Silicon With Afterburners: New Process Could Be Boon To Electronics Manufacturer

SOURCE

ScienceDaily (July 24, 2009) — Scientists at Rice University and North Carolina State University have found a method of attaching molecules to semiconducting silicon that may help manufacturers reach beyond the current limits of Moore's Law as they make microprocessors both smaller and more powerful.

Moore's Law, suggested by Intel co-founder Gordon Moore in 1965, said the number of transistors that can be placed on an integrated circuit doubles about every two years. But even Moore has said the law cannot be sustained indefinitely.
The challenge is to get past the limits of doping, a process that has been essential to creating the silicon substrate that is at the heart of all modern integrated circuits, said James Tour, Rice's Chao Professor of Chemistry and professor of mechanical engineering and materials science and of computer science.
Doping introduces impurities into pure crystalline silicon as a way of tuning microscopic circuits to a particular need, and it's been effective so far even in concentrations as small as one atom of boron, arsenic or phosphorus per 100 million of silicon.
But as manufacturers pack more transistors onto integrated circuits by making the circuits ever smaller, doping gets problematic.
"When silicon gets really small, down to the nanoscale, you get structures that essentially have very little volume," Tour said. "You have to put dopant atoms in silicon for it to work as a semiconductor, but now, devices are so small you get inhomogeneities. You may have a few more dopant atoms in this device than in that one, so the irregularities between them become profound."
Manufacturers who put billions of devices on a single chip need them all to work the same way, but that becomes more difficult with the size of a state-of-the-art circuit at 45 nanometers wide -- a human hair is about 100,000 nanometers wide -- and smaller ones on the way.
The paper suggests that monolayer molecular grafting -- basically, attaching molecules to the surface of the silicon rather than mixing them in -- essentially serves the same function as doping, but works better at the nanometer scale. "We call it silicon with afterburners," Tour said. "We're putting an even layer of molecules on the surface. These are not doping in the same way traditional dopants do, but they're effectively doing the same thing."
Tour said years of research into molecular computing with an eye toward replacing silicon has yielded little fruit. "It's hard to compete with something that has trillions of dollars and millions of person-years invested into it. So we decided it would be good to complement silicon, rather than try to supplant it."
He anticipates wide industry interest in the process, in which carbon molecules could be bonded with silicon either through a chemical bath or evaporation. "This is a nice entry point for molecules into the silicon industry. We can go to a manufacturer and say, 'Let us make your fabrication line work for you longer. Let us complement what you have.'
"This gives the Intels and the Microns and the Samsungs of the world another tool to try, and I guarantee you they'll be trying this."
Journal reference:
He et al. Controllable Molecular Modulation of Conductivity in Silicon-Based Devices. Journal of the American Chemical Society, 2009; 131 (29): 10023 DOI: 10.1021/ja9002537
Adapted from materials provided by Rice University.

Music Is The Engine Of New Lab-on-a-chip Device

SOURCE

ScienceDaily (July 23, 2009) — Music, rather than electromechanical valves, can drive experimental samples through a lab-on-a-chip in a new system developed at the University of Michigan. This development could significantly simplify the process of conducting experiments in microfluidic devices.

