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    Promoting systemic change in the Middle East, the “MIT way”

    The Middle East is a region that is facing complicated challenges. MIT programs have been committed to building scalable methodologies through which students and the broader MIT community can learn and make an impact. These processes ensure programs work alongside others across cultures to support change aligned with their needs. Through MIT International Science and Technology Initiatives (MISTI), faculty and staff at the Institute continue to build opportunities to connect with and support the region.

    In this spirit, MISTI launched the Leaders Journey Workshop in 2021. This program partnered MIT students with Palestinian and Israeli alumni from three associate organizations: Middle East Entrepreneurs for Tomorrow (MEET), Our Generation Speaks (OGS), and Tech2Peace. Teams met monthly to engage with speakers and work with one another to explore the best ways to leverage science, technology, and entrepreneurship across borders.

    Building on the success of this workshop, the program piloted a for-credit course: SP.258 (MISTI: Middle East Cross-Border Development and Leadership) in fall 2021. The course involved engaging with subject matter experts through five mini-consulting projects in collaboration with regional stakeholders. Topics included climate, health care, and economic development. The course was co-instructed by associate director of the MIT Regional Entrepreneurship Acceleration Program (REAP) Sinan AbuShanab, managing director of MISTI programs in the Middle East David Dolev, and Kathleen Schwind ’19, with MIT CIS/ MISTI Research Affiliate Steven Koltai as lead mentor. The course also drew support from alumni mentors and regional industry partners.

    The course was developed during the height of the pandemic and thus successfully leveraged the intense culture of online engagement prevalent at the time by layering in-person coursework with strategic digital group engagement. Pedagogically, the structure was inspired by multiple MIT methodologies: MISTI preparation and training courses, Sloan Action Learning, REAP/REAL multi-party stakeholder model, the Media Lab Learning Initiative, and the multicultural framework of associate organizations.

    “We worked to develop a series of aims and a methodology that would enrich MIT students and their peers in the region and support the important efforts of Israelis and Palestinians to make systemic change,” said Dolev.

    During the on-campus sessions, MIT students explored the region’s political and historical complexities and the meaning of being a global leader and entrepreneur. Guest presenters included: Boston College Associate Professor Peter Krause (MIT Security Studies Program alumnus), Gilad Rosenzweig (MITdesignX), Ari Jacobovits (MIT-Africa), and Mollie Laffin-Rose Agbiboa (MIT-REAP). Group projects focused on topics that fell under three key regional verticals: water, health care, and economic development. The teams were given a technical or business challenge they were tasked with solving. These challenges were sourced directly from for-profit and nonprofit organizations in the region.

    “This was a unique opportunity for me to learn so much about the area I live in, work on a project together with people from the ‘other side,’ MIT students, and incredible mentors,” shared a participant from the region. “Furthermore, getting a glimpse of the world of MIT was a great experience for me.”

    For their final presentations, teams pitched their solutions, including their methodology for researching/addressing the problem, a description of solutions to be applied, what is needed to execute the idea itself, and potential challenges encountered. Teams received feedback and continued to deepen their experience in cross-cultural teamwork.

    “As an education manager, I needed guidance with these digital tools and how to approach them,” says an EcoPeace representative. “The MIT program provided me with clear deliverables I can now implement in my team’s work.”

    “This course has broadened my knowledge of conflicts, relationships, and how geography plays an important role in the region,” says an MIT student participant. “Moving forward, I feel more confident working with business and organizations to develop solutions for problems in real time, using the skills I have to supplement the project work.”

    Layers of engagement with mentors, facilitators, and whole-team leadership ensured that participants gained project management experience, learning objectives were met, and professional development opportunities were available. Each team was assigned an MIT-MEET alumni mentor with whom they met throughout the course. Mentors coached the teams on methods for managing a client project and how to collaborate for successful completion. Joint sessions with MIT guest speakers deepened participants’ regional understanding of water, health care, economic development, and their importance in the region. Speakers included: Mohamed Aburawi, Phil Budden (MIT-REAP) Steven Koltai, Shari Loessberg, Dina Sherif (MIT Legatum Center, Greg Sixt (J-WAFS), and Shriya Srinivasan.

    “The program is unlike any other I’ve come across,” says one of the alumni mentors. “The chance for MIT students to work directly with peers from the region, to propose and create technical solutions to real problems on the ground, and partner with local organizations is an incredibly meaningful opportunity. I wish I had been able to participate in something like this when I was at MIT.”

    Each team also assigned a fellow group member as a facilitator, who served as the main point of contact for the team and oversaw project management: organizing workstreams, ensuring deadlines were met, and mediating any group disagreements. This model led to successful project outcomes and innovative suggestions.

    “The superb work of the MISTI group gave us a critical eye and made significant headway on a product that can hopefully be a game changer to over 150 Israeli and Palestinian organizations,” says a representative from Alliance for Middle East Peace (ALLMEP).

    Leadership team meetings included MIT staff and Israeli and Palestinian leadership of the partner organizations for discussing process, content, recent geopolitical developments, and how to adapt the class to the ongoing changing situation.

    “The topic of Palestine/Israel is contentious: globally, in the region, and also, at times, on the MIT campus,” says Dolev. “I myself have questioned how we can make a systemic impact with our partners from the region. How can we be side-by-side on that journey for the betterment of all? I have now seen first-hand how this multilayered model can work.”

