Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography
Artikel i vetenskaplig tidskrift, 2021

Characterization of suspended nanoparticles in their native environment plays a central role in a wide range of fields, from medical diagnostics and nanoparticleenhanced drug delivery to nanosafety and environmental nanopollution assessment. Standard optical approaches for nanoparticle sizing assess the size via the diffusion constant and, as a consequence, require long trajectories and that the medium has a known and uniform viscosity. However, in most biological applications, only short trajectories are available, while simultaneously, the medium viscosity is unknown and tends to display spatiotemporal variations. In this work, we demonstrate a label-free method to quantify not only size but also refractive index of individual subwavelength particles using 2 orders of magnitude shorter trajectories than required by standard methods and without prior knowledge about the physicochemical properties of the medium. We achieved this by developing a weighted average convolutional neural network to analyze holographic images of single particles, which was successfully applied to distinguish and quantify both size and refractive index of subwavelength silica and
polystyrene particles without prior knowledge of solute viscosity or refractive index. We further demonstrate how these features make it possible to temporally resolve aggregation dynamics of 31 nm polystyrene nanoparticles, revealing previously unobserved time-resolved dynamics of the monomer number and fractal dimension of individual subwavelength aggregates.

deep learning

optical microscopy

holography

particle characterization

aggregation kinetics

Författare

Benjamin Midtvedt

Göteborgs universitet

Erik Olsén

Chalmers, Fysik, Nano- och biofysik

Fredrik Eklund

Chalmers, Fysik, Nano- och biofysik

Fredrik Höök

Chalmers, Fysik, Nano- och biofysik

Caroline Adiels

Göteborgs universitet

Giovanni Volpe

Göteborgs universitet

Daniel Midtvedt

Göteborgs universitet

ACS Nano

1936-0851 (ISSN) 1936-086X (eISSN)

Vol. 15 2 2240-2250

Ämneskategorier

Biofysik

Den kondenserade materiens fysik

DOI

10.1021/acsnano.0c06902

PubMed

33399450

Mer information

Senast uppdaterat

2021-04-15