A paper on the research will be published online in the Proceedings of the National Academy of Sciences the week of July 20.
A lab-on-a-chip, or microfluidic device, integrates multiple laboratory functions onto one chip just millimeters or centimeters in size. The devices allow researchers to experiment on tiny sample sizes, and also to simultaneously perform multiple experiments on the same material. There is hope that they could lead to instant home tests for illnesses, food contaminants and toxic gases, among other advances.
To do an experiment in a microfluidic device today, researchers often use dozens of air hoses, valves and electrical connections between the chip and a computer to move, mix and split pin-prick drops of fluid in the device's microscopic channels and divots.
"You quickly lose the advantage of a small microfluidic system," said Mark Burns, professor and chair of the Department of Chemical Engineering and a professor in the Department of Biomedical Engineering.
"You'd really like to see something the size of an iPhone that you could sneeze onto and it would tell you if you have the flu. What hasn't been developed for such a small system is the pneumatics—the mechanisms for moving chemicals and samples around on the device."
The U-M researchers use sound waves to drive a unique pneumatic system that does not require electromechanical valves. Instead, musical notes produce the air pressure to control droplets in the device. The U-M system requires only one "off-chip" connection.
"This system is a lot like fiberoptics, or cable television. Nobody's dragging 200 separate wires all over your house to power all those channels," Burns said. "There's one cable signal that gets decoded."
The system developed by Burns, chemical engineering doctoral student Sean Langelier, and their collaborators replaces these air hoses, valves and electrical connections with what are called resonance cavities. The resonance cavities are tubes of specific lengths that amplify particular musical notes.
These cavities are connected on one end to channels in the microfluidic device, and on the other end to a speaker, which is connected to a computer. The computer generates the notes, or chords. The resonance cavities amplify those notes and the sound waves push air through a hole in the resonance cavity to their assigned channel. The air then nudges the droplets in the microfluidic device along.
"Each resonance cavity on the device is designed to amplify a specific tone and turn it into a useful pressure," Langelier said. "If I play one note, one droplet moves. If I play a three-note chord, three move, and so on. And because the cavities don't communicate with each other, I can vary the strength of the individual notes within the chords to move a given drop faster or slower."
Burns describes the set-up as the reverse of a bell choir. Rather than ringing a bell to create sound waves in the air, which are heard as music, this system uses music to create sound waves in the device, which in turn, move the experimental droplets.
"I think this is a very clever system," Burns said. "It's a way to make the connections between the microfluidic world and the real world much simpler."
The new system is still external to the chip, but the researchers are working to make it smaller and incorporate it on a microfluidic device. That would be a step closer to a smartphone-sized home flu test.
The paper is called, "Acoustically-driven programmable liquid motion using resonance cavities." Other authors are U-M chemical engineering graduate students Dustin Chang and Ramsey Zeitoun. The research is funded by the National Institutes of Health and the National Science Foundation. The University is pursuing patent protection for the intellectual property.
Adapted from materials provided by University of Michigan.

Cell Phones Turned Into Fluorescent Microscopes

SOURCE

ScienceDaily (July 22, 2009) — Researchers at the University of California, Berkeley, are proving that a camera phone can capture far more than photos of people or pets at play. They have now developed a cell phone microscope, or CellScope, that not only takes color images of malaria parasites, but of tuberculosis bacteria labeled with fluorescent markers.

