Maria Yang named vice provost for faculty

Maria Yang ’91, the William E. Leonhard (1940) Professor in the Department of Mechanical Engineering, has been appointed vice provost for faculty at MIT, a role in which she will oversee programs and strategies to recruit and retain faculty members and support them throughout their careers.

Provost Anantha Chandrakasan announced Yang’s appointment, which is effective Feb. 16, in an email to MIT faculty and staff today.

“In the nearly two decades since Maria joined the MIT faculty, she has exemplified dedicated service to the Institute and deep interdisciplinary collaboration,” Chandrakasan wrote. He added that, in a series of leadership positions within the School of Engineering, Yang “consistently demonstrated her skill as a leader, her empathy as a colleague, and her values-driven decision-making.”

As vice provost for faculty, Yang will play a pivotal role in creating an environment where MIT’s faculty members are able to do their best work, “pursuing bold ideas with excellence and creativity,” according to Chandrakasan’s letter. She will partner with school and department leaders on faculty recruitment and retention, mentorship, and strategic planning, and she will oversee programs to support faculty members’ professional development at every stage of their careers.

“Part of what makes MIT unique is the way it provides faculty the room and the encouragement to do work that they think is important, impactful, and sometimes unexpected,” says Yang. “I think it’s vital to foster a culture and a sense of community that really enables our faculty to perform at their best — as researchers, of course, but also as educators and mentors, and as citizens of MIT.”

In addition to her role supporting MIT faculty, Yang will also handle oversight and planning responsibilities for campus academic and research spaces, in partnership with the Office of the Executive Vice President and Treasurer. She will also serve as the principal investigator for the National Science Foundation’s New England Innovation Corps Hub, oversee MIT Solve, and represent the provost on various boards and committees, such as MIT International and the Axim Collaborative.

Yang, who attended MIT as an undergraduate in mechanical engineering as part of the Class of 1991 before earning her master’s and PhD degrees from the design division of the mechanical engineering department at Stanford University, returned to MIT in 2007 as an assistant professor. She has held a number of leadership positions at MIT, including associate dean, deputy dean, and interim dean of the School of Engineering. 

In 2021, Yang co-chaired an Institute-wide committee on the future of design, which recommended the creation of a center to support design opportunities at MIT. Through a generous gift from the Morningside Foundation, the recommendation came to life as the interdisciplinary Morningside Academy for Design (MAD), where Yang has served as associate director since inception. Yang has been instrumental in the development of several new programs at MAD, including design-focused graduate fellowships open to students across MIT and a new design-themed first-year learning community.

Since 2017, Yang has also served as academic faculty director for MIT D-Lab, which uses participatory design to collaborate with communities around the world on the development of solutions to poverty challenges. And since 2024, Yang has served as a co-chair of the SHASS+ Connectivity Fund, which funds research projects in which scholars in the School of Humanities, Arts, and Social Sciences collaborate with faculty colleagues from other schools at MIT.

Given Yang’s extensive track record of working across disciplinary lines, Chandrakasan said in his letter that he had “no doubt that in her new role she will be an effective and trusted champion for colleagues across the Institute.”

An internationally recognized leader in design theory and methodology, Yang is currently focused on researching the early-stage processes used to create successful designs for everything from consumer products to complex, large-scale engineering systems, and the role that these early-stage processes play in determining design outcomes.

Yang, a fellow of the American Society of Mechanical Engineers (ASME), received the 2024 ASME Design Theory and Methodology Award, recognizing “sustained and meritorious contributions” in the field. She has also been recognized with a National Science Foundation CAREER award and the American Society of Engineering Education Fred Merryfield Design Award. In 2017 Yang was named a MacVicar Faculty Fellow, one of MIT’s highest teaching honors.

Yang succeeds Institute Professor Paula Hammond, who served in the role from 2023 before being named dean of the School of Engineering, a role she assumed in January.

