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      in Security

      These neurons have food on the brain

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      A gooey slice of pizza. A pile of crispy French fries. Ice cream dripping down a cone on a hot summer day. When you look at any of these foods, a specialized part of your visual cortex lights up, according to a new study from MIT neuroscientists.

      This newly discovered population of food-responsive neurons is located in the ventral visual stream, alongside populations that respond specifically to faces, bodies, places, and words. The unexpected finding may reflect the special significance of food in human culture, the researchers say. 

      “Food is central to human social interactions and cultural practices. It’s not just sustenance,” says Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience and a member of MIT’s McGovern Institute for Brain Research and Center for Brains, Minds, and Machines. “Food is core to so many elements of our cultural identity, religious practice, and social interactions, and many other things that humans do.”

      The findings, based on an analysis of a large public database of human brain responses to a set of 10,000 images, raise many additional questions about how and why this neural population develops. In future studies, the researchers hope to explore how people’s responses to certain foods might differ depending on their likes and dislikes, or their familiarity with certain types of food.

      MIT postdoc Meenakshi Khosla is the lead author of the paper, along with MIT research scientist N. Apurva Ratan Murty. The study appears today in the journal Current Biology.

      Visual categories

      More than 20 years ago, while studying the ventral visual stream, the part of the brain that recognizes objects, Kanwisher discovered cortical regions that respond selectively to faces. Later, she and other scientists discovered other regions that respond selectively to places, bodies, or words. Most of those areas were discovered when researchers specifically set out to look for them. However, that hypothesis-driven approach can limit what you end up finding, Kanwisher says.

      “There could be other things that we might not think to look for,” she says. “And even when we find something, how do we know that that’s actually part of the basic dominant structure of that pathway, and not something we found just because we were looking for it?”

      To try to uncover the fundamental structure of the ventral visual stream, Kanwisher and Khosla decided to analyze a large, publicly available dataset of full-brain functional magnetic resonance imaging (fMRI) responses from eight human subjects as they viewed thousands of images.

      “We wanted to see when we apply a data-driven, hypothesis-free strategy, what kinds of selectivities pop up, and whether those are consistent with what had been discovered before. A second goal was to see if we could discover novel selectivities that either haven’t been hypothesized before, or that have remained hidden due to the lower spatial resolution of fMRI data,” Khosla says.

      To do that, the researchers applied a mathematical method that allows them to discover neural populations that can’t be identified from traditional fMRI data. An fMRI image is made up of many voxels — three-dimensional units that represent a cube of brain tissue. Each voxel contains hundreds of thousands of neurons, and if some of those neurons belong to smaller populations that respond to one type of visual input, their responses may be drowned out by other populations within the same voxel.

      The new analytical method, which Kanwisher’s lab has previously used on fMRI data from the auditory cortex, can tease out responses of neural populations within each voxel of fMRI data.

      Using this approach, the researchers found four populations that corresponded to previously identified clusters that respond to faces, places, bodies, and words. “That tells us that this method works, and it tells us that the things that we found before are not just obscure properties of that pathway, but major, dominant properties,” Kanwisher says.

      Intriguingly, a fifth population also emerged, and this one appeared to be selective for images of food.

      “We were first quite puzzled by this because food is not a visually homogenous category,” Khosla says. “Things like apples and corn and pasta all look so unlike each other, yet we found a single population that responds similarly to all these diverse food items.”

      The food-specific population, which the researchers call the ventral food component (VFC), appears to be spread across two clusters of neurons, located on either side of the FFA. The fact that the food-specific populations are spread out between other category-specific populations may help explain why they have not been seen before, the researchers say.

      “We think that food selectivity had been harder to characterize before because the populations that are selective for food are intermingled with other nearby populations that have distinct responses to other stimulus attributes. The low spatial resolution of fMRI prevents us from seeing this selectivity because the responses of different neural population get mixed in a voxel,” Khosla says.

      “The technique which the researchers used to identify category-sensitive cells or areas is impressive, and it recovered known category-sensitive systems, making the food category findings most impressive,” says Paul Rozin, a professor of psychology at the University of Pennsylvania, who was not involved in the study. “I can’t imagine a way for the brain to reliably identify the diversity of foods based on sensory features. That makes this all the more fascinating, and likely to clue us in about something really new.”

