Carter to lead Fusion Energy Division at Oak Ridge National Laboratory
Oak Ridge National Laboratory researchers serendipitously discovered when they automated the beam of an electron microscope to precisely drill holes in the atomically thin lattice of graphene, the drilled holes closed up.
Researchers at ORNL have developed a tool that provides accurate measurements and positioning directions to those installing energy-efficient panels over existing building exteriors. This method will decrease installation time and cost by more than 25%.
Researchers at ORNL have developed an online tool that offers industrial plants an easier way to track and download information about their energy footprint and carbon emissions.
Global carbon emissions from inland waters such as lakes, rivers, streams and ponds are being undercounted by about 13% and will likely continue to rise given climate events and land use changes, ORNL scientists found.
ORNL researchers demonstrated a process for producing a moisture-stable, lightweight thermal insulation material using hollow silica particles, or HSPs.
To further the potential benefits of the nation’s hydropower resources, researchers at Oak Ridge National Laboratory have developed and maintain a comprehensive water energy digital platform called HydroSource.
Oak Ridge National Laboratory researchers demonstrated that microchannel heat exchangers in heating, ventilation and air conditioning units can keep refrigerants evenly and continually distributed by inserting a device called a piezoelectric-driven
Oak Ridge National Laboratory researchers demonstrated that an electrochemical sensor paired with a transmitter not only detects propane leaks within seconds, but it can also send a signal to alert emergency services.
Oak Ridge National Laboratory researchers developed an invertible neural network, a type of artificial intelligence that mimics the human brain, to improve accuracy in climate-change models and predictions.
An Oak Ridge National Laboratory team developed a novel technique using sensors to monitor seismic and acoustic activity and machine learning to differentiate operational activities at facilities from “noise” in the recorded data.