Tag Archives: supercomputers

How does sticky tape make graphene?

As I understand it, Andre Geim one of the two men (the other was Konstantin Novoselov) to first isolate graphene from a block of graphite by using sticky tape is not thrilled that it’s known in some quarters as the graphene sticky tape method. Still, the technique caught the imagination as Steve Connor’s March 18, 2013 article for the Independent made clear.

It seems scientists are still just as fascinated as anyone else as a February 27, 2018 news item for Nanowerk describes,

Scientists at UCL [University College London] have explained for the first time the mystery of why adhesive tape is so useful for graphene production.

The study, published in Advanced Materials (“Graphene–Graphene Interactions: Friction, Superlubricity, and Exfoliation”), used supercomputers to model the process through which graphene sheets are exfoliated from graphite, the material in pencils.

A February 26, 2018 UCL press release, which originated the news item, provides more detail,

There are various methods for exfoliating graphene, including the famous adhesive tape method developed by Nobel Prize winner Andre Geim. However little has been known until now about how the process of exfoliating graphene using sticky tape works.

Academics at UCL are now able to demonstrate how individual flakes of graphite can be exfoliated to make one atom thick layers. They also reveal that the process of peeling a layer of graphene demands 40% less energy than that of another common method called shearing. This is expected to have far reaching impacts for the commercial production of graphene.

“The sticky tape method works rather like peeling egg boxes apart with a vertical motion, it is easier than pulling one horizontally across another when they are neatly stacked,” explained Professor Peter Coveney, Director of the Centre for Computational Science (UCL Chemistry).

“If shearing, then you get held up by this egg carton configuration. But if you peel, you can get them apart much more easily. The polymethyl methacrylate adhesive on traditional sticky tape is ideal for picking up the edge of the graphene sheet so it can be lifted and peeled,” added Professor Coveney.

Graphite occurs naturally, its basic crystalline structure is stacks of flat sheets of strongly bonded carbon atoms in a honeycomb pattern. Graphite’s many layers are bound together by weak interactions and can easily slide large distances over one another with little friction due to their superlubricity.

The scientists at UCL simulated an experiment conducted in 2015 at Lawrence Berkeley Laboratory in Berkeley, California, which used a special microscope with atomic resolution to see how graphene flakes move around on a graphite surface.

The supercomputer’s results matched Berkeley’s observations showing that there is less movement when the graphene atoms neatly line up with the atoms below.

“Despite the vast amount of research carried out on graphene since its discovery, it is clear that until now our understanding of its behaviour on an atomic length scale was very poor,” explains PhD student Robert Sinclair (UCL Chemistry).

“The one reason above all others why the material is difficult to use is because it is hard to make. Even now, a dozen years after its discovery, companies have to apply sticky tape methods to pull it apart, as the Laureates did to uncover it; hardly a hi-tech and industrially simple process to implement. We’re now in a position to assist experimentalists to figure out how to prise it apart, or make it to order. That could have big cost implications for the emerging graphene industry,” said Professor Coveney.

Here’s a link to and a citation for the paper,

Graphene–Graphene Interactions: Friction, Superlubricity, and Exfoliation by Robert C. Sinclair, James L. Suter, and Peter V. Coveney. Advanced Materials DOI: 10.1002/adma.201705791 First published: 13 February 2018

This paper is open access.

Handling massive digital datasets the quantum way

A Jan. 25, 2016 news item on phys.org describes a new approach to analyzing and managing huge datasets,

From gene mapping to space exploration, humanity continues to generate ever-larger sets of data—far more information than people can actually process, manage, or understand.

Machine learning systems can help researchers deal with this ever-growing flood of information. Some of the most powerful of these analytical tools are based on a strange branch of geometry called topology, which deals with properties that stay the same even when something is bent and stretched every which way.

Such topological systems are especially useful for analyzing the connections in complex networks, such as the internal wiring of the brain, the U.S. power grid, or the global interconnections of the Internet. But even with the most powerful modern supercomputers, such problems remain daunting and impractical to solve. Now, a new approach that would use quantum computers to streamline these problems has been developed by researchers at [Massachusetts Institute of Technology] MIT, the University of Waterloo, and the University of Southern California [USC}.

A Jan. 25, 2016 MIT news release (*also on EurekAlert*), which originated the news item, describes the theory in more detail,

… Seth Lloyd, the paper’s lead author and the Nam P. Suh Professor of Mechanical Engineering, explains that algebraic topology is key to the new method. This approach, he says, helps to reduce the impact of the inevitable distortions that arise every time someone collects data about the real world.

In a topological description, basic features of the data (How many holes does it have? How are the different parts connected?) are considered the same no matter how much they are stretched, compressed, or distorted. Lloyd [ explains that it is often these fundamental topological attributes “that are important in trying to reconstruct the underlying patterns in the real world that the data are supposed to represent.”

It doesn’t matter what kind of dataset is being analyzed, he says. The topological approach to looking for connections and holes “works whether it’s an actual physical hole, or the data represents a logical argument and there’s a hole in the argument. This will find both kinds of holes.”

Using conventional computers, that approach is too demanding for all but the simplest situations. Topological analysis “represents a crucial way of getting at the significant features of the data, but it’s computationally very expensive,” Lloyd says. “This is where quantum mechanics kicks in.” The new quantum-based approach, he says, could exponentially speed up such calculations.

Lloyd offers an example to illustrate that potential speedup: If you have a dataset with 300 points, a conventional approach to analyzing all the topological features in that system would require “a computer the size of the universe,” he says. That is, it would take 2300 (two to the 300th power) processing units — approximately the number of all the particles in the universe. In other words, the problem is simply not solvable in that way.

