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Media Contacts
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.
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.
Oak Ridge National Laboratory researchers have developed a new family of cathodes with the potential to replace the costly cobalt-based cathodes typically found in today’s lithium-ion batteries that power electric vehicles and consumer electronics.
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
Seven ORNL scientists have been named among the 2020 Highly Cited Researchers list, according to Clarivate, a data analytics firm that specializes in scientific and academic research.
Planning for a digitized, sustainable smart power grid is a challenge to which Suman Debnath is using not only his own applied mathematics expertise, but also the wider communal knowledge made possible by his revival of a local chapter of the IEEE professional society.
Soteria Battery Innovation Group has exclusively licensed and optioned a technology developed by Oak Ridge National Laboratory designed to eliminate thermal runaway in lithium ion batteries due to mechanical damage.
Four research teams from the Department of Energy’s Oak Ridge National Laboratory and their technologies have received 2020 R&D 100 Awards.
Oak Ridge National Laboratory researchers have developed artificial intelligence software for powder bed 3D printers that assesses the quality of parts in real time, without the need for expensive characterization equipment.
Oak Ridge National Laboratory researchers have developed a machine learning model that could help predict the impact pandemics such as COVID-19 have on fuel demand in the United States.