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

Charged grain boundaries limit short-circuit endurance in garnet solid-state battery electrolytes.

Nature nanotechnology·2026
Same author

Unbiased Structure Prediction of Sophisticated Cage Structures.

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

Galvanic Replacement Incorporation of Ultrasmall Gold-Silver Nanoparticles within a Titanium Aminoterephthalate Framework.

Chemistry of materials : a publication of the American Chemical Society·2026
Same author

Mechanistic Insights Into Nitric Oxide Capture and Release in a Radical-Scavenging Zinc Ascorbate Metal-Organic Framework.

Small science·2026
Same author

Chiral Au@Ag Core-Shell Nanoparticles for Enantioselective SERS Detection of Bio-Relevant Chiral Molecules.

ACS nanoscience Au·2026
Same author

Chirality Transfer via Orientational Order of Micellar Assemblies on Gold Nanocrystals.

Advanced materials (Deerfield Beach, Fla.)·2026

Related Experiment Video

Updated: Jun 28, 2025

High Resolution Physical Characterization of Single Metallic Nanoparticles
09:56

High Resolution Physical Characterization of Single Metallic Nanoparticles

Published on: June 28, 2019

5.8K

Sampling Real-Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine

Matteo Cioni1, Massimo Delle Piane1, Daniela Polino2

  • 1Department of Applied Science and Technology, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|April 24, 2024
PubMed
Summary

This study reveals metal nanoparticle (NP) dynamics using combined imaging and simulations. Machine learning analyzes molecular dynamics to show real-time atomic motion in NPs under realistic conditions.

Keywords:
ADF‐STEMatomic dynamicsmetal nanoparticlesmolecular dynamics simulationsunsupervised Machine Learning

More Related Videos

Laser-induced Breakdown Spectroscopy: A New Approach for Nanoparticle's Mapping and Quantification in Organ Tissue
10:17

Laser-induced Breakdown Spectroscopy: A New Approach for Nanoparticle's Mapping and Quantification in Organ Tissue

Published on: June 18, 2014

13.8K
All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics
11:33

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics

Published on: January 19, 2018

9.6K

Related Experiment Videos

Last Updated: Jun 28, 2025

High Resolution Physical Characterization of Single Metallic Nanoparticles
09:56

High Resolution Physical Characterization of Single Metallic Nanoparticles

Published on: June 28, 2019

5.8K
Laser-induced Breakdown Spectroscopy: A New Approach for Nanoparticle's Mapping and Quantification in Organ Tissue
10:17

Laser-induced Breakdown Spectroscopy: A New Approach for Nanoparticle's Mapping and Quantification in Organ Tissue

Published on: June 18, 2014

13.8K
All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics
11:33

All-electronic Nanosecond-resolved Scanning Tunneling Microscopy: Facilitating the Investigation of Single Dopant Charge Dynamics

Published on: January 19, 2018

9.6K

Area of Science:

  • Materials Science
  • Nanotechnology
  • Computational Chemistry

Background:

  • Metal nanoparticles (NPs) exhibit crucial atomic dynamics at low temperatures, yet these are difficult to study.
  • Experimental methods provide snapshots but struggle to reconstruct full dynamics due to data limitations.
  • Molecular simulations offer temporal data but often rely on idealized structures and face sampling challenges.

Purpose of the Study:

  • To overcome limitations of individual experimental and computational methods for studying NP dynamics.
  • To develop a robust approach for resolving atomistic dynamics in metal NPs under realistic conditions.
  • To combine high-resolution imaging with molecular simulations for accurate NP characterization.

Main Methods:

  • Utilized annular dark-field scanning transmission electron microscopy (ADF-STEM) to capture high-resolution images of an gold (Au) NP.
  • Reconstructed atomistic 3D models from experimental images to serve as starting points for simulations.
  • Performed multiple independent molecular dynamics (MD) simulations based on the reconstructed models.
  • Applied machine learning (ML) techniques to analyze MD trajectories and resolve atomic motion.

Main Results:

  • Successfully reconstructed realistic 3D atomistic models of an Au NP from experimental data.
  • Generated simulation trajectories capturing the real-time atomic dynamics within the NP.
  • Machine learning analysis effectively resolved the complex structural dynamics of the NP.
  • Demonstrated the capability of the combined approach to characterize NP behavior under realistic conditions.

Conclusions:

  • A novel, integrated experimental-computational strategy enables the detailed characterization of metal NP structural dynamics.
  • This approach overcomes the limitations of isolated techniques, providing insights into NP behavior.
  • The method offers a robust pathway for studying atomic dynamics in metal nanoparticles relevant to various applications.