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Tech Today: Stay Safe with Battery Testing for Space - NASA

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Tech Today: Stay Safe with Battery Testing for Space - NASA

NASA battery safety exams influence commercial product testing

Battery safety is of paramount importance in space, where the risk of thermal runaway looms large. This dangerous reaction, characterized by a continuous escalation of temperatures within the battery, can potentially lead to a fire or explosion.

For two decades, Judy Jeevarajan was the NASA engineer in charge of testing. Thanks to that experience, batteries for everything from industrial equipment to home appliances are tested using methods she originally developed for spaceflight.

Jeevarajan began working at NASA’s Johnson Space Center in Houston in the 1990s, developing advanced battery testing technologies, eventually becoming responsible for approving all batteries flown for human spaceflight. In 1999, shuttle astronauts wanted to bring a digital camcorder aboard. Previous video cameras on the space shuttle used battery chemistries already authorized for space, but the emerging use of lithium-ion cells was new territory for space missions.

To test these batteries, her team used a hydraulic press to test the tolerance to internal short circuits and they devised a vibration test that would ensure the intense shaking at launch wouldn’t lead to failure. After the camcorder’s lithium-ion batteries were approved to fly, her work expanded to testing batteries for every consumer-grade device brought aboard the International Space Station.

For more than 100 years, Underwriters Laboratories Inc. (UL) of Northbrook, Illinois, has developed standards and testing modes for all modern appliances and technologies, ensuring everything is as safe as possible. After Jeevarajan met engineers from UL at a battery safety conference, she became a member of the UL Standards Technical Panel for battery safety. Over the next decade, she helped verify the workings of a new battery-testing machine and used her NASA experience as UL further developed and promoted the adoption of new testing methods.

Jeevarajan joined UL’s nonprofit arm full-time in 2015, bringing with her decades of experience gained working at Johnson, including her techniques for inducing thermal runaway. These are now part of a UL-defined test method for testing cells in large lithium-ion battery systems, like those found in batteries for storing power on the electrical grid.

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MIT faculty, instructors, students experiment with generative AI in teaching and learning

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MIT faculty, instructors, students experiment with generative AI in teaching and learning

How can MIT’s community leverage generative AI to support learning and work on campus and beyond?

At MIT’s Festival of Learning 2024, faculty and instructors, students, staff, and alumni exchanged perspectives about the digital tools and innovations they’re experimenting with in the classroom. Panelists agreed that generative AI should be used to scaffold — not replace — learning experiences.

This annual event, co-sponsored by MIT Open Learning and the Office of the Vice Chancellor, celebrates teaching and learning innovations. When introducing new teaching and learning technologies, panelists stressed the importance of iteration and teaching students how to develop critical thinking skills while leveraging technologies like generative AI.

“The Festival of Learning brings the MIT community together to explore and celebrate what we do every day in the classroom,” said Christopher Capozzola, senior associate dean for open learning. “This year’s deep dive into generative AI was reflective and practical — yet another remarkable instance of ‘mind and hand’ here at the Institute.”  

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2024 Festival of Learning: Highlights

Incorporating generative AI into learning experiences 

MIT faculty and instructors aren’t just willing to experiment with generative AI — some believe it’s a necessary tool to prepare students to be competitive in the workforce. “In a future state, we will know how to teach skills with generative AI, but we need to be making iterative steps to get there instead of waiting around,” said Melissa Webster, lecturer in managerial communication at MIT Sloan School of Management. 

Some educators are revisiting their courses’ learning goals and redesigning assignments so students can achieve the desired outcomes in a world with AI. Webster, for example, previously paired written and oral assignments so students would develop ways of thinking. But, she saw an opportunity for teaching experimentation with generative AI. If students are using tools such as ChatGPT to help produce writing, Webster asked, “how do we still get the thinking part in there?”

One of the new assignments Webster developed asked students to generate cover letters through ChatGPT and critique the results from the perspective of future hiring managers. Beyond learning how to refine generative AI prompts to produce better outputs, Webster shared that “students are thinking more about their thinking.” Reviewing their ChatGPT-generated cover letter helped students determine what to say and how to say it, supporting their development of higher-level strategic skills like persuasion and understanding audiences.

Takako Aikawa, senior lecturer at the MIT Global Studies and Languages Section, redesigned a vocabulary exercise to ensure students developed a deeper understanding of the Japanese language, rather than just right or wrong answers. Students compared short sentences written by themselves and by ChatGPT and developed broader vocabulary and grammar patterns beyond the textbook. “This type of activity enhances not only their linguistic skills but stimulates their metacognitive or analytical thinking,” said Aikawa. “They have to think in Japanese for these exercises.”

While these panelists and other Institute faculty and instructors are redesigning their assignments, many MIT undergraduate and graduate students across different academic departments are leveraging generative AI for efficiency: creating presentations, summarizing notes, and quickly retrieving specific ideas from long documents. But this technology can also creatively personalize learning experiences. Its ability to communicate information in different ways allows students with different backgrounds and abilities to adapt course material in a way that’s specific to their particular context. 

Generative AI, for example, can help with student-centered learning at the K-12 level. Joe Diaz, program manager and STEAM educator for MIT pK-12 at Open Learning, encouraged educators to foster learning experiences where the student can take ownership. “Take something that kids care about and they’re passionate about, and they can discern where [generative AI] might not be correct or trustworthy,” said Diaz.

Panelists encouraged educators to think about generative AI in ways that move beyond a course policy statement. When incorporating generative AI into assignments, the key is to be clear about learning goals and open to sharing examples of how generative AI could be used in ways that align with those goals. 

The importance of critical thinking

Although generative AI can have positive impacts on educational experiences, users need to understand why large language models might produce incorrect or biased results. Faculty, instructors, and student panelists emphasized that it’s critical to contextualize how generative AI works. “[Instructors] try to explain what goes on in the back end and that really does help my understanding when reading the answers that I’m getting from ChatGPT or Copilot,” said Joyce Yuan, a senior in computer science. 

Jesse Thaler, professor of physics and director of the National Science Foundation Institute for Artificial Intelligence and Fundamental Interactions, warned about trusting a probabilistic tool to give definitive answers without uncertainty bands. “The interface and the output needs to be of a form that there are these pieces that you can verify or things that you can cross-check,” Thaler said.

When introducing tools like calculators or generative AI, the faculty and instructors on the panel said it’s essential for students to develop critical thinking skills in those particular academic and professional contexts. Computer science courses, for example, could permit students to use ChatGPT for help with their homework if the problem sets are broad enough that generative AI tools wouldn’t capture the full answer. However, introductory students who haven’t developed the understanding of programming concepts need to be able to discern whether the information ChatGPT generated was accurate or not.

Ana Bell, senior lecturer of the Department of Electrical Engineering and Computer Science and MITx digital learning scientist, dedicated one class toward the end of the semester of Course 6.100L (Introduction to Computer Science and Programming Using Python) to teach students how to use ChatGPT for programming questions. She wanted students to understand why setting up generative AI tools with the context for programming problems, inputting as many details as possible, will help achieve the best possible results. “Even after it gives you a response back, you have to be critical about that response,” said Bell. By waiting to introduce ChatGPT until this stage, students were able to look at generative AI’s answers critically because they had spent the semester developing the skills to be able to identify whether problem sets were incorrect or might not work for every case. 

A scaffold for learning experiences

The bottom line from the panelists during the Festival of Learning was that generative AI should provide scaffolding for engaging learning experiences where students can still achieve desired learning goals. The MIT undergraduate and graduate student panelists found it invaluable when educators set expectations for the course about when and how it’s appropriate to use AI tools. Informing students of the learning goals allows them to understand whether generative AI will help or hinder their learning. Student panelists asked for trust that they would use generative AI as a starting point, or treat it like a brainstorming session with a friend for a group project. Faculty and instructor panelists said they will continue iterating their lesson plans to best support student learning and critical thinking. 

Panelists from both sides of the classroom discussed the importance of generative AI users being responsible for the content they produce and avoiding automation bias — trusting the technology’s response implicitly without thinking critically about why it produced that answer and whether it’s accurate. But since generative AI is built by people making design decisions, Thaler told students, “You have power to change the behavior of those tools.”

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An AI dataset carves new paths to tornado detection

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An AI dataset carves new paths to tornado detection

The return of spring in the Northern Hemisphere touches off tornado season. A tornado’s twisting funnel of dust and debris seems an unmistakable sight. But that sight can be obscured to radar, the tool of meteorologists. It’s hard to know exactly when a tornado has formed, or even why.

A new dataset could hold answers. It contains radar returns from thousands of tornadoes that have hit the United States in the past 10 years. Storms that spawned tornadoes are flanked by other severe storms, some with nearly identical conditions, that never did. MIT Lincoln Laboratory researchers who curated the dataset, called TorNet, have now released it open source. They hope to enable breakthroughs in detecting one of nature’s most mysterious and violent phenomena.

“A lot of progress is driven by easily available, benchmark datasets. We hope TorNet will lay a foundation for machine learning algorithms to both detect and predict tornadoes,” says Mark Veillette, the project’s co-principal investigator with James Kurdzo. Both researchers work in the Air Traffic Control Systems Group. 

Along with the dataset, the team is releasing models trained on it. The models show promise for machine learning’s ability to spot a twister. Building on this work could open new frontiers for forecasters, helping them provide more accurate warnings that might save lives. 

Swirling uncertainty

About 1,200 tornadoes occur in the United States every year, causing millions to billions of dollars in economic damage and claiming 71 lives on average. Last year, one unusually long-lasting tornado killed 17 people and injured at least 165 others along a 59-mile path in Mississippi.  

Yet tornadoes are notoriously difficult to forecast because scientists don’t have a clear picture of why they form. “We can see two storms that look identical, and one will produce a tornado and one won’t. We don’t fully understand it,” Kurdzo says.

A tornado’s basic ingredients are thunderstorms with instability caused by rapidly rising warm air and wind shear that causes rotation. Weather radar is the primary tool used to monitor these conditions. But tornadoes lay too low to be detected, even when moderately close to the radar. As the radar beam with a given tilt angle travels further from the antenna, it gets higher above the ground, mostly seeing reflections from rain and hail carried in the “mesocyclone,” the storm’s broad, rotating updraft. A mesocyclone doesn’t always produce a tornado.

With this limited view, forecasters must decide whether or not to issue a tornado warning. They often err on the side of caution. As a result, the rate of false alarms for tornado warnings is more than 70 percent. “That can lead to boy-who-cried-wolf syndrome,” Kurdzo says.  

In recent years, researchers have turned to machine learning to better detect and predict tornadoes. However, raw datasets and models have not always been accessible to the broader community, stifling progress. TorNet is filling this gap.

The dataset contains more than 200,000 radar images, 13,587 of which depict tornadoes. The rest of the images are non-tornadic, taken from storms in one of two categories: randomly selected severe storms or false-alarm storms (those that led a forecaster to issue a warning but that didn’t produce a tornado).

Each sample of a storm or tornado comprises two sets of six radar images. The two sets correspond to different radar sweep angles. The six images portray different radar data products, such as reflectivity (showing precipitation intensity) or radial velocity (indicating if winds are moving toward or away from the radar).

A challenge in curating the dataset was first finding tornadoes. Within the corpus of weather radar data, tornadoes are extremely rare events. The team then had to balance those tornado samples with difficult non-tornado samples. If the dataset were too easy, say by comparing tornadoes to snowstorms, an algorithm trained on the data would likely over-classify storms as tornadic.

“What’s beautiful about a true benchmark dataset is that we’re all working with the same data, with the same level of difficulty, and can compare results,” Veillette says. “It also makes meteorology more accessible to data scientists, and vice versa. It becomes easier for these two parties to work on a common problem.”

