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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.
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Related Experiment Video

Updated: May 30, 2025

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

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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.