Tag Archives: DARPA

Entanglement and biological systems

I think it was about five years ago thatI wrote a paper on something I called ‘cognitive entanglement’ (mentioned in my July 20,2012 posting) so the latest from Northwestern University (Chicago, Illinois, US) reignited my interest in entanglement. A December 5, 2017 news item on ScienceDaily describes the latest ‘entanglement’ research,

Nearly 75 years ago, Nobel Prize-winning physicist Erwin Schrödinger wondered if the mysterious world of quantum mechanics played a role in biology. A recent finding by Northwestern University’s Prem Kumar adds further evidence that the answer might be yes.

Kumar and his team have, for the first time, created quantum entanglement from a biological system. This finding could advance scientists’ fundamental understanding of biology and potentially open doors to exploit biological tools to enable new functions by harnessing quantum mechanics.

A December 5, 2017 Northwestern University news release (also on EurekAlert), which originated the news item, provides more detail,

“Can we apply quantum tools to learn about biology?” said Kumar, professor of electrical engineering and computer science in Northwestern’s McCormick School of Engineering and of physics and astronomy in the Weinberg College of Arts and Sciences. “People have asked this question for many, many years — dating back to the dawn of quantum mechanics. The reason we are interested in these new quantum states is because they allow applications that are otherwise impossible.”

Partially supported by the [US] Defense Advanced Research Projects Agency [DARPA], the research was published Dec. 5 [2017] in Nature Communications.

Quantum entanglement is one of quantum mechanics’ most mystifying phenomena. When two particles — such as atoms, photons, or electrons — are entangled, they experience an inexplicable link that is maintained even if the particles are on opposite sides of the universe. While entangled, the particles’ behavior is tied one another. If one particle is found spinning in one direction, for example, then the other particle instantaneously changes its spin in a corresponding manner dictated by the entanglement. Researchers, including Kumar, have been interested in harnessing quantum entanglement for several applications, including quantum communications. Because the particles can communicate without wires or cables, they could be used to send secure messages or help build an extremely fast “quantum Internet.”

“Researchers have been trying to entangle a larger and larger set of atoms or photons to develop substrates on which to design and build a quantum machine,” Kumar said. “My laboratory is asking if we can build these machines on a biological substrate.”

In the study, Kumar’s team used green fluorescent proteins, which are responsible for bioluminescence and commonly used in biomedical research. The team attempted to entangle the photons generated from the fluorescing molecules within the algae’s barrel-shaped protein structure by exposing them to spontaneous four-wave mixing, a process in which multiple wavelengths interact with one another to produce new wavelengths.

Through a series of these experiments, Kumar and his team successfully demonstrated a type of entanglement, called polarization entanglement, between photon pairs. The same feature used to make glasses for viewing 3D movies, polarization is the orientation of oscillations in light waves. A wave can oscillate vertically, horizontally, or at different angles. In Kumar’s entangled pairs, the photons’ polarizations are entangled, meaning that the oscillation directions of light waves are linked. Kumar also noticed that the barrel-shaped structure surrounding the fluorescing molecules protected the entanglement from being disrupted.

“When I measured the vertical polarization of one particle, we knew it would be the same in the other,” he said. “If we measured the horizontal polarization of one particle, we could predict the horizontal polarization in the other particle. We created an entangled state that correlated in all possibilities simultaneously.”

Now that they have demonstrated that it’s possible to create quantum entanglement from biological particles, next Kumar and his team plan to make a biological substrate of entangled particles, which could be used to build a quantum machine. Then, they will seek to understand if a biological substrate works more efficiently than a synthetic one.

Here’s an image accompanying the news release,

Featured in the cuvette on the left, green fluorescent proteins responsible for bioluninescence in jellyfish. Courtesy: Northwestern University

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

Generation of photonic entanglement in green fluorescent proteins by Siyuan Shi, Prem Kumar & Kim Fook Lee. Nature Communications 8, Article number: 1934 (2017) doi:10.1038/s41467-017-02027-9 Published online: 05 December 2017

This paper is open access.

Leftover 2017 memristor news bits

i have two bits of news, one from this October 2017 about using light to control a memristor’s learning properties and one from December 2017 about memristors and neural networks.

Shining a light on the memristor

Michael Berger wrote an October 30, 2017 Nanowerk Sportlight article about some of the latest work concerning memristors and light,

Memristors – or resistive memory – are nanoelectronic devices that are very promising components for next generation memory and computing devices. They are two-terminal electric elements similar to a conventional resistor – however, the electric resistance in a memristor is dependent on the charge passing through it; which means that its conductance can be precisely modulated by charge or flux through it. Its special property is that its resistance can be programmed (resistor function) and subsequently remains stored (memory function).

In this sense, a memristor is similar to a synapse in the human brain because it exhibits the same switching characteristics, i.e. it is able, with a high level of plasticity, to modify the efficiency of signal transfer between neurons under the influence of the transfer itself. That’s why researchers are hopeful to use memristors for the fabrication of electronic synapses for neuromorphic (i.e. brain-like) computing that mimics some of the aspects of learning and computation in human brains.

Human brains may be slow at pure number crunching but they are excellent at handling fast dynamic sensory information such as image and voice recognition. Walking is something that we take for granted but this is quite challenging for robots, especially over uneven terrain.

“Memristors present an opportunity to make new types of computers that are different from existing von Neumann architectures, which traditional computers are based upon,” Dr Neil T. Kemp, a Lecturer in Physics at the University of Hull [UK], tells Nanowerk. “Our team at the University of Hull is focussed on making memristor devices dynamically reconfigurable and adaptive – we believe this is the route to making a new generation of artificial intelligence systems that are smarter and can exhibit complex behavior. Such systems would also have the advantage of memristors, high density integration and lower power usage, so these systems would be more lightweight, portable and not need re-charging so often – which is something really needed for robots etc.”

In their new paper in Nanoscale (“Reversible Optical Switching Memristors with Tunable STDP Synaptic Plasticity: A Route to Hierarchical Control in Artificial Intelligent Systems”), Kemp and his team demonstrate the ability to reversibly control the learning properties of memristors via optical means.

The reversibility is achieved by changing the polarization of light. The researchers have used this effect to demonstrate tuneable learning in a memristor. One way this is achieved is through something called Spike Timing Dependent Plasticity (STDP), which is an effect known to occur in human brains and is linked with sensory perception, spatial reasoning, language and conscious thought in the neocortex.

STDP learning is based upon differences in the arrival time of signals from two adjacent neurons. The University of Hull team has shown that they can modulate the synaptic plasticity via optical means which enables the devices to have tuneable learning.

“Our research findings are important because it demonstrates that light can be used to control the learning properties of a memristor,” Kemp points out. “We have shown that light can be used in a reversible manner to change the connection strength (or conductivity) of artificial memristor synapses and as well control their ability to forget i.e. we can dynamically change device to have short-term or long-term memory.”

According to the team, there are many potential applications, such as adaptive electronic circuits controllable via light, or in more complex systems, such as neuromorphic computing, the development of optically reconfigurable neural networks.

Having optically controllable memristors can also facilitate the implementation of hierarchical control in larger artificial-brain like systems, whereby some of the key processes that are carried out by biological molecules in human brains can be emulated in solid-state devices through patterning with light.

