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      Energizing communities in Africa

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      Growing up in Lagos, Nigeria, Ayomikun Ayodeji enjoyed the noisy hustle and bustle of his neighborhood. The cacophony included everything from vendors hawking water sachets and mini sausages, to commuters shouting for the next bus.

      Another common sound was the cry of “Up NEPA!” — an acronym for the Nigerian Electrical Power Authority — which Ayodeji would chant in unison with other neighborhood children when power had been restored after an outage. He remembers these moments fondly because, despite the difficulties of the frequent outages, the call also meant that people finally did have long-awaited electricity in their homes.

      “I grew up without reliable electricity, so power is something I’ve always been interested in,” says Ayodeji, who is now a senior studying chemical engineering. He hopes to use the knowledge he has gained during his time at MIT to expand energy access in his home country and elsewhere in Africa.

      Before coming to MIT, Ayodeji spent two years in Italy at United World College, where he embarked on chemistry projects, specifically focusing on dye-sensitized solar cells. He then transferred to the Institute, seeking a more technical grounding. He hoped that the knowledge gained in and out of the classroom would equip him with the tools to help combat the energy crisis in Lagos.

      “The questions that remained in the back of my mind were: How can I give back to the community I came from? How can I use the resources around me to help others?”  he says.

      This community-oriented mindset led Ayodeji to team up with a group of friends and brainstorm ideas for how they could help communities close to them. They eventually partnered with the Northeast Children’s Trust (NECT), an organization that helps children affected by the extremist group Boko Haram. Ayodeji and his friends looked at how to expand NECT’s educational program, and decided to build an offline, portable classroom server with a repository of books, animations, and activities for students at the primary and secondary education levels. The project was sponsored by Davis Projects for Peace and MIT’s PKG Center.

      Because of travel restrictions, Ayodeji was the only member of his team able to fly to Nigeria in the summer of 2019 to facilitate installing the servers. He says he wished his team could have been there, but he appreciated the opportunity to speak with the children directly, inspired by their excitement to learn and grow. The experience reaffirmed Ayodeji’s desire to pursue social impact projects, especially in Nigeria.

      “We knew we hadn’t just taken a step in providing the kids with a well-rounded education, but we also supported the center, NECT, in raising the next generation of future leaders that would guide that region to a sustainable, peaceful future,” he says.

      Ayodeji has also sought out energy-related opportunities on campus, pursuing an undergraduate research program (UROP) in the Buonassisi Lab in his sophomore year. He was tasked with testing perovskite solar cells, which have the potential to reach high efficiencies at low production costs. He characterized the cells using X-ray diffraction, studying their stability and degradation pathways. While Ayodeji enjoyed his first experience doing hands-on energy research, he found he was more curious about how energy technologies were implemented to reach various communities. “I wanted to see how things were being done in the industry,” he says.

      In the summer after his sophomore year, Ayodeji interned with Pioneer Natural Resources, an independent oil and gas company in Texas. Ayodeji worked as part of the completions projects team to assess the impact of design changes on cluster efficiency, that is, how evenly fluid is distributed along the wellbore. By using fiberoptic and photographic data to analyze perforation erosion, he discovered ways to lower costs while maintaining environmental stability during completions. The experience taught Ayodeji about the corporate side of the energy industry and enabled him to observe how approaches to alternative energy sources differ across countries, especially in the U.S. and Nigeria.

      “Some developing economies don’t have the capacity to pour resources into expanding renewable energy infrastructure at the rate that most developed economies do. While it is important to think sustainably for the long run, it is also important for us to understand that a clean energy transition is not something that can be done overnight,” he says.

      Ayodeji also employs his community-oriented mindset on campus. He is currently the vice president of the African Students’ Association (ASA), where he formerly chaired the African Learning Circle, a weekly discussion panel spotlighting key development and innovation events taking place on the African continent. He is also involved with student outreach, both within the ASA and as an international orientation student coordinator for the International Students Office.

      As a member of Cru, a Christian community on campus, Ayodeji helps lead a bible study and says the group supports him as he navigates college life. “It is a wonderful community of people I explore faith with and truly lean on when things get tough,” he says.

      After graduating, Ayodeji plans to start work at Boston Consulting Group, where he interned last summer. He expects he’ll have opportunities to engage with private equity issues and tackle energy-related cases while learning more about where the industry is headed.

      His long-term goal is to help expand renewable energy access and production across the African continent.

