Posts Tagged ‘US DoE’

Gold nanoparticles and the air that you breathe

Wednesday, June 12th, 2013

Scientists at the (US Dept. of Energy) Brookhaven National Laboratory can turn gold nanoparticles into catalysts using a room temperature process. From the June 11, 2013 news item on ScienceDaily,

Gold bars may signify great wealth, but the precious metal packs a much more practical punch when shrunk down to just billionths of a meter. Unfortunately, unlocking gold’s potential often requires complex synthesis techniques that produce delicate structures with extreme sensitivity to heat.

Now, scientists at the U.S. Department of Energy’s Brookhaven National Laboratory have discovered a process of creating uniquely structured gold-indium nanoparticles that combine high stability, great catalytic potential, and a simple synthesis process. The new nanostructures — detailed online June 10 in the Proceedings of the National Academy of Sciences — might enhance many different commercial and industrial processes, including acting as an efficient material for catalytic converters in cars.

“We discovered a room-temperature process that transforms a simple alloy into a nanostructure with remarkable properties,” said physicist Eli Sutter, lead author on the study. “By exposing the gold-indium alloy nanoparticles to air, ambient oxygen was able to drive an oxidation reaction that converted them into an active core-shell structure.”

The Brookhaven National Laboratory June 11, 2013 news release, which originated the news item, explains the issues with gold nanoparticles and how the ‘room temperature’ discovery was made,

The Brookhaven Lab researchers were studying oxidation processes through which metals and alloys combine with oxygen when they made the discovery. For this study, they examined alloys of a noble metal and a non-noble metal through a remarkably simple reaction technique: giving gold-indium nanoparticles a little room to breathe. Once nanoparticles of the metal alloy were exposed to oxygen, highly reactive shells of gold-indium oxide formed across their surfaces.

“Conventional wisdom would say that oxidation should push the gold atoms into the center while pulling the less noble indium to the surface, creating a noble metal core that is surrounded by a shell of non-reactive indium-oxide,” Peter Sutter said. “Instead, the oxygen actually penetrated the alloy. After oxidation, the alloy core of the nanoparticles was encapsulated by a newly formed thin shell of mixed gold-indium oxide.”

Trapping gold in the amorphous oxide shell retains its catalytic properties and prevents the gold from sintering and becoming inert. The new nanostructures proved capable of converting oxygen and carbon monoxide into carbon dioxide, demonstrating their activity as a catalyst.

“The indium and gold in the shell are not mobile, but are frozen in the amorphous, oxide,” Eli Sutter said. “Importantly, the structural integrity holds without sintering at temperatures of up to 300 degrees Celsius, making these remarkably resilient compared to other gold nanocatalysts.”

The research was conducted at Brookhaven Lab’s Center for Functional Nanomaterials (CFN), whose unique facilities for nanoscale synthesis and characterization proved central to the discovery of this new process.

“The CFN brings a wide range of state-of-the-art instruments and expertise together under one roof, accelerating research and facilitating collaboration,” Eli Sutter said. “We used transmission electron microscopy to characterize the structures and their composition, x-ray photoelectron spectroscopy to determine the chemical bonding at the surface, and ion-scattering spectroscopy to identify the outermost atoms of the nanoparticle shell.”

Further investigations will help determine the properties of the gold-indium oxide particles in different catalytic reactions, and the same oxidation process will be applied to other metal alloys to create an entire family of new functional materials.

You can find a citation and a link to the researchers’ paper if you click on the ScienceDaily news item link earlier in this posting.

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Sifting through Twitter with your computer cluster of more than 600 nodes named Olympus—one of the Top 500 fastest supercomputers in the world.

Monday, June 10th, 2013

Here are two (seemingly) contradictory pieces of information (1) the US Library of Congress takes over 24 hours to complete a single search of tweets archived from 2006 – 2010, according to my Jan. 16, 2013 posting, and (2) Court (Courtney) Corley, a data scientist at the US Dept. of Energy’s Pacific Northwest National Laboratory (PNNL), has a system (SALSA; SociAL Sensor Analytics) that analyzes billions of tweets in seconds. It’s a little hard to make sense out of these two very different perspectives on accessing data from tweets.

The news from Corley and the PNNL is more recent and, before I speculate further, here’s a bit more about Corley’s work, from the June 6, 2013 PNNL news release (also on EurekAlert)

If you think keeping up with what’s happening via Twitter, Facebook and other social media is like drinking from a fire hose, multiply that by 7 billion – and you’ll have a sense of what Court Corley wakes up to every morning.

Corley, a data scientist at the Department of Energy’s Pacific Northwest National Laboratory, has created a powerful digital system capable of analyzing billions of tweets and other social media messages in just seconds, in an effort to discover patterns and make sense of all the information. His social media analysis tool, dubbed “SALSA” (SociAL Sensor Analytics), combined with extensive know-how – and a fair degree of chutzpah – allows someone like Corley to try to grasp it all.

