Scientists Produce World's First Magnetic Soap

The liquid crystal progression of each surfactant was investigated by the solvent penetration method (i.e. phase cut). A small amount of surfactant was placed on a microscope slide under a coverslip. The slide was mounted on the cover slide and heated until the sample was fluid and completely isotropic. After slow cooling (1.0 °C min-1) to 25 °C, a drop of water was added to the edge of the coverslip. As the water penetrated the surfactant, a concentration gradient was established, from water at one side to pure surfactant at the other, enabling the entire range of mesophases to be observed in the field of view. (Credit: Image courtesy of Institut Laue-Langevin (ILL))


ScienceDaily (Jan. 23, 2012) — Scientists from Bristol University have developed a soap, composed of iron rich salts dissolved in water, that responds to a magnetic field when placed in solution. The soap’s magnetic properties were shown with neutrons at the Institut Laue-Langevin to result from tiny iron-rich clumps that sit within the watery solution. The generation of this property in a fully functional soap could calm concerns over the use of soaps in oil-spill clean ups and revolutionise industrial cleaning products.


Scientists have long been searching for a way to control soaps (or surfactants as they are known in industry) once they are in solution to increase their ability to dissolve oils in water and then remove them from a system. The team at Bristol University have previously worked on soaps sensitive to light, carbon dioxide or changes in pH, temperature or pressure. Their latest breakthrough, reported inAngewandte Chemie, is the world’s first soap sensitive to a magnetic field.

Ionic liquid surfactants, composed mostly of water with some transition metal complexes (heavy metals like iron bound to halides such as bromine or chlorine) have been suggested as potentially controllable by magnets for some time, but it had always been assumed that their metallic centres were too isolated within the solution, preventing the long-range interactions required to be magnetically active.

The team at Bristol, lead by Professor Julian Eastoe produced their magnetic soap by dissolving iron in a range of inert surfactant materials composed of chloride and bromide ions, very similar to those found in everyday mouthwash or fabric conditioner. The addition of the iron creates metallic centres within the soap particles.

To test its properties, the team introduced a magnet to a test tube containing their new soap lying beneath a less dense organic solution. When the magnet was introduced the iron-rich soap overcame both gravity and surface tension between the water and oil, to levitate through the organic solvent and reach the source of the magnetic energy, proving its magnetic properties.

Once the surfactant was developed and shown to be magnetic, Prof Eastoe’s team took it to the Institut Laue-Langevin, the world’s flagship centre for neutron science, and home to the world’s most intense neutron source, to investigate the science behind its remarkable property.

When surfactants are added to water they are known to form tiny clumps (particles called micelles). Scientists at ILL used a technique called “small angle neutron scattering (SANS)” to confirm that it was this clumping of the iron-rich surfactant that brought about its magnetic properties.

Dr Isabelle Grillo, responsible of the Chemistry Laboratories at ILL: “The particles of surfactant in solution are small and thus difficult to see using light but are easily revealed by SANS which we use to investigate the structure and behaviour of all types of materials with typical sizes ranging from the nanometer to the tenth of micrometer.”

The potential applications of magnetic surfactants are huge. Their responsiveness to external stimuli allows a range of properties, such as their electrical conductivity, melting point, the size and shape of aggregates and how readily its dissolves in water to be altered by a simple magnetic on and off switch. Traditionally these factors, which are key to the effective application of soaps in a variety of industrial settings, could only be controlled by adding an electric charge or changing the pH, temperature or pressure of the system, all changes that irreversibly alter the system composition and cost money to remediate.

Its magnetic properties also makes it easier to round up and remove from a system once it has been added, suggesting further applications in environmental clean ups and water treatment. Scientific experiments which require precise control of liquid droplets could also be made easier with the addition of this surfactant and a magnetic field.

Professor Julian Eastoe, University of Bristol: “As most magnets are metals, from a purely scientific point of view these ionic liquid surfactants are highly unusual, making them a particularly interesting discovery. From a commercial point of view, though these exact liquids aren’t yet ready to appear in any household product, by proving that magnetic soaps can be developed, future work can reproduce the same phenomenon in more commercially viable liquids for a range of applications from water treatment to industrial cleaning products.”

