Stephen Forrest, Peter A. Franken Distinguished University Professor of Engineering and Paul G. Goebel Professor of Engineering, is the co-recipient of the 2017 IEEE Jun-ichi Nishizawa Medal, along with Ching W. Tang and Mark Thompson, “For their pioneering work on organic devices, leading to organic light-emitting diode displays.” http://eecs.umich.edu/eecs/about/articles/2016/stephen-forrest-receives-ieee-jun-ichi-nishizawa-medal-for-pioneering-work-in-oleds.html
Researchers from Brown University have demonstrated an unusual method of putting the brakes on superconductivity, the ability of a material to conduct an electrical current with zero resistance.
A University of California, Riverside assistant professor has combined photosynthesis and physics to make a key discovery that could help make solar cells more efficient. The findings were recently published in the journal Nano Letters. https://ucrtoday.ucr.edu/42499
Four Dartmouth professors have been named fellows of the American Association for the Advancement of Science (AAAS), the world’s largest general scientific society and the publisher of the journal Science. In its announcement, AAAS writes, “These individuals have been elevated to this rank because of their efforts toward advancing science applications that are deemed scientifically or socially distinguished.”
Now a team led by Stanford electrical engineering Associate Professor Eric Pop has demonstrated how it might be possible to mass-produce such atomically thin materials and electronics. Why would this be useful? Because such thin materials would be transparent and flexible as well, in ways that would enable electronic devices that wouldn’t be possible to make with silicon.
In a paper published Friday, November 25, 2016, in the journal Science Advances, researchers from the Georgia Institute of Technology and the University of Toledo report on an X-ray-determined structure that authenticates the a priori prediction, and in conjunction with first-principles theoretical analysis, supports the underlying forecasting methodology.