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

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

You might also read

Related Articles

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

Sort by

Same author

Monoalcohol-Directed Shape Control in a Surfactant-Free Gold Nanoplate Synthesis.

Small methods·2026

Same author

The efficacy and safety of lower-dose aspirin for primary and secondary prevention of cardiovascular disease in the elderly: an interim analysis of a multicenter, prospective, observational study.

Frontiers in cardiovascular medicine·2026

Same author

Monte Carlo methods, 70 years after "Equation of state calculations by fast computing machines" by Nicholas Metropolis, Arianna Rosenbluth, Marshall Rosenbluth, Augusta Teller, and Edward Teller (1953).

The Journal of chemical physics·2025

Same author

Anisotropic surface potentials induced by competitive ion adsorption enable the synthesis of branched cubic Pt mesocrystals.

Nature communications·2025

Same author

Patchy nanoparticles by atomic stencilling.

Nature·2025

Same author

Predicting the shapes of Au55 and Au147: Force fields vs density-functional theory.

The Journal of chemical physics·2025

Search research articles

Related Experiment Video

Updated: Jun 28, 2026

Evaluating Plasmonic Transport in Current-carrying Silver Nanowires

Evaluating Plasmonic Transport in Current-carrying Silver Nanowires

Published on: December 11, 2013

Structural classification of Ag and Cu nanocrystals with machine learning.

Huaizhong Zhang¹, Kristen A Fichthorn²

¹Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, 16802, USA.

|August 28, 2024

Summary

Machine learning (ML) effectively classifies nanoparticle structures. This approach categorizes both crystalline and amorphous metal nanoparticles, aiding in understanding their properties and applications.

More Related Videos

Synthesis, Characterization, and Functionalization of Hybrid Au/CdS and Au/ZnS Core/Shell Nanoparticles

Synthesis, Characterization, and Functionalization of Hybrid Au/CdS and Au/ZnS Core/Shell Nanoparticles

Published on: March 2, 2016

High Resolution Physical Characterization of Single Metallic Nanoparticles

High Resolution Physical Characterization of Single Metallic Nanoparticles

Published on: June 28, 2019

Related Experiment Videos

Last Updated: Jun 28, 2026

Evaluating Plasmonic Transport in Current-carrying Silver Nanowires

Evaluating Plasmonic Transport in Current-carrying Silver Nanowires

Published on: December 11, 2013

Synthesis, Characterization, and Functionalization of Hybrid Au/CdS and Au/ZnS Core/Shell Nanoparticles

Synthesis, Characterization, and Functionalization of Hybrid Au/CdS and Au/ZnS Core/Shell Nanoparticles

Published on: March 2, 2016

High Resolution Physical Characterization of Single Metallic Nanoparticles

High Resolution Physical Characterization of Single Metallic Nanoparticles

Published on: June 28, 2019

Area of Science:

Materials Science
Computational Chemistry
Nanotechnology

Background:

Nanoparticles exhibit diverse structures, influencing their properties.
Classifying these structures is crucial for targeted applications.
Existing methods may lack the precision for complex nanoparticle architectures.

Purpose of the Study:

To develop and validate machine learning (ML) models for classifying mono-metallic copper (Cu) and silver (Ag) nanoparticle structures.
To explore the effectiveness of common neighbor analysis (CNA) and dimensionality reduction techniques in nanoparticle structure characterization.
To identify distinct structural classes and sub-classes within simulated Cu and Ag nanoparticles.

Main Methods:

Utilized parallel-tempering molecular dynamics simulations to generate nanoparticle structures (100-200 atoms).
Extracted structural features using common neighbor analysis (CNA) signatures.
Applied principal component analysis (PCA) for dimensionality reduction of CNA features.
Employed K-means clustering and Gaussian mixture models (GMM) for structural classification.
Evaluated clustering performance using gap statistic and silhouette score.

Main Results:

Identified five structural classes and 14 detailed sub-classes for Ag nanoparticles.
Discovered two broad classes (crystalline and amorphous) for Cu nanoparticles, with five shared classes and 15 detailed sub-classes.
Demonstrated high accuracy in classifying nanoparticles into physically relevant structural categories.
Validated the efficacy of ML methods in discerning complex nanoparticle architectures.

Conclusions:

Machine learning models, combined with CNA and PCA, provide a robust framework for nanoparticle structure classification.
The identified structural classes offer a foundation for understanding structure-property relationships in nanoparticles.
This ML-driven approach enhances the ability to predict and control nanoparticle behavior for diverse applications.