在动态光散射颗粒测量时, 为了从含噪的自相关函数数据中准确地反演出颗粒粒度分布, 对Tikhonov正则化算法进行改进, 将噪声作为一个独立的未知变量应用到正则化方程中进行粒度反演.在计算过程中, 相应增加方程中各系数矩阵的行数和列数, 对求解的粒度分布数值则仍取其原来方程的行数和列数, 从而达到对部分噪声的剔除作用.不同噪声水平下的颗粒粒度反演结果表明, 改进后的算法能够显著提高低信噪比动态光散射数据粒度反演结果的准确性, 适用于宽分布较大粒径的颗粒粒度反演.

In dynamic light scattering measurements, noise often makes inversion of the autocorrelation function to obtain the particle size distribution unreliable. To obtain accurate particle size distributions from noisy dynamic light scattering data, a modified inversion method based on the original Tikhonov regularization algorithm is proposed. In the method, the noise in the data is considered an independent variable. During the inversion process the number of rows and columns of the coefficient matrix equation is increased to accommodate this. Finally, using the dimensions of the coefficient matrix, the poor particle size distribution data is separated from the recovered particle size distributions, reducing the influence of noise in the data. The particle size distributions recovered from the dynamic light scatteringdata show that the modified Tikhonov regularization inversion algorithm can give rise to improved accuracy compared with the original inversion algorithm, especially for low signal-to-noise ratio data.