A material difference

Eesha Khare has always seen a world of matter. The daughter of a hardware engineer and a biologist, she has an insatiable interest in what substances — both synthetic and biological — have in common. Not surprisingly, that perspective led her to the study of materials.

“I recognized early on that everything around me is a material,” she says. “How our phones respond to touches, how trees in nature to give us both structural wood and foldable paper, or how we are able to make high skyscrapers with steel and glass, it all comes down to the fundamentals: This is materials science and engineering.”

As a rising fourth-year PhD student in the MIT Department of Materials Science and Engineering (DMSE), Khare now studies the metal-coordination bonds that allow mussels to bind to rocks along turbulent coastlines. But Khare’s scientific enthusiasm has also led to expansive interests from science policy to climate advocacy and entrepreneurship.

A material world

A Silicon Valley native, Khare recalls vividly how excited she was about science as a young girl, both at school and at myriad science fairs and high school laboratory internships. One such internship at the University of California at Santa Cruz introduced her to the study of nanomaterials, or materials that are smaller than a single human cell. The project piqued her interest in how research could lead to energy-storage applications, and she began to ponder the connections between materials, science policy, and the environment.

As an undergraduate at Harvard University, Khare pursued a degree in engineering sciences and chemistry while also working at the Harvard Kennedy School Institute of Politics. There, she grew fascinated by environmental advocacy in the policy space, working for then-professor Gina McCarthy, who is currently serving in the Biden administration as the first-ever White House climate advisor.

Following her academic explorations in college, Khare wanted to consider science in a new light before pursuing her doctorate in materials science and engineering. She deferred her program acceptance at MIT in order to attend Cambridge University in the U.K., where she earned a master’s degree in the history and philosophy of science. “Especially in a PhD program, it can often feel like your head is deep in the science as you push new research frontiers, but I wanted take a step back and be inspired by how scientists in the past made their discoveries,” she says.

Her experience at Cambridge was both challenging and informative, but Khare quickly found that her mechanistic curiosity remained persistent — a realization that came in the form of a biological material.

“My very first master’s research project was about environmental pollution indicators in the U.K., and I was looking specifically at lichen to understand the social and political reasons why they were adopted by the public as pollution indicators,” Khare explains. “But I found myself wondering more about how lichen can act as pollution indicators. And I found that to be quite similar for most of my research projects: I was more interested in how the technology or discovery actually worked.”

Enthusiasm for innovation

Fittingly, these bioindicators confirmed for her that studying materials at MIT was the right course. Now Khare works on a different organism altogether, conducting research on the metal-coordination chemical interactions of a biopolymer secreted by mussels.

“Mussels secrete this thread and can adhere to ocean walls. So, when ocean waves come, mussels don’t get dislodged that easily,” Khare says. “This is partly because of how metal ions in this material bind to different amino acids in the protein. There’s no input from the mussel itself to control anything there; all the magic is in this biological material that is not only very sticky, but also doesn’t break very readily, and if you cut it, it can re-heal that interface as well! If we could better understand and replicate this biological material in our own world, we could have materials self-heal and never break and thus eliminate so much waste.”

To study this natural material, Khare combines computational and experimental techniques, experimentally synthesizing her own biopolymers and studying their properties with in silico molecular dynamics. Her co-advisors — Markus Buehler, the Jerry McAfee Professor of Engineering in Civil and Environmental Engineering, and Niels Holten-Andersen, professor of materials science and engineering — have embraced this dual-approach to her project, as well as her abundant enthusiasm for innovation.

Khare likes to take one exploratory course per semester, and a recent offering in the MIT Sloan School of Management inspired her to pursue entrepreneurship. These days she is spending much of her free time on a startup called Taxie, formed with fellow MIT students after taking the course 15.390 (New Enterprises). Taxie attempts to electrify the rideshare business by making electric rental cars available to rideshare drivers. Khare hopes this project will initiate some small first steps in making the ridesharing industry environmentally cleaner — and in democratizing access to electric vehicles for rideshare drivers, who often hail from lower-income or immigrant backgrounds.

