首页|Findings in the Area of Machine Learning Reported from University of Cambridge (Aqueous Dissolution of Li-na Borosilicates: Insights From Machine Learning and Experiments)
Findings in the Area of Machine Learning Reported from University of Cambridge (Aqueous Dissolution of Li-na Borosilicates: Insights From Machine Learning and Experiments)
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NETL
NSTL
Elsevier
Fresh data on Machine Learning are presented in a new report. According to news reporting from Cambridge, United Kingdom, by NewsRx journalists, research stated, “Previously acquired data could be utilised in predicting glass dissolution kinetics at long times, but the application of machine learning methods needs to be assessed. Here, the dissolution processes of two Li-Na borosilicate ‘base glasses’ at 40 and 90 degrees C were investigated by SEM-EDS, NMR and Raman spectroscopy.” Funders for this research include Engineering & Physical Sciences Research Council (EPSRC), Nuclear Decommissioning Authority (NDA), Nuclear Waste Services (NWS). The news correspondents obtained a quote from the research from the University of Cambridge, “Boron and sodium machine learning predictions were excellent when considering other normalised releases as features. However, extrapolating the training feature space yielded poorer performance and the absence of incorporated waste elements resulted in underestimated predicted long-term lithium and silicon releases. Faster dissolution kinetics were observed for MW than MW-1/2Li but the MW-1/2Li gel layer at 40 degrees C trapped more water. Whilst BO3 rings leached preferentially at 90 degrees C, surface enrichment of BO3 at 40 degrees C suggested [BO4]- transformed prior to dissolution.”
CambridgeUnited KingdomEuropeCyborgsEmerging TechnologiesMachine LearningUniversity of Cambridge
NETL
NSTL
Elsevier