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Accelerating science with AI and simulations

For more than a decade, MIT Associate Professor Rafael Gómez-Bombarelli has used artificial intelligence to create new materials. As the technology has expanded, so have his ambitions.

Now, the newly tenured professor in materials science and engineering believes AI is poised to transform science in ways never before possible. His work at MIT and beyond is devoted to accelerating that future.

“We’re at a second inflection point,” Gómez-Bombarelli says. “The first one was around 2015 with the first wave of representation learning, generative AI, and high-throughput data in some areas of science. Those are some of the techniques I first brought into my lab at MIT. Now I think we’re at a second inflection point, mixing language and merging multiple modalities into general scientific intelligence. We’re going to have all the model classes and scaling laws needed to reason about language, reason over material structures, and reason over synthesis recipes.”

Gómez Bombarelli’s research combines physics-based simulations with approaches like machine learning and generative AI to discover new materials with promising real-world applications. His work has led to new materials for batteries, catalysts, plastics, and organic light-emitting diodes (OLEDs). He has also co-founded multiple companies and served on scientific advisory boards for startups applying AI to drug discovery, robotics, and more. His latest company, Lila Sciences, is working to build a scientific superintelligence platform for the life sciences, chemical, and materials science industries.

All of that work is designed to ensure the future of scientific research is more seamless and productive than research today.

“AI for science is one of the most exciting and aspirational uses of AI,” Gómez-Bombarelli says. “Other applications for AI have more downsides and ambiguity. AI for science is about bringing a better future forward in time.”

From experiments to simulations

Gómez-Bombarelli grew up in Spain and gravitated toward the physical sciences from an early age. In 2001, he won a Chemistry Olympics competition, setting him on an academic track in chemistry, which he studied as an undergraduate at his hometown college, the University of Salamanca. Gómez-Bombarelli stuck around for his PhD, where he investigated the function of DNA-damaging chemicals.

“My PhD started out experimental, and then I got bitten by the bug of simulation and computer science about halfway through,” he says. “I started simulating the same chemical reactions I was measuring in the lab. I like the way programming organizes your brain; it felt like a natural way to organize one’s thinking. Programming is also a lot less limited by what you can do with your hands or with scientific instruments.”

Next, Gómez-Bombarelli went to Scotland for a postdoctoral position, where he studied quantum effects in biology. Through that work, he connected with Alán Aspuru-Guzik, a chemistry professor at Harvard University, whom he joined for his next postdoc in 2014.

“I was one of the first people to use generative AI for chemistry in 2016, and I was on the first team to use neural networks to understand molecules in 2015,” Gómez-Bombarelli says. “It was the early, early days of deep learning for science.”

Gómez-Bombarelli also began working to eliminate manual parts of molecular simulations to run more high-throughput experiments. He and his collaborators ended up running hundreds of thousands of calculations across materials, discovering hundreds of promising materials for testing.

After two years in the lab, Gómez-Bombarelli and Aspuru-Guzik started a general-purpose materials computation company, which eventually pivoted to focus on producing organic light-emitting diodes. Gómez-Bombarelli joined the company full-time and calls it the hardest thing he’s ever done in his career.

“It was amazing to make something tangible,” he says. “Also, after seeing Aspuru-Guzik run a lab, I didn’t want to become a professor. My dad was a professor in linguistics, and I thought it was a mellow job. Then I saw Aspuru-Guzik with a 40-person group, and he was on the road 120 days a year. It was insane. I didn’t think I had that type of energy and creativity in me.”

In 2018, Aspuru-Guzik suggested Gómez-Bombarelli apply for a new position in MIT’s Department of Materials Science and Engineering. But, with his trepidation about a faculty job, Gómez-Bombarelli let the deadline pass. Aspuru-Guzik confronted him in his office, slammed his hands on the table, and told him, “You need to apply for this.” It was enough to get Gómez-Bombarelli to put together a formal application.

Fortunately at his startup, Gómez-Bombarelli had spent a lot of time thinking about how to create value from computational materials discovery. During the interview process, he says, he was attracted to the energy and collaborative spirit at MIT. He also began to appreciate the research possibilities.