Some of these processes include synaptic pruning, conversion of short term memory to long term memory, erasing of certain memories that are no longer needed or changing the sensitivity of synapses to be more adept at learning new information.

“The ability to control this dynamically, both spatially and temporally, is particularly interesting since it would allow neural networks to be reconfigurable on the fly through either spatial patterning or by adjusting the intensity of the light source,” notes Kemp.

In their new paper in Nanoscale Currently, the devices are more suited to neuromorphic computing applications, which do not need to be as fast. Optical control of memristors opens the route to dynamically tuneable and reprogrammable synaptic circuits as well the ability (via optical patterning) to have hierarchical control in larger and more complex artificial intelligent systems.

“Artificial Intelligence is really starting to come on strong in many areas, especially in the areas of voice/image recognition and autonomous systems – we could even say that this is the next revolution, similarly to what the industrial revolution was to farming and production processes,” concludes Kemp. “There are many challenges to overcome though. …

That excerpt should give you the gist of Berger’s article and, for those who need more information, there’s Berger’s article and, also, a link to and a citation for the paper,

Reversible optical switching memristors with tunable STDP synaptic plasticity: a route to hierarchical control in artificial intelligent systems by Ayoub H. Jaafar, Robert J. Gray, Emanuele Verrelli, Mary O’Neill, Stephen. M. Kelly, and Neil T. Kemp. Nanoscale, 2017,9, 17091-17098 DOI: 10.1039/C7NR06138B First published on 24 Oct 2017

This paper is behind a paywall.

The memristor and the neural network

It would seem machine learning could experience a significant upgrade if the work in Wei Lu’s University of Michigan laboratory can be scaled for general use. From a December 22, 2017 news item on ScienceDaily,

A new type of neural network made with memristors can dramatically improve the efficiency of teaching machines to think like humans.

The network, called a reservoir computing system, could predict words before they are said during conversation, and help predict future outcomes based on the present.

The research team that created the reservoir computing system, led by Wei Lu, professor of electrical engineering and computer science at the University of Michigan, recently published their work in Nature Communications.

A December 19, 2017 University of Michigan news release (also on EurekAlert) by Dan Newman, which originated the news item, expands on the theme,

Reservoir computing systems, which improve on a typical neural network’s capacity and reduce the required training time, have been created in the past with larger optical components. However, the U-M group created their system using memristors, which require less space and can be integrated more easily into existing silicon-based electronics.

Memristors are a special type of resistive device that can both perform logic and store data. This contrasts with typical computer systems, where processors perform logic separate from memory modules. In this study, Lu’s team used a special memristor that memorizes events only in the near history.

Inspired by brains, neural networks are composed of neurons, or nodes, and synapses, the connections between nodes.

To train a neural network for a task, a neural network takes in a large set of questions and the answers to those questions. In this process of what’s called supervised learning, the connections between nodes are weighted more heavily or lightly to minimize the amount of error in achieving the correct answer.

Once trained, a neural network can then be tested without knowing the answer. For example, a system can process a new photo and correctly identify a human face, because it has learned the features of human faces from other photos in its training set.

“A lot of times, it takes days or months to train a network,” says Lu. “It is very expensive.”

Image recognition is also a relatively simple problem, as it doesn’t require any information apart from a static image. More complex tasks, such as speech recognition, can depend highly on context and require neural networks to have knowledge of what has just occurred, or what has just been said.

“When transcribing speech to text or translating languages, a word’s meaning and even pronunciation will differ depending on the previous syllables,” says Lu.

This requires a recurrent neural network, which incorporates loops within the network that give the network a memory effect. However, training these recurrent neural networks is especially expensive, Lu says.

Reservoir computing systems built with memristors, however, can skip most of the expensive training process and still provide the network the capability to remember. This is because the most critical component of the system – the reservoir – does not require training.

When a set of data is inputted into the reservoir, the reservoir identifies important time-related features of the data, and hands it off in a simpler format to a second network. This second network then only needs training like simpler neural networks, changing weights of the features and outputs that the first network passed on until it achieves an acceptable level of error.

Enlargereservoir computing system

IMAGE:  Schematic of a reservoir computing system, showing the reservoir with internal dynamics and the simpler output. Only the simpler output needs to be trained, allowing for quicker and lower-cost training. Courtesy Wei Lu.

 

“The beauty of reservoir computing is that while we design it, we don’t have to train it,” says Lu.

The team proved the reservoir computing concept using a test of handwriting recognition, a common benchmark among neural networks. Numerals were broken up into rows of pixels, and fed into the computer with voltages like Morse code, with zero volts for a dark pixel and a little over one volt for a white pixel.

Using only 88 memristors as nodes to identify handwritten versions of numerals, compared to a conventional network that would require thousands of nodes for the task, the reservoir achieved 91% accuracy.

Reservoir computing systems are especially adept at handling data that varies with time, like a stream of data or words, or a function depending on past results.

To demonstrate this, the team tested a complex function that depended on multiple past results, which is common in engineering fields. The reservoir computing system was able to model the complex function with minimal error.

Lu plans on exploring two future paths with this research: speech recognition and predictive analysis.

“We can make predictions on natural spoken language, so you don’t even have to say the full word,” explains Lu.

“We could actually predict what you plan to say next.”

In predictive analysis, Lu hopes to use the system to take in signals with noise, like static from far-off radio stations, and produce a cleaner stream of data. “It could also predict and generate an output signal even if the input stopped,” he says.

EnlargeWei Lu

IMAGE:  Wei Lu, Professor of Electrical Engineering & Computer Science at the University of Michigan holds a memristor he created. Photo: Marcin Szczepanski.

 

The work was published in Nature Communications in the article, “Reservoir computing using dynamic memristors for temporal information processing”, with authors Chao Du, Fuxi Cai, Mohammed Zidan, Wen Ma, Seung Hwan Lee, and Prof. Wei Lu.

The research is part of a $6.9 million DARPA [US Defense Advanced Research Projects Agency] project, called “Sparse Adaptive Local Learning for Sensing and Analytics [also known as SALLSA],” that aims to build a computer chip based on self-organizing, adaptive neural networks. The memristor networks are fabricated at Michigan’s Lurie Nanofabrication Facility.

Lu and his team previously used memristors in implementing “sparse coding,” which used a 32-by-32 array of memristors to efficiently analyze and recreate images.

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

Reservoir computing using dynamic memristors for temporal information processing by Chao Du, Fuxi Cai, Mohammed A. Zidan, Wen Ma, Seung Hwan Lee & Wei D. Lu. Nature Communications 8, Article number: 2204 (2017) doi:10.1038/s41467-017-02337-y Published online: 19 December 2017

This is an open access paper.

Congratulate China on the world’s first quantum communication network

China has some exciting news about the world’s first quantum network; it’s due to open in late August 2017 so you may want to have your congratulations in order for later this month.

An Aug. 4, 2017 news item on phys.org makes the announcement,

As malicious hackers find ever more sophisticated ways to launch attacks, China is about to launch the Jinan Project, the world’s first unhackable computer network, and a major milestone in the development of quantum technology.

Named after the eastern Chinese city where the technology was developed, the network is planned to be fully operational by the end of August 2017. Jinan is the hub of the Beijing-Shanghai quantum network due to its strategic location between the two principal Chinese metropolises.

