Inverse characterization of nanoparticle clusters using unpolarized optical scattering without ex-situ measurements

Abstract

Characterization of nanostructures using light scattering experiments without using polarization information and a priori particle size or number measurements is investigated through numerical experiments. The study focuses on particle clusters in the form of carbon nanoparticle aggregates that are generated with Filippov's particle-cluster algorithm. Seven cases of monodisperse aggregates with less than 30 nanoparticles with primary particle radius between 10 and 40nm are investigated, together with one polydisperse case with lognormal particle size distribution. In all these cases, the scattering behavior of an ensemble of well separated, similar aggregates are represented by the behavior of a single aggregate considering orientation averaging using discrete dipole approximation. A database is developed and used for the solution of the direct problem considering the high computational time required for the solution. The inverse characterization problem is formulated as a least squares minimization. Use of Tabu Search algorithm along with gradient based Levenberg–Marquardt algorithm is investigated as problem topology is prone to multiple extrema. It is found that the proposed method relying on Tabu search algorithm is able to predict the particle size and number for monodisperse aggregates with effective radius larger than 20nm using a UV light source at a wavelength of 266nm. The proposed method also characterizes the polydisperse aggregate case with reasonable accuracy.