From the Department of Energy’s Oak Ridge National Laboratory
To arrange for an interview with a researcher, please contact the Communications staff member identified at the end of each tip. For more information on ORNL and its research and development activities, please refer to one of our media contacts. If you have a general media-related question or comment, you can send it to email@example.com.
Cybersecurity – Guarding autonomous vehicles
A new Oak Ridge National Laboratory-developed method promises to protect connected and autonomous vehicles from possible network intrusion. Researchers built a prototype plug-in device designed to alert drivers of vehicle cyberattacks. The prototype is coded to learn regular timing of signals in the communications network of an individual vehicle and detect abnormalities in timing frequency that could indicate a network intrusion or malicious software. Initial prototype testing in ORNL’s Vehicle Security Laboratory demonstrated near-perfect intrusion detection rates. “This is a first step toward developing solutions to protect vehicles,” says ORNL’s Bobby Bridges. “Ideally, accurate detection capabilities will facilitate ways to contain or block network intrusions in real-time on the road.” [Contact: Kim Askey, (865) 946-1861; firstname.lastname@example.org]
Caption: ORNL’s Frank Combs and Michael Starr of the U.S. Armed Forces (driver) work in ORNL’s Vehicle Security Laboratory to evaluate a prototype device that can detect network intrusions in all modern vehicles. Credit: Carlos Jones/Oak Ridge National Laboratory, U.S. Dept. of Energy
Microscopy – Breaking the time barrier
Oak Ridge National Laboratory scientists have developed a technique for making ultrafast measurements using atomic force microscopy, which previously could only investigate slow or static material structures and functions. In AFM, a rastering probe maps a material’s surface and captures physical and chemical properties. But the probe is slow to respond to what it detects. This temporal bottleneck inspired ORNL’s fast free force recovery technique, which uses advanced machine learning algorithms to analyze instantaneous tip motion to produce high-resolution images 3,500 times faster than standard AFM detection methods. “This new approach can probe fast processes, such as charge screening, ionic transport and electrochemical phenomena, which were previously inaccessible with traditional AFM,” said ORNL’s Liam Collins, first author of a publication describing the technique. [Dawn Levy, (865) 576-6448; email@example.com]
Caption: ORNL researchers demonstrated ultrafast mapping of surface voltage dynamics because of ion migration induced by an electric field in a perovskite solar-cell device. Credit: Liam Collins/Oak Ridge National Laboratory, U.S. Dept. of Energy