Label-free probing of the material composition of (bio)nano-objects directly in solution at the single-particle level is crucial in various fields, including colloid analysis and medical diagnostics. However, it remains challenging to decipher the constituents of heterogeneous mixtures of nano-objects with high sensitivity and resolution. Here, we present deep-learning plasmonic scattering interferometric microscopy, which is capable of identifying the composition of nanoparticles automatically with high throughput at the single-particle level. By employing deep learning to decode the quantitative relationship between the interferometric scattering patterns of nanoparticles and their intrinsic material properties, this technique is capable of high-throughput, label-free identification of diverse nanoparticle types. We demonstrate its versatility in analyzing dynamic surface chemical reactions on single nanoparticles, revealing its potential as a universal platform for nanoparticle imaging and reaction analysis. This technique not only streamlines the process of nanoparticle characterization, but also proposes a methodology for a deeper understanding of nanoscale dynamics, holding great potential for addressing extensive fundamental questions in nanoscience and nanotechnology.
Keywords: deep learning; high-throughput; identification; nanoparticles; plasmonic imaging.