“We plan to use the network for national defence, finance and other fields, and hope to spread it out as a pilot that if successful can be used across China and the whole world,” commented Zhou Fei, assistant director of the Jinan Institute of Quantum Technology, who was speaking to Britain’s Financial Times.

An Aug. 3, 2017 CORDIS (Community Research and Development Research Information Service [for the European Commission]) press release, which originated the news item, provides more detail about the technology,

By launching the network, China will become the first country worldwide to implement quantum technology for a real life, commercial end. It also highlights that China is a key global player in the rush to develop technologies based on quantum principles, with the EU and the United States also vying for world leadership in the field.

The network, known as a Quantum Key Distribution (QKD) network, is more secure than widely used electronic communication equivalents. Unlike a conventional telephone or internet cable, which can be tapped without the sender or recipient being aware, a QKD network alerts both users to any tampering with the system as soon as it occurs. This is because tampering immediately alters the information being relayed, with the disturbance being instantly recognisable. Once fully implemented, it will make it almost impossible for other governments to listen in on Chinese communications.

In the Jinan network, some 200 users from China’s military, government, finance and electricity sectors will be able to send messages safe in the knowledge that only they are reading them. It will be the world’s longest land-based quantum communications network, stretching over 2 000 km.

Also speaking to the ‘Financial Times’, quantum physicist Tim Byrnes, based at New York University’s (NYU) Shanghai campus commented: ‘China has achieved staggering things with quantum research… It’s amazing how quickly China has gotten on with quantum research projects that would be too expensive to do elsewhere… quantum communication has been taken up by the commercial sector much more in China compared to other countries, which means it is likely to pull ahead of Europe and US in the field of quantum communication.’

However, Europe is also determined to also be at the forefront of the ‘quantum revolution’ which promises to be one of the major defining technological phenomena of the twenty-first century. The EU has invested EUR 550 million into quantum technologies and has provided policy support to researchers through the 2016 Quantum Manifesto.

Moreover, with China’s latest achievement (and a previous one already notched up from July 2017 when its quantum satellite – the world’s first – sent a message to Earth on a quantum communication channel), it looks like the race to be crowned the world’s foremost quantum power is well and truly underway…

Prior to this latest announcement, Chinese scientists had published work about quantum satellite communications, a development that makes their imminent terrestrial quantum network possible. Gabriel Popkin wrote about the quantum satellite in a June 15, 2017 article Science magazine,

Quantum entanglement—physics at its strangest—has moved out of this world and into space. In a study that shows China’s growing mastery of both the quantum world and space science, a team of physicists reports that it sent eerily intertwined quantum particles from a satellite to ground stations separated by 1200 kilometers, smashing the previous world record. The result is a stepping stone to ultrasecure communication networks and, eventually, a space-based quantum internet.

“It’s a huge, major achievement,” says Thomas Jennewein, a physicist at the University of Waterloo in Canada. “They started with this bold idea and managed to do it.”

Entanglement involves putting objects in the peculiar limbo of quantum superposition, in which an object’s quantum properties occupy multiple states at once: like Schrödinger’s cat, dead and alive at the same time. Then those quantum states are shared among multiple objects. Physicists have entangled particles such as electrons and photons, as well as larger objects such as superconducting electric circuits.

Theoretically, even if entangled objects are separated, their precarious quantum states should remain linked until one of them is measured or disturbed. That measurement instantly determines the state of the other object, no matter how far away. The idea is so counterintuitive that Albert Einstein mocked it as “spooky action at a distance.”

Starting in the 1970s, however, physicists began testing the effect over increasing distances. In 2015, the most sophisticated of these tests, which involved measuring entangled electrons 1.3 kilometers apart, showed once again that spooky action is real.

Beyond the fundamental result, such experiments also point to the possibility of hack-proof communications. Long strings of entangled photons, shared between distant locations, can be “quantum keys” that secure communications. Anyone trying to eavesdrop on a quantum-encrypted message would disrupt the shared key, alerting everyone to a compromised channel.

But entangled photons degrade rapidly as they pass through the air or optical fibers. So far, the farthest anyone has sent a quantum key is a few hundred kilometers. “Quantum repeaters” that rebroadcast quantum information could extend a network’s reach, but they aren’t yet mature. Many physicists have dreamed instead of using satellites to send quantum information through the near-vacuum of space. “Once you have satellites distributing your quantum signals throughout the globe, you’ve done it,” says Verónica Fernández Mármol, a physicist at the Spanish National Research Council in Madrid. …

Popkin goes on to detail the process for making the discovery in easily accessible (for the most part) writing and in a video and a graphic.

Russell Brandom writing for The Verge in a June 15, 2017 article about the Chinese quantum satellite adds detail about previous work and teams in other countries also working on the challenge (Note: Links have been removed),

Quantum networking has already shown promise in terrestrial fiber networks, where specialized routing equipment can perform the same trick over conventional fiber-optic cable. The first such network was a DARPA-funded connection established in 2003 between Harvard, Boston University, and a private lab. In the years since, a number of companies have tried to build more ambitious connections. The Swiss company ID Quantique has mapped out a quantum network that would connect many of North America’s largest data centers; in China, a separate team is working on a 2,000-kilometer quantum link between Beijing and Shanghai, which would rely on fiber to span an even greater distance than the satellite link. Still, the nature of fiber places strict limits on how far a single photon can travel.

According to ID Quantique, a reliable satellite link could connect the existing fiber networks into a single globe-spanning quantum network. “This proves the feasibility of quantum communications from space,” ID Quantique CEO Gregoire Ribordy tells The Verge. “The vision is that you have regional quantum key distribution networks over fiber, which can connect to each other through the satellite link.”

China isn’t the only country working on bringing quantum networks to space. A collaboration between the UK’s University of Strathclyde and the National University of Singapore is hoping to produce the same entanglement in cheap, readymade satellites called Cubesats. A Canadian team is also developing a method of producing entangled photons on the ground before sending them into space.

I wonder if there’s going to be an invitational event for scientists around the world to celebrate the launch.

3-D integration of nanotechnologies on a single computer chip

By integrating nanomaterials , a new technique for a 3D computer chip capable of handling today’s huge amount of data has been developed. Weirdly, the first two paragraphs of a July 5, 2017 news item on Nanowerk do not convey the main point (Note: A link has been removed),

As embedded intelligence is finding its way into ever more areas of our lives, fields ranging from autonomous driving to personalized medicine are generating huge amounts of data. But just as the flood of data is reaching massive proportions, the ability of computer chips to process it into useful information is stalling.

Now, researchers at Stanford University and MIT have built a new chip to overcome this hurdle. The results are published today in the journal Nature (“Three-dimensional integration of nanotechnologies for computing and data storage on a single chip”), by lead author Max Shulaker, an assistant professor of electrical engineering and computer science at MIT. Shulaker began the work as a PhD student alongside H.-S. Philip Wong and his advisor Subhasish Mitra, professors of electrical engineering and computer science at Stanford. The team also included professors Roger Howe and Krishna Saraswat, also from Stanford.

