Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
In experiment after experiment, the synthetic radioisotope actinium-225 has shown promise for targeting and attacking certain types of cancer cells.
A team from ORNL, Stanford University and Purdue University developed and demonstrated a novel, fully functional quantum local area network, or QLAN, to enable real-time adjustments to information shared with geographically isolated systems at ORNL
The Department of Energy’s Oak Ridge National Laboratory has licensed its award-winning artificial intelligence software system, the Multinode Evolutionary Neural Networks for Deep Learning, to General Motors for use in vehicle technology and design.
A rare isotope in high demand for treating cancer is now more available to pharmaceutical companies developing and testing new drugs.
Balendra Sutharshan, deputy associate laboratory director for operational systems at DOE’s Pacific Northwest National Laboratory, has joined ORNL as associate laboratory director for the Isotope Science and Engineering Directorate.
Brian Damiano, head of the Centrifuge Engineering and Fabrication Section, has been elected fellow of the American Society of Mechanical Engineers.
In the quest for advanced vehicles with higher energy efficiency and ultra-low emissions, ORNL researchers are accelerating a research engine that gives scientists and engineers an unprecedented view inside the atomic-level workings of combustion engines in real time.
Six scientists at the Department of Energy’s Oak Ridge National Laboratory were named Battelle Distinguished Inventors, in recognition of obtaining 14 or more patents during their careers at the lab.
The annual Director's Awards recognized four individuals and teams including awards for leadership in quantum simulation development and application on high-performance computing platforms, and revolutionary advancements in the area of microbial
Researchers at ORNL used quantum optics to advance state-of-the-art microscopy and illuminate a path to detecting material properties with greater sensitivity than is possible with traditional tools.