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      in Energy

      Engineers enlist AI to help scale up advanced solar cell manufacturing

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      Perovskites are a family of materials that are currently the leading contender to potentially replace today’s silicon-based solar photovoltaics. They hold the promise of panels that are far thinner and lighter, that could be made with ultra-high throughput at room temperature instead of at hundreds of degrees, and that are cheaper and easier to transport and install. But bringing these materials from controlled laboratory experiments into a product that can be manufactured competitively has been a long struggle.

      Manufacturing perovskite-based solar cells involves optimizing at least a dozen or so variables at once, even within one particular manufacturing approach among many possibilities. But a new system based on a novel approach to machine learning could speed up the development of optimized production methods and help make the next generation of solar power a reality.

      The system, developed by researchers at MIT and Stanford University over the last few years, makes it possible to integrate data from prior experiments, and information based on personal observations by experienced workers, into the machine learning process. This makes the outcomes more accurate and has already led to the manufacturing of perovskite cells with an energy conversion efficiency of 18.5 percent, a competitive level for today’s market.

      The research is reported today in the journal Joule, in a paper by MIT professor of mechanical engineering Tonio Buonassisi, Stanford professor of materials science and engineering Reinhold Dauskardt, recent MIT research assistant Zhe Liu, Stanford doctoral graduate Nicholas Rolston, and three others.

      Perovskites are a group of layered crystalline compounds defined by the configuration of the atoms in their crystal lattice. There are thousands of such possible compounds and many different ways of making them. While most lab-scale development of perovskite materials uses a spin-coating technique, that’s not practical for larger-scale manufacturing, so companies and labs around the world have been searching for ways of translating these lab materials into a practical, manufacturable product.

      “There’s always a big challenge when you’re trying to take a lab-scale process and then transfer it to something like a startup or a manufacturing line,” says Rolston, who is now an assistant professor at Arizona State University. The team looked at a process that they felt had the greatest potential, a method called rapid spray plasma processing, or RSPP.

      The manufacturing process would involve a moving roll-to-roll surface, or series of sheets, on which the precursor solutions for the perovskite compound would be sprayed or ink-jetted as the sheet rolled by. The material would then move on to a curing stage, providing a rapid and continuous output “with throughputs that are higher than for any other photovoltaic technology,” Rolston says.

      “The real breakthrough with this platform is that it would allow us to scale in a way that no other material has allowed us to do,” he adds. “Even materials like silicon require a much longer timeframe because of the processing that’s done. Whereas you can think of [this approach as more] like spray painting.”

      Within that process, at least a dozen variables may affect the outcome, some of them more controllable than others. These include the composition of the starting materials, the temperature, the humidity, the speed of the processing path, the distance of the nozzle used to spray the material onto a substrate, and the methods of curing the material. Many of these factors can interact with each other, and if the process is in open air, then humidity, for example, may be uncontrolled. Evaluating all possible combinations of these variables through experimentation is impossible, so machine learning was needed to help guide the experimental process.

      But while most machine-learning systems use raw data such as measurements of the electrical and other properties of test samples, they don’t typically incorporate human experience such as qualitative observations made by the experimenters of the visual and other properties of the test samples, or information from other experiments reported by other researchers. So, the team found a way to incorporate such outside information into the machine learning model, using a probability factor based on a mathematical technique called Bayesian Optimization.

      Using the system, he says, “having a model that comes from experimental data, we can find out trends that we weren’t able to see before.” For example, they initially had trouble adjusting for uncontrolled variations in humidity in their ambient setting. But the model showed them “that we could overcome our humidity challenges by changing the temperature, for instance, and by changing some of the other knobs.”

      The system now allows experimenters to much more rapidly guide their process in order to optimize it for a given set of conditions or required outcomes. In their experiments, the team focused on optimizing the power output, but the system could also be used to simultaneously incorporate other criteria, such as cost and durability — something members of the team are continuing to work on, Buonassisi says.

      The researchers were encouraged by the Department of Energy, which sponsored the work, to commercialize the technology, and they’re currently focusing on tech transfer to existing perovskite manufacturers. “We are reaching out to companies now,” Buonassisi says, and the code they developed has been made freely available through an open-source server. “It’s now on GitHub, anyone can download it, anyone can run it,” he says. “We’re happy to help companies get started in using our code.”

      Already, several companies are gearing up to produce perovskite-based solar panels, even though they are still working out the details of how to produce them, says Liu, who is now at the Northwestern Polytechnical University in Xi’an, China. He says companies there are not yet doing large-scale manufacturing, but instead starting with smaller, high-value applications such as building-integrated solar tiles where appearance is important. Three of these companies “are on track or are being pushed by investors to manufacture 1 meter by 2-meter rectangular modules [comparable to today’s most common solar panels], within two years,” he says.

      ‘The problem is, they don’t have a consensus on what manufacturing technology to use,” Liu says. The RSPP method, developed at Stanford, “still has a good chance” to be competitive, he says. And the machine learning system the team developed could prove to be important in guiding the optimization of whatever process ends up being used.

      “The primary goal was to accelerate the process, so it required less time, less experiments, and less human hours to develop something that is usable right away, for free, for industry,” he says.

