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      in Energy

      MIT Energy Initiative awards seven Seed Fund grants for early-stage energy research

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      The MIT Energy Initiative (MITEI) has awarded seven Seed Fund grants to support novel, early-stage energy research by faculty and researchers at MIT. The awardees hail from a range of disciplines, but all strive to bring their backgrounds and expertise to address the global climate crisis by improving the efficiency, scalability, and adoption of clean energy technologies.

      “Solving climate change is truly an interdisciplinary challenge,” says MITEI Director Robert C. Armstrong. “The Seed Fund grants foster collaboration and innovation from across all five of MIT’s schools and one college, encouraging an ‘all hands on deck approach’ to developing the energy solutions that will prove critical in combatting this global crisis.”

      This year, MITEI’s Seed Fund grant program received 70 proposals from 86 different principal investigators (PIs) across 25 departments, labs, and centers. Of these proposals, 31 involved collaborations between two or more PIs, including 24 that involved multiple departments.

      The winning projects reflect this collaborative nature with topics addressing the optimization of low-energy thermal cooling in buildings; the design of safe, robust, and resilient distributed power systems; and how to design and site wind farms with consideration of wind resource uncertainty due to climate change.

      Increasing public support for low-carbon technologies

      One winning team aims to leverage work done in the behavioral sciences to motivate sustainable behaviors and promote the adoption of clean energy technologies.

      “Objections to scalable low-carbon technologies such as nuclear energy and carbon sequestration have made it difficult to adopt these technologies and reduce greenhouse gas emissions,” says Howard Herzog, a senior research scientist at MITEI and co-PI. “These objections tend to neglect the sheer scale of energy generation required and the inability to meet this demand solely with other renewable energy technologies.”

      This interdisciplinary team — which includes researchers from MITEI, the Department of Nuclear Science and Engineering, and the MIT Sloan School of Management — plans to convene industry professionals and academics, as well as behavioral scientists, to identify common objections, design messaging to overcome them, and prove that these messaging campaigns have long-lasting impacts on attitudes toward scalable low-carbon technologies.

      “Our aim is to provide a foundation for shifting the public and policymakers’ views about these low-carbon technologies from something they, at best, tolerate, to something they actually welcome,” says co-PI David Rand, the Erwin H. Schell Professor and professor of management science and brain and cognitive sciences at MIT Sloan School of Management.

      Siting and designing wind farms

      Michael Howland, an assistant professor of civil and environmental engineering, will use his Seed Fund grant to develop a foundational methodology for wind farm siting and design that accounts for the uncertainty of wind resources resulting from climate change.

      “The optimal wind farm design and its resulting cost of energy is inherently dependent on the wind resource at the location of the farm,” says Howland. “But wind farms are currently sited and designed based on short-term climate records that do not account for the future effects of climate change on wind patterns.”

      Wind farms are capital-intensive infrastructure that cannot be relocated and often have lifespans exceeding 20 years — all of which make it especially important that developers choose the right locations and designs based not only on wind patterns in the historical climate record, but also based on future predictions. The new siting and design methodology has the potential to replace current industry standards to enable a more accurate risk analysis of wind farm development and energy grid expansion under climate change-driven energy resource uncertainty.

      Membraneless electrolyzers for hydrogen production

      Producing hydrogen from renewable energy-powered water electrolyzers is central to realizing a sustainable and low-carbon hydrogen economy, says Kripa Varanasi, a professor of mechanical engineering and a Seed Fund award recipient. The idea of using hydrogen as a fuel has existed for decades, but it has yet to be widely realized at a considerable scale. Varanasi hopes to change that with his Seed Fund grant.

      “The critical economic hurdle for successful electrolyzers to overcome is the minimization of the capital costs associated with their deployment,” says Varanasi. “So, an immediate task at hand to enable electrochemical hydrogen production at scale will be to maximize the effectiveness of the most mature, least complex, and least expensive water electrolyzer technologies.”

      To do this, he aims to combine the advantages of existing low-temperature alkaline electrolyzer designs with a novel membraneless electrolyzer technology that harnesses a gas management system architecture to minimize complexity and costs, while also improving efficiency. Varanasi hopes his project will demonstrate scalable concepts for cost-effective electrolyzer technology design to help realize a decarbonized hydrogen economy.

      Since its establishment in 2008, the MITEI Seed Fund Program has supported 194 energy-focused seed projects through grants totaling more than $26 million. This funding comes primarily from MITEI’s founding and sustaining members, supplemented by gifts from generous donors.