The prototype CellScope, described in the journal PLoS One, moves a major step forward in taking clinical microscopy out of specialized laboratories and into field settings for disease screening and diagnoses.
"The same regions of the world that lack access to adequate health facilities are, paradoxically, well-served by mobile phone networks," said Dan Fletcher, UC Berkeley associate professor of bioengineering and head of the research team developing the CellScope. "We can take advantage of these mobile networks to bring low-cost, easy-to-use lab equipment out to more remote settings."
The engineers attached compact microscope lenses to a holder fitted to a cell phone. Using samples of infected blood and sputum, the researchers were able to use the camera phone to capture bright field images of Plasmodium falciparum, the parasite that causes malaria in humans, and sickle-shaped red blood cells. They were also able to take fluorescent images of Mycobacterium tuberculosis, the bacterial culprit that causes TB in humans. Moreover, the researchers showed that the TB bacteria could be automatically counted using image analysis software.
"The images can either be analyzed on site or wirelessly transmitted to clinical centers for remote diagnosis," said David Breslauer, co-lead author of the study and a graduate student in the UC San Francisco/UC Berkeley Bioengineering Graduate Group. "The system could be used to help provide early warning of outbreaks by shortening the time needed to screen, diagnose and treat infectious diseases."
The engineers had previously shown that a portable microscope mounted on a mobile phone could be used for bright field microscopy, which uses simple white light - such as from a bulb or sunlight - to illuminate samples. The latest development adds to the repertoire fluorescent microscopy, in which a special dye emits a specific fluorescent wavelength to tag a target - such as a parasite, bacteria or cell - in the sample.
"Fluorescence microscopy requires more equipment - such as filters and special lighting - than a standard light microscope, which makes them more expensive," said Fletcher. "In this paper we've shown that the whole fluorescence system can be constructed on a cell phone using the existing camera and relatively inexpensive components."
The researchers used filters to block out background light and to restrict the light source, a simple light-emitting diode (LED), to the 460 nanometer wavelength necessary to excite the green fluorescent dye in the TB-infected blood. Using an off-the-shelf phone with a 3.2 megapixel camera, they were able to achieve a spatial resolution of 1.2 micrometers. In comparison, a human red blood cell is about 7 micrometers in diameter.
"LEDs are dramatically more powerful now than they were just a few years ago, and they are only getting better and cheaper," said Fletcher. "We had to disabuse ourselves of the notion that we needed to spend many thousands on a mercury arc lamp and high-sensitivity camera to get a meaningful image. We found that a high-powered LED - which retails for just a few dollars - coupled with a typical camera phone could produce a clinical quality image sufficient for our goal of detecting in a field setting some of the most common diseases in the developing world."
The researchers pointed out that while fluorescent microscopes include additional parts, less training is needed to interpret fluorescent images. Instead of sorting out pathogens from normal cells in the images from standard light microscopes, health workers simply need to look for something the right size and shape to light up on the screen.
"Viewing fluorescent images is a bit like looking at stars at night," said Breslauer. "The bright green fluorescent light stands out clearly from the dark background. It's this contrast in fluorescent imaging that allowed us to use standard computer algorithms to analyze the sample containing TB bacteria."
Breslauer added that these software programs can be easily installed onto a typical cell phone, turning the mobile phone into a self-contained field lab and a "good platform for epidemiological monitoring."
While the CellScope is particularly valuable in resource-poor countries, Fletcher noted that it may have a place in this country's health care system, famously plagued with cost overruns.
"A CellScope device with fluorescence could potentially be used by patients undergoing chemotherapy who need to get regular blood counts," said Fletcher. "The patient could transmit from home the image or analyzed data to a health care professional, reducing the number of clinic visits necessary."
The CellScope developers have even been approached by experts in agriculture interested in using it to help diagnose diseases in crops. Instead of sending in a leaf sample to a lab for diagnosis, farmers could upload an image of the diseased leaf for analysis.
The researchers are currently developing more robust prototypes of the CellScope in preparation for further field testing.
Other researchers on the team include Robi Maamari, a UC Berkeley research associate in bioengineering and co-lead author of the study; Neil Switz, a graduate student in UC Berkeley's Biophysics Graduate Group; and Wilbur Lam, a UC Berkeley post-doctoral fellow in bioengineering and a UCSF pediatric hematologist.
Funding for the CellScope project comes from the Center for Information Technology Research in the Interest of Society (CITRIS) and the Blum Center for Developing Economies, both at UC Berkeley, and from Microsoft Research, Intel and the Vodafone Americas Foundation.
Journal reference:
David N. Breslauer et al. Mobile Phone Based Clinical Microscopy for Global Health Applications. PLoS One, July 22, 2009 DOI: 10.1371/journal.pone.0006320
Adapted from materials provided by University of California - Berkeley.

Electronic Nose Created To Detect Skin Vapors

SOURCE

ScienceDaily (July 21, 2009) — A team of researchers from the Yale University (United States) and a Spanish company have developed a system to detect the vapours emitted by human skin in real time. The scientists think that these substances, essentially made up of fatty acids, are what attract mosquitoes and enable dogs to identify their owners.

