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Related Concept Videos

Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

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...
Scanning Electron Microscopy01:07

Scanning Electron Microscopy

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.
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Atomic Emission Spectroscopy: Overview01:20

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Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...
Overview of Electron Microscopy01:25

Overview of Electron Microscopy

The wavelengths of visible light ultimately limit the maximum theoretical resolution of images created by light microscopes. Most light microscopes can only magnify 1000X, and a few can magnify up to 1500X. Electrons, like electromagnetic radiation, can behave like waves, but with wavelengths of 0.005 nm, they produce significantly greater resolution up to 0.05 nm as compared to 500 nm for visible light. An electron microscope (EM) can create a sharp image that is magnified up to 2,000,000X.

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AI-Assisted Electron Microscopy in Structure-Performance Analysis of Advanced Catalysts: From Atomic Resolution to

Yao Lv1, Xuan Tang1, Sheng Dai1

  • 1Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, School of Chemistry and Molecular Engineering, East China University of Science & Technology, Shanghai 200237, China.

Nano Letters
|July 6, 2026
PubMed
Summary

Artificial intelligence (AI) enhances electron microscopy for analyzing catalysts. AI enables large-scale, statistically grounded analysis of electron microscopy data, connecting material structure to performance.

Keywords:
Artificial intelligenceCatalystsElectron microscopyQuantitative analysisStructure−performance relationships

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Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Computational Science

Background:

  • Electron microscopy (EM) offers direct structural insights into catalysts but faces limitations like artifacts and data analysis challenges.
  • Extracting statistically significant information from complex EM datasets of catalytic systems is difficult.
  • Practical application of EM in catalysis is hindered by imaging constraints and data volume.

Purpose of the Study:

  • To review how artificial intelligence (AI) transforms electron microscopy for catalyst analysis.
  • To highlight AI's role in enabling quantitative, high-throughput EM studies of catalytic materials.
  • To discuss AI-driven structure-performance relationship establishment in catalysis.

Main Methods:

  • Review of recent advances in AI, particularly deep learning, applied to electron microscopy data analysis.
  • Focus on AI algorithms for processing large, complex EM datasets from catalytic systems.
  • Integration of AI with structural descriptors across atomic, nanoscale, and dynamic regimes.

Main Results:

  • AI transforms electron microscopy into a quantitative, high-throughput analytical platform for catalysts.
  • AI facilitates large-scale, statistically grounded analysis of EM data, overcoming previous limitations.
  • AI enables rigorous connection between material structure descriptors and catalytic performance.

Conclusions:

  • AI is revolutionizing electron microscopy for catalyst characterization and performance prediction.
  • AI-powered EM analysis allows for deeper understanding of structure-property relationships in catalysis.
  • Future directions include addressing data scarcity, model transferability, and integrating AI with other analytical techniques.