      “A key element of what the world needs to develop and grow is access to reliable energy. I hope to keep expanding my problem-solving toolkit so that, one day, it can be useful in electrifying communities back home,” he says. More

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      Preparing global online learners for the clean energy transition

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      After a career devoted to making the electric power system more efficient and resilient, Marija Ilic came to MIT in 2018 eager not just to extend her research in new directions, but to prepare a new generation for the challenges of the clean-energy transition.

      To that end, Ilic, a senior research scientist in MIT’s Laboratory for Information and Decisions Systems (LIDS) and a senior staff member at Lincoln Laboratory in the Energy Systems Group, designed an edX course that captures her methods and vision: Principles of Modeling, Simulation, and Control for Electric Energy Systems.

      EdX is a provider of massive open online courses produced in partnership with MIT, Harvard University, and other leading universities. Ilic’s class made its online debut in June 2021, running for 12 weeks, and it is one of an expanding set of online courses funded by the MIT Energy Initiative (MITEI) to provide global learners with a view of the shifting energy landscape.

      Ilic first taught a version of the class while a professor at Carnegie Mellon University, rolled out a second iteration at MIT just as the pandemic struck, and then revamped the class for its current online presentation. But no matter the course location, Ilic focuses on a central theme: “With the need for decarbonization, which will mean accommodating new energy sources such as solar and wind, we must rethink how we operate power systems,” she says. “This class is about how to pose and solve the kinds of problems we will face during this transformation.”

      Hot global topic

      The edX class has been designed to welcome a broad mix of students. In summer 2021, more than 2,000 signed up from 109 countries, ranging from high school students to retirees. In surveys, some said they were drawn to the class by the opportunity to advance their knowledge of modeling. Many others hoped to learn about the move to decarbonize energy systems.

      “The energy transition is a hot topic everywhere in the world, not just in the U.S.,” says teaching assistant Miroslav Kosanic. “In the class, there were veterans of the oil industry and others working in investment and finance jobs related to energy who wanted to understand the potential impacts of changes in energy systems, as well as students from different fields and professors seeking to update their curricula — all gathered into a community.”

      Kosanic, who is currently a PhD student at MIT in electrical engineering and computer science, had taken this class remotely in the spring semester of 2021, while he was still in college in Serbia. “I knew I was interested in power systems, but this course was eye-opening for me, showing how to apply control theory and to model different components of these systems,” he says. “I finished the course and thought, this is just the beginning, and I’d like to learn a lot more.” Kosanic performed so well online that Ilic recruited him to MIT, as a LIDS researcher and edX course teaching assistant, where he grades homework assignments and moderates a lively learner community forum.

      A platform for problem-solving

      The course starts with fundamental concepts in electric power systems operations and management, and it steadily adds layers of complexity, posing real-world problems along the way. Ilic explains how voltage travels from point to point across transmission lines and how grid managers modulate systems to ensure that enough, but not too much, electricity flows. “To deliver power from one location to the next one, operators must constantly make adjustments to ensure that the receiving end can handle the voltage transmitted, optimizing voltage to avoid overheating the wires,” she says.

      In her early lectures, Ilic notes the fundamental constraints of current grid operations, organized around a hierarchy of regional managers dealing with a handful of very large oil, gas, coal, and nuclear power plants, and occupied primarily with the steady delivery of megawatt-hours to far-flung customers. But historically, this top-down structure doesn’t do a good job of preventing loss of energy due to sub-optimal transmission conditions or due to outages related to extreme weather events.

      These issues promise to grow for grid operators as distributed resources such as solar and wind enter the picture, Ilic tells students. In the United States, under new rules dictated by the Federal Energy Regulatory Commission, utilities must begin to integrate the distributed, intermittent electricity produced by wind farms, solar complexes, and even by homes and cars, which flows at voltages much lower than electricity produced by large power plants.

      Finding ways to optimize existing energy systems and to accommodate low- and zero-carbon energy sources requires powerful new modes of analysis and problem-solving. This is where Ilic’s toolbox comes in: a mathematical modeling strategy and companion software that simplifies the input and output of electrical systems, no matter how large or how small. “In the last part of the course, we take up modeling different solutions to electric service in a way that is technology-agnostic, where it only matters how much a black-box energy source produces, and the rates of production and consumption,” says Ilic.

      This black-box modeling approach, which Ilic pioneered in her research, enables students to see, for instance, “what is happening with their own household consumption, and how it affects the larger system,” says Rupamathi Jaddivada PhD ’20, a co-instructor of the edX class and a postdoc in electrical engineering and computer science. “Without getting lost in details of current or voltage, or how different components work, we think about electric energy systems as dynamical components interacting with each other, at different spatial scales.” This means that with just a basic knowledge of physical laws, high school and undergraduate students can take advantage of the course “and get excited about cleaner and more reliable energy,” adds Ilic.