      “Existing work on machine-learning-driven perovskite PV fabrication largely focuses on spin-coating, a lab-scale technique,” says Ted Sargent, University Professor at the University of Toronto, who was not associated with this work, which he says demonstrates “a workflow that is readily adapted to the deposition techniques that dominate the thin-film industry. Only a handful of groups have the simultaneous expertise in engineering and computation to drive such advances.” Sargent adds that this approach “could be an exciting advance for the manufacture of a broader family of materials” including LEDs, other PV technologies, and graphene, “in short, any industry that uses some form of vapor or vacuum deposition.” 

      The team also included Austin Flick and Thomas Colburn at Stanford and Zekun Ren at the Singapore-MIT Alliance for Science and Technology (SMART). In addition to the Department of Energy, the work was supported by a fellowship from the MIT Energy Initiative, the Graduate Research Fellowship Program from the National Science Foundation, and the SMART program. More

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      in Energy

      A better way to separate gases

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      Industrial processes for chemical separations, including natural gas purification and the production of oxygen and nitrogen for medical or industrial uses, are collectively responsible for about 15 percent of the world’s energy use. They also contribute a corresponding amount to the world’s greenhouse gas emissions. Now, researchers at MIT and Stanford University have developed a new kind of membrane for carrying out these separation processes with roughly 1/10 the energy use and emissions.

      Using membranes for separation of chemicals is known to be much more efficient than processes such as distillation or absorption, but there has always been a tradeoff between permeability — how fast gases can penetrate through the material — and selectivity — the ability to let the desired molecules pass through while blocking all others. The new family of membrane materials, based on “hydrocarbon ladder” polymers, overcomes that tradeoff, providing both high permeability and extremely good selectivity, the researchers say.

      The findings are reported today in the journal Science, in a paper by Yan Xia, an associate professor of chemistry at Stanford; Zachary Smith, an assistant professor of chemical engineering at MIT; Ingo Pinnau, a professor at King Abdullah University of Science and Technology, and five others.

      Gas separation is an important and widespread industrial process whose uses include removing impurities and undesired compounds from natural gas or biogas, separating oxygen and nitrogen from air for medical and industrial purposes, separating carbon dioxide from other gases for carbon capture, and producing hydrogen for use as a carbon-free transportation fuel. The new ladder polymer membranes show promise for drastically improving the performance of such separation processes. For example, separating carbon dioxide from methane, these new membranes have five times the selectivity and 100 times the permeability of existing cellulosic membranes for that purpose. Similarly, they are 100 times more permeable and three times as selective for separating hydrogen gas from methane.

      The new type of polymers, developed over the last several years by the Xia lab, are referred to as ladder polymers because they are formed from double strands connected by rung-like bonds, and these linkages provide a high degree of rigidity and stability to the polymer material. These ladder polymers are synthesized via an efficient and selective chemistry the Xia lab developed called CANAL, an acronym for catalytic arene-norbornene annulation, which stitches readily available chemicals into ladder structures with hundreds or even thousands of rungs. The polymers are synthesized in a solution, where they form rigid and kinked ribbon-like strands that can easily be made into a thin sheet with sub-nanometer-scale pores by using industrially available polymer casting processes. The sizes of the resulting pores can be tuned through the choice of the specific hydrocarbon starting compounds. “This chemistry and choice of chemical building blocks allowed us to make very rigid ladder polymers with different configurations,” Xia says.

      To apply the CANAL polymers as selective membranes, the collaboration made use of Xia’s expertise in polymers and Smith’s specialization in membrane research. Holden Lai, a former Stanford doctoral student, carried out much of the development and exploration of how their structures impact gas permeation properties. “It took us eight years from developing the new chemistry to finding the right polymer structures that bestow the high separation performance,” Xia says.

      The Xia lab spent the past several years varying the structures of CANAL polymers to understand how their structures affect their separation performance. Surprisingly, they found that adding additional kinks to their original CANAL polymers significantly improved the mechanical robustness of their membranes and boosted their selectivity  for molecules of similar sizes, such as oxygen and nitrogen gases, without losing permeability of the more permeable gas. The selectivity actually improves as the material ages. The combination of high selectivity and high permeability makes these materials outperform all other polymer materials in many gas separations, the researchers say.

      Today, 15 percent of global energy use goes into chemical separations, and these separation processes are “often based on century-old technologies,” Smith says. “They work well, but they have an enormous carbon footprint and consume massive amounts of energy. The key challenge today is trying to replace these nonsustainable processes.” Most of these processes require high temperatures for boiling and reboiling solutions, and these often are the hardest processes to electrify, he adds.

      For the separation of oxygen and nitrogen from air, the two molecules only differ in size by about 0.18 angstroms (ten-billionths of a meter), he says. To make a filter capable of separating them efficiently “is incredibly difficult to do without decreasing throughput.” But the new ladder polymers, when manufactured into membranes produce tiny pores that achieve high selectivity, he says. In some cases, 10 oxygen molecules permeate for every nitrogen, despite the razor-thin sieve needed to access this type of size selectivity. These new membrane materials have “the highest combination of permeability and selectivity of all known polymeric materials for many applications,” Smith says.

      “Because CANAL polymers are strong and ductile, and because they are soluble in certain solvents, they could be scaled for industrial deployment within a few years,” he adds. An MIT spinoff company called Osmoses, led by authors of this study, recently won the MIT $100K entrepreneurship competition and has been partly funded by The Engine to commercialize the technology.