      Recipients of the 2021 MITEI Seed Fund grants are:

      “Design automation of safe, robust, and resilient distributed power systems” — Chuchu Fan of the Department of Aeronautics and Astronautics
      “Advanced MHD topping cycles: For fission, fusion, solar power plants” — Jeffrey Freidberg of the Department of Nuclear Science and Engineering and Dennis Whyte of the Plasma Science and Fusion Center
      “Robust wind farm siting and design under climate-change‐driven wind resource uncertainty” — Michael Howland of the Department of Civil and Environmental Engineering
      “Low-energy thermal comfort for buildings in the Global South: Optimal design of integrated structural-thermal systems” — Leslie Norford of the Department of Architecture and Caitlin Mueller of the departments of Architecture and Civil and Environmental Engineering
      “New low-cost, high energy-density boron-based redox electrolytes for nonaqueous flow batteries” — Alexander Radosevich of the Department of Chemistry
      “Increasing public support for scalable low-carbon energy technologies using behavorial science insights” — David Rand of the MIT Sloan School of Management, Koroush Shirvan of the Department of Nuclear Science and Engineering, Howard Herzog of the MIT Energy Initiative, and Jacopo Buongiorno of the Department of Nuclear Science and Engineering
      “Membraneless electrolyzers for efficient hydrogen production using nanoengineered 3D gas capture electrode architectures” — Kripa Varanasi of the Department of Mechanical Engineering More

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      in Security

      Saving seaweed with machine learning

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      Last year, Charlene Xia ’17, SM ’20 found herself at a crossroads. She was finishing up her master’s degree in media arts and sciences from the MIT Media Lab and had just submitted applications to doctoral degree programs. All Xia could do was sit and wait. In the meantime, she narrowed down her career options, regardless of whether she was accepted to any program.

      “I had two thoughts: I’m either going to get a PhD to work on a project that protects our planet, or I’m going to start a restaurant,” recalls Xia.

      Xia poured over her extensive cookbook collection, researching international cuisines as she anxiously awaited word about her graduate school applications. She even looked into the cost of a food truck permit in the Boston area. Just as she started hatching plans to open a plant-based skewer restaurant, Xia received word that she had been accepted into the mechanical engineering graduate program at MIT.

      Shortly after starting her doctoral studies, Xia’s advisor, Professor David Wallace, approached her with an interesting opportunity. MathWorks, a software company known for developing the MATLAB computing platform, had announced a new seed funding program in MIT’s Department of Mechanical Engineering. The program encouraged collaborative research projects focused on the health of the planet.

      “I saw this as a super-fun opportunity to combine my passion for food, my technical expertise in ocean engineering, and my interest in sustainably helping our planet,” says Xia.

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      From MIT Mechanical Engineering: “Saving Seaweed with Machine Learning”

      Wallace knew Xia would be up to the task of taking an interdisciplinary approach to solve an issue related to the health of the planet. “Charlene is a remarkable student with extraordinary talent and deep thoughtfulness. She is pretty much fearless, embracing challenges in almost any domain with the well-founded belief that, with effort, she will become a master,” says Wallace.

      Alongside Wallace and Associate Professor Stefanie Mueller, Xia proposed a project to predict and prevent the spread of diseases in aquaculture. The team focused on seaweed farms in particular.

      Already popular in East Asian cuisines, seaweed holds tremendous potential as a sustainable food source for the world’s ever-growing population. In addition to its nutritive value, seaweed combats various environmental threats. It helps fight climate change by absorbing excess carbon dioxide in the atmosphere, and can also absorb fertilizer run-off, keeping coasts cleaner.

      As with so much of marine life, seaweed is threatened by the very thing it helps mitigate against: climate change. Climate stressors like warm temperatures or minimal sunlight encourage the growth of harmful bacteria such as ice-ice disease. Within days, entire seaweed farms are decimated by unchecked bacterial growth.

      To solve this problem, Xia turned to the microbiota present in these seaweed farms as a predictive indicator of any threat to the seaweed or livestock. “Our project is to develop a low-cost device that can detect and prevent diseases before they affect seaweed or livestock by monitoring the microbiome of the environment,” says Xia.

      The team pairs old technology with the latest in computing. Using a submersible digital holographic microscope, they take a 2D image. They then use a machine learning system known as a neural network to convert the 2D image into a representation of the microbiome present in the 3D environment.

      “Using a machine learning network, you can take a 2D image and reconstruct it almost in real time to get an idea of what the microbiome looks like in a 3D space,” says Xia.

      The software can be run in a small Raspberry Pi that could be attached to the holographic microscope. To figure out how to communicate these data back to the research team, Xia drew upon her master’s degree research.

      In that work, under the guidance of Professor Allan Adams and Professor Joseph Paradiso in the Media Lab, Xia focused on developing small underwater communication devices that can relay data about the ocean back to researchers. Rather than the usual $4,000, these devices were designed to cost less than $100, helping lower the cost barrier for those interested in uncovering the many mysteries of our oceans. The communication devices can be used to relay data about the ocean environment from the machine learning algorithms.

      By combining these low-cost communication devices along with microscopic images and machine learning, Xia hopes to design a low-cost, real-time monitoring system that can be scaled to cover entire seaweed farms.

      “It’s almost like having the ‘internet of things’ underwater,” adds Xia. “I’m developing this whole underwater camera system alongside the wireless communication I developed that can give me the data while I’m sitting on dry land.”

      Armed with these data about the microbiome, Xia and her team can detect whether or not a disease is about to strike and jeopardize seaweed or livestock before it is too late.