Both researchers represent the progress that can come from cross-collaboration. Veillette is a mathematician and algorithm developer who has long been fascinated by tornadoes. Kurdzo is a meteorologist by training and a signal processing expert. In grad school, he chased tornadoes with custom-built mobile radars, collecting data to analyze in new ways.

“This dataset also means that a grad student doesn’t have to spend a year or two building a dataset. They can jump right into their research,” Kurdzo says.

This project was funded by Lincoln Laboratory’s Climate Change Initiative, which aims to leverage the laboratory’s diverse technical strengths to help address climate problems threatening human health and global security.

Chasing answers with deep learning

Using the dataset, the researchers developed baseline artificial intelligence (AI) models. They were particularly eager to apply deep learning, a form of machine learning that excels at processing visual data. On its own, deep learning can extract features (key observations that an algorithm uses to make a decision) from images across a dataset. Other machine learning approaches require humans to first manually label features. 

“We wanted to see if deep learning could rediscover what people normally look for in tornadoes and even identify new things that typically aren’t searched for by forecasters,” Veillette says.

The results are promising. Their deep learning model performed similar to or better than all tornado-detecting algorithms known in literature. The trained algorithm correctly classified 50 percent of weaker EF-1 tornadoes and over 85 percent of tornadoes rated EF-2 or higher, which make up the most devastating and costly occurrences of these storms.

They also evaluated two other types of machine-learning models, and one traditional model to compare against. The source code and parameters of all these models are freely available. The models and dataset are also described in a paper submitted to a journal of the American Meteorological Society (AMS). Veillette presented this work at the AMS Annual Meeting in January.

“The biggest reason for putting our models out there is for the community to improve upon them and do other great things,” Kurdzo says. “The best solution could be a deep learning model, or someone might find that a non-deep learning model is actually better.”

TorNet could be useful in the weather community for others uses too, such as for conducting large-scale case studies on storms. It could also be augmented with other data sources, like satellite imagery or lightning maps. Fusing multiple types of data could improve the accuracy of machine learning models.

Taking steps toward operations

On top of detecting tornadoes, Kurdzo hopes that models might help unravel the science of why they form.

“As scientists, we see all these precursors to tornadoes — an increase in low-level rotation, a hook echo in reflectivity data, specific differential phase (KDP) foot and differential reflectivity (ZDR) arcs. But how do they all go together? And are there physical manifestations we don’t know about?” he asks.

Teasing out those answers might be possible with explainable AI. Explainable AI refers to methods that allow a model to provide its reasoning, in a format understandable to humans, of why it came to a certain decision. In this case, these explanations might reveal physical processes that happen before tornadoes. This knowledge could help train forecasters, and models, to recognize the signs sooner. 

“None of this technology is ever meant to replace a forecaster. But perhaps someday it could guide forecasters’ eyes in complex situations, and give a visual warning to an area predicted to have tornadic activity,” Kurdzo says.

Such assistance could be especially useful as radar technology improves and future networks potentially grow denser. Data refresh rates in a next-generation radar network are expected to increase from every five minutes to approximately one minute, perhaps faster than forecasters can interpret the new information. Because deep learning can process huge amounts of data quickly, it could be well-suited for monitoring radar returns in real time, alongside humans. Tornadoes can form and disappear in minutes.

But the path to an operational algorithm is a long road, especially in safety-critical situations, Veillette says. “I think the forecaster community is still, understandably, skeptical of machine learning. One way to establish trust and transparency is to have public benchmark datasets like this one. It’s a first step.”

The next steps, the team hopes, will be taken by researchers across the world who are inspired by the dataset and energized to build their own algorithms. Those algorithms will in turn go into test beds, where they’ll eventually be shown to forecasters, to start a process of transitioning into operations.

In the end, the path could circle back to trust.

“We may never get more than a 10- to 15-minute tornado warning using these tools. But if we could lower the false-alarm rate, we could start to make headway with public perception,” Kurdzo says. “People are going to use those warnings to take the action they need to save their lives.”

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Exploring the history of data-driven arguments in public life

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Exploring the history of data-driven arguments in public life

Political debates today may not always be exceptionally rational, but they are often infused with numbers. If people are discussing the economy or health care or climate change, sooner or later they will invoke statistics.

It was not always thus. Our habit of using numbers to make political arguments has a history, and William Deringer is a leading historian of it. Indeed, in recent years Deringer, an associate professor in MIT’s Program in Science, Technology, and Society (STS), has carved out a distinctive niche through his scholarship showing how quantitative reasoning has become part of public life.

In his prize-winning 2018 book “Calculated Values” (Harvard University Press), Deringer identified a time in British public life from the 1680s to the 1720s as a key moment when the practice of making numerical arguments took hold — a trend deeply connected with the rise of parliamentary power and political parties. Crucially, freedom of the press also expanded, allowing greater scope for politicians and the public to have frank discussions about the world as it was, backed by empirical evidence.

Deringer’s second book project, in progress and under contract to Yale University Press, digs further into a concept from the first book — the idea of financial discounting. This is a calculation to estimate what money (or other things) in the future is worth today, to assign those future objects a “present value.” Some skilled mathematicians understood discounting in medieval times; its use expanded in the 1600s; today it is very common in finance and is the subject of debate in relation to climate change, as experts try to estimate ideal spending levels on climate matters.

“The book is about how this particular technique came to have the power to weigh in on profound social questions,” Deringer says. “It’s basically about compound interest, and it’s at the center of the most important global question we have to confront.”

Numbers alone do not make a debate rational or informative; they can be false, misleading, used to entrench interests, and so on. Indeed, a key theme in Deringer’s work is that when quantitiative reasoning gains more ground, the question is why, and to whose benefit. In this sense his work aligns with the long-running and always-relevant approach of the Institute’s STS faculty, in thinking carefully about how technology and knowledge is applied to the world.

“The broader culture more has become attuned to STS, whether it’s conversations about AI or algorithmic fairness or climate change or energy, these are simultaneously technical and social issues,” Deringer says. “Teaching undergraduates, I’ve found the awareness of that at MIT has only increased.” For both his research and teaching, Deringer received tenure from MIT earlier this year.

Dig in, work outward

Deringer has been focused on these topics since he was an undergraduate at Harvard University.

“I found myself becoming really interested in the history of economics, the history of practical mathematics, data, statistics, and how it came to be that so much of our world is organized quantitatively,” he says.

Deringer wrote a college thesis about how England measured the land it was seizing from Ireland in the 1600s, and then, after graduating, went to work in the finance sector, which gave him a further chance to think about the application of quantification to modern life.

“That was not what I wanted to do forever, but for some of the conceptual questions I was interested in, the societal life of calculations, I found it to be a really interesting space,” Deringer says.

He returned to academia by pursuing his PhD in the history of science at Princeton University. There, in his first year of graduate school, in the archives, Deringer found 18th-century pamphlets about financial calculations concering the value of stock involved in the infamous episode of speculation known as the South Sea Bubble. That became part of his dissertation; skeptics of the South Sea Bubble were among the prominent early voices bringing data into public debates. It has also helped inform his second book.

First, though, Deringer earned his doctorate from Princeton in 2012, then spent three years as a Mellon Postdoctoral Research Fellow at Columbia University. He joined the MIT faculty in 2015. At the Institute, he finished turning his dissertation into the “Calculated Values” book — which won the 2019 Oscar Kenshur Prize for the best book from the Center for Eighteenth-Century Studies at Indiana University, and was co-winner of the 2021 Joseph J. Spengler Prize for best book from the History of Economics Society.

“My method as a scholar is to dig into the technical details, then work outward historically from them,” Deringer says.

A long historical chain

Even as Deringer was writing his first book, the idea for the second one was taking root in his mind. Those South Sea Bubble pamphets he had found while at Princeton incorporated discounting, which was intermittently present in “Calculated Values.” Deringer was intrigued by how adept 18th-century figures were at discounting.

“Something that I thought of as a very modern technique seemed to be really well-known by a lot of people in the 1720s,” he says.

At the same time, a conversation with an academic colleague in philosophy made it clear to Deringer how different conclusions about discounting had become debated in climate change policy. He soon resolved to write the “biography of a calculation” about financial discounting.

“I knew my next book had to be about this,” Deringer says. “I was very interested in the deep historical roots of discounting, and it has a lot of present urgency.”

Deringer says the book will incorporate material about the financing of English cathedrals, the heavy use of discounting in the mining industry during the Industrial Revolution, a revival of discounting in 1960s policy circles, and climate change, among other things. In each case, he is carefully looking at the interests and historical dynamics behind the use of discounting.

“For people who use discounting regularly, it’s like gravity: It’s very obvious that to be rational is to discount the future according to this formula,” Deringer says. “But if you look at history, what is thought of as rational is part of a very long historical chain of people applying this calculation in various ways, and over time that’s just how things are done. I’m really interested in pulling apart that idea that this is a sort of timeless rational calculation, as opposed to a product of this interesting history.”

Working in STS, Deringer notes, has helped encourage him to link together numerous historical time periods into one book about the numerous ways discounting has been used.

“I’m not sure that pursuing a book that stretches from the 17th century to the 21st century is something I would have done in other contexts,” Deringer says. He is also quick to credit his colleagues in STS and in other programs for helping create the scholarly environment in which he is thriving.

“I came in with a really amazing cohort of other scholars in SHASS,” Deringer notes, referring to the MIT School of Humanities, Arts, and Social Sciences. He cites others receiving tenure in the last year such as his STS colleague Robin Scheffler, historian Megan Black, and historian Caley Horan, with whom Deringer has taught graduate classes on the concept of risk in history. In all, Deringer says, the Institute has been an excellent place for him to pursue interdisciplinary work on technical thought in history.

“I work on very old things and very technical things,” Deringer says. “But I’ve found a wonderful welcoming at MIT from people in different fields who light up when they hear what I’m interested in.”

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NASA’s ORCA, AirHARP Projects Paved Way for PACE to Reach Space - NASA

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NASA’s ORCA, AirHARP Projects Paved Way for PACE to Reach Space - NASA

It took the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission just 13 minutes to reach low-Earth orbit from Cape Canaveral Space Force Station in February 2024. It took a network of scientists at NASA and research institutions around the world more than 20 years to carefully craft and test the novel instruments that allow PACE to study the ocean and atmosphere with unprecedented clarity.

In the early 2000s, a team of scientists at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, prototyped the Ocean Radiometer for Carbon Assessment (ORCA) instrument, which ultimately became PACE’s primary research tool: the Ocean Color instrument (OCI). Then, in the 2010s, a team from the University of Maryland, Baltimore County (UMBC), worked with NASA to prototype the Hyper Angular Rainbow Polarimeter (HARP), a shoebox-sized instrument that will collect groundbreaking measurements of atmospheric aerosols.

Neither PACE’s OCI nor HARP2 — a nearly exact copy of the HARP prototype — would exist were it not for NASA’s early investments in novel technologies for Earth observation through competitive grants distributed by the agency’s Earth Science Technology Office (ESTO). Over the last 25 years, ESTO has managed the development of more than 1,100 new technologies for gathering science measurements.

“All of this investment in the tech development early on basically made it much, much easier for us to build the observatory into what it is today,” said Jeremy Werdell, an oceanographer at NASA Goddard and project scientist for PACE.

Charles “Chuck” McClain, who led the ORCA research team until his retirement in 2013, said NASA’s commitment to technology development is a cornerstone of PACE’s success. “Without ESTO, it wouldn’t have happened. It was a long and winding road, getting to where we are today.”

It was ORCA that first demonstrated a telescope rotating at a speed of six revolutions per second could synchronize perfectly with an array of charge-coupled devices — microchips that transform telescopic projections into digital images. This innovation made it possible for OCI to observe hyperspectral shades of ocean color previously unobtainable using space-based sensors.

But what made ORCA especially appealing to PACE was its pedigree of thorough testing. “One really important consideration was technology readiness,” said Gerhard Meister, who took over ORCA after McClain retired and serves as OCI instrument scientist. Compared to other ocean radiometer designs that were considered for PACE, “we had this instrument that was ready, and we had shown that it would work.”