"The spectrum of the vapours emitted by human skin is dominated by fatty acids. These substances are not very volatile, but we have developed an 'electronic nose' able to detect them", Juan Fernández de la Mora, of the Department of Mechanical Engineering at Yale University (United States) and co-author of a study recently published in the Journal of the American Society for Mass Spectrometry, says.
The system, created at the Boecillo Technology Park in Valladolid, works by ionising the vapours with an electrospray (a cloud of electrically-charged drops), and later analysing these using mass spectrometry. This technique can be used to identify many of the vapour compounds emitted by a hand, for example.
"The great novelty of this study is that, despite the almost non-existent volatility of fatty acids, which have chains of up to 18 carbon atoms, the electronic nose is so sensitive that it can detect them instantaneously", says Fernández de la Mora. The results show that the volatile compounds given off by the skin are primarily fatty acids, although there are also others such as lactic acid and pyruvic acid.
The researcher stresses that the great chemical wealth of fatty acids, made up of hundreds of different molecules, "is well known, and seems to prove the hypothesis that these are the key substances that enable dogs to identify people". The enormous range of vapours emitted by human skin and breath may not only enable dogs to recognise their owners, but also help mosquitoes to locate their hosts, according to several studies.
World record for detecting explosives
Aside from identifying people from their skin vapours, another of the important applications of the new system is that it is able to detect tiny amounts of explosives. The system can "smell" levels below a few parts per trillion, and has been able to set a world sensitivity record at "2x10-14 atmospheres of partial pressure of TNT (the explosive trinitrotoluene)".
The "father" of ionisation using the mass spectrometry electrospray is Professor John B. Fenn, who is currently a researcher at the University of Virginia (United States), and in 2002 won the Nobel Prize in Chemistry for using this technique in the analysis of proteins.
Journal references:
Pablo Martínez Lozano y Juan Fernández de la Mora. Online Detection of Human Skin Vapors. Journal of the American Society for Mass Spectrometry, 20 (6): 1060-1063, 2009
Martínez-Lozano et al. Secondary Electrospray Ionization (SESI) of Ambient Vapors for Explosive Detection at Concentrations Below Parts Per Trillion. Journal of the American Society for Mass Spectrometry, 2009; 20 (2): 287 DOI: 10.1016/j.jasms.2008.10.006
Adapted from materials provided by FECYT - Spanish Foundation for Science and Technology, via EurekAlert!, a service of AAAS.

Human-like Vision Lets Robots Navigate Naturally

SOURCE

ScienceDaily (July 17, 2009) — A robotic vision system that mimics key visual functions of the human brain promises to let robots manoeuvre quickly and safely through cluttered environments, and to help guide the visually impaired.

It’s something any toddler can do – cross a cluttered room to find a toy.
It's also one of those seemingly trivial skills that have proved to be extremely hard for computers to master. Analysing shifting and often-ambiguous visual data to detect objects and separate their movement from one’s own has turned out to be an intensely challenging artificial intelligence problem.
Three years ago, researchers at the European-funded research consortium Decisions in Motion (http://www.decisionsinmotion.org/) decided to look to nature for insights into this challenge.
In a rare collaboration, neuro- and cognitive scientists studied how the visual systems of advanced mammals, primates and people work, while computer scientists and roboticists incorporated their findings into neural networks and mobile robots.
The approach paid off. Decisions in Motion has already built and demonstrated a robot that can zip across a crowded room guided only by what it “sees” through its twin video cameras, and are hard at work on a head-mounted system to help visually impaired people get around.
“Until now, the algorithms that have been used are quite slow and their decisions are not reliable enough to be useful,” says project coordinator Mark Greenlee. “Our approach allowed us to build algorithms that can do this on the fly, that can make all these decisions within a few milliseconds using conventional hardware.”
How do we see movement?
The Decisions in Motion researchers used a wide variety of techniques to learn more about how the brain processes visual information, especially information about movement.
These included recording individual neurons and groups of neurons firing in response to movement signals, functional magnetic resonance imaging to track the moment-by-moment interactions between different brain areas as people performed visual tasks, and neuropsychological studies of people with visual processing problems.
The researchers hoped to learn more about how the visual system scans the environment, detects objects, discerns movement, distinguishes between the independent movement of objects and the organism’s own movements, and plans and controls motion towards a goal.
One of their most interesting discoveries was that the primate brain does not just detect and track a moving object; it actually predicts where the object will go.
“When an object moves through a scene, you get a wave of activity as the brain anticipates its trajectory,” says Greenlee. “It’s like feedback signals flowing from the higher areas in the visual cortex back to neurons in the primary visual cortex to give them a sense of what’s coming.”
Greenlee compares what an individual visual neuron sees to looking at the world through a peephole. Researchers have known for a long time that high-level processing is needed to build a coherent picture out of a myriad of those tiny glimpses. What's new is the importance of strong anticipatory feedback for perceiving and processing motion.
“This proved to be quite critical for the Decisions in Motion project,” Greenlee says. “It solves what is called the ‘aperture problem’, the problem of the neurons in the primary visual cortex looking through those little peepholes.”
Building a better robotic brain
Armed with a better understanding of how the human brain deals with movement, the project’s computer scientists and roboticists went to work. Using off-the-shelf hardware, they built a neural network with three levels mimicking the brain’s primary, mid-level, and higher-level visual subsystems.
They used what they had learned about the flow of information between brain regions to control the flow of information within the robotic “brain”.
“It’s basically a neural network with certain biological characteristics,” says Greenlee. “The connectivity is dictated by the numbers we have from our physiological studies.”
The computerised brain controls the behaviour of a wheeled robotic platform supporting a moveable head and eyes, in real time. It directs the head and eyes where to look, tracks its own movement, identifies objects, determines if they are moving independently, and directs the platform to speed up, slow down and turn left or right.
Greenlee and his colleagues were intrigued when the robot found its way to its first target – a teddy bear – just like a person would, speeding by objects that were at a safe distance, but passing nearby obstacles at a slower pace.
”That was very exciting,” Greenlee says. “We didn’t program it in – it popped out of the algorithm.”
In addition to improved guidance systems for robots, the consortium envisions a lightweight system that could be worn like eyeglasses by visually or cognitively impaired people to boost their mobility. One of the consortium partners, Cambridge Research Systems, is developing a commercial version of this, called VisGuide.
Decisions in Motion received funding from the ICT strand of the EU’s Sixth Framework Programme for research. The project’s work was featured in a video by the New Scientist in February this year.
Adapted from materials provided by ICT Results.

