Predicting radioactive waste glass dissolution with machine learning-Reference-Cited by-同舟云学术

Predicting radioactive waste glass dissolution with machine learning

Published:2020-04 Issue: Volume:533 Page:119852
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

Funder

Imperial-Cambridge-Open University

Publisher

Elsevier BV

Subject

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

Reference40 articles.

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4. NDA, Radioactive wastes in the UK: a summary of the 2016 inventory, 2017. https://ukinventory.nda.gov.uk/wp-content/uploads/sites/18/2017/03/High-Level-Summary-UK-Radwaste-Inventory-2016.pdfAccessed: 18/4/19.

5. Effect of clayey groundwater on the dissolution rate of the simulated nuclear waste glass SON68;Jollivet;J. Nucl. Mater.,2012

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