      What Jaddivada and Ilic describe as “zoom in, zoom out” systems thinking leverages the ubiquity of digital communications and the so-called “internet of things.” Energy devices of all scales can link directly to other devices in a network instead of just to a central operations hub, allowing for real-time adjustments in voltage, for instance, vastly improving the potential for optimizing energy flows.

      “In the course, we discuss how information exchange will be key to integrating new end-to-end energy resources and, because of this interactivity, how we can model better ways of controlling entire energy networks,” says Ilic. “It’s a big lesson of the course to show the value of information and software in enabling us to decarbonize the system and build resilience, rather than just building hardware.”

      By the end of the course, students are invited to pursue independent research projects. Some might model the impact of a new energy source on a local grid or investigate different options for reducing energy loss in transmission lines.

      “It would be nice if they see that we don’t have to rely on hardware or large-scale solutions to bring about improved electric service and a clean and resilient grid, but instead on information technologies such as smart components exchanging data in real time, or microgrids in neighborhoods that sustain themselves even when they lose power,” says Ilic. “I hope students walk away convinced that it does make sense to rethink how we operate our basic power systems and that with systematic, physics-based modeling and IT methods we can enable better, more flexible operation in the future.”

      This article appears in the Autumn 2021 issue of Energy Futures, the magazine of the MIT Energy Initiative More

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      MIT Energy Initiative launches the Future Energy Systems Center

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      The MIT Energy Initiative (MITEI) has launched a new research consortium — the Future Energy Systems Center — to address the climate crisis and the role energy systems can play in solving it. This integrated effort engages researchers from across all of MIT to help the global community reach its goal of net-zero carbon emissions. The center examines the accelerating energy transition and collaborates with industrial leaders to reform the world’s energy systems. The center is part of “Fast Forward: MIT’s Climate Action Plan for the Decade,” MIT’s multi-pronged effort announced last year to address the climate crisis.

      The Future Energy Systems Center investigates the emerging technology, policy, demographics, and economics reshaping the landscape of energy supply and demand. The center conducts integrative analysis of the entire energy system — a holistic approach essential to understanding the cross-sectorial impact of the energy transition.

      “We must act quickly to get to net-zero greenhouse gas emissions. At the same time, we have a billion people around the world with inadequate access, or no access, to electricity — and we need to deliver it to them,” says MITEI Director Robert C. Armstrong, the Chevron Professor of Chemical Engineering. “The Future Energy Systems Center combines MIT’s deep knowledge of energy science and technology with advanced tools for systems analysis to examine how advances in technology and system economics may respond to various policy scenarios.”  

      The overarching focus of the center is integrative analysis of the entire energy system, providing insights into the complex multi-sectorial transformations needed to alter the three major energy-consuming sectors of the economy — transportation, industry, and buildings — in conjunction with three major decarbonization-enabling technologies — electricity, energy storage and low-carbon fuels, and carbon management. “Deep decarbonization of our energy system requires an economy-wide perspective on the technology options, energy flows, materials flows, life-cycle emissions, costs, policies, and socioeconomics consequences,” says Randall Field, the center’s executive director. “A systems approach is essential in enabling cross-disciplinary teams to work collaboratively together to address the existential crisis of climate change.”

      Through techno-economic and systems-oriented research, the center analyzes these important interactions. For example:

      •  Increased reliance on variable renewable energy, such as wind and solar, and greater electrification of transportation, industry, and buildings will require expansion of demand management and other solutions for balancing of electricity supply and demand across these areas.

      •  Likewise, balancing supply and demand will require deploying grid-scale energy storage and converting the electricity to low-carbon fuels (hydrogen and liquid fuels), which can in turn play a vital role in the energy transition for hard-to-decarbonize segments of transportation, industry, and buildings.

      •  Carbon management (carbon dioxide capture from industry point sources and from air and oceans; utilization/conversion to valuable products; transport; storage) will also play a critical role in decarbonizing industry, electricity, and fuels — both as carbon-mitigation and negative-carbon solutions.

      As a member-supported research consortium, the center collaborates with industrial experts and leaders — from both energy’s consumer and supplier sides — to gain insights to help researchers anticipate challenges and opportunities of deploying technology at the scale needed to achieve decarbonization. “The Future Energy Systems Center gives us a powerful way to engage with industry to accelerate the energy transition,” says Armstrong. “Working together, we can better understand how our current technology toolbox can be more effectively put to use now to reduce emissions, and what new technologies and policies will ultimately be needed to reach net-zero.”