“Everything I had been doing as a postdoc and at the company was going to be a subset of what I could do at MIT,” he says. “I was making products, and I still get to do that. Suddenly, my universe of work was a subset of this new universe of things I could explore and do.”

It’s been nine years since Gómez Bombarelli joined MIT. Today his lab focuses on how the composition, structure, and reactivity of atoms impact material performance. He has also used high-throughput simulations to create new materials and helped develop tools for merging deep learning with physics-based modeling.

“Physics-based simulations make data and AI algorithms get better the more data you give them,” Gómez Bombarelli’s says. “There are all sorts of virtuous cycles between AI and simulations.”

The research group he has built is solely computational — they don’t run physical experiments.

“It’s a blessing because we can have a huge amount of breadth and do lots of things at once,” he says. “We love working with experimentalists and try to be good partners with them. We also love to create computational tools that help experimentalists triage the ideas coming from AI .”

Gómez-Bombarelli is also still focused on the real-world applications of the materials he invents. His lab works closely with companies and organizations like MIT’s Industrial Liaison Program to understand the material needs of the private sector and the practical hurdles of commercial development.

Accelerating science

As excitement around artificial intelligence has exploded, Gómez-Bombarelli has seen the field mature. Companies like Meta, Microsoft, and Google’s DeepMind now regularly conduct physics-based simulations reminiscent of what he was working on back in 2016. In November, the U.S. Department of Energy launched the Genesis Mission to accelerate scientific discovery, national security, and energy dominance using AI.

“AI for simulations has gone from something that maybe could work to a consensus scientific view,” Gómez-Bombarelli says. “We’re at an inflection point. Humans think in natural language, we write papers in natural language, and it turns out these large language models that have mastered natural language have opened up the ability to accelerate science. We’ve seen that scaling works for simulations. We’ve seen that scaling works for language. Now we’re going to see how scaling works for science.”

When he first came to MIT, Gómez-Bombarelli says he was blown away by how non-competitive things were between researchers. He tries to bring that same positive-sum thinking to his research group, which is made up of about 25 graduate students and postdocs.

“We’ve naturally grown into a really diverse group, with a diverse set of mentalities,” Gomez-Bombarelli says. “Everyone has their own career aspirations and strengths and weaknesses. Figuring out how to help people be the best versions of themselves is fun. Now I’ve become the one insisting that people apply to faculty positions after the deadline. I guess I’ve passed that baton.”

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Using synthetic biology and AI to address global antimicrobial resistance threat

James J. Collins, the Termeer Professor of Medical Engineering and Science at MIT and faculty co-lead of the Abdul Latif Jameel Clinic for Machine Learning in Health, is embarking on a multidisciplinary research project that applies synthetic biology and generative artificial intelligence to the growing global threat of antimicrobial resistance (AMR).

The research project is sponsored by Jameel Research, part of the Abdul Latif Jameel International network. The initial three-year, $3 million research project in MIT’s Department of Biological Engineering and Institute of Medical Engineering and Science focuses on developing and validating programmable antibacterials against key pathogens.

AMR — driven by the overuse and misuse of antibiotics — has accelerated the rise of drug-resistant infections, while the development of new antibacterial tools has slowed. The impact is felt worldwide, especially in low- and middle-income countries, where limited diagnostic infrastructure causes delays or ineffective treatment.

The project centers on developing a new generation of targeted antibacterials using AI to design small proteins to disable specific bacterial functions. These designer molecules would be produced and delivered by engineered microbes, providing a more precise and adaptable approach than traditional antibiotics.

“This project reflects my belief that tackling AMR requires both bold scientific ideas and a pathway to real-world impact,” Collins says. “Jameel Research is keen to address this crisis by supporting innovative, translatable research at MIT.”

