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Media Contacts
Used lithium-ion batteries from cell phones, laptops and a growing number of electric vehicles are piling up, but options for recycling them remain limited mostly to burning or chemically dissolving shredded batteries.
Researchers at ORNL have been leading a project to understand how a high-altitude electromagnetic pulse, or EMP, could threaten power plants.
As current courses through a battery, its materials erode over time. Mechanical influences such as stress and strain affect this trajectory, although their impacts on battery efficacy and longevity are not fully understood.
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.
A new nanoscience study led by a researcher at ORNL takes a big-picture look at how scientists study materials at the smallest scales.
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.
Like most scientists, Chengping Chai is not content with the surface of things: He wants to probe beyond to learn what’s really going on. But in his case, he is literally building a map of the world beneath, using seismic and acoustic data that reveal when and where the earth moves.
Marm Dixit, a Weinberg Distinguished Staff Fellow at ORNL has received the 2023 Rosalind Franklin Young Investigator Award.
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.