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: Jun 8, 2025

Large Area Substrate-Based Nanofabrication of Controllable and Customizable Gold Nanoparticles Via Capped Dewetting
05:51

Large Area Substrate-Based Nanofabrication of Controllable and Customizable Gold Nanoparticles Via Capped Dewetting

Published on: February 26, 2019

5.6K

Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and

Steven M E Demers1, Christopher Sobecki1, Larry Deschaine1

  • 1Savannah River National Laboratory, Aiken, SC 29808, USA.

Nanomaterials (Basel, Switzerland)
|November 8, 2024
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

Quantum Dot-DNA FRET Conjugates for Direct Analysis of Methylphosphonic Acid in Complex Media.

ACS omega·2023
See all related articles

The nanometal surface energy transfer (NSET) model was adapted for various metal nanoparticles, showing improved predictions over Förster resonance energy transfer. Machine learning further enhanced NSET equation accuracy for nanosensor development.

Area of Science:

  • Nanotechnology
  • Materials Science
  • Physical Chemistry

Background:

  • The nanometal surface energy transfer (NSET) mechanism explains interactions between metallic nanoparticles and molecular dyes.
  • Expanding the NSET model to diverse metal compositions is crucial for developing novel nanosensors.

Purpose of the Study:

  • To modify and test the NSET formula for gold, silver, copper, and platinum nanoparticles of varying sizes.
  • To investigate the predictive power of NSET compared to Förster resonance energy transfer (FRET) for different metals.

Main Methods:

  • Modified the NSET formula, adjusting size-dependent dampening and skin depth terms.
  • Tested the model with gold, silver, copper, and platinum nanoparticles.
  • Employed an exhaustive grid search and artificial intelligence/machine learning algorithms (multilayer perception, least absolute shrinkage and selection operator regression) for model optimization.
Keywords:
FRETLasso methodNSETexhaustive grid searchmachine learningmultilayer perceptronnanoparticlesoptimizationquenching

More Related Videos

Gold Nanoparticle Synthesis
13:42

Gold Nanoparticle Synthesis

Published on: July 10, 2021

14.4K
Asymmetrical Flow Field-Flow Fractionation for Sizing of Gold Nanoparticles in Suspension
09:33

Asymmetrical Flow Field-Flow Fractionation for Sizing of Gold Nanoparticles in Suspension

Published on: September 11, 2020

6.1K

Related Experiment Videos

Last Updated: Jun 8, 2025

Large Area Substrate-Based Nanofabrication of Controllable and Customizable Gold Nanoparticles Via Capped Dewetting
05:51

Large Area Substrate-Based Nanofabrication of Controllable and Customizable Gold Nanoparticles Via Capped Dewetting

Published on: February 26, 2019

5.6K
Gold Nanoparticle Synthesis
13:42

Gold Nanoparticle Synthesis

Published on: July 10, 2021

14.4K
Asymmetrical Flow Field-Flow Fractionation for Sizing of Gold Nanoparticles in Suspension
09:33

Asymmetrical Flow Field-Flow Fractionation for Sizing of Gold Nanoparticles in Suspension

Published on: September 11, 2020

6.1K

Main Results:

  • Modified NSET model showed closer agreement with experimental data for metal nanoparticles than FRET.
  • Scattering effects were observed for nanoparticles with diameters around 20 nm.
  • AI/ML algorithms achieved a correlation coefficient (R²) greater than 0.97, indicating robust model performance.

Conclusions:

  • The adapted NSET model provides a viable framework for understanding energy transfer with diverse metal nanoparticles.
  • Further research into physics-informed machine learning is warranted to refine NSET equations.
  • This work supports the development of advanced nanosensors for various applications.