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ORNL researcher Louise Evans is working to ensure safeguards approaches and verification technologies are integrated early in the design process of advanced reactor technologies. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy
Decades of nuclear expertise guides global nonproliferation innovation
To turn carbon dioxide, or CO2, into methanol, or CH3OH, copper (shown in yellow) on a hydride-substituted support speeds reactions mediated by hydrides and catalyzed by hydrogen atoms (shown in black) from surface-adsorbed formate, HCOO*. Credit: Yang He/ORNL, U.S. Dept. of Energy
Ion swap improves CO2-defeating catalyst
Caption: The Na-CO2 battery developed at ORNL, consisting of two electrodes in a saltwater solution, pulls atmospheric carbon dioxide into its electrochemical reaction, and releases only valuable biproducts. Credit: Andy Sproles/ORNL, U.S. Dept. of Energy
Carbon-capture batteries store renewable energy

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Researchers reveal how microbes cope in phosphorus-deficient tropical soil

A team led by the Department of Energy’s Oak Ridge National Laboratory has uncovered how certain soil microbes cope in a phosphorus-poor environment to survive in a tropical ecosystem. Their novel approach could be applied in other ecosystems to study various nutrient limitations and inform agriculture and terrestrial biosphere modeling.
ORNL’s Steven Young (left) and Travis Johnston used Titan to prove the design and training of deep learning networks could be greatly accelerated with a capable computing system.

ORNL researchers use Titan to accelerate design, training of deep learning networks

A team of researchers from the Department of Energy’s Oak Ridge National Laboratory has married artificial intelligence and high-performance computing to achieve a peak speed of 20 petaflops in the generation and training of deep learning networks on the

An example of a spiking neural network shows how data can be classified using the neuromorphic device. Credit: Catherine Schuman and Margaret Drouhard/Oak Ridge National Laboratory, U.S. Dept. of Energy

Data—Plug-in learning

For smarter data management and analysis, researchers have developed a low-power neuromorphic device based on spiking neural networks that can quickly and more efficiently analyze and classify data.