This is due to the inherent noise in the autocorrelation function (ACF) data and the scarce utilization of PSD information during the inversion process. In particle size measurement with dynamic light scattering (DLS), it is difficult to get an accurate recovery of a bimodal particle size distribution (PSD) with a peak position ratio less than ~2:1, especially when large particles (>350nm) are present. This was further verified with experimental results from samples of standard polystyrene spheres. The results for simulated DLS data at 151nm and 690nm diameters with average particle numbers of 6, 12, 24 and 48 in the scattering volume at four noise levels show that, compared with the usual DLS data processing method, inversion of the ACF after the separation of the number fluctuation term effectively eliminates the strong artifact peaks, and the relative errors of peak positions and distribution errors are significantly reduced. By differentiating the ACF we were able to identify and separate the number fluctuation term and then analyze the ACF to recover the PSD. To improve the accuracy of DLS at ultra-low concentrations, we analyzed the different decay characteristics of particle Brownian motion and particle number fluctuation in the ACF.
This gives a strong artifact peak in the particle size distribution (PSD) recovered. In ultra-low concentration suspensions, particle number fluctuations in the scattering volume add a long delay component to the intensity autocorrelation function (ACF) in dynamic light scattering (DLS) measurements. Results from simulated and measured ACF data show that, for both methods, noise on the ACF limits reliable PSD recovery. This greatly reduces the accuracy of inversion results. However, increasing noise at ultra-low concentrations can lead to errors in determining an effective baseline. Resetting the baseline can effectively remove the digital fluctuation term in ACF, which is also a feasible method to improve PSD recovery under ultra-low concentration. In particular, the baseline noise at the tail of long delay time of ACF overwhelms the number fluctuation term, making it difficult to recover reliable PSD data. This is because the measured intensity ACF contains more noise than the simulated ACF at ultra-low concentration. By including the number fluctuation term, the ideal recovered PSD can be obtained from the simulated data, but this will not happen in the experimental measurement data. The other is to remove the effect of the non-Gaussian term in the ACF by the baseline reset (BR) method. One is to directly establish the relationship between the non-Gaussian ACF and the PSD by the kernel function reconstruction (KFR) method while including the non-Gaussian term to recover the PSD. We propose two methods for inverting the DLS data and recovering the PSDs when number fluctuations are apparent.
This leads to an inaccurate particle size distribution (PSD) being recovered if the normal DLS analysis model is used. Number fluctuations add a non-Gaussian term to the scattered light intensity autocorrelation function (ACF). Dynamic light scattering (DLS) is a popular method of particle size measurement, but at ultra-low particle concentrations, the occurrence of number concentration fluctuations limits the use of the technique.