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Nuclear—Deep space travel

By automating the production of neptunium oxide-aluminum pellets, Oak Ridge National Laboratory scientists have eliminated a key bottleneck when producing plutonium-238 used by NASA to fuel deep space exploration.

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Oak Ridge National Laboratory scientists studying fuel cells as a potential alternative to internal combustion engines used sophisticated electron microscopy to investigate the benefits of replacing high-cost platinum with a lower cost, carbon-nitrogen-manganese-based catalyst.

Rose Ruther and Jagjit Nanda have been collaborating to develop a membrane for a low-cost redox flow battery for grid-scale energy storage.

Oak Ridge National Laboratory scientists have developed a crucial component for a new kind of low-cost stationary battery system utilizing common materials and designed for grid-scale electricity storage. Large, economical electricity storage systems can benefit the nation’s grid ...

Materials—Polymer-theory-problem

Scientists at Oak Ridge National Laboratory have conducted a series of breakthrough experimental and computational studies that cast doubt on a 40-year-old theory describing how polymers in plastic materials behave during processing.

ORNL Director Thomas Zacharia (center, seated) visited Robertsville Middle School to present a check in support of the school’s CubeSat efforts.

Last November a team of students and educators from Robertsville Middle School in Oak Ridge and scientists from Oak Ridge National Laboratory submitted a proposal to NASA for their Cube Satellite Launch Initiative in hopes of sending a student-designed nanosatellite named RamSat into...

ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.

A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the