      A steering committee, made up of 11 MIT professors and led by Armstrong, selects projects to create a research program with high impact on decarbonization, while leveraging MIT strengths and addressing interests of center members in pragmatic and scalable solutions. “MIT — through our recently released climate action plan — is committed to moving with urgency and speed to help wring carbon dioxide emissions out the global economy to resolve the growing climate crisis,” says Armstrong. “We have no time to waste.”

      The center members to date are: AECI, Analog Devices, Chevron, ConocoPhillips, Copec, Dominion, Duke Energy, Enerjisa, Eneva, Eni, Equinor, Eversource, Exelon, ExxonMobil, Ferrovial, Iberdrola, IHI, National Grid, Raizen, Repsol, Rio Tinto, Shell, Tata Power, Toyota Research Institute, and Washington Gas. More

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      Pricing carbon, valuing people

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      In November, inflation hit a 39-year high in the United States. The consumer price index was up 6.8 percent from the previous year due to major increases in the cost of rent, food, motor vehicles, gasoline, and other common household expenses. While inflation impacts the entire country, its effects are not felt equally. At greatest risk are low- and middle-income Americans who may lack sufficient financial reserves to absorb such economic shocks.

      Meanwhile, scientists, economists, and activists across the political spectrum continue to advocate for another potential systemic economic change that many fear will also put lower-income Americans at risk: the imposition of a national carbon price, fee, or tax. Framed by proponents as the most efficient and cost-effective way to reduce greenhouse gas emissions and meet climate targets, a carbon penalty would incentivize producers and consumers to shift expenditures away from carbon-intensive products and services (e.g., coal or natural gas-generated electricity) and toward low-carbon alternatives (e.g., 100 percent renewable electricity). But if not implemented in a way that takes differences in household income into account, this policy strategy, like inflation, could place an unequal and untenable economic burden on low- and middle-income Americans.         

      To garner support from policymakers, carbon-penalty proponents have advocated for policies that recycle revenues from carbon penalties to all or lower-income taxpayers in the form of payroll tax reductions or lump-sum payments. And yet some of these proposed policies run the risk of reducing the overall efficiency of the U.S. economy, which would lower the nation’s GDP and impede its economic growth.

      Which begs the question: Is there a sweet spot at which a national carbon-penalty revenue-recycling policy can both avoid inflicting economic harm on lower-income Americans at the household level and degrading economic efficiency at the national level?

      In search of that sweet spot, researchers at the MIT Joint Program on the Science and Policy of Global Change assess the economic impacts of four different carbon-penalty revenue-recycling policies: direct rebates from revenues to households via lump-sum transfers; indirect refunding of revenues to households via a proportional reduction in payroll taxes; direct rebates from revenues to households, but only for low- and middle-income groups, with remaining revenues recycled via a proportional reduction in payroll taxes; and direct, higher rebates for poor households, with remaining revenues recycled via a proportional reduction in payroll taxes.

      To perform the assessment, the Joint Program researchers integrate a U.S. economic model (MIT U.S. Regional Energy Policy) with a dataset (Bureau of Labor Statistics’ Consumer Expenditure Survey) providing consumption patterns and other socioeconomic characteristics for 15,000 U.S. households. Using the combined model, they evaluate the distributional impacts and potential trade-offs between economic equity and efficiency of all four carbon-penalty revenue-recycling policies.

      The researchers find that household rebates have progressive impacts on consumers’ financial well-being, with the greatest benefits going to the lowest-income households, while policies centered on improving the efficiency of the economy (e.g., payroll tax reductions) have slightly regressive household-level financial impacts. In a nutshell, the trade-off is between rebates that provide more equity and less economic efficiency versus tax cuts that deliver the opposite result. The latter two policy options, which combine rebates to lower-income households with payroll tax reductions, result in an optimal blend of sufficiently progressive financial results at the household level and economy efficiency at the national level. Results of the study are published in the journal Energy Economics.

      “We have determined that only a portion of carbon-tax revenues is needed to compensate low-income households and thus reduce inequality, while the rest can be used to improve the economy by reducing payroll or other distortionary taxes,” says Xaquin García-Muros, lead author of the study, a postdoc at the MIT Joint Program who is affiliated with the Basque Centre for Climate Change in Spain. “Therefore, we can eliminate potential trade-offs between efficiency and equity, and promote a just and efficient energy transition.”

