Accurate particle size distribution determination by nanoparticle tracking analysis based on 2-D Brownian dynamics simulation

A physical model is presented to simulate the average step length distribution during nanoparticle tracking analysis experiments as a function of the particle size distribution and the distribution of the number of steps within the tracks. Considering only tracks of at least five steps, numerical simulation could be replaced by a normal distribution approximation. Based on this model, simulation of a step length distribution allows obtaining a much more reliable estimation of the particle size distribution, thereby reducing the artificial broadening of the distribution, as is typically observed by direct conversion of step length to particle size data. As this fitting procedure also allowed including data from particles that were followed for a relatively low number of steps, the measurement time could be reduced for particles that are known to be monodisperse. Whereas the inversion is less sensitive towards the particle size distribution width, still similar values were obtained for both the average diameter and standard deviation of a polystyrene latex sample irrespective of the track length, provided that the latter included at least five steps.

Whereas direct inversion of the step length distribution obtained by nanoparticle tracking analysis gives rise to excessive broadening of the particle size distribution, simulation of the Brownian motion process yields much more accurate particle size distributions..Figure optionsDownload high-quality image (71 K)Download as PowerPoint slideResearch highlights
► Simulation of Brownian motion of submicron spherical particles.
► Simulation of mean step length distribution of a given particle size distribution.
► Inversion of experimental step length data using this approach yields reliable particle size distributions.
► Step length distribution broadening is mainly caused by stochastic nature of Brownian motion.