Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
Filter News
Area of Research
- (-) Materials (25)
- (-) Supercomputing (10)
- Advanced Manufacturing (6)
- Biology and Environment (1)
- Clean Energy (28)
- Climate and Environmental Systems (2)
- Computational Engineering (1)
- Computer Science (4)
- Fusion Energy (5)
- National Security (1)
- Neutron Science (7)
- Nuclear Science and Technology (1)
- Quantum information Science (1)
- Transportation Systems (1)
News Topics
- (-) 3-D Printing/Advanced Manufacturing (5)
- (-) Big Data (4)
- (-) Environment (6)
- (-) Fusion (2)
- (-) Grid (1)
- (-) Materials Science (18)
- (-) Physics (3)
- Advanced Reactors (2)
- Artificial Intelligence (6)
- Bioenergy (3)
- Biomedical (2)
- Clean Water (2)
- Composites (1)
- Computer Science (24)
- Cybersecurity (2)
- Energy Storage (5)
- Exascale Computing (2)
- Frontier (2)
- Isotopes (1)
- Microscopy (5)
- Molten Salt (1)
- Nanotechnology (6)
- Neutron Science (5)
- Nuclear Energy (8)
- Polymers (2)
- Quantum Science (5)
- Security (1)
- Space Exploration (2)
- Summit (9)
- Sustainable Energy (6)
- Transportation (6)
Media Contacts
A team of scientists led by Oak Ridge National Laboratory used machine learning methods to generate a high-resolution map of vegetation growing in the remote reaches of the Alaskan tundra.
Jon Poplawsky, a materials scientist at the Department of Energy’s Oak Ridge National Laboratory, develops and links advanced characterization techniques that improve our ability to see and understand atomic-scale features of diverse materials