      There are a variety of potential applications for these materials in the chemical processing industry, Smith says, including the separation of carbon dioxide from other gas mixtures as a form of emissions reduction. Another possibility is the purification of biogas fuel made from agricultural waste products in order to provide carbon-free transportation fuel. Hydrogen separation for producing a fuel or a chemical feedstock, could also be carried out efficiently, helping with the transition to a hydrogen-based economy.

      The close-knit team of researchers is continuing to refine the process to facilitate the development from laboratory to industrial scale, and to better understand the details on how the macromolecular structures and packing result in the ultrahigh selectivity. Smith says he expects this platform technology to play a role in multiple decarbonization pathways, starting with hydrogen separation and carbon capture, because there is such a pressing need for these technologies in order to transition to a carbon-free economy.

      “These are impressive new structures that have outstanding gas separation performance,” says Ryan Lively, am associate professor of chemical and biomolecular engineering at Georgia Tech, who was not involved in this work. “Importantly, this performance is improved during membrane aging and when the membranes are challenged with concentrated gas mixtures. … If they can scale these materials and fabricate membrane modules, there is significant potential practical impact.”

      The research team also included Jun Myun Ahn and Ashley Robinson at Stanford, Francesco Benedetti at MIT, now the chief executive officer at Osmoses, and Yingge Wang at King Abdullah University of Science and Technology in Saudi Arabia. The work was supported by the Stanford Natural Gas Initiative, the Sloan Research Fellowship, the U.S. Department of Energy Office of Basic Energy Sciences, and the National Science Foundation. More

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      in Environment

      Microbes and minerals may have set off Earth’s oxygenation

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      For the first 2 billion years of Earth’s history, there was barely any oxygen in the air. While some microbes were photosynthesizing by the latter part of this period, oxygen had not yet accumulated at levels that would impact the global biosphere.

      But somewhere around 2.3 billion years ago, this stable, low-oxygen equilibrium shifted, and oxygen began building up in the atmosphere, eventually reaching the life-sustaining levels we breathe today. This rapid infusion is known as the Great Oxygenation Event, or GOE. What triggered the event and pulled the planet out of its low-oxygen funk is one of the great mysteries of science.

      A new hypothesis, proposed by MIT scientists, suggests that oxygen finally started accumulating in the atmosphere thanks to interactions between certain marine microbes and minerals in ocean sediments. These interactions helped prevent oxygen from being consumed, setting off a self-amplifying process where more and more oxygen was made available to accumulate in the atmosphere.

      The scientists have laid out their hypothesis using mathematical and evolutionary analyses, showing that there were indeed microbes that existed before the GOE and evolved the ability to interact with sediment in the way that the researchers have proposed.

      Their study, appearing today in Nature Communications, is the first to connect the co-evolution of microbes and minerals to Earth’s oxygenation.

      “Probably the most important biogeochemical change in the history of the planet was oxygenation of the atmosphere,” says study author Daniel Rothman, professor of geophysics in MIT’s Department of Earth, Atmospheric, and Planetary Sciences (EAPS). “We show how the interactions of microbes, minerals, and the geochemical environment acted in concert to increase oxygen in the atmosphere.”

      The study’s co-authors include lead author Haitao Shang, a former MIT graduate student, and Gregory Fournier, associate professor of geobiology in EAPS.

      A step up

      Today’s oxygen levels in the atmosphere are a stable balance between processes that produce oxygen and those that consume it. Prior to the GOE, the atmosphere maintained a different kind of equilibrium, with producers and consumers of oxygen  in balance, but in a way that didn’t leave much extra oxygen for the atmosphere.

      What could have pushed the planet out of one stable, oxygen-deficient state to another stable, oxygen-rich state?

      “If you look at Earth’s history, it appears there were two jumps, where you went from a steady state of low oxygen to a steady state of much higher oxygen, once in the Paleoproterozoic, once in the Neoproterozoic,” Fournier notes. “These jumps couldn’t have been because of a gradual increase in excess oxygen. There had to have been some feedback loop that caused this step-change in stability.”

      He and his colleagues wondered whether such a positive feedback loop could have come from a process in the ocean that made some organic carbon unavailable to its consumers. Organic carbon is mainly consumed through oxidation, usually accompanied by the consumption of oxygen — a process by which microbes in the ocean use oxygen to break down organic matter, such as detritus that has settled in sediment. The team wondered: Could there have been some process by which the presence of oxygen stimulated its further accumulation?

      Shang and Rothman worked out a mathematical model that made the following prediction: If microbes possessed the ability to only partially oxidize organic matter, the partially-oxidized matter, or “POOM,” would effectively become “sticky,” and chemically bind to minerals in sediment in a way that would protect the material from further oxidation. The oxygen that would otherwise have been consumed to fully degrade the material would instead be free to build up in the atmosphere. This process, they found, could serve as a positive feedback, providing a natural pump to push the atmosphere into a new, high-oxygen equilibrium.

      “That led us to ask, is there a microbial metabolism out there that produced POOM?” Fourier says.

      In the genes

      To answer this, the team searched through the scientific literature and identified a group of microbes that partially oxidizes organic matter in the deep ocean today. These microbes belong to the bacterial group SAR202, and their partial oxidation is carried out through an enzyme, Baeyer-Villiger monooxygenase, or BVMO.