      While Xia still daydreams about opening a restaurant, she hopes the seaweed project will prompt people to rethink how they consider food production in general.

      “We should think about farming and food production in terms of the entire ecosystem,” she says. “My meta-goal for this project would be to get people to think about food production in a more holistic and natural way.” More

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      in Environment

      How marsh grass protects shorelines

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      Marsh plants, which are ubiquitous along the world’s shorelines, can play a major role in mitigating the damage to coastlines as sea levels rise and storm surges increase. Now, a new MIT study provides greater detail about how these protective benefits work under real-world conditions shaped by waves and currents.

      The study combined laboratory experiments using simulated plants in a large wave tank along with mathematical modeling. It appears in the journal Physical Review — Fluids, in a paper by former MIT visiting doctoral student Xiaoxia Zhang, now a postdoc at Dalian University of Technology, and professor of civil and environmental engineering Heidi Nepf.

      It’s already clear that coastal marsh plants provide significant protection from surges and devastating  storms. For example, it has been estimated that the damage caused by Hurricane Sandy was reduced by $625 million thanks to the damping of wave energy provided by extensive areas of marsh along the affected coasts. But the new MIT analysis incorporates details of plant morphology, such as the number and spacing of flexible leaves versus stiffer stems, and the complex interactions of currents and waves that may be coming from different directions.

      This level of detail could enable coastal restoration planners to determine the area of marsh needed to mitigate expected amounts of storm surge or sea-level rise, and to decide which types of plants to introduce to maximize protection.

      “When you go to a marsh, you often will see that the plants are arranged in zones,” says Nepf, who is the Donald and Martha Harleman Professor of Civil and Environmental Engineering. “Along the edge, you tend to have plants that are more flexible, because they are using their flexibility to reduce the wave forces they feel. In the next zone, the plants are a little more rigid and have a bit more leaves.”

      As the zones progress, the plants become stiffer, leafier, and more effective at absorbing wave energy thanks to their greater leaf area. The new modeling done in this research, which incorporated work with simulated plants in the 24-meter-long wave tank at MIT’s Parsons Lab, can enable coastal planners to take these kinds of details into account when planning protection, mitigation, or restoration projects.

      “If you put the stiffest plants at the edge, they might not survive, because they’re feeling very high wave forces. By describing why Mother Nature organizes plants in this way, we can hopefully design a more sustainable restoration,” Nepf says.

      Once established, the marsh plants provide a positive feedback cycle that helps to not only stabilize but also build up these delicate coastal lands, Zhang says. “After a few years, the marsh grasses start to trap and hold the sediment, and the elevation gets higher and higher, which might keep up with sea level rise,” she says.

      The new MIT analysis incorporates details of plant morphology, such as the number and spacing of flexible leaves versus stiffer stems, and the complex interactions of currents and waves that may be coming from different directions.

      Awareness of the protective effects of marshland has been growing, Nepf says. For example, the Netherlands has been restoring lost marshland outside the dikes that surround much of the nation’s agricultural land, finding that the marsh can protect the dikes from erosion; the marsh and dikes work together much more effectively than the dikes alone at preventing flooding.

      But most such efforts so far have been largely empirical, trial-and-error plans, Nepf says. Now, they could take advantage of this modeling to know just how much marshland with what types of plants would be needed to provide the desired level of protection.

      It also provides a more quantitative way to estimate the value provided by marshes, she says. “It could allow you to more accurately say, ‘40 meters of marsh will reduce waves this much and therefore will reduce overtopping of your levee by this much.’ Someone could use that to say, ‘I’m going to save this much money over the next 10 years if I reduce flooding by maintaining this marsh.’ It might help generate some political motivation for restoration efforts.”

      Nepf herself is already trying to get some of these findings included in coastal planning processes. She serves on a practitioner panel led by Chris Esposito of the Water Institute of the Gulf, which serves the storm-battered Louisiana coastline. “We’d like to get this work into the coatal simulations that are used for large-scale restoration and coastal planning,” she says.

      “Understanding the wave damping process in real vegetation wetlands is of critical value, as it is needed in the assessment of the coastal defense value of these wetlands,” says Zhan Hu, an associate professor of marine sciences at Sun Yat-Sen University, who was not associated with this work. “The challenge, however, lies in the quantitative representation of the wave damping process, in which many factors are at play, such as plant flexibility, morphology, and coexisting currents.”

      The new study, Hu says, “neatly combines experimental findings and analytical modeling to reveal the impact of each factor in the wave damping process. … Overall, this work is a solid step forward toward a more accurate assessment of wave damping capacity of real coastal wetlands, which is needed for science-based design and management of nature-based coastal protection.”

      The work was partly supported by the National Science Foundation and the China Scholarship Council.  More

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      in Environment

      Making roadway spending more sustainable

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      The share of federal spending on infrastructure has reached an all-time low, falling from 30 percent in 1960 to just 12 percent in 2018.

      While the nation’s ailing infrastructure will require more funding to reach its full potential, recent MIT research finds that more sustainable and higher performing roads are still possible even with today’s limited budgets.

