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Atomic Force Microscopy01:08

Atomic Force Microscopy

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.
The AFM Probe
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Polymer Microarrays for High Throughput Discovery of Biomaterials
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A macro-nano-atomic-scale high-throughput approach for material research.

Yiwei Ju1,2, Shuai Li1,3, Xiaofei Yuan4,5,6

  • 1National Center for Electron Microscopy in Beijing, School of Materials Science and Engineering, The State Key Laboratory of New Ceramics and Fine Processing, Key Laboratory of Advanced Materials (MOE), Tsinghua University, Beijing 100084, People's Republic of China.

Science Advances
|December 1, 2021
PubMed
Summary

High-throughput scanning electron microscopy combined with machine learning enables detailed multiscale analysis of material microstructure. This approach provides unprecedented insights into carbide evolution in superalloys, linking structure to performance.

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Area of Science:

  • Materials Science
  • Metallurgy
  • Microscopy

Background:

  • Material properties are intrinsically linked to their microstructure across various scales.
  • Characterizing microstructure over large areas is crucial for understanding material performance.
  • Nickel-base superalloys are critical in high-temperature applications, necessitating detailed structural analysis.

Purpose of the Study:

  • To demonstrate a high-throughput scanning electron microscopy technique for large-area structural characterization.
  • To integrate machine learning with microscopy for enhanced material research efficiency.
  • To conduct a multiscale investigation of carbide evolution in a superalloy during creep.

Main Methods:

  • Utilized a single-beam high-throughput scanning electron microscope (SEM) for secondary and backscattered electron imaging.
  • Applied machine learning algorithms to analyze large-scale panoramic SEM data.
  • Integrated SEM data with conventional electron microscopy for comprehensive analysis.

Main Results:

  • Generated terabyte-sized panoramic atlas data of the superalloy microstructure.
  • Enabled simultaneous multiscale analysis of carbide characteristics (type, location, composition, size, shape, matrix relationship).
  • Provided quantitative statistical data on carbide evolution during creep.

Conclusions:

  • The developed high-throughput SEM and machine learning approach significantly enhances material research efficiency.
  • This method offers precise insights into the role of carbides in superalloy performance during creep.
  • It facilitates a novel, comprehensive understanding of microstructure-property relationships.