Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
Oak Ridge National Laboratory scientists proved molybdenum titanium carbide, a refractory metal alloy that can withstand extreme temperature environments, can also be crack free and dense when produced with electron beam powder bed fusion.
A team of researchers at Oak Ridge National Laboratory and the University of Tennessee have developed a concrete mix that demonstrated high early strength within six hours of mixing, potentially doubling the production capacity for the precast industry.
A team of Oak Ridge National Laboratory researchers demonstrated that an additively manufactured hot stamping die – a tool used to create car body components – cooled faster than those produced by conventional manufacturing methods.
A method developed at Oak Ridge National Laboratory to print high-fidelity, passive sensors for energy applications can reduce the cost of monitoring critical power grid assets.
A new Department of Energy report produced by Oak Ridge National Laboratory details national and international trends in hydropower, including the role waterpower plays in enhancing the flexibility and resilience of the power grid.
Researchers at Oak Ridge National Laboratory developed self-healing elastomers that demonstrated unprecedented adhesion strength and the ability to adhere to many surfaces, which could broaden their potential use
Researchers at Oak Ridge National Laboratory were part of an international team that collected a treasure trove of data measuring precipitation, air particles, cloud patterns and the exchange of energy between the atmosphere and the sea ice.
Oak Ridge National Laboratory scientists have discovered a cost-effective way to significantly improve the mechanical performance of common polymer nanocomposite materials.
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.
Researchers at Oak Ridge National Laboratory developed a method that uses machine learning to predict seasonal fire risk in Africa, where half of the world’s wildfire-related carbon emissions originate.