Debjani Singh: Channeling a river of data for clean energy, sustainability
Electrons in atoms are pretty talented. They can form chemical bonds, get kicked out of the atom and even “jump” to different locations based on their energetic states.
Researchers at ORNL and the National Renewable Energy Laboratory took inspiration from flying insects to demonstrate a miniaturized gyroscope, a special sensor used in navigation technologies.
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.
Researchers at the Department of Energy’s Oak Ridge National Laboratory, the University of Tennessee and Texas A&M University demonstrated bio-inspired devices that accelerate routes to neuromorphic, or brain-like, computing.
ORNL and The University of Toledo have entered into a memorandum of understanding for collaborative research.
Researchers at Oak Ridge National Laboratory will present eight innovative technologies currently available for commercialization during a public event at ORNL on October 17.
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.
A modern, healthy transportation system is vital to the nation’s economic security and the American standard of living. The U.S. Department of Energy’s Oak Ridge National Laboratory (ORNL) is engaged in a broad portfolio of scientific research for improved mobility
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.