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      in Security

      J-WAFS awards $150K Solutions grant to Patrick Doyle and team for rapid removal of micropollutants from water

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      The Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) has awarded a 2022 J-WAFS Solutions grant to Patrick S. Doyle, the Robert T. Haslam Professor of Chemical Engineering at MIT, for his innovative system to tackle water pollution. Doyle will be working with co-Principal Investigator Rafael Gomez-Bombarelli, assistant professor in materials processing in the Department of Materials Science, as well as PhD students Devashish Gokhale and Tynan Perez. Building off of findings from a 2019 J-WAFS seed grant, Doyle and the research team will create cost-effective industry-scale processes to remove micropollutants from water. Project work will commence this month.

      The J-WAFS Solutions program provides one-year, renewable, commercialization grants to help move MIT technology from the laboratory to market. Grants of up to $150,000 are awarded to researchers with breakthrough technologies and inventions in water or food. Since its launch in 2015, J-WAFS Solutions grants have led to seven spinout companies and helped commercialize two products as open-source technologies. The grant program is supported by Community Jameel.

      A widespread problem 

      Micropollutants are contaminants that occur in low concentrations in the environment, yet continuous exposure and bioaccumulation of micropollutants make them a cause for concern. According to the U.S. Environmental Protection Agency, the plastics derivative Bisphenol A (BPA), the “forever chemicals” per-and polyfluoroalkyl substances (PFAS), and heavy metals like lead are common micropollutants known to be found in more than 85 percent of rivers, ponds, and lakes in the United States. Many of these bodies of water are sources of drinking water. Over long periods of time, exposure to micropollutants through drinking water can cause physiological damage in humans, increasing the risk of cancer, developmental disorders, and reproductive failure.

      Since micropollutants occur in low concentrations, it is difficult to detect and monitor their presence, and the chemical diversity of micropollutants makes it difficult to inexpensively remove them from water. Currently, activated carbon is the industry standard for micropollutant elimination, but this method cannot efficiently remove contaminants at parts-per-billion and parts-per-trillion concentrations. There are also strong sustainability concerns associated with activated carbon production, which is energy-intensive and releases large volumes of carbon dioxide.

      A solution with societal and economic benefits

      Doyle and his team are developing a technology that uses sustainable hydrogel microparticles to remove micropollutants from water. The polymeric hydrogel microparticles use chemically anchored structures including micelles and other chelating agents that act like a sponge by absorbing organic micropollutants and heavy metal ions. The microparticles are large enough to separate from water using simple gravitational settling. The system is sustainable because the microparticles can be recycled for continuous use. In testing, the long-lasting, reusable microparticles show quicker removal of contaminants than commercial activated carbon. The researchers plan to utilize machine learning to find optimal microparticle compositions that maximize performance on complex combinations of micropollutants in simulated and real wastewater samples.

      Economically, the technology is a new offering that has applications in numerous large markets where micropollutant elimination is vital, including municipal and industrial water treatment equipment, as well as household water purification systems. The J-WAFS Solutions grant will allow the team to build and test prototypes of the water treatment system, identify the best use cases and customers, and perform technoeconomic analyses and market research to formulate a preliminary business plan. With J-WAFS commercialization support, the project could eventually lead to a startup company.

      “Emerging micropollutants are a growing threat to drinking water supplies worldwide,” says J-WAFS Director John H. Lienhard, the Abdul Latif Jameel Professor of Water at MIT. “Cost-effective and scalable technologies for micropollutant removal are urgently needed. This project will develop and commercialize a promising new tool for water treatment, with the goal of improving water quality for millions of people.” More

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      in Energy

      A simple way to significantly increase lifetimes of fuel cells and other devices

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      In research that could jump-start work on a range of technologies including fuel cells, which are key to storing solar and wind energy, MIT researchers have found a relatively simple way to increase the lifetimes of these devices: changing the pH of the system.

