Polyphase wireless power transfer system achieves 270-kilowatt charge, s...
Oak Ridge National Laboratory researchers combined additive manufacturing with conventional compression molding to produce high-performance thermoplastic composites reinforced with short carbon fibers.
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 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.
Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences contributed to a groundbreaking experiment published in Science that tracks the real-time transport of individual molecules.
Oak Ridge National Laboratory and collaborators have discovered that signaling molecules known to trigger symbiosis between plants and soil bacteria are also used by almost all fungi as chemical signals to communicate with each other.
Scientists from Oak Ridge National Laboratory used high-performance computing to create protein models that helped reveal how the outer membrane is tethered to the cell membrane in certain bacteria.
Scientists at Oak Ridge National Laboratory and the University of Tennessee designed and demonstrated a method to make carbon-based materials that can be used as electrodes compatible with a specific semiconductor circuitry.
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.