Illustration of the optimized zeolite catalyst, or NbAlS-1, which enables a highly efficient chemical reaction to create butene, a renewable source of energy, without expending high amounts of energy for the conversion. Credit: Jill Hemman, Oak Ridge National Laboratory/U.S. Dept. of Energy
Students often participate in internships and receive formal training in their chosen career fields during college, but some pursue professional development opportunities even earlier.
Scientists at the U.S. Department of Energy’s Brookhaven National Laboratory have new experimental evidence and a predictive theory that solves a long-standing materials science mystery: why certain crystalline materials shrink when heated.
Two of the researchers who share the Nobel Prize in Chemistry announced Wednesday—John B. Goodenough of the University of Texas at Austin and M. Stanley Whittingham of Binghamton University in New York—have research ties to ORNL.
Artificial intelligence (AI) techniques have the potential to support medical decision-making, from diagnosing diseases to prescribing treatments. But to prioritize patient safety, researchers and practitioners must first ensure such methods are accurate.
Materials scientists, electrical engineers, computer scientists, and other members of the neuromorphic computing community from industry, academia, and government agencies gathered in downtown Knoxville July 23–25 to talk about what comes next in
In the shifting landscape of global manufacturing, American ingenuity is once again giving U.S companies an edge with radical productivity improvements as a result of advanced materials and robotic systems developed at the Department of Energy’s Manufacturing Demonstration Facility (MDF) at Oak Ridge National Laboratory.
Tempering, the heating process that gives chocolate its appealing sheen and creamy texture, is a crucial part of crafting quality chocolate. But, at the molecular level, it gets a little tricky, and when done incorrectly, can render entire batches of chocolate gritty and unappetizing.
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.
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.