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

Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

You might also read

Related Articles

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

Sort by

Same author

Spatial transcriptome for cell segmentation.

Nature computational science·2026

Same author

Ultra-broadband ultraviolet detection and imaging enabled by copper-halide inside transparent glass.

Nature communications·2026

Same author

Correction to "Thiolate DNAzymes on Gold Nanoparticles for Isothermal Amplification and Detection of Mesothelioma-derived Exosomal PD-L1 mRNA".

Analytical chemistry·2026

Same author

Lattice and ligand engineering for hierarchical heterogeneous nanocrystals.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Topology of the Cell Membrane Interface for the Physical Re-Encoding of Neural Signals.

Journal of the American Chemical Society·2026

Same author

Heterogeneity of Upconversion Bioconjugates Revealed by Single Nanoparticle Spectroscopy.

Nano letters·2025

Search research articles

Related Experiment Video

Updated: Oct 17, 2025

Triplet Fusion Upconversion Nanocapsule Synthesis

Triplet Fusion Upconversion Nanocapsule Synthesis

Published on: September 7, 2022

Optical Fingerprint Classification of Single Upconversion Nanoparticles by Deep Learning.

Jiayan Liao¹, Jiajia Zhou¹, Yiliao Song²

¹Institute for Biomedical Materials and Devices (IBMD), Faculty of Science, University of Technology Sydney, NSW 2007, Australia.

The Journal of Physical Chemistry Letters

|October 14, 2021

Summary

Deep learning decodes complex optical fingerprints from upconversion nanoparticles (UCNPs), enabling accurate, high-throughput classification of nanoscale barcodes for high-capacity information storage.

More Related Videos

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Published on: December 8, 2015

Biomolecular Imaging of Cellular Uptake of Nanoparticles using Multimodal Nonlinear Optical Microscopy

Biomolecular Imaging of Cellular Uptake of Nanoparticles using Multimodal Nonlinear Optical Microscopy

Published on: May 16, 2022

Related Experiment Videos

Last Updated: Oct 17, 2025

Triplet Fusion Upconversion Nanocapsule Synthesis

Triplet Fusion Upconversion Nanocapsule Synthesis

Published on: September 7, 2022

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping

Published on: December 8, 2015

Biomolecular Imaging of Cellular Uptake of Nanoparticles using Multimodal Nonlinear Optical Microscopy

Biomolecular Imaging of Cellular Uptake of Nanoparticles using Multimodal Nonlinear Optical Microscopy

Published on: May 16, 2022

Area of Science:

Nanotechnology
Optical Physics
Machine Learning

Background:

Upconversion nanoparticles (UCNPs) offer tunable optical properties through lanthanide ion doping, enabling nanoscale barcode creation for high-capacity information.
Decoding complex optical fingerprints from numerous UCNPs presents challenges in accuracy, throughput, and speed.

Purpose of the Study:

To develop a deep-learning approach for accurate and high-throughput decoding of optical fingerprints from diverse UCNPs.
To demonstrate the capability of deep learning in classifying UCNPs based on their unique optical signatures.

Main Methods:

Synthesis of UCNPs with controlled optical properties via heterogeneous design and lanthanide ion deposition.
Simultaneous collection of lifetime profiles from hundreds of single UCNPs using wide-field microscopy.
Development and application of deep-learning algorithms to analyze nanoparticle optical fingerprints.

Main Results:

High-accuracy classification (over 90%) of 14 distinct UCNP types was achieved using the deep-learning model.
The deep-learning approach effectively recognized the complexity of optical fingerprints from different UCNPs.
Lifetime profiles from hundreds of single nanoparticles provided sufficient data for robust algorithm development.

Conclusions:

Deep learning provides a powerful tool for decoding complex optical information from UCNPs.
This approach facilitates accurate and high-throughput classification of nanoscale optical barcodes.
The study opens new avenues for creating vast luminescent information carriers using UCNP-based barcodes.