      Fuel and electrolysis cells made of materials known as solid metal oxides are of interest for several reasons. For example, in the electrolysis mode, they are very efficient at converting electricity from a renewable source into a storable fuel like hydrogen or methane that can be used in the fuel cell mode to generate electricity when the sun isn’t shining or the wind isn’t blowing. They can also be made without using costly metals like platinum. However, their commercial viability has been hampered, in part, because they degrade over time. Metal atoms seeping from the interconnects used to construct banks of fuel/electrolysis cells slowly poison the devices.

      “What we’ve been able to demonstrate is that we can not only reverse that degradation, but actually enhance the performance above the initial value by controlling the acidity of the air-electrode interface,” says Harry L. Tuller, the R.P. Simmons Professor of Ceramics and Electronic Materials in MIT’s Department of Materials Science and Engineering (DMSE).

      The research, initially funded by the U.S. Department of Energy through the Office of Fossil Energy and Carbon Management’s (FECM) National Energy Technology Laboratory, should help the department meet its goal of significantly cutting the degradation rate of solid oxide fuel cells by 2035 to 2050.

      “Extending the lifetime of solid oxide fuels cells helps deliver the low-cost, high-efficiency hydrogen production and power generation needed for a clean energy future,” says Robert Schrecengost, acting director of FECM’s Division of Hydrogen with Carbon Management. “The department applauds these advancements to mature and ultimately commercialize these technologies so that we can provide clean and reliable energy for the American people.”

      “I’ve been working in this area my whole professional life, and what I’ve seen until now is mostly incremental improvements,” says Tuller, who was recently named a 2022 Materials Research Society Fellow for his career-long work in solid-state chemistry and electrochemistry. “People are normally satisfied with seeing improvements by factors of tens-of-percent. So, actually seeing much larger improvements and, as importantly, identifying the source of the problem and the means to work around it, issues that we’ve been struggling with for all these decades, is remarkable.”

      Says James M. LeBeau, the John Chipman Associate Professor of Materials Science and Engineering at MIT, who was also involved in the research, “This work is important because it could overcome [some] of the limitations that have prevented the widespread use of solid oxide fuel cells. Additionally, the basic concept can be applied to many other materials used for applications in the energy-related field.”

      A report describing the work was reported Aug. 11, in Energy & Environmental Science. Additional authors of the paper are Han Gil Seo, a DMSE postdoc; Anna Staerz, formerly a DMSE postdoc, now at Interuniversity Microelectronics Centre (IMEC) Belgium and soon to join the Colorado School of Mines faculty; Dennis S. Kim, a DMSE postdoc; Dino Klotz, a DMSE visiting scientist, now at Zurich Instruments; Michael Xu, a DMSE graduate student; and Clement Nicollet, formerly a DMSE postdoc, now at the Université de Nantes. Seo and Staerz contributed equally to the work.

      Changing the acidity

      A fuel/electrolysis cell has three principal parts: two electrodes (a cathode and anode) separated by an electrolyte. In the electrolysis mode, electricity from, say, the wind, can be used to generate storable fuel like methane or hydrogen. On the other hand, in the reverse fuel cell reaction, that storable fuel can be used to create electricity when the wind isn’t blowing.

      A working fuel/electrolysis cell is composed of many individual cells that are stacked together and connected by steel metal interconnects that include the element chrome to keep the metal from oxidizing. But “it turns out that at the high temperatures that these cells run, some of that chrome evaporates and migrates to the interface between the cathode and the electrolyte, poisoning the oxygen incorporation reaction,” Tuller says. After a certain point, the efficiency of the cell has dropped to a point where it is not worth operating any longer.

      “So if you can extend the life of the fuel/electrolysis cell by slowing down this process, or ideally reversing it, you could go a long way towards making it practical,” Tuller says.

      The team showed that you can do both by controlling the acidity of the cathode surface. They also explained what is happening.