“There are a lot of goals thrown around for reducing emissions or helping our environment. But we are slowly getting physical things on the road, physical things to real people, and I like to think that we are helping to accelerate the electric transition,” Khare says. “These small steps are helpful for learning, at the very least, how we can make a transition to electric or to a cleaner industry.”

Alongside her startup work, Khare has pursued a number of other extracurricular activities at MIT, including co-organizing her department’s Student Application Assistance Program and serving on DMSE’s Diversity, Equity, and Inclusion Council. Her varied interests also have led to a diverse group of friends, which suits her well, because she is a self-described “people-person.”

In a year where maintaining connections has been more challenging than usual, Khare has focused on the positive, spending her spring semester with family in California and practicing Bharatanatyam, a form of Indian classical dance, over Zoom. As she looks to the future, Khare hopes to bring even more of her interests together, like materials science and climate.

“I want to understand the energy and environmental sector at large to identify the most pressing technology gaps and how can I use my knowledge to contribute. My goal is to figure out where can I personally make a difference and where it can have a bigger impact to help our climate,” she says. “I like being outside of my comfort zone.”

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“To make even the smallest contribution to improving my country would be my dream”

Thailand has become an economic leader in Southeast Asia in recent decades, but while the country has rapidly industrialized, many Thai citizens have been left behind. As a child growing up in Bangkok, Pavarin Bhandtivej would watch the news and wonder why families in the nearby countryside had next to nothing. He aspired to become a policy researcher and create beneficial change.

But Bhandtivej knew his goal wouldn’t be easy. He was born with a visual impairment, making it challenging for him to see, read, and navigate. This meant he had to work twice as hard in school to succeed. It took achieving the highest grades for Bhandtivej to break through stigmas and have his talents recognized. Still, he persevered, with a determination to uplift others. “I would return to that initial motivation I had as a kid. For me, to make even the smallest contribution to improving my country would be my dream,” he says.

“When I would face these obstacles, I would tell myself that struggling people are waiting for someone to design policies for them to have better lives. And that person could be me. I cannot fall here in front of these obstacles. I must stay motivated and move on.”

Bhandtivej completed his undergraduate degree in economics at Thailand’s top college, Chulalongkorn University. His classes introduced him to many debates about development policy, such as universal basic income. During one debate, after both sides made compelling arguments about how to alleviate poverty, Bhandtivej realized there was no clear winner. “A question came to my mind: Who's right?” he says. “In terms of theory, both sides were correct. But how could we know what approach would work in the real world?”

A new approach to higher education

The search for those answers would lead Bhandtivej to become interested in data analysis. He began investigating online courses, eventually finding the MIT MicroMasters Program in Data, Economics, and Development Policy (DEDP), which was created by MIT’s Department of Economics and the Abdul Latif Jameel Poverty Action Lab (J-PAL). The program requires learners to complete five online courses that teach quantitative methods for evaluating social programs, leading to a MicroMasters credential. Students that pass the courses’ proctored exams are then also eligible to apply for a full-time, accelerated, on-campus master’s program at MIT, led by professors Esther Duflo, Abhijit Banerjee, and Benjamin Olken.

The program’s mission to make higher education more accessible worked well for Bhandtivej. He studied tirelessly, listening and relistening to online lectures and pausing to scrutinize equations. By the end, his efforts paid off — Bhandtivej was the MicroMasters program’s top scorer. He was soon admitted into the second cohort of the highly selective DEDP master’s program.

“You can imagine how time-consuming it was to use text-to-speech to get through a 30-page reading with numerous equations, tables, and graphs,” he explains. “Luckily, Disability and Access Services provided accommodations to timed exams and I was able to push through.”   

