Brain circuit keeps tabs on what just happened to aid judgment of what’s happening now

A brain must constantly cope with the highly variable, fast-paced nature of the world when trying to judge what’s going on around it. On one hand, it has to be open to whatever new sensory information may come its way, but on the other hand, just to keep up, it has to try to leverage prior experience to make predictions about what seems to be happening. 

In a new study published in Science, MIT neuroscientists identify a circuit that links a sensory decision-making region with one that advises it on how much sensory information just changed.

“This circuit organizes a comparison between what has just happened versus what is happening now in the sensory world in a manner that can be used to act,” says study senior author Mriganka Sur, Newton Professor in The Picower Institute for Learning and Memory and MIT’s Department of Brain and Cognitive Sciences.

Study lead author Ning Leow Phd ’23, a former graduate student in Sur’s lab who is now a postdoc at A*STAR in Singapore, says the study in mice sheds light on closely analogous circuitry in humans, in which an area of the prefrontal cortex (the anterior cingulate cortex, or ACC) makes sensory decisions. The new study shows it bases those decisions on advice about immediate past history from an area of the thalamus called the pulvinar (though in mice, it’s called the lateral posterior thalamus, or LP).

“The brain does not evaluate each new event from scratch,” Leow says. “The pulvinar has traditionally been studied for its role in attention and filtering visual information, but we found that it was also important for comparing present information with the immediate past and highlighting meaningful changes to influence whether we maintain or update a decision.”

As part of Sur’s long-standing interest in how the brain’s cortex integrates sensory perception and learning to produce behavior, Leow and Sur began comprehensively mapping the copious inputs to the LP-ACC circuit, culminating in a paper in 2022. It was clear from that study how the circuit would seem well-positioned to help focus attention, which is what it was known for at the time.

But in thinking more deeply about what focused attention is for, and about how these well-connected regions seemed to sit at the center of not only attention but also perception and action, Sur and Leow hypothesized that they might also have a hand in guiding decisions based on sensory information. The new study presents multiple lines of evidence that it does.

The findings not only shed light on a fundamental function of the brain, Sur says, but could also be applicable to studies of autism, in which many patients show significant differences in the predictions they make about the sensory world. Often, this manifests as difficulty filtering out stimuli that neurotypical people are able to regard as recurring, and therefore mundane.

Which way?

To conduct the study, the researchers trained lab mice to play a video game in which dots on a screen would drift around, but at least some would move together in the same direction (left or right). In each trial, the mice had to discern that trend. From one trial to the next, then, the sensory cue could vary not only by the direction of movement, but also by how what proportion of dots were participating. For instance, on one trial maybe 64 percent of the dots would move left and on the next trial maybe 16 percent of the dots would move right. In this way, the researchers could measure a whole continuum of differences from one trial to the next. 

Meanwhile, as mice played the game, the scientists used a two-photon microscope to record the activity of the LP-ACC circuit and the response of neurons in the ACC. In some experiments, they used a technique called optogenetics to artificially activate the circuit.

By tracking how mice performed the task trial after trial, the researchers were able to see that the mice indeed factored in not only what they were seeing in the moment, but also what they had just seen previously. For instance, when mice guessed right, they were very likely to repeat their guess if the new cue was very similar to the prior one, and very unlikely to if the cue was very different. But if they guessed wrong, then the opposite was true: They wouldn’t repeat that decision if the cue was similar to the last, but would if it looked very different.

Looking in the brain

Of course, behavioral observations only indicated that the mice indeed compared new cues to prior ones. Determining whether that was indeed because of the LP-ACC circuit required the researchers to use optogenetics to perturb it (by stimulating extra activity in the LP’s inputs into the ACC). For instance, optogenetic perturbation of the circuit in the left brain hemisphere made mice less likely to guess that dots were moving right, and perturbation in the right hemisphere made mice more likely to guess dots were moving to the right. But in both cases, the extent of these deviations from normal behavior was directly proportional to the difference between the current cue and the previous one. In other words, perturbing the circuit disrupted how mice used recent sensory history when evaluating new evidence, Leow says.

“That showed the pathway is causally involved in the comparison process that influences how current evidence is interpreted, rather than merely carrying the information,” Leow says.

Moreover, using the microscope imaging (which visualizes calcium levels in neurons, a close proxy of the electrical activity), the researchers extensively analyzed the activity patterns of the LP input into the ACC and how ACC neurons reacted to that input.

“The main takeaway is that the LP and ACC were performing different jobs,” Leow says. “The pulvinar doesn’t appear to be making the decision itself. Instead, it sends that history-referenced sensory comparison to the frontal cortex. The ACC then transforms that information into the neural activity that predicts the animal’s final choice.”

Essentially, the pulvinar advises the ACC on the degree of change so that the frontal cortex can consider whether it’s time to change a guess. After all, if a mouse is guessing right and little is changing, why not keep on trucking? But if there’s a big change, then it might make sense for the mouse to re-evaluate what it’s thinking.