Solar Power: New SunCatcher Power System Ready For Commercial Production In 2010

SOURCE

ScienceDaily (July 17, 2009) — Stirling Energy Systems (SES) and Tessera Solar recently unveiled four newly designed solar power collection dishes at Sandia National Laboratories’ National Solar Thermal Test Facility (NSTTF). Called SunCatchers™, the new dishes have a refined design that will be used in commercial-scale deployments of the units beginning in 2010.

“The four new dishes are the next-generation model of the original SunCatcher system. Six first-generation SunCatchers built over the past several years at the NSTTF have been producing up to 150KW [kilowatts] of grid-ready electrical power during the day,” says Chuck Andraka, the lead Sandia project engineer. “Every part of the new system has been upgraded to allow for a high rate of production and cost reduction.”
Sandia’s concentrating solar-thermal power (CSP) team has been working closely with SES over the past five years to improve the system design and operation.
The modular CSP SunCatcher uses precision mirrors attached to a parabolic dish to focus the sun’s rays onto a receiver, which transmits the heat to a Stirling engine. The engine is a sealed system filled with hydrogen. As the gas heats and cools, its pressure rises and falls. The change in pressure drives the piston inside the engine, producing mechanical power, which in turn drives a generator and makes electricity.
The new SunCatcher is about 5,000 pounds lighter than the original, is round instead of rectangular to allow for more efficient use of steel, has improved optics, and consists of 60 percent fewer engine parts. The revised design also has fewer mirrors — 40 instead of 80. The reflective mirrors are formed into a parabolic shape using stamped sheet metal similar to the hood of a car. The mirrors are made by using automobile manufacturing techniques. The improvements will result in high-volume production, cost reductions, and easier maintenance.
Among Sandia’s contributions to the new design was development of a tool to determine how well the mirrors work in less than 10 seconds, something that took the earlier design one hour.
“The new design of the SunCatcher represents more than a decade of innovative engineering and validation testing, making it ready for commercialization,” says Steve Cowman, Stirling Energy Systems CEO. “By utilizing the automotive supply chain to manufacture the SunCatcher, we’re leveraging the talents of an industry that has refined high-volume production through an assembly line process. More than 90 percent of the SunCatcher components will be manufactured in North America.”
In addition to improved manufacturability and easy maintenance, the new SunCatcher minimizes both cost and land use and has numerous environmental advantages, Andraka says.
“They have the lowest water use of any thermal electric generating technology, require minimal grading and trenching, require no excavation for foundations, and will not produce greenhouse gas emissions while converting sunlight into electricity,” he says.
Tessera Solar, the developer and operator of large-scale solar projects using the SunCatcher technology and sister company of SES, is building a 60-unit plant generating 1.5 MW (megawatts) by the end of the year either in Arizona or California. One megawatt powers about 800 homes. The proprietary solar dish technology will then be deployed to develop two of the world’s largest solar generating plants in Southern California with San Diego Gas & Electric in the Imperial Valley and Southern California Edison in the Mojave Desert, in addition to the recently announced project with CPS Energy in West Texas. The projects are expected to produce 1,000 MW by the end of 2012.
Last year one of the original SunCatchers set a new solar-to-grid system conversion efficiency record by achieving a 31.25 percent net efficiency rate, toppling the old 1984 record of 29.4.
Adapted from materials provided by Sandia National Laboratories.

