Anne Campbell, a researcher at ORNL, recently won the Young Leaders Professional Development Award from the Minerals, Metals & Materials Society, or TMS, and has been chosen as the first recipient of the Young Leaders International Scholar Program a
In response to a renewed international interest in molten salt reactors, researchers from the Department of Energy’s Oak Ridge National Laboratory have developed a novel technique to visualize molten salt intrusion in graphite.
In fiscal year 2023 — Oct. 1–Sept. 30, 2023 — Oak Ridge National Laboratory was awarded more than $8 million in technology maturation funding through the Department of Energy’s Technology Commercialization Fund, or TCF.
A team of scientists with ORNL has investigated the behavior of hafnium oxide, or hafnia, because of its potential for use in novel semiconductor applications.
Xiao-Ying Yu, a distinguished scientist at the Department of Energy’s Oak Ridge National Laboratory, has been named a Fellow of AVS: Science and Technology of Materials, Interfaces, and Processing, formerly American Vacuum Society.
Research into a new, unique technology to fabricate composite metal parts for a wide range of applications operating in extreme environments across the aviation, space and energy industries is showing promise for additive manufacturing.
Creating energy the way the sun and stars do — through nuclear fusion — is one of the grand challenges facing science and technology. What’s easy for the sun and its billions of relatives turns out to be particularly difficult on Earth.
Xiao-Ying Yu, a distinguished scientist in the Materials Science and Technology Division of the Department of Energy’s Oak Ridge National Laboratory, has recently been chosen for several prominent editorial roles.
Rama Vasudevan, a research scientist at the Department of Energy’s Oak Ridge National Laboratory, has been elected a Fellow of the American Physical Society, or APS.
Researchers from ORNL, the University of Tennessee at Chattanooga and Tuskegee University used mathematics to predict which areas of the SARS-CoV-2 spike protein are most likely to mutate.