It turns out, the brain has this dedicated circuit for doing so.

In addition to Leow and Sur, the paper’s other authors are Arundhati Natesan, Alexandria Barlowe, Sofie Ährlund-Richter, Tianyu (Cindy) Luo, and Mehrdad Jazayeri.

The National Institutes of Health, a MURI grant, the Simons Foundation Autism Research Initiative, A*STAR, and the Freedom Together Foundation funded the research.

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Meteorite dust holds records of magnetism that may have helped form the sun

Around 4.6 billion years ago, the solar system was little more than a giant ball of gas and dust. Over the next few million years, this “solar nebula” underwent a huge transformation, flattening into a disk of matter that then condensed to form the central sun and orbiting planets. 

Scientists have assumed that the early solar system was shaped mainly through gravity. But a new study finds that magnetism also likely played a role. 

MIT scientists have discovered records of ancient magnetism in the oldest samples of meteorites known today. The team analyzed microscopic grains embedded in a meteorite that was discovered in Antarctica in 2008. These grains, called calcium-aluminum-rich inclusions, or CAIs, originally formed during the solar system’s first 200,000 years, making the samples the oldest known solar system material.

The findings suggest that a magnetic field existed very early on, during the time of the solar nebula. The researchers estimate that this nebular magnetic field was stronger than Earth’s magnetic field today, and likely played a significant role in pulling together primordial matter to form the early sun.

“This transition, from a spherical cloud to a protoplanetary disk, is one of the most significant events in all of solar system history,” says Benjamin Weiss, the Robert R. Shrock Professor of Earth and Planetary Sciences at MIT. “It has long been theorized that gravity caused this, but our measurements show magnetism likely played a role.”

Weiss and his colleagues report their discovery in a paper appearing this week in the Proceedings of the National Academy of Sciences. The study’s MIT co-authors are first author Cauê Borlina PhD ’22, Elias Mansbach PhD ’24, and Nilanjan Chatterjee, along with Xue-Ning Bai of Tsinghua University, Po-Yen Tung and Richard Harrison of Cambridge University, François Tissot of Caltech, and Kevin McKeegan of the University of California at Los Angeles.

Spinning grains

Magnetic fields are generated by matter that is electrically charged and moving around. In the very early solar system, the collapsing cloud of gas and dust could have whipped up a plasma of charged particles. As these charges spun through the developing disk, they could have produced and sustained a magnetic field.

If this were the case, Weiss and his colleagues reasoned that such early magnetism would have affected material in the disk. As this material condensed, tiny magnetic minerals would have locked in the strength of the magnetic field, preserving its original intensity over billions of years. If these minerals somehow made it to Earth for scientists to measure, their “remanent magnetization” would be evidence that a magnetic field indeed existed and could have played a role in shaping the solar system. 

In fact, the team has previously discovered evidence of a magnetic field, as early as 2 million years into the solar system’s formation. At that time, scientists believe that the sun was already in place, and that the planets were just starting to come together. Thus, the magnetic field Weiss measured likely played a part in the formation of the early planets.

“Nowadays people don’t debate whether magnetism is present when planets are forming. But the debate is around the very early solar system, before planets are forming, when there’s just a disk,” says Borlina, who led the new study as an MIT graduate student and is now an assistant professor at Purdue University. “That’s where the debate still resides, and that’s where we’re operating now.”

Magnetic records

For their new study, the team investigated whether a magnetic field could have existed even earlier in the solar system, when the sun was first coming together. They analyzed samples of DOM 08006, a meteorite that was discovered in 2008 in Dominion Range, a mountain range located along the East Antarctic Ice Sheet. Since it was first recovered, the meteorite has been studied extensively. 

DOM 08006 is one of the most primitive meteorites discovered, and it contains mineral grains that date back to the earliest stages of solar system development, possibly even before the sun was formed. Surprisingly, the meteorite has managed to keep its original composition and minerals.

“Other meteorites went through many different processes over this 4.5 billion year history,” Weiss says. “They were formed in the solar nebula, then added to bodies with water, then got destroyed, moved to the asteroid belt, and then landed here. But somehow, DOM has experienced less alteration than any other meteorite.”

If the early solar system did harbor a magnetic field, records of that field could still be in place in some of DOM’s ancient mineral grains, including CAIs. 

“We know they are the oldest things we have of the early solar system,” Borlina says. “But CAI’s are very complex and are not all the same, even within a 1-millimeter piece of the meteorite. So we have to carefully identify what types they are.” 

From small samples of the parent meteorite, the team isolated tiny grains and identified a handful of CAIs that contained inherently magnetic minerals such as iron. They then put the grains through a series of tests to measure any magnetism they still carry. 