Peter Dowding an industrial chemist, not involved in the research: “Any systems which act only when responding to an outside stimulus that has no effect on its composition is a major breakthrough as you can create products which only work when they are needed to. Also the ability to remove the surfactant after it has been added widens the potential applications to environmentally sensitive areas like oil spill clean ups where in the past concerns have been raised.”

Mathematically detecting stock market bubbles before they burst

ScienceDaily (Oct. 31, 2011) — From the dotcom bust in the late nineties to the housing crash in the run-up to the 2008 crisis, financial bubbles have been a topic of major concern. Identifying bubbles is important in order to prevent collapses that can severely impact nations and economies.

A paper published this month in the SIAM Journal on Financial Mathematics addresses just this issue. Opening fittingly with a quote from New York Federal Reserve President William Dudley emphasizing the importance of developing tools to identify and address bubbles in real time, authors Robert Jarrow, Younes Kchia, and Philip Protter propose a mathematical model to detect financial bubbles.

A financial bubble occurs when prices for assets, such as stocks, rise far above their actual value. Such an economic cycle is usually characterized by rapid expansion followed by a contraction, or sharp decline in prices.

"It has been hard not to notice that financial bubbles play an important role in our economy, and speculation as to whether a given risky asset is undergoing bubble pricing has approached the level of an armchair sport. But bubbles can have real and often negative consequences," explains Protter, who has spent many years studying and analyzing financial markets.

"The ability to tell when an asset is or is not in a bubble could have important ramifications in the regulation of the capital reserves of banks as well as for individual investors and retirement funds holding assets for the long term. For banks, if their capital reserve holdings include large investments with unrealistic values due to bubbles, a shock to the bank could occur when the bubbles burst, potentially causing a run on the bank, as infamously happened with Lehman Brothers, and is currently happening with Dexia, a major European bank," he goes on to explain, citing the significance of such inflated prices.

Using sophisticated mathematical methods, Protter and his co-authors answer the question of whether the price increase of a particular asset represents a bubble in real time. "[In this paper] we show that by using tick data and some statistical techniques, one is able to tell with a large degree of certainty, whether or not a given financial asset (or group of assets) is undergoing bubble pricing," says Protter.

This question is answered by estimating an asset's price volatility, which is stochastic or randomly determined. The authors define an asset's price process in terms of a standard stochastic differential equation, which is driven by Brownian motion. Brownian motion, based on a natural process involving the erratic, random movement of small particles suspended in gas or liquid, has been widely used in mathematical finance. The concept is specifically used to model instances where previous change in the value of a variable is unrelated to past changes.

The key characteristic in determining a bubble is the volatility of an asset's price, which, in the case of bubbles is very high. The authors estimate the volatility by applying state of the art estimators to real-time tick price data for a given stock. They then obtain the best possible extension of this data for large values using a technique called Reproducing Kernel Hilbert Spaces (RKHS), which is a widely used method for statistical learning.

"First, one uses tick price data to estimate the volatility of the asset in question for various levels of the asset's price," Protter explains. "Then, a special technique (RKHS with an optimization addition) is employed to extrapolate this estimated volatility function to large values for the asset's price, where this information is not (and cannot be) available from tick data. Using this extrapolation, one can check the rate of increase of the volatility function as the asset price gets arbitrarily large. Whether or not there is a bubble depends on how fast this increase occurs (its asymptotic rate of increase)."

If it does not increase fast enough, there is no bubble within the model's framework.

The authors test their methodology by applying the model to several stocks from the dot-com bubble of the nineties. They find fairly successful rates in their predictions, with higher accuracies in cases where market volatilities can be modeled more efficiently. This helps establish the strengths and weaknesses of the method.

The authors have also used the model to test more recent price increases to detect bubbles. "We have found, for example, that the IPO [initial public offering] of LinkedIn underwent bubble pricing at its debut, and that the recent rise in gold prices was not a bubble, according to our models," Protter says.

