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Promethium’s secrets exposed
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tnAchieves students learn about lab careers
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Wagner receives ASME Medal

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The sun sets behind the ORNL Visitor Center in this aerial photo from April 2023. Credit: Kase Clapp/ORNL, U.S. Dept. of Energy

ORNL projects receive $8M from DOE Technology Commercialization Fund in FY23

In fiscal year 2023 — Oct. 1–Sept. 30, 2023 — Oak Ridge National Laboratory was awarded more than $8 million in technology maturation funding through the Department of Energy’s Technology Commercialization Fund, or TCF.

Tomonori Saito, Oak Ridge National Laboratory’s Inventor of the Year, was honored at Battelle’s Celebration of Solvers. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Inventor Saito honored at Battelle Celebration of Solvers

Tomonori Saito, a distinguished innovator in the field of polymer science and senior R&D staff member at ORNL, was honored on May 11 in Columbus, Ohio, at Battelle’s Celebration of Solvers.

Jeff Foster, Distinguished Staff Fellow at Oak Ridge National Laboratory, is looking for ways to control polymer sequencing for a variety of uses. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Research fellow Jeff Foster investigates ways to create designer molecules

Chemist Jeff Foster is looking for ways to control sequencing in polymers that could result in designer molecules to benefit a variety of industries, including medicine and energy.

From left are UWindsor students Isabelle Dib, Dominik Dziura, Stuart Castillo and Maksymilian Dziura at ORNL’s Neutron Spin Echo spectrometer. Their work advances studies on a natural cancer treatment. Credit: Genevieve Martin/ORNL, U.S. Dept. of Energy

Neutrons reveal how the spider lily preys on cancer, preserves healthy cells

A scientific instrument at ORNL could help create a noninvasive cancer treatment derived from a common tropical plant.

Molecular dynamics simulations of the Fs-peptide revealed the presence of at least eight distinct intermediate stages during the process of protein folding. The image depicts a fully folded helix (1), various transitional forms (2–8), and one misfolded state (9). By studying these protein folding pathways, scientists hope to identify underlying factors that affect human health.

Modified deep-learning algorithms unveil features of shape-shifting proteins

Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data. Applying this technique to science problems can help unearth historically elusive solutions.