A regularization algorithm of dynamic light scattering for estimating the particle size distribution of dual-substance mixture in water

Abstract

Dynamic light scattering (DLS) is a nondestructive, well-established technique for the size characterization of proteins, nanoparticles, polymers, and colloidal dispersions. However, current DLS techniques are only applied to particle groups of single composition due to the limitation of their inversion algorithm. In this study, we propose a particle size distribution inversion algorithm based on the Tikhnonov regularization method that can be applied to the dual-substance particle mixture. The algorithm retrieves the particle size distributions of two substances, respectively, by taking advantage of their refractive index differences. The simulation results reveal that the algorithm has excellent accuracy and stability when the scattering angle is 30°. Instead of the original identity matrix, the first-order difference matrix and second-order difference matrix are used as the regular matrix when utilizing the Tikhnonov algorithm, which obviously improves the anti-interference, accuracy, and stability of the algorithm. Furthermore, the inversion of particle size distribution is carried out at a 0.01%–1% noise level, which shows that the algorithm has an available antinoise ability. Finally, experimental particle size measurements for a mixture of polystyrene beads and toner particles demonstrate that the proposed algorithm is superior to the traditional Tikhnonov algorithm in applicability and accuracy.