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A nanobrush made by pulsed laser deposition of CeO2 and Y2O3 with dim and bright bands, respectively, is seen in cross-section with scanning transmission electron microscopy. Credit: Oak Ridge National Laboratory, U.S. Dept. of Energy

A team led by the Department of Energy’s Oak Ridge National Laboratory synthesized a tiny structure with high surface area and discovered how its unique architecture drives ions across interfaces to transport energy or information.

Gobet_Advincula Portrait

Rigoberto “Gobet” Advincula has been named Governor’s Chair of Advanced and Nanostructured Materials at Oak Ridge National Laboratory and the University of Tennessee.

Summit supercomputer

Processes like manufacturing aircraft parts, analyzing data from doctors’ notes and identifying national security threats may seem unrelated, but at the U.S. Department of Energy’s Oak Ridge National Laboratory, artificial intelligence is improving all of these tasks.

early prototype of the optical array developed by Oak Ridge National Laboratory.

IDEMIA Identity & Security USA has licensed an advanced optical array developed at Oak Ridge National Laboratory. The portable technology can be used to help identify individuals in challenging outdoor conditions.

(From left) ORNL Associate Laboratory Director for Computing and Computational Sciences Jeff Nichols; ORNL Health Data Sciences Institute Director Gina Tourassi; DOE Deputy Under Secretary for Science Thomas Cubbage; ORNL Task Lead for Biostatistics Blair Christian; and ORNL Research Scientist Ioana Danciu were invited to the White House to showcase an ORNL-developed digital tool aimed at better matching cancer patients with clinical trials.

OAK RIDGE, Tenn., March 4, 2019—A team of researchers from the Department of Energy’s Oak Ridge National Laboratory Health Data Sciences Institute have harnessed the power of artificial intelligence to better match cancer patients with clinical trials.

Sean Hearne has been named director of the Center for Nanophase Materials Sciences at Oak Ridge National Laboratory.

OAK RIDGE, Tenn., Feb. 8, 2019—The Department of Energy’s Oak Ridge National Laboratory has named Sean Hearne director of the Center for Nanophase Materials Sciences. The center is a DOE Office of Science User Facility that brings world-leading resources and capabilities to the nanoscience resear...

ORNL alanine_graphic.jpg

OAK RIDGE, Tenn., Jan. 31, 2019—A new electron microscopy technique that detects the subtle changes in the weight of proteins at the nanoscale—while keeping the sample intact—could open a new pathway for deeper, more comprehensive studies of the basic building blocks of life. 

After a monolayer MXene is heated, functional groups are removed from both surfaces. Titanium and carbon atoms migrate from one area to both surfaces, creating a pore and forming new structures. Credit: ORNL, USDOE; image by Xiahan Sang and Andy Sproles.

Scientists at the Department of Energy’s Oak Ridge National Laboratory induced a two-dimensional material to cannibalize itself for atomic “building blocks” from which stable structures formed. The findings, reported in Nature Communications, provide insights that ...

From left, Andrew Lupini and Juan Carlos Idrobo use ORNL’s new monochromated, aberration-corrected scanning transmission electron microscope, a Nion HERMES to take the temperatures of materials at the nanoscale. Image credit: Oak Ridge National Laboratory

A scientific team led by the Department of Energy’s Oak Ridge National Laboratory has found a new way to take the local temperature of a material from an area about a billionth of a meter wide, or approximately 100,000 times thinner than a human hair. This discove...

ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.

A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the