A select group gathered on the morning of Dec. 20 at the Department of Energy’s Oak Ridge National Laboratory for a symposium in honor of Liane B. Russell, the renowned ORNL mammalian geneticist who died in July.
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.
Elizabeth Herndon believes in going the distance whether she is preparing to compete in the 2020 Olympic marathon trials or examining how metals move through the environment as a geochemist at the Department of Energy’s Oak Ridge National Laboratory.
In the vast frozen whiteness of the central Arctic, the Polarstern, a German research vessel, has settled into the ice for a yearlong float.
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.
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.
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.
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.