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Media Contacts
Six scientists at the Department of Energy’s Oak Ridge National Laboratory were named Battelle Distinguished Inventors, in recognition of obtaining 14 or more patents during their careers at the lab.
Four research teams from the Department of Energy’s Oak Ridge National Laboratory and their technologies have received 2020 R&D 100 Awards.
Oak Ridge National Laboratory scientists evaluating northern peatland responses to environmental change recorded extraordinary fine-root growth with increasing temperatures, indicating that this previously hidden belowground mechanism may play an important role in how carbon-rich peatlands respond to warming.
Researchers at Oak Ridge National Laboratory developed a method that uses machine learning to predict seasonal fire risk in Africa, where half of the world’s wildfire-related carbon emissions originate.
Oak Ridge National Laboratory researchers have built a novel microscope that provides a “chemical lens” for viewing biological systems including cell membranes and biofilms.
Giri Prakash, data informatics scientist and director of the Department of Energy’s Atmospheric Radiation Measurement Data Center at Oak Ridge National Laboratory, has accepted an invitation from the National Academies of Sciences, Engineering, and Medicine to serve a four-year term on the U.S. National Committee for CODATA.
An international team of scientists found that rules governing plant growth hold true even at the edges of the world in the Arctic tundra.
To better determine the potential energy cost savings among connected homes, researchers at Oak Ridge National Laboratory developed a computer simulation to more accurately compare energy use on similar weather days.
Researchers at the Department of Energy’s Oak Ridge National Laboratory have received five 2019 R&D 100 Awards, increasing the lab’s total to 221 since the award’s inception in 1963.
A detailed study by Oak Ridge National Laboratory estimated how much more—or less—energy United States residents might consume by 2050 relative to predicted shifts in seasonal weather patterns