Technology readiness also made HARP an appealing solution to PACE’s polarimeter challenge. Mission engineers needed an instrument powerful enough to ensure PACE’s ocean color measurements weren’t jeopardized by atmospheric interference, but compact enough to fly on the PACE observatory platform.

By the time Vanderlei Martins, an atmospheric scientist at UMBC, first spoke to Werdell about incorporating a version of HARP into PACE in 2016, he had proven the technology with AirHARP, an airplane-mounted version of HARP, and was using an ESTO award to prepare HARP CubeSat for space.

HARP2 relies on the same optical system developed through AirHARP and HARP CubeSat. A wide-angle lens observes Earth’s surface from up to 60 different viewing angles with a spatial resolution of 1.62 miles (2.6kilometers) per pixel, all without any moving parts. This gives researchers a global view of aerosols from a tiny instrument that consumes very little energy.

Were it not for NASA’s early support of AirHARP and HARP CubeSat, said Martins, “I don’t think we would have HARP2 today.” He added: “We achieved every single goal, every single element, and that was because ESTO stayed with us.”

That support continues making a difference to researchers like Jessie Turner, an oceanographer at the University of Connecticut who will use PACE to study algal blooms and water clarity in the Chesapeake Bay.

“For my application that I’m building for early adopters of PACE data, I actually think that polarimeters are going to be really useful because that’s something we haven’t fully done before for the ocean,” Turner said. “Polarimetric data can actually help us see what kind of particles are in the water.”

Without the early development and test-drives of the instruments from McClain’s and Martins’ teams, PACE as we know it wouldn’t exist.

“It all kind of fell in place in a timely manner that allowed us to mature the instruments, along with the science, just in time for PACE,” said McClain.

To explore current opportunities to collaborate with NASA on new technologies for studying Earth, visit ESTO’s open solicitations page here.

By Gage Taylor NASA’s Goddard Space Flight Center, Greenbelt, Md.

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Two MIT teams selected for NSF sustainable materials grants

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Two MIT teams selected for NSF sustainable materials grants

Two teams led by MIT researchers were selected in December 2023 by the U.S. National Science Foundation (NSF) Convergence Accelerator, a part of the TIP Directorate, to receive awards of $5 million each over three years, to pursue research aimed at helping to bring cutting-edge new sustainable materials and processes from the lab into practical, full-scale industrial production. The selection was made after 16 teams from around the country were chosen last year for one-year grants to develop detailed plans for further research aimed at solving problems of sustainability and scalability for advanced electronic products.

Of the two MIT-led teams chosen for this current round of funding, one team, Topological Electric, is led by Mingda Li, an associate professor in the Department of Nuclear Science and Engineering. This team will be finding pathways to scale up sustainable topological materials, which have the potential to revolutionize next-generation microelectronics by showing superior electronic performance, such as dissipationless states or high-frequency response. The other team, led by Anuradha Agarwal, a principal research scientist at MIT’s Materials Research Laboratory, will be focusing on developing new materials, devices, and manufacturing processes for microchips that minimize energy consumption using electronic-photonic integration, and that detect and avoid the toxic or scarce materials used in today’s production methods.

Scaling the use of topological materials

Li explains that some materials based on quantum effects have achieved successful transitions from lab curiosities to successful mass production, such as blue-light LEDs, and giant magnetorestance (GMR) devices used for magnetic data storage. But he says there are a variety of equally promising materials that have shown promise but have yet to make it into real-world applications.

“What we really wanted to achieve is to bring newer-generation quantum materials into technology and mass production, for the benefit of broader society,” he says. In particular, he says, “topological materials are really promising to do many different things.”

Topological materials are ones whose electronic properties are fundamentally protected against disturbance. For example, Li points to the fact that just in the last two years, it has been shown that some topological materials are even better electrical conductors than copper, which are typically used for the wires interconnecting electronic components. But unlike the blue-light LEDs or the GMR devices, which have been widely produced and deployed, when it comes to topological materials, “there’s no company, no startup, there’s really no business out there,” adds Tomas Palacios, the Clarence J. Lebel Professor in Electrical Engineering at MIT and co-principal investigator on Li’s team. Part of the reason is that many versions of such materials are studied “with a focus on fundamental exotic physical properties with little or no consideration on the sustainability aspects,” says Liang Fu, an MIT professor of physics and also a co-PI. Their team will be looking for alternative formulations that are more amenable to mass production.

One possible application of these topological materials is for detecting terahertz radiation, explains Keith Nelson, an MIT professor of chemistry and co-PI. This extremely high-frequency electronics can carry far more information than conventional radio or microwaves, but at present there are no mature electronic devices available that are scalable at this frequency range. “There’s a whole range of possibilities for topological materials” that could work at these frequencies, he says. In addition, he says, “we hope to demonstrate an entire prototype system like this in a single, very compact solid-state platform.”

Li says that among the many possible applications of topological devices for microelectronics devices of various kinds, “we don’t know which, exactly, will end up as a product, or will reach real industrial scaleup. That’s why this opportunity from NSF is like a bridge, which is precious, to allow us to dig deeper to unleash the true potential.”

In addition to Li, Palacios, Fu, and Nelson, the Topological Electric team includes Qiong Ma, assistant professor of physics in Boston College; Farnaz Niroui, assistant professor of electrical engineering and computer science at MIT; Susanne Stemmer, professor of materials at the University of California at Santa Barbara; Judy Cha, professor of materials science and engineering at Cornell University; industrial partners including IBM, Analog Devices, and Raytheon; and professional consultants. “We are taking this opportunity seriously,” Li says. “We really want to see if the topological materials are as good as we show in the lab when being scaled up, and how far we can push to broadly industrialize them.”

Toward sustainable microchip production and use

The microchips behind everything from smartphones to medical imaging are associated with a significant percentage of greenhouse gas emissions today, and every year the world produces more than 50 million metric tons of electronic waste, the equivalent of about 5,000 Eiffel Towers. Further, the data centers necessary for complex computations and huge amount of data transfer — think AI and on-demand video — are growing and will require 10 percent of the world’s electricity by 2030.

“The current microchip manufacturing supply chain, which includes production, distribution, and use, is neither scalable nor sustainable, and cannot continue. We must innovate our way out of this crisis,” says Agarwal.

The name of Agarwal’s team, FUTUR-IC, is a reference to the future of the integrated circuits, or chips, through a global alliance for sustainable microchip manufacturing. Says Agarwal, “We bring together stakeholders from industry, academia, and government to co-optimize across three dimensions: technology, ecology, and workforce. These were identified as key interrelated areas by some 140 stakeholders. With FUTUR-IC we aim to cut waste and CO2-equivalent emissions associated with electronics by 50 percent every 10 years.”

The market for microelectronics in the next decade is predicted to be on the order of a trillion dollars, but most of the manufacturing for the industry occurs only in limited geographical pockets around the world. FUTUR-IC aims to diversify and strengthen the supply chain for manufacturing and packaging of electronics. The alliance has 26 collaborators and is growing. Current external collaborators include the International Electronics Manufacturing Initiative (iNEMI), Tyndall National Institute, SEMI, Hewlett Packard Enterprise, Intel, and the Rochester Institute of Technology.

Agarwal leads FUTUR-IC in close collaboration with others, including, from MIT, Lionel Kimerling, the Thomas Lord Professor of Materials Science and Engineering; Elsa Olivetti, the Jerry McAfee Professor in Engineering; Randolph Kirchain, principal research scientist in the Materials Research Laboratory; and Greg Norris, director of MIT’s Sustainability and Health Initiative for NetPositive Enterprise (SHINE). All are affiliated with the Materials Research Laboratory. They are joined by Samuel Serna, an MIT visiting professor and assistant professor of physics at Bridgewater State University. Other key personnel include Sajan Saini, education director for the Initiative for Knowledge and Innovation in Manufacturing in MIT’s Department of Materials Science and Engineering; Peter O’Brien, a professor from Tyndall National Institute; and Shekhar Chandrashekhar, CEO of iNEMI.

“We expect the integration of electronics and photonics to revolutionize microchip manufacturing, enhancing efficiency, reducing energy consumption, and paving the way for unprecedented advancements in computing speed and data-processing capabilities,” says Serna, who is the co-lead on the project’s technology “vector.”

Common metrics for these efforts are needed, says Norris, co-lead for the ecology vector, adding, “The microchip industry must have transparent and open Life Cycle Assessment (LCA) models and data, which are being developed by FUTUR-IC.” This is especially important given that microelectronics production transcends industries. “Given the scale and scope of microelectronics, it is critical for the industry to lead in the transition to sustainable manufacture and use,” says Kirchain, another co-lead and the co-director of the Concrete Sustainability Hub at MIT. To bring about this cross-fertilization, co-lead Olivetti, also co-director of the MIT Climate and Sustainability Consortium (MCSC), will collaborate with FUTUR-IC to enhance the benefits from microchip recycling, leveraging the learning across industries.

Saini, the co-lead for the workforce vector, stresses the need for agility. “With a workforce that adapts to a practice of continuous upskilling, we can help increase the robustness of the chip-manufacturing supply chain, and validate a new design for a sustainability curriculum,” he says.

“We have become accustomed to the benefits forged by the exponential growth of microelectronic technology performance and market size,” says Kimerling, who is also director of MIT’s Materials Research Laboratory and co-director of the MIT Microphotonics Center. “The ecological impact of this growth in terms of materials use, energy consumption and end-of-life disposal has begun to push back against this progress. We believe that concurrently engineered solutions for these three dimensions will build a common learning curve to power the next 40 years of progress in the semiconductor industry.”

The MIT teams are two of six that received awards addressing sustainable materials for global challenges through phase two of the NSF Convergence Accelerator program. Launched in 2019, the program targets solutions to especially compelling challenges at an accelerated pace by incorporating a multidisciplinary research approach.

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Study demonstrates efficacy of MIT-led Brave Behind Bars program

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Study demonstrates efficacy of MIT-led Brave Behind Bars program

Several years ago, a team of scientists from MIT and the University of Massachusetts at Lowell designed and deployed a first-of-its-kind web programming course for incarcerated individuals across multiple correctional facilities. The program, Brave Behind Bars, uses virtual classroom technology to deliver web design training to students behind prison walls. The program brought together men and women from gender-segregated facilities to learn fundamentals in HTML, CSS, and JavaScript, helping them to create websites addressing social issues of their own choosing.

The program is accredited through three collaborating universities: Georgetown University, Benjamin Franklin Institute of Technology, and Washington County Community College. In a new open-access paper about the project, the team analyzed its impact: They used a multi-pronged approach, gathering insights through comprehensive surveys with participants from dichotomous and open-ended questions. The results painted a powerful narrative of increased self-efficacy — a crucial marker for successful reentry into the workforce and society — among incarcerated learners.

“Education has long been recognized as a pivotal factor in reducing recidivism and fostering successful reentry,” says Martin Nisser, an MIT PhD candidate in electrical engineering and computer science (EECS), affiliate of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), and lead author of the paper. “By equipping incarcerated learners with invaluable digital literacy skills and boosting their self-efficacy, our program aims to foster the skills necessary to thrive in today’s technology-driven world.”

The strength of Brave Behind Bars is manifested vividly through the impactful websites created by the students. One project, “End Homelessness Statewide,” provided vital resources to help unhoused individuals find temporary and permanent shelter. Another website, “The PinkPrint,” addressed the unique challenges incarcerated women face, serving as a “blueprint” with educational resources and gender-responsive support. Equally remarkable was “No Excuse for Domestic Abuse,” which raised awareness about the prevalence of domestic violence while offering a lifeline to victims seeking help.

