New knowledge about 'flawed' diamonds could speed the development of diamond-based quantum computers

ScienceDaily (Oct. 12, 2011) — A University at Buffalo-led research team has established the presence of a dynamic Jahn-Teller effect in defective diamonds, a finding that will help advance the development of diamond-based systems in applications such as quantum information processing.

"We normally want things to be perfect, but defects are actually very important in terms of electronic applications," said Peihong Zhang, the UB associate professor of physics who led the study. "There are many proposals for the application of defective diamonds, ranging from quantum computing to biological imaging, and our research is one step toward a better understanding of these defect systems."

The research was published online Sept. 30 in Physical Review Letters.

The findings deal with diamonds whose crystal structure contains a particular defect: a nitrogen atom that sits alongside a vacant space in an otherwise perfect lattice made only of carbon.

At the point of the imperfection -- the so-called "nitrogen-vacancy center" -- a single electron can jump between different energy states. (The electron rises to a higher, "excited" energy state when it absorbs a photon and falls back to a lower energy state when it emits a photon).

Understanding how the diamond system behaves when the electron rises to an excited state called a "3E" state is critical to the success of such proposed applications as quantum computing.

The problem is that at the nitrogen-vacancy center, the 3E state has two orbital components with exactly the same energy -- a configuration that is inherently unstable.

In response, the lattice "stabilizes" by rearranging itself. Atoms near the nitrogen-vacancy center move slightly, resulting in a new geometry that has a lower energy and is more stable.

This morphing is known as the Jahn-Teller effect, and until recently, the effect's precise parameters in defective diamonds remained unknown.

Zhang and colleagues from the Rensselaer Polytechnic Institute in Troy, N.Y., are the first to crack that mystery. Using UB's supercomputing facility, the Center for Computational Research, the team conducted calculations that reveal how, exactly, the diamond lattice distorts.

Their findings align with experimental results from other research studies, and shed light on important topics such as how long an excited electron at the nitrogen-vacancy center will stay coherently at a higher energy state.

The UB-Rensselaer study was funded by the Department of Energy.

Recommend this story on Facebook, Twitter,
and Google +1:

Other bookmarking and sharing tools:

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by University at Buffalo.

Journal Reference:

Tesfaye Abtew, Y. Sun, Bi-Ching Shih, Pratibha Dev, S. Zhang, Peihong Zhang. Dynamic Jahn-Teller Effect in the NV- Center in Diamond. Physical Review Letters, 2011; 107 (14) DOI: 10.1103/PhysRevLett.107.146403

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

Quantum knowledge cools computers: New understanding of entropy

ScienceDaily (June 1, 2011) — From a laptop warming a knee to a supercomputer heating a room, the idea that computers generate heat is familiar to everyone. But theoretical physicists have discovered something astonishing: not only do computational processes sometimes generate no heat, under certain conditions they can even have a cooling effect. Behind this finding are fundamental considerations relating to knowledge and a lack of knowledge. The researchers publish their findings in the journal Nature.

When computers compute, the energy they consume eventually ends up as heat. This isn't all due to the engineering of the computer -- physics has something to say about the fundamental energy cost of processing information.

Recent research by a team of physicists reveals a surprise at this fundamental level. ETH-Professor Renato Renner, and Vlatko Vedral of the Centre for Quantum Technologies at the National University of Singapore and the University of Oxford, UK, and their colleagues describe in the scientific journal Nature how the deletion of data, under certain conditions, can create a cooling effect instead of generating heat. The cooling effect appears when the strange quantum phenomenon of entanglement is invoked. Ultimately, it may be possible to harness this effect to cool supercomputers that have their performance held back by heat generation. "Achieving the control at the quantum level that would be required to implement this in supercomputers is a huge technological challenge, but it may not be impossible. We have seen enormous progress is quantum technologies over the past 20 years," says Vedral. With the technology in quantum physics labs today, it should be possible to do a proof of principle experiment on a few bits of data.

