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    Building better batteries, faster

    To help combat climate change, many car manufacturers are racing to add more electric vehicles in their lineups. But to convince prospective buyers, manufacturers need to improve how far these cars can go on a single charge. One of their main challenges? Figuring out how to make extremely powerful but lightweight batteries.

    Typically, however, it takes decades for scientists to thoroughly test new battery materials, says Pablo Leon, an MIT graduate student in materials science. To accelerate this process, Leon is developing a machine-learning tool for scientists to automate one of the most time-consuming, yet key, steps in evaluating battery materials.

    With his tool in hand, Leon plans to help search for new materials to enable the development of powerful and lightweight batteries. Such batteries would not only improve the range of EVs, but they could also unlock potential in other high-power systems, such as solar energy systems that continuously deliver power, even at night.

    From a young age, Leon knew he wanted to pursue a PhD, hoping to one day become a professor of engineering, like his father. Growing up in College Station, Texas, home to Texas A&M University, where his father worked, many of Leon’s friends also had parents who were professors or affiliated with the university. Meanwhile, his mom worked outside the university, as a family counselor in a neighboring city.

    In college, Leon followed in his father’s and older brother’s footsteps to become a mechanical engineer, earning his bachelor’s degree at Texas A&M. There, he learned how to model the behaviors of mechanical systems, such as a metal spring’s stiffness. But he wanted to delve deeper, down to the level of atoms, to understand exactly where these behaviors come from.

    So, when Leon applied to graduate school at MIT, he switched fields to materials science, hoping to satisfy his curiosity. But the transition to a different field was “a really hard process,” Leon says, as he rushed to catch up to his peers.

    To help with the transition, Leon sought out a congenial research advisor and found one in Rafael Gómez-Bombarelli, an assistant professor in the Department of Materials Science and Engineering (DMSE). “Because he’s from Spain and my parents are Peruvian, there’s a cultural ease with the way we talk,” Leon says. According to Gómez-Bombarelli, sometimes the two of them even discuss research in Spanish — a “rare treat.” That connection has empowered Leon to freely brainstorm ideas or talk through concerns with his advisor, enabling him to make significant progress in his research.

    Leveraging machine learning to research battery materials

    Scientists investigating new battery materials generally use computer simulations to understand how different combinations of materials perform. These simulations act as virtual microscopes for batteries, zooming in to see how materials interact at an atomic level. With these details, scientists can understand why certain combinations do better, guiding their search for high-performing materials.

    But building accurate computer simulations is extremely time-intensive, taking years and sometimes even decades. “You need to know how every atom interacts with every other atom in your system,” Leon says. To create a computer model of these interactions, scientists first make a rough guess at a model using complex quantum mechanics calculations. They then compare the model with results from real-life experiments, manually tweaking different parts of the model, including the distances between atoms and the strength of chemical bonds, until the simulation matches real life.

    With well-studied battery materials, the simulation process is somewhat easier. Scientists can buy simulation software that includes pre-made models, Leon says, but these models often have errors and still require additional tweaking.

    To build accurate computer models more quickly, Leon is developing a machine-learning-based tool that can efficiently guide the trial-and-error process. “The hope with our machine learning framework is to not have to rely on proprietary models or do any hand-tuning,” he says. Leon has verified that for well-studied materials, his tool is as accurate as the manual method for building models.

    With this system, scientists will have a single, standardized approach for building accurate models in lieu of the patchwork of approaches currently in place, Leon says.

    Leon’s tool comes at an opportune time, when many scientists are investigating a new paradigm of batteries: solid-state batteries. Compared to traditional batteries, which contain liquid electrolytes, solid-state batteries are safer, lighter, and easier to manufacture. But creating versions of these batteries that are powerful enough for EVs or renewable energy storage is challenging.

    This is largely because in battery chemistry, ions dislike flowing through solids and instead prefer liquids, in which atoms are spaced further apart. Still, scientists believe that with the right combination of materials, solid-state batteries can provide enough electricity for high-power systems, such as EVs. 

    Leon plans to use his machine-learning tool to help look for good solid-state battery materials more quickly. After he finds some powerful candidates in simulations, he’ll work with other scientists to test out the new materials in real-world experiments.

    Helping students navigate graduate school

    To get to where he is today, doing exciting and impactful research, Leon credits his community of family and mentors. Because of his upbringing, Leon knew early on which steps he would need to take to get into graduate school and work toward becoming a professor. And he appreciates the privilege of his position, even more so as a Peruvian American, given that many Latino students are less likely to have access to the same resources. “I understand the academic pipeline in a way that I think a lot of minority groups in academia don’t,” he says.

    Now, Leon is helping prospective graduate students from underrepresented backgrounds navigate the pipeline through the DMSE Application Assistance Program. Each fall, he mentors applicants for the DMSE PhD program at MIT, providing feedback on their applications and resumes. The assistance program is student-run and separate from the admissions process.

    Knowing firsthand how invaluable mentorship is from his relationship with his advisor, Leon is also heavily involved in mentoring junior PhD students in his department. This past year, he served as the academic chair on his department’s graduate student organization, the Graduate Materials Council. With MIT still experiencing disruptions from Covid-19, Leon noticed a problem with student cohesiveness. “I realized that traditional [informal] modes of communication across [incoming class] years had been cut off,” he says, making it harder for junior students to get advice from their senior peers. “They didn’t have any community to fall back on.”