A mixed-methods research study evaluated how the 12-week, college-accredited course was faring. “Our qualitative study in 2022 involving thematic analyses of post-course surveys from 34 students revealed overwhelmingly positive feedback, with students reporting increased self-confidence, motivation, and a sense of empowerment from learning web programming skills. The themes we uncovered highlighted the powerful effect of the program on students’ self-beliefs,” says Nisser.

The urgency of such work cannot be understated, as underscored by the alarmingly high rates of recidivism, the rate at which formerly incarcerated individuals are rearrested leading to re-conviction. A central cause of mass incarceration, data shows that an estimated 68 percent of people released from U.S. jails or prisons were arrested within three years between 2005 and 2014, rising to 83 percent within nine years. However, a meta-analysis spanning 37 years of research (1980-2017) revealed a promising trend: Incarcerated individuals who participate in post-secondary educational programs are 28 percent less likely to return to prison.

Joblessness among the formerly incarcerated can be as high as 60 percent a year after release. Almost two-thirds of those who secure employment enter jobs typically available to people with little or no education, such as waste management, manufacturing, and construction — jobs increasingly being automated or outsourced. 

While both the demand and supply of AI curricula in higher education have sky-rocketed, these have not typically served disadvantaged people, who must be caught up in foundational digital literacy. The ability to skillfully navigate computers and the internet is becoming essential for post-release employment in the modern workplace, as well as to navigate the economic, social, and health-related resources that are now embedded in our digital technologies.

The other part was a quantitative study in 2023, with 37 participants measuring general computer programming self-efficacy using validated scales before and after the course. The authors saw an increase in mean scores for general self-efficacy and digital literacy after the course, but the pre- and post-course measures of self-efficacy were not statistically significantly different. This challenge, the team says, is common in carceral environments, where meta-analyses of multiple studies with less significant results are often needed to achieve statistical significance and draw meaningful conclusions. The authors also acknowledge that their quantitative study contributes to this data pool, and they are conducting new courses to gather more data for future comprehensive statistical analyses.

“By providing incarcerated individuals with an opportunity to develop digital literacy, the Brave Behind Bars program facilitates self-efficacy through a novel education model designed not only to expand access to the internet for individuals but also to teach them the navigation and web design skills needed to connect and engage with the communities to which they will return,” says UMass Lowell professor and chair of the School of Criminology and Justice Studies April Pattavina, who was not involved in the research. “I applaud the team’s dedication in implementing the program and look forward to longer-term evaluations on graduates when they leave prison so we can learn about the extent to which the program transforms lives on the outside.”

One student, reflecting on the impact of the Brave Behind Bars program, says, “This class has shown me that I am human again, and I deserve to have a better quality of life post-incarceration.” In an environment where individuals can too often be made to feel like numbers, a program is underway to demonstrate that these individuals can be seen once more as people.

The research was conducted by a team of experts from MIT and UMass Lowell. Leading the team was Martin Nisser, who wrote the paper alongside Marisa Gaetz, a PhD student in the MIT Department of Mathematics; Andrew Fishberg, a PhD student in the MIT Department of Aeronautics and Astronautics; and Raechel Soicher, assistant director of research and evaluation at the MIT Teaching and Learning Laboratory. Faraz Faruqi, an MIT PhD student in EECS and CSAIL affiliate, contributed significantly to the project. Completing the team, Joshua Long brought his expertise from UMass Lowell, adding a unique perspective to the collaborative effort.

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A closed-loop drug-delivery system could improve chemotherapy

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A closed-loop drug-delivery system could improve chemotherapy

When cancer patients undergo chemotherapy, the dose of most drugs is calculated based on the patient’s body surface area. This is estimated by plugging the patient’s height and weight into an equation, dating to 1916, that was formulated from data on just nine patients.

This simplistic dosing doesn’t take into account other factors and can lead to patients receiving either too much or too little of a drug. As a result, some patients likely experience avoidable toxicity or insufficient benefit from the chemotherapy they receive.

To make chemotherapy dosing more accurate, MIT engineers have come up with an alternative approach that can enable the dose to be personalized to the patient. Their system measures how much drug is in the patient’s system, and these measurements are fed into a controller that can adjust the infusion rate accordingly.

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This approach could help to compensate for differences in drug pharmacokinetics caused by body composition, genetic makeup, chemotherapy-induced toxicity of the organs that metabolize the drugs, interactions with other medications being taken and foods consumed, and circadian fluctuations in the enzymes responsible for breaking down chemotherapy drugs, the researchers say.

“Recognizing the advances in our understanding of how drugs are metabolized, and applying engineering tools to facilitate personalized dosing, we believe, can help transform the safety and efficacy of many drugs,” says Giovanni Traverso, an associate professor of mechanical engineering at MIT, a gastroenterologist at Brigham and Women’s Hospital, and the senior author of the study.

Louis DeRidder, an MIT graduate student, is the lead author of the paper, which appears today in the journal Med.

Continuous monitoring

In this study, the researchers focused on a drug called 5-fluorouracil, which is used to treat colorectal cancers, among others. The drug is typically infused over a 46-hour period, and the dosage is determined using a formula based on the patient’s height and weight, which gives the estimated body surface area.

However, that approach doesn’t account for differences in body composition that can affect how the drug spreads through the body, or genetic variations that influence how it is metabolized. Those differences can lead to harmful side effects, if too much drug is present. If not enough drug is circulating, it may not kill the tumor as expected.

“People with the same body surface area could have very different heights and weights, could have very different muscle masses or genetics, but as long as the height and the weight plugged into this equation give the same body surface area, their dose is identical,” says DeRidder, a PhD candidate in the Medical Engineering and Medical Physics program within the Harvard-MIT Program in Health Sciences and Technology.

Another factor that can alter the amount of drug in the bloodstream at any given time is circadian fluctuations of an enzyme called dihydropyrimidine dehydrogenase (DPD), which breaks down 5-fluorouracil. DPD’s expression, like many other enzymes in the body, is regulated on a circadian rhythm. Thus, the degradation of 5-FU by DPD is not constant but changes according to the time of the day. These circadian rhythms can lead to tenfold fluctuations in the amount of 5-fluorouracil in a patient’s bloodstream over the course of an infusion.

“Using body surface area to calculate a chemotherapy dose, we know that two people can have profoundly different toxicity from 5-fluorouracil chemotherapy. Looking at one patient, they can have cycles of treatment with minimal toxicity and then have a cycle with miserable toxicity. Something changed in how that patient metabolized chemo from one cycle to the next. Our antiquated dosing fails to capture that change, and patients suffer as a result,” says Douglas Rubinson, a clinical oncologist at Dana-Farber Cancer Institute and an author of the paper.

One way to try to counteract the variability in chemotherapy pharmacokinetics is a strategy called therapeutic drug monitoring, in which the patient gives a blood sample at the end of one treatment cycle. After this sample is analyzed for the drug concentration, the dosage can be adjusted, if needed, at the beginning of the next cycle (usually two weeks later for 5-fluorouracil). This approach has been shown to result in better outcomes for patients, but it is not widely used for chemotherapies such as 5-fluorouracil.

The MIT researchers wanted to develop a similar type of monitoring, but in a manner that is automated and enables real-time drug personalization, which could result in better outcomes for patients. In their “closed-loop” system, drug concentrations can be continually monitored, and that information is used to automatically adjust the infusion rate of the chemotherapy drug and keep the dose within the target range. Such a closed-loop system enables personalization of the drug dose in a manner that considers circadian rhythm changes in the levels of drug-metabolizing enzymes, as well as any changes in the patient’s pharmacokinetics since their last treatment, such as chemotherapy-induced toxicity of the organs that metabolize the drugs.

The new system they designed, known as CLAUDIA (Closed-Loop AUtomated Drug Infusion regulAtor), makes use of commercially available equipment for each step. Blood samples are taken every five minutes and rapidly prepared for analysis. The concentration of 5-fluorouracil in the blood is measured and compared to the target range. The difference between the target and measured concentration is input to a control algorithm, which then adjusts the infusion rate if necessary, to keep the dose within the range of concentrations between which the drug is effective and nontoxic.

“What we’ve developed is a system where you can constantly measure the concentration of drug and adjust the infusion rate accordingly, to keep the drug concentration within the therapeutic window,” DeRidder says.

Rapid adjustment

In tests in animals, the researchers found that using CLAUDIA, they could keep the amount of drug circulating in the body within the target range around 45 percent of the time. Drug levels in animals that received chemotherapy without CLAUDIA remained in the target range only 13 percent of the time, on average. In this study, the researchers did not do any tests of the effectiveness of the drug levels, but keeping the concentration within the target window is believed to lead to better outcomes and less toxicity.

CLAUDIA was also able to keep the dose of 5-fluorouracil within the target range even when the researchers administered a drug that inhibits the DPD enzyme. In animals that received this inhibitor without continuous monitoring and adjustment, levels of 5-fluorouracil increased by up to eightfold.

For this demonstration, the researchers manually performed each step of the process, using off-the-shelf equipment, but they now plan to work on automating each step so that the monitoring and dose adjustment can be done without any human intervention.

To measure drug concentrations, the researchers used high-performance liquid chromatography mass spectroscopy (HPLC-MS), a technique that could be adapted to detect nearly any type of drug.

“We foresee a future where we’re able to use CLAUDIA for any drug that has the right pharmacokinetic properties and is detectable with HPLC-MS, thereby enabling the personalization of dosing for many different drugs,” DeRidder says.

The research was funded by the National Science Foundation Graduate Research Fellowship Program, a MathWorks Fellowship, MIT’s Karl van Tassel Career Development Professorship, the MIT Department of Mechanical Engineering, and the Bridge Project, a partnership between the Koch Institute for Integrative Cancer Research at MIT and the Dana-Farber/Harvard Cancer Center.

Other authors of the paper include Kyle A. Hare, Aaron Lopes, Josh Jenkins, Nina Fitzgerald, Emmeline MacPherson, Niora Fabian, Josh Morimoto, Jacqueline N. Chu, Ameya R. Kirtane, Wiam Madani, Keiko Ishida, Johannes L. P. Kuosmanen, Naomi Zecharias, Christopher M. Colangelo, Hen-Wei Huang, Makaya Chilekwa, Nikhil B. Lal, Shriya S. Srinivasan, Alison M Hayward, Brian M. Wolpin, David Trumper, Troy Quast, and Robert Langer.

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MIT scientists tune the entanglement structure in an array of qubits

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MIT scientists tune the entanglement structure in an array of qubits

Entanglement is a form of correlation between quantum objects, such as particles at the atomic scale. This uniquely quantum phenomenon cannot be explained by the laws of classical physics, yet it is one of the properties that explains the macroscopic behavior of quantum systems.

Because entanglement is central to the way quantum systems work, understanding it better could give scientists a deeper sense of how information is stored and processed efficiently in such systems.

Qubits, or quantum bits, are the building blocks of a quantum computer. However, it is extremely difficult to make specific entangled states in many-qubit systems, let alone investigate them. There are also a variety of entangled states, and telling them apart can be challenging.

Now, MIT researchers have demonstrated a technique to efficiently generate entanglement among an array of superconducting qubits that exhibit a specific type of behavior.

Over the past years, the researchers at the Engineering Quantum Systems (EQuS) group have developed techniques using microwave technology to precisely control a quantum processor composed of superconducting circuits. In addition to these control techniques, the methods introduced in this work enable the processor to efficiently generate highly entangled states and shift those states from one type of entanglement to another — including between types that are more likely to support quantum speed-up and those that are not.

“Here, we are demonstrating that we can utilize the emerging quantum processors as a tool to further our understanding of physics. While everything we did in this experiment was on a scale which can still be simulated on a classical computer, we have a good roadmap for scaling this technology and methodology beyond the reach of classical computing,” says Amir H. Karamlou ’18, MEng ’18, PhD ’23, the lead author of the paper.

