Researchers Use Lasers to Transform Material Properties

MIT scientists have conducted research that could let them shine precise laser beams on substances to create new materials, change their electronic properties, and turn them into semiconductors. The researchers accomplished this by developing a way to produce and measure photon and electron coupling on a topological-insulator material – a material that has both an insulating interior and a conductive surface. This work could enable scientists to create new kinds of electronic states in solid-state systems. The researchers shone a polarized laser beam at bismuth selenide crystals and found they could change their bandgap—the energy difference between it’s a material’s nonconductive and conductive states—and turn them into a semiconductor. They add  that, although they have only experimented with bismuth selenide, the technique might be useful with other materials. They published their work in Science.(SlashDot)(MIT News Office)
 

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Understanding complex relationships: How global properties of networks become apparent locally

ScienceDaily (June 7, 2012) — From infections spreading around the globe to the onset of an epileptic seizure in the brain: Many phenomena can be seen as the effects of network activity. Often it is vitally important to understand the properties of these networks. However, they are often too complex to be described completely. Scientists from the Bernstein Center at the University of Freiburg were now able to show how global features of complex networks can be discovered in local statistical properties -- which are much more accessible for scientific investigation. The researchers were able to benefit from the high-performance computing facilities of the Bernstein Center, which are normally used to simulate the activity of nerve cells in the brain.

In an article appearing in the scientific journal PLoS ONE, Stefano Cardanobile and colleagues describe how they analysed 200,000 networks which they generated in a computer -- using models that are employed by scientists to understand the properties of naturally occurring networks. The researchers compared the results obtained from these models with well-understood networks from the real world: the metabolism of a bacterium, the relationship of synonyms in a thesaurus, and the nervous system of a worm. Thus, they were able to assess which model networks can predict the behaviour of its real-life counterpart the best. These insights can help colleagues from other fields to choose the right model in their specific research.

Most importantly, the scientists from Freiburg could demonstrate that it is possible to draw conclusions about global properties of complex networks from local statistical data. This means that one can discover important properties of networks even if they are not completely analysed -- very often an impossible task in large systems such as human social contacts or connections in the brain. Therefore, the authors see their study to represent an important step towards a better understanding of complex networks.

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The above story is reprinted from materials provided by Albert-Ludwigs-Universität Freiburg.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

Stefano Cardanobile, Volker Pernice, Moritz Deger, Stefan Rotter. Inferring General Relations between Network Characteristics from Specific Network Ensembles. PLoS ONE, 2012; 7 (6): e37911 DOI: 10.1371/journal.pone.0037911

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Understanding complex relationships: How global properties of networks become apparent locally

ScienceDaily (June 7, 2012) — From infections spreading around the globe to the onset of an epileptic seizure in the brain: Many phenomena can be seen as the effects of network activity. Often it is vitally important to understand the properties of these networks. However, they are often too complex to be described completely. Scientists from the Bernstein Center at the University of Freiburg were now able to show how global features of complex networks can be discovered in local statistical properties -- which are much more accessible for scientific investigation. The researchers were able to benefit from the high-performance computing facilities of the Bernstein Center, which are normally used to simulate the activity of nerve cells in the brain.

In an article appearing in the scientific journal PLoS ONE, Stefano Cardanobile and colleagues describe how they analysed 200,000 networks which they generated in a computer -- using models that are employed by scientists to understand the properties of naturally occurring networks. The researchers compared the results obtained from these models with well-understood networks from the real world: the metabolism of a bacterium, the relationship of synonyms in a thesaurus, and the nervous system of a worm. Thus, they were able to assess which model networks can predict the behaviour of its real-life counterpart the best. These insights can help colleagues from other fields to choose the right model in their specific research.

Most importantly, the scientists from Freiburg could demonstrate that it is possible to draw conclusions about global properties of complex networks from local statistical data. This means that one can discover important properties of networks even if they are not completely analysed -- very often an impossible task in large systems such as human social contacts or connections in the brain. Therefore, the authors see their study to represent an important step towards a better understanding of complex networks.

Share this story on Facebook, Twitter, and Google:

Other social bookmarking and sharing tools:

Story Source:

The above story is reprinted from materials provided by Albert-Ludwigs-Universität Freiburg.

Note: Materials may be edited for content and length. For further information, please contact the source cited above.

Journal Reference:

Stefano Cardanobile, Volker Pernice, Moritz Deger, Stefan Rotter. Inferring General Relations between Network Characteristics from Specific Network Ensembles. PLoS ONE, 2012; 7 (6): e37911 DOI: 10.1371/journal.pone.0037911

Note: If no author is given, the source is cited instead.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

View the original article here