Skip to main content
ORNL’s Alexey Serov will serve as a deputy director of the R2R Consortium. Credit: Carlos Jones/ORNL, US Department of Energy

The Department of Energy’s Oak Ridge National Laboratory is providing national leadership in a new collaboration among five national laboratories to accelerate U.S. production of clean hydrogen fuel cells and electrolyzers.  

The operating phases of an eVTOL need varying amounts of power; some require the battery to discharge high amounts of current rapidly, reducing the distance the vehicle can travel before its battery must be recharged. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy

Researchers at ORNL are taking cleaner transportation to the skies by creating and evaluating new batteries for airborne electric vehicles that take off and land vertically. 

The illustration depicts ocean surface currents simulated by MPAS-Ocean. Credit: Los Alamos National Laboratory, E3SM, U.S. Dept. of Energy

A team from DOE’s Oak Ridge, Los Alamos and Sandia National Laboratories has developed a new solver algorithm that reduces the total run time of the Model for Prediction Across Scales-Ocean, or MPAS-Ocean, E3SM’s ocean circulation model, by 45%. 

A collaboration between Oak Ridge National Laboratory and Caterpillar Inc. will investigate using methanol as an alternative fuel source for marine vessels. Members of the research team kicked off the project with the installation of a 6-cylinder engine at the Department of Energy’s National Transportation Research Center at ORNL.

ORNL and Caterpillar Inc. have entered into a cooperative research and development agreement, or CRADA, to investigate using methanol as an alternative fuel source for four-stroke internal combustion marine engines.

2023 Battelle Distinguished Inventors

Four scientists affiliated with ORNL were named Battelle Distinguished Inventors during the lab’s annual Innovation Awards on Dec. 1 in recognition of being granted 14 or more United States patents.

ORNL researchers Lu Yu and Yaocai Bai examine vials that contain a chemical solution that causes the cobalt and lithium to separate from a spent battery, followed by a second stage when cobalt precipitates in the bottom. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

Used lithium-ion batteries from cell phones, laptops and a growing number of electric vehicles are piling up, but options for recycling them remain limited mostly to burning or chemically dissolving shredded batteries.

Sam Hollifield displays a prototype of the Secure Hijack, Intrusion and Exploit Layered Detector, or SHIELD, the device monitoring the cybersecurity of the semi-truck. Credit: Lena Shoemaker/ORNL, U.S. Dept. of Energy

As vehicles gain technological capabilities, car manufacturers are using an increasing number of computers and sensors to improve situational awareness and enhance the driving experience.

The Department of Energy’s Oak Ridge National Laboratory announced the establishment of its Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making. Credit: Rachel Green/ORNL, U.S. Dept. of Energy

The Department of Energy’s Oak Ridge National Laboratory announced the establishment of the Center for AI Security Research, or CAISER, to address threats already present as governments and industries around the world adopt artificial intelligence and take advantage of the benefits it promises in data processing, operational efficiencies and decision-making.

Oak Ridge National Laboratory entrance sign

The Department of Energy’s Office of Science has selected three ORNL research teams to receive funding through DOE’s new Biopreparedness Research Virtual Environment initiative.

ZEISS Head of Additive Manufacturing Technology Claus Hermannstaedter, left, and ORNL Interim Associate Laboratory Director for Energy Science and Technology Rick Raines sign a licensing agreement that allows ORNL’s machine-learning algorithm, Simurgh, to be used for rapid evaluations of 3D-printed components with industrial X-ray computed tomography, or CT. Using machine learning in CT scanning is expected to reduce the time and cost of inspections of 3D-printed parts by more than ten times.

A licensing agreement between the Department of Energy’s Oak Ridge National Laboratory and research partner ZEISS will enable industrial X-ray computed tomography, or CT, to perform rapid evaluations of 3D-printed components using ORNL’s machine