The senior author is William D. Oliver, the Henry Ellis Warren professor of electrical engineering and computer science and of physics, director of the Center for Quantum Engineering, leader of the EQuS group, and associate director of the Research Laboratory of Electronics. Karamlou and Oliver are joined by Research Scientist Jeff Grover, postdoc Ilan Rosen, and others in the departments of Electrical Engineering and Computer Science and of Physics at MIT, at MIT Lincoln Laboratory, and at Wellesley College and the University of Maryland. The research appears today in Nature.

Assessing entanglement

In a large quantum system comprising many interconnected qubits, one can think about entanglement as the amount of quantum information shared between a given subsystem of qubits and the rest of the larger system.

The entanglement within a quantum system can be categorized as area-law or volume-law, based on how this shared information scales with the geometry of subsystems. In volume-law entanglement, the amount of entanglement between a subsystem of qubits and the rest of the system grows proportionally with the total size of the subsystem.

On the other hand, area-law entanglement depends on how many shared connections exist between a subsystem of qubits and the larger system. As the subsystem expands, the amount of entanglement only grows along the boundary between the subsystem and the larger system.

In theory, the formation of volume-law entanglement is related to what makes quantum computing so powerful.

“While have not yet fully abstracted the role that entanglement plays in quantum algorithms, we do know that generating volume-law entanglement is a key ingredient to realizing a quantum advantage,” says Oliver.

However, volume-law entanglement is also more complex than area-law entanglement and practically prohibitive at scale to simulate using a classical computer.

“As you increase the complexity of your quantum system, it becomes increasingly difficult to simulate it with conventional computers. If I am trying to fully keep track of a system with 80 qubits, for instance, then I would need to store more information than what we have stored throughout the history of humanity,” Karamlou says.

The researchers created a quantum processor and control protocol that enable them to efficiently generate and probe both types of entanglement.

Their processor comprises superconducting circuits, which are used to engineer artificial atoms. The artificial atoms are utilized as qubits, which can be controlled and read out with high accuracy using microwave signals.

The device used for this experiment contained 16 qubits, arranged in a two-dimensional grid. The researchers carefully tuned the processor so all 16 qubits have the same transition frequency. Then, they applied an additional microwave drive to all of the qubits simultaneously.

If this microwave drive has the same frequency as the qubits, it generates quantum states that exhibit volume-law entanglement. However, as the microwave frequency increases or decreases, the qubits exhibit less volume-law entanglement, eventually crossing over to entangled states that increasingly follow an area-law scaling.

Careful control

“Our experiment is a tour de force of the capabilities of superconducting quantum processors. In one experiment, we operated the processor both as an analog simulation device, enabling us to efficiently prepare states with different entanglement structures, and as a digital computing device, needed to measure the ensuing entanglement scaling,” says Rosen.

To enable that control, the team put years of work into carefully building up the infrastructure around the quantum processor.

By demonstrating the crossover from volume-law to area-law entanglement, the researchers experimentally confirmed what theoretical studies had predicted. More importantly, this method can be used to determine whether the entanglement in a generic quantum processor is area-law or volume-law.

“The MIT experiment underscores the distinction between area-law and volume-law entanglement in two-dimensional quantum simulations using superconducting qubits. This beautifully complements our work on entanglement Hamiltonian tomography with trapped ions in a parallel publication published in Nature in 2023,” says Peter Zoller, a professor of theoretical physics at the University of Innsbruck, who was not involved with this work.

“Quantifying entanglement in large quantum systems is a challenging task for classical computers but a good example of where quantum simulation could help,” says Pedram Roushan of Google, who also was not involved in the study. “Using a 2D array of superconducting qubits, Karamlou and colleagues were able to measure entanglement entropy of various subsystems of various sizes. They measure the volume-law and area-law contributions to entropy, revealing crossover behavior as the system’s quantum state energy is tuned. It powerfully demonstrates the unique insights quantum simulators can offer.”

In the future, scientists could utilize this technique to study the thermodynamic behavior of complex quantum systems, which is too complex to be studied using current analytical methods and practically prohibitive to simulate on even the world’s most powerful supercomputers.

“The experiments we did in this work can be used to characterize or benchmark larger-scale quantum systems, and we may also learn something more about the nature of entanglement in these many-body systems,” says Karamlou.

Additional co-authors of the study are Sarah E. Muschinske, Cora N. Barrett, Agustin Di Paolo, Leon Ding, Patrick M. Harrington, Max Hays, Rabindra Das, David K. Kim, Bethany M. Niedzielski, Meghan Schuldt, Kyle Serniak, Mollie E. Schwartz, Jonilyn L. Yoder, Simon Gustavsson, and Yariv Yanay.

This research is funded, in part, by the U.S. Department of Energy, the U.S. Defense Advanced Research Projects Agency, the U.S. Army Research Office, the National Science Foundation, the STC Center for Integrated Quantum Materials, the Wellesley College Samuel and Hilda Levitt Fellowship, NASA, and the Oak Ridge Institute for Science and Education.

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Ian Waitz named vice president for research

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Ian Waitz named vice president for research

In a letter to the MIT community today, President Sally Kornbluth announced the appointment of Ian A. Waitz to the position of vice president for research. In the role, Waitz will report to the president and oversee MIT’s vast research enterprise. The appointment is effective May 1.

Waitz, who is also the Jerome C. Hunsaker Professor of Aeronautics and Astronautics, brings deep knowledge of MIT to the position. Over more than 30 years, he has served in a wide range of roles across the Institute, where he has made his mark through energy, optimism, persistence, and a commitment to MIT’s mission of using education and innovation to create a better world.

“Ian brings a rare range and depth of understanding of MIT’s research and educational enterprise, our daily operations, our institutional challenges and opportunities, our history and our values — and an unmatched record of solving hard problems and getting big, high-stakes things done well,” Kornbluth wrote. 

“MIT’s research enterprise is a critical part of our mission, not just for the impact that innovation and discovery have on the world, but also for the way it enables us to educate people by giving them problems that no one else has ever solved before,” Waitz says. “That builds a sort of intellectual capacity and resilience to work on really hard problems, and the nation and the world need us to work on hard problems.”

Waitz will step down from his current role as vice chancellor overseeing undergraduate and graduate education, where he was instrumental in advancing the priorities of the Chancellor’s Office, currently led by Melissa Nobles.

In that role, which he has held since 2017, Waitz worked with students, faculty, and staff from across the Institute to revamp the first-year undergraduate academic experience, helped steer the Institute through the Covid-19 pandemic, and led efforts to respond to graduate student unionization. Waitz also led a strategic restructuring to integrate the former offices of the Dean for Undergraduate Education and the Dean for Graduate Education, creating the Office of the Vice Chancellor and leading to a more aligned and efficient organization. And, he spearheaded projects to expand professional development opportunities for graduate students, created the MIT Undergraduate Advising Center, worked to significantly expand undergraduate financial aid, and broadly expanded support for graduate students.

“I think my experience gives me a unique perspective on research and education at MIT,” Waitz says. “Education is obviously an amazing part of MIT, and working with students bridges education and the research. That’s one of the things that’s special about a research university. I’m excited for this new role and to continue to work to further strengthen MIT’s exceptional research enterprise.”

Waitz will be filling a role previously held by Maria Zuber, the E. A. Griswold Professor of Geophysics, who now serves as MIT’s presidential advisor for science and technology policy. Waitz says he’s eager to dive in and work to identify ways to help MIT’s prolific research engine run more smoothly. The move is just the latest example of Waitz leaning into new opportunities in service to MIT.

Prior to assuming his current role as vice chancellor, Waitz served as the dean of the School of Engineering between 2011 and 2017, supporting the school’s ability to attract and support exceptional students and faculty. He oversaw the launch of programs including the Institute for Data, Systems, and Society (IDSS), the Institute for Medical Engineering and Science (IMES), the Sandbox Innovation Fund, and the MIT Beaver Works program with Lincoln Laboratory. He also strengthened co-curricular and enrichment programs for undergraduate and graduate students, and worked with department heads to offer more flexible degrees.

Prior to that, Waitz served as the head of MIT’s Department of Aeronautics and Astronautics, where he has been a faculty member since 1991. His research focuses on developing technological, operational, and policy options to mitigate the environmental impacts of aviation. He is a member of the National Academy of Engineering, a fellow of the American Institute of Aeronautics and Astronautics, and has worked closely with industry and government throughout his career.

“One lesson I’ve learned is that the greatest strength of MIT is our students, faculty, and staff,” Waitz says. “We identify people who are real intellectual entrepreneurs. Those are the people that really thrive here, and what you want to do is create a low-friction, high-resource environment for them. Amazing things bubble up from that.”

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Circadian rhythms can influence drugs’ effectiveness

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Circadian rhythms can influence drugs’ effectiveness

Giving drugs at different times of day could significantly affect how they are metabolized in the liver, according to a new study from MIT.

Using tiny, engineered livers derived from cells from human donors, the researchers found that many genes involved in drug metabolism are under circadian control. These circadian variations affect how much of a drug is available and how effectively the body can break it down. For example, they found that enzymes that break down Tylenol and other drugs are more abundant at certain times of day.

Overall, the researchers identified more than 300 liver genes that follow a circadian clock, including many involved in drug metabolism, as well as other functions such as inflammation. Analyzing these rhythms could help researchers develop better dosing schedules for existing drugs.

“One of the earliest applications for this method could be fine-tuning drug regimens of already approved drugs to maximize their efficacy and minimize their toxicity,” says Sangeeta Bhatia, the John and Dorothy Wilson Professor of Health Sciences and Technology and of Electrical Engineering and Computer Science at MIT, and a member of MIT’s Koch Institute for Integrative Cancer Research and the Institute for Medical Engineering and Science (IMES).

The study also revealed that the liver is more susceptible to infections such as malaria at certain points in the circadian cycle, when fewer inflammatory proteins are being produced.

Bhatia is the senior author of the new study, which appears today in Science Advances. The paper’s lead author is Sandra March, a research scientist in IMES.

Metabolic cycles

It is estimated that about 50 percent of human genes follow a circadian cycle, and many of these genes are active in the liver. However, exploring how circadian cycles affect liver function has been difficult because many of these genes are not identical in mice and humans, so mouse models can’t be used to study them.

Bhatia’s lab has previously developed a way to grow miniaturized livers using liver cells called hepatocytes, from human donors. In this study, she and her colleagues set out to investigate whether these engineered livers have their own circadian clocks.

Working with Charles Rice’s group at Rockefeller University, they identified culture conditions that support the circadian expression of a clock gene called Bmal1. This gene, which regulates the cyclic expression of a wide range of genes, allowed the liver cells to develop synchronized circadian oscillations. Then, the researchers measured gene expression in these cells every three hours for 48 hours, enabling them to identify more than 300 genes that were expressed in waves.

Most of these genes clustered in two groups — about 70 percent of the genes peaked together, while the remaining 30 percent were at their lowest point when the others peaked. These included genes involved in a variety of functions, including drug metabolism, glucose and lipid metabolism, and several immune processes.

Once the engineered livers established these circadian cycles, the researchers could use them to explore how circadian cycles affect liver function. First, they set out to study how time of day would affect drug metabolism, looking at two different drugs — acetaminophen (Tylenol) and atorvastatin, a drug used to treat high cholesterol.

When Tylenol is broken down in the liver, a small fraction of the drug is converted into a toxic byproduct known as NAPQI. The researchers found that the amount of NAPQI produced can vary by up to 50 percent, depending on what time of day the drug is administered. They also found that atorvastatin generates higher toxicity at certain times of day.

Both of these drugs are metabolized in part by an enzyme called CYP3A4, which has a circadian cycle. CYP3A4 is involved in processing about 50 percent of all drugs, so the researchers now plan to test more of those drugs using their liver models.

“In this set of drugs, it will be helpful to identify the time of the day to administer the drug to reach the highest effectiveness of the drug and minimize the adverse effects,” March says.

