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Media Contacts
ORNL and The University of Toledo have entered into a memorandum of understanding for collaborative research.
A team including Oak Ridge National Laboratory and University of Tennessee researchers demonstrated a novel 3D printing approach called Z-pinning that can increase the material’s strength and toughness by more than three and a half times compared to conventional additive manufacturing processes.
Craig Blue, a program director at the Department of Energy’s Oak Ridge National Laboratory, has been elected a 2019 fellow for SME (formerly known as the Society for Manufacturing Engineers).
IDEMIA Identity & Security USA has licensed an advanced optical array developed at Oak Ridge National Laboratory. The portable technology can be used to help identify individuals in challenging outdoor conditions.
Oak Ridge National Laboratory is training next-generation cameras called dynamic vision sensors, or DVS, to interpret live information—a capability that has applications in robotics and could improve autonomous vehicle sensing.
Using additive manufacturing, scientists experimenting with tungsten at Oak Ridge National Laboratory hope to unlock new potential of the high-performance heat-transferring material used to protect components from the plasma inside a fusion reactor. Fusion requires hydrogen isotopes to reach millions of degrees.
Scientists at Oak Ridge National Laboratory studying quantum communications have discovered a more practical way to share secret messages among three parties, which could ultimately lead to better cybersecurity for the electric grid
Researchers at Oak Ridge National Laboratory are taking inspiration from neural networks to create computers that mimic the human brain—a quickly growing field known as neuromorphic computing.
Researchers have developed high-fidelity modeling capabilities for predicting radiation interactions outside of the reactor core—a tool that could help keep nuclear reactors running longer.
A study led by Oak Ridge National Laboratory explored the interface between the Department of Veterans Affairs’ healthcare data system and the data itself to detect the likelihood of errors and designed an auto-surveillance tool