      The research, conducted by a team of current and former MIT Concrete Sustainability Hub (MIT CSHub) scientists and published in Transportation Research D, finds that a set of innovative planning strategies could improve pavement network environmental and performance outcomes even if budgets don’t increase.

      The paper presents a novel budget allocation tool and pairs it with three innovative strategies for managing pavement networks: a mix of paving materials, a mix of short- and long-term paving actions, and a long evaluation period for those actions.

      This novel approach offers numerous benefits. When applied to a 30-year case study of the Iowa U.S. Route network, the MIT CSHub model and management strategies cut emissions by 20 percent while sustaining current levels of road quality. Achieving this with a conventional planning approach would require the state to spend 32 percent more than it does today. The key to its success is the consideration of a fundamental — but fraught — aspect of pavement asset management: uncertainty.

      Predicting unpredictability

      The average road must last many years and support the traffic of thousands — if not millions — of vehicles. Over that time, a lot can change. Material prices may fluctuate, budgets may tighten, and traffic levels may intensify. Climate (and climate change), too, can hasten unexpected repairs.

      Managing these uncertainties effectively means looking long into the future and anticipating possible changes.

      “Capturing the impacts of uncertainty is essential for making effective paving decisions,” explains Fengdi Guo, the paper’s lead author and a departing CSHub research assistant.

      “Yet, measuring and relating these uncertainties to outcomes is also computationally intensive and expensive. Consequently, many DOTs [departments of transportation] are forced to simplify their analysis to plan maintenance — often resulting in suboptimal spending and outcomes.”

      To give DOTs accessible tools to factor uncertainties into their planning, CSHub researchers have developed a streamlined planning approach. It offers greater specificity and is paired with several new pavement management strategies.

      The planning approach, known as Probabilistic Treatment Path Dependence (PTPD), is based on machine learning and was devised by Guo.

      “Our PTPD model is composed of four steps,” he explains. “These steps are, in order, pavement damage prediction; treatment cost prediction; budget allocation; and pavement network condition evaluation.”

      The model begins by investigating every segment in an entire pavement network and predicting future possibilities for pavement deterioration, cost, and traffic.

      “We [then] run thousands of simulations for each segment in the network to determine the likely cost and performance outcomes for each initial and subsequent sequence, or ‘path,’ of treatment actions,” says Guo. “The treatment paths with the best cost and performance outcomes are selected for each segment, and then across the network.”

      The PTPD model not only seeks to minimize costs to agencies but also to users — in this case, drivers. These user costs can come primarily in the form of excess fuel consumption due to poor road quality.

      “One improvement in our analysis is the incorporation of electric vehicle uptake into our cost and environmental impact predictions,” Randolph Kirchain, a principal research scientist at MIT CSHub and MIT Materials Research Laboratory (MRL) and one of the paper’s co-authors. “Since the vehicle fleet will change over the next several decades due to electric vehicle adoption, we made sure to consider how these changes might impact our predictions of excess energy consumption.”

      After developing the PTPD model, Guo wanted to see how the efficacy of various pavement management strategies might differ. To do this, he developed a sophisticated deterioration prediction model.

      A novel aspect of this deterioration model is its treatment of multiple deterioration metrics simultaneously. Using a multi-output neural network, a tool of artificial intelligence, the model can predict several forms of pavement deterioration simultaneously, thereby, accounting for their correlations among one another.

      The MIT team selected two key metrics to compare the effectiveness of various treatment paths: pavement quality and greenhouse gas emissions. These metrics were then calculated for all pavement segments in the Iowa network.

      Improvement through variation

       The MIT model can help DOTs make better decisions, but that decision-making is ultimately constrained by the potential options considered.

      Guo and his colleagues, therefore, sought to expand current decision-making paradigms by exploring a broad set of network management strategies and evaluating them with their PTPD approach. Based on that evaluation, the team discovered that networks had the best outcomes when the management strategy includes using a mix of paving materials, a variety of long- and short-term paving repair actions (treatments), and longer time periods on which to base paving decisions.

      They then compared this proposed approach with a baseline management approach that reflects current, widespread practices: the use of solely asphalt materials, short-term treatments, and a five-year period for evaluating the outcomes of paving actions.

      With these two approaches established, the team used them to plan 30 years of maintenance across the Iowa U.S. Route network. They then measured the subsequent road quality and emissions.

      Their case study found that the MIT approach offered substantial benefits. Pavement-related greenhouse gas emissions would fall by around 20 percent across the network over the whole period. Pavement performance improved as well. To achieve the same level of road quality as the MIT approach, the baseline approach would need a 32 percent greater budget.

      “It’s worth noting,” says Guo, “that since conventional practices employ less effective allocation tools, the difference between them and the CSHub approach should be even larger in practice.”

      Much of the improvement derived from the precision of the CSHub planning model. But the three treatment strategies also play a key role.

      “We’ve found that a mix of asphalt and concrete paving materials allows DOTs to not only find materials best-suited to certain projects, but also mitigates the risk of material price volatility over time,” says Kirchain.

