РОССИЙСКАЯ АКАДЕМИЯ НАУК УРАЛЬСКОЕ ОТДЕЛЕНИЕ ИНСТИТУТ ХИМИИ TBEPДОГО ТЕЛА |
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11.06.2008 | Карта сайта Language |
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Nørskov's team used DFT to quickly screen about 70 alternative bimetallic compounds, comparing their cost, stability, activity and selectivity of ethyne hydrogenation. They picked out a nickel-zinc alloy that performed well, and after synthesising and testing it with an oxide support, they showed it was indeed highly stable and selective for the required reaction. 'This shows not only that our theoretical descriptions of these reactions are correct,' says Nørskov, 'but also that we can use DFT to predict something we don't know in order to find new materials.' Comprehensive catalysts Density functional theory calculations have been used for about 15 years to calculate the structure and properties of metals and metal oxides in order to explain how molecules are reacting on them - but it's only in the past few years that researchers have tried to use such computational methods to design better catalysts, says Jeff Greeley, who works at the Center for Nanoscale Materials, Argonne National Laboratory, Illinois, US. Nørskov's group has led the way with the first few examples of successful theoretical catalyst design.[2] But the latest study shows the most comprehensive approach yet, says Greeley, because it incorporates a study of catalyst selectivity. The key to the team's success was cutting down on computation time by finding that simply determining how effectively a methyl group clung to the surface of the alloys was a good approximation to the behaviour of more complex molecules, explains Matt Neurock, of the University of Virginia, Charlottesville, US. 'This is a very elegant study,' he says. 'From knowing there are good systems out there to developing a real catalyst is an enormous step, but we are of course taking this further,' says Nørskov. Ten months ago he and colleagues launched a company, Computational Materials Design ApS, which takes on research contracts from industrial partners to improve catalysts, using the computational high-throughput methods the group has developed. Richard Van Noorden
Enjoy this story? Spread the word using the 'tools' menu on the left. References1 F Studt et al, Science, 2008, 320, 1320 (DOI: 10.1126/science.1156660) 2 J Greeley et al, Nature Materials, 2006, 5, 909
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