Mohammed Abdul Latif Jameel, chair of Abdul Latif Jameel, says, “antimicrobial resistance is one of the most urgent challenges we face today, and addressing it will require ambitious science and sustained collaboration. We are pleased to support this new research, building on our long-standing relationship with MIT and our commitment to advancing research across the world, to strengthen global health and contribute to a more resilient future.”

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Magnetic mixer improves 3D bioprinting

3D bioprinting, in which living tissues are printed with cells mixed into soft hydrogels, or “bio-inks,” is widely used in the field of bioengineering for modeling or replacing the tissues in our bodies. The print quality and reproducibility of tissues, however, can face challenges. One of the most significant challenges is created simply by gravity — cells naturally sink to the bottom of the bioink-extruding printer syringe because the cells are heavier than the hydrogel around them.

“This cell settling, which becomes worse during the long print sessions required to print large tissues, leads to clogged nozzles, uneven cell distribution, and inconsistencies between printed tissues,” explains Ritu Raman, the Eugene Bell Career Development Professor of Tissue Engineering and assistant professor of mechanical engineering at MIT. “Existing solutions, such as manually stirring bioinks before loading them into the printer, or using passive mixers, cannot maintain uniformity once printing begins.”

In a study published Feb. 2 in the journal Device, Raman’s team introduces a new approach that aims to solve this core limitation by actively preventing cell sedimentation within bioinks during printing, allowing for more reliable and biologically consistent 3D printed tissues.

“Precise control over the bioink’s physical and biological properties is essential for recreating the structure and function of native tissues,” says Ferdows Afghah, a postdoc in mechanical engineering at MIT and lead author of the study.

“If we can print tissues that more closely mimic those in our bodies, we can use them as models to understand more about human diseases, or to test the safety and efficacy of new therapeutic drugs,” adds Raman. Such models could help researchers move away from techniques like animal testing, which supports recent interest from the U.S. Food and Drug Administration in developing faster, less expensive, and more informative new approaches to establish the safety and efficacy of new treatment paths.

“Eventually, we are working towards regenerative medicine applications such as replacing diseased or injured tissues in our bodies with 3D printed tissues that can help restore healthy function,” says Raman.

MagMix, a magnetically actuated mixer, is composed of two parts: a small magnetic propeller that fits inside the syringes used by bioprinters to deposit bioinks, layer by layer, into 3D tissues, and a permanent magnet attached to a motor that moves up and down near the syringe, controlling the movement of the propeller inside. Together, this compact system can be mounted onto any standard 3D bioprinter, keeping bioinks uniformly mixed during printing without changing the bioink formulation or interfering with the printer’s normal operation. To test the approach, the team used computer simulations to design the optimal mixing propeller geometry and speed and then validated its performance experimentally.

“Across multiple bioink types, MagMix prevented cell settling for more than 45 minutes of continuous printing, reducing clogging and preserving high cell viability,” says Raman. “Importantly, we showed that mixing speeds could be adjusted to balance effective homogenization for different bioinks while inducing minimal stress on the cells. As a proof-of-concept, we demonstrated that MagMix could be used to 3D print cells that could mature into muscle tissues over the course of several days.”

By maintaining uniform cell distribution throughout long or complex print jobs, MagMix enables the fabrication of high-quality tissues with more consistent biological function. Because the device is compact, low-cost, customizable, and easily integrated into existing 3D printers, it offers a broadly accessible solution for laboratories and industries working toward reproducible engineered tissues for applications in human health including disease modeling, drug screening, and regenerative medicine.

This work was supported, in part, by the Safety, Health, and Environmental Discovery Lab (SHED) at MIT, which provides infrastructure and interdisciplinary expertise to help translate biofabrication innovations from lab-scale demonstrations to scalable, reproducible applications.

“At the SHED, we focus on accelerating the translation of innovative methods into practical tools that researchers can reliably adopt,” says Tolga Durak, the SHED’s founding director. “MagMix is a strong example of how the right combination of technical infrastructure and interdisciplinary support can move biofabrication technologies toward scalable, real-world impact.”

