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Media Contacts
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.
Tomonori Saito, a distinguished innovator in the field of polymer science and senior R&D staff member at ORNL, was honored on May 11 in Columbus, Ohio, at Battelle’s Celebration of Solvers.
Growing up in China, Yue Yuan stood beneath the world’s largest hydroelectric dam, built to harness the world’s third-longest river. Her father brought her to Three Gorges Dam every year as it was being constructed across the Yangtze River so she could witness its progress.
Chemist Jeff Foster is looking for ways to control sequencing in polymers that could result in designer molecules to benefit a variety of industries, including medicine and energy.
A scientific instrument at ORNL could help create a noninvasive cancer treatment derived from a common tropical plant.
Environmental scientists at ORNL have recently expanded collaborations with minority-serving institutions and historically Black colleges and universities across the nation to broaden the experiences and skills of student scientists while bringing fresh insights to the national lab’s missions.
The U.S. Departments of Energy and Defense teamed up to create a series of weld filler materials that could dramatically improve high-strength steel repair in vehicles, bridges and pipelines.
A new paper published in Nature Communications adds further evidence to the bradykinin storm theory of COVID-19’s viral pathogenesis — a theory that was posited two years ago by a team of researchers at the Department of Energy’s Oak Ridge National Laboratory.
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.
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.