Building innovations from ORNL will be on display in Washington, D.C. on the National Mall June 7 to June 9, 2024, during the U.S. Department of Housing and Urban Development’s Innovation Housing Showcase. For the first time, ORNL’s real-time building evaluator was demonstrated outside of a laboratory setting and deployed for building construction.
Four ORNL teams and one researcher were recognized for excellence in technology transfer and technology transfer innovation.
ORNL has been selected to lead an Energy Earthshot Research Center, or EERC, focused on developing chemical processes that use sustainable methods instead of burning fossil fuels to radically reduce industrial greenhouse gas emissions to stem climate change and limit the crisis of a rapidly warming planet.
The Department of Energy’s Oak Ridge National Laboratory announced the establishment of the Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making.
Technologies developed by researchers at ORNL have received six 2023 R&D 100 Awards.
Bryan Maldonado, a dynamic systems and controls researcher at ORNL, has been recognized by the 2023 Hispanic Engineer National Achievements Awards Conference, or HENAAC, with the Most Promising Engineer Award.
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.
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.
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.