Artificial intelligence tools secure tomorrow’s electric grid
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.
Oak Ridge National Laboratory scientists demonstrated that an electron microscope can be used to selectively remove carbon atoms from graphene’s atomically thin lattice and stitch transition-metal dopant atoms in their place.
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.
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
Oak Ridge National Laboratory researchers proved that the heat transport ability of lithium-ion battery cathodes is much lower than previously determined, a finding that could help explain barriers to increasing energy storage capacity and boosting performance.
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.