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Related Experiment Video

Updated: Jun 3, 2025

Utilization of Plasmonic and Photonic Crystal Nanostructures for Enhanced Micro- and Nanoparticle Manipulation

Utilization of Plasmonic and Photonic Crystal Nanostructures for Enhanced Micro- and Nanoparticle Manipulation

Published on: September 27, 2011

Precise Sizing and Collision Detection of Functional Nanoparticles by Deep Learning Empowered Plasmonic Microscopy.

Jingan Wang¹, Yi Sun^2,3, Yuting Yang⁴

¹School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|January 10, 2025

Summary

Deep learning-powered plasmonic microscopy (Deep-SM) precisely sizes and detects collisions of nanoparticles. This advanced technique enhances signal and reduces noise for analyzing nanoparticles as small as 10 nm.

Keywords:

collision deep learning microscopy nanoparticle plasmonic

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Last Updated: Jun 3, 2025

Utilization of Plasmonic and Photonic Crystal Nanostructures for Enhanced Micro- and Nanoparticle Manipulation

Utilization of Plasmonic and Photonic Crystal Nanostructures for Enhanced Micro- and Nanoparticle Manipulation

Published on: September 27, 2011

Plasmonic Trapping and Release of Nanoparticles in a Monitoring Environment

Plasmonic Trapping and Release of Nanoparticles in a Monitoring Environment

Published on: April 4, 2017

Performing Spectroscopy on Plasmonic Nanoparticles with Transmission-Based Nomarski-Type Differential Interference Contrast Microscopy

Performing Spectroscopy on Plasmonic Nanoparticles with Transmission-Based Nomarski-Type Differential Interference Contrast Microscopy

Published on: June 5, 2019

Area of Science:

Nanotechnology
Biophysics
Materials Science

Background:

Precise analysis of single nanoparticles is vital across biology, materials, and energy sectors.
Weakly scattering nanoparticles present significant challenges for accurate profiling and monitoring.

Purpose of the Study:

To demonstrate deep learning-empowered plasmonic microscopy (Deep-SM) for precise nanoparticle sizing and collision detection.
To enhance signal detection and suppress noise in dynamic imaging of nanoparticles.

Main Methods:

Acquisition of image sequences using state-of-the-art plasmonic microscopy during single nanoparticle collisions.
Application of deep learning algorithms to leverage spatio-temporal correlations for signal enhancement and noise reduction.

Main Results:

Deep-SM achieved significant scattering signal enhancement and noise reduction for dynamic imaging of biological nanoparticles down to 10 nm.
The method enabled accurate collision detection for metallic nanoparticle electrochemistry and quantum coupling studies.

Conclusions:

Deep-SM offers a highly sensitive and simple approach for routine nanoparticle analysis.
This technique holds promise for diverse scientific fields requiring precise single nanoparticle characterization.