This image helps to convey the main points,

Instead of relying on silicon-based devices, a new chip uses carbon nanotubes and resistive random-access memory (RRAM) cells. The two are built vertically over one another, making a new, dense 3-D computer architecture with interleaving layers of logic and memory. Courtesy MIT

As I hove been quite impressed with their science writing, it was a bit surprising to find that the Massachusetts Institute of Technology (MIT) had issued this news release (news item) as it didn’t follow the ‘rules’, i.e., cover as many of the journalistic questions (Who, What, Where, When, Why, and, sometimes, How) as possible in the first sentence/paragraph. This is written more in the style of a magazine article and so the details take a while to emerge, from a July 5, 2017 MIT news release, which originated the news item,

Computers today comprise different chips cobbled together. There is a chip for computing and a separate chip for data storage, and the connections between the two are limited. As applications analyze increasingly massive volumes of data, the limited rate at which data can be moved between different chips is creating a critical communication “bottleneck.” And with limited real estate on the chip, there is not enough room to place them side-by-side, even as they have been miniaturized (a phenomenon known as Moore’s Law).

To make matters worse, the underlying devices, transistors made from silicon, are no longer improving at the historic rate that they have for decades.

The new prototype chip is a radical change from today’s chips. It uses multiple nanotechnologies, together with a new computer architecture, to reverse both of these trends.

Instead of relying on silicon-based devices, the chip uses carbon nanotubes, which are sheets of 2-D graphene formed into nanocylinders, and resistive random-access memory (RRAM) cells, a type of nonvolatile memory that operates by changing the resistance of a solid dielectric material. The researchers integrated over 1 million RRAM cells and 2 million carbon nanotube field-effect transistors, making the most complex nanoelectronic system ever made with emerging nanotechnologies.

The RRAM and carbon nanotubes are built vertically over one another, making a new, dense 3-D computer architecture with interleaving layers of logic and memory. By inserting ultradense wires between these layers, this 3-D architecture promises to address the communication bottleneck.

However, such an architecture is not possible with existing silicon-based technology, according to the paper’s lead author, Max Shulaker, who is a core member of MIT’s Microsystems Technology Laboratories. “Circuits today are 2-D, since building conventional silicon transistors involves extremely high temperatures of over 1,000 degrees Celsius,” says Shulaker. “If you then build a second layer of silicon circuits on top, that high temperature will damage the bottom layer of circuits.”

The key in this work is that carbon nanotube circuits and RRAM memory can be fabricated at much lower temperatures, below 200 C. “This means they can be built up in layers without harming the circuits beneath,” Shulaker says.

This provides several simultaneous benefits for future computing systems. “The devices are better: Logic made from carbon nanotubes can be an order of magnitude more energy-efficient compared to today’s logic made from silicon, and similarly, RRAM can be denser, faster, and more energy-efficient compared to DRAM,” Wong says, referring to a conventional memory known as dynamic random-access memory.

“In addition to improved devices, 3-D integration can address another key consideration in systems: the interconnects within and between chips,” Saraswat adds.

“The new 3-D computer architecture provides dense and fine-grained integration of computating and data storage, drastically overcoming the bottleneck from moving data between chips,” Mitra says. “As a result, the chip is able to store massive amounts of data and perform on-chip processing to transform a data deluge into useful information.”

To demonstrate the potential of the technology, the researchers took advantage of the ability of carbon nanotubes to also act as sensors. On the top layer of the chip they placed over 1 million carbon nanotube-based sensors, which they used to detect and classify ambient gases.

Due to the layering of sensing, data storage, and computing, the chip was able to measure each of the sensors in parallel, and then write directly into its memory, generating huge bandwidth, Shulaker says.

Three-dimensional integration is the most promising approach to continue the technology scaling path set forth by Moore’s laws, allowing an increasing number of devices to be integrated per unit volume, according to Jan Rabaey, a professor of electrical engineering and computer science at the University of California at Berkeley, who was not involved in the research.

“It leads to a fundamentally different perspective on computing architectures, enabling an intimate interweaving of memory and logic,” Rabaey says. “These structures may be particularly suited for alternative learning-based computational paradigms such as brain-inspired systems and deep neural nets, and the approach presented by the authors is definitely a great first step in that direction.”

“One big advantage of our demonstration is that it is compatible with today’s silicon infrastructure, both in terms of fabrication and design,” says Howe.

“The fact that this strategy is both CMOS [complementary metal-oxide-semiconductor] compatible and viable for a variety of applications suggests that it is a significant step in the continued advancement of Moore’s Law,” says Ken Hansen, president and CEO of the Semiconductor Research Corporation, which supported the research. “To sustain the promise of Moore’s Law economics, innovative heterogeneous approaches are required as dimensional scaling is no longer sufficient. This pioneering work embodies that philosophy.”

The team is working to improve the underlying nanotechnologies, while exploring the new 3-D computer architecture. For Shulaker, the next step is working with Massachusetts-based semiconductor company Analog Devices to develop new versions of the system that take advantage of its ability to carry out sensing and data processing on the same chip.

So, for example, the devices could be used to detect signs of disease by sensing particular compounds in a patient’s breath, says Shulaker.

“The technology could not only improve traditional computing, but it also opens up a whole new range of applications that we can target,” he says. “My students are now investigating how we can produce chips that do more than just computing.”

“This demonstration of the 3-D integration of sensors, memory, and logic is an exceptionally innovative development that leverages current CMOS technology with the new capabilities of carbon nanotube field–effect transistors,” says Sam Fuller, CTO emeritus of Analog Devices, who was not involved in the research. “This has the potential to be the platform for many revolutionary applications in the future.”

This work was funded by the Defense Advanced Research Projects Agency [DARPA], the National Science Foundation, Semiconductor Research Corporation, STARnet SONIC, and member companies of the Stanford SystemX Alliance.

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

Three-dimensional integration of nanotechnologies for computing and data storage on a single chip by Max M. Shulaker, Gage Hills, Rebecca S. Park, Roger T. Howe, Krishna Saraswat, H.-S. Philip Wong, & Subhasish Mitra. Nature 547, 74–78 (06 July 2017) doi:10.1038/nature22994 Published online 05 July 2017

This paper is behind a paywall.

IBM to build brain-inspired AI supercomputing system equal to 64 million neurons for US Air Force

This is the second IBM computer announcement I’ve stumbled onto within the last 4 weeks or so,  which seems like a veritable deluge given the last time I wrote about IBM’s computing efforts was in an Oct. 8, 2015 posting about carbon nanotubes,. I believe that up until now that was my  most recent posting about IBM and computers.

Moving onto the news, here’s more from a June 23, 3017 news item on Nanotechnology Now,

IBM (NYSE: IBM) and the U.S. Air Force Research Laboratory (AFRL) today [June 23, 2017] announced they are collaborating on a first-of-a-kind brain-inspired supercomputing system powered by a 64-chip array of the IBM TrueNorth Neurosynaptic System. The scalable platform IBM is building for AFRL will feature an end-to-end software ecosystem designed to enable deep neural-network learning and information discovery. The system’s advanced pattern recognition and sensory processing power will be the equivalent of 64 million neurons and 16 billion synapses, while the processor component will consume the energy equivalent of a dim light bulb – a mere 10 watts to power.