      To achieve their results, the team coated the fuel/electrolysis cell cathode with lithium oxide, a compound that changes the relative acidity of the surface from being acidic to being more basic. “After adding a small amount of lithium, we were able to recover the initial performance of a poisoned cell,” Tuller says. When the engineers added even more lithium, the performance improved far beyond the initial value. “We saw improvements of three to four orders of magnitude in the key oxygen reduction reaction rate and attribute the change to populating the surface of the electrode with electrons needed to drive the oxygen incorporation reaction.”

      The engineers went on to explain what is happening by observing the material at the nanoscale, or billionths of a meter, with state-of-the-art transmission electron microscopy and electron energy loss spectroscopy at MIT.nano. “We were interested in understanding the distribution of the different chemical additives [chromium and lithium oxide] on the surface,” says LeBeau.

      They found that the lithium oxide effectively dissolves the chromium to form a glassy material that no longer serves to degrade the cathode performance.

      Applications for sensors, catalysts, and more

      Many technologies like fuel cells are based on the ability of the oxide solids to rapidly breathe oxygen in and out of their crystalline structures, Tuller says. The MIT work essentially shows how to recover — and speed up — that ability by changing the surface acidity. As a result, the engineers are optimistic that the work could be applied to other technologies including, for example, sensors, catalysts, and oxygen permeation-based reactors.

      The team is also exploring the effect of acidity on systems poisoned by different elements, like silica.

      Concludes Tuller: “As is often the case in science, you stumble across something and notice an important trend that was not appreciated previously. Then you test that concept further, and you discover that it is really very fundamental.”

      In addition to the DOE, this work was also funded by the National Research Foundation of Korea, the MIT Department of Materials Science and Engineering via Tuller’s appointment as the R.P. Simmons Professor of Ceramics and Electronic Materials, and the U.S. Air Force Office of Scientific Research. More

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      in Energy

      Designing zeolites, porous materials made to trap molecules

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      Zeolites are a class of minerals used in everything from industrial catalysts and chemical filters to laundry detergents and cat litter. They are mostly composed of silicon and aluminum — two abundant, inexpensive elements — plus oxygen; they have a crystalline structure; and most significantly, they are porous. Among the regularly repeating atomic patterns in them are tiny interconnected openings, or pores, that can trap molecules that just fit inside them, allow smaller ones to pass through, or block larger ones from entering. A zeolite can remove unwanted molecules from gases and liquids, or trap them temporarily and then release them, or hold them while they undergo rapid chemical reactions.

      Some zeolites occur naturally, but they take unpredictable forms and have variable-sized pores. “People synthesize artificial versions to ensure absolute purity and consistency,” says Rafael Gómez-Bombarelli, the Jeffrey Cheah Career Development Chair in Engineering in the Department of Materials Science and Engineering (DMSE). And they work hard to influence the size of the internal pores in hopes of matching the molecule or other particle they’re looking to capture.

      The basic recipe for making zeolites sounds simple. Mix together the raw ingredients — basically, silicon dioxide and aluminum oxide — and put them in a reactor for a few days at a high temperature and pressure. Depending on the ratio between the ingredients and the temperature, pressure, and timing, as the initial gel slowly solidifies into crystalline form, different zeolites emerge.

      But there’s one special ingredient to add “to help the system go where you want it to go,” says Gómez-Bombarelli. “It’s a molecule that serves as a template so that the zeolite you want will crystallize around it and create pores of the desired size and shape.”

      The so-called templating molecule binds to the material before it solidifies. As crystallization progresses, the molecule directs the structure, or “framework,” that forms around it. After crystallization, the temperature is raised and the templating molecule burns off, leaving behind a solid aluminosilicate material filled with open pores that are — given the correct templating molecule and synthesis conditions — just the right size and shape to recognize the targeted molecule.

      The zeolite conundrum

      Theoretical studies suggest that there should be hundreds of thousands of possible zeolites. But despite some 60 years of intensive research, only about 250 zeolites have been made. This is sometimes called the “zeolite conundrum.” Why haven’t more been made — especially now, when they could help ongoing efforts to decarbonize energy and the chemical industry?

