Three researchers at ORNL have been named ORNL Corporate Fellows in recognition of significant career accomplishments and continued leadership in their scientific fields.
The presence of minerals called ash in plants makes little difference to the fitness of new naturally derived compound materials designed for additive manufacturing, an Oak Ridge National Laboratory-led team found.
Oak Ridge National Laboratory researchers serendipitously discovered when they automated the beam of an electron microscope to precisely drill holes in the atomically thin lattice of graphene, the drilled holes closed up.
Oak Ridge National Laboratory scientists designed a recyclable polymer for carbon-fiber composites to enable circular manufacturing of parts that boost energy efficiency in automotive, wind power and aerospace applications.
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.
Researchers at ORNL have developed an online tool that offers industrial plants an easier way to track and download information about their energy footprint and carbon emissions.
Researchers at the Department of Energy’s Oak Ridge National Laboratory and their technologies have received seven 2022 R&D 100 Awards, plus special recognition for a battery-related green technology product.
ORNL Corporate Fellow and Center for Nanophase Materials Sciences researcher Bobby Sumpter has been named fellow of two scientific professional societies: the Institute of Physics and the International Association of Advanced Materials.
Scientists are using Oak Ridge National Laboratory’s Multicharged Ion Research Facility to simulate the cosmic origin of X-ray emissions resulting when highly charged ions collide with neutral atoms and molecules, such as helium and gaseous hydrogen.
An Oak Ridge National Laboratory team developed a novel technique using sensors to monitor seismic and acoustic activity and machine learning to differentiate operational activities at facilities from “noise” in the recorded data.