In the gap year before the master’s program began, Bhandtivej returned to Chulalongkorn University as a research assistant with Professor Thanyaporn Chankrajang. He began applying his newfound quantitative skills to study the impacts of climate change in Thailand. His contributions helped uncover how rising temperatures and irregular rainfall are leading to reduced rice crop yields. “Thailand is the world’s second largest exporter of rice, and the vast majority of Thais rely heavily on rice for its nutritional and commercial value. We need more data to encourage leaders to act now,” says Bhandtivej. “As a Buddhist, it was meaningful to be part of generating this evidence, as I am always concerned about my impact on other humans and sentient beings.”

Staying true to his mission

Now pursuing his master’s on campus, Bhandtivej is taking courses like 14.320 (Econometric Data Science) and studying how to design, conduct, and analyze empirical studies. “The professors I’ve had have opened a whole new world for me,” says Bhandtivej. “They’ve inspired me to see how we can take rigorous scientific practices and apply them to make informed policy decisions. We can do more than rely on theories.”

The final portion of the program requires a summer capstone experience, which Bhandtivej is using to work at Innovations for Poverty Action. He has recently begun to analyze how remote learning interventions in Bangladesh have performed since Covid-19. Many teachers are concerned, since disruptions in childhood education can lead to intergenerational poverty. “We have tried interventions that connect students with teachers, provide discounted data packages, and send information on where to access adaptive learning technologies and other remote learning resources,” he says. “It will be interesting to see the results. This is a truly urgent topic, as I don’t believe Covid-19 will be the last pandemic of our lifetime.”

Enhancing education has always been one of Bhandtivej’s priority interests. He sees education as the gateway that brings a person’s innate talent to light. “There is a misconception in many developing countries that disabled people cannot learn, which is untrue,” says Bhandtivej. “Education provides a critical signal to future employers and overall society that we can work and perform just as well, as long as we have appropriate accommodations.”

In the future, Bhandtivej plans on returning to Thailand to continue his journey as a policy researcher. While he has many issues he would like to tackle, his true purpose still lies in doing work that makes a positive impact on people’s lives. “My hope is that my story encourages people to think of not only what they are capable of achieving themselves, but also what they can do for others.”

“You may think you are just a small creature on a large planet. That you have just a tiny role to play. But I think — even if we are just a small part — whatever we can do to make life better for our communities, for our country, for our planet ... it’s worth it.”

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Five MIT School of Science professors receive tenure for 2021

MIT has granted tenure to five faculty members in the MIT School of Science in the departments of Brain and Cognitive Sciences, Chemistry, and Physics.

Physicist Joseph Checkelsky investigates exotic electronic states of matter through the synthesis, measurement, and control of solid-state materials. His research aims to uncover new physical phenomena that expand the boundaries of understanding of quantum mechanical condensed matter systems and open doorways to new technologies by realizing emergent electronic and magnetic functionalities. Checkelsky joined the Department of Physics in 2014 after a postdoc appointment at Japan’s Institute for Physical and Chemical Research and a lectureship at the University of Tokyo. He earned a bachelor’s degree in physics from Harvey Mudd College in 2004; and in 2010, he received a doctoral degree in physics from Princeton University.

A molecular neurobiologist and geneticist, Myriam Heiman studies the selective vulnerability and pathophysiology seen in neurodegenerative diseases of the brain’s basal ganglia, including Huntington’s disease and Parkinson’s disease. Using a revolutionary transcriptomic technique called translating ribosome affinity purification, she aims to understand the early molecular changes that eventually lead to cell death in these diseases. Heiman joined the Department of Brain and Cognitive Sciences, the Picower Institute for Learning and Memory, and the Broad Institute of Harvard and MIT in 2011 after completing her postdoctoral training at The Rockefeller University. She holds a PhD from Johns Hopkins University and a BA from Princeton University.

Particle physicist Kerstin Perez is interested in using cosmic particles to look beyond Standard Model physics, in particular evidence of dark matter interactions. Her work focuses on opening sensitivity to unexplored cosmic signatures with impact at the intersection of particle physics, astrophysics, and advanced instrumental techniques. Perez joined the Department of Physics in 2016, after a National Science Foundation astronomy and astrophysics postdoctoral fellowship at Columbia University and a faculty appointment at Haverford College. She earned her BA in physics from Columbia University in 2005, and her PhD from Caltech in 2011.

