Nanoparticles Characterization Using Non-learning and Learning Based Methods

Nanoparticles play a crucial role in the field of research and development. These entities are versatile and its important to learn their physical and chemical properties. Understanding the physical and chemical properties of nanoparticles is indeed critical for optimizing their performance and expanding their applications in various fields. Characterizing the morphology of nanoparticles is done using either optical microscopy, scanning electron microscopy or transmission electron microscopy which is an expensive way, labor-intensive and time-consuming process. In this study, the authors employ cutting-edge deep learning techniques, specifically You Only Look Once (YOLO) and Convolutional Neural Networks (CNN), to characterize Zinc Oxide (ZnO), Copper(II) Sulphide (CuS), and Magnesium Dioxide (MnO2) nanoparticles. The nanoparticle characterization involves the tasks of nanoparticle detection, classification, and instance segmentation. The YOLO model achieves a nanoparticle detection accuracy of 82.67%, while the CNN model demonstrates an accuracy of 83.33% for object detection. These results highlight the potential of deep learning techniques in streamlining and enhancing the efficiency of nanoparticle characterization processes, providing a more cost-effective and time-efficient alternative to traditional methods.