Particle size distribution recovery from non-Gaussian intensity autocorrelation functions obtained from dynamic light scattering at ultra-low particle concentrations

Dynamic light scattering (DLS) from ultra-low concentration suspensions (the average number of particles in the scattering volume is less than similar to 100) gives rise to autocorrelation functions (ACFs) containing a non-Gaussian term due to particle number fluctuations. This term is difficult to characterize and account for and makes recovery of particle size distribution (PSD) information unreliable. We show that an initial analysis of the intensity ACF to determine parameters describing the amplitude and relaxation rate of the non-Gaussian term and then using these parameters to create a better theoretical non-Gaussian ACF model allows a more accurate recovery of the PSD. The modified model is consistent with the measured ACF data, and a reconstructed kernel matrix matching the measured data is obtained. When compared with the usual kernel function reconstruction (KFR) method, the proposed method gives significantly improved PSD recovery accuracy with experimental data. Furthermore, the PSDs obtained have no obvious differences to those obtained from measurements at normal particle concentrations.

Automated summary

Dynamic light scattering from ultra-low concentration suspensions often leads to unreliable recovery of particle size distribution due to non-Gaussian terms in the autocorrelation functions. This study proposes an improved method that analyzes the intensity autocorrelation function to determine parameters describing the non-Gaussian term and then uses these parameters to create a better theoretical model. The proposed method significantly improves the accuracy of particle size distribution recovery and yields similar results to measurements at normal particle concentrations.