      One challenge is figuring out the best recipe for making them: Factors such as the best ratio between the silicon and aluminum, what cooking temperature to use, and whether to stir the ingredients all influence the outcome. But the real key, the researchers say, lies in choosing a templating molecule that’s best for producing the intended zeolite framework. Making that match is difficult: There are hundreds of known templating molecules and potentially a million zeolites, and researchers are continually designing new molecules because millions more could be made and might work better.

      For decades, the exploration of how to synthesize a particular zeolite has been done largely by trial and error — a time-consuming, expensive, inefficient way to go about it. There has also been considerable effort to use “atomistic” (atom-by-atom) simulation to figure out what known or novel templating molecule to use to produce a given zeolite. But the experimental and modeling results haven’t generated reliable guidance. In many cases, researchers have carefully selected or designed a molecule to make a particular zeolite, but when they tried their molecule in the lab, the zeolite that formed wasn’t what they expected or desired. So they needed to start over.

      Those experiences illustrate what Gómez-Bombarelli and his colleagues believe is the problem that’s been plaguing zeolite design for decades. All the efforts — both experimental and theoretical — have focused on finding the templating molecule that’s best for forming a specific zeolite. But what if that templating molecule is also really good — or even better — at forming some other zeolite?

      To determine the “best” molecule for making a certain zeolite framework, and the “best” zeolite framework to act as host to a particular molecule, the researchers decided to look at both sides of the pairing. Daniel Schwalbe-Koda PhD ’22, a former member of Gómez-Bombarelli’s group and now a postdoc at Lawrence Livermore National Laboratory, describes the process as a sort of dance with molecules and zeolites in a room looking for partners. “Each molecule wants to find a partner zeolite, and each zeolite wants to find a partner molecule,” he says. “But it’s not enough to find a good dance partner from the perspective of only one dancer. The potential partner could prefer to dance with someone else, after all. So it needs to be a particularly good pairing.” The upshot: “You need to look from the perspective of each of them.”

      To find the best match from both perspectives, the researchers needed to try every molecule with every zeolite and quantify how well the pairings worked.

      A broader metric for evaluating pairs

      Before performing that analysis, the researchers defined a new “evaluating metric” that they could use to rank each templating molecule-zeolite pair. The standard metric for measuring the affinity between a molecule and a zeolite is “binding energy,” that is, how strongly the molecule clings to the zeolite or, conversely, how much energy is required to separate the two. While recognizing the value of that metric, the MIT-led team wanted to take more parameters into account.

      Their new evaluating metric therefore includes not only binding energy but also the size, shape, and volume of the molecule and the opening in the zeolite framework. And their approach calls for turning the molecule to different orientations to find the best possible fit.

      Affinity scores for all molecule-zeolite pairs based on that evaluating metric would enable zeolite researchers to answer two key questions: What templating molecule will form the zeolite that I want? And if I use that templating molecule, what other zeolites might it form instead? Using the molecule-zeolite affinity scores, researchers could first identify molecules that look good for making a desired zeolite. They could then rule out the ones that also look good for forming other zeolites, leaving a set of molecules deemed to be “highly selective” for making the desired zeolite.  

      Validating the approach: A rich literature

      But does their new metric work better than the standard one? To find out, the team needed to perform atomistic simulations using their new evaluating metric and then benchmark their results against experimental evidence reported in the literature. There are many thousands of journal articles reporting on experiments involving zeolites — in many cases, detailing not only the molecule-zeolite pairs and outcomes but also synthesis conditions and other details. Ferreting out articles with the information the researchers needed was a job for machine learning — in particular, for natural language processing.

      For that task, Gómez-Bombarelli and Schwalbe-Koda turned to their DMSE colleague Elsa Olivetti PhD ’07, the Esther and Harold E. Edgerton Associate Professor in Materials Science and Engineering. Using a literature-mining technique that she and a group of collaborators had developed, she and her DMSE team processed more than 2 million materials science papers, found some 90,000 relating to zeolites, and extracted 1,338 of them for further analysis. The yield was 549 templating molecules tested, 209 zeolite frameworks produced, and 5,663 synthesis routes followed.