The SHED’s involvement reflects a broader vision of strengthening technology pathways that enhance reproducibility and accessibility across engineering and the life sciences by providing equitable access to advanced equipment and fostering cross-disciplinary collaboration.

“As the field advances toward larger-scale and more standardized systems, integrated labs like SHED are essential for building sustainable capacity,” Durak adds. “Our goal is not only to enable discovery, but to ensure that new technologies can be reliably adopted and sustained over time.”

The team is also interested in non-medical applications of engineered tissues, such as using printed muscles to power safer and more efficient “biohybrid” robots.

The researchers believe this work can improve the reliability and scalability of 3D bioprinting, making the potential impacts on the field of 3D bioprinting and on human health significant. Their paper, “Advancing Bioink Homogeneity in Extrusion 3D Bioprinting with Active In Situ Magnetic Mixing,” is available now from the journal Device. 

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3 Questions: Using AI to help Olympic skaters land a quint

Olympic figure skating looks effortless. Athletes sail across the ice, then soar into the air, spinning like a top, before landing on a single blade just 4-5 millimeters wide. To help figure skaters land quadruple axels, Salchows, Lutzes, and maybe even the elusive quintuple without looking the least bit stressed, Jerry Lu MFin ’24 developed an optical tracking system called OOFSkate that uses artificial intelligence to analyze video of a figure skater’s jump and make recommendations on how to improve. Lu, a former researcher at the MIT Sports Lab, has been aiding elite skaters on Team USA with their technical performance and will be working with NBC Sports during the 2026 Winter Olympics to help commentators and TV viewers make better sense of the complex scoring system in figure skating, snowboarding, and skiing. He’ll be applying AI technologies to explain nuanced judging decisions and demonstrate just how technically challenging these sports can be.

Meanwhile, Professor Anette “Peko” Hosoi, co-founder and faculty director of the MIT Sports Lab, is embarking on new research aimed at understanding how AI systems evaluate aesthetic performance in figure skating. Hosoi and Lu recently chatted with MIT News about applying AI to sports, whether AI systems could ever be used to judge Olympic figure skating, and when we might see a skater land a quint.

Q: Why apply AI to figure skating?

Lu: Skaters can always keep pushing, higher, faster, stronger. OOFSkate is all about helping skaters figure out a way to rotate a little bit faster in their jumps or jump a little bit higher. The system helps skaters catch things that perhaps could pass an eye test, but that might allow them to target some high-value areas of opportunity. The artistic side of skating is much harder to evaluate than the technical elements because it’s subjective.

To use mobile training app, you just need to take a video of an athlete’s jump, and it will spit out the physical metrics that drive how many rotations you can do. It tracks those metrics and builds in all of the other current elite and former elite athletes. You can see your data and then see, “This is how an Olympic champion did this element, perhaps I should try that.” You get the comparison and the automated classifier, which shows you if you did this trick at World Championships and it were judged by an international panel, this is approximately the grade of execution score they would give you.

Hosoi: There are a lot of AI tools that are coming online, especially things like pose estimators, where you can approximate skeletal configurations from video. The challenge with these pose estimators is that if you only have one camera angle, they do very well in the plane of the camera, but they do very poorly with depth. For example, if you’re trying to critique somebody’s form in fencing, and they’re moving toward the camera, you get very bad data. But with figure skating, Jerry has found one of the few areas where depth challenges don’t really matter. In figure skating, you need to understand: How high did this person jump, how many times did they go around, and how well did they land? None of those rely on depth. He’s found an application that pose estimators do really well, and that doesn’t pay a penalty for the things they do badly.

Q: Could you ever see a world in which AI is used to evaluate the artistic side of figure skating?

