Identification of particle mixtures using machine-learning-assisted laser diffraction analysis

Abstract

We demonstrate a smart laser-diffraction analysis technique for particle mixture identification. We retrieve information about the size, geometry, and ratio concentration of two-component heterogeneous particle mixtures with an efficiency above 92%. In contrast to commonly-used laser diffraction schemes -- in which a large number of detectors is needed -- our machine-learning-assisted protocol makes use of a single far-field diffraction pattern, contained within a small angle ($\sim 0.26^{\circ }$) around the light propagation axis. Because of its reliability and ease of implementation, our work may pave the way towards the development of novel smart identification technologies for sample classification and particle contamination monitoring in industrial manufacturing processes.

arXiv comment7 pages, 4 figures

References in corpus (6)

• Deep Learning Microscopy
• Deep learning enhanced mobile-phone microscopy
• Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles
• Endurance of quantum coherence due to particle indistinguishability in noisy quantum networks
• CATCH: Characterizing and Tracking Colloids Holographically using deep neural networks
• Machine learning techniques applied for detection of nanoparticles on surfaces using Coherent Fourier Scatterometry