      Based on those findings, the researchers used their new evaluating metric and a novel atomistic simulation technique to examine more than half-a-million templating molecule-zeolite pairs. Their results reproduced experimental outcomes reported in more than a thousand journal articles. Indeed, the new metric outperformed the traditional binding energy metric, and their simulations were orders of magnitude faster than traditional approaches.

      Ready for experimental investigations

      Now the researchers were ready to put their approach to the test: They would use it to design new templating molecules and try them out in experiments performed by a team led by Yuriy Román-Leshkov, the Robert T. Haslam (1911) Professor of Chemical Engineering, and a team from the Instituto de Tecnologia Química in Valencia, Spain, led by Manuel Moliner and Avelino Corma.

      One set of experiments focused on a zeolite called chabazite, which is used in catalytic converters for vehicles. Using their techniques, the researchers designed a new templating molecule for synthesizing chabazite, and the experimental results confirmed their approach. Their analyses had shown that the new templating molecule would be good for forming chabazite and not for forming anything else. “Its binding strength isn’t as high as other molecules for chabazite, so people hadn’t used it,” says Gómez-Bombarelli. “But it’s pretty good, and it’s not good for anything else, so it’s selective — and it’s way cheaper than the usual ones.”

      In addition, in their new molecule, the electrical charge is distributed differently than in the traditional ones, which led to new possibilities. The researchers found that by adjusting both the shape and charge of the molecule, they could control where the negative charge occurs on the pore that’s created in the final zeolite. “The charge placement that results can make the chabazite a much better catalyst than it was before,” says Gómez-Bombarelli. “So our same rules for molecule design also determine where the negative charge is going to end up, which can lead to whole different classes of catalysts.”

      Schwalbe-Koda describes another experiment that demonstrates the importance of molecular shape as well as the types of new materials made possible using the team’s approach. In one striking example, the team designed a templating molecule with a height and width that’s halfway between those of two molecules that are now commonly used—one for making chabazite and the other for making a zeolite called AEI. (Every new zeolite structure is examined by the International Zeolite Association and — once approved — receives a three-letter designation.)

      Experiments using that in-between templating molecule resulted in the formation of not one zeolite or the other, but a combination of the two in a single solid. “The result blends two different structures together in a way that the final result is better than the sum of its parts,” says Schwalbe-Koda. “The catalyst is like the one used in catalytic converters in today’s trucks — only better.” It’s more efficient in converting nitrogen oxides to harmless nitrogen gases and water, and — because of the two different pore sizes and the aluminosilicate composition — it works well on exhaust that’s fairly hot, as during normal operation, and also on exhaust that’s fairly cool, as during startup.

      Putting the work into practice

      As with all materials, the commercial viability of a zeolite will depend in part on the cost of making it. The researchers’ technique can identify promising templating molecules, but some of them may be difficult to synthesize in the lab. As a result, the overall cost of that molecule-zeolite combination may be too high to be competitive.

      Gómez-Bombarelli and his team therefore include in their assessment process a calculation of cost for synthesizing each templating molecule they identified — generally the most expensive part of making a given zeolite. They use a publicly available model devised in 2018 by Connor Coley PhD ’19, now the Henri Slezynger (1957) Career Development Assistant Professor of Chemical Engineering at MIT. The model takes into account all the starting materials and the step-by-step chemical reactions needed to produce the targeted templating molecule.

      However, commercialization decisions aren’t based solely on cost. Sometimes there’s a trade-off between cost and performance. “For instance, given our chabazite findings, would customers or the community trade a little bit of activity for a 100-fold decrease in the cost of the templating molecule?” says Gómez-Bombarelli. “The answer is likely yes. So we’ve made a tool that can help them navigate that trade-off.” And there are other factors to consider. For example, is this templating molecule truly novel, or have others already studied it — or perhaps even hold a patent on it?

      “While an algorithm can guide development of templating molecules and quantify specific molecule-zeolite matches, other types of assessments are best left to expert judgment,” notes Schwalbe-Koda. “We need a partnership between computational analysis and human intuition and experience.”

