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Media Contacts
![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 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](/sites/default/files/styles/list_page_thumbnail/public/news/images/2018-P00413.jpg?itok=UKejk7r2)
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 researchers Todd Toops, Charles Finney, and Melanie DeBusk (left to right) hold an example of a particulate filter used to collect harmful emissions in vehicles. ORNL researchers Todd Toops, Charles Finney, and Melanie DeBusk (left to right) hold an example of a particulate filter used to collect harmful emissions in vehicles.](/sites/default/files/styles/list_page_thumbnail/public/news/images/CG-1D%20user%20-%20ETSD_Toops-2878R_r1.jpg?itok=sRbVXIkF)
Researchers are looking to neutrons for new ways to save fuel during the operation of filters that clean the soot, or carbon and ash-based particulate matter, emitted by vehicles. A team of researchers from the Energy and Transportation Science Division at the Department of En...
![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. 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.](/sites/default/files/styles/list_page_thumbnail/public/news/images/RAvENNA%20release%20pic.png?itok=2bDpK5Mo)
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
![ORNL Image](/sites/default/files/styles/list_page_thumbnail/public/2017-S00094_2.jpg?itok=ZGWBnMOv)
Researchers used neutrons to probe a running engine at ORNL’s Spallation Neutron Source
![ORNL’s Xiahan Sang unambiguously resolved the atomic structure of MXene, a 2D material promising for energy storage, catalysis and electronic conductivity. Image credit: Oak Ridge National Laboratory, U.S. Dept. of Energy; photographer Carlos Jones ORNL’s Xiahan Sang unambiguously resolved the atomic structure of MXene, a 2D material promising for energy storage, catalysis and electronic conductivity. Image credit: Oak Ridge National Laboratory, U.S. Dept. of Energy; photographer Carlos Jones](/sites/default/files/styles/list_page_thumbnail/public/Sang_2016-P07680_0.jpg?itok=w0e5eR_U)
Researchers have long sought electrically conductive materials for economical energy-storage devices. Two-dimensional (2D) ceramics called MXenes are contenders. Unlike most 2D ceramics, MXenes have inherently good conductivity because they are molecular sheets made from the carbides ...