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An Oak Ridge National Laboratory-led team used a scanning transmission electron microscope to selectively position single atoms below a crystal’s surface for the first time.

ORNL cybersecurity researchers Jared Smith (left) and Elliot Greenlee (right) participate in a demonstration day event to showcase how Akatosh, a new security analysis tool, quickly sorts through data to identify potential threats.

As technology continues to evolve, cybersecurity threats do as well. To better safeguard digital information, a team of researchers at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory (ORNL) has developed Akatosh, a security analysis tool that works in conjunctio...

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Qrypt, Inc., has exclusively licensed a novel cyber security technology from the Department of Energy’s Oak Ridge National Laboratory, promising a stronger defense against cyberattacks including those posed by quantum computing.

Sergei Kalinin, director of the Institute for Functional Imaging of Materials at Oak Ridge National Laboratory, convenes experts in microscopy and computing to gain scientific insights that will inform design of advanced materials for energy and informati

Sergei Kalinin of the Department of Energy’s Oak Ridge National Laboratory knows that seeing something is not the same as understanding it. As director of ORNL’s Institute for Functional Imaging of Materials, he convenes experts in microscopy and computing to gain scientific insigh...

Schematic drawing of the boron nitride cell. Credit: University of Illinois at Chicago.

A new microscopy technique developed at the University of Illinois at Chicago allows researchers to visualize liquids at the nanoscale level — about 10 times more resolution than with traditional transmission electron microscopy — for the first time. By trapping minute amounts of...

Ryan Kerekes is leader of the RF, Communications, and Cyber-Physical Security Group at Oak Ridge National Laboratory. Photos by Genevieve Martin, ORNL.

As leader of the RF, Communications, and Cyber-Physical Security Group at Oak Ridge National Laboratory, Kerekes heads an accelerated lab-directed research program to build virtual models of critical infrastructure systems like the power grid that can be used to develop ways to detect and repel cyber-intrusion and to make the network resilient when disruption occurs.

Illustration of satellite in front of glowing orange celestial body

A shield assembly that protects an instrument measuring ion and electron fluxes for a NASA mission to touch the Sun was tested in extreme experimental environments at Oak Ridge National Laboratory—and passed with flying colors. Components aboard Parker Solar Probe, which will endure th...

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 Director Thomas Zacharia (center, seated) visited Robertsville Middle School to present a check in support of the school’s CubeSat efforts.

Last November a team of students and educators from Robertsville Middle School in Oak Ridge and scientists from Oak Ridge National Laboratory submitted a proposal to NASA for their Cube Satellite Launch Initiative in hopes of sending a student-designed nanosatellite named RamSat into...

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