Filter News
Area of Research
- (-) Clean Energy (101)
- (-) National Security (11)
- (-) Nuclear Science and Technology (11)
- Advanced Manufacturing (18)
- Biology and Environment (24)
- Building Technologies (1)
- Computational Biology (1)
- Computational Engineering (2)
- Computer Science (6)
- Fusion and Fission (3)
- Fusion Energy (1)
- Isotope Development and Production (1)
- Isotopes (10)
- Materials (59)
- Materials for Computing (11)
- Neutron Science (67)
- Supercomputing (35)
- Transportation Systems (2)
News Type
News Topics
- (-) 3-D Printing/Advanced Manufacturing (53)
- (-) Artificial Intelligence (10)
- (-) High-Performance Computing (4)
- (-) Isotopes (4)
- (-) Machine Learning (9)
- (-) Neutron Science (12)
- (-) Space Exploration (5)
- (-) Transportation (45)
- Advanced Reactors (9)
- Big Data (4)
- Bioenergy (17)
- Biology (8)
- Biomedical (6)
- Biotechnology (3)
- Buildings (21)
- Chemical Sciences (11)
- Clean Water (5)
- Climate Change (13)
- Composites (14)
- Computer Science (27)
- Coronavirus (7)
- Critical Materials (8)
- Cybersecurity (11)
- Decarbonization (14)
- Energy Storage (47)
- Environment (30)
- Exascale Computing (2)
- Fossil Energy (1)
- Frontier (1)
- Fusion (4)
- Grid (25)
- Hydropower (2)
- Materials (29)
- Materials Science (22)
- Mathematics (1)
- Mercury (2)
- Microscopy (6)
- Molten Salt (4)
- Nanotechnology (6)
- National Security (12)
- Net Zero (2)
- Nuclear Energy (21)
- Partnerships (11)
- Physics (3)
- Polymers (10)
- Quantum Science (1)
- Renewable Energy (1)
- Security (7)
- Simulation (2)
- Statistics (1)
- Summit (3)
- Sustainable Energy (51)
- Transformational Challenge Reactor (3)
Media Contacts
A new deep-learning framework developed at ORNL is speeding up the process of inspecting additively manufactured metal parts using X-ray computed tomography, or CT, while increasing the accuracy of the results. The reduced costs for time, labor, maintenance and energy are expected to accelerate expansion of additive manufacturing, or 3D printing.
Researchers at ORNL have developed an online tool that offers industrial plants an easier way to track and download information about their energy footprint and carbon emissions.
Five technologies invented by scientists at the Department of Energy’s Oak Ridge National Laboratory have been selected for targeted investment through ORNL’s Technology Innovation Program.
Burak Ozpineci, a Corporate Fellow and section head for Vehicle and Mobility Systems Research at Oak Ridge National Laboratory, is one of six international recipients of the eighth Nagamori Award.
Oak Ridge National Laboratory researchers determined that for every 5 miles per hour that drivers travel over a 50-mph speed limit, fuel economy decreases by 7% and equates to paying an extra 28 cents per gallon at current.
ORNL and the Tennessee Valley Authority, or TVA, are joining forces to advance decarbonization technologies from discovery through deployment through a new memorandum of understanding, or MOU.
ORNL, TVA and TNECD were recognized by the Federal Laboratory Consortium for their impactful partnership that resulted in a record $2.3 billion investment by Ultium Cells, a General Motors and LG Energy Solution joint venture, to build a battery cell manufacturing plant in Spring Hill, Tennessee.
Three ORNL scientists have been elected fellows of the American Association for the Advancement of Science, or AAAS, the world’s largest general scientific society and publisher of the Science family of journals.
ORNL and Tuskegee University have formed a partnership to develop new biodegradable materials for use in buildings, transportation and biomedical applications.
Oak Ridge National Laboratory has released the federal government’s new 2022 Fuel Economy Guide. The report provides the latest fuel efficiency stats and money-saving tips for new and used vehicles.