    To help fix this problem, Leon served as a go-to mentor for many junior students. He helped second-year PhD students prepare for their doctoral qualification exam, an often-stressful rite of passage. He also hosted seminars for first-year students to teach them how to make the most of their classes and help them acclimate to the department’s fast-paced classes. For fun, Leon organized an axe-throwing event to further facilitate student cameraderie.

    Leon’s efforts were met with success. Now, “newer students are building back the community,” he says, “so I feel like I can take a step back” from being academic chair. He will instead continue mentoring junior students through other programs within the department. He also plans to extend his community-building efforts among faculty and students, facilitating opportunities for students to find good mentors and work on impactful research. With these efforts, Leon hopes to help others along the academic pipeline that he’s become familiar with, journeying together over their PhDs. More

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    A better way to quantify radiation damage in materials

    It was just a piece of junk sitting in the back of a lab at the MIT Nuclear Reactor facility, ready to be disposed of. But it became the key to demonstrating a more comprehensive way of detecting atomic-level structural damage in materials — an approach that will aid the development of new materials, and could potentially support the ongoing operation of carbon-emission-free nuclear power plants, which would help alleviate global climate change.

    A tiny titanium nut that had been removed from inside the reactor was just the kind of material needed to prove that this new technique, developed at MIT and at other institutions, provides a way to probe defects created inside materials, including those that have been exposed to radiation, with five times greater sensitivity than existing methods.

    The new approach revealed that much of the damage that takes place inside reactors is at the atomic scale, and as a result is difficult to detect using existing methods. The technique provides a way to directly measure this damage through the way it changes with temperature. And it could be used to measure samples from the currently operating fleet of nuclear reactors, potentially enabling the continued safe operation of plants far beyond their presently licensed lifetimes.

    The findings are reported today in the journal Science Advances in a paper by MIT research specialist and recent graduate Charles Hirst PhD ’22; MIT professors Michael Short, Scott Kemp, and Ju Li; and five others at the University of Helsinki, the Idaho National Laboratory, and the University of California at Irvine.

    Rather than directly observing the physical structure of a material in question, the new approach looks at the amount of energy stored within that structure. Any disruption to the orderly structure of atoms within the material, such as that caused by radiation exposure or by mechanical stresses, actually imparts excess energy to the material. By observing and quantifying that energy difference, it’s possible to calculate the total amount of damage within the material — even if that damage is in the form of atomic-scale defects that are too small to be imaged with microscopes or other detection methods.

    The principle behind this method had been worked out in detail through calculations and simulations. But it was the actual tests on that one titanium nut from the MIT nuclear reactor that provided the proof — and thus opened the door to a new way of measuring damage in materials.

    The method they used is called differential scanning calorimetry. As Hirst explains, this is similar in principle to the calorimetry experiments many students carry out in high school chemistry classes, where they measure how much energy it takes to raise the temperature of a gram of water by one degree. The system the researchers used was “fundamentally the exact same thing, measuring energetic changes. … I like to call it just a fancy furnace with a thermocouple inside.”

    The scanning part has to do with gradually raising the temperature a bit at a time and seeing how the sample responds, and the differential part refers to the fact that two identical chambers are measured at once, one empty, and one containing the sample being studied. The difference between the two reveals details of the energy of the sample, Hirst explains.

    “We raise the temperature from room temperature up to 600 degrees Celsius, at a constant rate of 50 degrees per minute,” he says. Compared to the empty vessel, “your material will naturally lag behind because you need energy to heat your material. But if there are changes in the energy inside the material, that will change the temperature. In our case, there was an energy release when the defects recombine, and then it will get a little bit of a head start on the furnace … and that’s how we are measuring the energy in our sample.”

    Hirst, who carried out the work over a five-year span as his doctoral thesis project, found that contrary to what had been believed, the irradiated material showed that there were two different mechanisms involved in the relaxation of defects in titanium at the studied temperatures, revealed by two separate peaks in calorimetry. “Instead of one process occurring, we clearly saw two, and each of them corresponds to a different reaction that’s happening in the material,” he says.

    They also found that textbook explanations of how radiation damage behaves with temperature weren’t accurate, because previous tests had mostly been carried out at extremely low temperatures and then extrapolated to the higher temperatures of real-life reactor operations. “People weren’t necessarily aware that they were extrapolating, even though they were, completely,” Hirst says.

    “The fact is that our common-knowledge basis for how radiation damage evolves is based on extremely low-temperature electron radiation,” adds Short. “It just became the accepted model, and that’s what’s taught in all the books. It took us a while to realize that our general understanding was based on a very specific condition, designed to elucidate science, but generally not applicable to conditions in which we actually want to use these materials.”

    Now, the new method can be applied “to materials plucked from existing reactors, to learn more about how they are degrading with operation,” Hirst says.

    “The single biggest thing the world can do in order to get cheap, carbon-free power is to keep current reactors on the grid. They’re already paid for, they’re working,” Short adds.  But to make that possible, “the only way we can keep them on the grid is to have more certainty that they will continue to work well.” And that’s where this new way of assessing damage comes into play.

