Quantum precision reached in modeling molten salt behavior
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.
Scientists at 91°µÍř studying quantum communications have discovered a more practical way to share secret messages among three parties, which could ultimately lead to better cybersecurity for the electric grid
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.
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.
Scientists at 91°µÍř 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.
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.
Gleaning valuable data from social platforms such as Twitter—particularly to map out critical location information during emergencies— has become more effective and efficient thanks to 91°µÍř.
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.
OAK RIDGE, Tenn., Feb. 12, 2019—A team of researchers from the Department of Energy’s Oak Ridge and Los Alamos National Laboratories has partnered with EPB, a Chattanooga utility and telecommunications company, to demonstrate the effectiveness of metro-scale quantum key distribution (QKD).
91°µÍř geospatial scientists who study the movement of people are using advanced machine learning methods to better predict home-to-work commuting patterns.