Researchers find no evidence of an overall reduction in the world’s consumption of materials.
Are humans taking more resources from the Earth than the planet can safely produce? The answer lies partly in whether we can “dematerialize,” or reduce the amount of materials needed to produce goods and services.
While some scientists believe that the world can achieve significant dematerialization through improvements in technology, a new MIT-led study finds that technological advances alone will not bring about dematerialization and, ultimately, a sustainable world.
The researchers found that no matter how much more efficient and compact a product is made, consumers will only demand more of that product and in the long run increase the total amount of materials used in making that product.
Take, for instance, one of the world’s fastest-improving technologies: silicon-based semiconductors. Over the last few decades, technological improvements in the efficiency of semiconductors have greatly reduced the amount of material needed to make a single transistor. As a result, today’s smartphones, tablets, and computers are far more powerful and compact than computers built in the 1970s.
Nonetheless, the researchers find that consumers’ demand for silicon has outpaced the rate of its technological change, and that the world’s consumption of silicon has grown by 345 percent over the last four decades. As others have found, by 2005, there were more transistors used than printed text characters.
“Despite how fast technology is racing, there’s actually more silicon used today, because we now just put more stuff on, like movies, and photos, and things we couldn’t even think of 20 years ago,” says Christopher Magee, a professor of the practice of engineering systems in MIT’s Institute for Data, Systems, and Society.
“So we’re still using a little more material all the time.”
The researchers found similar trends in 56 other materials, goods, and services, from basic resources such as aluminum and formaldehyde to hardware and energy technologies such as hard disk drives, transistors, wind energy, and photovoltaics. In all cases, they found no evidence of dematerialization, or an overall reduction in their use, despite technological improvements to their performance.
“There is a techno-optimist’s position that says technological change will fix the environment,” Magee observes. “This says, probably not.”
Magee and his co-author, Tessaleno Devezas, a professor at the University of Beira Interior, in Portugal, published their findings recently in the journal Technological Forecasting and Social Change.
Tracking a rebound
In their research, Magee and Devezas examined whether the world’s use of materials has been swayed by an effect known as Jevons’ Paradox. In 1865, the English economist William Stanley Jevons observed that as improvements to coal-fired steam engines reduced the price of coal, England’s consumption of coal actually increased.
While experts believed technological improvements would reduce coal consumption, Jevons countered the opposite was true: Improving coal-fired power’s efficiency would only increase consumer demand for electricity and further deplete coal reserves.
Magee and Devezas looked to see whether Jevons’ Paradox, and consumer demand in general, has prevented dematerialization of today’s goods and services. They sought to identify a general relationship between dematerialization, technological change, and Jevons’s Paradox — also referred to as a rebound effect.
The team developed a simple model, or equation, to calculate whether dematerialization is taking place for a given product. The model considers a number of variables, including population and economic growth, a product’s yearly increase in technological performance, and demand elasticity — the degree to which demand for a product varies with its price.
Not surprisingly, the researchers’ model indicates that dematerialization is more likely when demand elasticity for a product is relatively low and the rate of its technological improvement is high. But when they applied the equation to common goods and services used today, they found that demand elasticity and technological change worked against each other — the better a product was made to perform, the more consumers wanted it.
“It seems we haven’t seen a saturation in demand,” Magee says. “People haven’t said, ‘That’s enough,’ at least in anything that we can get data to test for.”
A growing appetite
Magee and Devezas gathered data for 57 common goods and services, including widely used chemical components such as ammonia, formaldehyde, polyester fiber, and styrene, along with hardware and energy technologies such as transistors, laser diodes, crude oil, photovoltaics, and wind energy. They worked the data for each product into their equation, and, despite seeing technological improvements in almost all cases, they failed to find a single case in which dematerialization — an overall reduction in materials — was taking place.
In follow-up work, the researchers were eventually able to identify six cases in which an absolute decline in materials usage has occurred. However, these cases mostly include toxic chemicals such as asbestos and thallium, whose dematerialization was due not to technological advances, but to government intervention.
There was one other case in which researchers observed dematerialization: wool. The material’s usage has significantly fallen, due to innovations in synthetic alternatives, such as nylon and polyester fabrics. In this case, Magee argues that substitution, and not dematerialization, has occurred. In other words, wool has simply been replaced by another material to fill the same function.
So what will it take to reduce our materials consumption and achieve a sustainable world?
“What it’s going to take is much more difficult than just letting technological change do it,” Magee says. “Social and cultural change, people talking to each other, cooperating, might do it. That’s not the way we’re going right now, but that doesn’t mean we can’t do it.”
However, others are more hopeful that technology will bring about sustainability, albeit at significant cost.
“[Technology] will get us to a sustainable world — it has to,” says J. Doyne Farmer, a professor of mathematics at the University of Oxford who was not involved in the research. “I say this not only because we need it, but because there is only so much we can suck out of the Earth, and eventually we will be forced into a sustainable world, one way or another. The question is whether we can do that without great pain. Magee’s paper shows that we need to expect more pain than some of us thought.”
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Founded in 1861 in response to the increasing industrialization of the United States, the institute used a polytechnic university model and stressed laboratory instruction. MIT’s early emphasis on applied technology at the undergraduate and graduate levels led to close cooperation with industry. Curricular reforms under Karl Compton and Vannevar Bush in the 1930s re-emphasized basic scientific research. MIT was elected to the Association of American Universities in 1934. Researchers worked on computers, radar, and inertial guidance during World War II and the Cold War. Post-war defense research contributed to the rapid expansion of the faculty and campus under James Killian. The current 168-acre (68.0 ha) campus opened in 1916 and extends over 1 mile (1.6 km) along the northern bank of the Charles River basin.
Today, the University comprises various academic departments with a strong emphasis on scientific, engineering, and technological education and research. It has five schools and one college, which contain a total of 32 departments. Eighty-one Nobel laureates, 52 National Medal of Science recipients, 45 Rhodes Scholars, and 38 MacArthur Fellows have been affiliated with the university. It is one of the most selective higher learning institutions, and received 18,109 undergraduate applicants for the class of 2016—only admitting 1,620, an acceptance rate of 8.9%.
MIT also has a strong entrepreneurial culture. The aggregated revenues of companies founded by MIT alumni would rank as the eleventh-largest economy in the world.
Porous, 3-D forms of graphene developed at MIT can be 10 times as strong as steel but much lighter.
