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Media Contacts
In partnership with the National Cancer Institute, researchers from ORNL and Louisiana State University developed a long-sequenced AI transformer capable of processing millions of pathology reports to provide experts researching cancer diagnoses and management with exponentially more accurate information on cancer reporting.
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.
A team of researchers at ORNL demonstrated that a light-duty passenger electric vehicle can be wirelessly charged at 100-kW with 96% efficiency using polyphase electromagnetic coupling coils with rotating magnetic fields.
Anuj J. Kapadia, who heads the Advanced Computing Methods for Health Sciences Section at ORNL, has been elected as president of the Southeastern Chapter of the American Association of Physicists in Medicine.
To capitalize on AI and researcher strengths, scientists developed a human-AI collaboration recommender system for improved experimentation performance.
ORNL climate modeling expertise contributed to a project that assessed global emissions of ammonia from croplands now and in a warmer future, while also identifying solutions tuned to local growing conditions.
Gina Tourassi, associate laboratory director for computing and computational sciences at the US Department of Energy’s (DOE’s) Oak Ridge National Laboratory, has been named a fellow of the Institute of Electrical and Electronics Engineers, the world’s largest organization for technical professionals.
Electric vehicles can drive longer distances if their lithium-ion batteries deliver more energy in a lighter package. A prime weight-loss candidate is the current collector, a component that often adds 10% to the weight of a battery cell without contributing energy.
Oak Ridge National Laboratory researchers have identified the most energy-efficient 2024 model year vehicles available in the United States, including electric and hybrids, in the latest edition of the Department of Energy’s
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%.