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Overview of Microscopy Techniques01:22

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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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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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Immunoelectron microscopy utilizes immunogold labeling of endogenous proteins with specific antibodies to detect and localize these proteins in cells and tissues. The procedure provides insights into the distribution and quantification of protein under different stimulation conditions offering clues about their functions. Conjugating highly electron-dense gold particles with primary or secondary antibodies allow antigen detection on and within cells, with high resolution and specificity.
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Conventional electron microscopy (EM) involves dehydration, fixation, and staining of biological samples, which distorts the native state of biological molecules and results in several artifacts. Also, the high-energy electron beam damages the sample and makes it difficult to obtain high-resolution images. These issues can be addressed using cryo-EM, which uses frozen samples and gentler electron beams. The technique was developed by Jacques Dubochet, Joachim Frank, and Richard Henderson, for...
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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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Author Spotlight: A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management
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Application of machine learning techniques to electron microscopic/spectroscopic image data analysis.

Shunsuke Muto1, Motoki Shiga2,3,4

  • 1Electron Nanoscopy Division, Advanced Measurement Technology Center, Institute of Materials and Systems for Sustainability, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan.

Microscopy (Oxford, England)
|November 5, 2019
PubMed
Summary
This summary is machine-generated.

Non-negative matrix factorization (NMF) is a machine learning technique that separates entangled spectral and image data from materials science. This method enhances analysis in scanning transmission electron microscopy (STEM) and spectroscopy.

Keywords:
electron energy-loss spectroscopyhyperspectral image analysisnon-negative matrix factorizationscanning transmission electron microscopytensor decomposition

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Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Data Science

Background:

  • Scanning transmission electron microscopy (STEM) combined with analytical instruments is crucial for materials science.
  • Microscopic and spectral data often contain entangled physical/chemical components, requiring advanced analysis.
  • Existing methods for data unmixing are often complex and field-specific.

Purpose of the Study:

  • To introduce non-negative matrix factorization (NMF) as a powerful statistical technique for materials analysis.
  • To demonstrate NMF's application in resolving complex data from STEM, electron energy-loss spectroscopy (EELS), and energy-dispersive X-ray spectroscopy (EDS).
  • To review NMF's basic concepts, advantages, limitations, and extensions for flexible applications.

Main Methods:

  • Review of non-negative matrix factorization (NMF) as a multivariate data analysis technique.
  • Discussion of NMF's application in hyperspectral image analysis and blind source separation.
  • Exploration of strategies to overcome NMF limitations and tensor decomposition extensions.

Main Results:

  • NMF effectively separates spatially and spectrally entangled information in materials data.
  • The technique provides a robust framework for analyzing complex datasets from STEM-EELS and STEM-EDS.
  • Various strategies and extensions enhance NMF's applicability and flexibility.

Conclusions:

  • Non-negative matrix factorization is an indispensable tool for advanced materials characterization.
  • NMF offers a versatile machine learning approach to extract meaningful information from complex analytical data.
  • Further developments in tensor decomposition promise even broader applications in materials science.