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Researchers at ORNL have developed a machine-learning inspired software package that provides end-to-end image analysis of electron and scanning probe microscopy images.
A new report published by ORNL assessed how advanced manufacturing and materials, such as 3D printing and novel component coatings, could offer solutions to modernize the existing fleet and design new approaches to hydropower.
ORNL researchers Ben Ollis and Max Ferrari will be in Adjuntas to join the March 18 festivities but also to hammer out more technical details of their contribution to the project: making the microgrids even more reliable.
When aging vehicle batteries lack the juice to power your car anymore, they may still hold energy. Yet it’s tough to find new uses for lithium-ion batteries with different makers, ages and sizes. A solution is urgently needed because battery recycling options are scarce.
Oak Ridge National Laboratory scientists have devised a method to control the heating and cooling systems of a large network of buildings for power grid stability—all while ensuring the comfort of occupants.
The Department of Energy’s Oak Ridge National Laboratory is now producing actinium-227 (Ac-227) to meet projected demand for a highly effective cancer drug through a 10-year contract between the U.S. DOE Isotope Program and Bayer.
A shield assembly that protects an instrument measuring ion and electron fluxes for a NASA mission to touch the Sun was tested in extreme experimental environments at Oak Ridge National Laboratory—and passed with flying colors. Components aboard Parker Solar Probe, which will endure th...
A novel method developed at Oak Ridge National Laboratory creates supertough renewable plastic with improved manufacturability. Working with polylactic acid, a biobased plastic often used in packaging, textiles, biomedical implants and 3D printing, the research team added tiny amo...
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