      It’s a similar story with a mix of paving actions. Employing a mix of short- and long-term fixes gives DOTs the flexibility to choose the right action for the right project.

      The final strategy, a long-term evaluation period, enables DOTs to see the entire scope of their choices. If the ramifications of a decision are predicted over only five years, many long-term implications won’t be considered. Expanding the window for planning, then, can introduce beneficial, long-term options.

      It’s not surprising that paving decisions are daunting to make; their impacts on the environment, driver safety, and budget levels are long-lasting. But rather than simplify this fraught process, the CSHub method aims to reflect its complexity. The result is an approach that provides DOTs with the tools to do more with less.

      This research was supported through the MIT Concrete Sustainability Hub by the Portland Cement Association and the Ready Mixed Concrete Research and Education Foundation. More

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      in Energy

      Institute Professor Paula Hammond named to White House science council

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      Paula Hammond, an MIT Institute Professor and head of MIT’s Department of Chemical Engineering, has been chosen to serve on the President’s Council of Advisors on Science and Technology (PCAST), the White House announced today.

      The council advises the president on matters involving science, technology, education, and innovation policy. It also provides the White House with scientific and technical information that is needed to inform public policy relating to the U.S. economy, U.S. workers, and national security.

      “For me, this is an exciting opportunity,” Hammond says. “I have always been interested in considering how science can solve important problems in our community, in our country, and globally. It’s very meaningful for me to have a chance to have an advisory role at that level.”

      Hammond is one of 30 members named to the council, which is co-chaired by Frances Arnold, a professor at Caltech, and Maria Zuber, MIT’s vice president for research.

      “Paula is an extraordinary engineer, teacher, and colleague, and President Biden’s decision to appoint her to the council is an excellent one,” Zuber says. “I think about the work ahead of us — not just to restore science and technology to their proper place in policymaking, but also to make sure that they lead to real improvements in the lives of everyone in our country — and I can’t think of anyone better suited to the challenge than Paula.”

      Hammond, whose research as a chemical engineer touches on the fields of both medicine and energy, said she hopes to help address critical issues such as equal access to health care and efforts to mitigate climate change.

      “I’m very excited about the opportunities presented at the interface of engineering and health, and in particular, how we might be able to expand the benefits that we gain from our work to a broader set of communities, so that we’re able to address some of the disparities we see in health, which have been so obvious during the pandemic,” says Hammond, who is also a member of MIT’s Koch Institute for Integrative Cancer Research. “How we might be able to use everything from computational modeling and data science to technological innovation to equalize access to health is one area that I care a lot about.”

      Hammond’s research focuses on developing novel polymers and nanomaterials for a variety of applications in drug delivery, noninvasive imaging, solar cells, and battery technology. Using techniques for building polymers with highly controlled architectures, she has designed drug-delivering nanoparticles that can home in on tumors, as well as polymer films that dramatically improve the efficiency of methanol fuel cells.

      As an MIT faculty member and mentor to graduate students, Hammond has worked to increase opportunities for underrepresented minorities in science and engineering fields. That is a goal she also hopes to pursue in her new role.

      “There’s a lot of work to be done when we look at the low numbers of students of color who are actually going on to science and engineering fields,” she says. “When I think about my work related to increasing diversity in those areas, part of the reason I do it is because that’s where we gain excellence, and where we gain solutions and the foresight to work on the right problems. I also think that it’s important for there to be broad access to the power that science brings.”

      Hammond, who earned both her bachelor’s degree and PhD from MIT, has been a member of the faculty since 1995. She has been a full professor since 2006 and has chaired the Department of Chemical Engineering since 2015. Earlier this year, she was named an Institute Professor, MIT’s highest faculty honor. She is also one of only 25 people who have been elected to all three National Academies — Engineering, Science, and Medicine.

      She has previously served on the U.S. Secretary of Energy Scientific Advisory Board, the NIH Center for Scientific Review Advisory Council, and the Board of Directors of the American Institute of Chemical Engineers. She also chaired or co-chaired two committees that contributed landmark reports on gender and race at MIT: the Initiative for Faculty Race and Diversity, and the Academic and Organizational Relationships Working Group. More

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      in Energy

      A new method for removing lead from drinking water

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      Engineers at MIT have developed a new approach to removing lead or other heavy-metal contaminants from water, in a process that they say is far more energy-efficient than any other currently used system, though there are others under development that come close. Ultimately, it might be used to treat lead-contaminated water supplies at the home level, or to treat contaminated water from some chemical or industrial processes.

      The new system is the latest in a series of applications based on initial findings six years ago by members of the same research team, initially developed for desalination of seawater or brackish water, and later adapted for removing radioactive compounds from the cooling water of nuclear power plants. The new version is the first such method that might be applicable for treating household water supplies, as well as industrial uses.

      The findings are published today in the journal Environmental Science and Technology – Water, in a paper by MIT graduate students Huanhuan Tian, Mohammad Alkhadra, and Kameron Conforti, and professor of chemical engineering Martin Bazant.

