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Researchers at Oak Ridge National Laboratory demonstrated that an additively manufactured polymer layer, when applied to carbon fiber reinforced plastic, or CFRP, can serve as an effective protector against aircraft lightning strikes.
An international team of scientists, led by the University of Manchester, has developed a metal-organic framework, or MOF, material
Oak Ridge National Laboratory will give college students the chance to practice cybersecurity skills in a real-world setting as a host of the Department of Energy’s fifth collegiate CyberForce Competition on Nov. 16. The event brings together student teams from across the country to compete at 10 of DOE’s national laboratories.
A team of scientists found that critical interactions between microbes and peat moss break down under warming temperatures, impacting moss health and ultimately carbon stored in soil.
Researchers demonstrated that an additively manufactured hot stamping die can withstand up to 25,000 usage cycles, proving that this technique is a viable solution for production.
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.
ORNL and The University of Toledo have entered into a memorandum of understanding for collaborative research.
The National Alliance for Water Innovation, a partnership of the Department of Energy’s Oak Ridge National Laboratory, other national labs, university and private sector partners, has been awarded a five-year, $100 million Energy-Water Desalination Hub by DOE to address water security issues in the United States.
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
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.