Super-Resolving Microscopy Images of Li-Ion Electrodes for Fine-Feature Quantification Using Generative Adversarial Networks: Article No. 68

Orkun Furat, Donal Finegan, Zhenzhen Yang, Tom Kirstein, Kandler Smith, Volker Schmidt

Research output: Contribution to journalArticlepeer-review

11 Scopus Citations

Abstract

For a deeper understanding of the functional behavior of energy materials, it is necessary to investigate their microstructure, e.g., via imaging techniques like scanning electron microscopy (SEM). However, active materials are often heterogeneous, necessitating quantification of features over large volumes to achieve representativity which often requires reduced resolution for large fields of view. Cracks within Li-ion electrode particles are an example of fine features, representative quantification of which requires large volumes of tens of particles. To overcome the trade-off between the imaged volume of the material and the resolution achieved, we deploy generative adversarial networks (GAN), namely SRGANs, to super-resolve SEM images of cracked cathode materials. A quantitative analysis indicates that SRGANs outperform various other networks for crack detection within aged cathode particles. This makes GANs viable for performing super-resolution on microscopy images for mitigating the trade-off between resolution and field of view, thus enabling representative quantification of fine features.

Original languageAmerican English
Article number68
Number of pages11
Journalnpj Computational Materials
Volume8
Issue number1
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s).

NREL Publication Number

  • NREL/JA-5700-81148

Keywords

  • diagnostics
  • generative adversarial networks
  • Li-ion batteries
  • machine learning

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