Filter News
Area of Research
News Type
News Topics
- (-) Artificial Intelligence (3)
- (-) Bioenergy (1)
- (-) Biomedical (9)
- (-) Machine Learning (3)
- (-) Physics (7)
- (-) Summit (5)
- 3-D Printing/Advanced Manufacturing (5)
- Advanced Reactors (6)
- Big Data (4)
- Biology (4)
- Chemical Sciences (2)
- Climate Change (5)
- Composites (1)
- Computer Science (11)
- Coronavirus (7)
- Cybersecurity (1)
- Energy Storage (9)
- Environment (11)
- Frontier (1)
- Fusion (5)
- Grid (4)
- Isotopes (2)
- Materials Science (9)
- Mathematics (1)
- Microscopy (3)
- Molten Salt (1)
- Nanotechnology (3)
- Neutron Science (6)
- Nuclear Energy (9)
- Polymers (2)
- Security (1)
- Sustainable Energy (8)
- Transportation (5)
Media Contacts
Marcel Demarteau is director of the Physics Division at the Department of Energy’s Oak Ridge National Laboratory. For topics from nuclear structure to astrophysics, he shapes ORNL’s physics research agenda.
Oak Ridge National Laboratory and collaborators have discovered that signaling molecules known to trigger symbiosis between plants and soil bacteria are also used by almost all fungi as chemical signals to communicate with each other.
Scientists from Oak Ridge National Laboratory used high-performance computing to create protein models that helped reveal how the outer membrane is tethered to the cell membrane in certain bacteria.
When Sandra Davern looks to the future, she sees individualized isotopes sent into the body with a specific target: cancer cells.
Scientists at Oak Ridge National Laboratory and the University of Tennessee designed and demonstrated a method to make carbon-based materials that can be used as electrodes compatible with a specific semiconductor circuitry.
Rufus Ritchie came from Kentucky coal country, a region not known for producing physicists.
Systems biologist Paul Abraham uses his fascination with proteins, the molecular machines of nature, to explore new ways to engineer more productive ecosystems and hardier bioenergy crops.
Oak Ridge National Laboratory researchers have developed a machine learning model that could help predict the impact pandemics such as COVID-19 have on fuel demand in the United States.