Several significant science and energy projects led by the ORNL will receive a total of $497 million in funding from the Inflation Reduction Act.
Matthew Craig grew up eagerly exploring the forest patches and knee-high waterfalls just beyond his backyard in central Illinois’ corn belt. Today, that natural curiosity and the expertise he’s cultivated in biogeochemistry and ecology are focused on how carbon cycles in and out of soils, a process that can have tremendous impact on the Earth’s climate.
U.S. Secretary of Energy Jennifer Granholm visited Oak Ridge National Laboratory today to attend a groundbreaking ceremony for the U.S. Stable Isotope Production and Research Center. The facility is slated to receive $75 million in funding from the Inflation Reduction Act.
Gang Seob “GS” Jung has known from the time he was in middle school that he was interested in science.
Researchers at ORNL explored radium’s chemistry to advance cancer treatments using ionizing radiation.
A multi-lab research team led by ORNL's Paul Kent is developing a computer application called QMCPACK to enable precise and reliable predictions of the fundamental properties of materials critical in energy research.
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.
Two decades in the making, a new flagship facility for nuclear physics opened on May 2, and scientists from the Department of Energy’s Oak Ridge National Laboratory have a hand in 10 of its first 34 experiments.
What’s getting Jim Szybist fired up these days? It’s the opportunity to apply his years of alternative fuel combustion and thermodynamics research to the challenge of cleaning up the hard-to-decarbonize, heavy-duty mobility sector — from airplanes to locomotives to ships and massive farm combines.
An Oak Ridge National Laboratory team developed a novel technique using sensors to monitor seismic and acoustic activity and machine learning to differentiate operational activities at facilities from “noise” in the recorded data.