Updated software improves slicing for large-format 3D printing
Five technologies invented by scientists at the Department of Energy’s Oak Ridge National Laboratory have been selected for targeted investment through ORNL’s Technology Innovation Program.
When the COVID-19 pandemic stunned the world in 2020, researchers at ORNL wondered how they could extend their support and help
Researchers at ORNL are tackling a global water challenge with a unique material designed to target not one, but two toxic, heavy metal pollutants for simultaneous removal.
Chemical and environmental engineer Samarthya Bhagia is focused on achieving carbon neutrality and a circular economy by designing new plant-based materials for a range of applications from energy storage devices and sensors to environmentally friendly bioplastics.
ORNL researchers have developed an upcycling approach that adds value to discarded plastics for reuse in additive manufacturing, or 3D printing.
Researchers at Oak Ridge National Laboratory are using state-of-the-art methods to shed light on chemical separations needed to recover rare-earth elements and secure critical materials for clean energy technologies.
The Frontier supercomputer at the Department of Energy’s Oak Ridge National Laboratory earned the top ranking today as the world’s fastest on the 59th TOP500 list, with 1.1 exaflops of performance. The system is the first to achieve an unprecedented level of computing performance known as exascale, a threshold of a quintillion calculations per second.
ORNL scientists will present new technologies available for licensing during the annual Technology Innovation Showcase. The event is 9 a.m. to 3 p.m. Thursday, June 16, at the Manufacturing Demonstration Facility at ORNL’s Hardin Valley campus.
Researchers at Oak Ridge National Laboratory have empirically quantified the shifts in routine daytime activities, such as getting a morning coffee or takeaway dinner, following safer at home orders during the early days of the COVID-19 pandemic.
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.