Carter to lead Fusion Energy Division at Oak Ridge National Laboratory
A new tool that simulates the energy profile of every building in America will give homeowners, utilities and companies a quick way to determine energy use and cost-effective retrofits that can reduce energy and carbon emissions.
Scientists at Oak Ridge National Laboratory added new plant data to a computer model that simulates Arctic ecosystems, enabling it to better predict how vegetation in rapidly warming northern environments may respond to climate change.
Scientists studying a unique whole-ecosystem warming experiment in the Minnesota peatlands found that microorganisms are increasing methane production faster than carbon dioxide production.
An ORNL-led team comprising researchers from multiple DOE national laboratories is using artificial intelligence and computational screening techniques – in combination with experimental validation – to identify and design five promising drug therapy approaches to target the SARS-CoV-2 virus.
Six science and technology innovators from across the United States will join the fifth cohort of Oak Ridge National Laboratory’s Innovation Crossroads program in June.
Researchers at Oak Ridge National Laboratory have developed a novel envelope system that diverts heat or coolness away from a building and stores it for future use.
Scientists at Oak Ridge National Laboratory have developed a solvent that results in a more environmentally friendly process to recover valuable materials from used lithium-ion batteries, supports a stable domestic supply chain for new batteries
Consumer buy-in is key to the future of a decarbonized transportation sector in which electric vehicles largely replace today’s conventionally fueled cars and trucks.
Belinda Akpa is a chemical engineer with a talent for tackling big challenges and fostering inclusivity and diversity in the next generation of scientists.
The Department of Energy’s Oak Ridge National Laboratory has licensed its award-winning artificial intelligence software system, the Multinode Evolutionary Neural Networks for Deep Learning, to General Motors for use in vehicle technology and design.