ABSTRACT
Dynamic Light Scattering (DLS) is a widely used technique for measuring the size distributions of nano- and micro-particles dispersed in a liquid. The principle is based on the analysis of the temporal fluctuations of light scattered by the particles at a given scattering angle. However, single-angle DLS measurements may lack resolution and robustness, particularly for multimodal and/or polydisperse samples. Multi-angle Dynamic Light Scattering (MDLS) provides more robust, reproducible and accurate Particle Size Distributions (PSDs) than single-angle DLS. In this paper, a novel inversion method based on Bayesian inference is proposed for the estimation of the number PSD from MDLS measurements. The efficiency and robustness of this method is demonstrated through simulated and real data.
