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Media Contacts
Oak Ridge National Laboratory scientists ingeniously created a sustainable, soft material by combining rubber with woody reinforcements and incorporating “smart” linkages between the components that unlock on demand.
Groundwater withdrawals are expected to peak in about one-third of the world’s basins by 2050, potentially triggering significant trade and agriculture shifts, a new analysis finds.
To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance.
ORNL scientists combined two ligands, or metal-binding molecules, to target light and heavy lanthanides simultaneously for exceptionally efficient separation.
Researchers from Yale University and ORNL collaborated on neutron scattering experiments to study hydrogen atom locations and their effects on iron in a compound similar to those commonly used in industrial catalysts.
Researchers at ORNL zoomed in on molecules designed to recover critical materials via liquid-liquid extraction — a method used by industry to separate chemically similar elements.
Critical Materials Institute researchers at Oak Ridge National Laboratory and Arizona State University studied the mineral monazite, an important source of rare-earth elements, to enhance methods of recovering critical materials for energy, defense and manufacturing applications.
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 Oak Ridge National Laboratory are using state-of-the-art methods to shed light on chemical separations needed to recover rare-earth elements and secure critical materials for clean energy technologies.
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.