    While most nuclear power plants have been licensed for 40 to 60 years of operation, “we’re now talking about running those same assets out to 100 years, and that depends almost fully on the materials being able to withstand the most severe accidents,” Short says. Using this new method, “we can inspect them and take them out before something unexpected happens.”

    In practice, plant operators could remove a tiny sample of material from critical areas of the reactor, and analyze it to get a more complete picture of the condition of the overall reactor. Keeping existing reactors running is “the single biggest thing we can do to keep the share of carbon-free power high,” Short stresses. “This is one way we think we can do that.”

    Sergei Dudarev, a fellow at the United Kingdom Atomic Energy Authority who was not associated with this work, says this “is likely going to be impactful, as it confirms, in a nice systematic manner, supported both by experiment and simulations, the unexpectedly significant part played by the small invisible defects in microstructural evolution of materials exposed to irradiation.”

    The process is not just limited to the study of metals, nor is it limited to damage caused by radiation, the researchers say. In principle, the method could be used to measure other kinds of defects in materials, such as those caused by stresses or shockwaves, and it could be applied to materials such as ceramics or semiconductors as well.

    In fact, Short says, metals are the most difficult materials to measure with this method, and early on other researchers kept asking why this team was focused on damage to metals. That was partly because reactor components tend to be made of metal, and also because “It’s the hardest, so, if we crack this problem, we have a tool to crack them all!”

    Measuring defects in other kinds of materials can be up to 10,000 times easier than in metals, he says. “If we can do this with metals, we can make this extremely, ubiquitously applicable.” And all of it enabled by a small piece of junk that was sitting at the back of a lab.

    The research team included Fredric Granberg and Kai Nordlund at the University of Helsinki in Finland; Boopathy Kombaiah and Scott Middlemas at Idaho National Laboratory; and Penghui Cao at the University of California at Irvine. The work was supported by the U.S. National Science Foundation, an Idaho National Laboratory research grant, and a Euratom Research and Training program grant. More

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    New hardware offers faster computation for artificial intelligence, with much less energy

    As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial intelligence called analog deep learning promises faster computation with a fraction of the energy usage.

    Programmable resistors are the key building blocks in analog deep learning, just like transistors are the core elements for digital processors. By repeating arrays of programmable resistors in complex layers, researchers can create a network of analog artificial “neurons” and “synapses” that execute computations just like a digital neural network. This network can then be trained to achieve complex AI tasks like image recognition and natural language processing.

    A multidisciplinary team of MIT researchers set out to push the speed limits of a type of human-made analog synapse that they had previously developed. They utilized a practical inorganic material in the fabrication process that enables their devices to run 1 million times faster than previous versions, which is also about 1 million times faster than the synapses in the human brain.

    Moreover, this inorganic material also makes the resistor extremely energy-efficient. Unlike materials used in the earlier version of their device, the new material is compatible with silicon fabrication techniques. This change has enabled fabricating devices at the nanometer scale and could pave the way for integration into commercial computing hardware for deep-learning applications.

    “With that key insight, and the very powerful nanofabrication techniques we have at MIT.nano, we have been able to put these pieces together and demonstrate that these devices are intrinsically very fast and operate with reasonable voltages,” says senior author Jesús A. del Alamo, the Donner Professor in MIT’s Department of Electrical Engineering and Computer Science (EECS). “This work has really put these devices at a point where they now look really promising for future applications.”

    “The working mechanism of the device is electrochemical insertion of the smallest ion, the proton, into an insulating oxide to modulate its electronic conductivity. Because we are working with very thin devices, we could accelerate the motion of this ion by using a strong electric field, and push these ionic devices to the nanosecond operation regime,” explains senior author Bilge Yildiz, the Breene M. Kerr Professor in the departments of Nuclear Science and Engineering and Materials Science and Engineering.

    “The action potential in biological cells rises and falls with a timescale of milliseconds, since the voltage difference of about 0.1 volt is constrained by the stability of water,” says senior author Ju Li, the Battelle Energy Alliance Professor of Nuclear Science and Engineering and professor of materials science and engineering, “Here we apply up to 10 volts across a special solid glass film of nanoscale thickness that conducts protons, without permanently damaging it. And the stronger the field, the faster the ionic devices.”

    These programmable resistors vastly increase the speed at which a neural network is trained, while drastically reducing the cost and energy to perform that training. This could help scientists develop deep learning models much more quickly, which could then be applied in uses like self-driving cars, fraud detection, or medical image analysis.

    “Once you have an analog processor, you will no longer be training networks everyone else is working on. You will be training networks with unprecedented complexities that no one else can afford to, and therefore vastly outperform them all. In other words, this is not a faster car, this is a spacecraft,” adds lead author and MIT postdoc Murat Onen.

    Co-authors include Frances M. Ross, the Ellen Swallow Richards Professor in the Department of Materials Science and Engineering; postdocs Nicolas Emond and Baoming Wang; and Difei Zhang, an EECS graduate student. The research is published today in Science.

    Accelerating deep learning

    Analog deep learning is faster and more energy-efficient than its digital counterpart for two main reasons. “First, computation is performed in memory, so enormous loads of data are not transferred back and forth from memory to a processor.” Analog processors also conduct operations in parallel. If the matrix size expands, an analog processor doesn’t need more time to complete new operations because all computation occurs simultaneously.