Alexander Radosevich works at the interface of inorganic and organic chemistry to design new chemical reactions. In particular, his interests concern the invention of compositionally new classes of molecular catalysts based on inexpensive and Earth-abundant elements of the p-block. This research explores the connection between molecular structure and reactivity in an effort to discover new efficient and sustainable approaches to chemical synthesis. Radosevich returned to the MIT Department of Chemistry, where he also held a postdoctoral appointment in 2016, after serving on the faculty at The Pennsylvania State University. He received a BS from the University of Notre Dame in 2002, and a PhD from University of California at Berkeley in 2007.

Alex K. Shalek creates and implements new experimental and computational approaches to identify the cellular and molecular features that inform tissue-level function and dysfunction across the spectrum of human health and disease. This encompasses both the development of broadly enabling technologies, such as Seq-Well, as well as their application to characterize, model, and rationally control complex multicellular systems. In addition to sharing this toolbox to empower mechanistic scientific inquiry across the global research community, Shalek is applying it to uncover principles that inform a wide range of problems in immunology, infectious diseases, and cancer. Shalek joined the Department of Chemistry and the Institute of Medical Engineering and Science in 2014 after postdoctoral training at Harvard University and the Broad Institute. He received his BA in chemical physics at Columbia University in 2004, followed by a PhD from Harvard University in 2011.

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How a sudden stratospheric warming affected the Northern Hemisphere

Weather is a tricky science — even more so at very high altitudes, with a mix of plasma and neutral particles.

In sudden stratospheric warmings (SSWs) — large meteorological disturbances related to the polar vortex in which the polar stratosphere temperature increases as it is affected by the winds around the pole — the polar vortex is weakened. SSWs also have profound atmospheric effects at great distances, causing changes in the hemisphere opposite from the location of the original SSW — changes that extend all the way to the upper thermosphere and ionosphere.

A study published on July 16 in Geophysical Research Letters by MIT Haystack Observatory’s Larisa Goncharenko and colleagues examines the effects of a recent major Antarctic SSW on the Northern Hemisphere by studying changes observed in the upper atmosphere over North America and Europe.

In an SSW-caused anomaly, changes over the pole cause changes in the opposite hemisphere. This important interhemispheric linkage was identified as drastic shifts at altitudes greater than 100 km — for example, in total electron content (TEC) measurements as well as variations in the thermospheric O/N₂ ratio.

SSWs are more frequent over the Arctic; these cause TEC and other related anomalies in the Southern Hemisphere, and thus more observations have been made on this linkage. Since the Antarctic SSWs are less common, there are fewer opportunities to study their effects on the Northern Hemisphere. However, the greater density of TEC observation locations in the Northern Hemisphere allows for precise measurement of these upper atmospheric anomalies when they do occur.

In September 2019, an extreme, record-breaking SSW event occurred over Antarctica. Goncharenko and colleagues found significant resulting changes in the upper atmosphere in mid-latitudes over the Northern Hemisphere following this event; more observations are available for this region than in the Southern Hemisphere. The changes were notable not only in severity, but also because they are limited to a narrow (20–40 degrees) longitude range, differ between North America and Europe, and persist for a long time.

In the figure above, red areas show where TEC levels are shifted over North America and Europe in the afternoon; red indicates an increase of up to 80 percent versus the baseline regular levels, and blue indicates a decrease of up to –40 percent versus regular levels. This TEC shift persisted throughout September 2019 over the western United States, but was short-lived over Europe, indicating different mechanisms at play.

The authors suggest that a change in the thermospheric zonal (east–west) winds are one reason for the variance between regions. Another factor is differences in magnetic declination angles; in areas with greater declination, the zonal winds can more efficiently transport plasma to higher or lower altitudes, leading to the build-up or depletion of plasma density.

More study is needed to determine the precise extent to which these factors affect the linkage between polar stratospheric events and near-Earth space in the opposite hemisphere. These studies remain a challenge, given the relative rarity of Antarctic SSWs and sparse availability of ionospheric data in the Southern Hemisphere.

