Updated software improves slicing for large-format 3D printing
Technologies developed by researchers at ORNL have received six 2023 R&D 100 Awards.
It was reading about current nuclear discoveries in textbooks that first made Ken Engle want to work at a national lab. It was seeing the real-world impact of the isotopes produced at ORNL
Wildfires are an ancient force shaping the environment, but they have grown in frequency, range and intensity in response to a changing climate. At ORNL, scientists are working on several fronts to better understand and predict these events and what they mean for the carbon cycle and biodiversity.
Scientist-inventors from ORNL will present seven new technologies during the Technology Innovation Showcase on Friday, July 14, from 8 a.m.–4 p.m. at the Joint Institute for Computational Sciences on ORNL’s campus.
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.
When reading the novel Jurassic Park as a teenager, Jerry Parks found the passages about gene sequencing and supercomputers fascinating, but never imagined he might someday pursue such futuristic-sounding science.
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.
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.
Andrew Lupini, a scientist and inventor at ORNL, has been elected Fellow of the Microscopy Society of America.
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.