It was exciting to be involved in this TEESMAT scientific case study. We contributed to the design and acquisition of ex situ X-ray scattering/diffraction tomography measurements of industrially-relevant prolonged cycled Li-ion batteries at beamline ID31 of the ESRF. The data handling of the raw data (ca. 2.25 TB) and the analysis of the ca. 6,500,000 reconstructed SAXS/XRD patterns was significantly accelerated by our inhouse developed deep learning methods, such as the PQ-Net (you see here the open access paper: https://www.nature.com/articles/s41524-021-00542-4). We had the opportunity to collaborate with CRF, CEA, VITO and CERTH on this project, looking at degradation mechanisms in Li-ion batteries.
Read more about the project at https://www.teesmat.eu/wp-content/uploads/2022/04/TEESMAT-Success-Stories-CRF-1.pdf