It is encouraging to see that mathematical analysis can play a role in the diagnosis and detection of bubbles, which have significantly impacted economic upheavals in the past few decades.

Robert Jarrow is a professor at the Johnson Graduate School of Management at Cornell University in Ithaca, NY, and managing director of the Kamakura Corporation. Younes Kchia is a graduate student at Ecole Polytechnique in Paris, and Philip Protter is a professor in the Statistics Department at Columbia University in New York.

Professor Protter's work was supported in part by NSF grant DMS-0906995.

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Journal Reference:

Robert Jarrow, Younes Kchia, and Philip Protter. How to Detect an Asset Bubble. SIAM J. Finan. Math., 2011; 2, pp. 839-865 [link]

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.

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Mathematically detecting stock market bubbles before they burst

ScienceDaily (Oct. 31, 2011) — From the dotcom bust in the late nineties to the housing crash in the run-up to the 2008 crisis, financial bubbles have been a topic of major concern. Identifying bubbles is important in order to prevent collapses that can severely impact nations and economies.

A paper published this month in the SIAM Journal on Financial Mathematics addresses just this issue. Opening fittingly with a quote from New York Federal Reserve President William Dudley emphasizing the importance of developing tools to identify and address bubbles in real time, authors Robert Jarrow, Younes Kchia, and Philip Protter propose a mathematical model to detect financial bubbles.

A financial bubble occurs when prices for assets, such as stocks, rise far above their actual value. Such an economic cycle is usually characterized by rapid expansion followed by a contraction, or sharp decline in prices.

"It has been hard not to notice that financial bubbles play an important role in our economy, and speculation as to whether a given risky asset is undergoing bubble pricing has approached the level of an armchair sport. But bubbles can have real and often negative consequences," explains Protter, who has spent many years studying and analyzing financial markets.

"The ability to tell when an asset is or is not in a bubble could have important ramifications in the regulation of the capital reserves of banks as well as for individual investors and retirement funds holding assets for the long term. For banks, if their capital reserve holdings include large investments with unrealistic values due to bubbles, a shock to the bank could occur when the bubbles burst, potentially causing a run on the bank, as infamously happened with Lehman Brothers, and is currently happening with Dexia, a major European bank," he goes on to explain, citing the significance of such inflated prices.

Using sophisticated mathematical methods, Protter and his co-authors answer the question of whether the price increase of a particular asset represents a bubble in real time. "[In this paper] we show that by using tick data and some statistical techniques, one is able to tell with a large degree of certainty, whether or not a given financial asset (or group of assets) is undergoing bubble pricing," says Protter.

This question is answered by estimating an asset's price volatility, which is stochastic or randomly determined. The authors define an asset's price process in terms of a standard stochastic differential equation, which is driven by Brownian motion. Brownian motion, based on a natural process involving the erratic, random movement of small particles suspended in gas or liquid, has been widely used in mathematical finance. The concept is specifically used to model instances where previous change in the value of a variable is unrelated to past changes.

The key characteristic in determining a bubble is the volatility of an asset's price, which, in the case of bubbles is very high. The authors estimate the volatility by applying state of the art estimators to real-time tick price data for a given stock. They then obtain the best possible extension of this data for large values using a technique called Reproducing Kernel Hilbert Spaces (RKHS), which is a widely used method for statistical learning.

"First, one uses tick price data to estimate the volatility of the asset in question for various levels of the asset's price," Protter explains. "Then, a special technique (RKHS with an optimization addition) is employed to extrapolate this estimated volatility function to large values for the asset's price, where this information is not (and cannot be) available from tick data. Using this extrapolation, one can check the rate of increase of the volatility function as the asset price gets arbitrarily large. Whether or not there is a bubble depends on how fast this increase occurs (its asymptotic rate of increase)."

If it does not increase fast enough, there is no bubble within the model's framework.

The authors test their methodology by applying the model to several stocks from the dot-com bubble of the nineties. They find fairly successful rates in their predictions, with higher accuracies in cases where market volatilities can be modeled more efficiently. This helps establish the strengths and weaknesses of the method.

