Engineering Papers⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1873306

Machine Learning 4 Optimization: Applications for DoE-EM-TDO IAA

Lee, James Edward·Hijazi, Hassan Lionel·Gopinath, Smitha·Collins, Adam Donald·Crawford, Brandon Michael·Schultz-Fellenz, Emily S.·Bent, Russell Whitford
View original source ↗Read source document ↗Export metadata ↓

Abstract

The source did not provide an abstract. Follow the original record for more information.

Explore related subjects

Earth Sciences

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Lee, James Edward, Hijazi, Hassan Lionel, Gopinath, Smitha, Collins, Adam Donald, Crawford, Brandon Michael, Schultz-Fellenz, Emily S., Bent, Russell Whitford. 2022-06-16. Machine Learning 4 Optimization: Applications for DoE-EM-TDO IAA. https://www.osti.gov/biblio/1873306

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2025

Neutron capture signatures determined from gadolinium isotope compositions of uranium ore concentrates using MC-ICPMS

We present Gd isotope compositions on 25 well-characterized uranium ore concentrates (UOCs). About half the UOCs have isotope depletions in 157 Gd coupled with anticorrelated excesses in 158 Gd due to neutron capture effects. UOCs from older ore bodies and with higher U contents show larger neutron capture effects than UOCs from younger ore bodies with lower U contents. These Gd data are correlated with their previously measured Sm isotope compositions, and we use the Sm data to estimate the neutron fluence of these samples. In conclusion, this work demonstrates how Gd isotope signatures can be employed as a new tool for nuclear forensics.

Earth Sciences↗