The MIT researchers are now working with collaborators to analyze a cancer drug they suspect may be affected by circadian cycles, and they hope to investigate whether this may also be true of drugs used in pain management.

Susceptibility to infection

Many of the liver genes that show circadian behavior are involved in immune responses such as inflammation, so the researchers wondered if this variation might influence susceptibility to infection. To answer that question, they exposed the engineered livers to Plasmodium falciparum, a parasite that causes malaria, at different points in the circadian cycle.

These studies revealed that the livers were more likely to become infected after exposure at different times of day. This is due to variations in the expression of genes called interferon-stimulated genes, which help to suppress infections.

“The inflammatory signals are much stronger at certain times of days than others,” Bhatia says. “This means that a virus like hepatitis or parasite like the one that causes malaria might be better at taking hold in your liver at certain times of the day.”

The researchers believe this cyclical variation may occur because the liver dampens its response to pathogens following meals, when it is typically exposed to an influx of microorganisms that might trigger inflammation even if they are not actually harmful.

Bhatia’s lab is now taking advantage of these cycles to study infections that are usually difficult to establish in engineered livers, including malaria infections caused by parasites other than Plasmodium falciparum.

“This is quite important for the field, because just by setting up the system and choosing the right time of infection, we can increase the infection rate of our culture by 25 percent, enabling drug screens that were otherwise impractical,” March says.

The research was funded by the MIT International Science and Technology Initiatives MIT-France program, the Koch Institute Support (core) Grant from the U.S. National Cancer Institute, the National Institute of Health and Medical Research of France, and the French National Research Agency.

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MIT announces 2024 Bose Grants

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MIT announces 2024 Bose Grants

MIT Provost Cynthia Barnhart announced four Professor Amar G. Bose Research Grants to support bold research projects across diverse areas of study, including a way to generate clean hydrogen from deep in the Earth, build an environmentally friendly house of basalt, design maternity clothing that monitors fetal health, and recruit sharks as ocean oxygen monitors.

This year’s recipients are Iwnetim Abate, assistant professor of materials science and engineering; Andrew Babbin, the Cecil and Ida Green Associate Professor in Earth, Atmospheric and Planetary Sciences; Yoel Fink, professor of materials science and engineering and of electrical engineering and computer science; and Skylar Tibbits, associate professor of design research in the Department of Architecture.

The program was named for the visionary founder of the Bose Corporation and MIT alumnus Amar G. Bose ’51, SM ’52, ScD ’56. After gaining admission to MIT, Bose became a top math student and a Fulbright Scholarship recipient. He spent 46 years as a professor at MIT, led innovations in sound design, and founded the Bose Corp. in 1964. MIT launched the Bose grant program 11 years ago to provide funding over a three-year period to MIT faculty who propose original, cross-disciplinary, and often risky research projects that would likely not be funded by conventional sources.

“The promise of the Bose Fellowship is to help bold, daring ideas become realities, an approach that honors Amar Bose’s legacy,” says Barnhart. “Thanks to support from this program, these talented faculty members have the freedom to explore their bold and innovative ideas.”

Deep and clean hydrogen futures

A green energy future will depend on harnessing hydrogen as a clean energy source, sequestering polluting carbon dioxide, and mining the minerals essential to building clean energy technologies such as advanced batteries. Iwnetim Abate thinks he has a solution for all three challenges: an innovative hydrogen reactor.

He plans to build a reactor that will create natural hydrogen from ultramafic mineral rocks in the crust. “The Earth is literally a giant hydrogen factory waiting to be tapped,” Abate explains. “A back-of-the-envelope calculation for the first seven kilometers of the Earth’s crust estimates that there is enough ultramafic rock to produce hydrogen for 250,000 years.”

The reactor envisioned by Abate injects water to create a reaction that releases hydrogen, while also supporting the injection of climate-altering carbon dioxide into the rock, providing a global carbon capacity of 100 trillion tons. At the same time, the reactor process could provide essential elements such as lithium, nickel, and cobalt — some of the most important raw materials used in advanced batteries and electronics.

“Ultimately, our goal is to design and develop a scalable reactor for simultaneously tapping into the trifecta from the Earth’s subsurface,” Abate says.

Sharks as oceanographers

If we want to understand more about how oxygen levels in the world’s seas are disturbed by human activities and climate change, we should turn to a sensing platform “that has been honed by 400 million years of evolution to perfectly sample the ocean: sharks,” says Andrew Babbin.

As the planet warms, oceans are projected to contain less dissolved oxygen, with impacts on the productivity of global fisheries, natural carbon sequestration, and the flux of climate-altering greenhouse gasses from the ocean to the air. While scientists know dissolved oxygen is important, it has proved difficult to track over seasons, decades, and underexplored regions both shallow and deep.

Babbin’s goal is to develop a low-cost sensor for dissolved oxygen that can be integrated with preexisting electronic shark tags used by marine biologists. “This fleet of sharks … will finally enable us to measure the extent of the low-oxygen zones of the ocean, how they change seasonally and with El Niño/La Niña oscillation, and how they expand or contract into the future.”

The partnership with sharks will also spotlight the importance of these often-maligned animals for global marine and fisheries health, Babbin says. “We hope in pursuing this work marrying microscopic and macroscopic life we will inspire future oceanographers and conservationists, and lead to a better appreciation for the chemistry that underlies global habitability.”

Maternity wear that monitors fetal health

There are 2 million stillbirths around the world each year, and in the United States alone, 21,000 families suffer this terrible loss. In many cases, mothers and their doctors had no warning of any abnormalities or changes in fetal health leading up to these deaths. Yoel Fink and colleagues are looking for a better way to monitor fetal health and provide proactive treatment.

Fink is building on years of research on acoustic fabrics to design an affordable shirt for mothers that would monitor and communicate important details of fetal health. His team’s original research drew inspiration from the function of the eardrum, designing a fiber that could be woven into other fabrics to create a kind of fabric microphone.

“Given the sensitivity of the acoustic fabrics in sensing these nanometer-scale vibrations, could a mother’s clothing transcend its conventional role and become a health monitor, picking up on the acoustic signals and subsequent vibrations that arise from her unborn baby’s heartbeat and motion?” Fink says. “Could a simple and affordable worn fabric allow an expecting mom to sleep better, knowing that her fetus is being listened to continuously?”

The proposed maternity shirt could measure fetal heart and breathing rate, and might be able to give an indication of the fetal body position, he says. In the final stages of development, he and his colleagues hope to develop machine learning approaches that would identify abnormal fetal heart rate and motion and deliver real-time alerts.

A basalt house in Iceland

In the land of volcanoes, Skylar Tibbits wants to build a case-study home almost entirely from the basalt rock that makes up the Icelandic landscape.

Architects are increasingly interested in building using one natural material — creating a monomaterial structure — that can be easily recycled. At the moment, the building industry represents 40 percent of carbon emissions worldwide, and consists of many materials and structures, from metal to plastics to concrete, that can’t be easily disassembled or reused.

The proposed basalt house in Iceland, a project co-led by J. Jih, associate professor of the practice in the Department of Architecture, is “an architecture that would be fully composed of the surrounding earth, that melts back into that surrounding earth at the end of its lifespan, and that can be recycled infinitely,” Tibbits explains.

Basalt, the most common rock form in the Earth’s crust, can be spun into fibers for insulation and rebar. Basalt fiber performs as well as glass and carbon fibers at a lower cost in some applications, although it is not widely used in architecture. In cast form, it can make corrosion- and heat-resistant plumbing, cladding and flooring.

“A monomaterial architecture is both a simple and radical proposal that unfortunately falls outside of traditional funding avenues,” says Tibbits. “The Bose grant is the perfect and perhaps the only option for our research, which we see as a uniquely achievable moonshot with transformative potential for the entire built environment.”

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Geologists discover rocks with the oldest evidence yet of Earth’s magnetic field

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Geologists discover rocks with the oldest evidence yet of Earth’s magnetic field

Geologists at MIT and Oxford University have uncovered ancient rocks in Greenland that bear the oldest remnants of Earth’s early magnetic field.

The rocks appear to be exceptionally pristine, having preserved their properties for billions of years. The researchers determined that the rocks are about 3.7 billion years old and retain signatures of a magnetic field with a strength of at least 15 microtesla. The ancient field is similar in magnitude to the Earth’s magnetic field today.

The open-access findings, appearing today in the Journal of Geophysical Research, represent some of the earliest evidence of a magnetic field surrounding the Earth. The results potentially extend the age of the Earth’s magnetic field by hundreds of millions of years, and may shed light on the planet’s early conditions that helped life take hold.

Claire Nichols and colleagues stand on the outcrop of a banded iron formation containing the oldest records of Earth’s magnetic field. The Greenland ice sheet is in the background.

Credit: Claire Nichols

“The magnetic field is, in theory, one of the reasons we think Earth is really unique as a habitable planet,” says Claire Nichols, a former MIT postdoc who is now an associate professor of the geology of planetary processes at Oxford University. “It’s thought our magnetic field protects us from harmful radiation from space, and also helps us to have oceans and atmospheres that can be stable for long periods of time.”

Previous studies have shown evidence for a magnetic field on Earth that is at least 3.5 billion years old. The new study is extending the magnetic field’s lifetime by another 200 million years.

“That’s important because that’s the time when we think life was emerging,” says Benjamin Weiss, the Robert R. Shrock Professor of Planetary Sciences in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “If the Earth’s magnetic field was around a few hundred million years earlier, it could have played a critical role in making the planet habitable.”

Nichols and Weiss are co-authors of the new study, which also includes Craig Martin and Athena Eyster at MIT, Adam Maloof at Princeton University, and additional colleagues from institutions including Tufts University and the University of Colorado at Boulder.

A slow churn

Today, the Earth’s magnetic field is powered by its molten iron core, which slowly churns up electric currents in a self-generating “dynamo.” The resulting magnetic field extends out and around the planet like a protective bubble. Scientists suspect that, early in its evolution, the Earth was able to foster life, in part due to an early magnetic field that was strong enough to retain a life-sustaining atmosphere and simultaneously shield the planet from damaging solar radiation.

Exactly how early and robust this magnetic shield was is up for debate, though there has been evidence dating its existence to about 3.5 billion years ago.

“We wanted to see if we could extend this record back beyond 3.5 billion years and nail down how strong that early field was,” Nichols says.

In 2018, as a postdoc working in Weiss’ lab at the time, Nichols and her team set off on an expedition to the Isua Supracrustal Belt, a 20-mile stretch of exposed rock formations surrounded by towering ice sheets in the southwest of Greenland. There, scientists have discovered the oldest preserved rocks on Earth, which have been extensively studied in hopes of answering a slew of scientific questions about Earth’s ancient conditions.

For Nichols and Weiss, the objective was to find rocks that still held signatures of the Earth’s magnetic field when the rocks first formed. Rocks form through many millions of years, as grains of sediment and minerals accumulate and are progressively packed and buried under subsequent deposition over time. Any magnetic minerals such as iron-oxides that are in the deposits follow the pull of the Earth’s magnetic field as they form. This collective orientation, and the imprint of the magnetic field, are preserved in the rocks.

However, this preserved magnetic field can be scrambled and completely erased if the rocks subsequently undergo extreme thermal or aqueous events such as hydrothermal activity or plate tectonics that can pressurize and crush up these deposits. Determining the age of a magnetic field in ancient rocks has therefore been a highly contested area of study.

To get to rocks that were hopefully preserved and unaltered since their original deposition, the team sampled from rock formations in the Isua Supracrustal Belt, a remote location that was only accessible by helicopter.

“It’s about 150 kilometers away from the capital city, and you get helicoptered in, right up against the ice sheet,” Nichols says. “Here, you have the world’s oldest rocks essentially, surrounded by this dramatic expression of the ice age. It’s a really spectacular place.”