A June 23, 2017 IBM news release, which originated the news item, describes the proposed collaboration, which is based on IBM’s TrueNorth brain-inspired chip architecture (see my Aug. 8, 2014 posting for more about TrueNorth),

IBM researchers believe the brain-inspired, neural network design of TrueNorth will be far more efficient for pattern recognition and integrated sensory processing than systems powered by conventional chips. AFRL is investigating applications of the system in embedded, mobile, autonomous settings where, today, size, weight and power (SWaP) are key limiting factors.

The IBM TrueNorth Neurosynaptic System can efficiently convert data (such as images, video, audio and text) from multiple, distributed sensors into symbols in real time. AFRL will combine this “right-brain” perception capability of the system with the “left-brain” symbol processing capabilities of conventional computer systems. The large scale of the system will enable both “data parallelism” where multiple data sources can be run in parallel against the same neural network and “model parallelism” where independent neural networks form an ensemble that can be run in parallel on the same data.

“AFRL was the earliest adopter of TrueNorth for converting data into decisions,” said Daniel S. Goddard, director, information directorate, U.S. Air Force Research Lab. “The new neurosynaptic system will be used to enable new computing capabilities important to AFRL’s mission to explore, prototype and demonstrate high-impact, game-changing technologies that enable the Air Force and the nation to maintain its superior technical advantage.”

“The evolution of the IBM TrueNorth Neurosynaptic System is a solid proof point in our quest to lead the industry in AI hardware innovation,” said Dharmendra S. Modha, IBM Fellow, chief scientist, brain-inspired computing, IBM Research – Almaden. “Over the last six years, IBM has expanded the number of neurons per system from 256 to more than 64 million – an 800 percent annual increase over six years.’’

The system fits in a 4U-high (7”) space in a standard server rack and eight such systems will enable the unprecedented scale of 512 million neurons per rack. A single processor in the system consists of 5.4 billion transistors organized into 4,096 neural cores creating an array of 1 million digital neurons that communicate with one another via 256 million electrical synapses.    For CIFAR-100 dataset, TrueNorth achieves near state-of-the-art accuracy, while running at >1,500 frames/s and using 200 mW (effectively >7,000 frames/s per Watt) – orders of magnitude lower speed and energy than a conventional computer running inference on the same neural network.

The IBM TrueNorth Neurosynaptic System was originally developed under the auspices of Defense Advanced Research Projects Agency’s (DARPA) Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program in collaboration with Cornell University. In 2016, the TrueNorth Team received the inaugural Misha Mahowald Prize for Neuromorphic Engineering and TrueNorth was accepted into the Computer History Museum.  Research with TrueNorth is currently being performed by more than 40 universities, government labs, and industrial partners on five continents.

There is an IBM video accompanying this news release, which seems more promotional than informational,

The IBM scientist featured in the video has a Dec. 19, 2016 posting on an IBM research blog which provides context for this collaboration with AFRL,

2016 was a big year for brain-inspired computing. My team and I proved in our paper “Convolutional networks for fast, energy-efficient neuromorphic computing” that the value of this breakthrough is that it can perform neural network inference at unprecedented ultra-low energy consumption. Simply stated, our TrueNorth chip’s non-von Neumann architecture mimics the brain’s neural architecture — giving it unprecedented efficiency and scalability over today’s computers.

The brain-inspired TrueNorth processor [is] a 70mW reconfigurable silicon chip with 1 million neurons, 256 million synapses, and 4096 parallel and distributed neural cores. For systems, we present a scale-out system loosely coupling 16 single-chip boards and a scale-up system tightly integrating 16 chips in a 4´4 configuration by exploiting TrueNorth’s native tiling.

For the scale-up systems we summarize our approach to physical placement of neural network, to reduce intra- and inter-chip network traffic. The ecosystem is in use at over 30 universities and government / corporate labs. Our platform is a substrate for a spectrum of applications from mobile and embedded computing to cloud and supercomputers.
TrueNorth Ecosystem for Brain-Inspired Computing: Scalable Systems, Software, and Applications

TrueNorth, once loaded with a neural network model, can be used in real-time as a sensory streaming inference engine, performing rapid and accurate classifications while using minimal energy. TrueNorth’s 1 million neurons consume only 70 mW, which is like having a neurosynaptic supercomputer the size of a postage stamp that can run on a smartphone battery for a week.

Recently, in collaboration with Lawrence Livermore National Laboratory, U.S. Air Force Research Laboratory, and U.S. Army Research Laboratory, we published our fifth paper at IEEE’s prestigious Supercomputing 2016 conference that summarizes the results of the team’s 12.5-year journey (see the associated graphic) to unlock this value proposition. [keep scrolling for the graphic]

Applying the mind of a chip

Three of our partners, U.S. Army Research Lab, U.S. Air Force Research Lab and Lawrence Livermore National Lab, contributed sections to the Supercomputing paper each showcasing a different TrueNorth system, as summarized by my colleagues Jun Sawada, Brian Taba, Pallab Datta, and Ben Shaw:

U.S. Army Research Lab (ARL) prototyped a computational offloading scheme to illustrate how TrueNorth’s low power profile enables computation at the point of data collection. Using the single-chip NS1e board and an Android tablet, ARL researchers created a demonstration system that allows visitors to their lab to hand write arithmetic expressions on the tablet, with handwriting streamed to the NS1e for character recognition, and recognized characters sent back to the tablet for arithmetic calculation.

Of course, the point here is not to make a handwriting calculator, it is to show how TrueNorth’s low power and real time pattern recognition might be deployed at the point of data collection to reduce latency, complexity and transmission bandwidth, as well as back-end data storage requirements in distributed systems.

U.S. Air Force Research Lab (AFRL) contributed another prototype application utilizing a TrueNorth scale-out system to perform a data-parallel text extraction and recognition task. In this application, an image of a document is segmented into individual characters that are streamed to AFRL’s NS1e16 TrueNorth system for parallel character recognition. Classification results are then sent to an inference-based natural language model to reconstruct words and sentences. This system can process 16,000 characters per second! AFRL plans to implement the word and sentence inference algorithms on TrueNorth, as well.

Lawrence Livermore National Lab (LLNL) has a 16-chip NS16e scale-up system to explore the potential of post-von Neumann computation through larger neural models and more complex algorithms, enabled by the native tiling characteristics of the TrueNorth chip. For the Supercomputing paper, they contributed a single-chip application performing in-situ process monitoring in an additive manufacturing process. LLNL trained a TrueNorth network to recognize seven classes related to track weld quality in welds produced by a selective laser melting machine. Real-time weld quality determination allows for closed-loop process improvement and immediate rejection of defective parts. This is one of several applications LLNL is developing to showcase TrueNorth as a scalable platform for low-power, real-time inference.

[downloaded from https://www.ibm.com/blogs/research/2016/12/the-brains-architecture-efficiency-on-a-chip/] Courtesy: IBM

I gather this 2017 announcement is the latest milestone on the TrueNorth journey.

Dr. Wei Lu and bio-inspired ‘memristor’ chips

It’s been a while since I’ve featured Dr. Wei Lu’s work here. This April  15, 2010 posting features Lu’s most relevant previous work.) Here’s his latest ‘memristor’ work , from a May 22, 2017 news item on Nanowerk (Note: A link has been removed),

Inspired by how mammals see, a new “memristor” computer circuit prototype at the University of Michigan has the potential to process complex data, such as images and video orders of magnitude, faster and with much less power than today’s most advanced systems.