A team of researchers at MIT has designed one of the strongest lightweight materials known, by compressing and fusing flakes of graphene, a two-dimensional form of carbon. The new material, a sponge-like configuration with a density of just 5 percent, can have a strength 10 times that of steel.
In its two-dimensional form, graphene is thought to be the strongest of all known materials. But researchers until now have had a hard time translating that two-dimensional strength into useful three-dimensional materials.
The new findings show that the crucial aspect of the new 3-D forms has more to do with their unusual geometrical configuration than with the material itself, which suggests that similar strong, lightweight materials could be made from a variety of materials by creating similar geometric features.
The findings are being reported today in the journal Science Advances, in a paper by Markus Buehler, the head of MIT’s Department of Civil and Environmental Engineering (CEE) and the McAfee Professor of Engineering; Zhao Qin, a CEE research scientist; Gang Seob Jung, a graduate student; and Min Jeong Kang MEng ’16, a recent graduate.
Other groups had suggested the possibility of such lightweight structures, but lab experiments so far had failed to match predictions, with some results exhibiting several orders of magnitude less strength than expected. The MIT team decided to solve the mystery by analyzing the material’s behavior down to the level of individual atoms within the structure. They were able to produce a mathematical framework that very closely matches experimental observations.
Two-dimensional materials — basically flat sheets that are just one atom in thickness but can be indefinitely large in the other dimensions — have exceptional strength as well as unique electrical properties. But because of their extraordinary thinness, “they are not very useful for making 3-D materials that could be used in vehicles, buildings, or devices,” Buehler says. “What we’ve done is to realize the wish of translating these 2-D materials into three-dimensional structures.”
The team was able to compress small flakes of graphene using a combination of heat and pressure. This process produced a strong, stable structure whose form resembles that of some corals and microscopic creatures called diatoms. These shapes, which have an enormous surface area in proportion to their volume, proved to be remarkably strong. “Once we created these 3-D structures, we wanted to see what’s the limit — what’s the strongest possible material we can produce,” says Qin. To do that, they created a variety of 3-D models and then subjected them to various tests. In computational simulations, which mimic the loading conditions in the tensile and compression tests performed in a tensile loading machine, “one of our samples has 5 percent the density of steel, but 10 times the strength,” Qin says.
Buehler says that what happens to their 3-D graphene material, which is composed of curved surfaces under deformation, resembles what would happen with sheets of paper. Paper has little strength along its length and width, and can be easily crumpled up. But when made into certain shapes, for example rolled into a tube, suddenly the strength along the length of the tube is much greater and can support substantial weight. Similarly, the geometric arrangement of the graphene flakes after treatment naturally forms a very strong configuration.
The new configurations have been made in the lab using a high-resolution, multimaterial 3-D printer. They were mechanically tested for their tensile and compressive properties, and their mechanical response under loading was simulated using the team’s theoretical models. The results from the experiments and simulations matched accurately.
The new, more accurate results, based on atomistic computational modeling by the MIT team, ruled out a possibility proposed previously by other teams: that it might be possible to make 3-D graphene structures so lightweight that they would actually be lighter than air, and could be used as a durable replacement for helium in balloons. The current work shows, however, that at such low densities, the material would not have sufficient strength and would collapse from the surrounding air pressure.
But many other possible applications of the material could eventually be feasible, the researchers say, for uses that require a combination of extreme strength and light weight. “You could either use the real graphene material or use the geometry we discovered with other materials, like polymers or metals,” Buehler says, to gain similar advantages of strength combined with advantages in cost, processing methods, or other material properties (such as transparency or electrical conductivity).
“You can replace the material itself with anything,” Buehler says. “The geometry is the dominant factor. It’s something that has the potential to transfer to many things.”
The unusual geometric shapes that graphene naturally forms under heat and pressure look something like a Nerf ball — round, but full of holes. These shapes, known as gyroids, are so complex that “actually making them using conventional manufacturing methods is probably impossible,” Buehler says. The team used 3-D-printed models of the structure, enlarged to thousands of times their natural size, for testing purposes.
For actual synthesis, the researchers say, one possibility is to use the polymer or metal particles as templates, coat them with graphene by chemical vapor deposit before heat and pressure treatments, and then chemically or physically remove the polymer or metal phases to leave 3-D graphene in the gyroid form. For this, the computational model given in the current study provides a guideline to evaluate the mechanical quality of the synthesis output.
The same geometry could even be applied to large-scale structural materials, they suggest. For example, concrete for a structure such a bridge might be made with this porous geometry, providing comparable strength with a fraction of the weight. This approach would have the additional benefit of providing good insulation because of the large amount of enclosed airspace within it.
Because the shape is riddled with very tiny pore spaces, the material might also find application in some filtration systems, for either water or chemical processing. The mathematical descriptions derived by this group could facilitate the development of a variety of applications, the researchers say.
“This is an inspiring study on the mechanics of 3-D graphene assembly,” says Huajian Gao, a professor of engineering at Brown University, who was not involved in this work. “The combination of computational modeling with 3-D-printing-based experiments used in this paper is a powerful new approach in engineering research. It is impressive to see the scaling laws initially derived from nanoscale simulations resurface in macroscale experiments under the help of 3-D printing,” he says.
This work, Gao says, “shows a promising direction of bringing the strength of 2-D materials and the power of material architecture design together.”
Ride-sharing, carpooling and traffic congestion
Traffic is not just a nuisance for drivers: it’s also a public-health hazard and bad news for the economy.
Transportation studies put the annual cost of congestion at $160 billion, which includes 7 billion hours of time lost to sitting in traffic and an extra 3 billion gallons of fuel burned.
One way to improve traffic is through ride-sharing – and a new MIT study suggests that using carpooling options from companies like Uber and Lyft could reduce the number of vehicles on the road 75 percent without significantly impacting travel time.
Led by Professor Daniela Rus of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), researchers developed an algorithm that found that 3,000 four-passenger cars could serve 98 percent of taxi demand in New York City, with an average wait-time of only 2.7 minutes.
“Instead of transporting people one at a time, drivers could transport two to four people at once, results in fewer trips, in less time, to make the same amount of money,” says Rus, who wrote a related paper with former CSAIL postdoc Javier Alonso-Mora, assistant professor Samitha Samaranayake of Cornell University, PhD student Alex Wallar and MIT professor Emilio Frazzoli. “A system like this could allow drivers to work shorter shifts, while also creating less traffic, cleaner air and shorter, less stressful commutes.”
