Filter News
Area of Research
- Advanced Manufacturing (1)
- Biological Systems (1)
- Biology and Environment (82)
- Biology and Soft Matter (1)
- Clean Energy (58)
- Climate and Environmental Systems (1)
- Electricity and Smart Grid (2)
- Fuel Cycle Science and Technology (1)
- Functional Materials for Energy (1)
- Fusion and Fission (5)
- Isotopes (2)
- Materials (31)
- Materials for Computing (5)
- National Security (34)
- Neutron Science (12)
- Nuclear Science and Technology (2)
- Quantum information Science (2)
- Supercomputing (39)
News Type
News Topics
- (-) Bioenergy (53)
- (-) Clean Water (14)
- (-) Composites (9)
- (-) Environment (113)
- (-) Frontier (24)
- (-) Grid (24)
- (-) Molten Salt (2)
- (-) Nanotechnology (28)
- (-) National Security (39)
- 3-D Printing/Advanced Manufacturing (51)
- Advanced Reactors (12)
- Artificial Intelligence (48)
- Big Data (24)
- Biology (62)
- Biomedical (32)
- Biotechnology (10)
- Buildings (22)
- Chemical Sciences (32)
- Climate Change (52)
- Computer Science (93)
- Coronavirus (21)
- Critical Materials (2)
- Cybersecurity (20)
- Decarbonization (45)
- Emergency (2)
- Energy Storage (43)
- Exascale Computing (24)
- Fossil Energy (4)
- Fusion (36)
- High-Performance Computing (52)
- Hydropower (5)
- Irradiation (1)
- Isotopes (30)
- ITER (3)
- Machine Learning (21)
- Materials (70)
- Materials Science (60)
- Mathematics (5)
- Mercury (7)
- Microelectronics (2)
- Microscopy (27)
- Net Zero (7)
- Neutron Science (57)
- Nuclear Energy (65)
- Partnerships (17)
- Physics (31)
- Polymers (12)
- Quantum Computing (17)
- Quantum Science (30)
- Renewable Energy (1)
- Security (13)
- Simulation (31)
- Software (1)
- Space Exploration (12)
- Summit (32)
- Sustainable Energy (47)
- Transformational Challenge Reactor (4)
- Transportation (34)
Media Contacts
Researchers at ORNL are using a machine-learning model to answer ‘what if’ questions stemming from major events that impact large numbers of people. By simulating an event, such as extreme weather, researchers can see how people might respond to adverse situations, and those outcomes can be used to improve emergency planning.
To balance personal safety and research innovation, researchers at ORNL are employing a mathematical technique known as differential privacy to provide data privacy guarantees.
Scientists at Oak Ridge National Laboratory and six other Department of Energy national laboratories have developed a United States-based perspective for achieving net-zero carbon emissions.
The U.S. Environmental Protection Agency has approved the registration and use of a renewable gasoline blendstock developed by Vertimass LLC and ORNL that can significantly reduce the emissions profile of vehicles when added to conventional fuels.
Integral to the functionality of ORNL's Frontier supercomputer is its ability to store the vast amounts of data it produces onto its file system, Orion. But even more important to the computational scientists running simulations on Frontier is their capability to quickly write and read to Orion along with effectively analyzing all that data. And that’s where ADIOS comes in.
ORNL researchers modeled how hurricane cloud cover would affect solar energy generation as a storm followed 10 possible trajectories over the Caribbean and Southern U.S.
Rigoberto “Gobet” Advincula, a scientist with joint appointments at ORNL and the University of Tennessee, has been named a Fellow of the American Institute for Medical and Biological Engineering.
ORNL’s Erin Webb is co-leading a new Circular Bioeconomy Systems Convergent Research Initiative focused on advancing production and use of renewable carbon from Tennessee to meet societal needs.
ORNL researchers are working to make EV charging more resilient by developing algorithms to deal with both internal and external triggers of charger failure. This will help charging stations remain available to traveling EV drivers, reducing range anxiety.
Nuclear nonproliferation scientists at ORNL have published the Compendium of Uranium Raman and Infrared Experimental Spectra, a public database and analysis of structure-spectral relationships for uranium minerals. This first-of-its-kind dataset and corresponding analysis fill a key gap in the existing body of knowledge for mineralogists and actinide scientists.