Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
Researchers at Oak Ridge National Laboratory have identified a statistical relationship between the growth of cities and the spread of paved surfaces like roads and sidewalks. These impervious surfaces impede the flow of water into the ground, affecting the water cycle and, by extension, the climate.
Oak Ridge National Laboratory researchers combined additive manufacturing with conventional compression molding to produce high-performance thermoplastic composites reinforced with short carbon fibers.
A better way of welding targets for Oak Ridge National Laboratory’s plutonium-238 production has sped up the process and improved consistency and efficiency. This advancement will ultimately benefit the lab’s goal to make enough Pu-238 – the isotope that powers NASA’s deep space missions – to yield 1.5 kilograms of plutonium oxide annually by 2026.
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
Irradiation may slow corrosion of alloys in molten salt, a team of Oak Ridge National Laboratory scientists has found in preliminary tests.
A developing method to gauge the occurrence of a nuclear reactor anomaly has the potential to save millions of dollars.
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.
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.
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.