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Media Contacts
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
Digital twins are exactly what they sound like: virtual models of physical reality that continuously update to reflect changes in the real world.
Oak Ridge National Laboratory scientists identified a gene “hotspot” in the poplar tree that triggers dramatically increased root growth. The discovery supports development of better bioenergy crops and other plants that can thrive in difficult conditions while storing more carbon belowground.
The Department of Energy’s Office of Science has selected three ORNL research teams to receive funding through DOE’s new Biopreparedness Research Virtual Environment initiative.
Nonfood, plant-based biofuels have potential as a green alternative to fossil fuels, but the enzymes required for production are too inefficient and costly to produce. However, new research is shining a light on enzymes from fungi that could make biofuels economically viable.
Craig Blue, Defense Manufacturing Program Director at the Department of Energy’s Oak Ridge National Laboratory, was recently elected to a two-year term on the Institute for Advanced Composites Manufacturing Innovation Consortium Council, a body of professionals from academia, state governments, and national laboratories that provides strategic direction and oversight to IACMI.
A partnership of ORNL, the Tennessee Department of Economic and Community Development, the Community Reuse Organization of East Tennessee and TVA that aims to attract nuclear energy-related firms to Oak Ridge has been recognized with a state and local economic development award from the Federal Laboratory Consortium.
How an Alvin M. Weinberg Fellow is increasing security for critical infrastructure components
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.