      Food vs non-food

      The researchers also used the data to train a computational model of the VFC, based on previous models Murty had developed for the brain’s face and place recognition areas. This allowed the researchers to run additional experiments and predict the responses of the VFC. In one experiment, they fed the model matched images of food and non-food items that looked very similar — for example, a banana and a yellow crescent moon.

      “Those matched stimuli have very similar visual properties, but the main attribute in which they differ is edible versus inedible,” Khosla says. “We could feed those arbitrary stimuli through the predictive model and see whether it would still respond more to food than non-food, without having to collect the fMRI data.”

      They could also use the computational model to analyze much larger datasets, consisting of millions of images. Those simulations helped to confirm that the VFC is highly selective for images of food.

      From their analysis of the human fMRI data, the researchers found that in some subjects, the VFC responded slightly more to processed foods such as pizza than unprocessed foods like apples. In the future they hope to explore how factors such as familiarity and like or dislike of a particular food might affect individuals’ responses to that food.

      They also hope to study when and how this region becomes specialized during early childhood, and what other parts of the brain it communicates with. Another question is whether this food-selective population will be seen in other animals such as monkeys, who do not attach the cultural significance to food that humans do.

      The research was funded by the National Institutes of Health, the National Eye Institute, and the National Science Foundation through the MIT Center for Brains, Minds, and Machines. More

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      in Energy

      A better way to quantify radiation damage in materials

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      It was just a piece of junk sitting in the back of a lab at the MIT Nuclear Reactor facility, ready to be disposed of. But it became the key to demonstrating a more comprehensive way of detecting atomic-level structural damage in materials — an approach that will aid the development of new materials, and could potentially support the ongoing operation of carbon-emission-free nuclear power plants, which would help alleviate global climate change.

      A tiny titanium nut that had been removed from inside the reactor was just the kind of material needed to prove that this new technique, developed at MIT and at other institutions, provides a way to probe defects created inside materials, including those that have been exposed to radiation, with five times greater sensitivity than existing methods.

      The new approach revealed that much of the damage that takes place inside reactors is at the atomic scale, and as a result is difficult to detect using existing methods. The technique provides a way to directly measure this damage through the way it changes with temperature. And it could be used to measure samples from the currently operating fleet of nuclear reactors, potentially enabling the continued safe operation of plants far beyond their presently licensed lifetimes.

      The findings are reported today in the journal Science Advances in a paper by MIT research specialist and recent graduate Charles Hirst PhD ’22; MIT professors Michael Short, Scott Kemp, and Ju Li; and five others at the University of Helsinki, the Idaho National Laboratory, and the University of California at Irvine.

      Rather than directly observing the physical structure of a material in question, the new approach looks at the amount of energy stored within that structure. Any disruption to the orderly structure of atoms within the material, such as that caused by radiation exposure or by mechanical stresses, actually imparts excess energy to the material. By observing and quantifying that energy difference, it’s possible to calculate the total amount of damage within the material — even if that damage is in the form of atomic-scale defects that are too small to be imaged with microscopes or other detection methods.

      The principle behind this method had been worked out in detail through calculations and simulations. But it was the actual tests on that one titanium nut from the MIT nuclear reactor that provided the proof — and thus opened the door to a new way of measuring damage in materials.

      The method they used is called differential scanning calorimetry. As Hirst explains, this is similar in principle to the calorimetry experiments many students carry out in high school chemistry classes, where they measure how much energy it takes to raise the temperature of a gram of water by one degree. The system the researchers used was “fundamentally the exact same thing, measuring energetic changes. … I like to call it just a fancy furnace with a thermocouple inside.”

      The scanning part has to do with gradually raising the temperature a bit at a time and seeing how the sample responds, and the differential part refers to the fact that two identical chambers are measured at once, one empty, and one containing the sample being studied. The difference between the two reveals details of the energy of the sample, Hirst explains.

      “We raise the temperature from room temperature up to 600 degrees Celsius, at a constant rate of 50 degrees per minute,” he says. Compared to the empty vessel, “your material will naturally lag behind because you need energy to heat your material. But if there are changes in the energy inside the material, that will change the temperature. In our case, there was an energy release when the defects recombine, and then it will get a little bit of a head start on the furnace … and that’s how we are measuring the energy in our sample.”

