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      in Energy

      MIT expands research collaboration with Commonwealth Fusion Systems to build net energy fusion machine, SPARC

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      MIT’s Plasma Science and Fusion Center (PSFC) will substantially expand its fusion energy research and education activities under a new five-year agreement with Institute spinout Commonwealth Fusion Systems (CFS).

      “This expanded relationship puts MIT and PSFC in a prime position to be an even stronger academic leader that can help deliver the research and education needs of the burgeoning fusion energy industry, in part by utilizing the world’s first burning plasma and net energy fusion machine, SPARC,” says PSFC director Dennis Whyte. “CFS will build SPARC and develop a commercial fusion product, while MIT PSFC will focus on its core mission of cutting-edge research and education.”

      Commercial fusion energy has the potential to play a significant role in combating climate change, and there is a concurrent increase in interest from the energy sector, governments, and foundations. The new agreement, administered by the MIT Energy Initiative (MITEI), where CFS is a startup member, will help PSFC expand its fusion technology efforts with a wider variety of sponsors. The collaboration enables rapid execution at scale and technology transfer into the commercial sector as soon as possible.

      This new agreement doubles CFS’ financial commitment to PSFC, enabling greater recruitment and support of students, staff, and faculty. “We’ll significantly increase the number of graduate students and postdocs, and just as important they will be working on a more diverse set of fusion science and technology topics,” notes Whyte. It extends the collaboration between PSFC and CFS that resulted in numerous advances toward fusion power plants, including last fall’s demonstration of a high-temperature superconducting (HTS) fusion electromagnet with record-setting field strength of 20 tesla.

      The combined magnetic fusion efforts at PSFC will surpass those in place during the operations of the pioneering Alcator C-Mod tokamak device that operated from 1993 to 2016. This increase in activity reflects a moment when multiple fusion energy technologies are seeing rapidly accelerating development worldwide, and the emergence of a new fusion energy industry that would require thousands of trained people.

      MITEI director Robert Armstrong adds, “Our goal from the beginning was to create a membership model that would allow startups who have specific research challenges to leverage the MITEI ecosystem, including MIT faculty, students, and other MITEI members. The team at the PSFC and MITEI have worked seamlessly to support CFS, and we are excited for this next phase of the relationship.”

      PSFC is supporting CFS’ efforts toward realizing the SPARC fusion platform, which facilitates rapid development and refinement of elements (including HTS magnets) needed to build ARC, a compact, modular, high-field fusion power plant that would set the stage for commercial fusion energy production. The concepts originated in Whyte’s nuclear science and engineering class 22.63 (Principles of Fusion Engineering) and have been carried forward by students and PSFC staff, many of whom helped found CFS; the new activity will expand research into advanced technologies for the envisioned pilot plant.

      “This has been an incredibly effective collaboration that has resulted in a major breakthrough for commercial fusion with the successful demonstration of revolutionary fusion magnet technology that will enable the world’s first commercially relevant net energy fusion device, SPARC, currently under construction,” says Bob Mumgaard SM ’15, PhD ’15, CEO of Commonwealth Fusion Systems. “We look forward to this next phase in the collaboration with MIT as we tackle the critical research challenges ahead for the next steps toward fusion power plant development.”

      In the push for commercial fusion energy, the next five years are critical, requiring intensive work on materials longevity, heat transfer, fuel recycling, maintenance, and other crucial aspects of power plant development. It will need innovation from almost every engineering discipline. “Having great teams working now, it will cut the time needed to move from SPARC to ARC, and really unleash the creativity. And the thing MIT does so well is cut across disciplines,” says Whyte.

      “To address the climate crisis, the world needs to deploy existing clean energy solutions as widely and as quickly as possible, while at the same time developing new technologies — and our goal is that those new technologies will include fusion power,” says Maria T. Zuber, MIT’s vice president for research. “To make new climate solutions a reality, we need focused, sustained collaborations like the one between MIT and Commonwealth Fusion Systems. Delivering fusion power onto the grid is a monumental challenge, and the combined capabilities of these two organizations are what the challenge demands.”

      On a strategic level, climate change and the imperative need for widely implementable carbon-free energy have helped orient the PSFC team toward scalability. “Building one or 10 fusion plants doesn’t make a difference — we have to build thousands,” says Whyte. “The design decisions we make will impact the ability to do that down the road. The real enemy here is time, and we want to remove as many impediments as possible and commit to funding a new generation of scientific leaders. Those are critically important in a field with as much interdisciplinary integration as fusion.” More

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      in Energy

      Team creates map for production of eco-friendly metals

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      In work that could usher in more efficient, eco-friendly processes for producing important metals like lithium, iron, and cobalt, researchers from MIT and the SLAC National Accelerator Laboratory have mapped what is happening at the atomic level behind a particularly promising approach called metal electrolysis.

      By creating maps for a wide range of metals, they not only determined which metals should be easiest to produce using this approach, but also identified fundamental barriers behind the efficient production of others. As a result, the researchers’ map could become an important design tool for optimizing the production of all these metals.

      The work could also aid the development of metal-air batteries, cousins of the lithium-ion batteries used in today’s electric vehicles.

      Most of the metals key to society today are produced using fossil fuels. These fuels generate the high temperatures necessary to convert the original ore into its purified metal. But that process is a significant source of greenhouse gases — steel alone accounts for some 7 percent of carbon dioxide emissions globally. As a result, researchers from around the world are working to identify more eco-friendly ways for the production of metals.