      “If climate policies increase the gap between rich and poor households or reduce the affordability of energy services, then these policies might be rejected by the public and, as a result, attempts to decarbonize the economy will be less efficient,” says Joint Program Deputy Director Sergey Paltsev, a co-author of the study. “Our findings provide guidance to decision-makers to advance more well-designed policies that deliver economic benefits to the nation as a whole.” 

      The study’s novel integration of a national economic model with household microdata creates a new and powerful platform to further investigate key differences among households that can help inform policies aimed at a just transition to a low-carbon economy. More

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      Seeing the plasma edge of fusion experiments in new ways with artificial intelligence

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      To make fusion energy a viable resource for the world’s energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel.

      Abhilash Mathews, a PhD candidate in the Department of Nuclear Science and Engineering working at MIT’s Plasma Science and Fusion Center (PSFC), believes this plasma edge to be a particularly rich source of unanswered questions. A turbulent boundary, it is central to understanding plasma confinement, fueling, and the potentially damaging heat fluxes that can strike material surfaces — factors that impact fusion reactor designs.

      To better understand edge conditions, scientists focus on modeling turbulence at this boundary using numerical simulations that will help predict the plasma’s behavior. However, “first principles” simulations of this region are among the most challenging and time-consuming computations in fusion research. Progress could be accelerated if researchers could develop “reduced” computer models that run much faster, but with quantified levels of accuracy.

      For decades, tokamak physicists have regularly used a reduced “two-fluid theory” rather than higher-fidelity models to simulate boundary plasmas in experiment, despite uncertainty about accuracy. In a pair of recent publications, Mathews begins directly testing the accuracy of this reduced plasma turbulence model in a new way: he combines physics with machine learning.

      “A successful theory is supposed to predict what you’re going to observe,” explains Mathews, “for example, the temperature, the density, the electric potential, the flows. And it’s the relationships between these variables that fundamentally define a turbulence theory. What our work essentially examines is the dynamic relationship between two of these variables: the turbulent electric field and the electron pressure.”

      In the first paper, published in Physical Review E, Mathews employs a novel deep-learning technique that uses artificial neural networks to build representations of the equations governing the reduced fluid theory. With this framework, he demonstrates a way to compute the turbulent electric field from an electron pressure fluctuation in the plasma consistent with the reduced fluid theory. Models commonly used to relate the electric field to pressure break down when applied to turbulent plasmas, but this one is robust even to noisy pressure measurements.

      In the second paper, published in Physics of Plasmas, Mathews further investigates this connection, contrasting it against higher-fidelity turbulence simulations. This first-of-its-kind comparison of turbulence across models has previously been difficult — if not impossible — to evaluate precisely. Mathews finds that in plasmas relevant to existing fusion devices, the reduced fluid model’s predicted turbulent fields are consistent with high-fidelity calculations. In this sense, the reduced turbulence theory works. But to fully validate it, “one should check every connection between every variable,” says Mathews.

      Mathews’ advisor, Principal Research Scientist Jerry Hughes, notes that plasma turbulence is notoriously difficult to simulate, more so than the familiar turbulence seen in air and water. “This work shows that, under the right set of conditions, physics-informed machine-learning techniques can paint a very full picture of the rapidly fluctuating edge plasma, beginning from a limited set of observations. I’m excited to see how we can apply this to new experiments, in which we essentially never observe every quantity we want.”

      These physics-informed deep-learning methods pave new ways in testing old theories and expanding what can be observed from new experiments. David Hatch, a research scientist at the Institute for Fusion Studies at the University of Texas at Austin, believes these applications are the start of a promising new technique.

      “Abhi’s work is a major achievement with the potential for broad application,” he says. “For example, given limited diagnostic measurements of a specific plasma quantity, physics-informed machine learning could infer additional plasma quantities in a nearby domain, thereby augmenting the information provided by a given diagnostic. The technique also opens new strategies for model validation.”

      Mathews sees exciting research ahead.

      “Translating these techniques into fusion experiments for real edge plasmas is one goal we have in sight, and work is currently underway,” he says. “But this is just the beginning.”

