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从动态光散射信号中反演纳米颗粒粒度分布,结果准确性和重复性受测量的自相关函数数据点影响,数据点长度不同会导致不同的反演结果.为了解决该问题,提出了一种根据拟合自相关函数的均方根误差来截断自相函数的方法,该方法通过设置拟合误差阈值来自适应地选择最佳自相关函数数据点数.实验结果表明,使用均方根误差阈值方法获得的颗粒粒度分布比其他方法获得的结果具有更高的准确性和更好的重复性.“,”Dynamic light scattering technology is a common measurement method that can calculate the particle size distribution of nanoparticles at present. The particle size distribution obtained by the inversion of the autocorrelation function belongs to the problem of solving the first type of Fredholm integral equation,which is a typical ill-posed problem. The data near the baseline of the autocorrelation function has a lot of noise. Therefore,the accuracy and repeatability of the inversion results are affected by the data points of the autocorrelation function,and different data points of the autocorrelation function may lead to different inversion results. To overcome the shortcomings,the characteristics of the root-mean-square error of the fitted correlation function with the number of autocorrelation function points are investigated. The root-mean-square error curve as a function of number of autocorrelation function points can be divided into two stages:in the first stage,the root-mean-square error value of the fitted correlation function grows smoothly with the increase of the number of autocorrelation function points,and in the second stage,as the number of correlation function points increases,the root-mean-square error value of the fitted correlation function grows rapidly.The rapid growth of the root-mean-square error indicates that the noise contained in the autocorrelation function data increases and the reliability of the correlation function data decreases with increasing number of data points. The point of inflection of the root-mean-square error curve marks where the noise contribution from subsequent data points significantly increases the fitting error and produces poorer inversion results. With this in mind,root-mean-square error threshold method as the criterion to truncate the autocorrelation function be proposed. This method adaptively selects the optimal number of autocorrelation function data points by setting the root-mean-square error threshold. It can truncate the autocorrelation function according to the measured particle size and the noise level of the autocorrelation function,and select the optimal autocorrelation function data point. To verify the proposed method,the ACFs for 5 unimodal samples and 1 bimodal sample were analyzed using the root-mean-square error threshold method and the Tikhonov regularization algorithm for inversion. Experimental results show that the particle size results obtained using the root-mean-square error threshold method have higher accuracy and better repeatability than the results obtained by other methods. The root-mean-square error of the fitted correlation function is affected by the noise in the autocorrelation function data,and a larger root-mean-square error value indicates a higher level of noise in the correlation function data,and vice versa. The data near the baseline of the autocorrelation function has a lot of noise,which causes the root-mean-square error value of the fitted correlation function to increase rapidly. Therefore,the root-mean-square error threshold method proposed is essentially realized by using the feature that the root-mean-square value of the fitted correlation function can be used to reflect the noise level of the autocorrelation function data, which can effectively reduce the effect of the noise near the baseline the autocorrelation function on the inversion results.