      One promising approach is metal electrolysis, in which a metal oxide, the ore, is zapped with electricity to create pure metal with oxygen as the byproduct. That is the reaction explored at the atomic level in new research reported in the April 8 issue of the journal Chemistry of Materials.

      Donald Siegel is department chair and professor of mechanical engineering at the University of Texas at Austin. Says Siegel, who was not involved in the Chemistry of Materials study: “This work is an important contribution to improving the efficiency of metal production from metal oxides. It clarifies our understanding of low-carbon electrolysis processes by tracing the underlying thermodynamics back to elementary metal-oxygen interactions. I expect that this work will aid in the creation of design rules that will make these industrially important processes less reliant on fossil fuels.”

      Yang Shao-Horn, the JR East Professor of Engineering in MIT’s Department of Materials Science and Engineering (DMSE) and Department of Mechanical Engineering, is a leader of the current work, with Michal Bajdich of SLAC.

      “Here we aim to establish some basic understanding to predict the efficiency of electrochemical metal production and metal-air batteries from examining computed thermodynamic barriers for the conversion between metal and metal oxides,” says Shao-Horn, who is on the research team for MIT’s new Center for Electrification and Decarbonization of Industry, a winner of the Institute’s first-ever Climate Grand Challenges competition. Shao-Horn is also affiliated with MIT’s Materials Research Laboratory and Research Laboratory of Electronics.

      In addition to Shao-Horn and Bajdich, other authors of the Chemistry of Materials paper are Jaclyn R. Lunger, first author and a DMSE graduate student; mechanical engineering senior Naomi Lutz; and DMSE graduate student Jiayu Peng.

      Other applications

      The work could also aid in developing metal-air batteries such as lithium-air, aluminum-air, and zinc-air batteries. These cousins of the lithium-ion batteries used in today’s electric vehicles have the potential to electrify aviation because their energy densities are much higher. However, they are not yet on the market due to a variety of problems including inefficiency.

      Charging metal-air batteries also involves electrolysis. As a result, the new atomic-level understanding of these reactions could not only help engineers develop efficient electrochemical routes for metal production, but also design more efficient metal-air batteries.

      Learning from water splitting

      Electrolysis is also used to split water into oxygen and hydrogen, which stores the resulting energy. That hydrogen, in turn, could become an eco-friendly alternative to fossil fuels. Since much more is known about water electrolysis, the focus of Bajdich’s work at SLAC, than the electrolysis of metal oxides, the team compared the two processes for the first time.

      The result: “Slowly, we uncovered the elementary steps involved in metal electrolysis,” says Bajdich. The work was challenging, says Lunger, because “it was unclear to us what those steps are. We had to figure out how to get from A to B,” or from a metal oxide to metal and oxygen.

      All of the work was conducted with supercomputer simulations. “It’s like a sandbox of atoms, and then we play with them. It’s a little like Legos,” says Bajdich. More specifically, the team explored different scenarios for the electrolysis of several metals. Each involved different catalysts, molecules that boost the speed of a reaction.

      Says Lunger, “To optimize the reaction, you want to find the catalyst that makes it most efficient.” The team’s map is essentially a guide for designing the best catalysts for each different metal.

      What’s next? Lunger noted that the current work focused on the electrolysis of pure metals. “I’m interested in seeing what happens in more complex systems involving multiple metals. Can you make the reaction more efficient if there’s sodium and lithium present, or cadmium and cesium?”

      This work was supported by a U.S. Department of Energy Office of Science Graduate Student Research award. It was also supported by an MIT Energy Initiative fellowship, the Toyota Research Institute through the Accelerated Materials Design and Discovery Program, the Catalysis Science Program of Department of Energy, Office of Basic Energy Sciences, and by the Differentiate Program through the U.S. Advanced Research Projects Agency — Energy.  More

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      in Security

      Engineers use artificial intelligence to capture the complexity of breaking waves

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      Waves break once they swell to a critical height, before cresting and crashing into a spray of droplets and bubbles. These waves can be as large as a surfer’s point break and as small as a gentle ripple rolling to shore. For decades, the dynamics of how and when a wave breaks have been too complex to predict.

      Now, MIT engineers have found a new way to model how waves break. The team used machine learning along with data from wave-tank experiments to tweak equations that have traditionally been used to predict wave behavior. Engineers typically rely on such equations to help them design resilient offshore platforms and structures. But until now, the equations have not been able to capture the complexity of breaking waves.

      The updated model made more accurate predictions of how and when waves break, the researchers found. For instance, the model estimated a wave’s steepness just before breaking, and its energy and frequency after breaking, more accurately than the conventional wave equations.

      Their results, published today in the journal Nature Communications, will help scientists understand how a breaking wave affects the water around it. Knowing precisely how these waves interact can help hone the design of offshore structures. It can also improve predictions for how the ocean interacts with the atmosphere. Having better estimates of how waves break can help scientists predict, for instance, how much carbon dioxide and other atmospheric gases the ocean can absorb.

      “Wave breaking is what puts air into the ocean,” says study author Themis Sapsis, an associate professor of mechanical and ocean engineering and an affiliate of the Institute for Data, Systems, and Society at MIT. “It may sound like a detail, but if you multiply its effect over the area of the entire ocean, wave breaking starts becoming fundamentally important to climate prediction.”

