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Media Contacts
Four firms doing business with the Department of Energy’s Oak Ridge National Laboratory received ORNL Small Business Awards during an awards ceremony on June 29.
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.
Working with Western Michigan University and other partners, ORNL engineers are placing low-powered sensors in the reflective raised pavement markers that are already used to help drivers identify lanes. Microchips inside the markers transmit information to passing cars about the road shape to help autonomous driving features function even when vehicle cameras or remote laser sensing, called LiDAR, are unreliable because of fog, snow, glare or other obstructions.
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.
Scientists at ORNL have invented a coating that could dramatically reduce friction in common load-bearing systems with moving parts, from vehicle drive trains to wind
An innovative and sustainable chemistry developed at ORNL for capturing carbon dioxide has been licensed to Holocene, a Knoxville-based startup focused on designing and building plants that remove carbon dioxide
A method using augmented reality to create accurate visual representations of ionizing radiation, developed at ORNL, has been licensed by Teletrix, a firm that creates advanced simulation tools to train the nation’s radiation control workforce.
SAE International has awarded ORNL Buildings and Transportation Science Division Director Robert Wagner with the SAE Medal of Honor for his dedication and support of the organization’s mission of advancing mobility solutions.
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.