    The key element of MIT’s new analog processor technology is known as a protonic programmable resistor. These resistors, which are measured in nanometers (one nanometer is one billionth of a meter), are arranged in an array, like a chess board.

    In the human brain, learning happens due to the strengthening and weakening of connections between neurons, called synapses. Deep neural networks have long adopted this strategy, where the network weights are programmed through training algorithms. In the case of this new processor, increasing and decreasing the electrical conductance of protonic resistors enables analog machine learning.

    The conductance is controlled by the movement of protons. To increase the conductance, more protons are pushed into a channel in the resistor, while to decrease conductance protons are taken out. This is accomplished using an electrolyte (similar to that of a battery) that conducts protons but blocks electrons.

    To develop a super-fast and highly energy efficient programmable protonic resistor, the researchers looked to different materials for the electrolyte. While other devices used organic compounds, Onen focused on inorganic phosphosilicate glass (PSG).

    PSG is basically silicon dioxide, which is the powdery desiccant material found in tiny bags that come in the box with new furniture to remove moisture. It is studied as a proton conductor under humidified conditions for fuel cells. It is also the most well-known oxide used in silicon processing. To make PSG, a tiny bit of phosphorus is added to the silicon to give it special characteristics for proton conduction.

    Onen hypothesized that an optimized PSG could have a high proton conductivity at room temperature without the need for water, which would make it an ideal solid electrolyte for this application. He was right.

    Surprising speed

    PSG enables ultrafast proton movement because it contains a multitude of nanometer-sized pores whose surfaces provide paths for proton diffusion. It can also withstand very strong, pulsed electric fields. This is critical, Onen explains, because applying more voltage to the device enables protons to move at blinding speeds.

    “The speed certainly was surprising. Normally, we would not apply such extreme fields across devices, in order to not turn them into ash. But instead, protons ended up shuttling at immense speeds across the device stack, specifically a million times faster compared to what we had before. And this movement doesn’t damage anything, thanks to the small size and low mass of protons. It is almost like teleporting,” he says.

    “The nanosecond timescale means we are close to the ballistic or even quantum tunneling regime for the proton, under such an extreme field,” adds Li.

    Because the protons don’t damage the material, the resistor can run for millions of cycles without breaking down. This new electrolyte enabled a programmable protonic resistor that is a million times faster than their previous device and can operate effectively at room temperature, which is important for incorporating it into computing hardware.

    Thanks to the insulating properties of PSG, almost no electric current passes through the material as protons move. This makes the device extremely energy efficient, Onen adds.

    Now that they have demonstrated the effectiveness of these programmable resistors, the researchers plan to reengineer them for high-volume manufacturing, says del Alamo. Then they can study the properties of resistor arrays and scale them up so they can be embedded into systems.

    At the same time, they plan to study the materials to remove bottlenecks that limit the voltage that is required to efficiently transfer the protons to, through, and from the electrolyte.

    “Another exciting direction that these ionic devices can enable is energy-efficient hardware to emulate the neural circuits and synaptic plasticity rules that are deduced in neuroscience, beyond analog deep neural networks. We have already started such a collaboration with neuroscience, supported by the MIT Quest for Intelligence,” adds Yildiz.

    “The collaboration that we have is going to be essential to innovate in the future. The path forward is still going to be very challenging, but at the same time it is very exciting,” del Alamo says.

    “Intercalation reactions such as those found in lithium-ion batteries have been explored extensively for memory devices. This work demonstrates that proton-based memory devices deliver impressive and surprising switching speed and endurance,” says William Chueh, associate professor of materials science and engineering at Stanford University, who was not involved with this research. “It lays the foundation for a new class of memory devices for powering deep learning algorithms.”

    “This work demonstrates a significant breakthrough in biologically inspired resistive-memory devices. These all-solid-state protonic devices are based on exquisite atomic-scale control of protons, similar to biological synapses but at orders of magnitude faster rates,” says Elizabeth Dickey, the Teddy & Wilton Hawkins Distinguished Professor and head of the Department of Materials Science and Engineering at Carnegie Mellon University, who was not involved with this work. “I commend the interdisciplinary MIT team for this exciting development, which will enable future-generation computational devices.”

    This research is funded, in part, by the MIT-IBM Watson AI Lab. More

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    Explained: Why perovskites could take solar cells to new heights

    Perovskites hold promise for creating solar panels that could be easily deposited onto most surfaces, including flexible and textured ones. These materials would also be lightweight, cheap to produce, and as efficient as today’s leading photovoltaic materials, which are mainly silicon. They’re the subject of increasing research and investment, but companies looking to harness their potential do have to address some remaining hurdles before perovskite-based solar cells can be commercially competitive.

    The term perovskite refers not to a specific material, like silicon or cadmium telluride, other leading contenders in the photovoltaic realm, but to a whole family of compounds. The perovskite family of solar materials is named for its structural similarity to a mineral called perovskite, which was discovered in 1839 and named after Russian mineralogist L.A. Perovski.

