Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.
A team led by ORNL and the University of Michigan have discovered that certain bacteria can steal an essential compound from other microbes to break down methane and toxic methylmercury in the environment.
Anyone familiar with ORNL knows it’s a hub for world-class science. The nearly 33,000-acre space surrounding the lab is less known, but also unique.
Moving to landlocked Tennessee isn’t an obvious choice for most scientists with new doctorate degrees in coastal oceanography.
Cory Stuart of ORNL applies his expertise as a systems engineer to ensure the secure and timely transfer of millions of measurements of Earth’s atmosphere, fueling science around the world.
Researchers at Oak Ridge National Laboratory were part of an international team that collected a treasure trove of data measuring precipitation, air particles, cloud patterns and the exchange of energy between the atmosphere and the sea ice.
New capabilities and equipment recently installed at the Department of Energy’s Oak Ridge National Laboratory are bringing a creek right into the lab to advance understanding of mercury pollution and accelerate solutions.
In the race to identify solutions to the COVID-19 pandemic, researchers at the Department of Energy’s Oak Ridge National Laboratory are joining the fight by applying expertise in computational science, advanced manufacturing, data science and neutron science.
ORNL computer scientist Catherine Schuman returned to her alma mater, Harriman High School, to lead Hour of Code activities and talk to students about her job as a researcher.
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.