Landauer's principle is given a quantum twist

The physicist Rolf Landauer calculated back in 1961 that during the deletion of data, some release of energy in the form of heat is unavoidable. Landauer's principle implies that when a certain number of arithmetical operations per second have been exceeded, the computer will produce so much heat that the heat is impossible to dissipate. In supercomputers today other sources of heat are more significant, but Renner thinks that the critical threshold where Landauer's erasure heat becomes important may be reached within the next 10 to 20 years. The heat emission from the deletion of a ten terabyte hard-drive amounts in principle to less than a millionth of a joule. However, if such a deletion process were repeated many times per second then the heat would accumulate correspondingly.

The new study revisits Landauer's principle for cases when the values of the bits to be deleted may be known. When the memory content is known, it should be possible to delete the bits in such a manner that it is theoretically possible to re-create them. It has previously been shown that such reversible deletion would generate no heat. In the new paper, the researchers go a step further. They show that when the bits to be deleted are quantum-mechanically entangled with the state of an observer, then the observer could even withdraw heat from the system while deleting the bits. Entanglement links the observer's state to that of the computer in such a way that they know more about the memory than is possible in classical physics.

Similar formulas -- two disciplines

In order to reach this result, the scientists combined ideas from information theory and thermodynamics about a concept known as entropy. Entropy appears differently in these two disciplines, which are, to a large extent, independent of each other. In information theory, entropy is a measurement of the information density. It describes, for instance, how much memory capacity a given set of data would take up when compressed optimally. In thermodynamics, on the other hand, entropy relates to the disorder in systems, for example to the arrangement of molecules in a gas. In thermodynamics, adding entropy to a system is usually equivalent to adding energy as heat.

The ETH physicist Renner says "We have now shown that in both cases, the term entropy is actually describing the same thing even in the quantum mechanical regime." As the formulas for the two entropies look the same, it had already been assumed that there was a connection between them. "Our study shows that in both cases, entropy is considered to be a type of lack of knowledge," says Renner. The new paper in Nature builds on work published earlier in the New Journal of Physics.

In measuring entropy, one should bear in mind that an object does not have a certain amount of entropy per se, instead an object's entropy is always dependent on the observer. Applied to the example of deleting data, this means that if two individuals delete data in a memory and one has more knowledge of this data, she perceives the memory to have lower entropy and can then delete the memory using less energy. Entropy in quantum physics has the unusual property of sometimes being negative when calculated from the information theory point of view. Perfect classical knowledge of a system means the observer perceives it to have zero entropy. This corresponds to the memory of the observer and that of the system being perfectly correlated, as much as allowed in classical physics. Entanglement gives the observer „more than complete knowledge" because quantum correlations are stronger than classical correlations. This leads to an entropy less than zero. Until now, theoretical physicists had used this negative entropy in calculations without understanding what it might mean in thermodynamic terms or experimentally.

No heat, even a cooling effect

In the case of perfect classical knowledge of a computer memory (zero entropy), deletion of the data requires in theory no energy at all. The researchers prove that "more than complete knowledge" from quantum entanglement with the memory (negative entropy) leads to deletion of the data being accompanied by removal of heat from the computer and its release as usable energy. This is the physical meaning of negative entropy.

Renner emphasizes, however, "This doesn't mean that we can develop a perpetual motion machine." The data can only be deleted once, so there is no possibility to continue to generate energy. The process also destroys the entanglement, and it would take an input of energy to reset the system to its starting state. The equations are consistent with what's known as the second law of thermodynamics: the idea that the entropy of the universe can never decrease. Vedral says "We're working on the edge of the second law. If you go any further, you will break it."

Fundamental findings

The scientists' new findings relating to entropy in thermodynamics and information theory may have usefulness beyond calculating the heat production of computers. For example, methods developed within information theory to handle entropy could lead to innovations in thermodynamics. The connection made between the two concepts of entropy is fundamental.

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by ETH Zurich/Swiss Federal Institute of Technology.

