Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
To better understand the spread of SARS-CoV-2, the virus that causes COVID-19, Oak Ridge National Laboratory researchers have harnessed the power of supercomputers to accurately model the spike protein that binds the novel coronavirus to a human cell receptor.
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.
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 are developing a first-of-a-kind toolkit drawing on video game development software to visualize radiation data.
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.
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 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.