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Precise Sizing and Collision Detection of Functional Nanoparticles by Deep Learning Empowered Plasmonic Microscopy
Jingan Wang1, Yi Sun2,3, 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.
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.