    The original mineral perovskite, which is calcium titanium oxide (CaTiO3), has a distinctive crystal configuration. It has a three-part structure, whose components have come to be labeled A, B and X, in which lattices of the different components are interlaced. The family of perovskites consists of the many possible combinations of elements or molecules that can occupy each of the three components and form a structure similar to that of the original perovskite itself. (Some researchers even bend the rules a little by naming other crystal structures with similar elements “perovskites,” although this is frowned upon by crystallographers.)

    “You can mix and match atoms and molecules into the structure, with some limits. For instance, if you try to stuff a molecule that’s too big into the structure, you’ll distort it. Eventually you might cause the 3D crystal to separate into a 2D layered structure, or lose ordered structure entirely,” says Tonio Buonassisi, professor of mechanical engineering at MIT and director of the Photovoltaics Research Laboratory. “Perovskites are highly tunable, like a build-your-own-adventure type of crystal structure,” he says.

    That structure of interlaced lattices consists of ions or charged molecules, two of them (A and B) positively charged and the other one (X) negatively charged. The A and B ions are typically of quite different sizes, with the A being larger. 

    Within the overall category of perovskites, there are a number of types, including metal oxide perovskites, which have found applications in catalysis and in energy storage and conversion, such as in fuel cells and metal-air batteries. But a main focus of research activity for more than a decade has been on lead halide perovskites, according to Buonassisi says.

    Within that category, there is still a legion of possibilities, and labs around the world are racing through the tedious work of trying to find the variations that show the best performance in efficiency, cost, and durability — which has so far been the most challenging of the three.

    Many teams have also focused on variations that eliminate the use of lead, to avoid its environmental impact. Buonassisi notes, however, that “consistently over time, the lead-based devices continue to improve in their performance, and none of the other compositions got close in terms of electronic performance.” Work continues on exploring alternatives, but for now none can compete with the lead halide versions.

    One of the great advantages perovskites offer is their great tolerance of defects in the structure, he says. Unlike silicon, which requires extremely high purity to function well in electronic devices, perovskites can function well even with numerous imperfections and impurities.

    Searching for promising new candidate compositions for perovskites is a bit like looking for a needle in a haystack, but recently researchers have come up with a machine-learning system that can greatly streamline this process. This new approach could lead to a much faster development of new alternatives, says Buonassisi, who was a co-author of that research.

    While perovskites continue to show great promise, and several companies are already gearing up to begin some commercial production, durability remains the biggest obstacle they face. While silicon solar panels retain up to 90 percent of their power output after 25 years, perovskites degrade much faster. Great progress has been made — initial samples lasted only a few hours, then weeks or months, but newer formulations have usable lifetimes of up to a few years, suitable for some applications where longevity is not essential.

    From a research perspective, Buonassisi says, one advantage of perovskites is that they are relatively easy to make in the lab — the chemical constituents assemble readily. But that’s also their downside: “The material goes together very easily at room temperature,” he says, “but it also comes apart very easily at room temperature. Easy come, easy go!”

    To deal with that issue, most researchers are focused on using various kinds of protective materials to encapsulate the perovskite, protecting it from exposure to air and moisture. But others are studying the exact mechanisms that lead to that degradation, in hopes of finding formulations or treatments that are more inherently robust. A key finding is that a process called autocatalysis is largely to blame for the breakdown.

    In autocatalysis, as soon as one part of the material starts to degrade, its reaction products act as catalysts to start degrading the neighboring parts of the structure, and a runaway reaction gets underway. A similar problem existed in the early research on some other electronic materials, such as organic light-emitting diodes (OLEDs), and was eventually solved by adding additional purification steps to the raw materials, so a similar solution may be found in the case of perovskites, Buonassisi suggests.

    Buonassisi and his co-researchers recently completed a study showing that once perovskites reach a usable lifetime of at least a decade, thanks to their much lower initial cost that would be sufficient to make them economically viable as a substitute for silicon in large, utility-scale solar farms.

    Overall, progress in the development of perovskites has been impressive and encouraging, he says. With just a few years of work, it has already achieved efficiencies comparable to levels that cadmium telluride (CdTe), “which has been around for much longer, is still struggling to achieve,” he says. “The ease with which these higher performances are reached in this new material are almost stupefying.” Comparing the amount of research time spent to achieve a 1 percent improvement in efficiency, he says, the progress on perovskites has been somewhere between 100 and 1000 times faster than that on CdTe. “That’s one of the reasons it’s so exciting,” he says. More

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    MIT engineers design surfaces that make water boil more efficiently

    The boiling of water or other fluids is an energy-intensive step at the heart of a wide range of industrial processes, including most electricity generating plants, many chemical production systems, and even cooling systems for electronics.

    Improving the efficiency of systems that heat and evaporate water could significantly reduce their energy use. Now, researchers at MIT have found a way to do just that, with a specially tailored surface treatment for the materials used in these systems.

    The improved efficiency comes from a combination of three different kinds of surface modifications, at different size scales. The new findings are described in the journal Advanced Materials in a paper by recent MIT graduate Youngsup Song PhD ’21, Ford Professor of Engineering Evelyn Wang, and four others at MIT. The researchers note that this initial finding is still at a laboratory scale, and more work is needed to develop a practical, industrial-scale process.

