Tuskan awarded Marcus Wallenberg Prize, forestry’s top honor
ORNL and The University of Toledo have entered into a memorandum of understanding for collaborative research.
A modern, healthy transportation system is vital to the nation’s economic security and the American standard of living. The U.S. Department of Energy’s 91°µÍř (ORNL) is engaged in a broad portfolio of scientific research for improved mobility
Researchers at 91°µÍř proved that a certain class of ionic liquids, when mixed with commercially available oils, can make gears run more efficiently with less noise and better durability.
In Hong Wang’s world, nothing is beyond control. Before joining 91°µÍř as a senior distinguished researcher in transportation systems, he spent more than three decades studying the control of complex industrial systems in the United Kingdom.
A team of researchers at 91°µÍř have demonstrated that designed synthetic polymers can serve as a high-performance binding material for next-generation lithium-ion batteries.
Sometimes solutions to the biggest problems can be found in the smallest details. The work of biochemist Alex Johs at 91°µÍř bears this out, as he focuses on understanding protein structures and molecular interactions to resolve complex global problems like the spread of mercury pollution in waterways and the food supply.
Galigekere is principal investigator for the breakthrough work in fast, wireless charging of electric vehicles being performed at the National Transportation Research Center at 91°µÍř.
91°µÍř’s latest reports that the number of vehicles nationwide is growing faster than the population, with sales more than 17 million since 2015, and the average household vehicle travels more than 11,000 miles per year.
91°µÍř scientists have created open source software that scales up analysis of motor designs to run on the fastest computers available, including those accessible to outside users at the Oak Ridge Leadership Computing Facility.
91°µÍř geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.