Filter News
Area of Research
- Advanced Manufacturing (7)
- Biological Systems (1)
- Biology and Environment (39)
- Building Technologies (2)
- Clean Energy (93)
- Computer Science (3)
- Electricity and Smart Grid (1)
- Energy Sciences (1)
- Fusion and Fission (2)
- Fusion Energy (1)
- Materials (47)
- Materials for Computing (5)
- National Security (8)
- Neutron Science (14)
- Nuclear Science and Technology (3)
- Quantum information Science (1)
- Sensors and Controls (1)
- Supercomputing (27)
News Type
News Topics
- (-) Bioenergy (56)
- (-) Composites (20)
- (-) Frontier (18)
- (-) Grid (42)
- (-) Physics (44)
- (-) Sustainable Energy (88)
- 3-D Printing/Advanced Manufacturing (85)
- Advanced Reactors (25)
- Artificial Intelligence (46)
- Big Data (29)
- Biology (60)
- Biomedical (36)
- Biotechnology (14)
- Buildings (39)
- Chemical Sciences (44)
- Clean Water (20)
- Climate Change (58)
- Computer Science (109)
- Coronavirus (34)
- Critical Materials (24)
- Cybersecurity (26)
- Decarbonization (40)
- Education (3)
- Element Discovery (1)
- Energy Storage (86)
- Environment (116)
- Exascale Computing (13)
- Fossil Energy (1)
- Fusion (30)
- High-Performance Computing (46)
- Hydropower (8)
- Irradiation (2)
- Isotopes (30)
- ITER (6)
- Machine Learning (28)
- Materials (100)
- Materials Science (95)
- Mathematics (5)
- Mercury (9)
- Microscopy (36)
- Molten Salt (7)
- Nanotechnology (44)
- National Security (36)
- Net Zero (6)
- Neutron Science (84)
- Nuclear Energy (57)
- Partnerships (27)
- Polymers (25)
- Quantum Computing (14)
- Quantum Science (38)
- Renewable Energy (1)
- Security (18)
- Simulation (18)
- Space Exploration (13)
- Statistics (3)
- Summit (28)
- Transformational Challenge Reactor (4)
- Transportation (71)
Media Contacts
John Lagergren, a staff scientist in Oak Ridge National Laboratory’s Plant Systems Biology group, is using his expertise in applied math and machine learning to develop neural networks to quickly analyze the vast amounts of data on plant traits amassed at ORNL’s Advanced Plant Phenotyping Laboratory.
ORNL scientists develop a sample holder that tumbles powdered photochemical materials within a neutron beamline — exposing more of the material to light for increased photo-activation and better photochemistry data capture.
ORNL researchers used electron-beam additive manufacturing to 3D-print the first complex, defect-free tungsten parts with complex geometries.
Researchers at ORNL are developing battery technologies to fight climate change in two ways, by expanding the use of renewable energy and capturing airborne carbon dioxide.
Scientists at the Department of Energy’s Oak Ridge National Laboratory have developed lubricant additives that protect both water turbine equipment and the surrounding environment.
A first-ever dataset bridging molecular information about the poplar tree microbiome to ecosystem-level processes has been released by a team of DOE scientists led by ORNL. The project aims to inform research regarding how natural systems function, their vulnerability to a changing climate and ultimately how plants might be engineered for better performance as sources of bioenergy and natural carbon storage.
Alyssa Carrell started her science career studying the tallest inhabitants in the forest, but today is focused on some of its smallest — the microbial organisms that play an outsized role in plant health.
The United States could triple its current bioeconomy by producing more than 1 billion tons per year of plant-based biomass for renewable fuels, while meeting projected demands for food, feed, fiber, conventional forest products and exports, according to the DOE’s latest Billion-Ton Report led by ORNL.
In a win for chemistry, inventors at ORNL have designed a closed-loop path for synthesizing an exceptionally tough carbon-fiber-reinforced polymer, or CFRP, and later recovering all of its starting materials.
Corning uses neutron scattering to study the stability of different types of glass. Recently, researchers for the company have found that understanding the stability of the rings of atoms in glass materials can help predict the performance of glass products.