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Media Contacts
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.
ORNL researchers discovered genetic mutations that underlie autism using a new approach that could lead to better diagnostics and drug therapies.
Oak Ridge National Laboratory scientists designed a recyclable polymer for carbon-fiber composites to enable circular manufacturing of parts that boost energy efficiency in automotive, wind power and aerospace applications.
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.
A multi-lab research team led by ORNL's Paul Kent is developing a computer application called QMCPACK to enable precise and reliable predictions of the fundamental properties of materials critical in energy research.
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 developed an invertible neural network, a type of artificial intelligence that mimics the human brain, to improve accuracy in climate-change models and predictions.
Researchers at Oak Ridge National Laboratory have empirically quantified the shifts in routine daytime activities, such as getting a morning coffee or takeaway dinner, following safer at home orders during the early days of the COVID-19 pandemic.
Scientists are using Oak Ridge National Laboratory’s Multicharged Ion Research Facility to simulate the cosmic origin of X-ray emissions resulting when highly charged ions collide with neutral atoms and molecules, such as helium and gaseous hydrogen.
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.