Automated Nanoparticle Analysis in Surface Plasmon Resonance Microscopy.

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Abstract

The unique capability of surface plasmon resonance microscopy (SPRM) in single nanoparticle analysis has found use in various chemical and biological applications. While SPRM offers exceptional sensitivity, the statistical analysis of numerous nanoparticles has been extremely laborious and time-consuming. Herein, we presented an image processing software package for nanoparticle analysis in SPRM, which is empowered by a deep learning algorithm. This package enabled fully automated nanoparticle identification, digital counting, three-dimensional tracking of particle locations, and quantification of dwell time and Brownian motion properties. With a built-in image filtering process to improve the contrast, robust identification and analysis have been achieved from SPRM images of low refractive index nanoparticles. This software tool would largely promote the translation of SPRM technology into the digital sensing platform for high throughput sample screening.