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Researchers at Oak Ridge National Laboratory discovered a tug-of-war strategy to enhance chemical separations needed to recover critical materials. Credit: Alex Ivanov/ORNL, U.S. Dept. of Energy

ORNL scientists combined two ligands, or metal-binding molecules, to target light and heavy lanthanides simultaneously for exceptionally efficient separation.

Researchers observe T-shaped cluster drives lanthanide separation system during liquid-liquid extraction. Credit: Alex Ivanov/ORNL, U.S. Dept. of Energy

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.

Researchers captured atomic-level insights on the rare-earth mineral monazite to inform future design of flotation collector molecules, illustrated above, that can aid in the recovery of critical materials. Credit: Chad Malone/ORNL, U.S. Dept. of Energy

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.

ORNL’s green solvent enables environmentally friendly recycling of valuable Li-ion battery materials. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy

Scientists at Oak Ridge National Laboratory have developed a solvent that results in a more environmentally friendly process to recover valuable materials from used lithium-ion batteries, supports a stable domestic supply chain for new batteries

The proposed Battery Identity Global Passport suggests a scannable QR code or other digital tag affixed to Li-ion batteries to identify materials for efficient end-of-life recycling. Credit: Andy Sproles, ORNL/U.S. Dept. of Energy

Scientists at Oak Ridge National Laboratory have devised a method to identify the unique chemical makeup of every lithium-ion battery around the world, information that could accelerate recycling, recover critical materials and resolve a growing waste stream.

An X-ray CT image of a 3D-printed metal turbine blade was reconstructed using ORNL’s neural network and advanced algorithms. Credit: Amir Ziabari/ORNL, U.S. Dept. of Energy

Algorithms developed at Oak Ridge National Laboratory can greatly enhance X-ray computed tomography images of 3D-printed metal parts, resulting in more accurate, faster scans.