Filter News
Area of Research
News Topics
- (-) Decarbonization (22)
- (-) Environment (22)
- 3-D Printing/Advanced Manufacturing (16)
- Advanced Reactors (3)
- Artificial Intelligence (26)
- Big Data (13)
- Bioenergy (11)
- Biology (13)
- Biomedical (6)
- Biotechnology (6)
- Buildings (15)
- Chemical Sciences (18)
- Clean Water (4)
- Climate Change (23)
- Composites (7)
- Computer Science (25)
- Critical Materials (7)
- Education (1)
- Emergency (1)
- Energy Storage (11)
- Exascale Computing (7)
- Fossil Energy (3)
- Frontier (8)
- Fusion (6)
- Grid (9)
- High-Performance Computing (17)
- Isotopes (12)
- ITER (1)
- Machine Learning (9)
- Materials (15)
- Materials Science (18)
- Mathematics (4)
- Microelectronics (2)
- Microscopy (2)
- Nanotechnology (2)
- National Security (21)
- Net Zero (6)
- Neutron Science (12)
- Nuclear Energy (11)
- Partnerships (17)
- Physics (6)
- Polymers (5)
- Quantum Computing (11)
- Quantum Science (13)
- Security (2)
- Simulation (14)
- Space Exploration (3)
- Statistics (2)
- Summit (6)
- Sustainable Energy (20)
- Transportation (12)
Media Contacts
A study found that beaches with manmade fortifications recover more slowly from hurricanes than natural beaches, losing more sand and vegetation. The researchers used satellite images and light detection and ranging data, or LIDAR, to measure elevation changes and vegetation coverage. Changes in elevation showed how much sand was depleted during the storm and how much sand returned throughout the following year.
Seven entrepreneurs comprise the next cohort of Innovation Crossroads, a DOE Lab-Embedded Entrepreneurship Program node based at ORNL. The program provides energy-related startup founders from across the nation with access to ORNL’s unique scientific resources and capabilities, as well as connect them with experts, mentors and networks to accelerate their efforts to take their world-changing ideas to the marketplace.
A research team led by the Department of Energy’s Oak Ridge National Laboratory demonstrated an effective and reliable new way to identify and quantify polyethylene glycols in various samples.
To better predict long-term flooding risk, scientists at the Department of Energy’s Oak Ridge National Laboratory developed a 3D modeling framework that captures the complex dynamics of water as it flows across the landscape. The framework seeks to provide valuable insights into which communities are most vulnerable as the climate changes, and was developed for a project that’s assessing climate risk and mitigation pathways for an urban area along the Southeast Texas coast.
In the wet, muddy places where America’s rivers and lands meet the sea, scientists from the Department of Energy’s Oak Ridge National Laboratory are unearthing clues to better understand how these vital landscapes are evolving under climate change.
Oak Ridge National Laboratory scientists have developed a method leveraging artificial intelligence to accelerate the identification of environmentally friendly solvents for industrial carbon capture, biomass processing, rechargeable batteries and other applications.
Researchers at ORNL have successfully demonstrated the first 270-kW wireless power transfer to a light-duty electric vehicle. The demonstration used a Porsche Taycan and was conducted in collaboration with Volkswagen Group of America using the ORNL-developed polyphase wireless charging system.
Erin Webb, lead for the Bioresources Science and Engineering group at Oak Ridge National Laboratory, has been elected a Fellow of the American Society of Agricultural and Biological Engineers — the society’s highest honor.
Building innovations from ORNL will be on display in Washington, D.C. on the National Mall June 7 to June 9, 2024, during the U.S. Department of Housing and Urban Development’s Innovation Housing Showcase. For the first time, ORNL’s real-time building evaluator was demonstrated outside of a laboratory setting and deployed for building construction.
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.