      Mathews was supported in this work by the Manson Benedict Fellowship, Natural Sciences and Engineering Research Council of Canada, and U.S. Department of Energy Office of Science under the Fusion Energy Sciences program.​ More

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      Helping to make nuclear fusion a reality

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      Up until she served in the Peace Corps in Malawi, Rachel Bielajew was open to a career reboot. Having studied nuclear engineering as an undergraduate at the University of Michigan at Ann Arbor, graduate school had been on her mind. But seeing the drastic impacts of climate change play out in real-time in Malawi — the lives of the country’s subsistence farmers swing wildly, depending on the rains — convinced Bielajew of the importance of nuclear engineering. Bielajew was struck that her high school students in the small town of Chisenga had a shaky understanding of math, but universally understood global warming. “The concept of the changing world due to human impact was evident, and they could see it,” Bielajew says.

      Bielajew was looking to work on solutions that could positively impact global problems and feed her love of physics. Nuclear engineering, especially the study of fusion as a carbon-free energy source, checked off both boxes. Bielajew is now a fourth-year doctoral candidate in the Department of Nuclear Science and Engineering (NSE). She researches magnetic confinement fusion in the Plasma Science and Fusion Center (PSFC) with Professor Anne White.

      Researching fusion’s big challenge

      You need to confine plasma effectively in order to generate the extremely high temperatures (100 million degrees Celsius) fusion needs, without melting the walls of the tokamak, the device that hosts these reactions. Magnets can do the job, but “plasmas are weird, they behave strangely and are challenging to understand,” Bielajew says. Small instabilities in plasma can coalesce into fluctuating turbulence that can drive heat and particles out of the machine.

      In high-confinement mode, the edges of the plasma have less tolerance for such unruly behavior. “The turbulence gets damped out and sheared apart at the edge,” Bielajew says. This might seem like a good thing, but high-confinement plasmas have their own challenges. They are so tightly bound that they create edge-localized modes (ELMs), bursts of damaging particles and energy, that can be extremely damaging to the machine.

      The questions Bielajew is looking to answer: How do we get high confinement without ELMs? How do turbulence and transport play a role in plasmas? “We do not fully understand turbulence, even though we have studied it for a long time,” Bielajew says, “It is a big and important problem to solve for fusion to be a reality. I like that challenge,” Bielajew adds.

      A love of science

      Confronting such challenges head-on has been part of Bielajew’s toolkit since she was a child growing up in Ann Arbor, Michigan. Her father, Alex Bielajew, is a professor of nuclear engineering at the University of Michigan, and Bielajew’s mother also pursued graduate studies.

      Bielajew’s parents encouraged her to follow her own path and she found it led to her father’s chosen profession: nuclear engineering. Once she decided to pursue research in fusion, MIT stood out as a school she could set her sights on. “I knew that MIT had an extensive program in fusion and a lot of faculty in the field,” Bielajew says. The mechanics of the application were challenging: Chisenga had limited internet access, so Bielajew had to ride on the back of a pickup truck to meet a friend in a city a few hours away and use his phone as a hotspot to send the documents.

      A similar tenacity has surfaced in Bielajew’s approach to research during the Covid-19 pandemic. Working off a blueprint, Bielajew built the Correlation Cyclotron Emission Diagnostic, which measures turbulent electron temperature fluctuations. Through a collaboration, Bielajew conducts her plasma research at the ASDEX Upgrade tokamak in Germany. Traditionally, Bielajew would ship the diagnostic to Germany, follow and install it, and conduct the research in person. The pandemic threw a wrench in the plans, so Bielajew shipped the diagnostic and relied on team members to install it. She Zooms into the control room and trusts others to run the plasma experiments.

      DEI advocate

      Bielajew is very hands-on with another endeavor: improving diversity, equity, and inclusion (DEI) in her own backyard. Having grown up with parental encouragement and in an environment that never doubted her place as a woman in engineering, Bielajew realizes not everyone has the same opportunities. “I wish that the world was in a place where all I had to do was care about my research, but it’s not,” Bielajew says. While science can solve many problems, more fundamental ones about equity need humans to act in specific ways, she points out. “I want to see more women represented, more people of color. Everyone needs a voice in building a better world,” Bielajew says.

      To get there, Bielajew co-launched NSE’s Graduate Application Assistance Program, which connects underrepresented student applicants with NSE mentors. She has been the DEI officer with NSE’s student group, ANS, and is very involved in the department’s DEI committee.

      As for future research, Bielajew hopes to concentrate on the experiments that make her question existing paradigms about plasmas under high confinement. Bielajew has registered more head-scratching “hmm” moments than “a-ha” ones. Measurements from her experiments drive the need for more intensive study.

      Bielajew’s dogs, Dobby and Winky, keep her company through it all. They came home with her from Malawi. More

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      Selective separation could help alleviate critical metals shortage

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      New processing methods developed by MIT researchers could help ease looming shortages of the essential metals that power everything from phones to automotive batteries, by making it easier to separate these rare metals from mining ores and recycled materials.