The team also found that 95 percent of demand would be covered by just 2,000 ten-person vehicles, compared to the nearly 14,000 taxis that currently operate in New York City.
Using data from 3 million taxi rides, the new algorithm works in real-time to reroute cars based on incoming requests, and can also proactively send idle cars to areas with high demand – a step that speeds up service 20 percent, according to Rus.
“To our knowledge, this is the first time that scientists have been able to experimentally quantify the trade-off between fleet size, capacity, waiting time, travel delay, and operational costs for a range of vehicles, from taxis to vans and shuttles,” says Rus. “What’s more, the system is particularly suited to autonomous cars, since it can continuously reroute vehicles based on real-time requests.”
The team’s article was published in this week’s issue of the Proceedings of the National Academy of the Sciences (PNAS).
A future of carpool tunnels
While the concept of carpooling has been around for decades, it’s only in the last two years that services like Uber and Lyft have leveraged smartphone data in a way that has made ride-sharing a cheap, convenient option. (In 2015 Lyft reported that half of its San Francisco trips were carpools.)
However, existing approaches are still limited in their complexity. For example, some ride-sharing systems require that user B be on the way for user A, and need to have all the requests submitted before they can create a route.
In contrast, the new system allows requests to be rematched to different vehicles. It can also analyze a range of different types of vehicles to determine, say, where or when a 10-person van would be of the greatest benefit.
The system works by first creating a graph of all of the requests and all of the vehicles. It then creates a second graph of all possible trip combinations, and uses a method called “integer linear programming” to compute the best assignment of vehicles to trips.
After cars are assigned, the algorithm can then rebalance the remaining idle vehicles by sending them to higher-demand areas.
“A key challenge was to develop a real-time solution that considers the thousands of vehicles and requests at once,” says Rus. “We can do this in our method because that first step enables us to understand and abstract the road network at a fine level of detail.”
The final product is what Rus calls an “anytime optimal algorithm,” which means that it gets better the more times you run it – and she says that she’s eager to see how much it can improve with further refinement.
“Ride-sharing services have enormous potential for positive societal impact with respect to congestion, pollution and energy consumption,” says Rus. “It’s important that we as researchers do everything we can to explore ways to make these transportation systems as efficient and reliable as possible.”
Learn more: STUDY: CARPOOLING APPS COULD REDUCE TRAFFIC 75%
Investigations of the skyrmion Hall effect reveal surprising results
Researchers at Johannes Gutenberg University Mainz (JGU) and the Massachusetts Institute of Technology (MIT) have made another important breakthrough in the field of future magnetic storage devices. Already in March 2016, the international team investigated structures, which could serve as magnetic shift register or racetrack memory devices. This type of storage promises low access times, high information density, and low energy consumption. Now, the research team achieved the billion-fold reproducible motion of special magnetic textures, so-called skyrmions, between different positions, which is exactly the process needed in magnetic shift registers thereby taking a critical step towards the application of skyrmions in devices. The work was published in the research journal Nature Physics.
The experiments were carried out in specially designed thin film structures, i.e., vertically asymmetric multilayer devices exhibiting broken inversion symmetry and thus stabilizing special spin structures called skyrmions. Those structures are similar to a hair whorl and like these are relatively difficult to destroy. This grants them unique stability, which is another argument for the application of skyrmions in such spintronic devices.
Since skyrmions can be shifted by electrical currents and feel a repulsive force from the edges of the magnetic track as well as from single defects in the wire, they can move relatively undisturbed through the track. This is a highly desired property for racetrack devices, which are supposed to consist of static read- and write-heads, while the magnetic bits are shifted in the track. However, it is another important aspect of skyrmion dynamics that the skyrmions do not only move parallel to the applied current, but also perpendicular to it. This leads to an angle between the skyrmion direction of motion and the current flow called the skyrmion Hall angle, which can be predicted theoretically. As a result, the skyrmions should move under this constant angle until they start getting repelled by the edge of the material and then keep a constant distance to it.
Within their latest research project, scientists of JGU and MIT now proved that the billion-fold reproducible displacement of skyrmions is indeed possible and can be achieved with high velocities. Furthermore, the skyrmion Hall angle was investigated in detail. Surprisingly, it turned out to be dependent on the velocity of the skyrmions, which means that the components of the motion parallel and perpendicular to the current flow do not scale equally with the velocity of the skyrmions. This is not predicted in the conventional theoretical description of skyrmions. Part of the solution of this unexpected behavior could be the deformation of the skyrmion spin structure, calling for more theoretical effort to fully understand the properties of skyrmions.
Noninvasive technique reduces beta amyloid plaques in mouse models of Alzheimer’s disease
Using LED lights flickering at a specific frequency, MIT researchers have shown that they can substantially reduce the beta amyloid plaques seen in Alzheimer’s disease, in the visual cortex of mice.
This treatment appears to work by inducing brain waves known as gamma oscillations, which the researchers discovered help the brain suppress beta amyloid production and invigorate cells responsible for destroying the plaques.
Further research will be needed to determine if a similar approach could help Alzheimer’s patients, says Li-Huei Tsai, the Picower Professor of Neuroscience, director of MIT’s Picower Institute for Learning and Memory, and senior author of the study, which appears in the Dec. 7 online edition of Nature.
“It’s a big ‘if,’ because so many things have been shown to work in mice, only to fail in humans,” Tsai says. “But if humans behave similarly to mice in response to this treatment, I would say the potential is just enormous, because it’s so noninvasive, and it’s so accessible.”
Tsai and Ed Boyden, an associate professor of biological engineering and brain and cognitive sciences at the MIT Media Lab and the McGovern Institute for Brain Research, who is also an author of the Nature paper, have started a company called Cognito Therapeutics to pursue tests in humans. The paper’s lead authors are graduate student Hannah Iaccarino and Media Lab research affiliate Annabelle Singer.
“This important announcement may herald a breakthrough in the understanding and treatment of Alzheimer’s disease, a terrible affliction affecting millions of people and their families around the world,” says Michael Sipser, dean of MIT’s School of Science. “Our MIT scientists have opened the door to an entirely new direction of research on this brain disorder and the mechanisms that may cause or prevent it. I find it extremely exciting.”
Brain wave stimulation
Alzheimer’s disease, which affects more than 5 million people in the United States, is characterized by beta amyloid plaques that are suspected to be harmful to brain cells and to interfere with normal brain function. Previous studies have hinted that Alzheimer’s patients also have impaired gamma oscillations. These brain waves, which range from 25 to 80 hertz (cycles per second), are believed to contribute to normal brain functions such as attention, perception, and memory.
