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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.

Verónica Melesse Vergara speaks with third and fourth graders at East Side Intermediate School in Brownsville. Credit: ORNL, U.S. Dept. of Energy

Twenty-seven ORNL researchers Zoomed into 11 middle schools across Tennessee during the annual Engineers Week in February. East Tennessee schools throughout Oak Ridge and Roane, Sevier, Blount and Loudon counties participated, with three West Tennessee schools joining in.

Pella Marion

A new Department of Energy report produced by Oak Ridge National Laboratory details national and international trends in hydropower, including the role waterpower plays in enhancing the flexibility and resilience of the power grid.

Small, 3D-printed neutron collimators, designed by ORNL’s Jamie Molaison, yield reduced costs and manufacturing times and could enable new types of experiments. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

The ExOne Company, the global leader in industrial sand and metal 3D printers using binder jetting technology, announced it has reached a commercial license agreement with Oak Ridge National Laboratory to 3D print parts in aluminum-infiltrated boron carbide.

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.