The team identified traces of a magnetic field in the ancient grains. Based on their measurements, they estimate that a magnetic field, of about 150 to 600 microteslas, existed in the early solar system. This field strength is about three to 12 times greater than the Earth’s magnetic field today. 

“We think these kinds of magnetic fields were helping to move gas from the protoplanetary disk, in toward this central star, the sun,” Borlina says. “Gravity is also playing a role. But we are now showing that, if you want to fully understand how the sun and planets formed, you should include magnetic fields in the ingredients that make them.”

This research was supported, in part, by NASA.

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Generating scenarios for extreme events, without extreme data

Can a city’s seawall stand up to a blockbuster storm? Will a region’s power grid hold against record-breaking heat? And can a town’s fire-fighting resources contain a major wildfire? 

To answer these questions, communities will first need to know how such extreme events could unfold. How far is a wildfire likely to spread? How much of a region might a storm impact? How long could a heat wave last? 

But extreme events are notoriously difficult to anticipate. By their nature, they are outliers. In the history of record keeping, extreme events are sporadic and rare. Yet most methods that assess a region’s risk depend on extreme events of the past to characterize even more extreme, worst-case scenarios in the future. 

Now, MIT engineers have developed a tool that generates plausible extreme events and worst-case scenarios, and maps their characteristics, such as an extreme storm’s likely duration, intensity, and area of impact. The key to their method is that it does not need to know about previous extreme events in order to generate plausible future extreme events.

Instead, the method, in the form of a machine-learning algorithm, learns from a dataset, such as a region’s daily weather records and maps. This record may or may not contain past extreme deviations, such as record-setting heat or rain. The team’s algorithm takes a statistical approach to learn from the available data, to exclude implausible weather scenarios. The method then generates plausible extreme events that are likely to occur in a region with a given frequency (such as once every 100 years), and projects how those extreme events might look in terms of their size, intensity, and duration.

“We are trying to model extreme, unprecedented events that no one has seen before, that are not in the dataset,” says Kai Chang, an MIT graduate student in mechanical engineering and affiliate of the MIT Center for Computational Science and Engineering. 

“An event like Hurricane Katrina is something that happens every 30 to 40 years,” adds Themis Sapsis, the William I. Koch Professor of Mechanical and Ocean Engineering at MIT, a core member of the Center for Computational Science and Engineering, and an affiliate of the MIT Institute for Data, Systems, and Society. “What will be the Katrina that happens every 100 years? How bad will it be? That’s exactly what we’re trying to quantify, to help planners prepare for plausible extreme scenarios.”

Beyond weather events, the approach, which the team has dubbed Extreme Event Aware, or “η-learning,” can be applied to other fields, such as robotic navigation and financial markets.

“Financial market crashes are extreme events that are a complicated combination of things, involving many different sectors,” Chang says. “What is the interaction that leads to a market crash? That is something that this method could explore.” 

Sapsis and Chang detail their new method in an open-access paper that appeared on Aug. 20 in the journal Nature Communications. 

“Riskier than everything”

To estimate a region’s risk of an extreme weather event, planners, policymakers, and insurance companies typically ask questions such as “What does a once-every-100-year storm look like for New York City?” For answers, they use computer simulations that must be trained on data that includes extreme, once-in-a-century events, in order to learn the conditions leading up to those events and generate scenarios of how those events might look in the future. 

“These methods assume there are very disastrous events that we have seen in the dataset, and they build a method to either estimate the risk of those events, or they try to predict exactly the events that have happened,” Chang says. “We are trying to see: What do unprecedented extreme events look like that are riskier than everything that has happened before and yet are still plausible?”

For example, if the most extreme rainfall measurement ever recorded in New York City is 200 millimeters, what kind of storm would produce an even more extreme measurement, of 300 millimeters? Such an event has never been recorded before and yet could still be plausible. City planners would want to know where such a storm would hit, how big an area it would cover, and how intense it would be. A simulation of the storm could help them assess infrastructure and plan reinforcements. 

“We want to predict maps of these worst-case scenarios,” Sapsis says. “There is no method that does this efficiently to predict events that happen rarely.”

Extreme learning

The team’s new algorithm generates plausible, unprecedented extreme scenarios, without needing to train on previous extreme event data. To do so, the algorithm combines and learns statistics, or probabilities, about the relationships between two types of data: point statistics and spatial maps.

To demonstrate, the researchers applied the method to generate maps of future extreme precipitation events over the continental United States. The researchers began with 25 years of hourly precipitation maps, which they pooled into daily maps. From the full record, they computed point statistics describing how often the maximum rainfall across a map reached a given level. They then trained the algorithm on paired low- and high-resolution spatial maps from just the first six months of the record, which contained few or no examples of the most extreme rainfall levels.

