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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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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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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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The early pioneers of microscopy opened a window into the invisible world of microorganisms. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes that leveraged nonvisible light, such as fluorescence microscopy that uses an ultraviolet light source and electron microscopy that uses short-wavelength electron beams. These advances significantly improved magnification, image resolution, and contrast. By comparison, the...
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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and

Marc Botifoll1, Ivan Pinto-Huguet1, Jordi Arbiol1,2

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Machine learning and artificial intelligence (AI) are revolutionizing electron microscopy by automating workflows and enabling new discoveries. This review guides researchers in applying AI tools to materials and nano-sciences.

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Area of Science:

  • Electron microscopy
  • Materials science
  • Nanoscience
  • Machine learning
  • Artificial intelligence

Background:

  • Electron microscopy workflows face bottlenecks and challenges.
  • Machine learning (ML) and artificial intelligence (AI) offer solutions for automation and exploration.
  • This review focuses on ML applications in electron microscopy and related fields.

Purpose of the Study:

  • To evaluate the state-of-the-art of ML in electron microscopy.
  • To cover traditional and advanced imaging techniques, spectroscopy, and tomography.
  • To provide a practical guide for applying ML tools to research.

Main Methods:

  • Review of current literature on ML applications in electron microscopy.
  • Analysis of ML integration across various microscopy techniques (imaging, spectroscopy, tomography).
  • Exploration of AI applications in related scientific and non-scientific disciplines.

Main Results:

  • ML and AI are transforming electron microscopy analytical workflows.
  • Practical guidance is provided for researchers to implement ML tools.
  • Insights from other fields suggest future directions for AI in microscopy.

Conclusions:

  • AI and ML are essential for the future of electron microscopy.
  • Interdisciplinary insights can accelerate AI adoption in microscopy.
  • The review aims to bridge the gap between ML and microscopy research.