Journal Reference:

Lídia del Rio, Johan Åberg, Renato Renner, Oscar Dahlsten, Vlatko Vedral. The thermodynamic meaning of negative entropy. Nature, 2011; 474 (7349): 61 DOI: 10.1038/nature10123

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

Scientists afflict computers with 'schizophrenia' to better understand the human brain

ScienceDaily (May 6, 2011) — Computer networks that can't forget fast enough can show symptoms of a kind of virtual schizophrenia, giving researchers further clues to the inner workings of schizophrenic brains, researchers at The University of Texas at Austin and Yale University have found.

The researchers used a virtual computer model, or "neural network," to simulate the excessive release of dopamine in the brain. They found that the network recalled memories in a distinctly schizophrenic-like fashion.

Their results were published in April in Biological Psychiatry.

"The hypothesis is that dopamine encodes the importance-the salience-of experience," says Uli Grasemann, a graduate student in the Department of Computer Science at The University of Texas at Austin. "When there's too much dopamine, it leads to exaggerated salience, and the brain ends up learning from things that it shouldn't be learning from."

The results bolster a hypothesis known in schizophrenia circles as the hyperlearning hypothesis, which posits that people suffering from schizophrenia have brains that lose the ability to forget or ignore as much as they normally would. Without forgetting, they lose the ability to extract what's meaningful out of the immensity of stimuli the brain encounters. They start making connections that aren't real, or drowning in a sea of so many connections they lose the ability to stitch together any kind of coherent story.

The neural network used by Grasemann and his adviser, Professor Risto Miikkulainen, is called DISCERN. Designed by Miikkulainen, DISCERN is able to learn natural language. In this study it was used to simulate what happens to language as the result of eight different types of neurological dysfunction. The results of the simulations were compared by Ralph Hoffman, professor of psychiatry at the Yale School of Medicine, to what he saw when studying human schizophrenics.

In order to model the process, Grasemann and Miikkulainen began by teaching a series of simple stories to DISCERN. The stories were assimilated into DISCERN's memory in much the way the human brain stores information-not as distinct units, but as statistical relationships of words, sentences, scripts and stories.

"With neural networks, you basically train them by showing them examples, over and over and over again," says Grasemann. "Every time you show it an example, you say, if this is the input, then this should be your output, and if this is the input, then that should be your output. You do it again and again thousands of times, and every time it adjusts a little bit more towards doing what you want. In the end, if you do it enough, the network has learned."

In order to model hyperlearning, Grasemann and Miikkulainen ran the system through its paces again, but with one key parameter altered. They simulated an excessive release of dopamine by increasing the system's learning rate-essentially telling it to stop forgetting so much.

"It's an important mechanism to be able to ignore things," says Grasemann. "What we found is that if you crank up the learning rate in DISCERN high enough, it produces language abnormalities that suggest schizophrenia."

After being re-trained with the elevated learning rate, DISCERN began putting itself at the center of fantastical, delusional stories that incorporated elements from other stories it had been told to recall. In one answer, for instance, DISCERN claimed responsibility for a terrorist bombing.

In another instance, DISCERN began showing evidence of "derailment"-replying to requests for a specific memory with a jumble of dissociated sentences, abrupt digressions and constant leaps from the first- to the third-person and back again.

"Information processing in neural networks tends to be like information processing in the human brain in many ways," says Grasemann. "So the hope was that it would also break down in similar ways. And it did."

The parallel between their modified neural network and human schizophrenia isn't absolute proof the hyperlearning hypothesis is correct, says Grasemann. It is, however, support for the hypothesis, and also evidence of how useful neural networks can be in understanding the human brain.

"We have so much more control over neural networks than we could ever have over human subjects," he says. "The hope is that this kind of modeling will help clinical research."

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by University of Texas at Austin.

Journal Reference:

Ralph E. Hoffman, Uli Grasemann, Ralitza Gueorguieva, Donald Quinlan, Douglas Lane, Risto Miikkulainen. Using Computational Patients to Evaluate Illness Mechanisms in Schizophrenia. Biological Psychiatry, 2011; DOI: 10.1016/j.biopsych.2010.12.036

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

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Scientists afflict computers with 'schizophrenia' to better understand the human brain

ScienceDaily (May 6, 2011) — Computer networks that can't forget fast enough can show symptoms of a kind of virtual schizophrenia, giving researchers further clues to the inner workings of schizophrenic brains, researchers at The University of Texas at Austin and Yale University have found.