From these data, the algorithm learned how patterns in low-resolution maps correspond to detailed, high-resolution precipitation maps. It then used the point statistics to constrain the rainfall extremes represented in those maps. This combination enables the algorithm to generate plausible spatial patterns for events more extreme than those represented in the training data — for instance, the possible locations, sizes, and intensities of a once-in-a-century rainfall event with a maximum of 300 millimeters.

A user can prompt the trained algorithm with a question such as, “What could a once-in-a-century storm look like in New York City?” The algorithm then generates maps of statistically plausible storms that are likely to occur with that frequency, including characteristics such as the storm’s size, area of coverage, and intensity of rainfall.

“Someone can say, ‘I’m interested in building things to withstand the risk of an event that happens every 100 years,’” Chang says. “What we can do then is produce thousands of possible realizations that will happen with this sort of rare frequency.”

As long as relevant point statistics and spatial data are available, the method could be applied to visualize other unprecedented events such as extreme floods and wildfires.

“Extreme events have become a strategic concern, not just an environmental one — we’ve optimized global systems for efficiency, and the price of that efficiency is that there’s very little slack left anywhere. A single extreme event propagates through supply chains, energy markets, and food systems in weeks,” Sapsis says. “Being able to put a probability on an event that hasn’t happened yet is now a question of national and economic resilience.”

This research was supported, in part, by a Vannevar Bush Faculty Fellowship and the U.S. Air Force Office of Scientific Research. 

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Language skills stay strong in older adults, even while other cognitive abilities decline

As people age, many cognitive functions tend to decline. Brain scanning studies have revealed corresponding changes in the function of a brain network that is involved in many of these cognitive functions, including working memory and problem-solving.

When it comes to language skills, however, the picture is different. Unless impaired by a stroke or dementia, most older people retain their language skills and may even improve them as they steadily gain vocabulary throughout their lives.

A new brain imaging study from an MIT-Boston University collaboration now reveals the neural activity underlying this observation. The researchers found that in older adults, activity of the language processing network is nearly identical to that seen in the brains of younger adults during language tasks.

In contrast, the researchers found that activation patterns in the multiple demand network, a brain system involved in executive control tasks such as decision-making, were very different in older and younger adults. 

“In the language network, we couldn’t find any differences between older and younger groups. In contrast, the executive system showed decline across almost all of the measures. The network synchronization declined in older adults, the extent of activation was reduced, and the magnitude of activation was reduced as well,” says Anne Billot, one of the lead authors of the new study, who carried out this work while doing her PhD at BU and is now a postdoc at Harvard University.

The findings suggest that parts of the brain that are specialized for specific functions, such as language processing, may be more resilient to aging than the multiple demand network, a more general-purpose network that has greater flexibility in its function, the researchers say.

Former MIT research assistant Niharika Jhingan is also a lead author of the study, which appears today in Nature Communications. Evelina Fedorenko, an MIT associate professor of brain and cognitive sciences and member of MIT’s McGovern Institute for Brain Research, and Swathi Kiran, the James and Cecilia Tse Ying Professor in Neurorehabilitation at BU, are the paper’s senior co-authors.

The resilience of language

To study the effects of aging on the brain, the researchers looked at two groups of people, ages 17-39 and 41-80. Based on previous studies, they expected that the multiple demand network, which includes several regions in the frontal and parietal lobes of the brain, would look different in the brains of older people. 

“It’s well known that executive functions, such as attention, working memory, and cognitive control, tend to decline with age. And it’s also known that in opposition to that, language skills typically tend to remain quite stable or even improve with age,” Billot says. “These two types of functions really go in opposite directions in healthy aging. In terms of behavior, that’s quite well-established, and we wanted to see if that was also the case in the brain.”

In previous studies of the multiple demand network, scientists have found that as people age network activity becomes less synchronized. Some neuroscientists have hypothesized that this may also happen in the language network, but studies haven’t found definitive evidence for this.

The MIT researchers were able to look at both networks by designing tasks that elicit responses primarily in either the language network or the multiple demand network. This allowed them to identify, for each participant, the brain areas that belong to each network.

During a spatial memory task — remembering the location of squares in a grid — the researchers confirmed that the multiple demand network showed altered activity in older adults. Compared to the younger subjects, their networks were smaller and less well-synchronized, and the overall activation level was weaker.

To identify the language network, the researchers had participants listen to stories and read sentences. They found that in both groups, brain activity in response to language showed similar levels and spatial distribution across the network.

They also found that younger and older subjects showed similar brain responses when they encountered an unfamiliar word or an unusual grammatical construction.

“We have previously used similar kinds of materials to show that young adults show strong sensitivity to these points of linguistic difficulty: activity in the language areas goes up. Here we found that in older adults, you also see this sensitivity, which suggests that there’s nothing fundamentally different about how they process language,” Fedorenko says. 

