Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Molecular characterization and correlation with β-lactam resistance of penicillin-binding protein2x, 2b, and 1a of <i>Streptococcus pneumoniae</i> in clinical pneumococcal isolates.

Microbiology spectrum·2026

Same author

Magnetic Resonance Spectroscopy Deep Learning with Magnetic Resonance Background Generator Enables In Vivo Metabolite Quantification of Hepatic Encephalopathy.

IEEE transactions on bio-medical engineering·2026

Same author

PolyMamba-Net: a lightweight and boundary-aware network for real-time polyp segmentation in colonoscopy.

Frontiers in medicine·2026

Same author

Triglyceride-glucose index and systemic immune-inflammation index are associated with left ventricular hypertrophy in type 2 diabetes mellitus patients.

BMC endocrine disorders·2026

Same author

Phosphate Triggers Spontaneous Delamination at the Graphene-ITO Interface.

Nano letters·2026

Same author

Molecular-Level Perturbations of Dissolved Organic Matter Driven by Episodic Firecracker Residue Leaching.

Environmental science & technology·2026

Same journal

Nongenetic <i>in Vivo</i> Bimodal Neuromodulation via Photothermal Gold Nanorods and a Multifunctional Fiber Neural Probe.

ACS nano·2026

Same journal

Electric-Field-Driven Ferredoxin 1-Independent Cuproptosis Induction Overcomes Therapy-Induced Resistance in Glioblastoma.

ACS nano·2026

Same journal

Connecting and Engaging.

ACS nano·2026

Same journal

Efficient Photocatalytic Methane Conversion to Liquid Oxygenates by Constructing Charge-Directed Transfer Pathways.

ACS nano·2026

Same journal

Mechanochemically Coupled Multidimensional Modulation of Calcium Overload.

ACS nano·2026

Same journal

Electrical Control and High-Bias Enhancement of Magnetoresistance in van der Waals Antiferromagnetic Spin-Filter Tunnel Field-Effect Transistor.

ACS nano·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 30, 2025

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Published on: January 3, 2016

Deep-Learning Driven, High-Precision Plasmonic Scattering Interferometry for Single-Particle Identification.

Yi-Fan He¹, Si-Yu Yang¹, Wen-Li Lv¹

¹Hefei National Laboratory for Physical Sciences at the Microscale, Chinese Academy of Sciences Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei, 230026, China.

|March 21, 2024

Summary

This summary is machine-generated.

This study introduces a deep-learning microscopy method for label-free, high-throughput identification of nanoparticle composition in solution. It enables precise analysis of diverse nanomaterials and dynamic surface reactions at the single-particle level.

Keywords:

deep learning high-throughput identification nanoparticles plasmonic imaging

More Related Videos

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

Implementation of a Reference Interferometer for Nanodetection

Implementation of a Reference Interferometer for Nanodetection

Published on: April 26, 2014

Related Experiment Videos

Last Updated: Jun 30, 2025

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Published on: January 3, 2016

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

Implementation of a Reference Interferometer for Nanodetection

Implementation of a Reference Interferometer for Nanodetection

Published on: April 26, 2014

Area of Science:

Nanoscience and Nanotechnology
Materials Science
Analytical Chemistry

Background:

Label-free analysis of nano-objects in solution is vital for colloid analysis and medical diagnostics.
Characterizing heterogeneous nano-object mixtures at the single-particle level with high sensitivity and resolution remains a challenge.
Current methods often require labeling or lack the throughput for comprehensive analysis.

Purpose of the Study:

To develop a high-throughput, label-free technique for identifying the material composition of individual nano-objects in solution.
To leverage deep learning to correlate plasmonic scattering interferometric patterns with nanoparticle properties.
To establish a versatile platform for nanoparticle characterization and reaction analysis.

Main Methods:

Deep-learning plasmonic scattering interferometric microscopy was employed.
Quantitative relationships between interferometric scattering patterns and material properties were decoded using deep learning algorithms.
The technique was applied to analyze nanoparticle composition and dynamic surface chemical reactions.

Main Results:

High-throughput, label-free identification of diverse nanoparticle types at the single-particle level was achieved.
The method demonstrated high sensitivity and resolution in deciphering constituents of heterogeneous nano-object mixtures.
Versatility was shown in analyzing dynamic surface chemical reactions on single nanoparticles.

Conclusions:

Deep-learning plasmonic scattering interferometric microscopy offers a streamlined and powerful approach for nanoparticle characterization.
This technique provides a new methodology for understanding nanoscale dynamics and addressing fundamental questions in nanoscience.
The platform holds significant potential for applications in colloid analysis, medical diagnostics, and reaction monitoring.