Faster image processing could have big implications for autonomous systems such as self-driving cars, says Wei Lu, U-M professor of electrical engineering and computer science. Lu is lead author of a paper on the work published in the current issue of Nature Nanotechnology (“Sparse coding with memristor networks”).

Lu’s next-generation computer components use pattern recognition to shortcut the energy-intensive process conventional systems use to dissect images. In this new work, he and his colleagues demonstrate an algorithm that relies on a technique called “sparse coding” to coax their 32-by-32 array of memristors to efficiently analyze and recreate several photos.

A May 22, 2017 University of Michigan news release (also on EurekAlert), which originated the news item, provides more information about memristors and about the research,

Memristors are electrical resistors with memory—advanced electronic devices that regulate current based on the history of the voltages applied to them. They can store and process data simultaneously, which makes them a lot more efficient than traditional systems. In a conventional computer, logic and memory functions are located at different parts of the circuit.

“The tasks we ask of today’s computers have grown in complexity,” Lu said. “In this ‘big data’ era, computers require costly, constant and slow communications between their processor and memory to retrieve large amounts data. This makes them large, expensive and power-hungry.”

But like neural networks in a biological brain, networks of memristors can perform many operations at the same time, without having to move data around. As a result, they could enable new platforms that process a vast number of signals in parallel and are capable of advanced machine learning. Memristors are good candidates for deep neural networks, a branch of machine learning, which trains computers to execute processes without being explicitly programmed to do so.

“We need our next-generation electronics to be able to quickly process complex data in a dynamic environment. You can’t just write a program to do that. Sometimes you don’t even have a pre-defined task,” Lu said. “To make our systems smarter, we need to find ways for them to process a lot of data more efficiently. Our approach to accomplish that is inspired by neuroscience.”

A mammal’s brain is able to generate sweeping, split-second impressions of what the eyes take in. One reason is because they can quickly recognize different arrangements of shapes. Humans do this using only a limited number of neurons that become active, Lu says. Both neuroscientists and computer scientists call the process “sparse coding.”

“When we take a look at a chair we will recognize it because its characteristics correspond to our stored mental picture of a chair,” Lu said. “Although not all chairs are the same and some may differ from a mental prototype that serves as a standard, each chair retains some of the key characteristics necessary for easy recognition. Basically, the object is correctly recognized the moment it is properly classified—when ‘stored’ in the appropriate category in our heads.”

Image of a memristor chip Image of a memristor chip Similarly, Lu’s electronic system is designed to detect the patterns very efficiently—and to use as few features as possible to describe the original input.

In our brains, different neurons recognize different patterns, Lu says.

“When we see an image, the neurons that recognize it will become more active,” he said. “The neurons will also compete with each other to naturally create an efficient representation. We’re implementing this approach in our electronic system.”

The researchers trained their system to learn a “dictionary” of images. Trained on a set of grayscale image patterns, their memristor network was able to reconstruct images of famous paintings and photos and other test patterns.

If their system can be scaled up, they expect to be able to process and analyze video in real time in a compact system that can be directly integrated with sensors or cameras.

The project is titled “Sparse Adaptive Local Learning for Sensing and Analytics.” Other collaborators are Zhengya Zhang and Michael Flynn of the U-M Department of Electrical Engineering and Computer Science, Garrett Kenyon of the Los Alamos National Lab and Christof Teuscher of Portland State University.

The work is part of a $6.9 million Unconventional Processing of Signals for Intelligent Data Exploitation project that aims to build a computer chip based on self-organizing, adaptive neural networks. It is funded by the [US] Defense Advanced Research Projects Agency [DARPA].

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

Sparse coding with memristor networks by Patrick M. Sheridan, Fuxi Cai, Chao Du, Wen Ma, Zhengya Zhang, & Wei D. Lu. Nature Nanotechnology (2017) doi:10.1038/nnano.2017.83 Published online 22 May 2017

This paper is behind a paywall.

For the interested, there are a number of postings featuring memristors here (just use ‘memristor’ as your search term in the blog search engine). You might also want to check out ‘neuromorphic engineeering’ and ‘neuromorphic computing’ and ‘artificial brain’.

Tree-on-a-chip

It’s usually organ-on-a-chip or lab-on-a-chip or human-on-a-chip; this is my first tree-on-a-chip.

Engineers have designed a microfluidic device they call a “tree-on-a-chip,” which mimics the pumping mechanism of trees and other plants. Courtesy: MIT

From a March 20, 2017 news item on phys.org,

Trees and other plants, from towering redwoods to diminutive daisies, are nature’s hydraulic pumps. They are constantly pulling water up from their roots to the topmost leaves, and pumping sugars produced by their leaves back down to the roots. This constant stream of nutrients is shuttled through a system of tissues called xylem and phloem, which are packed together in woody, parallel conduits.

Now engineers at MIT [Massachusetts Institute of Technology] and their collaborators have designed a microfluidic device they call a “tree-on-a-chip,” which mimics the pumping mechanism of trees and plants. Like its natural counterparts, the chip operates passively, requiring no moving parts or external pumps. It is able to pump water and sugars through the chip at a steady flow rate for several days. The results are published this week in Nature Plants.

A March 20, 2017 MIT news release by Jennifer Chu, which originated the news item, describes the work in more detail,

Anette “Peko” Hosoi, professor and associate department head for operations in MIT’s Department of Mechanical Engineering, says the chip’s passive pumping may be leveraged as a simple hydraulic actuator for small robots. Engineers have found it difficult and expensive to make tiny, movable parts and pumps to power complex movements in small robots. The team’s new pumping mechanism may enable robots whose motions are propelled by inexpensive, sugar-powered pumps.

“The goal of this work is cheap complexity, like one sees in nature,” Hosoi says. “It’s easy to add another leaf or xylem channel in a tree. In small robotics, everything is hard, from manufacturing, to integration, to actuation. If we could make the building blocks that enable cheap complexity, that would be super exciting. I think these [microfluidic pumps] are a step in that direction.”

Hosoi’s co-authors on the paper are lead author Jean Comtet, a former graduate student in MIT’s Department of Mechanical Engineering; Kaare Jensen of the Technical University of Denmark; and Robert Turgeon and Abraham Stroock, both of Cornell University.

A hydraulic lift

The group’s tree-inspired work grew out of a project on hydraulic robots powered by pumping fluids. Hosoi was interested in designing hydraulic robots at the small scale, that could perform actions similar to much bigger robots like Boston Dynamic’s Big Dog, a four-legged, Saint Bernard-sized robot that runs and jumps over rough terrain, powered by hydraulic actuators.

“For small systems, it’s often expensive to manufacture tiny moving pieces,” Hosoi says. “So we thought, ‘What if we could make a small-scale hydraulic system that could generate large pressures, with no moving parts?’ And then we asked, ‘Does anything do this in nature?’ It turns out that trees do.”

The general understanding among biologists has been that water, propelled by surface tension, travels up a tree’s channels of xylem, then diffuses through a semipermeable membrane and down into channels of phloem that contain sugar and other nutrients.

