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Anne Campbell, a researcher at ORNL, recently won the Young Leaders Professional Development Award from the Minerals, Metals & Materials Society, or TMS, and has been chosen as the first recipient of the Young Leaders International Scholar Program award from TMS and the Korean Institute of Metals and Materials, or KIM.
Quantum computers process information using quantum bits, or qubits, based on fragile, short-lived quantum mechanical states. To make qubits robust and tailor them for applications, researchers from the Department of Energy’s Oak Ridge National Laboratory sought to create a new material system.
Neutron experiments can take days to complete, requiring researchers to work long shifts to monitor progress and make necessary adjustments. But thanks to advances in artificial intelligence and machine learning, experiments can now be done remotely and in half the time.
How did we get from stardust to where we are today? That’s the question NASA scientist Andrew Needham has pondered his entire career.
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.
While studying how bio-inspired materials might inform the design of next-generation computers, scientists at ORNL achieved a first-of-its-kind result that could have big implications for both edge computing and human health.
Researchers at the Department of Energy’s Oak Ridge National Laboratory and their technologies have received seven 2022 R&D 100 Awards, plus special recognition for a battery-related green technology product.
Oak Ridge National Laboratory researchers are developing a first-of-its-kind artificial intelligence device for neutron scattering called Hyperspectral Computed Tomography, or HyperCT.
Scientists at ORNL used neutron scattering to determine whether a specific material’s atomic structure could host a novel state of matter called a spiral spin liquid.
Researchers at ORNL are teaching microscopes to drive discoveries with an intuitive algorithm, developed at the lab’s Center for Nanophase Materials Sciences, that could guide breakthroughs in new materials for energy technologies, sensing and computing.