Robot Learns To Smile And Frown

SOURCE

ScienceDaily (July 8, 2009) — A hyper-realistic Einstein robot at the University of California, San Diego has learned to smile and make facial expressions through a process of self-guided learning. The UC San Diego researchers used machine learning to “empower” their robot to learn to make realistic facial expressions.

“As far as we know, no other research group has used machine learning to teach a robot to make realistic facial expressions,” said Tingfan Wu, the computer science Ph.D. student from the UC San Diego Jacobs School of Engineering who presented this advance on June 6 at the IEEE International Conference on Development and Learning.
The faces of robots are increasingly realistic and the number of artificial muscles that controls them is rising. In light of this trend, UC San Diego researchers from the Machine Perception Laboratory are studying the face and head of their robotic Einstein in order to find ways to automate the process of teaching robots to make lifelike facial expressions.
This Einstein robot head has about 30 facial muscles, each moved by a tiny servo motor connected to the muscle by a string. Today, a highly trained person must manually set up these kinds of realistic robots so that the servos pull in the right combinations to make specific face expressions. In order to begin to automate this process, the UCSD researchers looked to both developmental psychology and machine learning.
Developmental psychologists speculate that infants learn to control their bodies through systematic exploratory movements, including babbling to learn to speak. Initially, these movements appear to be executed in a random manner as infants learn to control their bodies and reach for objects.
“We applied this same idea to the problem of a robot learning to make realistic facial expressions,” said Javier Movellan, the senior author on the paper presented at ICDL 2009 and the director of UCSD’s Machine Perception Laboratory, housed in Calit2, the California Institute for Telecommunications and Information Technology.
Although their preliminary results are promising, the researchers note that some of the learned facial expressions are still awkward. One potential explanation is that their model may be too simple to describe the coupled interactions between facial muscles and skin.
To begin the learning process, the UC San Diego researchers directed the Einstein robot head (Hanson Robotics’ Einstein Head) to twist and turn its face in all directions, a process called “body babbling.” During this period the robot could see itself on a mirror and analyze its own expression using facial expression detection software created at UC San Diego called CERT (Computer Expression Recognition Toolbox). This provided the data necessary for machine learning algorithms to learn a mapping between facial expressions and the movements of the muscle motors.
Once the robot learned the relationship between facial expressions and the muscle movements required to make them, the robot learned to make facial expressions it had never encountered.
For example, the robot learned eyebrow narrowing, which requires the inner eyebrows to move together and the upper eyelids to close a bit to narrow the eye aperture.
“During the experiment, one of the servos burned out due to misconfiguration. We therefore ran the experiment without that servo. We discovered that the model learned to automatically compensate for the missing servo by activating a combination of nearby servos,” the authors wrote in the paper presented at the 2009 IEEE International Conference on Development and Learning.
“Currently, we are working on a more accurate facial expression generation model as well as systematic way to explore the model space efficiently,” said Wu, the computer science PhD student. Wu also noted that the “body babbling” approach he and his colleagues described in their paper may not be the most efficient way to explore the model of the face.
While the primary goal of this work was to solve the engineering problem of how to approximate the appearance of human facial muscle movements with motors, the researchers say this kind of work could also lead to insights into how humans learn and develop facial expressions.
“Learning to Make Facial Expressions,” by Tingfan Wu, Nicholas J. Butko, Paul Ruvulo, Marian S. Bartlett, Javier R. Movellan from Machine Perception Laboratory, University of California San Diego. Presented on June 6 at the 2009 IEEE 8th International Conference On Development And Learning.
Adapted from materials provided by University of California - San Diego.