A new nanoscience study led by a researcher at ORNL takes a big-picture look at how scientists study materials at the smallest scales.
A licensing agreement between the Department of Energy’s Oak Ridge National Laboratory and research partner ZEISS will enable industrial X-ray computed tomography, or CT, to perform rapid evaluations of 3D-printed components using ORNL’s machine
Technologies developed by researchers at ORNL have received six 2023 R&D 100 Awards.
Seven entrepreneurs will embark on a two-year fellowship as the seventh cohort of Innovation Crossroads kicks off this month at ORNL. Representing a range of transformative energy technologies, Cohort 7 is a diverse class of innovators with promising new companies.
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.
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.
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
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.