An improved Bayesian inversion method for the estimation of multimodal particle size distributions using multiangle Dynamic Light Scattering measurements

An improved and robust Bayesian method is proposed to estimate the number-weighted Particle Size-Distributions (PSD) from data obtained by Multiangle Dynamic Light Scattering (MDLS). Compared to former approach presented by Clementi, the originality of our method lies in the fact that it is directly applied to raw MDLS data without any preprocessing. Indeed, the PSD Bayesian estimation proposed by Clementi requires the prior evaluation of the harmonic intensity averaged particle diameters at different angles by means of the cumulants method. The spreading of the estimation errors is inherent in this method which makes the multimodal PSD estimation worsens. The efficiency and robustness of the method proposed are evaluated through simulated and experimental data. Comparisons with the Clementi method are also presented. We show an improvement of the resolution and the repeatability, and a better correlation between our results and the original distributions.