A language boost

The researchers also showed that in older people, the language network did not show any signs of becoming less synchronized. Additionally, the network did not show signs that it was blurring together with the multiple demand network, as some neuroscientists have hypothesized might happen.

While this study did not evaluate language ability, other studies have shown that not only do language skills not decline with age, for some people, their language processing improves in older age. This might be because vocabulary and reading skill can continually grow over time, the researchers say.

“Vocabulary keeps increasing as long as people have been measuring, which makes sense. People get exposed to more and more language, and older people sometimes start reading more, so they get an extra boost — it’s like a large language model trained on increasingly more data,” Fedorenko says. 

Given these findings, one possible generalization is that parts of the brain that are specialized for particular functions such as language may be less susceptible to age-related decline than the multiple demand network. That network is unique in its ability to give the human brain the flexibility to learn new skills and adapt to new situations. 

“The multiple demand network is a different system in the sense that it’s not accumulating knowledge over time. It’s more like a flexible resource that you can deploy in all sorts of ways. And somehow that’s the thing that is more vulnerable to aging,” Fedorenko says. “Why it’s so vulnerable — that is a very good question.”

The research was funded by the National Institute on Deafness and Other Communication Disorders, as well as MIT’s McGovern Institute, Simons Center for the Social Brain, Poitras Center for Psychiatric Disorders Research, and Quest for Intelligence.

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The importance of indoor airflow patterns in spreading airborne disease

Tuberculosis (TB) is a leading cause of infectious disease deaths, claiming over 1 million lives every year. It spreads through the air when an infected person coughs, sneezes, or exhales, and drug-resistant strains and asymptomatic spreading are growing concerns. Curbing TB transmission is an urgent public health challenge, yet scientists still don’t understand how airflow and other environmental factors influence that spread.

One problem is that studies of infectious disease transmission have focused mainly on population-level assessments or individual immune responses. But understanding how airflow and mixing influence transmission in indoor spaces requires expertise in fluid physics and computational modeling.

An interdisciplinary team including researchers at MIT and the University of Texas Southwestern Medical Center has now combined animal transmission experiments with quantitative particle tracking and flow modeling to understand how some lab-based environments can promote the spread of respiratory infectious diseases such as TB, while others mitigate that spread.

A key factor in predicting infectious transmission was not just the total ventilation rate but, more importantly, the local pattern of airflow driven by the design — such as air leakage, inflow and outflow locations, and forces created by an infected individual.

“The local airflow patterns turn out to be pivotal,” says Lydia Bourouiba, the Japan Steel Industry Chair Professor at MIT and faculty lead of the Fluid Dynamics of Disease Transmission Laboratory, part of the Fluids and Health Network within the Institute for Medical Engineering and Science (IMES). “Our team’s findings provide some of the clearest evidence I’m aware of showing the importance of accounting for [airflow] inhomogeneity and its effects when designing for airflow detailed patterns. This insight is critical when building or retrofitting an indoor space to mitigate airborne transmission, or when designing an airborne transmission study.”

The research is an important step toward connecting laboratory infectious disease studies with how people spread such diseases in the real world. The team hopes their insights can extend beyond their model system and show the importance of flow physics in building designs to prevent the spread of airborne diseases indoors.

“Despite recent pandemics and epidemics, there is still resistance to incorporating airflow in routine infectious disease prevention tools,” Bourouiba says. “Infrastructure could be retrofitted at relatively low cost, but the paucity and difficulty of gathering direct evidence prevents broader adoption of flow physics as a tool for indoor health. This study helps provide such evidence.”

Joining Bourouiba on a paper about the work are Yash Kulkarni, a postdoc at IMES, who led the fluid and aerosol physics components; Kubra Naqvi, lead author and a postdoc at UT Southwestern; Michael Shiloh, a professor at UT Southwestern, who led the multiyear effort to reestablish a classic tuberculosis transmission model; Hui Ouyang, an assistant professor of aerosol engineering at UT Dallas; Yuhui Guo, Deepak Sapkota, and Arabella Martin, all UT Southwestern PhD students; Pei Lu and Victoria Ektnitphong, research associates at UT Southwestern; Shibo Wang, a University of Minnesota researcher; Beatriz Dias, a UT Southwestern instructor; Bret Evers, an associate professor at UT Southwestern; and Lenette Lu, assistant professor at UT Southwestern.

Opening the black box

When people exhale, talk, cough, or sneeze, tiny microdroplets and bioaerosols launch from their mouths, carried forward by a cloud. If infected by a respiratory disease, these bioaerosols can contain pathogens that can infect others. Disease transmission depends on pathogen survival in the air, which is influenced by temperature, humidity, and ventilation.

In 1882, German physician and microbiologist Robert Koch first established an animal model for the study of tuberculosis pathogenesis. Decades later, researchers demonstrated airborne transmission of tuberculosis between people and animals.

