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Media Contacts
The Neutron Scattering Society of America, or NSSA, recognized Oak Ridge National Laboratory’s Ke An and Ken Herwig as fellows for their outstanding contributions to neutron scattering.
Jens Dilling has been named associate laboratory director for the Neutron Sciences Directorate at the Department of Energy’s Oak Ridge National Laboratory, effective April 1.
Scientists at ORNL have developed 3-D-printed collimator techniques that can be used to custom design collimators that better filter out noise during different types of neutron scattering experiments
Astrophysicists at the State University of New York, Stony Brook and University of California, Berkeley, used the Oak Ridge Leadership Computing Facility’s Summit supercomputer to compare models of X-ray bursts in 2D and 3D.
The United States could triple its current bioeconomy by producing more than 1 billion tons per year of plant-based biomass for renewable fuels, while meeting projected demands for food, feed, fiber, conventional forest products and exports, according to the DOE’s latest Billion-Ton Report led by ORNL.
Ilenne Del Valle is merging her expertise in synthetic biology and environmental science to develop new technologies to help scientists better understand and engineer ecosystems for climate resilience.
The 2023 top science achievements from HFIR and SNS feature a broad range of materials research published in high impact journals such as Nature and Advanced Materials.
A team of computational scientists at ORNL has generated and released datasets of unprecedented scale that provide the ultraviolet visible spectral properties of over 10 million organic molecules.
Scientists from Stanford University and the Department of Energy’s Oak Ridge National Laboratory are turning air into fertilizer without leaving a carbon footprint. Their discovery could deliver a much-needed solution to help meet worldwide carbon-neutral goals by 2050.
Lee's paper at the August conference in Bellevue, Washington, combined weather and power outage data for three states – Texas, Michigan and Hawaii – and used a machine learning model to predict how extreme weather such as thunderstorms, floods and tornadoes would affect local power grids and to estimate the risk for outages. The paper relied on data from the National Weather Service and the U.S. Department of Energy’s Environment for Analysis of Geo-Located Energy Information, or EAGLE-I, database.