In a study of mice that were genetically programmed to develop Alzheimer’s but did not yet show any plaque accumulation or behavioral symptoms, Tsai and her colleagues found impaired gamma oscillations during patterns of activity that are essential for learning and memory while running a maze.
Next, the researchers stimulated gamma oscillations at 40 hertz in a brain region called the hippocampus, which is critical in memory formation and retrieval. These initial studies relied on a technique known as optogenetics, co-pioneered by Boyden, which allows scientists to control the activity of genetically modified neurons by shining light on them. Using this approach, the researchers stimulated certain brain cells known as interneurons, which then synchronize the gamma activity of excitatory neurons.
After an hour of stimulation at 40 hertz, the researchers found a 40 to 50 percent reduction in the levels of beta amyloid proteins in the hippocampus. Stimulation at other frequencies, ranging from 20 to 80 hertz, did not produce this decline.
Tsai and colleagues then began to wonder if less-invasive techniques might achieve the same effect. Tsai and Emery Brown, the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience, a member of the Picower Institute, and an author of the paper, came up with the idea of using an external stimulus — in this case, light — to drive gamma oscillations in the brain. The researchers built a simple device consisting of a strip of LEDs that can be programmed to flicker at different frequencies.
Using this device, the researchers found that an hour of exposure to light flickering at 40 hertz enhanced gamma oscillations and reduced beta amyloid levels by half in the visual cortex of mice in the very early stages of Alzheimer’s. However, the proteins returned to their original levels within 24 hours.
The researchers then investigated whether a longer course of treatment could reduce amyloid plaques in mice with more advanced accumulation of amyloid plaques. After treating the mice for an hour a day for seven days, both plaques and free-floating amyloid were markedly reduced. The researchers are now trying to determine how long these effects last.
Furthermore, the researchers found that gamma rhythms also reduced another hallmark of Alzheimer’s disease: the abnormally modified Tau protein, which can form tangles in the brain.
“What this study does, in a very carefully designed and well-executed way, is show that gamma oscillations, which we have known for a long time are linked to cognitive function, play a critical role in the capacity of the brain to clean up deposits,” says Alvaro Pascual-Leone, a professor of neurology at Harvard Medical School who was not involved in the research. “That’s remarkable and surprising, and it opens up the exciting prospect of possible translation to application in humans.”
Tsai’s lab is now studying whether light can drive gamma oscillations in brain regions beyond the visual cortex, and preliminary data suggest that this is possible. They are also investigating whether the reduction in amyloid plaques has any effects on the behavioral symptoms of their Alzheimer’s mouse models, and whether this technique could affect other neurological disorders that involve impaired gamma oscillations.
Two modes of action
The researchers also performed studies to try to figure out how gamma oscillations exert their effects. They found that after gamma stimulation, the process for beta amyloid generation is less active. Gamma oscillations also improved the brain’s ability to clear out beta amyloid proteins, which is normally the job of immune cells known as microglia.
“They take up toxic materials and cell debris, clean up the environment, and keep neurons healthy,” Tsai says.
In Alzheimer’s patients, microglia cells become very inflammatory and secrete toxic chemicals that make other brain cells more sick. However, when gamma oscillations were boosted in mice, their microglia underwent morphological changes and became more active in clearing away the beta amyloid proteins.
“The bottom line is, enhancing gamma oscillations in the brain can do at least two things to reduced amyloid load. One is to reduce beta amyloid production from neurons. And second is to enhance the clearance of amyloids by microglia,” Tsai says.
The researchers also sequenced messenger RNA from the brains of the treated mice and found that hundreds of genes were over- or underexpressed, and they are now investigating the possible impact of those variations on Alzheimer’s disease.
New technique offers precise, durable control over tiny mirrors or stages.
Microelectromechanical systems, or MEMS, are tiny machines fabricated using equipment and processes developed for the production of electronic chips and devices. They’ve found a wide variety of applications in today’s consumer electronics, but their moving parts can wear out over time as a result of friction.
A new approach developed by researchers at MIT could offer a new way of making movable parts with no solid connections between the pieces, potentially eliminating a major source of wear and failure.
The new system uses a layer of liquid droplets to support a tiny, movable platform, which essentially floats on top of the droplets. The droplets can be water or some other fluid, and the precise movements of the platform can be controlled electrically, through a system that can alter the dimensions of the droplets to raise, lower, and tilt the platform.
The new findings are reported in a paper in Applied Physics Letters, co-authored by Daniel Preston, an MIT graduate student; Evelyn Wang, the Gail E. Kendall Associate Professor of Mechanical Engineering; and five others.
Preston explains that the new system could be used to make devices such as stages for microscope specimens. The focus of the microscope could be controlled by raising or lowering the stage, which would involve changing the shapes of supporting liquid droplets.
The system works by altering the way the droplets interact with the surface below them, governed by a characteristic known as the contact angle. This angle is a measure of how steep the edge of the droplet is at the point where it meets the surface. On hydrophilic, or water-attracting, surfaces, droplets spread out nearly flat, producing a very small contact angle, while hydrophobic, or water-repelling, surfaces cause droplets to be nearly spherical, barely touching the surface, with very large contact angles. On certain kinds of dielectric surfaces, these qualities can be “tuned” across that whole range by simply varying a voltage applied to the surface.
As the surface gets more hydrophobic and the droplets get rounder, their tops rise farther from the surface, thus raising the platform — in these tests, a thin sheet of copper — that floats on them. By selectively changing different droplets by different amounts, the platform can also be selectively tilted. This could be used, for example, to change the angle of a mirrored surface in order to aim a laser beam, Preston says. “There are a lot of experiments that use lasers, that could really benefit from a way to make these small-scale movements.”
The new system could be used to make devices such as stages for microscope specimens. The focus of the microscope could be controlled by raising or lowering the stage, which would involve changing the shapes of supporting liquid droplets. (Image: Daniel Preston/Device Research Lab)
In order to maintain the positioning of the droplets rather than letting them slide around, the team treated the underside of the floating platform. They made the overall surface hydrophobic, but with small circles of hydrophilic material. That way, all the droplets are securely “pinned” to those water-attracting surfaces, keeping the platform securely in position.
In the group’s initial test device, the vertical positioning can be controlled to within a precision of 10 microns, or millionths of a meter, over a range of motion of 130 microns.
