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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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Enabling autonomous scanning probe microscopy imaging of single molecules with deep learning.

Javier Sotres1, Hannah Boyd1, Juan F Gonzalez-Martinez1

  • 1Department of Biomedical Science, Faculty of Health and Society, Malmö University, 20506 Malmö, Sweden and Biofilms-Research Center for Biointerfaces, Malmö University, 20506 Malmö, Sweden. javier.sotres@mau.se.

Nanoscale
|April 22, 2021
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Summary

This study introduces an automated Atomic Force Microscope (AFM) using deep learning, reducing the need for expert users and supervision. The AI system autonomously captures high-resolution nanoscale images of molecules, advancing scanning probe microscopy towards full automation.

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Area of Science:

  • Nanotechnology
  • Microscopy
  • Artificial Intelligence

Background:

  • Scanning probe microscopies offer high-resolution nanoscale surface analysis but face limitations in user expertise, data analysis, and experiment duration.
  • Continuous user supervision is a significant bottleneck hindering wider adoption of techniques like Atomic Force Microscopy (AFM).

Purpose of the Study:

  • To develop an autonomous algorithm for Atomic Force Microscope (AFM) operation, minimizing the need for user intervention.
  • To enable the automated acquisition of multiple high-resolution images of specific molecules.

Main Methods:

  • Developed a novel algorithm integrating YOLOv3 object detection and Siamese networks for real-time scanning probe microscopy (SPM) automation.
  • Utilized DNA on mica as a model system to test the algorithm's capability in molecule identification and tracking.
  • Implemented deep learning for automated molecule localization, high-resolution imaging, and preventing redundant imaging cycles.

Main Results:

  • The algorithm successfully controlled the AFM without user intervention, acquiring multiple high-resolution images of different molecules.
  • YOLOv3 accurately located molecules, while the Siamese network enabled tracking and identification of individual molecules across images.
  • The system efficiently managed imaging sequences, increasing resolution and avoiding repetitive imaging of the same molecule.

Conclusions:

  • The developed deep learning-based algorithm significantly advances towards autonomous operation of scanning probe microscopes (SPM).
  • This automation reduces reliance on expert users and streamlines the nanoscale imaging process, making SPM more accessible.