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

Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.3K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
2.3K
Scanning Electron Microscopy01:07

Scanning Electron Microscopy

4.1K
A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
Accelerated...
4.1K

You might also read

Related Articles

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

Sort by
Same author

Author Correction: Operando monitoring of the functional role of tetrahedral cobalt centers for the oxygen evolution reaction.

Nature communications·2026
Same author

Ligand-Engineered Methylammonium Lead Bromide Nanoplatelets: Single-Photon Emission and Strong Light-Matter Coupling.

ACS nano·2026
Same author

Single atoms of indium on hafnia enable superior CO<sub>2</sub>-based methanol synthesis.

Nature nanotechnology·2026
Same author

Dynamics and Control of Dual Active Sites in Co-Substituted Ni Coordination Polymers for Enhanced Oxygen Evolution Catalysis.

Journal of the American Chemical Society·2026
Same author

In Situ Liquid-Phase Transmission Electron Microscopy Investigation of Anode-Electrolyte Interface Processes in Zn-Ion Batteries.

ACS nano·2025
Same author

Accelerating Surface Reconstruction in Cobalt Carbodiimides through Structural Defects for Enhanced Oxygen Evolution.

ACS nano·2025

Related Experiment Video

Updated: May 30, 2025

Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes
09:30

Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes

Published on: May 24, 2019

7.3K

Improving Nanoparticle Size Estimation from Scanning Transmission Electron Micrographs with a Multislice Surrogate

Henrik Eliasson1, Rolf Erni1,2

  • 1Electron Microscopy Center, Empa - Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland.

Nano Letters
|January 29, 2025
PubMed
Summary

We developed a faster method to simulate scanning transmission electron microscopy (STEM) images, enabling the creation of large datasets for machine learning. This significantly improves nanoparticle size estimation accuracy.

Keywords:
CharacterizationHeterogeneous catalysisMachine learningNanoparticlesScanning transmission electron microscopySynthetic data

More Related Videos

Nanoparticle Tracking Analysis for the Quantification and Size Determination of Extracellular Vesicles
09:19

Nanoparticle Tracking Analysis for the Quantification and Size Determination of Extracellular Vesicles

Published on: March 28, 2021

8.3K
Studying the Effects of Temperature on the Nucleation and Growth of Nanoparticles by Liquid-Cell Transmission Electron Microscopy
07:02

Studying the Effects of Temperature on the Nucleation and Growth of Nanoparticles by Liquid-Cell Transmission Electron Microscopy

Published on: February 17, 2021

4.1K

Related Experiment Videos

Last Updated: May 30, 2025

Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes
09:30

Using Nanoplasmon-Enhanced Scattering and Low-Magnification Microscope Imaging to Quantify Tumor-Derived Exosomes

Published on: May 24, 2019

7.3K
Nanoparticle Tracking Analysis for the Quantification and Size Determination of Extracellular Vesicles
09:19

Nanoparticle Tracking Analysis for the Quantification and Size Determination of Extracellular Vesicles

Published on: March 28, 2021

8.3K
Studying the Effects of Temperature on the Nucleation and Growth of Nanoparticles by Liquid-Cell Transmission Electron Microscopy
07:02

Studying the Effects of Temperature on the Nucleation and Growth of Nanoparticles by Liquid-Cell Transmission Electron Microscopy

Published on: February 17, 2021

4.1K

Area of Science:

  • Materials Science
  • Computational Science
  • Machine Learning

Background:

  • Simulating scanning transmission electron microscopy (STEM) images is computationally expensive, hindering the development of machine learning models for feature extraction.
  • Accurate nanoparticle size estimation requires diverse training data, which is difficult to obtain experimentally due to variations in size, shape, crystallinity, orientation, and diffraction effects.

Purpose of the Study:

  • To develop a computationally efficient method for generating large datasets of STEM images.
  • To improve the accuracy of nanoparticle size estimation using machine learning.

Main Methods:

  • A 3D convolutional neural network was trained to predict STEM images from voxelized atomic models, achieving a 100x speed-up over traditional multislice simulations.
  • A large dataset of 100,000 synthetic multislice STEM images was generated.
  • Various size-estimator architectures were evaluated based on training set size.

Main Results:

  • The developed method significantly accelerates STEM image simulation while maintaining high image quality.
  • A ResNet18-based model trained on both real and synthetic STEM images demonstrated superior performance.
  • The median size-estimation error was reduced from 9.89% to 5.26% by incorporating synthetic data.

Conclusions:

  • Accelerated STEM image simulation is crucial for generating large, diverse datasets for machine learning applications.
  • The integration of synthetic data significantly enhances the accuracy of nanoparticle size estimation models.
  • This approach offers a scalable solution for advancing deep feature extraction from atomically resolved STEM images.