MEMS devices, Preston says, “often fail when there’s a solid-solid contact that wears out, or just gets stuck. At these very small scales, things break down easily.”
While the basic technology behind the alteration of droplet shapes on a surface is not a new idea, Preston says, “nobody has used it to move a stage, without any solid-solid contact. The real innovation here is being able to move a stage up and down, and change its angle, without any solid material connections.”
In principle, it would be possible to use a large array of electrodes that could be adjusted to move a platform across a surface in precise ways, in addition to up and down. For example, it could be used for “lab on a chip” applications, where a biological sample could be mounted on the platform and then moved around from one test site to another on the microchip.
He says the system is relatively simple to implement and that it would be possible to develop it for specific real-world application fairly rapidly. “It depends how motivated people are,” he says. “But I don’t see any huge barriers to large-scale use. I think it could be done within a year.”
“Controlled movements of a stage in microscale are difficult, because the miniature versions of the familiar motors and gear mechanisms do not exist, and if they existed, they would be too weak and frictional, respectively,” says Chang-Jin Kim, a professor of mechanical and aerospace engineering at the University of California at Los Angeles, who was not involved in this research. “Since this challenge is rather fundamental, it is only logical (albeit exotic) to use liquid droplets as a mechanical element,” he says. “There are numerous questions remaining before this stage becomes practical, but this is a good start proving the main design concept.”
“This approach is simpler and hence is expected to provide a significant cost benefit over existing techniques in practical applications,” says Prosenjit Sen, an assistant professor at the Centre for Nano Science and Engineering at the Indian Institute of Science, who also was not involved in this work. “The use of droplet supports additionally provides some degree of vibration isolation, which is not possible in all-solid stages.”
New stamping technique creates functional features at nanoscale dimensions.
The next time you place your coffee order, imagine slapping onto your to-go cup a sticker that acts as an electronic decal, letting you know the precise temperature of your triple-venti no-foam latte. Someday, the high-tech stamping that produces such a sticker might also bring us food packaging that displays a digital countdown to warn of spoiling produce, or even a window pane that shows the day’s forecast, based on measurements of the weather conditions outside.
Engineers at MIT have invented a fast, precise printing process that may make such electronic surfaces an inexpensive reality. In a paper published today in Science Advances, the researchers report that they have fabricated a stamp made from forests of carbon nanotubes that is able to print electronic inks onto rigid and flexible surfaces.
A. John Hart, the Mitsui Career Development Associate Professor in Contemporary Technology and Mechanical Engineering at MIT, says the team’s stamping process should be able to print transistors small enough to control individual pixels in high-resolution displays and touchscreens. The new printing technique may also offer a relatively cheap, fast way to manufacture electronic surfaces for as-yet-unknown applications.
“There is a huge need for printing of electronic devices that are extremely inexpensive but provide simple computations and interactive functions,” Hart says. “Our new printing process is an enabling technology for high-performance, fully printed electronics, including transistors, optically functional surfaces, and ubiquitous sensors.”
Sanha Kim, a postdoc in MIT’s department of Mechanical Engineering, is the lead author, and Hart is the senior author. Their co-authors are Hossein Sojoudi, a postdoc in mechanical engineering and chemical engineering; Hangbo Zhao and Dhanushkodi Mariappan, graduate students in mechanical engineering; Gareth McKinley, the School of Engineering Professor of Teaching Innovation; and Karen Gleason, professor of chemical engineering and MIT’s associate provost.
A stamp from tiny pen quills
There have been other attempts in recent years to print electronic surfaces using inkjet printing and rubber stamping techniques, but with fuzzy results. Because such techniques are difficult to control at very small scales, they tend to produce “coffee ring” patterns where ink spills over the borders, or uneven prints that can lead to incomplete circuits.
“There are critical limitations to existing printing processes in the control they have over the feature size and thickness of the layer that’s printed,” Hart says. “For something like a transistor or thin film with particular electrical or optical properties, those characteristics are very important.”
Hart and his team sought to print electronics much more precisely, by designing “nanoporous” stamps. (Imagine a stamp that’s more spongy than rubber and shrunk to the size of a pinky fingernail, with patterned features that are much smaller than the width of a human hair.) They reasoned that the stamp should be porous, to allow a solution of nanoparticles, or “ink,” to flow uniformly through the stamp and onto whatever surface is to be printed. Designed in this way, the stamp should achieve much higher resolution than conventional rubber stamp printing, referred to as flexography.
Kim and Hart hit upon the perfect material to create their highly detailed stamp: carbon nanotubes — strong, microscopic sheets of carbon atoms, arranged in cylinders. Hart’s group has specialized in growing forests of vertically aligned nanotubes in carefully controlled patterns that can be engineered into highly detailed stamps.
“It’s somewhat serendipitous that the solution to high-resolution printing of electronics leverages our background in making carbon nanotubes for many years,” Hart says. “The forests of carbon nanotubes can transfer ink onto a surface like massive numbers of tiny pen quills.”
Printing circuits, roll by roll
To make their stamps, the researchers used the group’s previously developed techniques to grow the carbon nanotubes on a surface of silicon in various patterns, including honeycomb-like hexagons and flower-shaped designs. They coated the nanotubes with a thin polymer layer (developed by Gleason’s group) to ensure the ink would penetrate throughout the nanotube forest and the nanotubes would not shrink after the ink was stamped. Then they infused the stamp with a small volume of electronic ink containing nanoparticles such as silver, zinc oxide, or semiconductor quantum dots.
The key to printing tiny, precise, high-resolution patterns is in the amount of pressure applied to stamp the ink. The team developed a model to predict the amount of force necessary to stamp an even layer of ink onto a substrate, given the roughness of both the stamp and the substrate, and the concentration of nanoparticles in the ink.
To scale up the process, Mariappan built a printing machine, including a motorized roller, and attached to it various flexible substrates. The researchers fixed each stamp onto a platform attached to a spring, which they used to control the force used to press the stamp against the substrate.
“This would be a continuous industrial process, where you would have a stamp, and a roller on which you’d have a substrate you want to print on, like a spool of plastic film or specialized paper for electronics,” Hart says. “We found, limited by the motor we used in the printing system, we could print at 200 millimeters per second, continuously, which is already competitive with the rates of industrial printing technologies. This, combined with a tenfold improvement in the printing resolution that we demonstrated, is encouraging.”
After stamping ink patterns of various designs, the team tested the printed patterns’ electrical conductivity. After annealing, or heating, the designs after stamping — a common step in activating electronic features — the printed patterns were indeed highly conductive, and could serve, for example, as high-performance transparent electrodes.