      Selective adjustments within a chemical process called sulfidation allowed professor of metallurgy Antoine Allanore and his graduate student Caspar Stinn to successfully target and separate rare metals, such as the cobalt in a lithium-ion battery, from mixed-metal materials.

      As they report in the journal Nature, their processing techniques allow the metals to remain in solid form and be separated without dissolving the material. This avoids traditional but costly liquid separation methods that require significant energy. The researchers developed processing conditions for 56 elements and tested these conditions on 15 elements.

      Their sulfidation approach, they write in the paper, could reduce the capital costs of metal separation between 65 and 95 percent from mixed-metal oxides. Their selective processing could also reduce greenhouse gas emissions by 60 to 90 percent compared to traditional liquid-based separation.

      “We were excited to find replacements for processes that had really high levels of water usage and greenhouse gas emissions, such as lithium-ion battery recycling, rare-earth magnet recycling, and rare-earth separation,” says Stinn. “Those are processes that make materials for sustainability applications, but the processes themselves are very unsustainable.”

      The findings offer one way to alleviate a growing demand for minor metals like cobalt, lithium, and rare earth elements that are used in “clean” energy products like electric cars, solar cells, and electricity-generating windmills. According to a 2021 report by the International Energy Agency, the average amount of minerals needed for a new unit of power generation capacity has risen by 50 percent since 2010, as renewable energy technologies using these metals expand their reach.

      Opportunity for selectivity

      For more than a decade, the Allanore group has been studying the use of sulfide materials in developing new electrochemical routes for metal production. Sulfides are common materials, but the MIT scientists are experimenting with them under extreme conditions like very high temperatures — from 800 to 3,000 degrees Fahrenheit — that are used in manufacturing plants but not in a typical university lab.

      “We are looking at very well-established materials in conditions that are uncommon compared to what has been done before,” Allanore explains, “and that is why we are finding new applications or new realities.”

      In the process of synthetizing high-temperature sulfide materials to support electrochemical production, Stinn says, “we learned we could be very selective and very controlled about what products we made. And it was with that understanding that we realized, ‘OK, maybe there’s an opportunity for selectivity in separation here.’”

      The chemical reaction exploited by the researchers reacts a material containing a mix of metal oxides to form new metal-sulfur compounds or sulfides. By altering factors like temperature, gas pressure, and the addition of carbon in the reaction process, Stinn and Allanore found that they could selectively create a variety of sulfide solids that can be physically separated by a variety of methods, including crushing the material and sorting different-sized sulfides or using magnets to separate different sulfides from one another.

      Current methods of rare metal separation rely on large quantities of energy, water, acids, and organic solvents which have costly environmental impacts, says Stinn. “We are trying to use materials that are abundant, economical, and readily available for sustainable materials separation, and we have expanded that domain to now include sulfur and sulfides.”

      Stinn and Allanore used selective sulfidation to separate out economically important metals like cobalt in recycled lithium-ion batteries. They also used their techniques to separate dysprosium — a rare-earth element used in applications ranging from data storage devices to optoelectronics — from rare-earth-boron magnets, or from the typical mixture of oxides available from mining minerals such as bastnaesite.

      Leveraging existing technology

      Metals like cobalt and rare earths are only found in small amounts in mined materials, so industries must process large volumes of material to retrieve or recycle enough of these metals to be economically viable, Allanore explains. “It’s quite clear that these processes are not efficient. Most of the emissions come from the lack of selectivity and the low concentration at which they operate.”

      By eliminating the need for liquid separation and the extra steps and materials it requires to dissolve and then reprecipitate individual elements, the MIT researchers’ process significantly reduces the costs incurred and emissions produced during separation.

      “One of the nice things about separating materials using sulfidation is that a lot of existing technology and process infrastructure can be leveraged,” Stinn says. “It’s new conditions and new chemistries in established reactor styles and equipment.”

      The next step is to show that the process can work for large amounts of raw material — separating out 16 elements from rare-earth mining streams, for example. “Now we have shown that we can handle three or four or five of them together, but we have not yet processed an actual stream from an existing mine at a scale to match what’s required for deployment,” Allanore says.

      Stinn and colleagues in the lab have built a reactor that can process about 10 kilograms of raw material per day, and the researchers are starting conversations with several corporations about the possibilities.

      “We are discussing what it would take to demonstrate the performance of this approach with existing mineral and recycling streams,” Allanore says.

