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 Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Impact of nanoparticle morphologies on property prediction using explainable AI.

Nanoscale horizons·2025
Same author

Fractal Characterization of Simulated Metal Nanocatalysts in 3D.

Small science·2025
Same author

Materials "Economatics": Combining Chemical, Financial, Environmental, and Social Factors Using Machine Learning.

ACS nano·2025
Same author

Property Prediction for Complex Compounds Using Structure-Free Mendeleev Encoding and Machine Learning.

Journal of chemical information and modeling·2024
Same author

Classification of battery compounds using structure-free Mendeleev encodings.

Journal of cheminformatics·2024
Same author

Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations.

ACS nanoscience Au·2023

Related Experiment Video

Updated: Dec 11, 2025

Bio-inspired Polydopamine Surface Modification of Nanodiamonds and Its Reduction of Silver Nanoparticles
07:58

Bio-inspired Polydopamine Surface Modification of Nanodiamonds and Its Reduction of Silver Nanoparticles

Published on: November 14, 2018

8.6K

Machine learning reveals multiple classes of diamond nanoparticles.

Amanda J Parker1, Amanda S Barnard

  • 1Data61 CSIRO, Door 34 Goods Shed Village St, Docklands, Victoria, Australia. amanda.parker@data61.csiro.au.

Nanoscale Horizons
|August 26, 2020
PubMed
Summary

This study introduces a sustainable approach to nanoparticle production by classifying diamond nanoparticles using machine learning. This method focuses on structural diversity for targeted applications and cost-effectiveness.

More Related Videos

Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping
12:19

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

Published on: December 8, 2015

12.8K
Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction
09:13

Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction

Published on: April 1, 2017

14.0K

Related Experiment Videos

Last Updated: Dec 11, 2025

Bio-inspired Polydopamine Surface Modification of Nanodiamonds and Its Reduction of Silver Nanoparticles
07:58

Bio-inspired Polydopamine Surface Modification of Nanodiamonds and Its Reduction of Silver Nanoparticles

Published on: November 14, 2018

8.6K
Identification of Metal Oxide Nanoparticles in Histological Samples by Enhanced Darkfield Microscopy and Hyperspectral Mapping
12:19

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

Published on: December 8, 2015

12.8K
Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction
09:13

Characterization of Ultra-fine Grained and Nanocrystalline Materials Using Transmission Kikuchi Diffraction

Published on: April 1, 2017

14.0K

Area of Science:

  • Materials Science
  • Nanotechnology
  • Computational Chemistry

Background:

  • Sustainable nanoparticle production requires cost-effective methods for targeted applications.
  • Classifying nanoparticles and characterizing properties as a whole enables better industrial prospects.
  • Current methods often focus on structural precision, which can be less sustainable.

Purpose of the Study:

  • To develop a machine learning model for classifying diamond nanoparticles based on structural features.
  • To explore the characteristics of identified diamond nanoparticle classes, including size, shape, speciation, and charge transfer.
  • To provide a framework for rapid class assignment using microanalysis techniques.

Main Methods:

  • Utilized machine learning algorithms to predict nanoparticle classes.
  • Analyzed structural features of diamond nanoparticles.
  • Investigated size, shape, speciation, and charge transfer properties within each class.

Main Results:

  • Identified 9 distinct classes of diamond nanoparticles based on 17 similarity dimensions.
  • Found that the fraction of sp2 or sp3 hybridized atoms are not strong determinants of class.
  • Observed that nanoparticle classes are only weakly related to size.

Conclusions:

  • Machine learning can effectively classify diamond nanoparticles based on structural diversity, promoting sustainability.
  • The classification is independent of hybridization fractions and weakly correlated with size.
  • The defined classes facilitate rapid identification via microanalysis, aiding industrial applications.