The more sugar there is in the phloem, the more water flows from xylem to phloem to balance out the sugar-to-water gradient, in a passive process known as osmosis. The resulting water flow flushes nutrients down to the roots. Trees and plants are thought to maintain this pumping process as more water is drawn up from their roots.

“This simple model of xylem and phloem has been well-known for decades,” Hosoi says. “From a qualitative point of view, this makes sense. But when you actually run the numbers, you realize this simple model does not allow for steady flow.”

In fact, engineers have previously attempted to design tree-inspired microfluidic pumps, fabricating parts that mimic xylem and phloem. But they found that these designs quickly stopped pumping within minutes.

It was Hosoi’s student Comtet who identified a third essential part to a tree’s pumping system: its leaves, which produce sugars through photosynthesis. Comtet’s model includes this additional source of sugars that diffuse from the leaves into a plant’s phloem, increasing the sugar-to-water gradient, which in turn maintains a constant osmotic pressure, circulating water and nutrients continuously throughout a tree.

Running on sugar

With Comtet’s hypothesis in mind, Hosoi and her team designed their tree-on-a-chip, a microfluidic pump that mimics a tree’s xylem, phloem, and most importantly, its sugar-producing leaves.

To make the chip, the researchers sandwiched together two plastic slides, through which they drilled small channels to represent xylem and phloem. They filled the xylem channel with water, and the phloem channel with water and sugar, then separated the two slides with a semipermeable material to mimic the membrane between xylem and phloem. They placed another membrane over the slide containing the phloem channel, and set a sugar cube on top to represent the additional source of sugar diffusing from a tree’s leaves into the phloem. They hooked the chip up to a tube, which fed water from a tank into the chip.

With this simple setup, the chip was able to passively pump water from the tank through the chip and out into a beaker, at a constant flow rate for several days, as opposed to previous designs that only pumped for several minutes.

“As soon as we put this sugar source in, we had it running for days at a steady state,” Hosoi says. “That’s exactly what we need. We want a device we can actually put in a robot.”

Hosoi envisions that the tree-on-a-chip pump may be built into a small robot to produce hydraulically powered motions, without requiring active pumps or parts.

“If you design your robot in a smart way, you could absolutely stick a sugar cube on it and let it go,” Hosoi says.

This research was supported, in part, by the Defense Advance Research Projects Agency [DARPA].

This research’s funding connection to DARPA reminded me that MIT has an Institute of Soldier Nanotechnologies.

Getting back to the tree-on-a-chip, here’s a link to and a citation for the paper,

Passive phloem loading and long-distance transport in a synthetic tree-on-a-chip by Jean Comtet, Kaare H. Jensen, Robert Turgeon, Abraham D. Stroock & A. E. Hosoi. Nature Plants 3, Article number: 17032 (2017)  doi:10.1038/nplants.2017.32 Published online: 20 March 2017

This paper is behind a paywall.

The Imagineers of War: The Untold Story of DARPA, the Pentagon Agency That Changed the World on March 21, 2017 at the Woodrow Wilson International Center for Scholars

I received a March 17, 2017 Woodrow Wilson International Center for Scholars notice (via email) about this upcoming event,

The Imagineers of War: The Untold Story of DARPA [Defense Advanced Research Projects Agency], the Pentagon Agency That Changed the World

There will be a webcast of this event

In The Imagineers of War, Weinberger gives us a definitive history of the agency that has quietly shaped war and technology for nearly 60 years. Founded in 1958 in response to the launch of Sputnik, DARPA’s original mission was to create “the unimagined weapons of the future.” Over the decades, DARPA has been responsible for countless inventions and technologies that extend well beyond military technology.

Weinberger has interviewed more than one hundred former Pentagon officials and scientists involved in DARPA’s projects—many of whom have never spoken publicly about their work with the agency—and pored over countless declassified records from archives around the country, documents obtained under the Freedom of Information Act, and exclusive materials provided by sources. The Imagineers of War is a compelling and groundbreaking history in which science, technology, and politics collide.

Speakers


  • Sharon Weinberger

    Global Fellow
    Author, Imagineers of War, National Security Editor at The Intercept and former Wilson Center Fellow

  • Richard Whittle

    Global Fellow
    Author, Predator: The Secret Origins of the Drone Revolution and Wilson Center Global Fellow

The logistics:

6th Floor, Woodrow Wilson Center

I first heard about DARPA in reference to the internet. A developer I was working with noted that ARPA (DARPA’s predecessor agency) was instrumental in the development of the internet.

You can register for the event here. Should you be interested in the webcast, you can check this page.

As a point of interest, the Wilson Center (also known as the Woodrow Wilson International Center for Scholars) is one of the independent agencies slated to be defunded in the 2017 US budget as proposed by President Donald Trump according to a March 16, 2017 article by Elaine Godfrey for The Atlantic.

Metamaterial could supply air conditioning with zero energy consumption

This is exciting provided they can scale up the metamaterial for industrial use. A Feb. 9, 2017 news item on Nanowerk announces a new metamaterial that could change air conditioning  from the University of Colorado at Boulder (Note: A link has been removed),

A team of University of Colorado Boulder engineers has developed a scalable manufactured metamaterial — an engineered material with extraordinary properties not found in nature — to act as a kind of air conditioning system for structures. It has the ability to cool objects even under direct sunlight with zero energy and water consumption.

When applied to a surface, the metamaterial film cools the object underneath by efficiently reflecting incoming solar energy back into space while simultaneously allowing the surface to shed its own heat in the form of infrared thermal radiation.

The new material, which is described today in the journal Science (“Scalable-manufactured randomized glass-polymer hybrid metamaterial for daytime radiative cooling”), could provide an eco-friendly means of supplementary cooling for thermoelectric power plants, which currently require large amounts of water and electricity to maintain the operating temperatures of their machinery.

A Feb. 9, 2017 University of Colorado at Boulder news release (also on EurekAlert), which originated the news item, expands on the theme (Note: Links have been removed),

The researchers’ glass-polymer hybrid material measures just 50 micrometers thick — slightly thicker than the aluminum foil found in a kitchen — and can be manufactured economically on rolls, making it a potentially viable large-scale technology for both residential and commercial applications.

“We feel that this low-cost manufacturing process will be transformative for real-world applications of this radiative cooling technology,” said Xiaobo Yin, co-director of the research and an assistant professor who holds dual appointments in CU Boulder’s Department of Mechanical Engineering and the Materials Science and Engineering Program. Yin received DARPA’s [US Defense Advanced Research Projects Agency] Young Faculty Award in 2015.

The material takes advantage of passive radiative cooling, the process by which objects naturally shed heat in the form of infrared radiation, without consuming energy. Thermal radiation provides some natural nighttime cooling and is used for residential cooling in some areas, but daytime cooling has historically been more of a challenge. For a structure exposed to sunlight, even a small amount of directly-absorbed solar energy is enough to negate passive radiation.

The challenge for the CU Boulder researchers, then, was to create a material that could provide a one-two punch: reflect any incoming solar rays back into the atmosphere while still providing a means of escape for infrared radiation. To solve this, the researchers embedded visibly-scattering but infrared-radiant glass microspheres into a polymer film. They then added a thin silver coating underneath in order to achieve maximum spectral reflectance.

