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Media Contacts
Oak Ridge National Laboratory scientists led the development of a supply chain model revealing the optimal places to site farms, biorefineries, pipelines and other infrastructure for sustainable aviation fuel production.
Oak Ridge National Laboratory researchers have developed an online resource to help consumers understand the electric vehicle tax credits available through the Inflation Reduction Act.
A new report published by ORNL assessed how advanced manufacturing and materials, such as 3D printing and novel component coatings, could offer solutions to modernize the existing fleet and design new approaches to hydropower.
When aging vehicle batteries lack the juice to power your car anymore, they may still hold energy. Yet it’s tough to find new uses for lithium-ion batteries with different makers, ages and sizes. A solution is urgently needed because battery recycling options are scarce.
Researchers at Oak Ridge National Laboratory have designed architecture, software and control strategies for a futuristic EV truck stop that can draw megawatts of power and reduce carbon emissions.
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 determined that for every 5 miles per hour that drivers travel over a 50-mph speed limit, fuel economy decreases by 7% and equates to paying an extra 28 cents per gallon at current.
Scientists are using Oak Ridge National Laboratory’s Multicharged Ion Research Facility to simulate the cosmic origin of X-ray emissions resulting when highly charged ions collide with neutral atoms and molecules, such as helium and gaseous hydrogen.
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.