A weighted Bayesian inversion method is proposed for estimation of unimodal particle size distribution in multi-angle dynamic light scattering measurements. Particle size informiation distribution in autocorrelation function is used as base and adjustment parameters as exponent in the weight coefficient. Traditional Bayesian inversion method is recovered. Simulation and experimental data at different noise levels show that weighted Bayesian inversion method obtains inversion results with smaller distribution error. It weakens effectively influence of data noise and improves accuracy of particle size distribution.

A self-optimizing cumulative inversion for particle size distribution with photon correlation spectroscopy is proposed.It improves precision and stability of measurement.From a detailed fundamental of photon correlation spectroscopy and conventional cumulative inversion,we analyze instability of cumulative inversion.A self-optimizing method is presented by using the minimum mean-square error.It inverts particle size distribution stably with scattered autocorrelation curve with random noise.Experimental results illustrate validity and feasibility of the approach.

A real-code hybrid genetic algorithm for nano-particle sizing with photon correlation spectroscopy is presented.With ergodic properties of chaos and simulated annealing algorithm,our algorithm shortens inversion range automatically.It has a relatively strong local searching ability.Numerical simulations are performed for mono-disperse particle system under noise-free condition and noise with various noise levels.Inversion of experimental results of 90nm standard samples of polystyrene particles is made.It shows effectiveness,reliability and ability of noise resistance of the algorithm.