Machine Learning Enhanced Optical Microscopy for the Rapid Morphology Characterization of Silver Nanoparticles.

Abstract

The rapid characterization of nanoparticles for morphological information such as size and shape is essential for material synthesis as they are the determining factors for the optical, mechanical, and chemical properties and related applications. In this paper, we report a computational imaging platform to characterize nanoparticle size and morphology under conventional optical microscopy. We established a machine learning model based on a series of images acquired by through-focus scanning optical microscopy (TSOM) on a conventional optical microscope. This model predicts the size of silver nanocubes with an estimation error below 5% on individual particles. At the ensemble level, the estimation error is 1.6% for the averaged size and 0.4 nm for the standard deviation. The method can also identify the tip morphology of silver nanowires from the mix of sharp-tip and blunt-tip samples at an accuracy of 82%. Furthermore, we demonstrated online monitoring for the evolution of the size distribution of nanoparticles during synthesis. This method can be potentially extended to more complicated nanomaterials such as anisotropic and dielectric nanoparticles.