      The team carried out a phylogenetic analysis to see how far back the microbe, and the gene for the enzyme, could be traced. They found that the bacteria did indeed have ancestors dating back before the GOE, and that the gene for the enzyme could be traced across various microbial species, as far back as pre-GOE times.

      What’s more, they found that the gene’s diversification, or the number of species that acquired the gene, increased significantly during times when the atmosphere experienced spikes in oxygenation, including once during the GOE’s Paleoproterozoic, and again in the Neoproterozoic.

      “We found some temporal correlations between diversification of POOM-producing genes, and the oxygen levels in the atmosphere,” Shang says. “That supports our overall theory.”

      To confirm this hypothesis will require far more follow-up, from experiments in the lab to surveys in the field, and everything in between. With their new study, the team has introduced a new suspect in the age-old case of what oxygenated Earth’s atmosphere.

      “Proposing a novel method, and showing evidence for its plausibility, is the first but important step,” Fournier says. “We’ve identified this as a theory worthy of study.”

      This work was supported in part by the mTerra Catalyst Fund and the National Science Foundation. More

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      in Environment

      Study: Ice flow is more sensitive to stress than previously thought

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      The rate of glacier ice flow is more sensitive to stress than previously calculated, according to a new study by MIT researchers that upends a decades-old equation used to describe ice flow.

      Stress in this case refers to the forces acting on Antarctic glaciers, which are primarily influenced by gravity that drags the ice down toward lower elevations. Viscous glacier ice flows “really similarly to honey,” explains Joanna Millstein, a PhD student in the Glacier Dynamics and Remote Sensing Group and lead author of the study. “If you squeeze honey in the center of a piece of toast, and it piles up there before oozing outward, that’s the exact same motion that’s happening for ice.”

      The revision to the equation proposed by Millstein and her colleagues should improve models for making predictions about the ice flow of glaciers. This could help glaciologists predict how Antarctic ice flow might contribute to future sea level rise, although Millstein said the equation change is unlikely to raise estimates of sea level rise beyond the maximum levels already predicted under climate change models.

      “Almost all our uncertainties about sea level rise coming from Antarctica have to do with the physics of ice flow, though, so this will hopefully be a constraint on that uncertainty,” she says.

      Other authors on the paper, published in Nature Communications Earth and Environment, include Brent Minchew, the Cecil and Ida Green Career Development Professor in MIT’s Department of Earth, Atmospheric, and Planetary Sciences, and Samuel Pegler, a university academic fellow at the University of Leeds.

      Benefits of big data

      The equation in question, called Glen’s Flow Law, is the most widely used equation to describe viscous ice flow. It was developed in 1958 by British scientist J.W. Glen, one of the few glaciologists working on the physics of ice flow in the 1950s, according to Millstein.

      With relatively few scientists working in the field until recently, along with the remoteness and inaccessibility of most large glacier ice sheets, there were few attempts to calibrate Glen’s Flow Law outside the lab until recently. In the recent study, Millstein and her colleagues took advantage of a new wealth of satellite imagery over Antarctic ice shelves, the floating extensions of the continent’s ice sheet, to revise the stress exponent of the flow law.

      “In 2002, this major ice shelf [Larsen B] collapsed in Antarctica, and all we have from that collapse is two satellite images that are a month apart,” she says. “Now, over that same area we can get [imagery] every six days.”

      The new analysis shows that “the ice flow in the most dynamic, fastest-changing regions of Antarctica — the ice shelves, which basically hold back and hug the interior of the continental ice — is more sensitive to stress than commonly assumed,” Millstein says. She’s optimistic that the growing record of satellite data will help capture rapid changes on Antarctica in the future, providing insights into the underlying physical processes of glaciers.   

      But stress isn’t the only thing that affects ice flow, the researchers note. Other parts of the flow law equation represent differences in temperature, ice grain size and orientation, and impurities and water contained in the ice — all of which can alter flow velocity. Factors like temperature could be especially important in understanding how ice flow impacts sea level rise in the future, Millstein says.

      Cracking under strain

      Millstein and colleagues are also studying the mechanics of ice sheet collapse, which involves different physical models than those used to understand the ice flow problem. “The cracking and breaking of ice is what we’re working on now, using strain rate observations,” Millstein says.

      The researchers use InSAR, radar images of the Earth’s surface collected by satellites, to observe deformations of the ice sheets that can be used to make precise measurements of strain. By observing areas of ice with high strain rates, they hope to better understand the rate at which crevasses and rifts propagate to trigger collapse.

      The research was supported by the National Science Foundation. More

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      in Environment

      Study reveals chemical link between wildfire smoke and ozone depletion

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      The Australian wildfires in 2019 and 2020 were historic for how far and fast they spread, and for how long and powerfully they burned. All told, the devastating “Black Summer” fires blazed across more than 43 million acres of land, and extinguished or displaced nearly 3 billion animals. The fires also injected over 1 million tons of smoke particles into the atmosphere, reaching up to 35 kilometers above Earth’s surface — a mass and reach comparable to that of an erupting volcano.

      Now, atmospheric chemists at MIT have found that the smoke from those fires set off chemical reactions in the stratosphere that contributed to the destruction of ozone, which shields the Earth from incoming ultraviolet radiation. The team’s study, appearing this week in the Proceedings of the National Academy of Sciences, is the first to establish a chemical link between wildfire smoke and ozone depletion.

