A particle morphology characterization system and its method based on particle scattering image recognition. (April 2023)

• Record Type: Journal Article
• Authors: Ding, Xinrui; Liu, Xin; Shao, Changkun; Chen, Bowen; Li, Weihong; Li, Zongtao
• Is Part Of: Optics and lasers in engineering. Volume 163(2023)
• Publication Date: 2023-04
• Subjects: Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2022.107448
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: The transit method in astronomy was firstly referred to detect the shape of microscope particles. Comparing with the previous work, the accuracy of optical fluid field couple (OFFC) model improved 16.08%. Comparing with the previous work, the mix sample could be detected and the maximum error of different particles' proportions was less than 6%. The scattering patterns and flow trajectory could be analyzed automatically by the algorithm and system proposed in this work. Abstract: The morphology of particles is usually detected by microscopic imaging methods such as scanning electron microscope (SEM). However, this type of direct observation relies on expensive equipment and requires complex preparations. Inspired by the transit method in astronomy, a characterization system based on the scattering patterns change due to the relative motion between the particle and facula was proposed in this work. The scattering patterns of particles could be recognized and analyzed automatically to detect the particle shape. The flow trajectory was studied by the optical fluid field couple (OFFC) model proposed in this work to calculate the size and specific surface area of the particle. For getting more accurate results, particle surface roughness was measured by image recognition to correct the results of specific surface areas. The accuracy of the classification machine learning (CML) model was about 97%, and the errors of the specific surface area results were lower than 10%. Comparing with the previous work, the proportions of different particles in mix sample could be detected successfully by this system with a maximum error less than 6%. This work can offer a valuable reference for the fields of characterization morphology systems based on scattering.