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Related Concept Videos

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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Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
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Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning.

Alexey G Okunev1,2, Mikhail Yu Mashukov3, Anna V Nartova2,3

  • 1Novosibirsk State University Higher College of Informatics, Russkaja Str. 35, 630058 Novosibirsk, Russia.

Nanomaterials (Basel, Switzerland)
|July 8, 2020
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Summary
This summary is machine-generated.

Deep learning accurately identifies and measures metal nanoparticles from scanning tunneling microscopy images. A new web service, ParticlesNN, offers this automated particle recognition for researchers worldwide.

Keywords:
deep neural networksparticle recognitionparticlesscanning tunneling microscopy

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Computational Science

Background:

  • Manual particle analysis using software rulers is time-consuming and subjective.
  • Conventional automated image processing methods lack universality and require empirical parameter tuning.
  • Scanning Tunneling Microscopy (STM) generates high-resolution images crucial for nanoparticle characterization.

Purpose of the Study:

  • To apply deep learning for automated recognition and measurement of metal nanoparticles on highly oriented pyrolytic graphite.
  • To evaluate the performance of the Cascade Mask-RCNN model for this task.
  • To develop a user-friendly, open-access tool for researchers.

Main Methods:

  • Utilized the Cascade Mask-RCNN deep learning model for nanoparticle detection and segmentation.
  • Trained the model on a dataset of 5157 nanoparticles from 23 STM images.
  • Employed a 2D Gaussian function for refining predicted particle contours.

Main Results:

  • Achieved a precision of 0.93 and recall of 0.78 in nanoparticle recognition on a verification set.
  • Calculated mean particle size accuracies ranged from 0.87 to 0.99 compared to ground truth.
  • Demonstrated superior performance of deep learning over conventional image processing techniques.

Conclusions:

  • Deep learning, specifically the Cascade Mask-RCNN model, offers a robust and accurate solution for automated nanoparticle analysis from STM images.
  • The developed "ParticlesNN" web service provides a free, accessible platform for researchers to leverage this advanced technology.
  • This approach significantly improves the efficiency and reliability of nanoparticle characterization in scientific studies.