Hosoi: When it comes to AI and aesthetic evaluation, we have new work underway thanks to a MIT Human Insight Collaborative (MITHIC) grant. This work is in collaboration with Professor Arthur Bahr and IDSS graduate student Eric Liu. When you ask an AI platform for an aesthetic evaluation such as “What do you think of this painting?” it will respond with something that sounds like it came from a human. What we want to understand is, to get to that assessment, are the AIs going through the same sort of reasoning pathways or using the same intuitive concepts that humans go through to arrive at, “I like that painting,” or “I don’t like that painting”? Or are they just parrots? Are they just mimicking what they heard a person say? Or is there some concept map of aesthetic appeal? Figure skating is a perfect place to look for this map because skating is aesthetically judged. And there are numbers. You can’t go around a museum and find scores, “This painting is a 35.” But in skating, you’ve got the data.

That brings up another even more interesting question, which is the difference between novices and experts. It’s known that expert humans and novice humans will react differently to seeing the same thing. Somebody who is an expert judge may have a different opinion of a skating performance than a member of the general population. We’re trying to understand differences between reactions from experts, novices, and AI. Do these reactions have some common ground in where they are coming from, or is the AI coming from a different place than both the expert and the novice?

Lu: Figure skating is interesting because everybody working in the field of AI is trying to figure out AGI or artificial general intelligence and trying to build this extremely sound AI that replicates human beings. Working on applying AI to sports like figure skating helps us understand how humans think and approach judging. This has down-the-line impacts for AI research and companies that are developing AI models. By gaining a deeper understanding of how current state-of-the-art AI models work with these sports, and how you need to do training and fine-tuning of these models to make them work for specific sports, it helps you understand how AI needs to advance.

Q: What will you be watching for in the Milan Cortina Olympics figure skating competitions, now that you’ve been studying and working in this area? Do you think someone will land a quint?

Lu: For the winter games, I am working with NBC for the figure skating, ski, and snowboarding competitions to help them tell a data-driven story for the American people. The goal is to make these sports more relatable. Skating looks slow on television, but it’s not. Everything is supposed to look effortless. If it looks hard, you are probably going to get penalized. Skaters need to learn how to spin very fast, jump extremely high, float in the air, and land beautifully on one foot. The data we are gathering can help showcase how hard skating actually is, even though it is supposed to look easy.

I’m glad we are working in the Olympics sports realm because the world watches once every four years, and it is traditionally coaching-intensive and talent-driven sports, unlike a sport like baseball, where if you don’t have an elite-level optical tracking system you are not maximizing the value that you currently have. I’m glad we get to work with these Olympic sports and athletes and make an impact here.

Hosoi: I have always watched Olympic figure skating competitions, ever since I could turn on the TV. They’re always incredible. One of the things that I’m going to be practicing is identifying the jumps, which is very hard to do if you’re an amateur “judge.”

I have also done some back-of-the-envelope calculations to see if a quint is possible. I am now totally convinced it’s possible. We will see one in our lifetime, if not relatively soon. Not in this Olympics, but soon. When I saw we were so close on the quint, I thought, what about six? Can we do six rotations? Probably not. That’s where we start to come up against the limits of human physical capability. But five, I think, is in reach.

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Times Higher Education ranks MIT No. 1 in arts and humanities, business and economics, and social sciences for 2026

The 2026 Times Higher Education World University Ranking has ranked MIT first in three subject categories: Arts and Humanities, Business and Economics, and Social Sciences, repeating the Institute’s top spot in the same subjects in 2025.

The Times Higher Education World University Ranking is an annual publication of university rankings by Times Higher Education, a leading British education magazine. The subject rankings are based on 18 rigorous performance indicators categorized under five core pillars: teaching, research environment, research quality, industry, and international outlook.

Disciplines included in MIT’s top-ranked subjects are housed in the School of Humanities, Arts, and Social Sciences (SHASS), the School of Architecture and Planning (SA+P), and the MIT Sloan School of Management.

“SHASS is a vibrant crossroads of ideas, bringing together extraordinary people,” says Agustín Rayo, the Kenan Sahin Dean of SHASS. “These rankings reflect the strength of this remarkable community and MIT’s ongoing commitment to the humanities, arts, and social sciences.” 

