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Media Contacts
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.
Researchers at ORNL are helping modernize power management and enhance reliability in an increasingly complex electric grid.
ORNL is teaming with the National Energy Technology Laboratory to jointly explore a range of technology innovations for carbon management and strategies for economic development and sustainable energy transitions in the Appalachian region.
David McCollum, a senior scientist at the ORNL and lead for the lab’s contributions to the Net Zero World Initiative, was one of more than 35,000 attendees in Egypt at the November 2022 Sharm El-Sheikh United Nations Framework Convention on Climate Change, or UNFCCC, Conference of the Parties, also known as COP27.
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.
Mechanical engineer Marm Dixit’s work is all about getting electricity to flow efficiently from one end of a solid-state battery to the other. It’s a high-stakes problem
It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.
What’s getting Jim Szybist fired up these days? It’s the opportunity to apply his years of alternative fuel combustion and thermodynamics research to the challenge of cleaning up the hard-to-decarbonize, heavy-duty mobility sector — from airplanes to locomotives to ships and massive farm combines.
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.