Automatic electron hologram acquisition of catalyst nanoparticles using particle detection with image processing and machine learning

To enable better statistical analysis of catalyst nanoparticles by high-resolution electron holography, we improved the particle detection accuracy of our previously developed automated hologram acquisition system by using an image classifier trained with machine learning. The detection accuracy of 83% was achieved with the small training data of just 232 images showing nanoparticles by utilizing transfer learning based on VGG16 to train the image classifier. Although the construction of training data generally requires much effort, the time needed to select the training data candidates was significantly shortened by utilizing a pattern matching technique. Experimental results showed that the high-resolution hologram acquisition efficiency was improved by factors of about 100 and 6 compared to a scan method and a pattern-matching-only method, respectively.& nbsp;(c) 2022 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

Researchers have improved an automated hologram acquisition system by using machine learning and pattern matching techniques, resulting in increased detection accuracy and improved efficiency in acquiring high-resolution holograms for catalyst nanoparticles.