| Scope |
Increased corrosion resistance of the nuclear waste materials is critical to prevent their premature degradation and to restrict the escape of radioactive products into environment. Solutions offered to enhance their corrosion resistance and confine active fission products within nuclear storage could alleviate the damaging effects of corrosion on these materials, thereby preventing environment contamination.This symposium will enclose two major research topics: 1) Development of environmental safe nuclear storage materials during both through their manufacturing and their long-term usage at the geological repository. 2) Improvement of the corrosion resistance of nuclear waste storage materials currently in use, including glass, ceramics, and stainless steel, as well as searching for alternative, novel materials system of nuclear waste storage that demonstrates superior corrosion resistance.This symposium will give researchers worldwide an opportunity to discuss developments in the specific characterization techniques, including Neutron Diffraction, High-Energy X-ray Diffraction, Extended X-ray absorption fine structure (EXAFS) and Raman Spectroscopy, as well as new techniques for ceramic coating of steel canisters to circumvent the deleterious effect of chloride -containing brines on steel corrosion, as well as the detrimental effect of steel corrosion on the nuclear waste glass in the presence of underground water. Further, the symposium will attract outstanding scientists to present experimental models and atomistic simulation and predictive modeling - Quantitative Structural Property Relationship (QSPR)and Molecular Dynamics Simulations (MD) to model corrosion/dissolution of glasses.Insights into nuclear glass corrosion kinetics by underground water as well as thermodynamic views into the process are sought and encouraged. Of particular interest are experimental and modeling approaches to study groundwater corrosion and toughness of glass canisters and corrosion of steel canisters for nuclear waste storage and machine learning (ML) predictions on best glass and/or glass-ceramics candidates for glass/glass-ceramics canisters and best candidates for steel canisters.Experimental and modeling approaches to study interactions between steel canisters and nuclear glass in the presence of underground water are welcome. |