“The human dimension is capital to our school's mission and programs, be they architecture, planning, media arts and sciences, or the arts, and whether at the scale of individuals, communities, or societies,” says Hashim Sarkis, dean of SA+P. “The acknowledgment and celebration of their centrality by the Times Higher Education only renews our deep commitment to human values.”

“MIT and MIT Sloan are providing students with an education that ensures they have the skills, experience, and problem-solving abilities they need in order to succeed in our world today,” says Richard M. Locke, the John C Head III Dean at the MIT Sloan School of Management. “It’s not just what we teach them, but how we teach them. The interdisciplinary nature of a school like MIT combines analytical reasoning skills, deep functional knowledge, and, at MIT Sloan, a hands-on management education that teaches students how to collaborate, lead teams, and navigate challenges, now and in the future."

The Arts and Humanities ranking evaluated 817 universities from 74 countries in the disciplines of languages; literature and linguistics; history; philosophy; theology; architecture; archaeology; and art, performing arts, and design. This is the second consecutive year MIT has earned the top spot in this subject.

The ranking for Business and Economics evaluated 1,067 institutions from 91 countries and territories across three core disciplines: business and management; accounting and finance; and economics and econometrics. This is the fifth consecutive year MIT has been ranked first in this subject.

The Social Sciences ranking evaluated 1,202 institutions from 104 countries and territories in the disciplines of political science and international studies, sociology, geography, communication and media studies, and anthropology. MIT claimed the top spot in this subject for the second consecutive year.

In other subjects, MIT was also named among the top universities, ranking third in Engineering and Life Sciences, and fourth in Computer Science and Physical Sciences. Overall, MIT ranked second in the Times Higher Education 2026 World University Ranking. 

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A quick stretch switches this polymer’s capacity to transport heat

Most materials have an inherent capacity to handle heat. Plastic, for instance, is typically a poor thermal conductor, whereas materials like marble move heat more efficiently. If you were to place one hand on a marble countertop and the other on a plastic cutting board, the marble would conduct more heat away from your hand, creating a colder sensation compared to the plastic.

Typically, a material’s thermal conductivity cannot be changed without re-manufacturing it. But MIT engineers have now found that a relatively common material can switch its thermal conductivity. Simply stretching the material quickly dials up its heat conductance, from a baseline similar to that of plastic to a higher capacity closer to that of marble. When the material springs back to its unstretched form, it returns to its plastic-like properties.

The thermally reversible material is an olefin block copolymer — a soft and flexible polymer that is used in a wide range of commercial products. The team found that when the material is quickly stretched, its ability to conduct heat more than doubles. This transition occurs within just 0.22 seconds, which is the fastest thermal switching that has been observed in any material.

This material could be used to engineer systems that adapt to changing temperatures in real time. For instance, switchable fibers could be woven into apparel that normally retains heat. When stretched, the fabric would instantly conduct heat away from a person’s body to cool them down. Similar fibers can be built into laptops and infrastructure to keep devices and buildings from overheating. The researchers are working on further optimizing the polymer and on engineering new materials with similar properties.

“We need cheap and abundant materials that can quickly adapt to environmental temperature changes,” says Svetlana Boriskina, principal research scientist in MIT’s Department of Mechanical Engineering. “Now that we’ve seen this thermal switching, this changes the direction where we can look for and build new adaptive materials.”

Boriskina and her colleagues have published their results in a study appearing today in the journal Advanced Materials. The study’s co-authors include Duo Xu, Buxuan Li, You Lyu, and Vivian Santamaria-Garcia of MIT, and Yuan Zhu of Southern University of Science and Technology in Shenzhen, China.

Elastic chains

The key to the new phenomenon is that when the material is stretched, its microscopic structures align in ways that suddenly allow heat to travel through easily, increasing the material’s thermal conductivity. In its unstretched state, the same microstructures are tangled and bunched, effectively blocking heat’s path.