      The study’s co-authors include lead author and MIT postdoc Debbie Eeltink, Hubert Branger and Christopher Luneau of Aix-Marseille University, Amin Chabchoub of Kyoto University, Jerome Kasparian of the University of Geneva, and T.S. van den Bremer of Delft University of Technology.

      Learning tank

      To predict the dynamics of a breaking wave, scientists typically take one of two approaches: They either attempt to precisely simulate the wave at the scale of individual molecules of water and air, or they run experiments to try and characterize waves with actual measurements. The first approach is computationally expensive and difficult to simulate even over a small area; the second requires a huge amount of time to run enough experiments to yield statistically significant results.

      The MIT team instead borrowed pieces from both approaches to develop a more efficient and accurate model using machine learning. The researchers started with a set of equations that is considered the standard description of wave behavior. They aimed to improve the model by “training” the model on data of breaking waves from actual experiments.

      “We had a simple model that doesn’t capture wave breaking, and then we had the truth, meaning experiments that involve wave breaking,” Eeltink explains. “Then we wanted to use machine learning to learn the difference between the two.”

      The researchers obtained wave breaking data by running experiments in a 40-meter-long tank. The tank was fitted at one end with a paddle which the team used to initiate each wave. The team set the paddle to produce a breaking wave in the middle of the tank. Gauges along the length of the tank measured the water’s height as waves propagated down the tank.

      “It takes a lot of time to run these experiments,” Eeltink says. “Between each experiment you have to wait for the water to completely calm down before you launch the next experiment, otherwise they influence each other.”

      Safe harbor

      In all, the team ran about 250 experiments, the data from which they used to train a type of machine-learning algorithm known as a neural network. Specifically, the algorithm is trained to compare the real waves in experiments with the predicted waves in the simple model, and based on any differences between the two, the algorithm tunes the model to fit reality.

      After training the algorithm on their experimental data, the team introduced the model to entirely new data — in this case, measurements from two independent experiments, each run at separate wave tanks with different dimensions. In these tests, they found the updated model made more accurate predictions than the simple, untrained model, for instance making better estimates of a breaking wave’s steepness.

      The new model also captured an essential property of breaking waves known as the “downshift,” in which the frequency of a wave is shifted to a lower value. The speed of a wave depends on its frequency. For ocean waves, lower frequencies move faster than higher frequencies. Therefore, after the downshift, the wave will move faster. The new model predicts the change in frequency, before and after each breaking wave, which could be especially relevant in preparing for coastal storms.

      “When you want to forecast when high waves of a swell would reach a harbor, and you want to leave the harbor before those waves arrive, then if you get the wave frequency wrong, then the speed at which the waves are approaching is wrong,” Eeltink says.

      The team’s updated wave model is in the form of an open-source code that others could potentially use, for instance in climate simulations of the ocean’s potential to absorb carbon dioxide and other atmospheric gases. The code can also be worked into simulated tests of offshore platforms and coastal structures.

      “The number one purpose of this model is to predict what a wave will do,” Sapsis says. “If you don’t model wave breaking right, it would have tremendous implications for how structures behave. With this, you could simulate waves to help design structures better, more efficiently, and without huge safety factors.”

      This research is supported, in part, by the Swiss National Science Foundation, and by the U.S. Office of Naval Research. More

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      in Environment

      Five MIT PhD students awarded 2022 J-WAFS fellowships for water and food solutions

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      The Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) recently announced the selection of its 2022-23 cohort of graduate fellows. Two students were named Rasikbhai L. Meswani Fellows for Water Solutions and three students were named J-WAFS Graduate Student Fellows. All five fellows will receive full tuition and a stipend for one semester, and J-WAFS will support the students throughout the 2022-23 academic year by providing networking, mentorship, and opportunities to showcase their research.

      New this year, fellowship nominations were open not only to students pursuing water research, but food-related research as well. The five students selected were chosen for their commitment to solutions-based research that aims to alleviate problems such as water supply or purification, food security, or agriculture. Their projects exemplify the wide range of research that J-WAFS supports, from enhancing nutrition through improved methods to deliver micronutrients to developing high-performance drip irrigation technology. The strong applicant pool reflects the passion MIT students have to address the water and food crises currently facing the planet.

      “This year’s fellows are drawn from a dynamic and engaged community across the Institute whose creativity and ingenuity are pushing forward transformational water and food solutions,” says J-WAFS executive director Renee J. Robins. “We congratulate these students as we recognize their outstanding achievements and their promise as up-and-coming leaders in global water and food sectors.”

      2022-23 Rasikbhai L. Meswani Fellows for Water SolutionsThe Rasikbhai L. Meswani Fellowship for Water Solutions is a fellowship for students pursuing water-related research at MIT. The Rasikbhai L. Meswani Fellowship for Water Solutions was made possible by a generous gift from Elina and Nikhil Meswani and family.