These early experiments have proven difficult to replicate in today’s modern, biosafety-grade facilities. This new study reveals the difficulty comes from stringent containment and ventilation requirements, which can dramatically influence airflow in experiments.

“Host-to-host transmission is an obligatory evolutionary phase of respiratory pathogens, yet it has been considered too intractable or complex to be amenable to systematic investigation, hence is commonly relegated to a black box. Our work opens that black box,” says Bourouiba, who is professor in MIT’s departments of Mechanical and Civil and Environmental Engineering, and an IMES core faculty member.

To quantify how local airflow patterns impact infectious disease transmission, the researchers redesigned and modelled the early studies for modern high-containment lab facilities — including their seal, inflow, outflow, and exhaust pathways — and quantified particle and bacteria-laden particle release and dispersal. They released tracer particles and bacteria into a compartment and modelled recovery from air sampled on the other side under differing airflow rates, designs, and leak configurations.

The MIT team carried out computations, benchmarked against particle release experiments. The results revealed how important seemingly small details such as leakage paths could be.

“Even a small leak could short-circuit the airflow by drawing fresh air directly toward the exhaust, rather than drawing contaminated air across the containment chambers,” says Kulkarni. 

Advancing TB research

To date, uneven indoor airflow patterns have not been fully harnessed as part of a risk mitigation strategy. 

“By systematically defining how airflow and design influence biological exposure, we were ultimately able to restore transmission and create a system that can now be used to ask fundamental questions about the bacterial, host, and environmental factors that determine tuberculosis spread,” says Naqvi.

“I began working to reestablish this seminal TB animal transmission model nearly 10 years ago, and it proved far more challenging than I anticipated,” says Shiloh. “I hope this work serves as a reminder that meaningful scientific advances often require patience and perseverance.”

“This work illustrates how crucial it is to support synergistic collaborations integrating complementary disciplines to tackle research bottlenecks — and to standardize reporting norms across laboratories,” Bourouiba says. “If different labs have varying airflow patterns from uncontrolled leaks or seal details, that physical variability can overwhelm the biological signals researchers seek. Beyond its foundational impact for TB transmission studies, our work shows that opening the black box of transmission provides mechanistic insights: Detailed airflow pattern control can enhance or mitigate airborne transmission — making it exploitable as a prevention measure in crowded gathering spaces.”

This work was supported, in part, by the National Institutes of Health, the National Science Foundation, the Burroughs Wellcome Fund, MathWorks, and the Translational Research Institute for Space Health.

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Paving the way for greener ammonia production

Ammonia is one of the most important chemicals produced in the world, ranking second only to sulfuric acid in the total volume produced each year. It is used mostly to make fertilizer, which is essential to feeding the world’s population. Yet its production accounts for up to 2 percent of the world’s energy consumption and about 1.5 percent of greenhouse gas emissions, so the search has been underway for ways to produce ammonia more sustainably.

The traditional way of making ammonia, in use for more than a century and accounting for the vast majority of production, is the Haber-Bosch process, which relies on fossil fuels to provide the needed heat. Hydrogen used in the process is also largely produced from fossil fuels.

There is another way, using electrochemistry instead of heat and pressure, but so far this method has not been anywhere near economically competitive at the scales needed.

Now, researchers at MIT have developed a way to predict which materials could be most promising as catalysts in electrochemical ammonia production. Catalysts help drive chemical reactions, and their properties determine how efficiently those reactions proceed. Rather than using trial and error to test each possible combination out of the millions of possible alloys — which can take years — the new approach could greatly speed up the search for materials that could make this low-emissions method competitive with the Haber-Bosch process. 

“Our approach identifies the key physical properties that drive catalytic activity in ammonia production,” says Bilge Yildiz, the Breen M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering (DMSE). The results can guide the search for new and more effective catalyst compounds.

The open-access findings were published Aug. 11 in the Royal Society of Chemistry journal EES Catalysis, in a paper by Yildiz and doctoral students Constantine Athanitis of DMSE and Filip Grajkowski of the Department of Chemistry. 

The challenge of greener ammonia

As the world’s population grows, Athanitis says, “we’re just going to need more and more food, and the only reason why we’re able to sustain so many people is because of fertilizer.” But more than 90 percent of the ammonia needed for fertilizer is still made by that energy-intensive Haber-Bosch process, which “has been hyper-optimized since it first came out more than a century ago,” he says.

“If we’re trying to keep in line with society’s sustainability and energy targets and climate change targets, we really need to come up with another alternative,” he explains. The world currently uses about 200 million metric tons of ammonia each year, “so ideally we want to be able to find a way to produce the same amount of ammonia, or even more, but in a more energy-efficient way and also with lower CO₂ emissions,” he says.

Using electricity to produce ammonia is not a new idea. “It’s really just the electrochemical reaction between proton-electron pairs and nitrogen gas. And these technologies exist,” he says. The approach uses the same basic principles as electrolyzers, which use electricity to drive chemical reactions in devices.