      Hirst, who carried out the work over a five-year span as his doctoral thesis project, found that contrary to what had been believed, the irradiated material showed that there were two different mechanisms involved in the relaxation of defects in titanium at the studied temperatures, revealed by two separate peaks in calorimetry. “Instead of one process occurring, we clearly saw two, and each of them corresponds to a different reaction that’s happening in the material,” he says.

      They also found that textbook explanations of how radiation damage behaves with temperature weren’t accurate, because previous tests had mostly been carried out at extremely low temperatures and then extrapolated to the higher temperatures of real-life reactor operations. “People weren’t necessarily aware that they were extrapolating, even though they were, completely,” Hirst says.

      “The fact is that our common-knowledge basis for how radiation damage evolves is based on extremely low-temperature electron radiation,” adds Short. “It just became the accepted model, and that’s what’s taught in all the books. It took us a while to realize that our general understanding was based on a very specific condition, designed to elucidate science, but generally not applicable to conditions in which we actually want to use these materials.”

      Now, the new method can be applied “to materials plucked from existing reactors, to learn more about how they are degrading with operation,” Hirst says.

      “The single biggest thing the world can do in order to get cheap, carbon-free power is to keep current reactors on the grid. They’re already paid for, they’re working,” Short adds.  But to make that possible, “the only way we can keep them on the grid is to have more certainty that they will continue to work well.” And that’s where this new way of assessing damage comes into play.

      While most nuclear power plants have been licensed for 40 to 60 years of operation, “we’re now talking about running those same assets out to 100 years, and that depends almost fully on the materials being able to withstand the most severe accidents,” Short says. Using this new method, “we can inspect them and take them out before something unexpected happens.”

      In practice, plant operators could remove a tiny sample of material from critical areas of the reactor, and analyze it to get a more complete picture of the condition of the overall reactor. Keeping existing reactors running is “the single biggest thing we can do to keep the share of carbon-free power high,” Short stresses. “This is one way we think we can do that.”

      Sergei Dudarev, a fellow at the United Kingdom Atomic Energy Authority who was not associated with this work, says this “is likely going to be impactful, as it confirms, in a nice systematic manner, supported both by experiment and simulations, the unexpectedly significant part played by the small invisible defects in microstructural evolution of materials exposed to irradiation.”

      The process is not just limited to the study of metals, nor is it limited to damage caused by radiation, the researchers say. In principle, the method could be used to measure other kinds of defects in materials, such as those caused by stresses or shockwaves, and it could be applied to materials such as ceramics or semiconductors as well.

      In fact, Short says, metals are the most difficult materials to measure with this method, and early on other researchers kept asking why this team was focused on damage to metals. That was partly because reactor components tend to be made of metal, and also because “It’s the hardest, so, if we crack this problem, we have a tool to crack them all!”

      Measuring defects in other kinds of materials can be up to 10,000 times easier than in metals, he says. “If we can do this with metals, we can make this extremely, ubiquitously applicable.” And all of it enabled by a small piece of junk that was sitting at the back of a lab.

      The research team included Fredric Granberg and Kai Nordlund at the University of Helsinki in Finland; Boopathy Kombaiah and Scott Middlemas at Idaho National Laboratory; and Penghui Cao at the University of California at Irvine. The work was supported by the U.S. National Science Foundation, an Idaho National Laboratory research grant, and a Euratom Research and Training program grant. More

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      in Security

      Structures considered key to gene expression are surprisingly fleeting

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      In human chromosomes, DNA is coated by proteins to form an exceedingly long beaded string. This “string” is folded into numerous loops, which are believed to help cells control gene expression and facilitate DNA repair, among other functions. A new study from MIT suggests that these loops are very dynamic and shorter-lived than previously thought.

      In the new study, the researchers were able to monitor the movement of one stretch of the genome in a living cell for about two hours. They saw that this stretch was fully looped for only 3 to 6 percent of the time, with the loop lasting for only about 10 to 30 minutes. The findings suggest that scientists’ current understanding of how loops influence gene expression may need to be revised, the researchers say.

