| Scope |
To increase the Long-term Corrosion Resistance of Nuclear Waste Forms' Materials in Permanent Storage to Restrict Radionuclides' Escapes in the Neighboring Subterrain Waters
The symposium will enclose two topics:
1)Improvement of Nuclear Waste Immobilization Glasses' (Borosilicate and Iron Phosphate)Long-term Durability at their final disposal, through fully understanding their Aqueous Corrosion Behavior, Details of Dissolution Kinetics, relevant Mechanical Properties (Toughness, Strength, Hardness) including protective 2D Materials Coatings for Scratch-free Glass surfaces, through increasing and predicting their Aqueous Corrosion Resistance. We seek studies of the Parameters that control Glass's Mechanical Properties of interest, as they arise from Composition, Processing and Structure.
We consider 2 different Nuclear Waste Forms Systems at the Geological Repository:
a)A vitreous waste form as a Container made of Iron Phosphate Glass immobilizing the nuclear waste
b)Borosilicate or Iron Phosphate Glass immobilizing the Nuclear Waste hosted in Metallic Canisters
2)Improvement of Metallic Canisters' Nuclear Waste Forms:
a)For temporary storage, Stainless Steel Canisters for Spent Nuclear Fuel, through understanding their mechanical mechanism of Chloride Induced Stress Corrosion Cracking (CISCC), measurement of the Residual Stress in welds of Stainless Steel (SS) Canisters' produced by Wire Arc Direct Energy Deposition and by classical technology, increasing and predicting the SS Canisters' resistance to CISCC
b)For permanent storage, Corrosion Resistant Alloys (CRA): SS, Nickel-based alloys, such as Ni-Cr alloys, through atomic understanding the underlying physics that controls the aqueous corrosion of metals, means to mitigate it, models to reliably predict the transition from passivation to corrosion
Correlation Composition-Processing-Structure-Properties are sought for.
Modeling Corrosion and Mechanical Properties by Atomistic Simulations, Quantitative Structure-property Relationship (QSPR) analysis, Machine Learning (ML), Physics based, and Artificial Intelligence (AI), and Predicting the Nuclear Waste Materials' Properties, Designing Iron Phosphate Vitreous Waste Forms as Containers, and Borosilicate or Iron Phosphate Nuclear Waste Forms to be contained in Metal Canisters for final storage, and improving SS Canisters for temporary storage
Experimental work to further investigate details of the Materials' Corrosion process, as well as details of the structure of Glasses that immobilize Radionuclides and Evaluate and Investigate means to Improve relevant Mechanical Properties of the Vitreous Waste Forms
Developments in the Characterization Techniques of the Nuclear Waste Forms' Microstructure and Atomic Structure and their Changes during the Corrosion process, such as Neutron Diffraction (also for Measurement of the Residual Stress in the Waste Forms), High-energy X-ray Diffraction, Extended X-ray Absorption Fine Structure (EXAFS), X-ray Diffraction (XRD), Nuclear Magnetic Resonance (NMR), Spectroscopy (Raman, Infrared, Terahertz, Mosbauer, Energy Dispersive, X-ray Photoelectron (XPS)), Electron Microscopy (including 4D STEM), 3D Automated Mineralogical Classification-Characterization and Quantification of Nuclear Concrete (using X-ray Microscopy, Deep Learning, and AI)-, ML for Image/Microstructure Analysis of Oxide Glasses, Atom probe Tomography vs. NanoSims for unraveling glasses' aqueous corrosion mechanism |