      Aditya Ghodgaonkar is a PhD candidate in the Department of Mechanical Engineering at MIT, where he works in the Global Engineering and Research (GEAR) Lab under Professor Amos Winter. Ghodgaonkar received a bachelor’s degree in mechanical engineering from the RV College of Engineering in India. He then moved to the United States and received a master’s degree in mechanical engineering from Purdue University.Ghodgaonkar is currently designing hydraulic components for drip irrigation that could support the development of water-efficient irrigation systems that are off-grid, inexpensive, and low-maintenance. He has focused on designing drip irrigation emitters that are resistant to clogging, seeking inspiration about flow regulation from marine fauna such as manta rays, as well as turbomachinery concepts. Ghodgaonkar notes that clogging is currently an expensive technical challenge to diagnose, mitigate, and resolve. With an eye on hundreds of millions of farms in developing countries, he aims to bring the benefits of irrigation technology to even the poorest farmers.Outside of his research, Ghodgaonkar is a mentor in MIT Makerworks and has been a teaching assistant for classes such as 2.007 (Design and Manufacturing I). He also helped organize the annual MIT Water Summit last fall.

      Devashish Gokhale is a PhD candidate advised by Professor Patrick Doyle in the Department of Chemical Engineering. He received a bachelor’s degree in chemical engineering from the Indian Institute of Technology Madras, where he researched fluid flow in energy-efficient pumps. Gokhale’s commitment to global water security stemmed from his experience growing up in India, where water sources are threatened by population growth, industrialization, and climate change.As a researcher in the Doyle group, Devashish is developing sustainable and reusable materials for water treatment, with a focus on the elimination of emerging contaminants and other micropollutants from water through cost-effective processes. Many of these contaminants are carcinogens or endocrine disruptors, posing significant threats to both humans and animals. His advisor notes that Devashish was the first researcher in the Doyle group to work on water purification, bringing his passion for the topic to the lab.Gokhale’s research won an award for potential scalability in last year’s J-WAFS World Water Day competition. He also serves as the lecture series chair in the MIT Water Club.

      2022-23 J-WAFS Graduate Student FellowsThe J-WAFS Fellowship for Water and Food Solutions is funded by the J-WAFS Research Affiliate Program, which offers companies the opportunity to collaborate with MIT on water and food research. A portion of each research affiliate’s fees supports this fellowship. The program is central to J-WAFS’ efforts to engage across sector and disciplinary boundaries in solving real-world problems. Currently, there are two J-WAFS Research Affiliates: Xylem, Inc., a water technology company, and GoAigua, a company leading the digital transformation of the water industry.

      James Zhang is a PhD candidate in the Department of Mechanical Engineering at MIT, where he has worked in the NanoEngineering Laboratory with Professor Gang Chen since 2019. As an undergraduate at Carnegie Mellon University, he double majored in mechanical engineering and engineering public policy. He then received a master’s degree in mechanical engineering from MIT. In addition to working in the NanoEngineering Laboratory, James has also worked in the Zhao Laboratory and in the Boriskina Research Group at MIT.Zhang is developing a technology that uses light-induced evaporation to clean water. He is currently investigating the fundamental properties of how light interacts with brackish water surfaces. With strong theoretical as well as experimental components, his research could lead to innovations in desalinating brackish water at high energy efficiencies. Outside of his research, Zhang has served as a student moderator for the MIT International Colloquia on Thermal Innovations.

      Katharina Fransen is a PhD candidate advised by Professor Bradley Olsen in the Department of Chemical Engineering at MIT. She received a bachelor’s degree in chemical engineering from the University of Minnesota, where she was involved in the Society of Women Engineers. Fransen is motivated by the challenge of protecting the most vulnerable global communities from the large quantities of plastic waste associated with traditional food packaging materials. As a researcher in the Olsen Lab, Fransen is developing new plastics that are biologically-based and biodegradable, so they can degrade in the environment instead of polluting communities with plastic waste. These polymers are also optimized for food packaging applications to keep food fresher for longer, preventing food waste.Outside of her research, Fransen is involved in Diversity in Chemical Engineering as the coordinator for the graduate application mentorship program for underrepresented groups. She is also an active member of Graduate Womxn in ChemE and mentors an Undergraduate Research Opportunities Program student.

      Linzixuan (Rhoda) Zhang is a PhD candidate advised by Professor Robert Langer and Ana Jaklenec in the Department of Chemical Engineering at MIT. She received a bachelor’s degree in chemical engineering from the University of Illinois at Urbana-Champaign, where she researched how to genetically engineer microorganisms for the efficient production of advanced biofuels and chemicals.Zhang is currently developing a micronutrient delivery platform that fortifies foods with essential vitamins and nutrients. She has helped develop a group of biodegradable polymers that can stabilize micronutrients under harsh conditions, enabling local food companies to fortify food with essential vitamins. This work aims to tackle a hidden crisis in low- and middle-income countries, where a chronic lack of essential micronutrients affects an estimated 2 billion people.Zhang is also working on the development of self-boosting vaccines to promote more widespread vaccine access and serves as a research mentor in the Langer Lab. More

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      in Environment

      A community approach to improving the health of the planet

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      Earlier this month, MIT’s Department of Mechanical Engineering (MechE) hosted a Health of the Planet Showcase. The event was the culmination of a four-year long community initiative to focus on what the mechanical engineering community at MIT can do to solve some of the biggest challenges the planet faces on a local and global scale. Structured like an informal poster session, the event marked the first time that administrative staff joined students, researchers, and postdocs in sharing their own research.

      When Evelyn Wang started her tenure as mechanical engineering department head in July 2018, she and associate department heads Pierre Lermusiaux and Rohit Karnik made the health of the planet a top priority for the department. Their goal was to bring students, faculty, and staff together to develop solutions that address the many problems related to the health of the planet.

