Assessing static glass leaching predictions from large datasets using machine learning-Reference-Cited by-同舟云学术

Assessing static glass leaching predictions from large datasets using machine learning

Published:2020-10 Issue: Volume:546 Page:120276
ISSN:0022-3093
Container-title:Journal of Non-Crystalline Solids
language:en
Short-container-title:Journal of Non-Crystalline Solids

Author:

Lillington Joseph N.P.,Goût Thomas L.^ORCID,Harrison Mike T.,Farnan Ian^ORCID

Publisher

Elsevier BV

Subject

Materials Chemistry,Condensed Matter Physics,Ceramics and Composites,Electronic, Optical and Magnetic Materials

Reference16 articles.

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3. ASTM, Standard Test Methods for Determining Chemical Durability of Nuclear, Hazardous, and Mixed Waste Glasses and Multiphase Glass Ceramics : the Product Consistency Test (PCT) Designation: C1285-14, 2002. doi:10.1520/C1285-14.2.

4. Evaluating the temperature dependence of Magnox waste glass dissolution;Goût;J. Non. Cryst. Solids.,2019

5. Impacts of lithium on Magnox waste glass dissolution;Goût;J. Non. Cryst. Solids.,2019

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