But while the process works, it’s not efficient enough for industrial-scale production. “Production rates and yields are still too low,” Athanitis says. “Even though a technology might be better for the world or for the climate, companies and capitalism won’t really allow it unless it’s cost competitive.”

How to make it more competitive? The key ingredient in the electrochemical process is a metallic catalyst, whose properties govern the reaction that takes place on its surface. “If we can somehow find a catalyst that reduces the energy needed and is more selective for ammonia production,” Athanitis says, “then we could essentially hit the jackpot.” A more selective catalyst would produce more ammonia while reducing unwanted side reactions.

Finding better catalysts

But finding that ideal catalyst is not simply a matter of identifying one perfect material. Different materials can improve different parts of the reaction, and researchers are seeking combinations that can make ammonia production efficient, affordable, and practical at large scale.

“Metal nitride compounds make an ideal material system for this reaction and for identifying the electronic, chemical, and structural properties that determine reactivity in nitrogen reduction and ammonia electrosynthesis,” Yildiz says. 

Transition metals could form promising nitride alloys for this purpose, and historically, “materials research has been pretty much trial and error,” Athanitis says.

The usual process is to take some existing material and “tweak it in some way,” he says. “It’s all somewhat guided by scientific and chemical intuition.” 

Now, increasingly, computational tools are being used to model the physical interactions and predict outcomes. A method called density functional theory uses quantum mechanics to simulate the properties and behavior of materials, allowing researchers to predict how different atomic arrangements may perform before making them in the lab. Rather than searching randomly through every possible alloy combination, Yildiz says, “we first assessed what microscopic properties of the material make them tick for nitrogen reduction.” 

For ammonia-producing catalysts, “we’re looking at transition metal nitrides,” Athanitis says, because they have been found to be effective in these electrochemical nitrogen reactions. They are especially effective because “the nitrogen inherent to the catalyst itself becomes part of the reaction.”

This produces a series of chemical steps in which one step provides part of the energy needed to drive the next, reducing the amount of input energy needed. This helps solve one of the major bottlenecks in the nitrogen reduction reaction: the high energy required to break the strong bonds in nitrogen molecules, he says.

But the process is far from perfect, Athanitis says. It is “still limited by certain steps throughout the reaction pathway, including nitrogen dissociation and hydrogen transfer.” The study attempted to identify those bottlenecks and, with the help of machine learning, determine which alloys of these metals might overcome them.

With that understanding, “it can give us insights and open up potential strategies for how we can tune these materials to create next-generation better nitride catalysts,” Athanitis says.

Pushing past theory

The approach is “exciting work” that could help develop a foundation for designing new catalysts for ammonia production, says Dane Morgan, a professor of engineering at the University of Wisconsin who was not involved in this study.

“This work helps clarify how fundamental electronic properties of a material relate to its role as a catalyst in making ammonia,” Morgan says. “Such understanding can help guide researchers in designing new catalysts, both through better qualitative understanding and by accelerating computational screening.” 

So far, the study is purely theoretical: The researchers have used computer models to identify promising alloys, but those materials still need to be made and tested. Morgan notes that “translating these calculations into practical catalysts will require many additional steps, so meaningful real-world impact is likely still some distance away.”

The next step will be to build a working reaction cell, a laboratory device that uses the catalyst to produce ammonia and test its performance under real operating conditions. “For this to really make an impact in society, we need to bring it to the experimental lab,” Athanitis says.

“There have always been pushes at the frontiers of what’s possible,” he adds. “We like to think we’ve pushed the boundary of candidate materials here beyond what was thought of before, and hopefully we’re almost there. But even if we’re not almost there, we’re still pushing in the right direction.”

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MIT engineers design a better controller for operating construction diggers

Anyone who’s ever wrestled with a claw machine at an arcade can appreciate the difficulty in pulling and pushing on joysticks, in just the right way, to get a mechanical arm to scoop up that one special toy. Coordinating the joysticks and connecting their movement to the claw’s motion is a type of “mental mapping” that is not immediately intuitive. (And it’s what arcade owners depend on to bring players back, again and again). 

In fact, the mechanics of a claw machine are broadly similar to driving an excavator: An operator uses joysticks to control the digger’s boom, arm, and bucket, and the direction of its cab. But an excavator’s maneuvers are far more complex than anything an arcade claw can do. Operators must learn more complicated mental maps to direct a digger to move rocks, grade soil, clear debris, and dig foundations, among other essential on-site jobs. Indeed, it can often take years for operators to build up expertise in maneuvering the heavy machines.

MIT engineers are looking to shorten the learning curve for excavator operators with a new training interface. Instead of using joysticks, the team has designed a more intuitive controller, which itself resembles a miniature excavator’s arm and bucket. Trainees grasp the device and use their arm and hand to make it move the way an excavator does. A digital excavator projected on an immersive six-screen display mirrors the trainee’s movements in a virtual environment.

