In a finding that helps elucidate how molten salts in advanced nuclear reactors might behave, scientists have shown how electrons interacting with the ions of the molten salt can form three states with different properties. Understanding these states can help predict the impact of radiation on the performance of salt-fueled reactors.
The Department of Energy’s Oak Ridge National Laboratory and Tennessee State University have signed a memorandum of understanding to strengthen research cooperation and provide diverse undergraduate students enriching educational research opportunities at the lab.
ORNL and the University of Texas Rio Grande Valley, known as UTRGV, have signed a memorandum of understanding to strengthen research cooperation and establish a collaborative program for undergraduate research and education, further cementing hi
Susan Hubbard, diputada de Ciencia y Tecnología en ORNL, Can (John) Saygin, vicepresidente mayor de investigación y decano del Colegio de la Escuela de Postgrados en UTGRV, firman un Memorándum de Entendimiento comprometiéndose a fortalecer
Technologies developed by researchers at ORNL have received six 2023 R&D 100 Awards.
Scientist-inventors from ORNL will present seven new technologies during the Technology Innovation Showcase on Friday, July 14, from 8 a.m.–4 p.m. at the Joint Institute for Computational Sciences on ORNL’s campus.
Like most scientists, Chengping Chai is not content with the surface of things: He wants to probe beyond to learn what’s really going on. But in his case, he is literally building a map of the world beneath, using seismic and acoustic data that reveal when and where the earth moves.
A method using augmented reality to create accurate visual representations of ionizing radiation, developed at ORNL, has been licensed by Teletrix, a firm that creates advanced simulation tools to train the nation’s radiation control workforce.
Andrew Lupini, a scientist and inventor at ORNL, has been elected Fellow of the Microscopy Society of America.
Researchers at ORNL have developed a machine-learning inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.