Statistically Representative Metrology of Nanoparticles via Unsupervised Machine Learning of TEM Images

The morphology of nanoparticles governs their properties for a range of important applications. Thus, the ability to statistically correlate this key particle performance parameter is paramount in achieving accurate control of nanoparticle properties. Among several effective techniques for morphological characterization of nanoparticles, transmission electron microscopy (TEM) can provide a direct, accurate characterization of the details of nanoparticle structures and morphology at atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this work, we demonstrate an efficient, robust and highly automated unsupervised machine learning method for the metrology of nanoparticle systems based on TEM images. Our method not only can achieve statistically significant analysis, but it is also robust against variable image quality, imaging modalities, and particle dispersions. The ability to efficiently gain statistically significant particle metrology is critical in advancing precise particle synthesis and accurate property control.

Automated summary

The morphology of nanoparticles plays a crucial role in determining their properties for various applications. Transmission electron microscopy (TEM) is an effective technique for characterizing nanoparticle morphology at atomic resolution. Developing efficient and automated methods for statistically significant particle metrology is essential for advancing precise particle synthesis and property control.