      In March 2020, shortly after the fires subsided, the team observed a sharp drop in nitrogen dioxide in the stratosphere, which is the first step in a chemical cascade that is known to end in ozone depletion. The researchers found that this drop in nitrogen dioxide directly correlates with the amount of smoke that the fires released into the stratosphere. They estimate that this smoke-induced chemistry depleted the column of ozone by 1 percent.

      To put this in context, they note that the phaseout of ozone-depleting gases under a worldwide agreement to stop their production has led to about a 1 percent ozone recovery from earlier ozone decreases over the past 10 years — meaning that the wildfires canceled those hard-won diplomatic gains for a short period. If future wildfires grow stronger and more frequent, as they are predicted to do with climate change, ozone’s projected recovery could be delayed by years. 

      “The Australian fires look like the biggest event so far, but as the world continues to warm, there is every reason to think these fires will become more frequent and more intense,” says lead author Susan Solomon, the Lee and Geraldine Martin Professor of Environmental Studies at MIT. “It’s another wakeup call, just as the Antarctic ozone hole was, in the sense of showing how bad things could actually be.”

      The study’s co-authors include Kane Stone, a research scientist in MIT’s Department of Earth, Atmospheric, and Planetary Sciences, along with collaborators at multiple institutions including the University of Saskatchewan, Jinan University, the National Center for Atmospheric Research, and the University of Colorado at Boulder.

      Chemical trace

      Massive wildfires are known to generate pyrocumulonimbus — towering clouds of smoke that can reach into the stratosphere, the layer of the atmosphere that lies between about 15 and 50 kilometers above the Earth’s surface. The smoke from Australia’s wildfires reached well into the stratosphere, as high as 35 kilometers.

      In 2021, Solomon’s co-author, Pengfei Yu at Jinan University, carried out a separate study of the fires’ impacts and found that the accumulated smoke warmed parts of the stratosphere by as much as 2 degrees Celsius — a warming that persisted for six months. The study also found hints of ozone destruction in the Southern Hemisphere following the fires.

      Solomon wondered whether smoke from the fires could have depleted ozone through a chemistry similar to volcanic aerosols. Major volcanic eruptions can also reach into the stratosphere, and in 1989, Solomon discovered that the particles in these eruptions can destroy ozone through a series of chemical reactions. As the particles form in the atmosphere, they gather moisture on their surfaces. Once wet, the particles can react with circulating chemicals in the stratosphere, including dinitrogen pentoxide, which reacts with the particles to form nitric acid.

      Normally, dinitrogen pentoxide reacts with the sun to form various nitrogen species, including nitrogen dioxide, a compound that binds with chlorine-containing chemicals in the stratosphere. When volcanic smoke converts dinitrogen pentoxide into nitric acid, nitrogen dioxide drops, and the chlorine compounds take another path, morphing into chlorine monoxide, the main human-made agent that destroys ozone.

      “This chemistry, once you get past that point, is well-established,” Solomon says. “Once you have less nitrogen dioxide, you have to have more chlorine monoxide, and that will deplete ozone.”

      Cloud injection

      In the new study, Solomon and her colleagues looked at how concentrations of nitrogen dioxide in the stratosphere changed following the Australian fires. If these concentrations dropped significantly, it would signal that wildfire smoke depletes ozone through the same chemical reactions as some volcanic eruptions.

      The team looked to observations of nitrogen dioxide taken by three independent satellites that have surveyed the Southern Hemisphere for varying lengths of time. They compared each satellite’s record in the months and years leading up to and following the Australian fires. All three records showed a significant drop in nitrogen dioxide in March 2020. For one satellite’s record, the drop represented a record low among observations spanning the last 20 years.

      To check that the nitrogen dioxide decrease was a direct chemical effect of the fires’ smoke, the researchers carried out atmospheric simulations using a global, three-dimensional model that simulates hundreds of chemical reactions in the atmosphere, from the surface on up through the stratosphere.

      The team injected a cloud of smoke particles into the model, simulating what was observed from the Australian wildfires. They assumed that the particles, like volcanic aerosols, gathered moisture. They then ran the model multiple times and compared the results to simulations without the smoke cloud.

      In every simulation incorporating wildfire smoke, the team found that as the amount of smoke particles increased in the stratosphere, concentrations of nitrogen dioxide decreased, matching the observations of the three satellites.

      “The behavior we saw, of more and more aerosols, and less and less nitrogen dioxide, in both the model and the data, is a fantastic fingerprint,” Solomon says. “It’s the first time that science has established a chemical mechanism linking wildfire smoke to ozone depletion. It may only be one chemical mechanism among several, but it’s clearly there. It tells us these particles are wet and they had to have caused some ozone depletion.”

      She and her collaborators are looking into other reactions triggered by wildfire smoke that might further contribute to stripping ozone. For the time being, the major driver of ozone depletion remains chlorofluorocarbons, or CFCs — chemicals such as old refrigerants that have been banned under the Montreal Protocol, though they continue to linger in the stratosphere. But as global warming leads to stronger, more frequent wildfires, their smoke could have a serious, lasting impact on ozone.

