
Guided by machine learning, chemists at ORNL designed a record-setting carbonaceous supercapacitor material that stores four times more energy than the best commercial material.
Guided by machine learning, chemists at ORNL designed a record-setting carbonaceous supercapacitor material that stores four times more energy than the best commercial material.
ORNL, a bastion of nuclear physics research for the past 80 years, is poised to strengthen its programs and service to the United States over the next decade if national recommendations of the Nuclear Science Advisory Committee, or NSAC, are enacted.
When the second collaborative ORNL-Vanderbilt University workshop took place on Sept. 18-19 at ORNL, about 70 researchers and students assembled to share thoughts concerning a broad spectrum of topics.
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.
Led by Kelly Chipps of ORNL, scientists working in the lab have produced a signature nuclear reaction that occurs on the surface of a neutron star gobbling mass from a companion star.
Few things carry the same aura of mystery as dark matter. The name itself radiates secrecy, suggesting something hidden in the shadows of the Universe.
Warming a crystal of the mineral fresnoite, ORNL scientists discovered that excitations called phasons carried heat three times farther and faster than phonons, the excitations that usually carry heat through a material.
Nine student physicists and engineers from the #1-ranked Nuclear Engineering and Radiological Sciences Program at the University of Michigan, or UM, attended a scintillation detector workshop at Oak Ridge National Laboratory Oct. 10-13.
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.