As it happens, Boriskina and her colleagues didn’t set out to find a heat-switching material. They were initially looking for more sustainable alternatives to spandex, which is a synthetic fabric made from petroleum-based plastics that is traditionally difficult to recycle. As a potential replacement, the team was investigating fibers made from a different polymer known as polyethylene.

“Once we started working with the material, we realized it had other properties that were more interesting than the fact that it was elastic,” Boriskina says. “What makes polyethylene unique is it has this backbone of carbon atoms arranged along a simple chain. And carbon is a very good conductor of heat.”

The microstructure of most polymer materials, including polyethylene, contains many carbon chains. However, these chains exist in a messy, spaghetti-like tangle known as an amorphous phase. Despite the fact that carbon is a good heat conductor, the disordered arrangement of chains typically impedes heat flow. Polyethylene and most other polymers, therefore, generally have low thermal conductivity.

In previous work, MIT Professor Gang Chen and his collaborators found ways to untangle the mess of carbon chains and push polyethylene to shift from a disordered amorphous state to a more aligned, crystalline phase. This transition effectively straightened the carbon chains, providing clear highways for heat to flow through and increasing the material’s thermal conductivity. In those experiments however, the switch was permanent; once the material’s phase changed, it could not be reversed.

As Boriskina’s team explored polyethylene, they also considered other closely related materials, including olefin block copolymer (OBC). OBC is predominantly an amorphous material, made from highly tangled chains of carbon and hydrogen atoms. Scientists had therefore assumed that OBC would exhibit low thermal conductivity. If its conductance could be increased, it would likely be permanent, similar to polyethylene.

But when the team carried out experiments to test the elasticity of OBC, they found something quite different.

“As we stretched and released the material, we realized that its thermal conductivity was really high when it was stretched and lower when it was relaxed, over thousands of cycles,” says study co-author and MIT graduate student Duo Xu. “This switch was reversible, while the material stayed mostly amorphous. That was unexpected.”

A stretchy mess

The team then took a closer look at OBC, and how it might be changing as it was stretched. The researchers used a combination of X-ray and Raman spectroscopy to observe the material’s microscopic structure as they stretched and relaxed it repeatedly. They observed that, in its unstretched state, the material consists mainly of amorphous tangles of carbon chains, with just a few islands of ordered, crystalline domains scattered here and there. When stretched, the crystalline domains seemed to align and the amorphous tangles straightened out, similar to what Gang Chen observed in polyethylene.

However, rather than transitioning entirely into a crystalline phase, the straightened tangles stayed in their amorphous state. In this way, the team found that the tangles were able to switch back and forth, from straightened to bunched and back again, as the material was stretched and relaxed repeatedly.

“Our material is always in a mostly amorphous state; it never crystallizes under strain,” Xu notes. “So it leaves you this opportunity to go back and forth in thermal conductivity a thousand times. It’s very reversible.”

The team also found that this thermal switching happens extremely fast: The material’s thermal conductivity more than doubled within just 0.22 seconds of being stretched.

“The resulting difference in heat dissipation through this material is comparable to a tactile difference between touching a plastic cutting board versus a marble countertop,” Boriskina says.

She and her colleagues are now taking the results of their experiments and working them into models to see how they can tweak a material’s amorphous structure, to trigger an even bigger change when stretched.

“Our fibers can quickly react to dissipate heat, for electronics, fabrics, and building infrastructure.” Boriskina says. “If we could make further improvements to switch their thermal conductivity from that of plastic to that closer to diamond, it would have a huge industrial and societal impact.”

This research was supported, in part, by the U.S. Department of Energy, the Office of Naval Research Global via Tec de Monterrey, MIT Evergreen Graduate Innovation Fellowship, MathWorks MechE Graduate Fellowship, and the MIT-SUSTech Centers for Mechanical Engineering Research and Education, and carried out, in part, with the use of MIT.nano and ISN facilities.

de MIT News https://ift.tt/MEaklcs