      “As a field, mechanical engineering is unique in its diversity,” says Wang, the Ford Professor of Engineering. “We have researchers who are world-leading experts on desalination, ocean engineering, energy storage, and photovoltaics, just to name a few. One of our driving motivations has been getting those experts to collaborate and work on new health of the planet research projects together.”

      Wang also saw an opportunity to tap into the passions of the department’s students and staff, many of whom devote their extracurricular and personal time to environmental causes. She enlisted the help of a team of faculty and staff to launch what has become known as the MechE Health of the Planet Initiative.

      The initiative, which capitalizes on the diverse range of research fields in mechanical engineering, encouraged both grand research ideas that could have impact on a global scale, and smaller personal habits that could help on a smaller scale.

      “We wanted to encourage everyone in our community to think about their daily routine and make small changes that really add up over time,” says Dorothy Hanna, program administrator at MIT and one of the staff members leading the initiative.

      The Health of the Planet team started small. They hosted an office supply swap day to encourage recycling and reuse of everyday office products. This idea expanded to include the launch of “Lab Reuse Days.” Members of the Rohsenow Kendall Lab, including members of the research groups of professors Gang Chen, John Lienhard, and Evelyn Wang, gathered extra materials for reuse. Researchers from other labs picked up Arduino kits, tubing, and electrical wiring to use for their own projects.

      While individuals were encouraged to adopt small habits at home and at work to help the health of the planet, research teams were encouraged to work together on solutions on a larger scale.

      Seed funding for collaborative research

      In early 2020, the MIT Department of Mechanical Engineering launched a new collaborative seed research program based on funding from MathWorks, the computing software company that developed MATLAB. The first seed funding supported health of the planet research projects led by two or more mechanical engineering faculty members.

      “One of the driving goals of MechE has been fostering collaborations and supporting interdisciplinary research on the grand challenges our world faces,” says Pierre Lermusiaux, the Nam P. Suh Professor and associate department head for operations. “The seed funding from MathWorks was a great opportunity to build upon the diverse expertise and creativity our researchers have to address health of the planet related issues.” 

      The research projects supported by the seed funding ranged from lithium-ion batteries for electric vehicles to high-performance household energy products for low- and middle-income countries. Each project differs in scope and application, and draws upon the expertise of at least two different research groups at MIT.

      Throughout the past two years, faculty presented about these research projects in several community seminars. They also participated in a full-day faculty research retreat focused on health of the planet research that included presentations from local Cambridge and Boston city leaders, as well as experts from other MIT departments and Harvard University.

      These projects have helped break down barriers and increased collaboration among research groups that focus on different areas. The third round of seed funding for collaborative research projects was recently announced and new projects will be chosen in the coming weeks.

      A community showcase

      Upon returning to the campus last fall, the Health of the Planet team began planning an event to bring the community together and celebrate the department’s research efforts. The Health of the Planet Showcase, which took place on April 4, featured 26 presenters from across the mechanical engineering community at MIT.

      Projects included a marine coastal monitoring robot, solar hydrogen production with thermochemical cycles, and a portable atmospheric water extractor for dry climates. Among the presenters was Administrative Assistant Tony Pulsone, who presented on how honeybees navigate their surroundings, as well as program manager Theresa Werth and program administrator Dorothy Hanna, who presented on reducing bottled water use and practical strategies developed by staff to overcome functional barriers on campus.

      The event concluded with the announcement of the Fay and Alfred D. Chandler Jr. Research Fellowship, awarded to a MechE student-led effort to propose a new paradigm to improve the health of our planet. Graduate student Charlene Xia won for her work developing a real-time opto-fluidics system for monitoring the soil microbiome.

      “The soil microbiome governs the biogeochemical cycling of macronutrients, micronutrients, and other elements vital for the growth of plants and animal life,” Xia said. “Understanding and predicting the impact of climate change on soil microbiomes and the ecosystem services they provide present a grand challenge and major opportunity.”

      The Chandler Fellowship will continue during the 2022-23 academic year, when another student-led project will be chosen. The department also hopes to make the Health of the Planet Showcase an annual gathering.

      “The showcase was such a vibrant event,” adds Wang. “It really energized the department and renewed our commitment to growing community efforts and continuing to advance research to help improve and protect the health of our planet.” More

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      in Security

      MIT engineers introduce the Oreometer

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      When you twist open an Oreo cookie to get to the creamy center, you’re mimicking a standard test in rheology — the study of how a non-Newtonian material flows when twisted, pressed, or otherwise stressed. MIT engineers have now subjected the sandwich cookie to rigorous materials tests to get to the center of a tantalizing question: Why does the cookie’s cream stick to just one wafer when twisted apart?

      “There’s the fascinating problem of trying to get the cream to distribute evenly between the two wafers, which turns out to be really hard,” says Max Fan, an undergraduate in MIT’s Department of Mechanical Engineering.

      In pursuit of an answer, the team subjected cookies to standard rheology tests in the lab and found that no matter the flavor or amount of stuffing, the cream at the center of an Oreo almost always sticks to one wafer when twisted open. Only for older boxes of cookies does the cream sometimes separate more evenly between both wafers.

