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.