Going forward, Hart and his team plan to pursue the possibility of fully printed electronics.
“Another exciting next step is the integration of our printing technologies with 2-D materials, such as graphene, which together could enable new, ultrathin electronic and energy conversion devices,” Hart says.
Learn more: Printable electronics
How the brain recognizes faces
MIT researchers and their colleagues have developed a new computational model of the human brain’s face-recognition mechanism that seems to capture aspects of human neurology that previous models have missed.
The researchers designed a machine-learning system that implemented their model, and they trained it to recognize particular faces by feeding it a battery of sample images. They found that the trained system included an intermediate processing step that represented a face’s degree of rotation — say, 45 degrees from center — but not the direction — left or right.
This property wasn’t built into the system; it emerged spontaneously from the training process. But it duplicates an experimentally observed feature of the primate face-processing mechanism. The researchers consider this an indication that their system and the brain are doing something similar.
“This is not a proof that we understand what’s going on,” says Tomaso Poggio, a professor of brain and cognitive sciences at MIT and director of the Center for Brains, Minds, and Machines (CBMM), a multi-institution research consortium funded by the National Science Foundation and headquartered at MIT. “Models are kind of cartoons of reality, especially in biology. So I would be surprised if things turn out to be this simple. But I think it’s strong evidence that we are on the right track.”
Indeed, the researchers’ new paper includes a mathematical proof that the particular type of machine-learning system they use, which was intended to offer what Poggio calls a “biologically plausible” model of the nervous system, will inevitably yield intermediary representations that are indifferent to angle of rotation.
Poggio, who is also a primary investigator at MIT’s McGovern Institute for Brain Research, is the senior author on a paper describing the new work, which appeared today in the journal Computational Biology. He’s joined on the paper by several other members of both the CBMM and the McGovern Institute: first author Joel Leibo, a researcher at Google DeepMind, who earned his PhD in brain and cognitive sciences from MIT with Poggio as his advisor; Qianli Liao, an MIT graduate student in electrical engineering and computer science; Fabio Anselmi, a postdoc in the [email protected] Laboratory for Computational and Statistical Learning, a joint venture of MIT and the Italian Institute of Technology; and Winrich Freiwald, an associate professor at the Rockefeller University.
The new paper is “a nice illustration of what we want to do in [CBMM], which is this integration of machine learning and computer science on one hand, neurophysiology on the other, and aspects of human behavior,” Poggio says. “That means not only what algorithms does the brain use, but what are the circuits in the brain that implement these algorithms.”
Poggio has long believed that the brain must produce “invariant” representations of faces and other objects, meaning representations that are indifferent to objects’ orientation in space, their distance from the viewer, or their location in the visual field. Magnetic resonance scans of human and monkey brains suggested as much, but in 2010, Freiwald published a study describing the neuroanatomy of macaque monkeys’ face-recognition mechanism in much greater detail.
Freiwald showed that information from the monkey’s optic nerves passes through a series of brain locations, each of which is less sensitive to face orientation than the last. Neurons in the first region fire only in response to particular face orientations; neurons in the final region fire regardless of the face’s orientation — an invariant representation.
But neurons in an intermediate region appear to be “mirror symmetric”: That is, they’re sensitive to the angle of face rotation without respect to direction. In the first region, one cluster of neurons will fire if a face is rotated 45 degrees to the left, and a different cluster will fire if it’s rotated 45 degrees to the right. In the final region, the same cluster of neurons will fire whether the face is rotated 30 degrees, 45 degrees, 90 degrees, or anywhere in-between. But in the intermediate region, a particular cluster of neurons will fire if the face is rotated by 45 degrees in either direction, another if it’s rotated 30 degrees, and so on.
This is the behavior that the researchers’ machine-learning system reproduced. “It was not a model that was trying to explain mirror symmetry,” Poggio says. “This model was trying to explain invariance, and in the process, there is this other property that pops out.”
The researchers’ machine-learning system is a neural network, so called because it roughly approximates the architecture of the human brain. A neural network consists of very simple processing units, arranged into layers, that are densely connected to the processing units — or nodes — in the layers above and below. Data are fed into the bottom layer of the network, which processes them in some way and feeds them to the next layer, and so on. During training, the output of the top layer is correlated with some classification criterion — say, correctly determining whether a given image depicts a particular person.
In earlier work, Poggio’s group had trained neural networks to produce invariant representations by, essentially, memorizing a representative set of orientations for just a handful of faces, which Poggio calls “templates.” When the network was presented with a new face, it would measure its difference from these templates. That difference would be smallest for the templates whose orientations were the same as that of the new face, and the output of their associated nodes would end up dominating the information signal by the time it reached the top layer. The measured difference between the new face and the stored faces gives the new face a kind of identifying signature.
In experiments, this approach produced invariant representations: A face’s signature turned out to be roughly the same no matter its orientation. But the mechanism — memorizing templates — was not, Poggio says, biologically plausible.
So instead, the new network uses a variation on Hebb’s rule, which is often described in the neurological literature as “neurons that fire together wire together.” That means that during training, as the weights of the connections between nodes are being adjusted to produce more accurate outputs, nodes that react in concert to particular stimuli end up contributing more to the final output than nodes that react independently (or not at all).
This approach, too, ended up yielding invariant representations. But the middle layers of the network also duplicated the mirror-symmetric responses of the intermediate visual-processing regions of the primate brain.
“I think it’s a significant step forward,” says Christof Koch, president and chief scientific officer at the Allen Institute for Brain Science. “In this day and age, when everything is dominated by either big data or huge computer simulations, this shows you how a principled understanding of learning can explain some puzzling findings.”
“They’re very careful,” Koch adds. “They’re only looking at the feed-forward pathway — in other words, the first 80, 100 milliseconds. The monkey opens its eyes, and within 80 to 100 milliseconds, it can recognize a face and push a button signaling that. The question is what goes on in those 80 to 100 milliseconds, and the model that they have seems to explain that quite well.”
Learn more: How the brain recognizes faces
Technology could aid in elimination of malaria and treatment of many other diseases.
Researchers at MIT and Brigham and Women’s Hospital have developed a new drug capsule that remains in the stomach for up to two weeks after being swallowed, gradually releasing its drug payload. This type of drug delivery could replace inconvenient regimens that require repeated doses, which would help to overcome one of the major obstacles to treating and potentially eliminating diseases such as malaria.
