The presence of minerals called ash in plants makes little difference to the fitness of new naturally derived compound materials designed for additive manufacturing, an Oak Ridge National Laboratory-led team found.
Oak Ridge National Laboratory scientists designed a recyclable polymer for carbon-fiber composites to enable circular manufacturing of parts that boost energy efficiency in automotive, wind power and aerospace applications.
Several significant science and energy projects led by the ORNL will receive a total of $497 million in funding from the Inflation Reduction Act.
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.
Tomás Rush began studying the mysteries of fungi in fifth grade and spent his college intern days tromping through forests, swamps and agricultural lands searching for signs of fungal plant pathogens causing disease on host plants.
Researchers at ORNL explored radium’s chemistry to advance cancer treatments using ionizing radiation.
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.
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.
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.
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.