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Lincoln Laboratory convenes top network scientists for Graph Exploitation Symposium

As the Covid-19 pandemic has shown, we live in a richly connected world, facilitating not only the efficient spread of a virus but also of information and influence. What can we learn by analyzing these connections? This is a core question of network science, a field of research that models interactions across physical, biological, social, and information systems to solve problems.

The 2021 Graph Exploitation Symposium (GraphEx), hosted by MIT Lincoln Laboratory, brought together top network science researchers to share the latest advances and applications in the field.

"We explore and identify how exploitation of graph data can offer key technology enablers to solve the most pressing problems our nation faces today," says Edward Kao, a symposium organizer and technical staff in Lincoln Laboratory's AI Software Architectures and Algorithms Group.

The themes of the virtual event revolved around some of the year's most relevant issues, such as analyzing disinformation on social media, modeling the pandemic's spread, and using graph-based machine learning models to speed drug design.

"The special sessions on influence operations and Covid-19 at GraphEx reflect the relevance of network and graph-based analysis for understanding the phenomenology of these complicated and impactful aspects of modern-day life, and also may suggest paths forward as we learn more and more about graph manipulation," says William Streilein, who co-chaired the event with Rajmonda Caceres, both of Lincoln Laboratory.

Social networks

Several presentations at the symposium focused on the role of network science in analyzing influence operations (IO), or organized attempts by state and/or non-state actors to spread disinformation narratives.  

Lincoln Laboratory researchers have been developing tools to classify and quantify the influence of social media accounts that are likely IO accounts, such as those willfully spreading false Covid-19 treatments to vulnerable populations.

"A cluster of IO accounts acts as an echo chamber to amplify the narrative. The vulnerable population is then engaging in these narratives," says Erika Mackin, a researcher developing the tool, called RIO or Reconnaissance of Influence Operations.

To classify IO accounts, Mackin and her team trained an algorithm to detect probable IO accounts in Twitter networks based on a specific hashtag or narrative. One example they studied was #MacronLeaks, a disinformation campaign targeting Emmanuel Macron during the 2017 French presidential election. The algorithm is trained to label accounts within this network as being IO on the basis of several factors, such as the number of interactions with foreign news accounts, the number of links tweeted, or number of languages used. Their model then uses a statistical approach to score an account's level of influence in spreading the narrative within that network.

The team has found that their classifier outperforms existing detectors of IO accounts, because it can identify both bot accounts and human-operated ones. They've also discovered that IO accounts that pushed the 2017 French election disinformation narrative largely overlap with accounts influentially spreading Covid-19 pandemic disinformation today. "This suggests that these accounts will continue to transition to disinformation narratives," Mackin says.

Pandemic modeling

Throughout the Covid-19 pandemic, leaders have been looking to epidemiological models, which predict how disease will spread, to make sound decisions. Alessandro Vespignani, director of the Network Science Institute at Northeastern University, has been leading Covid-19 modeling efforts in the United States, and shared a keynote on this work at the symposium.

Besides taking into account the biological facts of the disease, such as its incubation period, Vespignani's model is especially powerful in its inclusion of community behavior. To run realistic simulations of disease spread, he develops "synthetic populations" that are built by using publicly available, highly detailed datasets about U.S. households. "We create a population that is not real, but is statistically real, and generate a map of the interactions of those individuals," he says. This information feeds back into the model to predict the spread of the disease. 

Today, Vespignani is considering how to integrate genomic analysis of the virus into this kind of population modeling in order to understand how variants are spreading. "It's still a work in progress that is extremely interesting," he says, adding that this approach has been useful in modeling the dispersal of the Delta variant of SARS-CoV-2. 

As researchers model the virus' spread, Lucas Laird at Lincoln Laboratory is considering how network science can be used to design effective control strategies. He and his team are developing a model for customizing strategies for different geographic regions. The effort was spurred by the differences in Covid-19 spread across U.S. communities, and what the researchers found to be a gap in intervention modeling to address those differences.

