dynamic Light Scattering analysis methods. This paper presents a simple setup that can accomplish the signal conditioning and conversion to a digital time series.
Using Dynamic Light Scattering Experimental Setup and Neural Networks For Particle Sizing
Abstract Using a Lorentzian function fit as reference, a basic experiment was designed for processing Dynamic Light Scattering time series, allowing to estimate the average particle size of a suspension. For fitting the averaged power spectrum of the time series, several neural network configurations were tested in order to compare the results with the reference. The results of this comparison revealed a good match, serving as a proof of concept for using neural networks as an alternative for DLS time series processing.
Using Dynamic Light Scattering for Monitoring the Size of the Suspended Particles in Wastewater
Abstract A coherent light scattering experiment on wastewater samples extracted from several stages of water processing within a wastewater processing plant was carried out. The samples were allowed to sediment while they were the subject of a Dynamic Light Scattering (DLS) measurement. The recorded time series were processed using an Artificial Neural Network based DLS procedure to produce the average diameter of the particles in suspension. The method, using a single physical procedure for monitoring the variation of the average diameter in time, indicates the dominant type of suspensions in water.