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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Jin, Tongan
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article
Glass transition temperature of low‐activity waste nuclear glasses
Abstract
<jats:title>Abstract</jats:title><jats:p>The glass transition temperature (<jats:italic>T</jats:italic><jats:sub>g</jats:sub>) is a parameter used in many glass melt viscosity models as it denotes a temperature around which liquid‐glass transition occurs. In this work, <jats:italic>T</jats:italic><jats:sub>g</jats:sub> values were measured for a series of low‐activity waste (LAW) glasses using differential scanning calorimetry. These data were combined with <jats:italic>T</jats:italic><jats:sub>g</jats:sub> data of other waste glasses available from literature. The combined dataset, consisting of 137 data points, was used for the development of several models to estimate <jats:italic>T</jats:italic><jats:sub>g</jats:sub> from glass composition. When testing the number of influential components and different supervised learning methods, we demonstrated that using more than 10 components or using non‐linear methods brought marginal improvement to the model accuracy. The best model predicts <jats:italic>T</jats:italic><jats:sub>g</jats:sub> of both LAW and high‐level waste glasses with reasonable accuracy.</jats:p>