      The researchers also measured the torque required to twist open an Oreo, and found it to be similar to the torque required to turn a doorknob and about 1/10th what’s needed to twist open a bottlecap. The cream’s failure stress — i.e. the force per area required to get the cream to flow, or deform — is twice that of cream cheese and peanut butter, and about the same magnitude as mozzarella cheese. Judging from the cream’s response to stress, the team classifies its texture as “mushy,” rather than brittle, tough, or rubbery.

      So, why does the cookie’s cream glom to one side rather than splitting evenly between both? The manufacturing process may be to blame.

      “Videos of the manufacturing process show that they put the first wafer down, then dispense a ball of cream onto that wafer before putting the second wafer on top,” says Crystal Owens, an MIT mechanical engineering PhD candidate who studies the properties of complex fluids. “Apparently that little time delay may make the cream stick better to the first wafer.”

      The team’s study isn’t simply a sweet diversion from bread-and-butter research; it’s also an opportunity to make the science of rheology accessible to others. To that end, the researchers have designed a 3D-printable “Oreometer” — a simple device that firmly grasps an Oreo cookie and uses pennies and rubber bands to control the twisting force that progressively twists the cookie open. Instructions for the tabletop device can be found here.

      The new study, “On Oreology, the fracture and flow of ‘milk’s favorite cookie,’” appears today in Kitchen Flows, a special issue of the journal Physics of Fluids. It was conceived of early in the Covid-19 pandemic, when many scientists’ labs were closed or difficult to access. In addition to Owens and Fan, co-authors are mechanical engineering professors Gareth McKinley and A. John Hart.

      Confection connection

      A standard test in rheology places a fluid, slurry, or other flowable material onto the base of an instrument known as a rheometer. A parallel plate above the base can be lowered onto the test material. The plate is then twisted as sensors track the applied rotation and torque.

      Owens, who regularly uses a laboratory rheometer to test fluid materials such as 3D-printable inks, couldn’t help noting a similarity with sandwich cookies. As she writes in the new study:

      “Scientifically, sandwich cookies present a paradigmatic model of parallel plate rheometry in which a fluid sample, the cream, is held between two parallel plates, the wafers. When the wafers are counter-rotated, the cream deforms, flows, and ultimately fractures, leading to separation of the cookie into two pieces.”

      While Oreo cream may not appear to possess fluid-like properties, it is considered a “yield stress fluid” — a soft solid when unperturbed that can start to flow under enough stress, the way toothpaste, frosting, certain cosmetics, and concrete do.

      Curious as to whether others had explored the connection between Oreos and rheology, Owens found mention of a 2016 Princeton University study in which physicists first reported that indeed, when twisting Oreos by hand, the cream almost always came off on one wafer.

      “We wanted to build on this to see what actually causes this effect and if we could control it if we mounted the Oreos carefully onto our rheometer,” she says.

      Play video

      Cookie twist

      In an experiment that they would repeat for multiple cookies of various fillings and flavors, the researchers glued an Oreo to both the top and bottom plates of a rheometer and applied varying degrees of torque and angular rotation, noting the values  that successfully twisted each cookie apart. They plugged the measurements into equations to calculate the cream’s viscoelasticity, or flowability. For each experiment, they also noted the cream’s “post-mortem distribution,” or where the cream ended up after twisting open.

      In all, the team went through about 20 boxes of Oreos, including regular, Double Stuf, and Mega Stuf levels of filling, and regular, dark chocolate, and “golden” wafer flavors. Surprisingly, they found that no matter the amount of cream filling or flavor, the cream almost always separated onto one wafer.

      “We had expected an effect based on size,” Owens says. “If there was more cream between layers, it should be easier to deform. But that’s not actually the case.”

      Curiously, when they mapped each cookie’s result to its original position in the box, they noticed the cream tended to stick to the inward-facing wafer: Cookies on the left side of the box twisted such that the cream ended up on the right wafer, whereas cookies on the right side separated with cream mostly on the left wafer. They suspect this box distribution may be a result of post-manufacturing environmental effects, such as heating or jostling that may cause cream to peel slightly away from the outer wafers, even before twisting.

      The understanding gained from the properties of Oreo cream could potentially be applied to the design of other complex fluid materials.

      “My 3D printing fluids are in the same class of materials as Oreo cream,” she says. “So, this new understanding can help me better design ink when I’m trying to print flexible electronics from a slurry of carbon nanotubes, because they deform in almost exactly the same way.”

      As for the cookie itself, she suggests that if the inside of Oreo wafers were more textured, the cream might grip better onto both sides and split more evenly when twisted.

      “As they are now, we found there’s no trick to twisting that would split the cream evenly,” Owens concludes.

      This research was supported, in part, by the MIT UROP program and by the National Defense Science and Engineering Graduate Fellowship Program. More

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      in Energy

      A new heat engine with no moving parts is as efficient as a steam turbine

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      Engineers at MIT and the National Renewable Energy Laboratory (NREL) have designed a heat engine with no moving parts. Their new demonstrations show that it converts heat to electricity with over 40 percent efficiency — a performance better than that of traditional steam turbines.

      The heat engine is a thermophotovoltaic (TPV) cell, similar to a solar panel’s photovoltaic cells, that passively captures high-energy photons from a white-hot heat source and converts them into electricity. The team’s design can generate electricity from a heat source of between 1,900 to 2,400 degrees Celsius, or up to about 4,300 degrees Fahrenheit.