“That’s where our algorithm kicks in,” he says. Solving the same problem with the new system, using a quantum computer, would require just 300 quantum bits — and a device this size may be achieved in the next few years, according to Lloyd.

“Our algorithm shows that you don’t need a big quantum computer to kick some serious topological butt,” he says.

There are many important kinds of huge datasets where the quantum-topological approach could be useful, Lloyd says, for example understanding interconnections in the brain. “By applying topological analysis to datasets gleaned by electroencephalography or functional MRI, you can reveal the complex connectivity and topology of the sequences of firing neurons that underlie our thought processes,” he says.

The same approach could be used for analyzing many other kinds of information. “You could apply it to the world’s economy, or to social networks, or almost any system that involves long-range transport of goods or information,” says Lloyd, who holds a joint appointment as a professor of physics. But the limits of classical computation have prevented such approaches from being applied before.

While this work is theoretical, “experimentalists have already contacted us about trying prototypes,” he says. “You could find the topology of simple structures on a very simple quantum computer. People are trying proof-of-concept experiments.”

Ignacio Cirac, a professor at the Max Planck Institute of Quantum Optics in Munich, Germany, who was not involved in this research, calls it “a very original idea, and I think that it has a great potential.” He adds “I guess that it has to be further developed and adapted to particular problems. In any case, I think that this is top-quality research.”

Here’s a link to and a citation for the paper,

Quantum algorithms for topological and geometric analysis of data by Seth Lloyd, Silvano Garnerone, & Paolo Zanardi. Nature Communications 7, Article number: 10138 doi:10.1038/ncomms10138 Published 25 January 2016

This paper is open access.

ETA Jan. 25, 2016 1245 hours PST,

Shown here are the connections between different regions of the brain in a control subject (left) and a subject under the influence of the psychedelic compound psilocybin (right). This demonstrates a dramatic increase in connectivity, which explains some of the drug’s effects (such as “hearing” colors or “seeing” smells). Such an analysis, involving billions of brain cells, would be too complex for conventional techniques, but could be handled easily by the new quantum approach, the researchers say. Courtesy of the researchers

Shown here are the connections between different regions of the brain in a control subject (left) and a subject under the influence of the psychedelic compound psilocybin (right). This demonstrates a dramatic increase in connectivity, which explains some of the drug’s effects (such as “hearing” colors or “seeing” smells). Such an analysis, involving billions of brain cells, would be too complex for conventional techniques, but could be handled easily by the new quantum approach, the researchers say. Courtesy of the researchers

*’also on EurekAlert’ text and link added Jan. 26, 2016.

Hector Barron Escobar and his virtual nanomaterial atomic models for the oil, mining, and energy industries

I think there’s some machine translation at work in the Aug. 27, 2015 news item about Hector Barron Escobar on Azonano,

By using supercomputers the team creates virtual atomic models that interact under different conditions before being taken to the real world, allowing savings in time and money.

With the goal of potentiate the oil, mining and energy industries, as well as counteract the emission of greenhouse gases, the nanotechnologist Hector Barron Escobar, designs more efficient and profitable nanomaterials.

The Mexican who lives in Australia studies the physical and chemical properties of platinum and palladium, metal with excellent catalytic properties that improve processes in petrochemistry, solar cells and fuel cells, which because of their scarcity have a high and unprofitable price, hence the need to analyze their properties and make them long lasting.

Structured materials that the specialist in nanotechnology designs can be implemented in the petrochemical and automotive industries. In the first, they accelerate reactions in the production of hydrocarbons, and in the second, nanomaterials are placed in catalytic converters of vehicles to transform the pollutants emitted by combustion into less harmful waste.

An August 26, 2015 Investigación y Desarrollo press release on Alpha Galileo, which originated the news item, continues Barron Escobar’s profile,

PhD Barron Escobar, who majored in physics at the National University of Mexico (UNAM), says that this are created by using virtual supercomputers to interact with atomic models under different conditions before being taken to the real world.

Barron recounts how he came to Australia with an invitation of his doctoral advisor, Amanda Partner with whom he analyzed the electronic properties of gold in the United States.

He explains that using computer models in the Virtual Nanoscience Laboratory (VNLab) in Australia, he creates nanoparticles that interact in different environmental conditions such as temperature and pressure. He also analyzes their mechanical and electronic properties, which provide specific information about behavior and gives the best working conditions. Together, these data serve to establish appropriate patterns or trends in a particular application.

The work of the research team serves as a guide for experts from the University of New South Wales in Australia, with which they cooperate, to build nanoparticles with specific functions. “This way we perform virtual experiments, saving time, money and offer the type of material conditions and ideal size for a specific catalytic reaction, which by the traditional way would cost a lot of money trying to find what is the right substance” Barron Escobar comments.

Currently he designs nanomaterials for the mining company Orica, because in this industry explosives need to be controlled in order to avoid damaging the minerals or the environment.

Research is also immersed in the creation of fuel cells, with the use of the catalysts designed by Barron is possible to produce more electricity without polluting.

Additionally, they enhance the effectiveness of catalytic converters in petrochemistry, where these materials help accelerate oxidation processes of hydrogen and carbon, which are present in all chemical reactions when fuel and gasoline are created. “We can identify the ideal particles for improving this type of reactions.”

The nanotechnology specialist also seeks to analyze the catalytic properties of bimetallic materials like titanium, ruthenium and gold, as their reaction according to size, shape and its components.

Escobar Barron chose to study nanomaterials because it is interesting to see how matter at the nano level completely changes its properties: at large scale it has a definite color, but keep another at a nanoscale, besides many applications can be obtained with these metals.

For anyone interested in Orica, there’s more here on their website; as for Dr. Hector Barron Escobar, there’s this webpage on  Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO) website.