      To that end, the MIT researchers and their colleagues decided to share their techniques and findings with other zeolite researchers. Led by Schwalbe-Koda, they created an online database that they made publicly accessible and easy to use — an unusual step, given the competitive industries that rely on zeolites. The interactive website — zeodb.mit.edu — contains the researchers’ final metrics for templating molecule-zeolite pairs resulting from hundreds of thousands of simulations; all the identified journal articles, along with which molecules and zeolites were examined and what synthesis conditions were used; and many more details. Users are free to search and organize the data in any way that suits them.

      Gómez-Bombarelli, Schwalbe-Koda, and their colleagues hope that their techniques and the interactive website will help other researchers explore and discover promising new templating molecules and zeolites, some of which could have profound impacts on efforts to decarbonize energy and tackle climate change.

      This research involved a team of collaborators at MIT, the Instituto de Tecnologia Química (UPV-CSIC), and Stockholm University. The work was supported in part by the MIT Energy Initiative Seed Fund Program and by seed funds from the MIT International Science and Technology Initiative. Daniel Schwalbe-Koda was supported by an ExxonMobil-MIT Energy Fellowship in 2020–21.

      This article appears in the Spring 2022 issue of Energy Futures, the magazine of the MIT Energy Initiative. More

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      in Energy

      A new concept for low-cost batteries

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      As the world builds out ever larger installations of wind and solar power systems, the need is growing fast for economical, large-scale backup systems to provide power when the sun is down and the air is calm. Today’s lithium-ion batteries are still too expensive for most such applications, and other options such as pumped hydro require specific topography that’s not always available.

      Now, researchers at MIT and elsewhere have developed a new kind of battery, made entirely from abundant and inexpensive materials, that could help to fill that gap.

      The new battery architecture, which uses aluminum and sulfur as its two electrode materials, with a molten salt electrolyte in between, is described today in the journal Nature, in a paper by MIT Professor Donald Sadoway, along with 15 others at MIT and in China, Canada, Kentucky, and Tennessee.

      “I wanted to invent something that was better, much better, than lithium-ion batteries for small-scale stationary storage, and ultimately for automotive [uses],” explains Sadoway, who is the John F. Elliott Professor Emeritus of Materials Chemistry.

      In addition to being expensive, lithium-ion batteries contain a flammable electrolyte, making them less than ideal for transportation. So, Sadoway started studying the periodic table, looking for cheap, Earth-abundant metals that might be able to substitute for lithium. The commercially dominant metal, iron, doesn’t have the right electrochemical properties for an efficient battery, he says. But the second-most-abundant metal in the marketplace — and actually the most abundant metal on Earth — is aluminum. “So, I said, well, let’s just make that a bookend. It’s gonna be aluminum,” he says.

      Then came deciding what to pair the aluminum with for the other electrode, and what kind of electrolyte to put in between to carry ions back and forth during charging and discharging. The cheapest of all the non-metals is sulfur, so that became the second electrode material. As for the electrolyte, “we were not going to use the volatile, flammable organic liquids” that have sometimes led to dangerous fires in cars and other applications of lithium-ion batteries, Sadoway says. They tried some polymers but ended up looking at a variety of molten salts that have relatively low melting points — close to the boiling point of water, as opposed to nearly 1,000 degrees Fahrenheit for many salts. “Once you get down to near body temperature, it becomes practical” to make batteries that don’t require special insulation and anticorrosion measures, he says.

      The three ingredients they ended up with are cheap and readily available — aluminum, no different from the foil at the supermarket; sulfur, which is often a waste product from processes such as petroleum refining; and widely available salts. “The ingredients are cheap, and the thing is safe — it cannot burn,” Sadoway says.

      In their experiments, the team showed that the battery cells could endure hundreds of cycles at exceptionally high charging rates, with a projected cost per cell of about one-sixth that of comparable lithium-ion cells. They showed that the charging rate was highly dependent on the working temperature, with 110 degrees Celsius (230 degrees Fahrenheit) showing 25 times faster rates than 25 C (77 F).