    There are two key parameters that describe the boiling process: the heat transfer coefficient (HTC) and the critical heat flux (CHF). In materials design, there’s generally a tradeoff between the two, so anything that improves one of these parameters tends to make the other worse. But both are important for the efficiency of the system, and now, after years of work, the team has achieved a way of significantly improving both properties at the same time, through their combination of different textures added to a material’s surface.

    “Both parameters are important,” Song says, “but enhancing both parameters together is kind of tricky because they have intrinsic trade off.” The reason for that, he explains, is “because if we have lots of bubbles on the boiling surface, that means boiling is very efficient, but if we have too many bubbles on the surface, they can coalesce together, which can form a vapor film over the boiling surface.” That film introduces resistance to the heat transfer from the hot surface to the water. “If we have vapor in between the surface and water, that prevents the heat transfer efficiency and lowers the CHF value,” he says.

    Song, who is now a postdoc at Lawrence Berkeley National Laboratory, carried out much of the research as part of his doctoral thesis work at MIT. While the various components of the new surface treatment he developed had been previously studied, the researchers say this work is the first to show that these methods could be combined to overcome the tradeoff between the two competing parameters.

    Adding a series of microscale cavities, or dents, to a surface is a way of controlling the way bubbles form on that surface, keeping them effectively pinned to the locations of the dents and preventing them from spreading out into a heat-resisting film. In this work, the researchers created an array of 10-micrometer-wide dents separated by about 2 millimeters to prevent film formation. But that separation also reduces the concentration of bubbles at the surface, which can reduce the boiling efficiency. To compensate for that, the team introduced a much smaller-scale surface treatment, creating tiny bumps and ridges at the nanometer scale, which increases the surface area and promotes the rate of evaporation under the bubbles.

    In these experiments, the cavities were made in the centers of a series of pillars on the material’s surface. These pillars, combined with nanostructures, promote wicking of liquid from the base to their tops, and this enhances the boiling process by providing more surface area exposed to the water. In combination, the three “tiers” of the surface texture — the cavity separation, the posts, and the nanoscale texturing — provide a greatly enhanced efficiency for the boiling process, Song says.

    “Those micro cavities define the position where bubbles come up,” he says. “But by separating those cavities by 2 millimeters, we separate the bubbles and minimize the coalescence of bubbles.” At the same time, the nanostructures promote evaporation under the bubbles, and the capillary action induced by the pillars supplies liquid to the bubble base. That maintains a layer of liquid water between the boiling surface and the bubbles of vapor, which enhances the maximum heat flux.

    Although their work has confirmed that the combination of these kinds of surface treatments can work and achieve the desired effects, this work was done under small-scale laboratory conditions that could not easily be scaled up to practical devices, Wang says. “These kinds of structures we’re making are not meant to be scaled in its current form,” she says, but rather were used to prove that such a system can work. One next step will be to find alternative ways of creating these kinds of surface textures so these methods could more easily be scaled up to practical dimensions.

    “Showing that we can control the surface in this way to get enhancement is a first step,” she says. “Then the next step is to think about more scalable approaches.” For example, though the pillars on the surface in these experiments were created using clean-room methods commonly used to produce semiconductor chips, there are other, less demanding ways of creating such structures, such as electrodeposition. There are also a number of different ways to produce the surface nanostructure textures, some of which may be more easily scalable.

    There may be some significant small-scale applications that could use this process in its present form, such as the thermal management of electronic devices, an area that is becoming more important as semiconductor devices get smaller and managing their heat output becomes ever more important. “There’s definitely a space there where this is really important,” Wang says.

    Even those kinds of applications will take some time to develop because typically thermal management systems for electronics use liquids other than water, known as dielectric liquids. These liquids have different surface tension and other properties than water, so the dimensions of the surface features would have to be adjusted accordingly. Work on these differences is one of the next steps for the ongoing research, Wang says.

    This same multiscale structuring technique could also be applied to different liquids, Song says, by adjusting the dimensions to account for the different properties of the liquids. “Those kinds of details can be changed, and that can be our next step,” he says.

    The team also included Carlos Diaz-Martin, Lenan Zhang, Hyeongyun Cha, and Yajing Zhao, all at MIT. The work was supported by the Advanced Research Projects Agency-Energy (ARPA-E), the Air Force Office of Scientific Research, and the Singapore-MIT Alliance for Research and Technology, and made use of the MIT.nano facilities. More

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    Donald Sadoway wins European Inventor Award for liquid metal batteries

    MIT Professor Donald Sadoway has won the 2022 European Inventor Award, in the category for Non-European Patent Office Countries, for his work on liquid metal batteries that could enable the long-term storage of renewable energy.

    Sadoway is the John F. Elliott Professor of Materials Chemistry in MIT’s Department of Materials Science and Engineering, and a longtime supporter and friend of the Materials Research Laboratory.

    “By enabling the large-scale storage of renewable energy, Donald Sadoway’s invention is a huge step towards the deployment of carbon-free electricity generation,” says António Campinos, president of the European Patent Office. “He has spent his career studying electrochemistry and has transformed this expertise into an invention that represents a huge step forward in the transition to green energy.”

    Sadoway was honored at the 2022 European Inventor Award ceremony on June 21. The award is one of Europe’s most prestigious innovation prizes and is presented annually to outstanding inventors from Europe and beyond who have made an exceptional contribution to society, technological progress, and economic growth.

