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Image Segmentation for FIB-SEM Serial Sectioning of a Si/C-Graphite Composite Anode Microstructure Based on

Dongjae Kim1, Sihyung Lee2, Wooram Hong2

  • 1School of Chemical and Biological Engineering, Seoul National University, Seoul 08826, Republic of Korea.

Microscopy and Microanalysis : the Official Journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
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Summary
This summary is machine-generated.

This study presents an algorithm to analyze lithium-ion battery anode microstructures from FIB-SEM images. It effectively preprocesses images and reconstructs 3D structures for better battery design.

Keywords:
FIB-SEMLi-ion batteryanode microstructureglobal thresholdingpreprocessing

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Image Analysis

Background:

  • Electrode microstructure significantly impacts lithium-ion battery performance, energy density, and power density.
  • Controlling electrode microstructure during manufacturing is crucial for high-performance batteries.
  • Understanding microstructure aids in designing cost-effective, high-performance batteries.

Purpose of the Study:

  • To develop a systematic algorithm workflow for analyzing anode microstructures from focused ion beam scanning electron microscope (FIB-SEM) images.
  • To address common issues in raw FIB-SEM images and propose effective preprocessing and segmentation methods.
  • To reconstruct three-dimensional (3D) microstructures for improved battery design.

Main Methods:

  • Preprocessing of raw FIB-SEM images to resolve typical issues.
  • Application of a Fourier transform-based filter to reduce curtain artifacts.
  • Implementation of a global-thresholding method for identifying active materials and pores.
  • 3D reconstruction by concatenating segmented 2D images.

Main Results:

  • A systematic algorithm workflow for FIB-SEM image analysis of battery microstructures was established.
  • A Fourier transform filter effectively mitigated curtain artifacts in the images.
  • A stable global-thresholding method achieved high segmentation performance for active materials and pores.
  • 3D microstructures were successfully reconstructed, although with some manual user interaction required.

Conclusions:

  • The proposed algorithm workflow provides a robust method for analyzing battery electrode microstructures.
  • The developed image processing techniques, particularly the global-thresholding method, offer stable and effective segmentation.
  • While not fully automated, the workflow aids in understanding and designing high-performance lithium-ion batteries.