| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
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Advanced Characterization of Materials for Nuclear, Radiation, and Extreme Environments V
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| Presentation Title |
Quantitative Phase Characterization of Nuclear Cements and Concretes Using Non-Destructive 3D Automated Mineralogy and Enhanced Deep-Learning Reconstruction via X-ray Microscopy |
| Author(s) |
Andy Holwell, Ria Mitchell, Stephen Kelly, John Provis, Giacomo Torelli, Kajanan Selvaranjan |
| On-Site Speaker (Planned) |
Andy Holwell |
| Abstract Scope |
Concretes degrade in nuclear environments, making processes such as hydration, cracking, corrosion and phase transformations under differing conditions, important to characterize.
In characterizing these dynamic physico-chemical processes, it is essential to understand these micro-to-nano 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 breaks new ground for concrete phase identification, 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. |