      This research was supported by the U.S. Department of Energy and the U.S. National Science Foundation. More

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      in Environment

      A tool to speed development of new solar cells

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      In the ongoing race to develop ever-better materials and configurations for solar cells, there are many variables that can be adjusted to try to improve performance, including material type, thickness, and geometric arrangement. Developing new solar cells has generally been a tedious process of making small changes to one of these parameters at a time. While computational simulators have made it possible to evaluate such changes without having to actually build each new variation for testing, the process remains slow.

      Now, researchers at MIT and Google Brain have developed a system that makes it possible not just to evaluate one proposed design at a time, but to provide information about which changes will provide the desired improvements. This could greatly increase the rate for the discovery of new, improved configurations.

      The new system, called a differentiable solar cell simulator, is described in a paper published today in the journal Computer Physics Communications, written by MIT junior Sean Mann, research scientist Giuseppe Romano of MIT’s Institute for Soldier Nanotechnologies, and four others at MIT and at Google Brain.

      Traditional solar cell simulators, Romano explains, take the details of a solar cell configuration and produce as their output a predicted efficiency — that is, what percentage of the energy of incoming sunlight actually gets converted to an electric current. But this new simulator both predicts the efficiency and shows how much that output is affected by any one of the input parameters. “It tells you directly what happens to the efficiency if we make this layer a little bit thicker, or what happens to the efficiency if we for example change the property of the material,” he says.

      In short, he says, “we didn’t discover a new device, but we developed a tool that will enable others to discover more quickly other higher performance devices.” Using this system, “we are decreasing the number of times that we need to run a simulator to give quicker access to a wider space of optimized structures.” In addition, he says, “our tool can identify a unique set of material parameters that has been hidden so far because it’s very complex to run those simulations.”

      While traditional approaches use essentially a random search of possible variations, Mann says, with his tool “we can follow a trajectory of change because the simulator tells you what direction you want to be changing your device. That makes the process much faster because instead of exploring the entire space of opportunities, you can just follow a single path” that leads directly to improved performance.

      Since advanced solar cells often are composed of multiple layers interlaced with conductive materials to carry electric charge from one to the other, this computational tool reveals how changing the relative thicknesses of these different layers will affect the device’s output. “This is very important because the thickness is critical. There is a strong interplay between light propagation and the thickness of each layer and the absorption of each layer,” Mann explains.

      Other variables that can be evaluated include the amount of doping (the introduction of atoms of another element) that each layer receives, or the dielectric constant of insulating layers, or the bandgap, a measure of the energy levels of photons of light that can be captured by different materials used in the layers.

      This simulator is now available as an open-source tool that can be used immediately to help guide research in this field, Romano says. “It is ready, and can be taken up by industry experts.” To make use of it, researchers would couple this device’s computations with an optimization algorithm, or even a machine learning system, to rapidly assess a wide variety of possible changes and home in quickly on the most promising alternatives.

      At this point, the simulator is based on just a one-dimensional version of the solar cell, so the next step will be to expand its capabilities to include two- and three-dimensional configurations. But even this 1D version “can cover the majority of cells that are currently under production,” Romano says. Certain variations, such as so-called tandem cells using different materials, cannot yet be simulated directly by this tool, but “there are ways to approximate a tandem solar cell by simulating each of the individual cells,” Mann says.

      The simulator is “end-to-end,” Romano says, meaning it computes the sensitivity of the efficiency, also taking into account light absorption. He adds: “An appealing future direction is composing our simulator with advanced existing differentiable light-propagation simulators, to achieve enhanced accuracy.”

      Moving forward, Romano says, because this is an open-source code, “that means that once it’s up there, the community can contribute to it. And that’s why we are really excited.” Although this research group is “just a handful of people,” he says, now anyone working in the field can make their own enhancements and improvements to the code and introduce new capabilities.

      “Differentiable physics is going to provide new capabilities for the simulations of engineered systems,” says Venkat Viswanathan, an associate professor of mechanical engineering at Carnegie Mellon University, who was not associated with this work. “The  differentiable solar cell simulator is an incredible example of differentiable physics, that can now provide new capabilities to optimize solar cell device performance,” he says, calling the study “an exciting step forward.”

      In addition to Mann and Romano, the team included Eric Fadel and Steven Johnson at MIT, and Samuel Schoenholz and Ekin Cubuk at Google Brain. The work was supported in part by Eni S.p.A. and the MIT Energy Initiative, and the MIT Quest for Intelligence. More