Counting and mapping of subwavelength nanoparticles from a single shot scattering pattern

Abstract

Abstract Particle counting is of critical importance for nanotechnology, environmental monitoring, pharmaceutical, food and semiconductor industries. Here we introduce a super-resolution single-shot optical method for counting and mapping positions of subwavelength particles on a surface. The method is based on the deep learning analysis of the intensity profile of the coherent light scattered on the group of particles. In a proof of principle experiment, we demonstrated particle counting accuracies of more than 90%. We also demonstrate that the particle locations can be mapped on a 4 × 4 grid with a nearly perfect accuracy (16-pixel binary imaging of the particle ensemble). Both the retrieval of number of particles and their mapping is achieved with super-resolution: accuracies are similar for sets with closely located optically unresolvable particles and sets with sparsely located particles. As the method does not require fluorescent labelling of the particles, is resilient to small variations of particle sizes, can be adopted to counting various types of nanoparticulates and high rates, it can find applications in numerous particles counting tasks in nanotechnology, life sciences and beyond.