| Abstract Scope |
Concretes degrade in nuclear environments, making processes such as hydration, cracking, corrosion, and mineralogical/phase transformations under differing conditions important to characterize.
In characterizing these dynamic physico-chemical processes, it is essential to understand these micro-to-nano scale transformations in multiple dimensions, with complementary analytical modes.
In this work, we use novel non-destructive 3D imaging techniques via X-ray Microscopy, combined with novel 3D automated quantitative mineralogy techniques, to identify and spatially characterize mineralogical phases in various fresh and aged nuclear containment concretes. Additionally, we apply deep learning-based reconstruction to improve quantification of pores and cracks. This combined workflow applies advanced denoising and improves artefact detection in 10 mm+ diameter cores, enhancing quantification and throughput.
3D automated mineralogy provides novel concrete phase identification methods, non-destructively with minimal sample preparation. 3D measurements allow recognition of minor phases, and non-destructive techniques allow the use of further imaging and analytical modes including time-resolved workflows. |