As examples, they applied their planning algorithm to three counties in Florida, Massachusetts, and California. Taking into account the characteristics of a specific geographic center, such as the number of susceptible individuals and number of infections there, their planner institutes different strategies in those communities throughout the outbreak duration.

"Our approach eradicates disease in 100 days, but it also is able to do it with much more targeted interventions than any of the global interventions. In other words, you don't have to shut down a full country." Laird adds that their planner offers a "sandbox environment" for exploring intervention strategies in the future.

Machine learning with graphs

Graph-based machine learning is receiving increasing attention for its potential to "learn" the complex relationships between graphical data, and thus extract new insights or predictions about these relationships. This interest has given rise to a new class of algorithms called graph neural networks. Today, graph neural networks are being applied in areas such as drug discovery and material design, with promising results.

"We can now apply deep learning much more broadly, not only to medical images and biological sequences. This creates new opportunities in data-rich biology and medicine," says Marinka Zitnik, an assistant professor at Harvard University who presented her research at GraphEx.

Zitnik's research focuses on the rich networks of interactions between proteins, drugs, disease, and patients, at the scale of billions of interactions. One application of this research is discovering drugs to treat diseases with no or few approved drug treatments, such as for Covid-19. In April, Zitnik's team published a paper on their research that used graph neural networks to rank 6,340 drugs for their expected efficacy against SARS-CoV-2, identifying four that could be repurposed to treat Covid-19.

At Lincoln Laboratory, researchers are similarly applying graph neural networks to the challenge of designing advanced materials, such as those that can withstand extreme radiation or capture carbon dioxide. Like the process of designing drugs, the trial-and-error approach to materials design is time-consuming and costly. The laboratory's team is developing graph neural networks that can learn relationships between a material’s crystalline structure and its properties. This network can then be used to predict a variety of properties from any new crystal structure, greatly speeding up the process of screening materials with desired properties for specific applications.

"Graph representation learning has emerged as a rich and thriving research area for incorporating inductive bias and structured priors during the machine learning process, with broad applications such as drug design, accelerated scientific discovery, and personalized recommendation systems," Caceres says. 

A vibrant community

Lincoln Laboratory has hosted the GraphEx Symposium annually since 2010, with the exception of last year's cancellation due to Covid-19. "One key takeaway is that despite the postponement from last year and the need to be virtual, the GraphEx community is as vibrant and active as it's ever been," Streilein says. "Network-based analysis continues to expand its reach and is applied to ever-more important areas of science, society, and defense with increasing impact."

In addition to those from Lincoln Laboratory, technical committee members and co-chairs of the GraphEx Symposium included researchers from Harvard University, Arizona State University, Stanford University, Smith College, Duke University, the U.S. Department of Defense, and Sandia National Laboratories.

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“Magic-angle” trilayer graphene may be a rare, magnet-proof superconductor

MIT physicists have observed signs of a rare type of superconductivity in a material called magic-angle twisted trilayer graphene. In a study appearing today in Nature, the researchers report that the material exhibits superconductivity at surprisingly high magnetic fields of up to 10 Tesla, which is three times higher than what the material is predicted to endure if it were a conventional superconductor.

The results strongly imply that magic-angle trilayer graphene, which was initially discovered by the same group, is a very rare type of superconductor, known as a “spin-triplet,” that is impervious to high magnetic fields. Such exotic superconductors could vastly improve technologies such as magnetic resonance imaging, which uses superconducting wires under a magnetic field to resonate with and image biological tissue. MRI machines are currently limited to magnet fields of 1 to 3 Tesla. If they could be built with spin-triplet superconductors, MRI could operate under higher magnetic fields to produce sharper, deeper images of the human body.

The new evidence of spin-triplet superconductivity in trilayer graphene could also help scientists design stronger superconductors for practical quantum computing.

“The value of this experiment is what it teaches us about fundamental superconductivity, about how materials can behave, so that with those lessons learned, we can try to design principles for other materials which would be easier to manufacture, that could perhaps give you better superconductivity,” says Pablo Jarillo-Herrero, the Cecil and Ida Green Professor of Physics at MIT.

