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Media Contacts
Scientists at the Department of Energy’s Oak Ridge National Laboratory are working to understand both the complex nature of uranium and the various oxide forms it can take during processing steps that might occur throughout the nuclear fuel cycle.
Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data. Applying this technique to science problems can help unearth historically elusive solutions.
Kevin Field at the Department of Energy’s Oak Ridge National Laboratory synthesizes and scrutinizes materials for nuclear power systems that must perform safely and efficiently over decades of irradiation.
Oak Ridge National Laboratory is using ultrasonic additive manufacturing to embed highly accurate fiber optic sensors in heat- and radiation-resistant materials, allowing for real-time monitoring that could lead to greater insights and safer reactors.
OAK RIDGE, Tenn., March 4, 2019—A team of researchers from the Department of Energy’s Oak Ridge National Laboratory Health Data Sciences Institute have harnessed the power of artificial intelligence to better match cancer patients with clinical trials.
Gleaning valuable data from social platforms such as Twitter—particularly to map out critical location information during emergencies— has become more effective and efficient thanks to Oak Ridge National Laboratory.
Scientists have tested a novel heat-shielding graphite foam, originally created at Oak Ridge National Laboratory, at Germany’s Wendelstein 7-X stellarator with promising results for use in plasma-facing components of fusion reactors.
OAK RIDGE, Tenn., Feb. 12, 2019—A team of researchers from the Department of Energy’s Oak Ridge and Los Alamos National Laboratories has partnered with EPB, a Chattanooga utility and telecommunications company, to demonstrate the effectiveness of metro-scale quantum key distribution (QKD).
Oak Ridge National Laboratory geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.
OAK RIDGE, Tenn., Jan. 31, 2019—A new electron microscopy technique that detects the subtle changes in the weight of proteins at the nanoscale—while keeping the sample intact—could open a new pathway for deeper, more comprehensive studies of the basic building blocks of life.