      “Wildfire smoke is a toxic brew of organic compounds that are complex beasts,” Solomon says. “And I’m afraid ozone is getting pummeled by a whole series of reactions that we are now furiously working to unravel.”

      This research was supported in part by the National Science Foundation and NASA. More

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      in Security

      Solar-powered system offers a route to inexpensive desalination

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      An estimated two-thirds of humanity is affected by shortages of water, and many such areas in the developing world also face a lack of dependable electricity. Widespread research efforts have thus focused on ways to desalinate seawater or brackish water using just solar heat. Many such efforts have run into problems with fouling of equipment caused by salt buildup, however, which often adds complexity and expense.

      Now, a team of researchers at MIT and in China has come up with a solution to the problem of salt accumulation — and in the process developed a desalination system that is both more efficient and less expensive than previous solar desalination methods. The process could also be used to treat contaminated wastewater or to generate steam for sterilizing medical instruments, all without requiring any power source other than sunlight itself.

      The findings are described today in the journal Nature Communications, in a paper by MIT graduate student Lenan Zhang, postdoc Xiangyu Li, professor of mechanical engineering Evelyn Wang, and four others.

      “There have been a lot of demonstrations of really high-performing, salt-rejecting, solar-based evaporation designs of various devices,” Wang says. “The challenge has been the salt fouling issue, that people haven’t really addressed. So, we see these very attractive performance numbers, but they’re often limited because of longevity. Over time, things will foul.”

      Many attempts at solar desalination systems rely on some kind of wick to draw the saline water through the device, but these wicks are vulnerable to salt accumulation and relatively difficult to clean. The team focused on developing a wick-free system instead. The result is a layered system, with dark material at the top to absorb the sun’s heat, then a thin layer of water above a perforated layer of material, sitting atop a deep reservoir of the salty water such as a tank or a pond. After careful calculations and experiments, the researchers determined the optimal size for the holes drilled through the perforated material, which in their tests was made of polyurethane. At 2.5 millimeters across, these holes can be easily made using commonly available waterjets.

      The holes are large enough to allow for a natural convective circulation between the warmer upper layer of water and the colder reservoir below. That circulation naturally draws the salt from the thin layer above down into the much larger body of water below, where it becomes well-diluted and no longer a problem. “It allows us to achieve high performance and yet also prevent this salt accumulation,” says Wang, who is the Ford Professor of Engineering and head of the Department of Mechanical Engineering.

      Li says that the advantages of this system are “both the high performance and the reliable operation, especially under extreme conditions, where we can actually work with near-saturation saline water. And that means it’s also very useful for wastewater treatment.”

      He adds that much work on such solar-powered desalination has focused on novel materials. “But in our case, we use really low-cost, almost household materials.” The key was analyzing and understanding the convective flow that drives this entirely passive system, he says. “People say you always need new materials, expensive ones, or complicated structures or wicking structures to do that. And this is, I believe, the first one that does this without wicking structures.”

      This new approach “provides a promising and efficient path for desalination of high salinity solutions, and could be a game changer in solar water desalination,” says Hadi Ghasemi, a professor of chemical and biomolecular engineering at the University of Houston, who was not associated with this work. “Further work is required for assessment of this concept in large settings and in long runs,” he adds.

      Just as hot air rises and cold air falls, Zhang explains, natural convection drives the desalination process in this device. In the confined water layer near the top, “the evaporation happens at the very top interface. Because of the salt, the density of water at the very top interface is higher, and the bottom water has lower density. So, this is an original driving force for this natural convection because the higher density at the top drives the salty liquid to go down.” The water evaporated from the top of the system can then be collected on a condensing surface, providing pure fresh water.

      The rejection of salt to the water below could also cause heat to be lost in the process, so preventing that required careful engineering, including making the perforated layer out of highly insulating material to keep the heat concentrated above. The solar heating at the top is accomplished through a simple layer of black paint.

      This gif shows fluid flow visualized by food dye. The left-side shows the slow transport of colored de-ionized water from the top to the bottom bulk water. The right-side shows the fast transport of colored saline water from the top to the bottom bulk water driven by the natural convection effect.

      So far, the team has proven the concept using small benchtop devices, so the next step will be starting to scale up to devices that could have practical applications. Based on their calculations, a system with just 1 square meter (about a square yard) of collecting area should be sufficient to provide a family’s daily needs for drinking water, they say. Zhang says they calculated that the necessary materials for a 1-square-meter device would cost only about $4.

      Their test apparatus operated for a week with no signs of any salt accumulation, Li says. And the device is remarkably stable. “Even if we apply some extreme perturbation, like waves on the seawater or the lake,” where such a device could be installed as a floating platform, “it can return to its original equilibrium position very fast,” he says.

      The necessary work to translate this lab-scale proof of concept into workable commercial devices, and to improve the overall water production rate, should be possible within a few years, Zhang says. The first applications are likely to be providing safe water in remote off-grid locations, or for disaster relief after hurricanes, earthquakes, or other disruptions of normal water supplies.

      Zhang adds that “if we can concentrate the sunlight a little bit, we could use this passive device to generate high-temperature steam to do medical sterilization” for off-grid rural areas.

      “I think a real opportunity is the developing world,” Wang says. “I think that is where there’s most probable impact near-term, because of the simplicity of the design.” But, she adds, “if we really want to get it out there, we also need to work with the end users, to really be able to adopt the way we design it so that they’re willing to use it.”

