Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
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.
An advance in a topological insulator material — whose interior behaves like an electrical insulator but whose surface behaves like a conductor — could revolutionize the fields of next-generation electronics and quantum computing, according to scientists at ORNL.
ORNL scientists found that a small tweak created big performance improvements in a type of solid-state battery, a technology considered vital to broader electric vehicle adoption.
Having passed the midpoint of his career, physicist Mali Balasubramanian was part of a tight-knit team at a premier research facility for X-ray spectroscopy. But then another position opened, at ORNL— one that would take him in a new direction.
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.
A study led by Oak Ridge National Laboratory researchers identifies a new potential application in quantum computing that could be part of the next computational revolution.
Marm Dixit, a Weinberg Distinguished Staff Fellow at ORNL has received the 2023 Rosalind Franklin Young Investigator Award.
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.