      The researchers plan to incorporate the TPV cell into a grid-scale thermal battery. The system would absorb excess energy from renewable sources such as the sun and store that energy in heavily insulated banks of hot graphite. When the energy is needed, such as on overcast days, TPV cells would convert the heat into electricity, and dispatch the energy to a power grid.

      With the new TPV cell, the team has now successfully demonstrated the main parts of the system in separate, small-scale experiments. They are working to integrate the parts to demonstrate a fully operational system. From there, they hope to scale up the system to replace fossil-fuel-driven power plants and enable a fully decarbonized power grid, supplied entirely by renewable energy.

      “Thermophotovoltaic cells were the last key step toward demonstrating that thermal batteries are a viable concept,” says Asegun Henry, the Robert N. Noyce Career Development Professor in MIT’s Department of Mechanical Engineering. “This is an absolutely critical step on the path to proliferate renewable energy and get to a fully decarbonized grid.”

      Henry and his collaborators have published their results today in the journal Nature. Co-authors at MIT include Alina LaPotin, Kevin Schulte, Kyle Buznitsky, Colin Kelsall, Andrew Rohskopf, and Evelyn Wang, the Ford Professor of Engineering and head of the Department of Mechanical Engineering, along with collaborators at NREL in Golden, Colorado.

      Jumping the gap

      More than 90 percent of the world’s electricity comes from sources of heat such as coal, natural gas, nuclear energy, and concentrated solar energy. For a century, steam turbines have been the industrial standard for converting such heat sources into electricity.

      On average, steam turbines reliably convert about 35 percent of a heat source into electricity, with about 60 percent representing the highest efficiency of any heat engine to date. But the machinery depends on moving parts that are temperature- limited. Heat sources higher than 2,000 degrees Celsius, such as Henry’s proposed thermal battery system, would be too hot for turbines.

      In recent years, scientists have looked into solid-state alternatives — heat engines with no moving parts, that could potentially work efficiently at higher temperatures.

      “One of the advantages of solid-state energy converters are that they can operate at higher temperatures with lower maintenance costs because they have no moving parts,” Henry says. “They just sit there and reliably generate electricity.”

      Thermophotovoltaic cells offered one exploratory route toward solid-state heat engines. Much like solar cells, TPV cells could be made from semiconducting materials with a particular bandgap — the gap between a material’s valence band and its conduction band. If a photon with a high enough energy is absorbed by the material, it can kick an electron across the bandgap, where the electron can then conduct, and thereby generate electricity — doing so without moving rotors or blades.

      To date, most TPV cells have only reached efficiencies of around 20 percent, with the record at 32 percent, as they have been made of relatively low-bandgap materials that convert lower-temperature, low-energy photons, and therefore convert energy less efficiently.

      Catching light

      In their new TPV design, Henry and his colleagues looked to capture higher-energy photons from a higher-temperature heat source, thereby converting energy more efficiently. The team’s new cell does so with higher-bandgap materials and multiple junctions, or material layers, compared with existing TPV designs.

      The cell is fabricated from three main regions: a high-bandgap alloy, which sits over a slightly lower-bandgap alloy, underneath which is a mirror-like layer of gold. The first layer captures a heat source’s highest-energy photons and converts them into electricity, while lower-energy photons that pass through the first layer are captured by the second and converted to add to the generated voltage. Any photons that pass through this second layer are then reflected by the mirror, back to the heat source, rather than being absorbed as wasted heat.

      The team tested the cell’s efficiency by placing it over a heat flux sensor — a device that directly measures the heat absorbed from the cell. They exposed the cell to a high-temperature lamp and concentrated the light onto the cell. They then varied the bulb’s intensity, or temperature, and observed how the cell’s power efficiency — the amount of power it produced, compared with the heat it absorbed — changed with temperature. Over a range of 1,900 to 2,400 degrees Celsius, the new TPV cell maintained an efficiency of around 40 percent.

      “We can get a high efficiency over a broad range of temperatures relevant for thermal batteries,” Henry says.

      The cell in the experiments is about a square centimeter. For a grid-scale thermal battery system, Henry envisions the TPV cells would have to scale up to about 10,000 square feet (about a quarter of a football field), and would operate in climate-controlled warehouses to draw power from huge banks of stored solar energy. He points out that an infrastructure exists for making large-scale photovoltaic cells, which could also be adapted to manufacture TPVs.

      “There’s definitely a huge net positive here in terms of sustainability,” Henry says. “The technology is safe, environmentally benign in its life cycle, and can have a tremendous impact on abating carbon dioxide emissions from electricity production.”

      This research was supported, in part, by the U.S. Department of Energy. More

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      in Energy

      Engineers enlist AI to help scale up advanced solar cell manufacturing

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      Perovskites are a family of materials that are currently the leading contender to potentially replace today’s silicon-based solar photovoltaics. They hold the promise of panels that are far thinner and lighter, that could be made with ultra-high throughput at room temperature instead of at hundreds of degrees, and that are cheaper and easier to transport and install. But bringing these materials from controlled laboratory experiments into a product that can be manufactured competitively has been a long struggle.

      Manufacturing perovskite-based solar cells involves optimizing at least a dozen or so variables at once, even within one particular manufacturing approach among many possibilities. But a new system based on a novel approach to machine learning could speed up the development of optimized production methods and help make the next generation of solar power a reality.

