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ORNL and Department of Energy officials dedicated the launch of two clean energy research initiatives that focus on the recycling and recovery of advanced manufacturing materials and on connected and
Through a one-of-a-kind experiment at ORNL, nuclear physicists have precisely measured the weak interaction between protons and neutrons. The result quantifies the weak force theory as predicted by the Standard Model of Particle Physics.
Experts at the Department of Energy’s Oak Ridge National Laboratory are now offering short-term technical and scientific assistance to entities working to combat the coronavirus through the COVID-19 Technical Assistance Program, an initiative of DOE’s Office of Technology Transitions.
Oak Ridge National Laboratory researchers used additive manufacturing to build a first-of-its kind smart wall called EMPOWER.
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.
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.
The Society of Manufacturing Engineers, known as SME, has named William Peter, director of Oak Ridge National Laboratory’s Manufacturing Demonstration Facility in the Energy and Environmental Sciences Directorate, among its 2020 College of SME Fellows.
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.
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.