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Researchers from Yale University and ORNL collaborated on neutron scattering experiments to study hydrogen atom locations and their effects on iron in a compound similar to those commonly used in industrial catalysts.
Critical Materials Institute researchers at Oak Ridge National Laboratory and Arizona State University studied the mineral monazite, an important source of rare-earth elements, to enhance methods of recovering critical materials for energy, defense and manufacturing applications.
Researchers at Oak Ridge National Laboratory have identified a key need for future hydropower innovations – full-scale testing – to better inform developers and operators before making major investments.
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.
Neutron scattering techniques were used as part of a study of a novel nanoreactor material that grows crystalline hydrogen clathrates, or HCs, capable of storing hydrogen.
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.