      The system, developed by researchers at MIT and Stanford University over the last few years, makes it possible to integrate data from prior experiments, and information based on personal observations by experienced workers, into the machine learning process. This makes the outcomes more accurate and has already led to the manufacturing of perovskite cells with an energy conversion efficiency of 18.5 percent, a competitive level for today’s market.

      The research is reported today in the journal Joule, in a paper by MIT professor of mechanical engineering Tonio Buonassisi, Stanford professor of materials science and engineering Reinhold Dauskardt, recent MIT research assistant Zhe Liu, Stanford doctoral graduate Nicholas Rolston, and three others.

      Perovskites are a group of layered crystalline compounds defined by the configuration of the atoms in their crystal lattice. There are thousands of such possible compounds and many different ways of making them. While most lab-scale development of perovskite materials uses a spin-coating technique, that’s not practical for larger-scale manufacturing, so companies and labs around the world have been searching for ways of translating these lab materials into a practical, manufacturable product.

      “There’s always a big challenge when you’re trying to take a lab-scale process and then transfer it to something like a startup or a manufacturing line,” says Rolston, who is now an assistant professor at Arizona State University. The team looked at a process that they felt had the greatest potential, a method called rapid spray plasma processing, or RSPP.

      The manufacturing process would involve a moving roll-to-roll surface, or series of sheets, on which the precursor solutions for the perovskite compound would be sprayed or ink-jetted as the sheet rolled by. The material would then move on to a curing stage, providing a rapid and continuous output “with throughputs that are higher than for any other photovoltaic technology,” Rolston says.

      “The real breakthrough with this platform is that it would allow us to scale in a way that no other material has allowed us to do,” he adds. “Even materials like silicon require a much longer timeframe because of the processing that’s done. Whereas you can think of [this approach as more] like spray painting.”

      Within that process, at least a dozen variables may affect the outcome, some of them more controllable than others. These include the composition of the starting materials, the temperature, the humidity, the speed of the processing path, the distance of the nozzle used to spray the material onto a substrate, and the methods of curing the material. Many of these factors can interact with each other, and if the process is in open air, then humidity, for example, may be uncontrolled. Evaluating all possible combinations of these variables through experimentation is impossible, so machine learning was needed to help guide the experimental process.

      But while most machine-learning systems use raw data such as measurements of the electrical and other properties of test samples, they don’t typically incorporate human experience such as qualitative observations made by the experimenters of the visual and other properties of the test samples, or information from other experiments reported by other researchers. So, the team found a way to incorporate such outside information into the machine learning model, using a probability factor based on a mathematical technique called Bayesian Optimization.

      Using the system, he says, “having a model that comes from experimental data, we can find out trends that we weren’t able to see before.” For example, they initially had trouble adjusting for uncontrolled variations in humidity in their ambient setting. But the model showed them “that we could overcome our humidity challenges by changing the temperature, for instance, and by changing some of the other knobs.”

      The system now allows experimenters to much more rapidly guide their process in order to optimize it for a given set of conditions or required outcomes. In their experiments, the team focused on optimizing the power output, but the system could also be used to simultaneously incorporate other criteria, such as cost and durability — something members of the team are continuing to work on, Buonassisi says.

      The researchers were encouraged by the Department of Energy, which sponsored the work, to commercialize the technology, and they’re currently focusing on tech transfer to existing perovskite manufacturers. “We are reaching out to companies now,” Buonassisi says, and the code they developed has been made freely available through an open-source server. “It’s now on GitHub, anyone can download it, anyone can run it,” he says. “We’re happy to help companies get started in using our code.”

      Already, several companies are gearing up to produce perovskite-based solar panels, even though they are still working out the details of how to produce them, says Liu, who is now at the Northwestern Polytechnical University in Xi’an, China. He says companies there are not yet doing large-scale manufacturing, but instead starting with smaller, high-value applications such as building-integrated solar tiles where appearance is important. Three of these companies “are on track or are being pushed by investors to manufacture 1 meter by 2-meter rectangular modules [comparable to today’s most common solar panels], within two years,” he says.

      ‘The problem is, they don’t have a consensus on what manufacturing technology to use,” Liu says. The RSPP method, developed at Stanford, “still has a good chance” to be competitive, he says. And the machine learning system the team developed could prove to be important in guiding the optimization of whatever process ends up being used.

      “The primary goal was to accelerate the process, so it required less time, less experiments, and less human hours to develop something that is usable right away, for free, for industry,” he says.

      “Existing work on machine-learning-driven perovskite PV fabrication largely focuses on spin-coating, a lab-scale technique,” says Ted Sargent, University Professor at the University of Toronto, who was not associated with this work, which he says demonstrates “a workflow that is readily adapted to the deposition techniques that dominate the thin-film industry. Only a handful of groups have the simultaneous expertise in engineering and computation to drive such advances.” Sargent adds that this approach “could be an exciting advance for the manufacture of a broader family of materials” including LEDs, other PV technologies, and graphene, “in short, any industry that uses some form of vapor or vacuum deposition.” 

      The team also included Austin Flick and Thomas Colburn at Stanford and Zekun Ren at the Singapore-MIT Alliance for Science and Technology (SMART). In addition to the Department of Energy, the work was supported by a fellowship from the MIT Energy Initiative, the Graduate Research Fellowship Program from the National Science Foundation, and the SMART program. More