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November 2009 story tips from the US Department of Energy's Oak Ridge National Laboratory

TRANSPORTATION -- Gone hybrid . . .

Fuel economy ratings for the new 2010 model year automobiles are now posted at the web site, which Oak Ridge National Laboratory developed and maintains for the U.S. Department of Energy and the U.S. Environmental Protection Agency. Nine of the top 10 vehicles on the best fuel economy list are hybrid electrics, led by the Toyota Prius (51 MPG city; 48 highway). "This list proves that hybrids have gone mainstream," said project leader Bo Saulsbury of ORNL's Environmental Sciences Division. "There's now a hybrid for everyone. The 32 hybrid models available in 2010 offer car buyers the choice of an SUV, passenger car, economy or luxury model, from almost every major automobile maker." The site also now contains its first Spanish-language pages, including the "Find and Compare Cars" and "Gas Mileage Tips" pages which can be found at the main address or at Saulsbury said ORNL plans to post an entire Spanish-language site by the end of 2010. Many of the new models qualify for federal tax credit. Users can check out the new 2010 hybrids, clean diesels, alternative fuel vehicles and more energy efficient gasoline-powered vehicles at or at the abbreviated "mobile" site at [Contact: Mike Bradley, (865)576-9553;]

MATERIALS Ferroelectric fury...

By discovering a technique to guide the ferroelectric switching process in bismuth ferrite, a team led by Oak Ridge National Laboratory's Nina Balke has moved one step closer to developing more rugged memory and logic devices. One key to the success is that the material has both electrical and magnetic properties at the same time, which is unusual. By controlling the polarization switching it is possible to alter magnetization states as well due to parameter coupling, which can be used to store information. Using a one-of-a-kind scanning probe in research funded by ORNL's Laboratory Directed Research and Development program, Balke was able to select among final polarization states that have the same electrostatic energy but differ dramatically in elastic or magnetic order. Remarkably, the control of polarization switching allowed the authors to create the closure ferroelectric domain pattern, the precursor for the long-theorized ferroelectric vortex state. Funding for this work, published in Nature Nanotechnology, was provided by the Department of Energy's Office of Basic Energy Sciences and the National Science Foundation. The work was performed at ORNL's Center for Nanophase Materials Science. [Contact: Ron Walli, (865) 576-0226;]

MANUFACTURING -- Holey truck frames . . .

Heavy trucks are a little less heavy but just as safe and rugged because of steel rail frames provided by Metalsa Roanoke, which enlisted the help of Cam Hubbard and the High Temperature Materials Laboratory User Program at Oak Ridge National Laboratory. By working with ORNL, the Virginia manufacturer has refined a hole-cutting method that could cut 100 pounds to 200 pounds per truck for frames alone. This is the result of some beneficial residual effects of current hole-cutting methods. Metalsa supplies side rails and chassis components to about half of the North American heavy truck market, including Peterbilt, Kenworth, Volvo/Mack and Freightliner. Hubbard used the lab's neutron scattering residual stress mapping facility to determine the state of residual stresses associated with four different methods to cut holes in the steel. Through this effort, Metalsa hopes to eventually save up to 30 million pounds of steel per year. Total annual fuel savings on 150,000 trucks driven 100,000 miles is estimated to be 3.8 million gallons. This work was funded through the Department of Energy's Office of Energy Efficiency and Renewable Energy. [Contact: Ron Walli, (865) 576-0226;]

NANOSCIENCE -- Using neural networks . . .

A new approach to crunching massive volumes of data uses neural networks, an architecture of multiple elements that is figuratively taught to pool imbedded information into results, like an artificial brain. Researchers at ORNL's Center for Nanophase Materials Sciences developed and trained a neural net to recognize significant patterns in data from analytical experiments using methods such as atomic force microscopy. The work was recently published in Physical Review Letters. The neural-network approach provides a bridge between theoretical and experimental research by quickly extracting physical parameters from voluminous and complex data. The work is funded by ORNL's Laboratory Directed Research and Development program. [Contact: Bill Cabage, (865)574-4399;]


Contact: Ron Walli
DOE/Oak Ridge National Laboratory

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