“The world is equipped with human sensors – more than 7 billion and counting. It’s by far the most extensive sensor network on the planet. What can we learn by paying attention?” Corley said.

Among the payoffs Corley envisions are emergency responders who receive crucial early information about natural disasters such as tornadoes; a tool that public health advocates can use to better protect people’s health; and information about social unrest that could help nations protect their citizens. But finding those jewels amidst the effluent of digital minutia is a challenge.

“The task we all face is separating out the trivia, the useless information we all are blasted with every day, from the really good stuff that helps us live better lives. There’s a lot of noise, but there’s some very valuable information too.”

I was getting a little worried when I saw the bit about separating useless information from the good stuff since that can be a very personal choice. Thankfully, this followed,

One person’s digital trash is another’s digital treasure. For example, people known in social media circles as “Beliebers,” named after entertainer Justin Bieber, covet inconsequential tidbits about Justin Bieber, while “non-Beliebers” send that data straight to the recycle bin.

The amount of data is mind-bending. In social media posted just in the single year ending Aug. 31, 2012, each hour on average witnessed:

  • 30 million comments
  • 25 million search queries
  • 98,000 new tweets
  • 3.8 million blog views
  • 4.5 million event invites
  • 7.1 million photos uploaded
  • 5.5 million status updates
  • The equivalent of 453 years of video watched

Several firms routinely sift posts on LinkedIn, Facebook, Twitter, YouTube and other social media, then analyze the data to see what’s trending. These efforts usually require a great deal of software and a lot of person-hours devoted specifically to using that application. It’s what Corley terms a manual approach.

Corley is out to change that, by creating a systematic, science-based, and automated approach for understanding patterns around events found in social media.

It’s not so simple as scanning tweets. Indeed, if Corley were to sit down and read each of the more than 20 billion entries in his data set from just a two-year period, it would take him more than 3,500 years if he spent just 5 seconds on each entry. If he hired 1 million helpers, it would take more than a day.

But it takes less than 10 seconds when he relies on PNNL’s Institutional Computing resource, drawing on a computer cluster with more than 600 nodes named Olympus, which is among the Top 500 fastest supercomputers in the world.

“We are using the institutional computing horsepower of PNNL to analyze one of the richest data sets ever available to researchers,” Corley said.

At the same time that his team is creating the computing resources to undertake the task, Corley is constructing a theory for how to analyze the data. He and his colleagues are determining baseline activity, culling the data to find routine patterns, and looking for patterns that indicate something out of the ordinary. Data might include how often a topic is the subject of social media, who is putting out the messages, and how often.

Corley notes additional challenges posed by social media. His programs analyze data in more than 60 languages, for instance. And social media users have developed a lexicon of their own and often don’t use traditional language. A post such as “aw my avalanna wristband @Avalanna @justinbieber rip angel pic.twitter.com/yldGVV7GHk” poses a challenge to people and computers alike.

Nevertheless, Corley’s program is accurate much more often than not, catching the spirit of a social media comment accurately more than three out of every four instances, and accurately detecting patterns in social media more than 90 percent of the time.

Corley’s educational background may explain the interest in emergency responders and health crises mentioned in the early part of the news release (from Corley’s PNNL webpage),

B.S. Computer Science from University of North Texas; M.S. Computer Science from University of North Texas; Ph.D. Computer Science and Engineering from University of North Texas; M.P.H (expected 2013) Public Health from University of Washington.

The reference to public health and emergency response is further developed, from the news release,

Much of the work so far has been around public health. According to media reports in China, the current H7N9 flu situation in China was highlighted on Sina Weibo, a China-based social media platform, weeks before it was recognized by government officials. And Corley’s work with the social media working group of the International Society for Disease Surveillance focuses on the use of social media for effective public health interventions.

In collaboration with the Infectious Disease Society of America and Immunizations 4 Public Health, he has focused on the early identification of emerging immunization safety concerns.

“If you want to understand the concerns of parents about vaccines, you’re never going to have the time to go out there and read hundreds of thousands, perhaps millions of tweets about those questions or concerns,” Corley said. “By creating a system that can capture trends in just a few minutes, and observe shifts in opinion minute to minute, you can stay in front of the issue, for instance, by letting physicians in certain areas know how to customize the educational materials they provide to parents of young children.”

Corley has looked closely at reaction to the vaccine that protects against HPV, which causes cervical cancer. The first vaccine was approved in 2006, when he was a graduate student, and his doctoral thesis focused on an analysis of social media messages connected to HPV. He found that creators of messages that named a specific drug company were less likely to be positive about the vaccine than others who did not mention any company by name.

Other potential applications include helping emergency responders react more efficiently to disasters like tornadoes, or identifying patterns that might indicate coming social unrest or even something as specific as a riot after a soccer game. More than a dozen college students or recent graduates are working with Corley to look at questions like these and others.