    When accepting the award in Munich, Sadoway told the audience:

    “I am astonished. When I look at all the patented technologies that are represented at this event I see an abundance of excellence, all of them solutions to pressing problems. I wonder if the judges are assessing not only degrees of excellence but degrees of urgency. The liquid metal battery addresses an existential threat to the health of our atmosphere which is related to climate change.

    “By hosting this event the EPO celebrates invention. The thread that connects all the inventors is their efforts to make the world a better place. In my judgment there is no nobler pursuit. So perhaps this is a celebration of nobility.”

    Sadoway’s liquid metal batteries consist of three liquid layers of different densities, which naturally separate in the same way as oil and vinegar do in a salad dressing. The top and bottom layers are made from molten metals, with a middle layer of molten liquid salt.

    To keep the metals liquid, the batteries need to operate at extremely high temperatures, so Sadoway designed a system that is self-heating and insulated, requiring no external heating or cooling. They have a lifespan of more than 20 years, can maintain 99 percent of their capacity over 5,000 charging cycles, and have no combustible materials, meaning there is no fire risk.

    In 2010, with a patent for his invention and support from Bill Gates, Sadoway co-founded Ambri, based in Marlborough, Massachusetts just outside Boston, to develop a commercial product. The company will soon install a unit on a 3,700-acre development for a data center in Nevada. This battery will store energy from a reported 500 megawatts of on-site renewable generation, the same output as a natural gas power plant.

    Born in 1950 into a family of Ukrainian immigrants in Canada, Sadoway studied chemical metallurgy specializing in what he calls “extreme electrochemistry” — chemical reactions in molten salts and liquid metals that have been heated to over 500 degrees Celsius. After earning his BASc, MASc, and PhD, all from the University of Toronto, he joined the faculty at MIT in 1978. More

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    Using excess heat to improve electrolyzers and fuel cells

    Reducing the use of fossil fuels will have unintended consequences for the power-generation industry and beyond. For example, many industrial chemical processes use fossil-fuel byproducts as precursors to things like asphalt, glycerine, and other important chemicals. One solution to reduce the impact of the loss of fossil fuels on industrial chemical processes is to store and use the heat that nuclear fission produces. New MIT research has dramatically improved a way to put that heat toward generating chemicals through a process called electrolysis. 

    Electrolyzers are devices that use electricity to split water (H2O) and generate molecules of hydrogen (H2) and oxygen (O2). Hydrogen is used in fuel cells to generate electricity and drive electric cars or drones or in industrial operations like the production of steel, ammonia, and polymers. Electrolyzers can also take in water and carbon dioxide (CO2) and produce oxygen and ethylene (C2H4), a chemical used in polymers and elsewhere.

    There are three main types of electrolyzers. One type works at room temperature, but has downsides; they’re inefficient and require rare metals, such as platinum. A second type is more efficient but runs at high temperatures, above 700 degrees Celsius. But metals corrode at that temperature, and the devices need expensive sealing and insulation. The third type would be a Goldilocks solution for nuclear heat if it were perfected, running at 300-600 C and requiring mostly cheap materials like stainless steel. These cells have never been operated as efficiently as theory says they should. The new work, published this month in Nature, both illuminates the problem and offers a solution.

    A sandwich mystery

    The intermediate-temperature devices use what are called protonic ceramic electrochemical cells. Each cell is a sandwich, with a dense electrolyte layered between two porous electrodes. Water vapor is pumped into the top electrode. A wire on the side connects the two electrodes, and externally generated electricity runs from the top to the bottom. The voltage pulls electrons out of the water, which splits the molecule, releasing oxygen. A hydrogen atom without an electron is just a proton. The protons get pulled through the electrolyte to rejoin with the electrons at the bottom electrode and form H2 molecules, which are then collected.

    On its own, the electrolyte in the middle, made mainly of barium, cerium, and zirconium, conducts protons very well. “But when we put the same material into this three-layer device, the proton conductivity of the full cell is pretty bad,” says Yanhao Dong, a postdoc in MIT’s Department of Nuclear Science and Engineering and a paper co-author. “Its conductivity is only about 50 percent of the bulk form’s. We wondered why there’s an inconsistency here.”

    A couple of clues pointed them in the right direction. First, if they don’t prepare the cell very carefully, the top layer, only about 20 microns (.02 millimeters) thick, doesn’t stay attached. “Sometimes if you use just Scotch tape, it will peel off,” Dong says. Second, when they looked at a cross section of a device using a scanning electron microscope, they saw that the top surface of the electrolyte layer was flat, whereas the bottom surface of the porous electrode sitting on it was bumpy, and the two came into contact in only a few places. They didn’t bond well. That precarious interface leads to both structural de-lamination and poor proton passage from the electrode to the electrolyte.

    Acidic solution

    The solution turned out to be simple: researchers roughed up the top of the electrolyte. Specifically, they applied acid for 10 minutes, which etched grooves into the surface. Ju Li, the Battelle Energy Alliance Professor in Nuclear Engineering and professor of materials science and engineering at MIT, and a paper co-author, likens it to sandblasting a surface before applying paint to increase adhesion. Their acid-treated cells produced about 200 percent more hydrogen per area at 1.5 volts at 600 C than did any previous cell of its type, and worked well down to 350 C with very little performance decay over extended operation. 

