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Media Contacts
Researchers at ORNL are developing battery technologies to fight climate change in two ways, by expanding the use of renewable energy and capturing airborne carbon dioxide.
Scientists at ORNL completed a study of how well vegetation survived extreme heat events in both urban and rural communities across the country in recent years. The analysis informs pathways for climate mitigation, including ways to reduce the effect of urban heat islands.
A collection of seven technologies for lithium recovery developed by scientists from ORNL has been licensed to Element3, a Texas-based company focused on extracting lithium from wastewater produced by oil and gas production.
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.
Kate Evans, director for the Computational Sciences and Engineering Division at ORNL, has been awarded the 2024 Society for Industrial and Applied Mathematicians Activity Group on Mathematics of Planet Earth Prize.
Chuck Greenfield, former assistant director of the DIII-D National Fusion Program at General Atomics, has joined ORNL as ITER R&D Lead.
Four ORNL teams and one researcher were recognized for excellence in technology transfer and technology transfer innovation.
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.
Ateios Systems licensed an ORNL technology for solvent-free battery component production using electron curing. Through Innovation Crossroads, Ateios continues to work with ORNL to enable readiness for production-quality battery components.
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%.