Leveraging Machine Learning for Size and Shape Analysis of Nanoparticles: A Shortcut to Electron Microscopy.

ABSTRACT: Characterizing nanoparticles (NPs) is crucial in nanoscience due to the direct influence of their physiochemical properties on their behavior. Various experimental techniques exist to analyze the size and shape of NPs, each with advantages, limitations, proneness to uncertainty, and resource requirements. One of them is electron microscopy (EM), often considered the gold standard, which offers visualization of the primary particles. However, despite its advantages, EM can be expensive, less accessible, and difficult to apply during dynamic processes. Therefore, using EM for specific experimental conditions, such as observing dynamic processes or visualizing low-contrast particles, is challenging. This study showcases the potential of machine learning in deriving EM parameters by utilizing

SUBMITTER: Glaubitz C 

PROVIDER: S-EPMC10788956 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature