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Scientists at Oak Ridge National Laboratory successfully demonstrated a technique to heal dendrites that formed in a solid electrolyte, resolving an issue that can hamper the performance of high energy-density, solid-state batteries.
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.
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 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.
Fuel economy can take a tumble when temperatures plummet, according to the Department of Energy’s 2021 Fuel Economy Guide. Compiled by researchers at Oak Ridge National Laboratory, the guide includes several tips to improve a vehicle’s fuel performance.
If air taxis become a viable mode of transportation, Oak Ridge National Laboratory researchers have estimated they could reduce fuel consumption significantly while alleviating traffic congestion.
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.