A Hybrid Particle Swarm Optimization-Tuning Algorithm for the Prediction of Nanoparticle Morphology from Microscopic Images

Aggregated nanoparticle structures are quite ubiquitous in aerosol and colloidal science, specifically in nanoparticle synthesis systems such as combustion processes where coagulation results in the formation of fractal-like structures. In addition to their size, morphology of the particles also plays a key role in defining various physicochemical properties. Electron microscopy based images are the most commonly used tools in visualizing these aggregates, and prediction of the 3-dimensional structures from the microscopic images is quite complex. Typically, 2-dimensional features from the images are compared to available structures in a database or regression equations are used to predict 3-dimensional morphological parameters including fractal dimension and pre-exponential factor. In this study, we propose a combination of evolutionary algorithm and forward tuning model to predict the best fit 3-dimensional structures of aggregates from their projection images. 2-dimensional features from a projection image are compared to the candidate projections generated using FracVAL code and optimized using Particle Swarm Optimization to obtain the 3-dimensional structure of the aggregate. Various 3-dimensional properties including hydrodynamic diameter and mobility diameter of the retrieved structures are then compared with the properties of the aggregate used to form the candidate projection image, to test the suitability of the algorithm. Results show that the hybrid algorithm can closely predict the 3-dimensional structures from the projection images with less than 10% difference in the predicted 3-dimensional properties including mobility diameter and radius of gyration.