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Media Contacts
Nuclear physicists at the Department of Energy’s Oak Ridge National Laboratory recently used Frontier, the world’s most powerful supercomputer, to calculate the magnetic properties of calcium-48’s atomic nucleus.
Researchers at ORNL are extending the boundaries of composite-based materials used in additive manufacturing, or AM. ORNL is working with industrial partners who are exploring AM, also known as 3D printing, as a path to higher production levels and fewer supply chain interruptions.
In fiscal year 2023 — Oct. 1–Sept. 30, 2023 — Oak Ridge National Laboratory was awarded more than $8 million in technology maturation funding through the Department of Energy’s Technology Commercialization Fund, or TCF.
ORNL hosted its annual Smoky Mountains Computational Sciences and Engineering Conference in person for the first time since the COVID-19 pandemic.
Researchers at ORNL are developing advanced automation techniques for desalination and water treatment plants, enabling them to save energy while providing affordable drinking water to small, parched communities without high-quality water supplies.
Yarom Polsky, director of the Manufacturing Science Division, or MSD, at the Department of Energy’s Oak Ridge National Laboratory, has been elected a Fellow of the American Society of Mechanical Engineers, or ASME.
Wildfires have shaped the environment for millennia, but they are increasing in frequency, range and intensity in response to a hotter climate. The phenomenon is being incorporated into high-resolution simulations of the Earth’s climate by scientists at the Department of Energy’s Oak Ridge National Laboratory, with a mission to better understand and predict environmental change.
A study led by researchers at ORNL could uncover new ways to produce more powerful, longer-lasting batteries and memory devices.
ORNL researchers used the nation’s fastest supercomputer to map the molecular vibrations of an important but little-studied uranium compound produced during the nuclear fuel cycle for results that could lead to a cleaner, safer world.
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.