In a study described in the Nov. 16 issue of Science Translational Medicine, the researchers used this approach to deliver a drug called ivermectin, which they believe could aid in malaria elimination efforts. However, this approach could be applicable to many other diseases, says Robert Langer, the David H. Koch Institute Professor at MIT and a member of MIT’s Koch Institute for Integrative Cancer Research.
“Until now, oral drugs would almost never last for more than a day,” Langer says. “This really opens the door to ultra-long-lasting oral systems, which could have an effect on all kinds of diseases, such as Alzheimer’s or mental health disorders. There are a lot of exciting things this could someday enable.”
Langer and Giovanni Traverso, a research affiliate at the Koch Institute and a gastroenterologist and biomedical engineer at Brigham and Women’s Hospital, are the senior authors of the paper. The paper’s lead authors are former MIT postdoc Andrew Bellinger, MIT postdoc Mousa Jafari, and former MIT postdocs Tyler Grant and Shiyi Zhang. The team also includes researchers from Harvard University, Imperial College London, and the Institute for Disease Modeling in Bellevue, Washington.
The research has led to the launching of Lyndra, a Cambridge-based company that is developing the technology with a focus on diseases for which patients would benefit the most from sustained drug delivery, including neuropsychiatric disorders, HIV, diabetes, and epilepsy.
Drugs taken orally tend to work for a limited time because they pass rapidly through the body and are exposed to harsh environments in the stomach and intestines. Langer’s lab has been working for several years to overcome this challenge, with an initial focus on malaria and ivermectin, which kills any mosquito that bites a person who is taking the drug. This can greatly reduce the transmission of malaria and other mosquito-borne illnesses.
The team envisions that long-term delivery of ivermectin could help with malaria elimination campaigns based on mass drug administration — the treatment of an entire population, whether infected or not, in an area where a disease is common. In this scenario, ivermectin would be paired with the antimalaria drug artemisinin.
“Getting patients to take medicine day after day after day is really challenging,” says Bellinger, now a cardiologist at Brigham and Women’s Hospital and chief scientific officer at Lyndra. “If the medicine could be effective for a long period of time, you could radically improve the efficacy of your mass drug administration campaigns.”
To achieve ultra-long-term delivery, drugs need to be packaged in a capsule that is stable enough to survive the harsh environment of the stomach and can release its contents over time. Once the drug is released, the capsule must break down and pass safely through the digestive tract.
Working with those criteria in mind, the team designed a star-shaped structure with six arms that can be folded inward and encased in a smooth capsule. Drug molecules are loaded into the arms, which are made of a rigid polymer called polycaprolactone. Each arm is attached to a rubber-like core by a linker that is designed to eventually break down.
After the capsule is swallowed, acid in the stomach dissolves the outer layer of the capsule, allowing the six arms to unfold. Once the star expands, it is large enough to stay in the stomach and resist the forces that would normally push an object further down the digestive tract. However, it is not large enough to cause any harmful blockage of the digestive tract.
“When the star opens up inside the stomach, it stays inside the stomach for the duration that you need,” says Grant, now a product development engineer at Lyndra.
In tests in pigs, the researchers confirmed that the drug is gradually released over two weeks. The linkers that join the arms to the core then dissolve, allowing the arms to break off. The pieces are small enough that they can pass harmlessly through the digestive tract.
“This is a platform into which you can incorporate any drug,” Jafari says. “This can be used with any drug that requires frequent dosing. We can replace that dosing with a single administration.”
This type of delivery could also help doctors to run better clinical trials by making it easier for patients to take the drugs, Zhang says. “It may help doctors and the pharma industry to better evaluate the efficacy of certain drugs, because currently a lot of patients in clinical trials have serious medication adherence problems that will mislead the clinical studies,” he says.
The new study includes mathematical modeling done by researchers at Imperial College London and the Institute for Disease Modeling to predict the potential impact of this approach. The models suggest that if this technology were used to deliver ivermectin along with antimalaria treatments to 70 percent of a population in a mass drug administration campaign, disease transmission could be reduced the same amount as if 90 percent were treated with antimalaria treatments alone.
“What we showed is that we stand to significantly amplify the effect of those campaigns,” Traverso says. “The introduction of this kind of system could have a substantial impact on the fight against malaria and transform clinical care in general by ensuring patients receive their medication.”
Peter Agre, director of the Johns Hopkins Malaria Research Institute, who was not involved in the research, described the new approach as a “remarkable” advance that could improve treatment of malaria and any other disease that requires long-term treatment.
“If you could reduce the frequency of dosing, and one treatment would continue to release medicine until the course is completed, that would be very beneficial,” Agre says.
Researchers led by Traverso are working on developing similar capsules to deliver drugs against other tropical diseases, as well as HIV and tuberculosis.
Learn more: New capsule achieves long-term drug delivery
Machine-learning system doesn’t require costly hand-annotated data.
In recent years, computers have gotten remarkably good at recognizing speech and images: Think of the dictation software on most cellphones, or the algorithms that automatically identify people in photos posted to Facebook.
But recognition of natural sounds — such as crowds cheering or waves crashing — has lagged behind. That’s because most automated recognition systems, whether they process audio or visual information, are the result of machine learning, in which computers search for patterns in huge compendia of training data. Usually, the training data has to be first annotated by hand, which is prohibitively expensive for all but the highest-demand applications.
Sound recognition may be catching up, however, thanks to researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). At the Neural Information Processing Systems conference next week, they will present a sound-recognition system that outperforms its predecessors but didn’t require hand-annotated data during training.
Instead, the researchers trained the system on video. First, existing computer vision systems that recognize scenes and objects categorized the images in the video. The new system then found correlations between those visual categories and natural sounds.
“Computer vision has gotten so good that we can transfer it to other domains,” says Carl Vondrick, an MIT graduate student in electrical engineering and computer science and one of the paper’s two first authors. “We’re capitalizing on the natural synchronization between vision and sound. We scale up with tons of unlabeled video to learn to understand sound.”
The researchers tested their system on two standard databases of annotated sound recordings, and it was between 13 and 15 percent more accurate than the best-performing previous system. On a data set with 10 different sound categories, it could categorize sounds with 92 percent accuracy, and on a data set with 50 categories it performed with 74 percent accuracy. On those same data sets, humans are 96 percent and 81 percent accurate, respectively.
