Hypothesis Test of the Photon Count Distribution for Dust Discrimination in Dynamic Light Scattering

Users of dynamic light scattering (DLS) are challenged when a sample of nanoparticles (NPs) contains dust. This is a frequently inevitable scenario and a major problem that critically affects the reproducibility and accuracy of DLS measurements. Current methods approach this problem via photon correlation spectroscopy, but remedy exists only for a few special cases. We introduce here a general criterion and a clearly defined measure to discriminate between NPs and dust particles. The experimental results show that, in contrast to photon correlation spectroscopy, hypothesis testing and the statistical moment analysis of the photon count distribution provides an accurate and precise way to characterize NPs and Brownian dynamics in the presence of dust. To demonstrate, analyses of silica, iron oxide, and gold NPs of low polydispersity are presented.