The researchers used a virtual computer model, or "neural network," to simulate the excessive release of dopamine in the brain. They found that the network recalled memories in a distinctly schizophrenic-like fashion.

Their results were published in April in Biological Psychiatry.

"The hypothesis is that dopamine encodes the importance-the salience-of experience," says Uli Grasemann, a graduate student in the Department of Computer Science at The University of Texas at Austin. "When there's too much dopamine, it leads to exaggerated salience, and the brain ends up learning from things that it shouldn't be learning from."

The results bolster a hypothesis known in schizophrenia circles as the hyperlearning hypothesis, which posits that people suffering from schizophrenia have brains that lose the ability to forget or ignore as much as they normally would. Without forgetting, they lose the ability to extract what's meaningful out of the immensity of stimuli the brain encounters. They start making connections that aren't real, or drowning in a sea of so many connections they lose the ability to stitch together any kind of coherent story.

The neural network used by Grasemann and his adviser, Professor Risto Miikkulainen, is called DISCERN. Designed by Miikkulainen, DISCERN is able to learn natural language. In this study it was used to simulate what happens to language as the result of eight different types of neurological dysfunction. The results of the simulations were compared by Ralph Hoffman, professor of psychiatry at the Yale School of Medicine, to what he saw when studying human schizophrenics.

In order to model the process, Grasemann and Miikkulainen began by teaching a series of simple stories to DISCERN. The stories were assimilated into DISCERN's memory in much the way the human brain stores information-not as distinct units, but as statistical relationships of words, sentences, scripts and stories.

"With neural networks, you basically train them by showing them examples, over and over and over again," says Grasemann. "Every time you show it an example, you say, if this is the input, then this should be your output, and if this is the input, then that should be your output. You do it again and again thousands of times, and every time it adjusts a little bit more towards doing what you want. In the end, if you do it enough, the network has learned."

In order to model hyperlearning, Grasemann and Miikkulainen ran the system through its paces again, but with one key parameter altered. They simulated an excessive release of dopamine by increasing the system's learning rate-essentially telling it to stop forgetting so much.

"It's an important mechanism to be able to ignore things," says Grasemann. "What we found is that if you crank up the learning rate in DISCERN high enough, it produces language abnormalities that suggest schizophrenia."

After being re-trained with the elevated learning rate, DISCERN began putting itself at the center of fantastical, delusional stories that incorporated elements from other stories it had been told to recall. In one answer, for instance, DISCERN claimed responsibility for a terrorist bombing.

In another instance, DISCERN began showing evidence of "derailment"-replying to requests for a specific memory with a jumble of dissociated sentences, abrupt digressions and constant leaps from the first- to the third-person and back again.

"Information processing in neural networks tends to be like information processing in the human brain in many ways," says Grasemann. "So the hope was that it would also break down in similar ways. And it did."

The parallel between their modified neural network and human schizophrenia isn't absolute proof the hyperlearning hypothesis is correct, says Grasemann. It is, however, support for the hypothesis, and also evidence of how useful neural networks can be in understanding the human brain.

"We have so much more control over neural networks than we could ever have over human subjects," he says. "The hope is that this kind of modeling will help clinical research."

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by University of Texas at Austin.

Journal Reference:

Ralph E. Hoffman, Uli Grasemann, Ralitza Gueorguieva, Donald Quinlan, Douglas Lane, Risto Miikkulainen. Using Computational Patients to Evaluate Illness Mechanisms in Schizophrenia. Biological Psychiatry, 2011; DOI: 10.1016/j.biopsych.2010.12.036

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

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

View the original article here

Toward faster transistors: Physicists discover physical phenomenon that could boost computers' clock speed

ScienceDaily (May 15, 2011) — In the 1980s and '90s, competition in the computer industry was all about "clock speed" -- how many megahertz, and ultimately gigahertz, a chip could boast. But clock speeds stalled out almost 10 years ago: Chips that run faster also run hotter, and with existing technology, there seems to be no way to increase clock speed without causing chips to overheat.

