A Nanoparticle Tracking Analysis Algorithm for Particle Size Estimation

To improve the accuracy of nanoparticle sizes estimation in polydisperse samples and flowing samples, we discuss a novel nanoparticle tracking analysis (NTA) algorithm in detail. A local adaptive threshold segmentation algorithm is used to improve the recognition efficiency of weak light intensity particles, and a new particle matching strategy including multiple optimization steps is proposed to improve the accuracy of trajectory tracking. A flow correction algorithm is introduced to remove the flow vector from the particle motion displacement and retain only the Brownian motion, which enables the NTA algorithm to be applied to flowing samples detection. The particle size measurement experiment of mixed nanosphere solution shows that the polydisperse sample particle sizes measured by the NTA algorithm are close to the true values. The particle size measurement experiment of the nanosphere flow sample shows that the measurement result with the flow correction algorithm is closer to the true value. In summary, we have demonstrated that the proposed NTA algorithm can accurately measure particle sizes of polydisperse samples and flowing samples.