Determination of Nanoparticle and Microdroplet Parameters in Levitating Microdroplets of Suspension by Speckle Image Analysis Using Convolutional Neural Networks
The optical response of a suspension microdroplet is governed not only by the properties of the dispersed phase, but also by the finite size and optical structure of the droplet itself. As a result, the interpretation of scattered-light patterns from such systems constitutes a non-trivial inverse problem. In this work, we examine whether laser speckle images recorded from single levitating microdroplets of suspension can be used for data-driven recognition of selected droplet and suspension parameters. Experiments were performed on slowly evaporating microdroplets of monodisperse TiO$_2$ nanoparticle suspensions in diethylene glycol confined in a linear electrodynamic quadrupole trap. Speckle images were analyzed with a convolutional neural network trained to classify droplet diameter, nanoparticle concentration, and nanoparticle diameter, first in separate tasks and then in combined two-parameter and three-parameter classifications. Under the present experimental conditions, droplet diameter was identified with good reliability, with an estimated accuracy better than approximately 6% for the tested dataset. Nanoparticle concentration was more difficult to resolve, but useful discrimination was obtained when concentration classes were sufficiently separated. Nanoparticle diameter was also classified unambiguously for the selected cases. In addition, simultaneous classification of up to three parameters across 27 classes was achieved. These results suggest that CNN-based analysis of speckle images may provide a viable route toward multi-parameter optical diagnostics of free suspension microdroplets and, potentially, more complex aerosol-like systems.