His co-authors on the paper include postdoc Yuan Cao and graduate student Jeong Min Park at MIT, and Kenji Watanabe and Takashi Taniguchi of the National Institute for Materials Science in Japan.

Strange shift

Superconducting materials are defined by their super-efficient ability to conduct electricity without losing energy. When exposed to an electric current, electrons in a superconductor couple up in “Cooper pairs” that then travel through the material without resistance, like passengers on an express train.

In a vast majority of superconductors, these passenger pairs have opposite spins, with one electron spinning up, and the other down — a configuration known as a “spin-singlet.” These pairs happily speed through a superconductor, except under high magnetic fields, which can shift the energy of each electron in opposite directions, pulling the pair apart. In this way, and through mechanisms, high magnetic fields can derail superconductivity in conventional spin-singlet superconductors.  

“That’s the ultimate reason why in a large-enough magnetic field, superconductivity disappears,” Park says.

But there exists a handful of exotic superconductors that are impervious to magnetic fields, up to very large strengths. These materials superconduct through pairs of electrons with the same spin — a property known as “spin-triplet.” When exposed to high magnetic fields, the energy of both electrons in a Cooper pair shift in the same direction, in a way that they are not pulled apart but continue superconducting unperturbed, regardless of the magnetic field strength.

Jarillo-Herrero’s group was curious whether magic-angle trilayer graphene might harbor signs of this more unusual spin-triplet superconductivity. The team has produced pioneering work in the study of graphene moiré structures — layers of atom-thin carbon lattices that, when stacked at specific angles, can give rise to surprising electronic behaviors.

The researchers initially reported such curious properties in two angled sheets of graphene, which they dubbed magic-angle bilayer graphene. They soon followed up with tests of trilayer graphene, a sandwich configuration of three graphene sheets that turned out to be even stronger than its bilayer counterpart, retaining superconductivity at higher temperatures. When the researchers applied a modest magnetic field, they noticed that trilayer graphene was able to superconduct at field strengths that would destroy superconductivity in bilayer graphene.

“We thought, this is something very strange,” Jarillo-Herrero says.

A super comeback

In their new study, the physicists tested trilayer graphene’s superconductivity under increasingly higher magnetic fields. They fabricated the material by peeling away atom-thin layers of carbon from a block of graphite, stacking three layers together, and rotating the middle one by 1.56 degrees with respect to the outer layers. They attached an electrode to either end of the material to run a current through and measure any energy lost in the process. Then they turned on a large magnet in the lab, with a field which they oriented parallel to the material.

As they increased the magnetic field around trilayer graphene, they observed that superconductivity held strong up to a point before disappearing, but then curiously reappeared at higher field strengths — a comeback that is highly unusual and not known to occur in conventional spin-singlet superconductors.

“In spin-singlet superconductors, if you kill superconductivity, it never comes back — it’s gone for good,” Cao says. “Here, it reappeared again. So this definitely says this material is not spin-singlet.”

They also observed that after “re-entry,” superconductivity persisted up to 10 Tesla, the maximum field strength that the lab’s magnet could produce. This is about three times higher than what the superconductor should withstand if it were a conventional spin-singlet, according to Pauli’s limit, a theory that predicts the maximum magnetic field at which a material can retain superconductivity.

Trilayer graphene’s reappearance of superconductivity, paired with its persistence at higher magnetic fields than predicted, rules out the possibility that the material is a run-of-the-mill superconductor. Instead, it is likely a very rare type, possibly a spin-triplet, hosting Cooper pairs that speed through the material, impervious to high magnetic fields. The team plans to drill down on the material to confirm its exact spin state, which could help to inform the design of more powerful MRI machines, and also more robust quantum computers.