      “This is a new strategy toward solving the salt accumulation problem in solar evaporation,” says Peng Wang, a professor at King Abdullah University of Science and Technology in Saudi Arabia, who was not associated with this research. “This elegant design will inspire new innovations in the design of advanced solar evaporators. The strategy is very promising due to its high energy efficiency, operation durability, and low cost, which contributes to low-cost and passive water desalination to produce fresh water from various source water with high salinity, e.g., seawater, brine, or brackish groundwater.”

      The team also included Yang Zhong, Arny Leroy, and Lin Zhao at MIT, and Zhenyuan Xu at Shanghai Jiao Tong University in China. The work was supported by the Singapore-MIT Alliance for Research and Technology, the U.S.-Egypt Science and Technology Joint Fund, and used facilities supported by the National Science Foundation. More

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      in Environment

      How marsh grass protects shorelines

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      Marsh plants, which are ubiquitous along the world’s shorelines, can play a major role in mitigating the damage to coastlines as sea levels rise and storm surges increase. Now, a new MIT study provides greater detail about how these protective benefits work under real-world conditions shaped by waves and currents.

      The study combined laboratory experiments using simulated plants in a large wave tank along with mathematical modeling. It appears in the journal Physical Review — Fluids, in a paper by former MIT visiting doctoral student Xiaoxia Zhang, now a postdoc at Dalian University of Technology, and professor of civil and environmental engineering Heidi Nepf.

      It’s already clear that coastal marsh plants provide significant protection from surges and devastating  storms. For example, it has been estimated that the damage caused by Hurricane Sandy was reduced by $625 million thanks to the damping of wave energy provided by extensive areas of marsh along the affected coasts. But the new MIT analysis incorporates details of plant morphology, such as the number and spacing of flexible leaves versus stiffer stems, and the complex interactions of currents and waves that may be coming from different directions.

      This level of detail could enable coastal restoration planners to determine the area of marsh needed to mitigate expected amounts of storm surge or sea-level rise, and to decide which types of plants to introduce to maximize protection.

      “When you go to a marsh, you often will see that the plants are arranged in zones,” says Nepf, who is the Donald and Martha Harleman Professor of Civil and Environmental Engineering. “Along the edge, you tend to have plants that are more flexible, because they are using their flexibility to reduce the wave forces they feel. In the next zone, the plants are a little more rigid and have a bit more leaves.”

      As the zones progress, the plants become stiffer, leafier, and more effective at absorbing wave energy thanks to their greater leaf area. The new modeling done in this research, which incorporated work with simulated plants in the 24-meter-long wave tank at MIT’s Parsons Lab, can enable coastal planners to take these kinds of details into account when planning protection, mitigation, or restoration projects.

      “If you put the stiffest plants at the edge, they might not survive, because they’re feeling very high wave forces. By describing why Mother Nature organizes plants in this way, we can hopefully design a more sustainable restoration,” Nepf says.

      Once established, the marsh plants provide a positive feedback cycle that helps to not only stabilize but also build up these delicate coastal lands, Zhang says. “After a few years, the marsh grasses start to trap and hold the sediment, and the elevation gets higher and higher, which might keep up with sea level rise,” she says.

      The new MIT analysis incorporates details of plant morphology, such as the number and spacing of flexible leaves versus stiffer stems, and the complex interactions of currents and waves that may be coming from different directions.

      Awareness of the protective effects of marshland has been growing, Nepf says. For example, the Netherlands has been restoring lost marshland outside the dikes that surround much of the nation’s agricultural land, finding that the marsh can protect the dikes from erosion; the marsh and dikes work together much more effectively than the dikes alone at preventing flooding.

      But most such efforts so far have been largely empirical, trial-and-error plans, Nepf says. Now, they could take advantage of this modeling to know just how much marshland with what types of plants would be needed to provide the desired level of protection.

      It also provides a more quantitative way to estimate the value provided by marshes, she says. “It could allow you to more accurately say, ‘40 meters of marsh will reduce waves this much and therefore will reduce overtopping of your levee by this much.’ Someone could use that to say, ‘I’m going to save this much money over the next 10 years if I reduce flooding by maintaining this marsh.’ It might help generate some political motivation for restoration efforts.”

      Nepf herself is already trying to get some of these findings included in coastal planning processes. She serves on a practitioner panel led by Chris Esposito of the Water Institute of the Gulf, which serves the storm-battered Louisiana coastline. “We’d like to get this work into the coatal simulations that are used for large-scale restoration and coastal planning,” she says.

      “Understanding the wave damping process in real vegetation wetlands is of critical value, as it is needed in the assessment of the coastal defense value of these wetlands,” says Zhan Hu, an associate professor of marine sciences at Sun Yat-Sen University, who was not associated with this work. “The challenge, however, lies in the quantitative representation of the wave damping process, in which many factors are at play, such as plant flexibility, morphology, and coexisting currents.”

      The new study, Hu says, “neatly combines experimental findings and analytical modeling to reveal the impact of each factor in the wave damping process. … Overall, this work is a solid step forward toward a more accurate assessment of wave damping capacity of real coastal wetlands, which is needed for science-based design and management of nature-based coastal protection.”