      “It’s notoriously difficult to remove toxic heavy metal that’s persistent and present in a lot of different water sources,” Alkhadra says. “Obviously there are competing methods today that do this function, so it’s a matter of which method can do it at lower cost and more reliably.”

      The biggest challenge in trying to remove lead is that it is generally present in such tiny concentrations, vastly exceeded by other elements or compounds. For example, sodium is typically present in drinking water at a concentration of tens of parts per million, whereas lead can be highly toxic at just a few parts per billion. Most existing processes, such as reverse osmosis or distillation, remove everything at once, Alkhadra explains. This not only takes much more energy than would be needed for a selective removal, but it’s counterproductive since small amounts of elements such as sodium and magnesium are actually essential for healthy drinking water.

      The new approach is to use a process called shock electrodialysis, in which an electric field is used to produce a shockwave inside a pipe carrying the contaminated water. The shockwave separates the liquid into two streams, selectively pulling certain electrically charged atoms, or ions, toward one side of the flow by tuning the properties of the shockwave to match the target ions, while leaving a stream of relatively pure water on the other side. The stream containing the concentrated lead ions can then be easily separated out using a mechanical barrier in the pipe.

      In principle, “this makes the process much cheaper,” Bazant says, “because the electrical energy that you’re putting in to do the separation is really going after the high-value target, which is the lead. You’re not wasting a lot of energy removing the sodium.” Because the lead is present at such low concentration, “there’s not a lot of current involved in removing those ions, so this can be a very cost-effective way.”

      The process still has its limitations, as it has only been demonstrated at small laboratory scale and at quite slow flow rates. Scaling up the process to make it practical for in-home use will require further research, and larger-scale industrial uses will take even longer. But it could be practical within a few years for some home-based systems, Bazant says.

      For example, a home whose water supply is heavily contaminated with lead might have a system in the cellar that slowly processes a stream of water, filling a tank with lead-free water to be used for drinking and cooking, while leaving most of the water untreated for uses like toilet flushing or watering the lawn. Such uses might be appropriate as an interim measure for places like Flint, Michigan, where the water, mostly contaminated by the distribution pipes, will take many years to remediate through pipe replacements.

      The process could also be adapted for some industrial uses such as cleaning water produced in mining or drilling operations, so that the treated water can be safely disposed of or reused. And in some cases, this could also provide a way of recovering metals that contaminate water but could actually be a valuable product if they were separated out; for example, some such minerals could be used to process semiconductors or pharmaceuticals or other high-tech products, the researchers say.

      Direct comparisons of the economics of such a system versus existing methods is difficult, Bazant says, because in filtration systems, for example, the costs are mainly for replacing the filter materials, which quickly clog up and become unusable, whereas in this system the costs are mostly for the ongoing energy input, which is very small. At this point, the shock electrodialysis system has been operated for several weeks, but it’s too soon to estimate the real-world longevity of such a system, he says.

      Developing the process into a scalable commercial product will take some time, but “we have shown how this could be done, from a technical standpoint,” Bazant says. “The main issue would be on the economic side,” he adds. That includes figuring out the most appropriate applications and developing specific configurations that would meet those uses. “We do have a reasonable idea of how to scale this up. So it’s a question of having the resources,” which might be a role for a startup company rather than an academic research lab, he adds.

      “I think this is an exciting result,” he says, “because it shows that we really can address this important application” of cleaning the lead from drinking water. For example, he says, there are places now that perform desalination of seawater using reverse osmosis, but they have to run this expensive process twice in a row, first to get the salt out, and then again to remove the low-level but highly toxic contaminants like lead. This new process might be used instead of the second round of reverse osmosis, at a far lower expenditure of energy.

      The research received support from a MathWorks Engineering Fellowship and a fellowship awarded by MIT’s Abdul Latif Jameel Water and Food Systems Lab, funded by Xylem, Inc. More

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      in Energy

      Predicting building emissions across the US

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      The United States is entering a building boom. Between 2017 and 2050, it will build the equivalent of New York City 20 times over. Yet, to meet climate targets, the nation must also significantly reduce the greenhouse gas (GHG) emissions of its buildings, which comprise 27 percent of the nation’s total emissions.

      A team of current and former MIT Concrete Sustainability Hub (CSHub) researchers is addressing these conflicting demands with the aim of giving policymakers the tools and information to act. They have detailed the results of their collaboration in a recent paper in the journal Applied Energy that projects emissions for all buildings across the United States under two GHG reduction scenarios.

      Their paper found that “embodied” emissions — those from materials production and construction — would represent around a quarter of emissions between 2016 and 2050 despite extensive construction.

      Further, many regions would have varying priorities for GHG reductions; some, like the West, would benefit most from reductions to embodied emissions, while others, like parts of the Midwest, would see the greatest payoff from interventions to emissions from energy consumption. If these regional priorities were addressed aggressively, building sector emissions could be reduced by around 30 percent between 2016 and 2050.

      Quantifying contradictions

      Modern buildings are far more complex — and efficient — than their predecessors. Due to new technologies and more stringent building codes, they can offer lower energy consumption and operational emissions. And yet, more-efficient materials and improved construction standards can also generate greater embodied emissions.

