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Media Contacts
John “Jack” Cahill is out to illuminate previously unseen processes with new technology, advancing our understanding of how chemicals interact to influence complex systems whether it’s in the human body or in the world beneath our feet.
Eight ORNL scientists are among the world’s most highly cited researchers, according to a bibliometric analysis conducted by the scientific publication analytics firm Clarivate.
ORNL researchers discovered genetic mutations that underlie autism using a new approach that could lead to better diagnostics and drug therapies.
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.
As the United States shifts away from fossil-fuel-burning cars and trucks, scientists at the Department of Energy’s Oak Ridge and Argonne national laboratories are exploring options for another form of transportation: trains. The research focuses on zero-carbon hydrogen and other low-carbon fuels as viable alternatives to diesel for the rail industry.
Laboratory Director Thomas Zacharia presented five Director’s Awards during Saturday night's annual Awards Night event hosted by UT-Battelle, which manages ORNL for the Department of Energy.
Several significant science and energy projects led by the ORNL will receive a total of $497 million in funding from the Inflation Reduction Act.
Over the past seven years, researchers in ORNL’s Geospatial Science and Human Security Division have mapped and characterized all structures within the United States and its territories to aid FEMA in its response to disasters. This dataset provides a consistent, nationwide accounting of the buildings where people reside and work.
Marc-Antoni Racing has licensed a collection of patented energy storage technologies developed at ORNL. The technologies focus on components that enable fast-charging, energy-dense batteries for electric and hybrid vehicles and grid storage.
A new deep-learning framework developed at ORNL is speeding up the process of inspecting additively manufactured metal parts using X-ray computed tomography, or CT, while increasing the accuracy of the results. The reduced costs for time, labor, maintenance and energy are expected to accelerate expansion of additive manufacturing, or 3D printing.