Super-resolving microscopy images of Li-ion electrodes for fine-feature quantification using generative adversarial networks

• Main Authors: Finegan, D.P (Author), Furat, O. (Author), Kirstein, T. (Author), Schmidt, V. (Author), Smith, K. (Author), Yang, Z. (Author)
• Format: Article
• Language: English
• Published: Nature Research 2022
• Subjects: Active material; Cathodes; Crack detection; Economic and social effects; Energy materials; Fine Feature; Functional behaviors; Generative adversarial networks; Ions electrodes; Large volumes; Microscopy images; Reduced resolution; Representativity; Scanning electron microscopy; Trade off
• Online Access: View Fulltext in Publisher

 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. © 2022, The Author(s).