      Concrete, in many ways, epitomizes this tradeoff. Though its durability can minimize energy-intensive repairs over a building’s operational life, the scale of its production means that it contributes to a large proportion of the embodied impacts in the building sector.

      As such, the team centered GHG reductions for concrete in its analysis.

      “We took a bottom-up approach, developing reference designs based on a set of residential and commercial building models,” explains Ehsan Vahidi, an assistant professor at the University of Nevada at Reno and a former CSHub postdoc. “These designs were differentiated by roof and slab insulation, HVAC efficiency, and construction materials — chiefly concrete and wood.”

      After measuring the operational and embodied GHG emissions for each reference design, the team scaled up their results to the county level and then national level based on building stock forecasts. This allowed them to estimate the emissions of the entire building sector between 2016 and 2050.

      To understand how various interventions could cut GHG emissions, researchers ran two different scenarios — a “projected” and an “ambitious” scenario — through their framework.

      The projected scenario corresponded to current trends. It assumed grid decarbonization would follow Energy Information Administration predictions; the widespread adoption of new energy codes; efficiency improvement of lighting and appliances; and, for concrete, the implementation of 50 percent low-carbon cements and binders in all new concrete construction and the adoption of full carbon capture, storage, and utilization (CCUS) of all cement and concrete emissions.

      “Our ambitious scenario was intended to reflect a future where more aggressive actions are taken to reduce GHG emissions and achieve the targets,” says Vahidi. “Therefore, the ambitious scenario took these same strategies [of the projected scenario] but featured more aggressive targets for their implementation.”

      For instance, it assumed a 33 percent reduction in grid emissions by 2050 and moved the projected deadlines for lighting and appliances and thermal insulation forward by five and 10 years, respectively. Concrete decarbonization occurred far more quickly as well.

      Reductions and variations

      The extensive growth forecast for the U.S. building sector will inevitably generate a sizable number of emissions. But how much can this figure be minimized?

      Without the implementation of any GHG reduction strategies, the team found that the building sector would emit 62 gigatons CO2 equivalent between 2016 and 2050. That’s comparable to the emissions generated from 156 trillion passenger vehicle miles traveled.

      But both GHG reduction scenarios could cut the emissions from this unmitigated, business-as-usual scenario significantly.

      Under the projected scenario, emissions would fall to 45 gigatons CO2 equivalent — a 27 percent decrease over the analysis period. The ambitious scenario would offer a further 6 percent reduction over the projected scenario, reaching 40 gigatons CO2 equivalent — like removing around 55 trillion passenger vehicle miles from the road over the period.

      “In both scenarios, the largest contributor to reductions was the greening of the energy grid,” notes Vahidi. “Other notable opportunities for reductions were from increasing the efficiency of lighting, HVAC, and appliances. Combined, these four attributes contributed to 85 percent of the emissions over the analysis period. Improvements to them offered the greatest potential emissions reductions.”

      The remaining attributes, such as thermal insulation and low-carbon concrete, had a smaller impact on emissions and, consequently, offered smaller reduction opportunities. That’s because these two attributes were only applied to new construction in the analysis, which was outnumbered by existing structures throughout the period.

      The disparities in impact between strategies aimed at new and existing structures underscore a broader finding: Despite extensive construction over the period, embodied emissions would comprise just 23 percent of cumulative emissions between 2016 and 2050, with the remainder coming primarily from operation.  

      “This is a consequence of existing structures far outnumbering new structures,” explains Jasmina Burek, a CSHub postdoc and an incoming assistant professor at the University of Massachusetts Lowell. “The operational emissions generated by all new and existing structures between 2016 and 2050 will always greatly exceed the embodied emissions of new structures at any given time, even as buildings become more efficient and the grid gets greener.”

      Yet the emissions reductions from both scenarios were not distributed evenly across the entire country. The team identified several regional variations that could have implications for how policymakers must act to reduce building sector emissions.

      “We found that western regions in the United States would see the greatest reduction opportunities from interventions to residential emissions, which would constitute 90 percent of the region’s total emissions over the analysis period,” says Vahidi.

      The predominance of residential emissions stems from the region’s ongoing population surge and its subsequent growth in housing stock. Proposed solutions would include CCUS and low-carbon binders for concrete production, and improvements to energy codes aimed at residential buildings.

      As with the West, ideal solutions for the Southeast would include CCUS, low-carbon binders, and improved energy codes.

      “In the case of Southeastern regions, interventions should equally target commercial and residential buildings, which we found were split more evenly among the building stock,” explains Burek. “Due to the stringent energy codes in both regions, interventions to operational emissions were less impactful than those to embodied emissions.”

      Much of the Midwest saw the inverse outcome. Its energy mix remains one of the most carbon-intensive in the nation and improvements to energy efficiency and the grid would have a large payoff — particularly in Missouri, Kansas, and Colorado.

      New England and California would see the smallest reductions. As their already-strict energy codes would limit further operational reductions, opportunities to reduce embodied emissions would be the most impactful.

