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While studying how bio-inspired materials might inform the design of next-generation computers, scientists at ORNL achieved a first-of-its-kind result that could have big implications for both edge computing and human health.
Although blockchain is best known for securing digital currency payments, researchers at the Department of Energy’s Oak Ridge National Laboratory are using it to track a different kind of exchange: It’s the first time blockchain has ever been used to validate communication among devices on the electric grid.
Marc-Antoni Racing has licensed a collection of patented energy storage technologies developed at ORNL. The technologies focus on components that enable fast-charging, energy-dense batteries for electric and hybrid vehicles and grid storage.
In human security research, Thomaz Carvalhaes says, there are typically two perspectives: technocentric and human centric. Rather than pick just one for his work, Carvalhaes uses data from both perspectives to understand how technology impacts the lives of people.
Researchers at ORNL explored radium’s chemistry to advance cancer treatments using ionizing radiation.
Scientists at ORNL used neutron scattering to determine whether a specific material’s atomic structure could host a novel state of matter called a spiral spin liquid.
To solve a long-standing puzzle about how long a neutron can “live” outside an atomic nucleus, physicists entertained a wild but testable theory positing the existence of a right-handed version of our left-handed universe.
ORNL scientists will present new technologies available for licensing during the annual Technology Innovation Showcase. The event is 9 a.m. to 3 p.m. Thursday, June 16, at the Manufacturing Demonstration Facility at ORNL’s Hardin Valley campus.
ORNL researchers used the nation’s fastest supercomputer to map the molecular vibrations of an important but little-studied uranium compound produced during the nuclear fuel cycle for results that could lead to a cleaner, safer world.
A team of researchers has developed a novel, machine learning–based technique to explore and identify relationships among medical concepts using electronic health record data across multiple healthcare providers.