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Media Contacts
The Department of Energy has selected Oak Ridge National Laboratory to lead a collaboration charged with developing quantum technologies that will usher in a new era of innovation.
A team led by Oak Ridge National Laboratory developed a novel, integrated approach to track energy-transporting ions within an ultra-thin material, which could unlock its energy storage potential leading toward faster charging, longer-lasting devices.
A team led by ORNL created a computational model of the proteins responsible for the transformation of mercury to toxic methylmercury, marking a step forward in understanding how the reaction occurs and how mercury cycles through the environment.
Oak Ridge National Laboratory researchers have designed and additively manufactured a first-of-its-kind aluminum device that enhances the capture of carbon dioxide emitted from fossil fuel plants and other industrial processes.
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.
Horizon31, LLC has exclusively licensed a novel communication system that allows users to reliably operate unmanned vehicles such as drones from anywhere in the world using only an internet connection.
An all-in-one experimental platform developed at Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences accelerates research on promising materials for future technologies.
Combining expertise in physics, applied math and computing, Oak Ridge National Laboratory scientists are expanding the possibilities for simulating electromagnetic fields that underpin phenomena in materials design and telecommunications.
Real-time measurements captured by researchers at ORNL provide missing insight into chemical separations to recover cobalt, a critical raw material used to make batteries and magnets for modern technologies.
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.