      This tremendous regional variation uncovered by the MIT team is in many ways a reflection of the great demographic and geographic diversity of the nation as a whole. And there are still further variables to consider.

      In addition to GHG emissions, future research could consider other environmental impacts, like water consumption and air quality. Other mitigation strategies to consider include longer building lifespans, retrofitting, rooftop solar, and recycling and reuse.

      In this sense, their findings represent the lower bounds of what is possible in the building sector. And even if further improvements are ultimately possible, they’ve shown that regional variation will invariably inform those environmental impact reductions.

      The MIT Concrete Sustainability Hub is a team of researchers from several departments across MIT working on concrete and infrastructure science, engineering, and economics. Its research is supported by the Portland Cement Association and the Ready Mixed Concrete Research and Education Foundation. More

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      in Energy

      Crossing disciplines, adding fresh eyes to nuclear engineering

      by

      Sometimes patterns repeat in nature. Spirals appear in sunflowers and hurricanes. Branches occur in veins and lightning. Limiao Zhang, a doctoral student in MIT’s Department of Nuclear Science and Engineering, has found another similarity: between street traffic and boiling water, with implications for preventing nuclear meltdowns.

      Growing up in China, Zhang enjoyed watching her father repair things around the house. He couldn’t fulfill his dream of becoming an engineer, instead joining the police force, but Zhang did have that opportunity and studied mechanical engineering at Three Gorges University. Being one of four girls among about 50 boys in the major didn’t discourage her. “My father always told me girls can do anything,” she says. She graduated at the top of her class.

      In college, she and a team of classmates won a national engineering competition. They designed and built a model of a carousel powered by solar, hydroelectric, and pedal power. One judge asked how long the system could operate safely. “I didn’t have a perfect answer,” she recalls. She realized that engineering means designing products that not only function, but are resilient. So for her master’s degree, at Beihang University, she turned to industrial engineering and analyzed the reliability of critical infrastructure, in particular traffic networks.

      “Among all the critical infrastructures, nuclear power plants are quite special,” Zhang says. “Although one can provide very enormous carbon-free energy, once it fails, it can cause catastrophic results.” So she decided to switch fields again and study nuclear engineering. At the time she had no nuclear background, and hadn’t studied in the United States, but “I tried to step out of my comfort zone,” she says. “I just applied and MIT welcomed me.” Her supervisor, Matteo Bucci, and her classmates explained the basics of fission reactions as she adjusted to the new material, language, and environment. She doubted herself — “my friend told me, ‘I saw clouds above your head’” — but she passed her first-year courses and published her first paper soon afterward.

      Much of the work in Bucci’s lab deals with what’s called the boiling crisis. In many applications, such as nuclear plants and powerful computers, water cools things. When a hot surface boils water, bubbles cling to the surface before rising, but if too many form, they merge into a layer of vapor that insulates the surface. The heat has nowhere to go — a boiling crisis.

      Bucci invited Zhang into his lab in part because she saw a connection between traffic and heat transfer. The data plots of both phenomena look surprisingly similar. “The mathematical tools she had developed for the study of traffic jams were a completely different way of looking into our problem” Bucci says, “by using something which is intuitively not connected.”

      One can view bubbles as cars. The more there are, the more they interfere with each other. People studying boiling had focused on the physics of individual bubbles. Zhang instead uses statistical physics to analyze collective patterns of behavior. “She brings a different set of skills, a different set of knowledge, to our research,” says Guanyu Su, a postdoc in the lab. “That’s very refreshing.”

      In her first paper on the boiling crisis, published in Physical Review Letters, Zhang used theory and simulations to identify scale-free behavior in boiling: just as in traffic, the same patterns appear whether zoomed in or out, in terms of space or time. Both small and large bubbles matter. Using this insight, the team found certain physical parameters that could predict a boiling crisis. Zhang’s mathematical tools both explain experimental data and suggest new experiments to try. For a second paper, the team collected more data and found ways to predict the boiling crisis in a wider variety of conditions.

      Zhang’s thesis and third paper, both in progress, propose a universal law for explaining the crisis. “She translated the mechanism into a physical law, like F=ma or E=mc2,” Bucci says. “She came up with an equally simple equation.” Zhang says she’s learned a lot from colleagues in the department who are pioneering new nuclear reactors or other technologies, “but for my own work, I try to get down to the very basics of a phenomenon.”

      Bucci describes Zhang as determined, open-minded, and commendably self-critical. Su says she’s careful, optimistic, and courageous. “If I imagine going from heat transfer to city planning, that would be almost impossible for me,” he says. “She has a strong mind.” Last year, Zhang gave birth to a boy, whom she’s raising on her own as she does her research. (Her husband is stuck in China during the pandemic.) “This, to me,” Bucci says, “is almost superhuman.”

      Zhang will graduate at the end of the year, and has started looking for jobs back in China. She wants to continue in the energy field, though maybe not nuclear. “I will use my interdisciplinary knowledge,” she says. “I hope I can design safer and more efficient and more reliable systems to provide energy for our society.” More