Identifying chemically similar multiphase nanoprecipitates in compositionally complex non-equilibrium oxides via machine learning

Autor:	Keyou S. Mao, Tyler J. Gerczak, Jason M. Harp, Casey S. McKinney, Timothy G. Lach, Omer Karakoc, Andrew T. Nelson, Kurt A. Terrani, Chad M. Parish, Philip D. Edmondson
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Materials of engineering and construction. Mechanics of materials TA401-492
Zdroj:	Communications Materials, Vol 3, Iss 1, Pp 1-13 (2022)
Druh dokumentu:	article
ISSN:	2662-4443
DOI:	10.1038/s43246-022-00244-4
Popis:	Characterizing fission products in uranium dioxide nuclear fuel is important for predicting its long-term properties. Here, machine learning is used to mine microscopy images of precipitates and nanoscale gas bubbles in high-burn-up fuels, providing detailed structural insight of nanoscale fission products.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/4d2fcfbad4f04161b2fb36c690f3ae95 Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