“Regular quantum computing is super fragile,” Jarillo-Herrero says. “You look at it and, poof, it disappears. About 20 years ago, theorists proposed a type of topological superconductivity that, if realized in any material, could [enable] a quantum computer where states responsible for computation are very robust. That would give infinite more power to do computing. The key ingredient to realize that would be spin-triplet superconductors, of a certain type. We have no idea if our type is of that type. But even if it’s not, this could make it easier to put trilayer graphene with other materials to engineer that kind of superconductivity. That could be a major breakthrough. But it’s still super early.”

This research was supported by the U.S. Department of Energy, the National Science Foundation, the Gordon and Betty Moore Foundation, the Fundacion Ramon Areces, and the CIFAR Quantum Materials Program.

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Reducing emissions by decarbonizing industry

A critical challenge in meeting the Paris Agreement’s long-term goal of keeping global warming well below 2 degrees Celsius is to vastly reduce carbon dioxide (CO₂) and other greenhouse gas emissions generated by the most energy-intensive industries. According to a recent report by the International Energy Agency, these industries — cement, iron and steel, chemicals — account for about 20 percent of global CO₂ emissions. Emissions from these industries are notoriously difficult to abate because, in addition to emissions associated with energy use, a significant portion of industrial emissions come from the process itself.

For example, in the cement industry, about half the emissions come from the decomposition of limestone into lime and CO₂. While a shift to zero-carbon energy sources such as solar or wind-powered electricity could lower CO₂ emissions in the power sector, there are no easy substitutes for emissions-intensive industrial processes.

Enter industrial carbon capture and storage (CCS). This technology, which extracts point-source carbon emissions and sequesters them underground, has the potential to remove up to 90-99 percent of CO₂ emissions from an industrial facility, including both energy-related and process emissions. And that begs the question: Might CCS alone enable hard-to-abate industries to continue to grow while eliminating nearly all of the CO₂ emissions they generate from the atmosphere?

The answer is an unequivocal yes in a new study in the journal Applied Energy co-authored by researchers at the MIT Joint Program on the Science and Policy of Global Change, MIT Energy Initiative, and ExxonMobil.

Using an enhanced version of the MIT Economic Projection and Policy Analysis (EPPA) model that represents different industrial CCS technology choices — and assuming that CCS is the only greenhouse gas emissions mitigation option available to hard-to-abate industries — the study assesses the long-term economic and environmental impacts of CCS deployment under a climate policy aimed at capping the rise in average global surface temperature at 2 C above preindustrial levels.

The researchers find that absent industrial CCS deployment, the global costs of implementing the 2 C policy are higher by 12 percent in 2075 and 71 percent in 2100, relative to policy costs with CCS. They conclude that industrial CCS enables continued growth in the production and consumption of energy-intensive goods from hard-to-abate industries, along with dramatic reductions in the CO₂ emissions they generate. Their projections show that as industrial CCS gains traction mid-century, this growth occurs globally as well as within geographical regions (primarily in China, Europe, and the United States) and the cement, iron and steel, and chemical sectors.

“Because it can enable deep reductions in industrial emissions, industrial CCS is an essential mitigation option in the successful implementation of policies aligned with the Paris Agreement’s long-term climate targets,” says Sergey Paltsev, the study’s lead author and a deputy director of the MIT Joint Program and senior research scientist at the MIT Energy Initiative. “As the technology advances, our modeling approach offers decision-makers a pathway for projecting the deployment of industrial CCS across industries and regions.”

But such advances will not take place without substantial, ongoing funding.

“Sustained government policy support across decades will be needed if CCS is to realize its potential to promote the growth of energy-intensive industries and a stable climate,” says Howard Herzog, a co-author of the study and senior research engineer at the MIT Energy Initiative.

The researchers also find that advanced CCS options such as cryogenic carbon capture (CCC), in which extracted CO₂ is cooled to solid form using far less power than conventional coal- and gas-fired CCS technologies, could help expand the use of CCS in industrial settings through further production cost and emissions reductions.

The study was supported by sponsors of the MIT Joint Program and by ExxonMobil through its membership in the MIT Energy Initiative.

de MIT News https://ift.tt/3zjLmJ1