Digital twins are exactly what they sound like: virtual models of physical reality that continuously update to reflect changes in the real world.
Anne Campbell, a researcher at ORNL, recently won the Young Leaders Professional Development Award from the Minerals, Metals & Materials Society, or TMS, and has been chosen as the first recipient of the Young Leaders International Scholar Program award from TMS and the Korean Institute of Metals and Materials, or KIM.
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.
Speakers, scientific workshops, speed networking, a student poster showcase and more energized the Annual User Meeting of the Department of Energy’s Center for Nanophase Materials Sciences, or CNMS, Aug. 7-10, near Market Square in downtown Knoxville, Tennessee.
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.
Eight ORNL scientists are among the world’s most highly cited researchers, according to a bibliometric analysis conducted by the scientific publication analytics firm Clarivate.
Rama Vasudevan, a research scientist at the Department of Energy’s Oak Ridge National Laboratory, has been elected a Fellow of the American Physical Society, or APS. The honor recognizes members who have made significant contributions to physics and its application to science and technology.
Researchers at the Department of Energy’s Oak Ridge National Laboratory and their technologies have received seven 2022 R&D 100 Awards, plus special recognition for a battery-related green technology product.
It’s a simple premise: To truly improve the health, safety, and security of human beings, you must first understand where those individuals are.
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.