As to why the US Library of Congress requires 24 hours to search one term in their archived tweets and Corley and the PNNL require seconds to sift through two years of tweets, only two possibilities come to my mind. (1) Corley is doing a stripped down version of an archival search so his searches are not comparable to the Library of Congress searches or (2) Corley and the PNNL have far superior technology.

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Computer simulation errors and corrections

Thursday, January 3rd, 2013

In addition to being a news release, this is a really good piece of science writing by Paul Preuss for the Lawrence Berkeley National Laboratory (Berkeley Lab), from the Jan. 3, 2013 Berkeley Lab news release,

Because modern computers have to depict the real world with digital representations of numbers instead of physical analogues, to simulate the continuous passage of time they have to digitize time into small slices. This kind of simulation is essential in disciplines from medical and biological research, to new materials, to fundamental considerations of quantum mechanics, and the fact that it inevitably introduces errors is an ongoing problem for scientists.

Scientists at the U.S. Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have now identified and characterized the source of tenacious errors and come up with a way to separate the realistic aspects of a simulation from the artifacts of the computer method. …

Here’s more detail about the problem and solution,

How biological molecules move is hardly the only field where computer simulations of molecular-scale motion are essential. The need to use computers to test theories and model experiments that can’t be done on a lab bench is ubiquitous, and the problems that Sivak and his colleagues encountered weren’t new.

“A simulation of a physical process on a computer cannot use the exact, continuous equations of motion; the calculations must use approximations over discrete intervals of time,” says Sivak. “It’s well known that standard algorithms that use discrete time steps don’t conserve energy exactly in these calculations.”

One workhorse method for modeling molecular systems is Langevin dynamics, based on equations first developed by the French physicist Paul Langevin over a century ago to model Brownian motion. Brownian motion is the random movement of particles in a fluid (originally pollen grains on water) as they collide with the fluid’s molecules – particle paths resembling a “drunkard’s walk,” which Albert Einstein had used just a few years earlier to establish the reality of atoms and molecules. Instead of impractical-to-calculate velocity, momentum, and acceleration for every molecule in the fluid, Langevin’s method substituted an effective friction to damp the motion of the particle, plus a series of random jolts.

When Sivak and his colleagues used Langevin dynamics to model the behavior of molecular machines, they saw significant differences between what their exact theories predicted and what their simulations produced. They tried to come up with a physical picture of what it would take to produce these wrong answers.

“It was as if extra work were being done to push our molecules around,” Sivak says. “In the real world, this would be a driven physical process, but it existed only in the simulation, so we called it ‘shadow work.’ It took exactly the form of a nonequilibrium driving force.”

They first tested this insight with “toy” models having only a single degree of freedom, and found that when they ignored the shadow work, the calculations were systematically biased. But when they accounted for the shadow work, accurate calculations could be recovered.

“Next we looked at systems with hundreds or thousands of simple molecules,” says Sivak. Using models of water molecules in a box, they simulated the state of the system over time, starting from a given thermal energy but with no “pushing” from outside. “We wanted to know how far the water simulation would be pushed by the shadow work alone.”

The result confirmed that even in the absence of an explicit driving force, the finite-time-step Langevin dynamics simulation acted by itself as a driving nonequilibrium process. Systematic errors resulted from failing to separate this shadow work from the actual “protocol work” that they explicitly modeled in their simulations. For the first time, Sivak and his colleagues were able to quantify the magnitude of the deviations in various test systems.

Such simulation errors can be reduced in several ways, for example by dividing the evolution of the system into ever-finer time steps, because the shadow work is larger when the discrete time steps are larger. But doing so increases the computational expense.

The better approach is to use a correction factor that isolates the shadow work from the physically meaningful work, says Sivak. “We can apply results from our calculation in a meaningful way to characterize the error and correct for it, separating the physically realistic aspects of the simulation from the artifacts of the computer method.”

You can find out more in the Berkeley Lab news release, or (H/T)  in the Jan. 3, 2013 news item on Nanowerk, or you can read the paper,

“Using nonequilibrium fluctuation theorems to understand and correct errors in equilibrium and nonequilibrium discrete Langevin dynamics simulations,” by David A. Sivak, John D. Chodera, and Gavin E. Crooks, will appear in Physical Review X (http://prx.aps.org/) and is now available as an arXiv preprint at http://arxiv.org/abs/1107.2967.

This casts a new light on the SPAUN (Semantic Pointer Architecture Unified Network) project, from Chris Eliasmith’s team at the University of Waterloo, which announced the most  successful attempt (my Nov. 29, 2012 posting) yet to simulate a brain using virtual neurons. Given the probability that Eliasmith’s team was not aware of this work from the Berkeley Lab, one imagines that once it has been integrated that SPAUN will be capable of even more extraordinary feats.

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