Dynamic history

The team returned to MIT with whole rock samples of banded iron formations — a rock type that appears as stripes of iron-rich and silica-rich rock. The iron-oxide minerals found in these rocks can act as tiny magnets that orient with any external magnetic field. Given their composition, the researchers suspect the rocks were originally formed in primordial oceans prior to the rise in atmospheric oxygen around 2.5 billion years ago.

“Back when there wasn’t oxygen in the atmosphere, iron didn’t oxidize so easily, so it was in solution in the oceans until it reached a critical concentration, when it precipitated out,” Nichols explains. “So, it’s basically a result of iron raining out of the oceans and depositing on the seafloor.”

“They’re very beautiful, weird rocks that don’t look like anything that forms on Earth today,” Weiss adds.

Previous studies had used uranium-lead dating to determine the age of the iron oxides in these rock samples. The ratio of uranium to lead (U-Pb) gives scientists an estimate of a rock’s age. This analysis found that some of the magnetized minerals were likely about 3.7 billion years old. The MIT team, in collaboration with researchers from Rensselaer Polytechnic Institute, showed in a paper published last year that the U-Pb age also dates the age of the magnetic record in these minerals.

The researchers then set out to determine whether the ancient rocks preserved magnetic field from that far back, and how strong that field might have been.

“The samples we think are best and have that very old signature, we then demagnetize in the lab, in steps. We apply a laboratory field that we know the strength of, and we remagnetize the rocks in steps, so you can compare the gradient of the demagnetization to the gradient of the lab magnetization. That gradient tells you how strong the ancient field was,” Nichols explains.

Through this careful process of remagnetization, the team concluded that the rocks likely harbored an ancient, 3.7-billion-year-old magnetic field, with a magnitude of at least 15 microtesla. Today, Earth’s magnetic field measures around 30 microtesla.

“It’s half the strength, but the same order of magnitude,” Nichols says. “The fact that it’s similar in strength as today’s field implies whatever is driving Earth’s magnetic field has not changed massively in power over billions of years.”

The team’s experiments also showed that the rocks retained the ancient field, despite having undergone two subsequent thermal events. Any extreme thermal event, such as a tectonic shake-up of the subsurface or hydrothermal eruptions, could potentially heat up and erase a rock’s magnetic field. But the team found that the iron in their samples likely oriented, then crystallized, 3.7 billion years ago, in some initial, extreme thermal event. Around 2.8 billion years ago, and then again at 1.5 billion years ago, the rocks may have been reheated, but not to the extreme temperatures that would have scrambled their magnetization.

“The rocks that the team has studied have experienced quite a bit during their long geological journey on our planet,” says Annique van der Boon, a planetary science researcher at the University of Oslo who was not involved in the study. “The authors have done a lot of work on constraining which geological events have affected the rocks at different times.” 

“The team have taken their time to deliver a very thorough study of these complex rocks, which do not give up their secrets easily,” says Andy Biggin, professor of geomagnetism at the University of Liverpool, who did not contribute to the study. “These new results tell us that the Earth’s magnetic field was alive and well 3.7 billion years ago. Knowing it was there and strong contributes a significant boundary constraint on the early Earth’s environment.”

The results also raise questions about how the ancient Earth could have powered such a robust magnetic field. While today’s field is powered by crystallization of the solid iron inner core, it’s thought that the inner core had not yet formed so early in the planet’s evolution.

“It seems like evidence for whatever was generating a magnetic field back then was a different power source from what we have today,” Weiss says. “And we care about Earth because there’s life here, but it’s also a touchstone for understanding other terrestrial planets. It suggests planets throughout the galaxy probably have lots of ways of powering a magnetic field, which is important for the question of habitability elsewhere.”

This research was supported, in part, by the Simons Foundation.

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Mapping the brain pathways of visual memorability

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Mapping the brain pathways of visual memorability

For nearly a decade, a team of MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have been seeking to uncover why certain images persist in a people’s minds, while many others fade. To do this, they set out to map the spatio-temporal brain dynamics involved in recognizing a visual image. And now for the first time, scientists harnessed the combined strengths of magnetoencephalography (MEG), which captures the timing of brain activity, and functional magnetic resonance imaging (fMRI), which identifies active brain regions, to precisely determine when and where the brain processes a memorable image. 

Their open-access study, published this month in PLOS Biology, used 78 pairs of images matched for the same concept but differing in their memorability scores — one was highly memorable and the other was easy to forget. These images were shown to 15 subjects, with scenes of skateboarding, animals in various environments, everyday objects like cups and chairs, natural landscapes like forests and beaches, urban scenes of streets and buildings, and faces displaying different expressions. What they found was that a more distributed network of brain regions than previously thought are actively involved in the encoding and retention processes that underpin memorability. 

“People tend to remember some images better than others, even when they are conceptually similar, like different scenes of a person skateboarding,” says Benjamin Lahner, an MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and first author of the study. “We’ve identified a brain signature of visual memorability that emerges around 300 milliseconds after seeing an image, involving areas across the ventral occipital cortex and temporal cortex, which processes information like color perception and object recognition. This signature indicates that highly memorable images prompt stronger and more sustained brain responses, especially in regions like the early visual cortex, which we previously underestimated in memory processing.”

While highly memorable images maintain a higher and more sustained response for about half a second, the response to less memorable images quickly diminishes. This insight, Lahner elaborated, could redefine our understanding of how memories form and persist. The team envisions this research holding potential for future clinical applications, particularly in early diagnosis and treatment of memory-related disorders. 

The MEG/fMRI fusion method, developed in the lab of CSAIL Senior Research Scientist Aude Oliva, adeptly captures the brain’s spatial and temporal dynamics, overcoming the traditional constraints of either spatial or temporal specificity. The fusion method had a little help from its machine-learning friend, to better examine and compare the brain’s activity when looking at various images. They created a “representational matrix,” which is like a detailed chart, showing how similar neural responses are in various brain regions. This chart helped them identify the patterns of where and when the brain processes what we see.

Picking the conceptually similar image pairs with high and low memorability scores was the crucial ingredient to unlocking these insights into memorability. Lahner explained the process of aggregating behavioral data to assign memorability scores to images, where they curated a diverse set of high- and low-memorability images with balanced representation across different visual categories. 

Despite strides made, the team notes a few limitations. While this work can identify brain regions showing significant memorability effects, it cannot elucidate the regions’ function in how it is contributing to better encoding/retrieval from memory.

“Understanding the neural underpinnings of memorability opens up exciting avenues for clinical advancements, particularly in diagnosing and treating memory-related disorders early on,” says Oliva. “The specific brain signatures we’ve identified for memorability could lead to early biomarkers for Alzheimer’s disease and other dementias. This research paves the way for novel intervention strategies that are finely tuned to the individual’s neural profile, potentially transforming the therapeutic landscape for memory impairments and significantly improving patient outcomes.”

“These findings are exciting because they give us insight into what is happening in the brain between seeing something and saving it into memory,” says Wilma Bainbridge, assistant professor of psychology at the University of Chicago, who was not involved in the study. “The researchers here are picking up on a cortical signal that reflects what’s important to remember, and what can be forgotten early on.” 

Lahner and Oliva, who is also the director of strategic industry engagement at the MIT Schwarzman College of Computing, MIT director of the MIT-IBM Watson AI Lab, and CSAIL principal investigator, join Western University Assistant Professor Yalda Mohsenzadeh and York University researcher Caitlin Mullin on the paper. The team acknowledges a shared instrument grant from the National Institutes of Health, and their work was funded by the Vannevar Bush Faculty Fellowship via an Office of Naval Research grant, a National Science Foundation award, Multidisciplinary University Research Initiative award via an Army Research Office grant, and the EECS MathWorks Fellowship. Their paper is published in PLOS Biology.

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How light can vaporize water without the need for heat

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How light can vaporize water without the need for heat

It’s the most fundamental of processes — the evaporation of water from the surfaces of oceans and lakes, the burning off of fog in the morning sun, and the drying of briny ponds that leaves solid salt behind. Evaporation is all around us, and humans have been observing it and making use of it for as long as we have existed.

And yet, it turns out, we’ve been missing a major part of the picture all along.

In a series of painstakingly precise experiments, a team of researchers at MIT has demonstrated that heat isn’t alone in causing water to evaporate. Light, striking the water’s surface where air and water meet, can break water molecules away and float them into the air, causing evaporation in the absence of any source of heat.

The astonishing new discovery could have a wide range of significant implications. It could help explain mysterious measurements over the years of how sunlight affects clouds, and therefore affect calculations of the effects of climate change on cloud cover and precipitation. It could also lead to new ways of designing industrial processes such as solar-powered desalination or drying of materials.

The findings, and the many different lines of evidence that demonstrate the reality of the phenomenon and the details of how it works, are described today in the journal PNAS, in a paper by Carl Richard Soderberg Professor of Power Engineering Gang Chen, postdocs Guangxin Lv and Yaodong Tu, and graduate student James Zhang.

The authors say their study suggests that the effect should happen widely in nature— everywhere from clouds to fogs to the surfaces of oceans, soils, and plants — and that it could also lead to new practical applications, including in energy and clean water production. “I think this has a lot of applications,” Chen says. “We’re exploring all these different directions. And of course, it also affects the basic science, like the effects of clouds on climate, because clouds are the most uncertain aspect of climate models.”

A newfound phenomenon

The new work builds on research reported last year, which described this new “photomolecular effect” but only under very specialized conditions: on the surface of specially prepared hydrogels soaked with water. In the new study, the researchers demonstrate that the hydrogel is not necessary for the process; it occurs at any water surface exposed to light, whether it’s a flat surface like a body of water or a curved surface like a droplet of cloud vapor.

Because the effect was so unexpected, the team worked to prove its existence with as many different lines of evidence as possible. In this study, they report 14 different kinds of tests and measurements they carried out to establish that water was indeed evaporating — that is, molecules of water were being knocked loose from the water’s surface and wafted into the air — due to the light alone, not by heat, which was long assumed to be the only mechanism involved.

One key indicator, which showed up consistently in four different kinds of experiments under different conditions, was that as the water began to evaporate from a test container under visible light, the air temperature measured above the water’s surface cooled down and then leveled off, showing that thermal energy was not the driving force behind the effect.

Other key indicators that showed up included the way the evaporation effect varied depending on the angle of the light, the exact color of the light, and its polarization. None of these varying characteristics should happen because at these wavelengths, water hardly absorbs light at all — and yet the researchers observed them.

The effect is strongest when light hits the water surface at an angle of 45 degrees. It is also strongest with a certain type of polarization, called transverse magnetic polarization. And it peaks in green light — which, oddly, is the color for which water is most transparent and thus interacts the least.

Chen and his co-researchers have proposed a physical mechanism that can explain the angle and polarization dependence of the effect, showing that the photons of light can impart a net force on water molecules at the water surface that is sufficient to knock them loose from the body of water. But they cannot yet account for the color dependence, which they say will require further study.

They have named this the photomolecular effect, by analogy with the photoelectric effect that was discovered by Heinrich Hertz in 1887 and finally explained by Albert Einstein in 1905. That effect was one of the first demonstrations that light also has particle characteristics, which had major implications in physics and led to a wide variety of applications, including LEDs. Just as the photoelectric effect liberates electrons from atoms in a material in response to being hit by a photon of light, the photomolecular effect shows that photons can liberate entire molecules from a liquid surface, the researchers say.

“The finding of evaporation caused by light instead of heat provides new disruptive knowledge of light-water interaction,” says Xiulin Ruan, professor of mechanical engineering at Purdue University, who was not involved in the study. “It could help us gain new understanding of how sunlight interacts with cloud, fog, oceans, and other natural water bodies to affect weather and climate. It has significant potential practical applications such as high-performance water desalination driven by solar energy. This research is among the rare group of truly revolutionary discoveries which are not widely accepted by the community right away but take time, sometimes a long time, to be confirmed.”

