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An ORNL-led team's observation of certain crystalline ice phases challenges accepted theories about super-cooled water and non-crystalline ice. Their findings, reported in the journal Nature, will also lead to better understanding of ice and its various phases found on other planets, moons and elsewhere in space.
OAK RIDGE, Tenn., May 8, 2019—Oak Ridge National Laboratory and Lincoln Electric (NASDAQ: LECO) announced their continued collaboration on large-scale, robotic additive manufacturing technology at the Department of Energy’s Advanced Manufacturing InnovationXLab Summit.
Researchers at the Department of Energy’s Oak Ridge National Laboratory, Pacific Northwest National Laboratory and Washington State University teamed up to investigate the complex dynamics of low-water liquids that challenge nuclear waste processing at federal cleanup sites.
A novel additive manufacturing method developed by researchers at Oak Ridge National Laboratory could be a promising alternative for low-cost, high-quality production of large-scale metal parts with less material waste.
When Scott Smith looks at a machine tool, he thinks not about what the powerful equipment used to shape metal can do – he’s imagining what it could do with the right added parts and strategies. As ORNL’s leader for a newly formed group, Machining and Machine Tool Research, Smith will have the opportunity to do just that.
Ionic conduction involves the movement of ions from one location to another inside a material. The ions travel through point defects, which are irregularities in the otherwise consistent arrangement of atoms known as the crystal lattice. This sometimes sluggish process can limit the performance and efficiency of fuel cells, batteries, and other energy storage technologies.
Scientists at Oak Ridge National Laboratory have developed a low-cost, printed, flexible sensor that can wrap around power cables to precisely monitor electrical loads from household appliances to support grid operations.
A team of scientists led by Oak Ridge National Laboratory used carbon nanotubes to improve a desalination process that attracts and removes ionic compounds such as salt from water using charged electrodes.
Using artificial neural networks designed to emulate the inner workings of the human brain, deep-learning algorithms deftly peruse and analyze large quantities of data. Applying this technique to science problems can help unearth historically elusive solutions.
Alex Roschli is no stranger to finding himself in unique situations. After all, the early career researcher in ORNL’s Manufacturing Systems Research group bears a last name that only 29 other people share in the United States, and he’s certain he’s the only Roschli (a moniker that hails from Switzerland) with the first name Alex.