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Media Contacts
Researchers from Oak Ridge National Laboratory and Northeastern University modeled how extreme conditions in a changing climate affect the land’s ability to absorb atmospheric carbon — a key process for mitigating human-caused emissions. They found that 88% of Earth’s regions could become carbon emitters by the end of the 21st century.
Oak Ridge National Laboratory scientists exploring bioenergy plant genetics have made a surprising discovery: a protein domain that could lead to new COVID-19 treatments.
A study by Oak Ridge National Laboratory researchers has demonstrated how satellites could enable more efficient, secure quantum networks.
Critical Materials Institute researchers at Oak Ridge National Laboratory and Arizona State University studied the mineral monazite, an important source of rare-earth elements, to enhance methods of recovering critical materials for energy, defense and manufacturing applications.
ORNL researchers discovered genetic mutations that underlie autism using a new approach that could lead to better diagnostics and drug therapies.
Global carbon emissions from inland waters such as lakes, rivers, streams and ponds are being undercounted by about 13% and will likely continue to rise given climate events and land use changes, ORNL scientists found.
A multi-lab research team led by ORNL's Paul Kent is developing a computer application called QMCPACK to enable precise and reliable predictions of the fundamental properties of materials critical in energy research.
Oak Ridge National Laboratory researchers are developing a first-of-its-kind artificial intelligence device for neutron scattering called Hyperspectral Computed Tomography, or HyperCT.
Oak Ridge National Laboratory researchers developed an invertible neural network, a type of artificial intelligence that mimics the human brain, to improve accuracy in climate-change models and predictions.
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.