    “The authors reported a surprisingly simple yet highly effective surface treatment to dramatically improve the interface,” says Liangbing Hu, the director of the Center for Materials Innovation at the Maryland Energy Innovation Institute, who was not involved in the work. He calls the cell performance “exceptional.”

    “We are excited and surprised” by the results, Dong says. “The engineering solution seems quite simple. And that’s actually good, because it makes it very applicable to real applications.” In a practical product, many such cells would be stacked together to form a module. MIT’s partner in the project, Idaho National Laboratory, is very strong in engineering and prototyping, so Li expects to see electrolyzers based on this technology at scale before too long. “At the materials level, this is a breakthrough that shows that at a real-device scale you can work at this sweet spot of temperature of 350 to 600 degrees Celsius for nuclear fission and fusion reactors,” he says.

    “Reduced operating temperature enables cheaper materials for the large-scale assembly, including the stack,” says Idaho National Laboratory researcher and paper co-author Dong Ding. “The technology operates within the same temperature range as several important, current industrial processes, including ammonia production and CO2 reduction. Matching these temperatures will expedite the technology’s adoption within the existing industry.”

    “This is very significant for both Idaho National Lab and us,” Li adds, “because it bridges nuclear energy and renewable electricity.” He notes that the technology could also help fuel cells, which are basically electrolyzers run in reverse, using green hydrogen or hydrocarbons to generate electricity. According to Wei Wu, a materials scientist at Idaho National Laboratory and a paper co-author, “this technique is quite universal and compatible with other solid electrochemical devices.”

    Dong says it’s rare for a paper to advance both science and engineering to such a degree. “We are happy to combine those together and get both very good scientific understanding and also very good real-world performance.”

    This work, done in collaboration with Idaho National Laboratory, New Mexico State University, and the University of Nebraska–Lincoln, was funded, in part, by the U.S. Department of Energy. More

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    Strengthening students’ knowledge and experience in climate and sustainability

    Tackling the climate crisis is central to MIT. Critical to this mission is harnessing the innovation, passion, and expertise of MIT’s talented students, from a variety of disciplines and backgrounds. To help raise this student involvement to the next level, the MIT Climate and Sustainability Consortium (MCSC) recently launched a program that will engage MIT undergraduates in a unique, year-long, interdisciplinary experience both developing and implementing climate and sustainability research projects.

    The MCSC Climate and Sustainability Scholars Program is a way for students to dive deeply and directly into climate and sustainability research, strengthen their skill sets in a variety of climate and sustainability-related areas, build their networks, and continue to embrace and grow their passion.The MCSC Climate and Sustainability Scholars Program is representative of MIT’s ambitious and bold initiatives on climate and sustainability — bringing together faculty and students across MIT to collaborate with industry on developing climate and sustainability solutions in the context of undergraduate education and research.

    The program, open to rising juniors and seniors from all majors and departments, is inspired by MIT’s SuperUROP program. Students will enroll in a year-long class while simultaneously engaging in research. Research projects will be climate- and sustainability-focused and can be on or off campus. The course will be initially facilitated by Desiree Plata, the Gilbert W. Winslow Career Development Professor in Civil and Environmental Engineering, and Elsa Olivetti, the Esther and Harold E. Edgerton Associate Professor in Materials Science and Engineering and MCSC co-director.“Climate and sustainability challenges face real barriers in science, technology, policy, and beyond,” says Plata, who also serves on the MCSC’s Faculty Steering Committee. “We need to motivate an all-hands effort to bring MIT talent to bear on these challenges, and we need to give our students the tools to make tangible benefits within and between their disciplines. This was our goal in designing the MCSC Scholars Program, and it’s what I’m most excited about.”

    The Climate and Sustainability Scholars Program has relevance across all five schools, and the number of places the course is cross-listed continues to grow. As is the broader goal of the MCSC, the Climate and Sustainability Scholars Program aims to amplify and extend MIT’s expertise — through engaging students of all backgrounds and majors, bringing in faculty mentors and instructors from around the Institute, and identifying research opportunities and principal investigators that span disciplines. The student cohort model will also build off of the successful community-building endeavors by the MIT Energy Initiative and Environmental Solutions Initiative, among others, to bring students with similar interests together into an interdisciplinary, problem-solving space.The program’s fall semester will focus on key climate and sustainability topics, such as decarbonization strategies, policy, environmental justice, and quantitative methods for evaluating social and environmental impacts, and humanities-based communication of climate topics, all while students engage in research. Students will simultaneously develop project proposals, participate in a project through MIT’s Undergraduate Research Opportunities Program, and communicate their work using written and oral media. The spring semester’s course will focus on research and experiential activities, and help students communicate their outputs in entrepreneurial or policy activities that would enable the research outcomes to be rapidly scaled for impact.Throughout the program, students will engage with their research mentors, additional mentors drawn from MCSC-affiliated faculty, postdoctoral Impact Fellows, and graduate students — and there will also be opportunities for interaction with representatives of MCSC member companies.“Providing opportunities for students to sharpen the skills and knowledge needed to pioneer solutions for climate change mitigation and adaptation is critical,” says Olivetti. “We are excited that the Climate and Sustainability Scholars Program can contribute to that important mission.” More