Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
Rich Giannone uses bioanalytical mass spectrometry to examine proteins, the primary driver in biological systems.
The Accelerating Therapeutics for Opportunities in Medicine , or ATOM, consortium today announced the U.S. Department of Energy’s Oak Ridge, Argonne and Brookhaven national laboratories are joining the consortium to further develop ATOM’s artificial intelligence, or AI-driven, drug discovery platform.
Oak Ridge National Laboratory was among an international team, led by Lawrence Livermore National Laboratory, who synthesized 108 elevated carbon dioxide, or CO2, experiments performed in various ecosystems to find out how much carbon is
An Oak Ridge National Laboratory research team discovered that aspen saplings emerging after wildfire have less diverse microbiomes and more pathogens in their leaves, providing new insights about how fire affects ecosystem recovery.
Stan Wullschleger has been selected as the associate laboratory director for the Biological and Environmental Systems Science Directorate, or BESSD, at ORNL.
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.
A new Department of Energy report produced by Oak Ridge National Laboratory details national and international trends in hydropower, including the role waterpower plays in enhancing the flexibility and resilience of the power grid.
For nearly three decades, scientists and engineers across the globe have worked on the Square Kilometre Array (SKA), a project focused on designing and building the world’s largest radio telescope. Although the SKA will collect enormous amounts of precise astronomical data in record time, scientific breakthroughs will only be possible with systems able to efficiently process that data.
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.
More than 6,000 veterans died by suicide in 2016, and from 2005 to 2016, the rate of veteran suicides in the United States increased by more than 25 percent.