      “Many models in the field have been these pictures of static loops regulating these processes. What our new paper shows is that this picture is not really correct,” says Anders Sejr Hansen, the Underwood-Prescott Career Development Assistant Professor of Biological Engineering at MIT. “We suggest that the functional state of these domains is much more dynamic.”

      Hansen is one of the senior authors of the new study, along with Leonid Mirny, a professor in MIT’s Institute for Medical Engineering and Science and the Department of Physics, and Christoph Zechner, a group leader at the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, Germany, and the Center for Systems Biology Dresden. MIT postdoc Michele Gabriele, recent Harvard University PhD recipient Hugo Brandão, and MIT graduate student Simon Grosse-Holz are the lead authors of the paper, which appears today in Science.

      Out of the loop

      Using computer simulations and experimental data, scientists including Mirny’s group at MIT have shown that loops in the genome are formed by a process called extrusion, in which a molecular motor promotes the growth of progressively larger loops. The motor stops each time it encounters a “stop sign” on DNA. The motor that extrudes such loops is a protein complex called cohesin, while the DNA-bound protein CTCF serves as the stop sign. These cohesin-mediated loops between CTCF sites were seen in previous experiments.

      However, those experiments only offered a snapshot of a moment in time, with no information on how the loops change over time. In their new study, the researchers developed techniques that allowed them to fluorescently label CTCF DNA sites so they could image the DNA loops over several hours. They also created a new computational method that can infer the looping events from the imaging data.

      “This method was crucial for us to distinguish signal from noise in our experimental data and quantify looping,” Zechner says. “We believe that such approaches will become increasingly important for biology as we continue to push the limits of detection with experiments.”

      The researchers used their method to image a stretch of the genome in mouse embryonic stem cells. “If we put our data in the context of one cell division cycle, which lasts about 12 hours, the fully formed loop only actually exists for about 20 to 45 minutes, or about 3 to 6 percent of the time,” Grosse-Holz says.

      “If the loop is only present for such a tiny period of the cell cycle and very short-lived, we shouldn’t think of this fully looped state as being the primary regulator of gene expression,” Hansen says. “We think we need new models for how the 3D structure of the genome regulates gene expression, DNA repair, and other functional downstream processes.”

      While fully formed loops were rare, the researchers found that partially extruded loops were present about 92 percent of the time. These smaller loops have been difficult to observe with the previous methods of detecting loops in the genome.

      “In this study, by integrating our experimental data with polymer simulations, we have now been able to quantify the relative extents of the unlooped, partially extruded, and fully looped states,” Brandão says.

      “Since these interactions are very short, but very frequent, the previous methodologies were not able to fully capture their dynamics,” Gabriele adds. “With our new technique, we can start to resolve transitions between fully looped and unlooped states.”

      Play video

      The researchers hypothesize that these partial loops may play more important roles in gene regulation than fully formed loops. Strands of DNA run along each other as loops begin to form and then fall apart, and these interactions may help regulatory elements such as enhancers and gene promoters find each other.

      “More than 90 percent of the time, there are some transient loops, and presumably what’s important is having those loops that are being perpetually extruded,” Mirny says. “The process of extrusion itself may be more important than the fully looped state that only occurs for a short period of time.”

      More loops to study

      Since most of the other loops in the genome are weaker than the one the researchers studied in this paper, they suspect that many other loops will also prove to be highly transient. They now plan to use their new technique study some of those other loops, in a variety of cell types.

      “There are about 10,000 of these loops, and we’ve looked at one,” Hansen says. “We have a lot of indirect evidence to suggest that the results would be generalizable, but we haven’t demonstrated that. Using the technology platform we’ve set up, which combines new experimental and computational methods, we can begin to approach other loops in the genome.”

      The researchers also plan to investigate the role of specific loops in disease. Many diseases, including a neurodevelopmental disorder called FOXG1 syndrome, could be linked to faulty loop dynamics. The researchers are now studying how both the normal and mutated form of the FOXG1 gene, as well as the cancer-causing gene MYC, are affected by genome loop formation.

      The research was funded by the National Institutes of Health, the National Science Foundation, the Mathers Foundation, a Pew-Stewart Cancer Research Scholar grant, the Chaires d’excellence Internationale Blaise Pascal, an American-Italian Cancer Foundation research scholarship, and the Max Planck Institute for Molecular Cell Biology and Genetics. More

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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