In this week's issue of the journal Science, MIT researchers and their colleagues at the University of Augsburg in Germany report the discovery of a new physical phenomenon that could yield transistors with greatly enhanced capacitance -- a measure of the voltage required to move a charge. And that, in turn, could lead to the revival of clock speed as the measure of a computer's power.

In today's computer chips, transistors are made from semiconductors, such as silicon. Each transistor includes an electrode called the gate; applying a voltage to the gate causes electrons to accumulate underneath it. The electrons constitute a channel through which an electrical current can pass, turning the semiconductor into a conductor.

Capacitance measures how much charge accumulates below the gate for a given voltage. The power that a chip consumes, and the heat it gives off, are roughly proportional to the square of the gate's operating voltage. So lowering the voltage could drastically reduce the heat, creating new room to crank up the clock.

MIT Professor of Physics Raymond Ashoori and Lu Li, a postdoc and Pappalardo Fellow in his lab -- together with Christoph Richter, Stefan Paetel, Thilo Kopp and Jochen Mannhart of the University of Augsburg -- investigated the unusual physical system that results when lanthanum aluminate is grown on top of strontium titanate. Lanthanum aluminate consists of alternating layers of lanthanum oxide and aluminum oxide. The lanthanum-based layers have a slight positive charge; the aluminum-based layers, a slight negative charge. The result is a series of electric fields that all add up in the same direction, creating an electric potential between the top and bottom of the material.

Ordinarily, both lanthanum aluminate and strontium titanate are excellent insulators, meaning that they don't conduct electrical current. But physicists had speculated that if the lanthanum aluminate gets thick enough, its electrical potential would increase to the point that some electrons would have to move from the top of the material to the bottom, to prevent what's called a "polarization catastrophe." The result is a conductive channel at the juncture with the strontium titanate -- much like the one that forms when a transistor is switched on. So Ashoori and his collaborators decided to measure the capacitance between that channel and a gate electrode on top of the lanthanum aluminate.

They were amazed by what they found: Although their results were somewhat limited by their experimental apparatus, it may be that an infinitesimal change in voltage will cause a large amount of charge to enter the channel between the two materials. "The channel may suck in charge -- shoomp! Like a vacuum," Ashoori says. "And it operates at room temperature, which is the thing that really stunned us."

Indeed, the material's capacitance is so high that the researchers don't believe it can be explained by existing physics. "We've seen the same kind of thing in semiconductors," Ashoori says, "but that was a very pure sample, and the effect was very small. This is a super-dirty sample and a super-big effect." It's still not clear, Ashoori says, just why the effect is so big: "It could be a new quantum-mechanical effect or some unknown physics of the material."

There is one drawback to the system that the researchers investigated: While a lot of charge will move into the channel between materials with a slight change in voltage, it moves slowly -- much too slowly for the type of high-frequency switching that takes place in computer chips. That could be because the samples of the material are, as Ashoori says, "super dirty"; purer samples might exhibit less electrical resistance. But it's also possible that, if researchers can understand the physical phenomena underlying the material's remarkable capacitance, they may be able to reproduce them in more practical materials.

Triscone cautions that wholesale changes to the way computer chips are manufactured will inevitably face resistance. "So much money has been injected into the semiconductor industry for decades that to do something new, you need a really disruptive technology," he says.

"It's not going to revolutionize electronics tomorrow," Ashoori agrees. "But this mechanism exists, and once we know it exists, if we can understand what it is, we can try to engineer it."

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by Massachusetts Institute of Technology. The original article was written by Larry Hardesty, MIT News Office.

Journal Reference:

L. Li, C. Richter, S. Paetel, T. Kopp, J. Mannhart, R. C. Ashoori. Very Large Capacitance Enhancement in a Two-Dimensional Electron System. Science, 2011; 332 (6031): 825 DOI: 10.1126/science.1204168

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

Toward faster transistors: Physicists discover physical phenomenon that could boost computers' clock speed

ScienceDaily (May 15, 2011) — In the 1980s and '90s, competition in the computer industry was all about "clock speed" -- how many megahertz, and ultimately gigahertz, a chip could boast. But clock speeds stalled out almost 10 years ago: Chips that run faster also run hotter, and with existing technology, there seems to be no way to increase clock speed without causing chips to overheat.