      Surprisingly, the molten salt the team chose as an electrolyte simply because of its low melting point turned out to have a fortuitous advantage. One of the biggest problems in battery reliability is the formation of dendrites, which are narrow spikes of metal that build up on one electrode and eventually grow across to contact the other electrode, causing a short-circuit and hampering efficiency. But this particular salt, it happens, is very good at preventing that malfunction.

      The chloro-aluminate salt they chose “essentially retired these runaway dendrites, while also allowing for very rapid charging,” Sadoway says. “We did experiments at very high charging rates, charging in less than a minute, and we never lost cells due to dendrite shorting.”

      “It’s funny,” he says, because the whole focus was on finding a salt with the lowest melting point, but the catenated chloro-aluminates they ended up with turned out to be resistant to the shorting problem. “If we had started off with trying to prevent dendritic shorting, I’m not sure I would’ve known how to pursue that,” Sadoway says. “I guess it was serendipity for us.”

      What’s more, the battery requires no external heat source to maintain its operating temperature. The heat is naturally produced electrochemically by the charging and discharging of the battery. “As you charge, you generate heat, and that keeps the salt from freezing. And then, when you discharge, it also generates heat,” Sadoway says. In a typical installation used for load-leveling at a solar generation facility, for example, “you’d store electricity when the sun is shining, and then you’d draw electricity after dark, and you’d do this every day. And that charge-idle-discharge-idle is enough to generate enough heat to keep the thing at temperature.”

      This new battery formulation, he says, would be ideal for installations of about the size needed to power a single home or small to medium business, producing on the order of a few tens of kilowatt-hours of storage capacity.

      For larger installations, up to utility scale of tens to hundreds of megawatt hours, other technologies might be more effective, including the liquid metal batteries Sadoway and his students developed several years ago and which formed the basis for a spinoff company called Ambri, which hopes to deliver its first products within the next year. For that invention, Sadoway was recently awarded this year’s European Inventor Award.

      The smaller scale of the aluminum-sulfur batteries would also make them practical for uses such as electric vehicle charging stations, Sadoway says. He points out that when electric vehicles become common enough on the roads that several cars want to charge up at once, as happens today with gasoline fuel pumps, “if you try to do that with batteries and you want rapid charging, the amperages are just so high that we don’t have that amount of amperage in the line that feeds the facility.” So having a battery system such as this to store power and then release it quickly when needed could eliminate the need for installing expensive new power lines to serve these chargers.

      The new technology is already the basis for a new spinoff company called Avanti, which has licensed the patents to the system, co-founded by Sadoway and Luis Ortiz ’96 ScD ’00, who was also a co-founder of Ambri. “The first order of business for the company is to demonstrate that it works at scale,” Sadoway says, and then subject it to a series of stress tests, including running through hundreds of charging cycles.

      Would a battery based on sulfur run the risk of producing the foul odors associated with some forms of sulfur? Not a chance, Sadoway says. “The rotten-egg smell is in the gas, hydrogen sulfide. This is elemental sulfur, and it’s going to be enclosed inside the cells.” If you were to try to open up a lithium-ion cell in your kitchen, he says (and please don’t try this at home!), “the moisture in the air would react and you’d start generating all sorts of foul gases as well. These are legitimate questions, but the battery is sealed, it’s not an open vessel. So I wouldn’t be concerned about that.”

      The research team included members from Peking University, Yunnan University and the Wuhan University of Technology, in China; the University of Louisville, in Kentucky; the University of Waterloo, in Canada; Oak Ridge National Laboratory, in Tennessee; and MIT. The work was supported by the MIT Energy Initiative, the MIT Deshpande Center for Technological Innovation, and ENN Group. More

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      in Energy

      Building better batteries, faster

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      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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      in Energy

      A better way to quantify radiation damage in materials

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      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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      in Energy

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

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      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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      in Energy

      Explained: Why perovskites could take solar cells to new heights

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      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