Solving a cloud conundrum

The finding may solve an 80-year-old mystery in climate science. Measurements of how clouds absorb sunlight have often shown that they are absorbing more sunlight than conventional physics dictates possible. The additional evaporation caused by this effect could account for the longstanding discrepancy, which has been a subject of dispute since such measurements are difficult to make.

“Those experiments are based on satellite data and flight data,“ Chen explains. “They fly an airplane on top of and below the clouds, and there are also data based on the ocean temperature and radiation balance. And they all conclude that there is more absorption by clouds than theory could calculate. However, due to the complexity of clouds and the difficulties of making such measurements, researchers have been debating whether such discrepancies are real or not. And what we discovered suggests that hey, there’s another mechanism for cloud absorption, which was not accounted for, and this mechanism might explain the discrepancies.”

Chen says he recently spoke about the phenomenon at an American Physical Society conference, and one physicist there who studies clouds and climate said they had never thought about this possibility, which could affect calculations of the complex effects of clouds on climate. The team conducted experiments using LEDs shining on an artificial cloud chamber, and they observed heating of the fog, which was not supposed to happen since water does not absorb in the visible spectrum. “Such heating can be explained based on the photomolecular effect more easily,” he says.

Lv says that of the many lines of evidence, “the flat region in the air-side temperature distribution above hot water will be the easiest for people to reproduce.” That temperature profile “is a signature” that demonstrates the effect clearly, he says.

Zhang adds: “It is quite hard to explain how this kind of flat temperature profile comes about without invoking some other mechanism” beyond the accepted theories of thermal evaporation. “It ties together what a whole lot of people are reporting in their solar desalination devices,” which again show evaporation rates that cannot be explained by the thermal input.

The effect can be substantial. Under the optimum conditions of color, angle, and polarization, Lv says, “the evaporation rate is four times the thermal limit.”

Already, since publication of the first paper, the team has been approached by companies that hope to harness the effect, Chen says, including for evaporating syrup and drying paper in a paper mill. The likeliest first applications will come in the areas of solar desalinization systems or other industrial drying processes, he says. “Drying consumes 20 percent of all industrial energy usage,” he points out.

Because the effect is so new and unexpected, Chen says, “This phenomenon should be very general, and our experiment is really just the beginning.” The experiments needed to demonstrate and quantify the effect are very time-consuming. “There are many variables, from understanding water itself, to extending to other materials, other liquids and even solids,” he says.

“The observations in the manuscript points to a new physical mechanism that foundationally alters our thinking on the kinetics of evaporation,” says Shannon Yee, an associate professor of mechanical engineering at Georgia Tech, who was not associated with this work. He adds, “Who would have thought that we are still learning about something as quotidian as water evaporating?”

“I think this work is very significant scientifically because it presents a new mechanism,” says University of Alberta Distinguished Professor Janet A.W. Elliott, who also was not associated with this work. “It may also turn out to be practically important for technology and our understanding of nature, because evaporation of water is ubiquitous and the effect appears to deliver significantly higher evaporation rates than the known thermal mechanism. …  My overall impression is this work is outstanding. It appears to be carefully done with many precise experiments lending support for one another.”

The work was partly supported by an MIT Bose Award.

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This tiny chip can safeguard user data while enabling efficient computing on a smartphone

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This tiny chip can safeguard user data while enabling efficient computing on a smartphone

Health-monitoring apps can help people manage chronic diseases or stay on track with fitness goals, using nothing more than a smartphone. However, these apps can be slow and energy-inefficient because the vast machine-learning models that power them must be shuttled between a smartphone and a central memory server.

Engineers often speed things up using hardware that reduces the need to move so much data back and forth. While these machine-learning accelerators can streamline computation, they are susceptible to attackers who can steal secret information.

To reduce this vulnerability, researchers from MIT and the MIT-IBM Watson AI Lab created a machine-learning accelerator that is resistant to the two most common types of attacks. Their chip can keep a user’s health records, financial information, or other sensitive data private while still enabling huge AI models to run efficiently on devices.

The team developed several optimizations that enable strong security while only slightly slowing the device. Moreover, the added security does not impact the accuracy of computations. This machine-learning accelerator could be particularly beneficial for demanding AI applications like augmented and virtual reality or autonomous driving.

While implementing the chip would make a device slightly more expensive and less energy-efficient, that is sometimes a worthwhile price to pay for security, says lead author Maitreyi Ashok, an electrical engineering and computer science (EECS) graduate student at MIT.

“It is important to design with security in mind from the ground up. If you are trying to add even a minimal amount of security after a system has been designed, it is prohibitively expensive. We were able to effectively balance a lot of these tradeoffs during the design phase,” says Ashok.

Her co-authors include Saurav Maji, an EECS graduate student; Xin Zhang and John Cohn of the MIT-IBM Watson AI Lab; and senior author Anantha Chandrakasan, MIT’s chief innovation and strategy officer, dean of the School of Engineering, and the Vannevar Bush Professor of EECS. The research will be presented at the IEEE Custom Integrated Circuits Conference.

Side-channel susceptibility

The researchers targeted a type of machine-learning accelerator called digital in-memory compute. A digital IMC chip performs computations inside a device’s memory, where pieces of a machine-learning model are stored after being moved over from a central server.

The entire model is too big to store on the device, but by breaking it into pieces and reusing those pieces as much as possible, IMC chips reduce the amount of data that must be moved back and forth.

But IMC chips can be susceptible to hackers. In a side-channel attack, a hacker monitors the chip’s power consumption and uses statistical techniques to reverse-engineer data as the chip computes. In a bus-probing attack, the hacker can steal bits of the model and dataset by probing the communication between the accelerator and the off-chip memory.

Digital IMC speeds computation by performing millions of operations at once, but this complexity makes it tough to prevent attacks using traditional security measures, Ashok says.

She and her collaborators took a three-pronged approach to blocking side-channel and bus-probing attacks.

First, they employed a security measure where data in the IMC are split into random pieces. For instance, a bit zero might be split into three bits that still equal zero after a logical operation. The IMC never computes with all pieces in the same operation, so a side-channel attack could never reconstruct the real information.

But for this technique to work, random bits must be added to split the data. Because digital IMC performs millions of operations at once, generating so many random bits would involve too much computing. For their chip, the researchers found a way to simplify computations, making it easier to effectively split data while eliminating the need for random bits.

Second, they prevented bus-probing attacks using a lightweight cipher that encrypts the model stored in off-chip memory. This lightweight cipher only requires simple computations. In addition, they only decrypted the pieces of the model stored on the chip when necessary.

Third, to improve security, they generated the key that decrypts the cipher directly on the chip, rather than moving it back and forth with the model. They generated this unique key from random variations in the chip that are introduced during manufacturing, using what is known as a physically unclonable function.

“Maybe one wire is going to be a little bit thicker than another. We can use these variations to get zeros and ones out of a circuit. For every chip, we can get a random key that should be consistent because these random properties shouldn’t change significantly over time,” Ashok explains.

They reused the memory cells on the chip, leveraging the imperfections in these cells to generate the key. This requires less computation than generating a key from scratch.

“As security has become a critical issue in the design of edge devices, there is a need to develop a complete system stack focusing on secure operation. This work focuses on security for machine-learning workloads and describes a digital processor that uses cross-cutting optimization. It incorporates encrypted data access between memory and processor, approaches to preventing side-channel attacks using randomization, and exploiting variability to generate unique codes. Such designs are going to be critical in future mobile devices,” says Chandrakasan.

Safety testing

To test their chip, the researchers took on the role of hackers and tried to steal secret information using side-channel and bus-probing attacks.

Even after making millions of attempts, they couldn’t reconstruct any real information or extract pieces of the model or dataset. The cipher also remained unbreakable. By contrast, it took only about 5,000 samples to steal information from an unprotected chip.

The addition of security did reduce the energy efficiency of the accelerator, and it also required a larger chip area, which would make it more expensive to fabricate.

The team is planning to explore methods that could reduce the energy consumption and size of their chip in the future, which would make it easier to implement at scale.

“As it becomes too expensive, it becomes harder to convince someone that security is critical. Future work could explore these tradeoffs. Maybe we could make it a little less secure but easier to implement and less expensive,” Ashok says.

The research is funded, in part, by the MIT-IBM Watson AI Lab, the National Science Foundation, and a Mathworks Engineering Fellowship.

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Researchers detect a new molecule in space

New Post has been published on https://sunalei.org/news/researchers-detect-a-new-molecule-in-space/

Researchers detect a new molecule in space

New research from the group of MIT Professor Brett McGuire has revealed the presence of a previously unknown molecule in space. The team’s open-access paper, “Rotational Spectrum and First Interstellar Detection of 2-Methoxyethanol Using ALMA Observations of NGC 6334I,” appears in April 12 issue of The Astrophysical Journal Letters.

Zachary T.P. Fried, a graduate student in the McGuire group and the lead author of the publication, worked to assemble a puzzle comprised of pieces collected from across the globe, extending beyond MIT to France, Florida, Virginia, and Copenhagen, to achieve this exciting discovery. 

“Our group tries to understand what molecules are present in regions of space where stars and solar systems will eventually take shape,” explains Fried. “This allows us to piece together how chemistry evolves alongside the process of star and planet formation. We do this by looking at the rotational spectra of molecules, the unique patterns of light they give off as they tumble end-over-end in space. These patterns are fingerprints (barcodes) for molecules. To detect new molecules in space, we first must have an idea of what molecule we want to look for, then we can record its spectrum in the lab here on Earth, and then finally we look for that spectrum in space using telescopes.”

Searching for molecules in space

The McGuire Group has recently begun to utilize machine learning to suggest good target molecules to search for. In 2023, one of these machine learning models suggested the researchers target a molecule known as 2-methoxyethanol. 

“There are a number of ‘methoxy’ molecules in space, like dimethyl ether, methoxymethanol, ethyl methyl ether, and methyl formate, but 2-methoxyethanol would be the largest and most complex ever seen,” says Fried. To detect this molecule using radiotelescope observations, the group first needed to measure and analyze its rotational spectrum on Earth. The researchers combined experiments from the University of Lille (Lille, France), the New College of Florida (Sarasota, Florida), and the McGuire lab at MIT to measure this spectrum over a broadband region of frequencies ranging from the microwave to sub-millimeter wave regimes (approximately 8 to 500 gigahertz). 

The data gleaned from these measurements permitted a search for the molecule using Atacama Large Millimeter/submillimeter Array (ALMA) observations toward two separate star-forming regions: NGC 6334I and IRAS 16293-2422B. Members of the McGuire group analyzed these telescope observations alongside researchers at the National Radio Astronomy Observatory (Charlottesville, Virginia) and the University of Copenhagen, Denmark. 

“Ultimately, we observed 25 rotational lines of 2-methoxyethanol that lined up with the molecular signal observed toward NGC 6334I (the barcode matched!), thus resulting in a secure detection of 2-methoxyethanol in this source,” says Fried. “This allowed us to then derive physical parameters of the molecule toward NGC 6334I, such as its abundance and excitation temperature. It also enabled an investigation of the possible chemical formation pathways from known interstellar precursors.”

Looking forward

Molecular discoveries like this one help the researchers to better understand the development of molecular complexity in space during the star formation process. 2-methoxyethanol, which contains 13 atoms, is quite large for interstellar standards — as of 2021, only six species larger than 13 atoms were detected outside the solar system, many by McGuire’s group, and all of them existing as ringed structures.  

“Continued observations of large molecules and subsequent derivations of their abundances allows us to advance our knowledge of how efficiently large molecules can form and by which specific reactions they may be produced,” says Fried. “Additionally, since we detected this molecule in NGC 6334I but not in IRAS 16293-2422B, we were presented with a unique opportunity to look into how the differing physical conditions of these two sources may be affecting the chemistry that can occur.”