Holographic characterisation of subwavelength particles enhanced by deep learning

TL;DR

This paper introduces a deep learning-based holographic method for rapid, label-free characterization of subwavelength nanoparticles, accurately determining size and refractive index from short trajectories without prior medium knowledge.

Contribution

It develops a convolutional neural network approach to analyze holograms, enabling nanoparticle characterization with minimal trajectory data and no assumptions about the medium's properties.

Findings

Accurately distinguishes silica and polystyrene particles.

Monitors nanoparticle aggregation dynamics.

Requires significantly shorter trajectories than traditional methods.

Abstract

The characterisation of the physical properties of nanoparticles in their native environment plays a central role in a wide range of fields, from nanoparticle-enhanced drug delivery to environmental nanopollution assessment. Standard optical approaches require long trajectories of nanoparticles dispersed in a medium with known viscosity to characterise their diffusion constant and, thus, their size. However, often only short trajectories are available, while the medium viscosity is unknown, e.g., in most biomedical applications. In this work, we demonstrate a label-free method to quantify size and refractive index of individual subwavelength particles using two orders of magnitude shorter trajectories than required by standard methods, and without assumptions about the physicochemical properties of the medium. We achieve this by developing a weighted average convolutional neural network to analyse the holographic images of the particles. As a proof of principle, we distinguish and quantify size and refractive index of silica and polystyrene particles without prior knowledge of solute viscosity or refractive index. As an example of an application beyond the state of the art, we demonstrate how this technique can monitor the aggregation of polystyrene nanoparticles, revealing the time-resolved dynamics of the monomer number and fractal dimension of individual subwavelength aggregates. This technique opens new possibilities for nanoparticle characterisation with a broad range of applications from biomedicine to environmental monitoring.