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Meeting MS&T22: Materials Science & Technology
Symposium Progressive Solutions to Improve Corrosion Resistance of Nuclear Waste Storage Materials
Presentation Title Diminished Diffusion in the Aged Hydrated Gels of Irradiated Borosilicate Glasses
Author(s) Amreen Jan, N M Anoop Krishnan
On-Site Speaker (Planned) Amreen Jan
Abstract Scope Under aqueous conditions, borosilicate glass, which is used as a matrix to store nuclear waste, forms a hydrated gel layer which plays a significant role in the durability of the glass matrix. However, the properties of this gel layer remain poorly understood. Here, we study the effect of irradiation on the structure and transport properties of the hydrated gels in a series of borosilicate glasses. By analyzing the gel structure of pristine and irradiated glasses, we observe that, indeed, there is a difference in the structure (connectivity and short- and medium-range order) of aged gels formed from pristine and irradiated glass. Particularly, aging has a pronounced effect on the irradiated gels as compared to gels obtained from pristine glasses. Furthermore, we observe that irradiated gels exhibit larger ring sizes in comparison to pristine gels. In contrast to the conventional hypothesis, we observe that diffusion of H decreases in gels from irradiated glasses in comparison to their pristine counterparts. We suggest that the topology of the ring plays a major role, in addition to their size, in governing the dynamics.
Keywords Ceramics,

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Designing Glasses for Nuclear Waste Immobilization with AI and ML
Diminished Diffusion in the Aged Hydrated Gels of Irradiated Borosilicate Glasses
Environmental Cracking Lifetime Prediction through the Development of Pitting and SCC Models for Nuclear Waste Storage Casks
From Preferential Bonding to Phase Separation in Boro-silicate Glasses
Microstructural Development and Chemical Durability of a Borosilicate Glass-ceramic Waste-form
Predicting the Long-term Durability of Nuclear Waste Immobilization Glasses using Machine Learning
SCC of Nuclear Waste Canisters: Mechanisms and Mitigation

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