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Media Contacts
New capabilities and equipment recently installed at the Department of Energy’s Oak Ridge National Laboratory are bringing a creek right into the lab to advance understanding of mercury pollution and accelerate solutions.
Popular wisdom holds tall, fast-growing trees are best for biomass, but new research by two U.S. Department of Energy national laboratories reveals that is only part of the equation.
Momentum Technologies Inc., a Dallas, Texas-based materials science company that is focused on extracting critical metals from electronic waste, has licensed an Oak Ridge National Laboratory process for recovering cobalt and other metals from spent
Four research teams from the Department of Energy’s Oak Ridge National Laboratory and their technologies have received 2020 R&D 100 Awards.
The combination of bioenergy with carbon capture and storage could cost-effectively sequester hundreds of millions of metric tons per year of carbon dioxide in the United States, making it a competitive solution for carbon management, according to a new analysis by ORNL scientists.
Prometheus Fuels has licensed an ethanol-to-jet-fuel conversion process developed by researchers at Oak Ridge National Laboratory. The ORNL technology will enable cost-competitive production of jet fuel and co-production of butadiene for use in renewable polymer synthesis.
Scientists at ORNL and the University of Nebraska have developed an easier way to generate electrons for nanoscale imaging and sensing, providing a useful new tool for material science, bioimaging and fundamental quantum research.
ORNL scientists have modified a single microbe to simultaneously digest five of the most abundant components of lignocellulosic biomass, a big step forward in the development of a cost-effective biochemical conversion process to turn plants into
Kübra Yeter-Aydeniz, a postdoctoral researcher, was recently named the Turkish Women in Science group’s “Scientist of the Week.”
Researchers at ORNL used quantum optics to advance state-of-the-art microscopy and illuminate a path to detecting material properties with greater sensitivity than is possible with traditional tools.