“This is a more intuitive way to command the machine,” says Hermano Krebs, principal research scientist in MIT’s Department of Mechanical Engineering. “With this new interface, we can eliminate a lot of the mental maps that an operator would need to build in order to operate an excavator.” 

Krebs sees the interface as a faster way to train excavator operators, as well as a new way to physically operate the machines, both on-site and remotely. 

“Instead of having joysticks, you might have this miniature arm on the side, where the operator would place their own arm, kind of like an exoskeleton, which would allow them to operate the excavator in the cab,” Krebs says. “If work has to be done in a difficult or unsafe environment, you could have an operator sitting off-site in a trailer and using this arm to remotely tele-operate the excavator.”

The team reports its open-access results this week in the Journal of Computing and Civil Engineering. MIT co-authors include Moises Alencastre-Miranda, Joao Buzzatto, and Eran Beeri Bamani, along with collaborators from Sumitomo Heavy Industries, an industrial machinery manufacturer based in Japan. 

A machine mimic

At MIT, Krebs’ group works on human-robot interactions, with a longtime focus on physical rehabilitation. Through this work, the team has accumulated knowledge about the ways in which humans control their limbs and how they can most intuitively interact with machines. 

In 2018, Krebs struck up a collaboration with researchers at Sumitomo Heavy Industries, who were looking for a faster way to train excavator operators. They noted that in Japan, the population of heavy machinery operators is aging rapidly; training their replacements takes time. 

Operators typically learn by driving actual excavators on a controlled driving course. As they operate the machine, novices must learn to relate the actions of the excavator’s joysticks with the movements of the arm, bucket, and cab. Coordinating these actions to carry out actual tasks adds another level of complexity that can take months to years to master. 

The team reasoned that if they could eliminate the need for this mental map, they might significantly shorten the training process. To do so, they looked for a more natural way to control the machine, as an alternative to the traditional joysticks. They soon landed on the mechanical arm design, reasoning that the physical resemblance to the digger’s own arm and bucket could enable operators to mimic and control the excavator’s movements directly, without much mental translation. 

Over the next few years, the researchers worked to build the mechanical arm, along with the software to pair its movements with a virtual simulation of an excavator. The combination of the mechanical arm and the virtual simulator constitutes a new training and control platform for digger operators, which the team has named the “World-Space Interface.”

“‘World-space’ refers to everything in the world that is outside of yourself, or in this case, outside of the excavator’s cab,” Krebs explains. “Normally, operators have to build a mental map of how to manipulate things in the world-space. But now, we can just mime picking up rocks or dirt, and the computer will do that translation to the world-space for us.”

Construction on day one

For their new study, the team ran training experiments with volunteers who used the World-Space Interface (WSI) as well as a more traditional, joystick-based excavator simulator. The researchers developed virtual simulations of 15 realistic excavation environments, including construction sites, highways, forest roads, riverbanks, mining areas, and urban and rural settings. Each virtual environment was associated with various excavation tasks, such as scooping and dumping sand or gravel, digging and grading trenches, clearing debris from roads, removing tree branches from water edges, and breaking up rocks. 

The team designed the experiment to resemble the tasks that an operator typically performs during a weeklong excavator driving course. For one hour each day for seven days, volunteers — both expert and novice — operated the WSI and the joystick counterpart, training on tasks with increasing difficulty. 

The researchers then compared the volunteers’ performance before and after the training period. For the joystick simulator, they found that novices were consistently worse than experts, though they did improve over the training period. In comparison, the team found that with the new World-Space Interface, novices were just as good as experts from the start. 

“In this case, joysticks are a non-intuitive way to control and coordinate the machine,” says study co-author and MIT postdoc Joao Buzzatto. “This is the first interface that does not require me to command the excavator with joysticks.”

The team is now working to add haptics, or feeling to the WSI’s physical arm. The idea is that, as an operator uses the arm to mime an action such as picking up a pile of rocks, the arm will generate a force in response, as if the operator can feel the heaviness of the rocks, as confirmation that the excavator is indeed picking them up. 

“Haptics would make this an even more intuitive system,” says co-author and visiting engineer Solmon Jeong.

The team says the new training interface can be a more natural alternative to excavator simulators that the construction industry is currently exploring. Companies such as Caterpillar, Hyundai, and Komatsu are developing virtual simulators, both to help train operators before they go on-site, and to one day remotely control excavators from a distance. However, these simulators are largely based on traditional joystick controllers that still take time to learn. 

If the team’s new arm-and-bucket controller were incorporated, as an appendage in an excavator cab, or in a virtual, teleoperational simulator, the researchers envision that even first-time operators could get to work, from day one. 

This research was supported, in part, by Sumitomo Heavy Industries.

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