Filter News
Area of Research
- Advanced Manufacturing (5)
- Biological Systems (1)
- Biology and Environment (15)
- Building Technologies (1)
- Clean Energy (53)
- Climate and Environmental Systems (1)
- Computational Biology (1)
- Computational Engineering (2)
- Computer Science (10)
- Electricity and Smart Grid (1)
- Fusion Energy (6)
- Isotopes (2)
- Materials (27)
- Materials for Computing (8)
- Mathematics (1)
- National Security (5)
- Neutron Science (9)
- Nuclear Science and Technology (11)
- Nuclear Systems Modeling, Simulation and Validation (1)
- Quantum information Science (3)
- Sensors and Controls (1)
- Supercomputing (18)
- Transportation Systems (2)
News Type
News Topics
- (-) Bioenergy (16)
- (-) Biomedical (11)
- (-) Computer Science (40)
- (-) Cybersecurity (3)
- (-) Grid (21)
- (-) Materials Science (34)
- (-) Microscopy (11)
- (-) Nuclear Energy (19)
- (-) Security (1)
- (-) Space Exploration (10)
- (-) Transportation (35)
- 3-D Printing/Advanced Manufacturing (31)
- Advanced Reactors (13)
- Artificial Intelligence (13)
- Big Data (17)
- Biology (18)
- Biotechnology (3)
- Buildings (19)
- Chemical Sciences (10)
- Clean Water (13)
- Climate Change (22)
- Composites (9)
- Coronavirus (11)
- Critical Materials (12)
- Decarbonization (9)
- Energy Storage (31)
- Environment (44)
- Exascale Computing (1)
- Fossil Energy (1)
- Frontier (1)
- Fusion (9)
- High-Performance Computing (11)
- Hydropower (6)
- Irradiation (2)
- Isotopes (5)
- ITER (3)
- Machine Learning (11)
- Materials (35)
- Mathematics (2)
- Mercury (3)
- Molten Salt (5)
- Nanotechnology (12)
- National Security (3)
- Net Zero (2)
- Neutron Science (27)
- Partnerships (1)
- Physics (4)
- Polymers (9)
- Quantum Computing (4)
- Quantum Science (10)
- Simulation (7)
- Statistics (1)
- Summit (6)
- Sustainable Energy (45)
Media Contacts
Scientists at Oak Ridge National Laboratory have developed a low-cost, printed, flexible sensor that can wrap around power cables to precisely monitor electrical loads from household appliances to support grid operations.
Oak Ridge National Laboratory scientists are evaluating paths for licensing remotely operated microreactors, which could provide clean energy sources to hard-to-reach communities, such as isolated areas in Alaska.
As the rise of antibiotic-resistant bacteria known as superbugs threatens public health, Oak Ridge National Laboratory’s Shuo Qian and Veerendra Sharma from the Bhaba Atomic Research Centre in India are using neutron scattering to study how an antibacterial peptide interacts with and fights harmful bacteria.
Oak Ridge National Laboratory is using ultrasonic additive manufacturing to embed highly accurate fiber optic sensors in heat- and radiation-resistant materials, allowing for real-time monitoring that could lead to greater insights and safer reactors.
Oak Ridge National Laboratory’s latest Transportation Energy Data Book: Edition 37 reports that the number of vehicles nationwide is growing faster than the population, with sales more than 17 million since 2015, and the average household vehicle travels more than 11,000 miles per year.
Gleaning valuable data from social platforms such as Twitter—particularly to map out critical location information during emergencies— has become more effective and efficient thanks to Oak Ridge National Laboratory.
Scientists have tested a novel heat-shielding graphite foam, originally created at Oak Ridge National Laboratory, at Germany’s Wendelstein 7-X stellarator with promising results for use in plasma-facing components of fusion reactors.
Oak Ridge National Laboratory scientists have created open source software that scales up analysis of motor designs to run on the fastest computers available, including those accessible to outside users at the Oak Ridge Leadership Computing Facility.
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.
Oak Ridge National Laboratory geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.