      The work was partly supported by the National Science Foundation and the China Scholarship Council.  More

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      in Security

      A robot that finds lost items

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      A busy commuter is ready to walk out the door, only to realize they’ve misplaced their keys and must search through piles of stuff to find them. Rapidly sifting through clutter, they wish they could figure out which pile was hiding the keys.

      Researchers at MIT have created a robotic system that can do just that. The system, RFusion, is a robotic arm with a camera and radio frequency (RF) antenna attached to its gripper. It fuses signals from the antenna with visual input from the camera to locate and retrieve an item, even if the item is buried under a pile and completely out of view.

      The RFusion prototype the researchers developed relies on RFID tags, which are cheap, battery-less tags that can be stuck to an item and reflect signals sent by an antenna. Because RF signals can travel through most surfaces (like the mound of dirty laundry that may be obscuring the keys), RFusion is able to locate a tagged item within a pile.

      Using machine learning, the robotic arm automatically zeroes-in on the object’s exact location, moves the items on top of it, grasps the object, and verifies that it picked up the right thing. The camera, antenna, robotic arm, and AI are fully integrated, so RFusion can work in any environment without requiring a special set up.

      While finding lost keys is helpful, RFusion could have many broader applications in the future, like sorting through piles to fulfill orders in a warehouse, identifying and installing components in an auto manufacturing plant, or helping an elderly individual perform daily tasks in the home, though the current prototype isn’t quite fast enough yet for these uses.

      “This idea of being able to find items in a chaotic world is an open problem that we’ve been working on for a few years. Having robots that are able to search for things under a pile is a growing need in industry today. Right now, you can think of this as a Roomba on steroids, but in the near term, this could have a lot of applications in manufacturing and warehouse environments,” said senior author Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science and director of the Signal Kinetics group in the MIT Media Lab.

      Co-authors include research assistant Tara Boroushaki, the lead author; electrical engineering and computer science graduate student Isaac Perper; research associate Mergen Nachin; and Alberto Rodriguez, the Class of 1957 Associate Professor in the Department of Mechanical Engineering. The research will be presented at the Association for Computing Machinery Conference on Embedded Networked Senor Systems next month.

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      Sending signals

      RFusion begins searching for an object using its antenna, which bounces signals off the RFID tag (like sunlight being reflected off a mirror) to identify a spherical area in which the tag is located. It combines that sphere with the camera input, which narrows down the object’s location. For instance, the item can’t be located on an area of a table that is empty.

      But once the robot has a general idea of where the item is, it would need to swing its arm widely around the room taking additional measurements to come up with the exact location, which is slow and inefficient.

      The researchers used reinforcement learning to train a neural network that can optimize the robot’s trajectory to the object. In reinforcement learning, the algorithm is trained through trial and error with a reward system.

      “This is also how our brain learns. We get rewarded from our teachers, from our parents, from a computer game, etc. The same thing happens in reinforcement learning. We let the agent make mistakes or do something right and then we punish or reward the network. This is how the network learns something that is really hard for it to model,” Boroushaki explains.

      In the case of RFusion, the optimization algorithm was rewarded when it limited the number of moves it had to make to localize the item and the distance it had to travel to pick it up.

      Once the system identifies the exact right spot, the neural network uses combined RF and visual information to predict how the robotic arm should grasp the object, including the angle of the hand and the width of the gripper, and whether it must remove other items first. It also scans the item’s tag one last time to make sure it picked up the right object.

      Cutting through clutter

      The researchers tested RFusion in several different environments. They buried a keychain in a box full of clutter and hid a remote control under a pile of items on a couch.

      But if they fed all the camera data and RF measurements to the reinforcement learning algorithm, it would have overwhelmed the system. So, drawing on the method a GPS uses to consolidate data from satellites, they summarized the RF measurements and limited the visual data to the area right in front of the robot.

      Their approach worked well — RFusion had a 96 percent success rate when retrieving objects that were fully hidden under a pile.

      “Sometimes, if you only rely on RF measurements, there is going to be an outlier, and if you rely only on vision, there is sometimes going to be a mistake from the camera. But if you combine them, they are going to correct each other. That is what made the system so robust,” Boroushaki says.

      In the future, the researchers hope to increase the speed of the system so it can move smoothly, rather than stopping periodically to take measurements. This would enable RFusion to be deployed in a fast-paced manufacturing or warehouse setting.

      Beyond its potential industrial uses, a system like this could even be incorporated into future smart homes to assist people with any number of household tasks, Boroushaki says.

      “Every year, billions of RFID tags are used to identify objects in today’s complex supply chains, including clothing and lots of other consumer goods. The RFusion approach points the way to autonomous robots that can dig through a pile of mixed items and sort them out using the data stored in the RFID tags, much more efficiently than having to inspect each item individually, especially when the items look similar to a computer vision system,” says Matthew S. Reynolds, CoMotion Presidential Innovation Fellow and associate professor of electrical and computer engineering at the University of Washington, who was not involved in the research. “The RFusion approach is a great step forward for robotics operating in complex supply chains where identifying and ‘picking’ the right item quickly and accurately is the key to getting orders fulfilled on time and keeping demanding customers happy.”

      The research is sponsored by the National Science Foundation, a Sloan Research Fellowship, NTT DATA, Toppan, Toppan Forms, and the Abdul Latif Jameel Water and Food Systems Lab. More