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Media Contacts
As scientists study approaches to best sustain a fusion reactor, a team led by Oak Ridge National Laboratory investigated injecting shattered argon pellets into a super-hot plasma, when needed, to protect the reactor’s interior wall from high-energy runaway electrons.
A technology developed at the ORNL and scaled up by Vertimass LLC to convert ethanol into fuels suitable for aviation, shipping and other heavy-duty applications can be price-competitive with conventional fuels
If humankind reaches Mars this century, an Oak Ridge National Laboratory-developed experiment testing advanced materials for spacecraft may play a key role.
Researchers at the Department of Energy’s Oak Ridge National Laboratory have received five 2019 R&D 100 Awards, increasing the lab’s total to 221 since the award’s inception in 1963.
A joint research team from Google Inc., NASA Ames Research Center, and the Department of Energy’s Oak Ridge National Laboratory has demonstrated that a quantum computer can outperform a classical computer
Jason Nattress, an Alvin M. Weinberg Fellow at the Department of Energy’s Oak Ridge National Laboratory, found his calling on a nuclear submarine.
The U.S. Department of Energy announced funding for 12 projects with private industry to enable collaboration with DOE national laboratories on overcoming challenges in fusion energy development.
Three researchers at Oak Ridge National Laboratory will lead or participate in collaborative research projects aimed at harnessing the power of quantum mechanics to advance a range of technologies including computing, fiber optics and network
Quanex Building Products has signed a non-exclusive agreement to license a method to produce insulating material from ORNL. The low-cost material can be used as an additive to increase thermal insulation performance and improve energy efficiency when applied to a variety of building products.
In collaboration with the Department of Veterans Affairs, a team at Oak Ridge National Laboratory has expanded a VA-developed predictive computing model to identify veterans at risk of suicide and sped it up to run 300 times faster, a gain that could profoundly affect the VA’s ability to reach susceptible veterans quickly.