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Media Contacts
ORNL scientists and researchers attended the annual American Geophysical Union meeting and came away inspired for the year ahead in geospatial, earth and climate science.
A key industrial isotope, iridium-192, has not been produced in the U.S. in almost 20 years. DOE's Isotope Program and QSA Global Inc. announced a joint product development agreement to initiate U.S. production of iridium-192.
New computational framework speeds discovery of fungal metabolites, key to plant health and used in drug therapies and for other uses.
Technology Transfer staff from Department of Energy’s Oak Ridge National Laboratory attended the 2024 Consumer Electronics Show, or CES, in Las Vegas, Jan. 8–12.
The 21st Symposium on Separation Science and Technology for Energy Applications, Oct. 23-26 at the Embassy Suites by Hilton West in Knoxville, attracted 109 researchers, including some from Austria and the Czech Republic. Besides attending many technical sessions, they had the opportunity to tour the Graphite Reactor, High Flux Isotope Reactor and both supercomputers at ORNL.
The 2023 top science achievements from HFIR and SNS feature a broad range of materials research published in high impact journals such as Nature and Advanced Materials.
A team of computational scientists at ORNL has generated and released datasets of unprecedented scale that provide the ultraviolet visible spectral properties of over 10 million organic molecules.
Nuclear engineering students from the United States Military Academy and United States Naval Academy are working with researchers at ORNL to complete design concepts for a nuclear propulsion rocket to go to space in 2027 as part of the Defense Advanced Research Projects Agency DRACO program.
A 19-member team of scientists from across the national laboratory complex won the Association for Computing Machinery’s 2023 Gordon Bell Special Prize for Climate Modeling for developing a model that uses the world’s first exascale supercomputer to simulate decades’ worth of cloud formations.
Lee's paper at the August conference in Bellevue, Washington, combined weather and power outage data for three states – Texas, Michigan and Hawaii – and used a machine learning model to predict how extreme weather such as thunderstorms, floods and tornadoes would affect local power grids and to estimate the risk for outages. The paper relied on data from the National Weather Service and the U.S. Department of Energy’s Environment for Analysis of Geo-Located Energy Information, or EAGLE-I, database.