Like most scientists, Chengping Chai is not content with the surface of things: He wants to probe beyond to learn what’s really going on. But in his case, he is literally building a map of the world beneath, using seismic and acoustic data that reveal when and where the earth moves.
In a discovery aimed at accelerating the development of process-advantaged crops for jet biofuels, scientists at ORNL developed a capability to insert multiple genes into plants in a single step.
ORNL will team up with six of eight companies that are advancing designs and research and development for fusion power plants with the mission to achieve a pilot-scale demonstration of fusion within a decade.
A study led by researchers at ORNL could uncover new ways to produce more powerful, longer-lasting batteries and memory devices.
Nature-based solutions are an effective tool to combat climate change triggered by rising carbon emissions, whether it’s by clearing the skies with bio-based aviation fuels or boosting natural carbon sinks.
ORNL’s electromagnetic isotope separator, or EMIS, made history in 2018 when it produced 500 milligrams of the rare isotope ruthenium-96, unavailable anywhere else in the world.
Computing pioneer Jack Dongarra has been elected to the National Academy of Sciences in recognition of his distinguished and continuing achievements in original research.
Inspired by one of the mysteries of human perception, an ORNL researcher invented a new way to hide sensitive electric grid information from cyberattack: within a constantly changing color palette.
Growing up in suburban Upper East Tennessee, Layla Marshall didn’t see a lot of STEM opportunities for children.
“I like encouraging young people to get involved in the kinds of things I’ve been doing in my career,” said Marshall. “I like seeing the students achieve their goals. It’s fun to watch them get excited about learning new things and teaching the robot to do things that they didn’t know it could do until they tried it.”
Marshall herself has a passion for learning new things.
Researchers at ORNL have developed a machine-learning inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.