Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Oct 20, 2025

Colloidal Synthesis of Nanopatch Antennas for Applications in Plasmonics and Nanophotonics
09:12

Colloidal Synthesis of Nanopatch Antennas for Applications in Plasmonics and Nanophotonics

Published on: May 28, 2016

11.4K

Deep learning: an efficient method for plasmonic design of geometric nanoparticles.

Qian Du1,2, Quan Zhang1,2, Guohua Liu1,2

  • 1College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, People's Republic of China.

Nanotechnology
|September 16, 2021
PubMed
Summary

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

Validation of the Weibull-derived Gini coefficient for quantifying the inequality of the aboveground biomass distribution using 2815 culms from 50 bamboo species.

Frontiers in plant science·2026
Same author

Integrated multi-platform genetic profiling reveals dual molecular pathology in 46, XY disorders of sex development through NR5A1 Haploinsufficiency and maternal chromosome 15 UPD.

Human molecular genetics·2026
Same author

Confined Cationic Covalent Organic Cages Enable Oxidant-Free Hofmann-Löffler-Freytag/Cyclization Sequences.

Angewandte Chemie (International ed. in English)·2026
Same author

A Machine Learning Ensemble Framework for Carbon Price Prediction and Decision Support Under Information Structure Heterogeneity in Regional Carbon Markets in China.

Entropy (Basel, Switzerland)·2026
Same author

Opioid-free vs. opioid-inclusive anaesthesia with or without regional anaesthesia for postoperative pain.

Anaesthesia·2026
Same author

Widespread marine and freshwater distributions of active sulfoquinovose-degrading bacteria.

The ISME journal·2026

This study introduces a novel artificial intelligence (AI) method for designing plasmonic nanoparticles, significantly improving spectral precision. The AI model accelerates the design process, reducing costly trial-and-error experiments in nanoparticle research.

Area of Science:

  • Nanotechnology
  • Materials Science
  • Computational Physics

Background:

  • Noble metal nanoparticles exhibit unique plasmonic properties crucial for energy transfer, sensing, and catalysis.
  • Designing nanoparticles with specific spectral characteristics is complex due to numerous interdependent parameters like shape and size.
  • Current plasmonic design methods are often hindered by high dimensionality and experimental costs.

Purpose of the Study:

  • To develop an efficient and accurate plasmonic design method using artificial intelligence (AI).
  • To enable the prediction of nanoparticle parameters based on desired spectral properties.
  • To overcome the limitations of traditional trial-and-error approaches in plasmonic design.

Main Methods:

  • A dual-strategy deep learning model was employed for plasmonic design.
Keywords:
neural networkparameters designplasmonscattering spectra

More Related Videos

Plasmonic Trapping and Release of Nanoparticles in a Monitoring Environment
09:13

Plasmonic Trapping and Release of Nanoparticles in a Monitoring Environment

Published on: April 4, 2017

7.8K
Utilization of Plasmonic and Photonic Crystal Nanostructures for Enhanced Micro- and Nanoparticle Manipulation
09:29

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

Published on: September 27, 2011

12.4K

Related Experiment Videos

Last Updated: Oct 20, 2025

Colloidal Synthesis of Nanopatch Antennas for Applications in Plasmonics and Nanophotonics
09:12

Colloidal Synthesis of Nanopatch Antennas for Applications in Plasmonics and Nanophotonics

Published on: May 28, 2016

11.4K
Plasmonic Trapping and Release of Nanoparticles in a Monitoring Environment
09:13

Plasmonic Trapping and Release of Nanoparticles in a Monitoring Environment

Published on: April 4, 2017

7.8K
Utilization of Plasmonic and Photonic Crystal Nanostructures for Enhanced Micro- and Nanoparticle Manipulation
09:29

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

Published on: September 27, 2011

12.4K
  • A large dataset (>1.2 million samples) of nanoparticle scattering spectra was generated using the boundary element method.
  • An AI model was trained to correlate spectral features with design parameters (morphology, material, volume).

    • Main Results:

    • The AI model achieved a high design precision exceeding 90% on a large test dataset (>120,000 samples).
    • Experimental validation using dark-field microscopy confirmed the AI-designed nanoparticle spectra closely matched target spectra.
    • The developed AI model demonstrated high accuracy and reliability in predicting nanoparticle designs.

    Conclusions:

    • The proposed AI-driven method offers a viable and efficient solution for plasmonic nanoparticle design.
    • This approach significantly reduces the cost and time associated with experimental design iterations.
    • The study highlights the potential of AI in advancing plasmonic research and applications.