The development of hierarchically structured layered oxide materials, crucial for advancing battery cell design, faces challenges not only due to the complexity of the materials but also because of the sheer volume of SEM (Scanning Electron Microscopy) images that need to be quantitatively evaluated. The complex morphology of these materials often requires manual analysis, which can be both time-consuming and error-prone, significantly slowing down research progress.

This project aims to overcome these challenges by leveraging the Karlsruhe Image Data Annotation Tool (KaIDA) to automate the segmentation and analysis of large volumes of SEM images. KaIDA assists researchers by integrating machine learning (ML) techniques, enabling the efficient identification of form factors, particle size distribution, and microstructural characteristics.