“Even humans are ambiguous,” says Yusuf Aytar, the paper’s other first author and a postdoc in the lab of MIT professor of electrical engineering and computer science Antonio Torralba. Torralba is the final co-author on the paper.
“We did an experiment with Carl,” Aytar says. “Carl was looking at the computer monitor, and I couldn’t see it. He would play a recording and I would try to guess what it was. It turns out this is really, really hard. I could tell indoor from outdoor, basic guesses, but when it comes to the details — ‘Is it a restaurant?’ — those details are missing. Even for annotation purposes, the task is really hard.”
Because it takes far less power to collect and process audio data than it does to collect and process visual data, the researchers envision that a sound-recognition system could be used to improve the context sensitivity of mobile devices.
When coupled with GPS data, for instance, a sound-recognition system could determine that a cellphone user is in a movie theater and that the movie has started, and the phone could automatically route calls to a prerecorded outgoing message. Similarly, sound recognition could improve the situational awareness of autonomous robots.
“For instance, think of a self-driving car,” Aytar says. “There’s an ambulance coming, and the car doesn’t see it. If it hears it, it can make future predictions for the ambulance — which path it’s going to take — just purely based on sound.”
The researchers’ machine-learning system is a neural network, so called because its architecture loosely resembles that of the human brain. A neural net consists of processing nodes that, like individual neurons, can perform only rudimentary computations but are densely interconnected. Information — say, the pixel values of a digital image — is fed to the bottom layer of nodes, which processes it and feeds it to the next layer, which processes it and feeds it to the next layer, and so on. The training process continually modifies the settings of the individual nodes, until the output of the final layer reliably performs some classification of the data — say, identifying the objects in the image.
Vondrick, Aytar, and Torralba first trained a neural net on two large, annotated sets of images: one, the ImageNet data set, contains labeled examples of images of 1,000 different objects; the other, the Places data set created by Torralba’s group, contains labeled images of 401 different scene types, such as a playground, bedroom, or conference room.
Once the network was trained, the researchers fed it the video from 26 terabytes of video data downloaded from the photo-sharing site Flickr. “It’s about 2 million unique videos,” Vondrick says. “If you were to watch all of them back to back, it would take you about two years.” Then they trained a second neural network on the audio from the same videos. The second network’s goal was to correctly predict the object and scene tags produced by the first network.
The result was a network that could interpret natural sounds in terms of image categories. For instance, it might determine that the sound of birdsong tends to be associated with forest scenes and pictures of trees, birds, birdhouses, and bird feeders.
To compare the sound-recognition network’s performance to that of its predecessors, however, the researchers needed a way to translate its language of images into the familiar language of sound names. So they trained a simple machine-learning system to associate the outputs of the sound-recognition network with a set of standard sound labels.
For that, the researchers did use a database of annotated audio — one with 50 categories of sound and about 2,000 examples. Those annotations had been supplied by humans. But it’s much easier to label 2,000 examples than to label 2 million. And the MIT researchers’ network, trained first on unlabeled video, significantly outperformed all previous networks trained solely on the 2,000 labeled examples.
“With the modern machine-learning approaches, like deep learning, you have many, many trainable parameters in many layers in your neural-network system,” says Mark Plumbley, a professor of signal processing at the University of Surrey. “That normally means that you have to have many, many examples to train that on. And we have seen that sometimes there’s not enough data to be able to use a deep-learning system without some other help. Here the advantage is that they are using large amounts of other video information to train the network and then doing an additional step where they specialize the network for this particular task. That approach is very promising because it leverages this existing information from another field.”
Plumbley says that both he and colleagues at other institutions have been involved in efforts to commercialize sound recognition software for applications such as home security, where it might, for instance, respond to the sound of breaking glass. Other uses might include eldercare, to identify potentially alarming deviations from ordinary sound patterns, or to control sound pollution in urban areas. “I really think that there’s a lot of potential in the sound-recognition area,” he says.
A new study by an international team of researchers led by the University of Minnesota highlights how manipulation of 2D materials could make our modern day devices faster, smaller, and better.
The findings are now online and will be published in Nature Materials, a leading scientific journal of materials science and engineering research.
Two-dimensional materials are a class of nanomaterials that are only a few atoms in thickness. Electrons in these materials are free to move in the two-dimensional plane, but their restricted motion in the third direction is governed by quantum mechanics. Research on these nanomaterials is still in its infancy, but 2D materials such as graphene, transition metal dichalcogenides and black phosphorus have garnered tremendous attention from scientists and engineers for their amazing properties and potential to improve electronic and photonic devices.
In this study, researchers from the University of Minnesota, MIT, Stanford, U.S. Naval Research Laboratory, IBM, and universities in Brazil, UK and Spain, teamed up to examine the optical properties of several dozens of 2D materials. The goal of the paper is to unify understanding of light-matter interactions in these materials among researchers and explore new possibilities for future research.
They discuss how polaritons, a class of quasiparticles formed through the coupling of photons with electric charge dipoles in solid, allow researchers to marry the speed of photon light particles and the small size of electrons.
“With our devices, we want speed, efficiency, and we want small. Polaritons could offer the answer,” said Tony Low, a University of Minnesota electrical and computer engineering assistant professor and lead author of the study.
By exciting the polaritons in 2D materials, electromagnetic energy can be focused down to a volume a million times smaller compared to when its propagating in free space.
“Layered two-dimensional materials have emerged as a fantastic toolbox for nano-photonics and nano-optoelectronics, providing tailored design and tunability for properties that are not possible to realize with conventional materials,” said Frank Koppens, group leader at the Institute of Photonic Sciences at Barcelona, Spain, and co-author of the study. “This will offer tremendous opportunities for applications.”
Others on the team from private industry also recognize the potential in practical applications.
“The study of the plasmon-polaritons in two-dimensions is not only a fascinating research subject, but also offers possibilities for important technological applications,” said Phaedon Avoruris, IBM Fellow at the IBM T. J. Watson Research Center and co-author of the study. “For example, an atomic layer material like graphene extends the field of plasmonics to the infrared and terahertz regions of the electromagnetic spectrum allowing unique applications ranging from sensing and fingerprinting minute amounts of biomolecules, to applications in optical communications, energy harvesting and security imaging.”
The new study also examined the possibilities of combining 2D materials. Researchers point out that every 2D material has advantages and disadvantages. Combining these materials create new materials that may have the best qualities of both.
“Every time we look at a new material, we find something new,” Low said. “Graphene is often considered a ‘wonder’ material, but combining it with another material may make it even better for a wide variety of applications.”