Abstract
Single nanoparticle analysis is crucial for various applications in biology, materials, and energy. However, precisely profiling and monitoring weakly scattering nanoparticles remains challenging. Here, it is demonstrated that deep learning-empowered plasmonic microscopy (Deep-SM) enables precise sizing and collision detection of functional chemical and biological nanoparticles. Image sequences are recorded by the state-of-the-art plasmonic microscopy during single nanoparticle collision onto the sensor surface. Deep-SM can enhance signal detection and suppresses noise by leveraging spatio-temporal correlations of the unique signal and noise characteristics in plasmonic microscopy image sequences. Deep-SM can provide significant scattering signal enhancement and noise reduction in dynamic imaging of biological nanoparticles as small as 10 nm, as well as the collision detection of metallic nanoparticle electrochemistry and quantum coupling with plasmonic microscopy. The high sensitivity and simplicity make this approach promising for routine use in nanoparticle analysis across diverse scientific fields.