Making complex fluids look simple

ScienceDaily (June 1, 2011) — An international research team has successfully developed a widely applicable method for discovering the physical foundations of complex fluids for the first time. Researchers at the University of Vienna and University of Rome have developed a microscopic theory that describes the interactions between the various components of a complex polymer mixture. This approach has now been experimentally proven by physicists from Jülich, who conducted neutron scattering experiments in Grenoble.

The results have been published in the June issue of the journal Physical Review Letters.

Some important materials from technology and nature are complex fluids: polymer melts for plastics production, mixtures of water, oil and amphiphiles, which can be found in both living cells and in your washing machine, or colloidal suspensions such as blood or dispersion paints. They are quite different from simple fluids consisting of small molecules, such as water, because they are made of mixtures of particles between a nanometre and a micrometre in size, and have a large number of so-called degrees of freedom. The latter include vibrations, movements of the functional groups of molecules or joint movements of several molecules. They can appear on widely varied length, time, and energy scales. This makes experimental and theoretical studies difficult and, so far, has impeded understanding of the properties of these systems and the targeted development of new materials with improved properties.

A method developed and tested by physicists at Forschungszentrum Jülich, the Institut Laue-Langevin in Grenoble, and the Universities of Vienna and Rome now permits realistic modelling of complex fluids for the first time. "Our microscopic theory describes the interactions between the various components of a complex mixture and in turn, enables us to draw realistic conclusions about their macroscopic properties, such as their structure or their flow properties," said Prof. Christos Likos of the University of Vienna, an expert on theory and simulation.

The team from Vienna and Rome developed the theory model. Since the researchers were unable to include all the details of the real system -- a mixture of larger star-shaped polymers and smaller polymer chains -- they systematically eliminated the rapidly moving degrees of freedom and focused on the relevant slow degrees of freedom, a time-consuming and challenging task. "To do this, we use a relatively new method called coarse graining and replace each complex macromolecule with a sphere of the appropriate size. The challenge involves integrating the degrees of freedom that have been eliminated in the simplified systems as averages so that the characteristics of the substances are retained," Likos explained.

The team from Jülich used elaborate small angle neutron scattering experiments with the instrument D11 at the Institut Laue-Langevin in Grenoble to prove that the interactions between the spheres of the coarse-grained model realistically simulate the conditions in the real system. "We were faced with the proverbial challenge of visualizing the needle in a haystack," explained Dr. Jörg Stellbrink, a physicist and neutron scattering expert at the Jülich Centre for Neutron Science (JCNS). For neutrons, the individual polymers of the mixture cannot be readily distinguished. For this reason, the physicists "coloured" the components they were interested in, so that they stood out of the crowd. This is one of the Jülich team's specialities. In this way, they were able to selectively examine the structures and interactions on a microscopic length scale.

The physicists are especially proud of the excellent agreement between theoretical predictions and experimental results. The method will now open up a spectrum of possibilities for studying the physical properties of a whole range of different complex mixtures.

Story Source:

The above story is reprinted (with editorial adaptations by ScienceDaily staff) from materials provided by Helmholtz Association of German Research Centres, via EurekAlert!, a service of AAAS.

Journal Reference:

B. Lonetti, M. Camargo, J. Stellbrink, C. Likos, E. Zaccarelli, L. Willner, P. Lindner, D. Richter. Ultrasoft Colloid-Polymer Mixtures: Structure and Phase Diagram. Physical Review Letters, 2011; 106 (22) DOI: 10.1103/PhysRevLett.106.228301

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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IBM's Watson Makes the Move From Answering Trivia Questions to Making Medical Diagnoses

What is...Toronto (General Hospital)?
Watson, Behind the Curtain Dan Nosowitz

When Watson was competing on Jeopardy!, its massive databanks were filled with encyclopedias, novels, film scripts, and history books. These days, Watson is more into medical journals and misspelled Yahoo Answers blog posts about weird rashes and vague abdominal pains. Watson is maturing, and prepping for his first non-trivia, real-world application: medical diagnoses. He's all *sniff* grown up!

We've known medicine was to be the next step for Watson for some time, but just recently, IBM gave a short demonstration of Watson's progress. Watson isn't the first attempt at an automated diagnosis program--we documented Artemis, and Isabel has been around for a few years--but Watson's incredible power, depth of knowledge, and ability to understand natural human language puts it in a totally different league. Diagnosing an ailment isn't really that much different from answering a trivia question; Watson takes in as much information as possible from the question, eliminating the potential answers as new information renders them impossible, and comes up with a list of likely answers. An example from the AP: "As more clues were unveiled - blurred vision, family history of arthritis, Connecticut residence - Watson's suggested diagnoses evolved from uveitis to Behcet's disease to Lyme disease. It gave the final diagnosis a 73 percent confidence rating."

While on Jeopardy!, Watson could only give one answer, but in medicine, it lists all possible answers, along with the percent likeliness. An 80% possibility of accuracy is enough for Watson to risk money on Jeopardy, but when working with possible diagnoses, that still leaves a one in five chance that the patient is afflicted with something else, so Watson is designed to divulge even the less likely answers.

Watson's human language recognition skills also allows it to input an entirely new sector of information: anecdotal evidence. Anecdotal evidence is not necessarily reliable, of course, but can still be extremely useful--it's worth noting that a patient's description of symptoms is anecdotal, and still very important to diagnosis. Watson is able to trawl through the internet, picking up the oodles of medical information out there and adding it to its memory banks. Being able to understand that, say, a "dry mouth" is the same as xerostomia can make legitimate use of all those confused forums.

Of course, Watson isn't designed to replace a doctor's diagnostic instincts. Instead, it's more like a futuristic reference book. There's simply too much information out there these days, in too many places and added too frequently, for any doctor to keep up. Watson could help keep track of all the new drugs, studies, journals, and anecdotal evidence.

Diagnosis systems using Watson are still likely a few years away, but IBM is working on ways to leverage Watson's abilities even to hospitals with budgets too small to afford a multimillion-dollar Watson of their own. iPad apps were mentioned as a distinct possibility--doctors could tap into an off-site Watson with an iPad, shoot off a few queries and receive an answer immediately. And as more medical data is digitized, Watson will only get stronger and more useful.

[Associated Press]

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