“Both the glass-polymer metamaterial formation and the silver coating are manufactured at scale on roll-to-roll processes,” added Ronggui Yang, also a professor of mechanical engineering and a Fellow of the American Society of Mechanical Engineers.

“Just 10 to 20 square meters of this material on the rooftop could nicely cool down a single-family house in summer,” said Gang Tan, an associate professor in the University of Wyoming’s Department of Civil and Architectural Engineering and a co-author of the paper.

In addition to being useful for cooling of buildings and power plants, the material could also help improve the efficiency and lifetime of solar panels. In direct sunlight, panels can overheat to temperatures that hamper their ability to convert solar rays into electricity.

“Just by applying this material to the surface of a solar panel, we can cool the panel and recover an additional one to two percent of solar efficiency,” said Yin. “That makes a big difference at scale.”

The engineers have applied for a patent for the technology and are working with CU Boulder’s Technology Transfer Office to explore potential commercial applications. They plan to create a 200-square-meter “cooling farm” prototype in Boulder in 2017.

The invention is the result of a $3 million grant awarded in 2015 to Yang, Yin and Tang by the Energy Department’s Advanced Research Projects Agency-Energy (ARPA-E).

“The key advantage of this technology is that it works 24/7 with no electricity or water usage,” said Yang “We’re excited about the opportunity to explore potential uses in the power industry, aerospace, agriculture and more.”

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

Scalable-manufactured randomized glass-polymer hybrid metamaterial for daytime radiative cooling by Yao Zhai, Yaoguang Ma, Sabrina N. David, Dongliang Zhao, Runnan Lou, Gang Tan, Ronggui Yang, Xiaobo Yin. Science  09 Feb 2017: DOI: 10.1126/science.aai7899

This paper is behind a paywall.

Members of the research team show off the metamaterial (?) Courtesy: University of Colorado at Boulder

I added the caption to this image, which was on the University of Colorado at Boulder’s home page where it accompanied the news release headline on the rotating banner.

Powering up your graphene implants so you don’t get fried in the process

A Sept. 23, 2016 news item on phys.org describes a way of making graphene-based medical implants safer,

In the future, our health may be monitored and maintained by tiny sensors and drug dispensers, deployed within the body and made from graphene—one of the strongest, lightest materials in the world. Graphene is composed of a single sheet of carbon atoms, linked together like razor-thin chicken wire, and its properties may be tuned in countless ways, making it a versatile material for tiny, next-generation implants.

But graphene is incredibly stiff, whereas biological tissue is soft. Because of this, any power applied to operate a graphene implant could precipitously heat up and fry surrounding cells.

Now, engineers from MIT [Massachusetts Institute of Technology] and Tsinghua University in Beijing have precisely simulated how electrical power may generate heat between a single layer of graphene and a simple cell membrane. While direct contact between the two layers inevitably overheats and kills the cell, the researchers found they could prevent this effect with a very thin, in-between layer of water.

A Sept. 23, 2016 MIT news release by Emily Chu, which originated the news item, provides more technical details,

By tuning the thickness of this intermediate water layer, the researchers could carefully control the amount of heat transferred between graphene and biological tissue. They also identified the critical power to apply to the graphene layer, without frying the cell membrane. …

Co-author Zhao Qin, a research scientist in MIT’s Department of Civil and Environmental Engineering (CEE), says the team’s simulations may help guide the development of graphene implants and their optimal power requirements.

“We’ve provided a lot of insight, like what’s the critical power we can accept that will not fry the cell,” Qin says. “But sometimes we might want to intentionally increase the temperature, because for some biomedical applications, we want to kill cells like cancer cells. This work can also be used as guidance [for those efforts.]”

Sandwich model

Typically, heat travels between two materials via vibrations in each material’s atoms. These atoms are always vibrating, at frequencies that depend on the properties of their materials. As a surface heats up, its atoms vibrate even more, causing collisions with other atoms and transferring heat in the process.

The researchers sought to accurately characterize the way heat travels, at the level of individual atoms, between graphene and biological tissue. To do this, they considered the simplest interface, comprising a small, 500-nanometer-square sheet of graphene and a simple cell membrane, separated by a thin layer of water.

“In the body, water is everywhere, and the outer surface of membranes will always like to interact with water, so you cannot totally remove it,” Qin says. “So we came up with a sandwich model for graphene, water, and membrane, that is a crystal clear system for seeing the thermal conductance between these two materials.”

Qin’s colleagues at Tsinghua University had previously developed a model to precisely simulate the interactions between atoms in graphene and water, using density functional theory — a computational modeling technique that considers the structure of an atom’s electrons in determining how that atom will interact with other atoms.

However, to apply this modeling technique to the group’s sandwich model, which comprised about half a million atoms, would have required an incredible amount of computational power. Instead, Qin and his colleagues used classical molecular dynamics — a mathematical technique based on a “force field” potential function, or a simplified version of the interactions between atoms — that enabled them to efficiently calculate interactions within larger atomic systems.

The researchers then built an atom-level sandwich model of graphene, water, and a cell membrane, based on the group’s simplified force field. They carried out molecular dynamics simulations in which they changed the amount of power applied to the graphene, as well as the thickness of the intermediate water layer, and observed the amount of heat that carried over from the graphene to the cell membrane.

Watery crystals

Because the stiffness of graphene and biological tissue is so different, Qin and his colleagues expected that heat would conduct rather poorly between the two materials, building up steeply in the graphene before flooding and overheating the cell membrane. However, the intermediate water layer helped dissipate this heat, easing its conduction and preventing a temperature spike in the cell membrane.

Looking more closely at the interactions within this interface, the researchers made a surprising discovery: Within the sandwich model, the water, pressed against graphene’s chicken-wire pattern, morphed into a similar crystal-like structure.

“Graphene’s lattice acts like a template to guide the water to form network structures,” Qin explains. “The water acts more like a solid material and makes the stiffness transition from graphene and membrane less abrupt. We think this helps heat to conduct from graphene to the membrane side.”

The group varied the thickness of the intermediate water layer in simulations, and found that a 1-nanometer-wide layer of water helped to dissipate heat very effectively. In terms of the power applied to the system, they calculated that about a megawatt of power per meter squared, applied in tiny, microsecond bursts, was the most power that could be applied to the interface without overheating the cell membrane.

Qin says going forward, implant designers can use the group’s model and simulations to determine the critical power requirements for graphene devices of different dimensions. As for how they might practically control the thickness of the intermediate water layer, he says graphene’s surface may be modified to attract a particular number of water molecules.

“I think graphene provides a very promising candidate for implantable devices,” Qin says. “Our calculations can provide knowledge for designing these devices in the future, for specific applications, like sensors, monitors, and other biomedical applications.”

This research was supported in part by the MIT International Science and Technology Initiative (MISTI): MIT-China Seed Fund, the National Natural Science Foundation of China, DARPA [US Defense Advanced Research Projects Agency], the Department of Defense (DoD) Office of Naval Research, the DoD Multidisciplinary Research Initiatives program, the MIT Energy Initiative, and the National Science Foundation.

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

Intercalated water layers promote thermal dissipation at bio–nano interfaces by Yanlei Wang, Zhao Qin, Markus J. Buehler, & Zhiping Xu. Nature Communications 7, Article number: 12854 doi:10.1038/ncomms12854 Published 23 September 2016

This paper is open access.