The authors have also used the model to test more recent price increases to detect bubbles. "We have found, for example, that the IPO [initial public offering] of LinkedIn underwent bubble pricing at its debut, and that the recent rise in gold prices was not a bubble, according to our models," Protter says.

It is encouraging to see that mathematical analysis can play a role in the diagnosis and detection of bubbles, which have significantly impacted economic upheavals in the past few decades.

Robert Jarrow is a professor at the Johnson Graduate School of Management at Cornell University in Ithaca, NY, and managing director of the Kamakura Corporation. Younes Kchia is a graduate student at Ecole Polytechnique in Paris, and Philip Protter is a professor in the Statistics Department at Columbia University in New York.

Professor Protter's work was supported in part by NSF grant DMS-0906995.

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

Other bookmarking and sharing tools:

Story Source:

The above story is reprinted from materials provided by Society for Industrial and Applied Mathematics.

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

Journal Reference:

Robert Jarrow, Younes Kchia, and Philip Protter. How to Detect an Asset Bubble. SIAM J. Finan. Math., 2011; 2, pp. 839-865 [link]

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

Fiber-Optic Transatlantic Cable Could Save Milliseconds, Millions by Speeding Data to Stock Traders

By Matt Dellinger Posted 04.25.2011 at 1:00 pm 4 Comments
Speed Sells Fiber-optic cables allow stocks to be bought and sold in an instant. Joshua Lott/Reuters

Traders used to all buy and sell stocks in the same crowded room. Everyone received information at the same time, and the first guy to shout or signal got the sale. Today, using algorithms that exploit slightly different prices changing at slightly different speeds, and computers connected to exclusive fiber-optic lines that can buy and sell stocks within fractions of a second, high-frequency traders are able to buy low and sell slightly higher in virtually the same instant.

“A couple of milliseconds can roll out to a $20-million difference in [a trader’s] account at the end of the month,” says Nigel Bayliff, the CEO of Huawei Marine Networks, one of the companies laying down superfast fiber-optic lines.

Companies like Bayliff’s are looking for ways to shave time, and the easiest method is to build a more direct route. Last year, Mississippi-based Spread Networks opened a shorter connection between New York and Chicago that saved about three milliseconds and was estimated to have cost $300 million to develop. Huawei is working with another company, Hibernia Atlantic, to lay the first transatlantic fiber-optic submarine cable in a decade, a $400-million-plus project that will save traders five milliseconds.

To do this, Hibernia is laying nearly 3,000 miles of cable across the Grand Banks off Canada and the North Atlantic, a shorter route that most companies have avoided because it traverses relatively shallow waters. Undersea-cable companies prefer to work at greater depths; they can just drop naked cable down to the ocean floor. At less than a mile deep, though, they must bury armored cable to protect it from ship anchors, fishing trawls, dredging gear, and attacks from sharks, which are drawn to the line’s electricity.

Remotely Operated Underwater Vehicle (ROV):  Courtesy Global Marine EnergyFor all the money Hibernia and its clients will make from a 60-millisecond trip across the Atlantic, the installation will be slow. Crews on two ships, the Sovereign and the Cable Innovator, will deploy 24-ton ploughs to cut a trench up to six feet into the seabed, into which they will lay the cable. The top speed is about one mile an hour.

Each ship is outfitted with a dynamic positioning system that keeps it in place while laying cable, regardless of currents or winds. If something gets in the way, such as another submarine cable, the crews will use a remotely operated vehicle equipped with a pair of high-pressure water “swords” to break apart sediment. The ROV then uses a mechanical arm to bury the new cable underneath the obstacle and into the temporarily softened earth. “The seabed always throws up something unexpected,” says Stuart Wilson, the manager of cable-route engineering for Global Marine Systems, the company installing the Hibernia line.

Hibernia says its cable will go live next year, connecting it to Hibernia’s Global Financial Network, which has fiber optics running 15,000 miles between financial centers from Chicago to Frankfurt. But the New York-to-London line could be the company’s biggest draw, providing a competitive advantage of just five milliseconds—about the amount of time it takes a bee to flap its wings.

View the original article here