In this week's issue of the journal Science, MIT researchers and their colleagues at the University of Augsburg in Germany report the discovery of a new physical phenomenon that could yield transistors with greatly enhanced capacitance -- a measure of the voltage required to move a charge. And that, in turn, could lead to the revival of clock speed as the measure of a computer's power.

In today's computer chips, transistors are made from semiconductors, such as silicon. Each transistor includes an electrode called the gate; applying a voltage to the gate causes electrons to accumulate underneath it. The electrons constitute a channel through which an electrical current can pass, turning the semiconductor into a conductor.

Capacitance measures how much charge accumulates below the gate for a given voltage. The power that a chip consumes, and the heat it gives off, are roughly proportional to the square of the gate's operating voltage. So lowering the voltage could drastically reduce the heat, creating new room to crank up the clock.

MIT Professor of Physics Raymond Ashoori and Lu Li, a postdoc and Pappalardo Fellow in his lab -- together with Christoph Richter, Stefan Paetel, Thilo Kopp and Jochen Mannhart of the University of Augsburg -- investigated the unusual physical system that results when lanthanum aluminate is grown on top of strontium titanate. Lanthanum aluminate consists of alternating layers of lanthanum oxide and aluminum oxide. The lanthanum-based layers have a slight positive charge; the aluminum-based layers, a slight negative charge. The result is a series of electric fields that all add up in the same direction, creating an electric potential between the top and bottom of the material.

Ordinarily, both lanthanum aluminate and strontium titanate are excellent insulators, meaning that they don't conduct electrical current. But physicists had speculated that if the lanthanum aluminate gets thick enough, its electrical potential would increase to the point that some electrons would have to move from the top of the material to the bottom, to prevent what's called a "polarization catastrophe." The result is a conductive channel at the juncture with the strontium titanate -- much like the one that forms when a transistor is switched on. So Ashoori and his collaborators decided to measure the capacitance between that channel and a gate electrode on top of the lanthanum aluminate.

They were amazed by what they found: Although their results were somewhat limited by their experimental apparatus, it may be that an infinitesimal change in voltage will cause a large amount of charge to enter the channel between the two materials. "The channel may suck in charge -- shoomp! Like a vacuum," Ashoori says. "And it operates at room temperature, which is the thing that really stunned us."

Indeed, the material's capacitance is so high that the researchers don't believe it can be explained by existing physics. "We've seen the same kind of thing in semiconductors," Ashoori says, "but that was a very pure sample, and the effect was very small. This is a super-dirty sample and a super-big effect." It's still not clear, Ashoori says, just why the effect is so big: "It could be a new quantum-mechanical effect or some unknown physics of the material."

There is one drawback to the system that the researchers investigated: While a lot of charge will move into the channel between materials with a slight change in voltage, it moves slowly -- much too slowly for the type of high-frequency switching that takes place in computer chips. That could be because the samples of the material are, as Ashoori says, "super dirty"; purer samples might exhibit less electrical resistance. But it's also possible that, if researchers can understand the physical phenomena underlying the material's remarkable capacitance, they may be able to reproduce them in more practical materials.

Triscone cautions that wholesale changes to the way computer chips are manufactured will inevitably face resistance. "So much money has been injected into the semiconductor industry for decades that to do something new, you need a really disruptive technology," he says.

"It's not going to revolutionize electronics tomorrow," Ashoori agrees. "But this mechanism exists, and once we know it exists, if we can understand what it is, we can try to engineer it."

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by Massachusetts Institute of Technology. The original article was written by Larry Hardesty, MIT News Office.

Journal Reference:

L. Li, C. Richter, S. Paetel, T. Kopp, J. Mannhart, R. C. Ashoori. Very Large Capacitance Enhancement in a Two-Dimensional Electron System. Science, 2011; 332 (6031): 825 DOI: 10.1126/science.1204168

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