    MIT International Science and Technology Initiatives (MISTI) is MIT’s hub for global experiences. MISTI’s unparalleled internship, research, teaching, and study abroad programs offer students unique experiences that bring MIT’s one-of-a-kind education model to life in countries around the world. MISTI programs are carefully designed to complement on-campus course work and research, and rigorous, country-specific preparation enables students to forge cultural connections and play a role in addressing important global challenges while abroad. Students come away from their experiences with invaluable perspectives that inform their education, career, and worldview. MISTI embodies MIT’s commitment to global engagement and prepares students to thrive in an increasingly interconnected world. More

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    Assay determines the percentage of Omicron, other variants in Covid wastewater

    Wastewater monitoring emerged amid the Covid-19 pandemic as an effective and noninvasive way to track a viral outbreak, and advances in the technology have enabled researchers to not only identify but also quantify the presence of particular variants of concern (VOCs) in wastewater samples.

    Last year, researchers with the Singapore-MIT Alliance for Research and Technology (SMART) made the news for developing a quantitative assay for the Alpha variant of SARS-CoV-2 in wastewater, while also working on a similar assay for the Delta variant. Previously, conventional wastewater detection methods could only detect the presence of SARS-CoV-2 viral material in a sample, without identifying the variant of the virus.

    Now, a team at SMART has developed a quantitative RT-qPCR assay that can detect the Omicron variant of SARS-CoV-2. This type of assay enables wastewater surveillance to accurately trace variant dynamics in any given community or population, and support and inform the implementation of appropriate public health measures tailored according to the specific traits of a particular viral pathogen.

    The capacity to count and assess particular VOCs is unique to SMART’s open-source assay, and allows researchers to accurately determine displacement trends in a community. Hence, the new assay can reveal what proportion of SARS-CoV-2 virus circulating in a community belongs to a particular variant. This is particularly significant, as different SARS-CoV-2 VOCs — Alpha, Delta, Omicron, and their offshoots — have emerged at various points throughout the pandemic, each causing a new wave of infections to which the population was more susceptible.

    The team’s new allele-specific RT-qPCR assay is described in a paper, “Rapid displacement of SARS-CoV-2 variant Delta by Omicron revealed by allele-specific PCR in wastewater,” published this month in Water Research. Senior author on the work is Eric Alm, professor of biological engineering at MIT and a principal investigator in the Antimicrobial Resistance (AMR) interdisciplinary research group within SMART, MIT’s research enterprise in Singapore. Co-authors include researchers from Nanyang Technological University (NTU), Singapore National University (NUS), MIT, Singapore Centre for Environmental Life Sciences Engineering (SCELSE), and Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia Romagna (IZSLER) in Italy.

    Omicron overtakes delta within three weeks in Italy study

    In their study, SMART researchers found that the increase in booster vaccine population coverage in Italy concurred with the complete displacement of the Delta variant by the Omicron variant in wastewater samples obtained from the Torbole Casaglia wastewater treatment plant, with a catchment size of 62,722 people. Taking less than three weeks, the rapid pace of this displacement can be attributed to Omicron’s infection advantage over the previously dominant Delta in vaccinated individuals, which may stem from Omicron’s more efficient evasion of vaccination-induced immunity.

    “In a world where Covid-19 is endemic, the monitoring of VOCs through wastewater surveillance will be an effective tool for the tracking of variants circulating in the community and will play an increasingly important role in guiding public health response,” says paper co-author Federica Armas, a senior postdoc at SMART AMR. “This work has demonstrated that wastewater surveillance can be used to quickly and quantitatively trace VOCs present in a community.”

    Wastewater surveillance vital for future pandemic responses

    As the global population becomes increasingly vaccinated and exposed to prior infections, nations have begun transitioning toward the classification of SARS-CoV-2 as an endemic disease, rolling back active clinical surveillance toward decentralized antigen rapid tests, and consequently reducing sequencing of patient samples. However, SARS-CoV-2 has been shown to produce novel VOCs that can swiftly emerge and spread rapidly across populations, displacing previously dominant variants of the virus. This was observed when Delta displaced Alpha across the globe after the former’s emergence in India in December 2020, and again when Omicron displaced Delta at an even faster rate following its discovery in South Africa in November 2021. The continuing emergence of novel VOCs therefore necessitates continued vigilance on the monitoring of circulating SARS-CoV-2 variants in communities.

    In a separate review paper on wastewater surveillance titled “Making Waves: Wastewater Surveillance of SARS-CoV-2 in an Endemic Future,” published in the journal Water Research, SMART researchers and collaborators found that the utility of wastewater surveillance in the near future could include 1) monitoring the trend of viral loads in wastewater for quantified viral estimates circulating in a community; 2) sampling of wastewater at the source — e.g., taking samples from particular neighborhoods or buildings — for pinpointing infections in neighborhoods and at the building level; 3) integrating wastewater and clinical surveillance for cost-efficient population surveillance; and 4) genome sequencing wastewater samples to track circulating and emerging variants in the population.

    “Our experience with SARS-CoV-2 has shown that clinical testing can often only paint a limited picture of the true extent of an outbreak or pandemic. With Covid-19 becoming prevalent and with the anticipated emergence of further variants of concern, qualitative and quantitative data from wastewater surveillance will be an integral component of a cost- and resource-efficient public health surveillance program, empowering authorities to make more informed policy decisions,” adds corresponding author Janelle Thompson, associate professor at SCELSE and NTU. “Our review provides a roadmap for the wider deployment of wastewater surveillance, with opportunities and challenges that, if addressed, will enable us to not only better manage Covid-19, but also future-proof societies for other viral pathogens and future pandemics.”

    In addition, the review suggests that future wastewater research should comply with a set of standardized wastewater processing methods to reduce inconsistencies in wastewater data toward improving epidemiological inference. Methods developed in the context of SARS-CoV-2 and its analyses could be of invaluable benefit for future wastewater monitoring work on discovering emerging zoonotic pathogens — pathogens that can be transmitted from animals to humans — and for early detection of future pandemics.

    Furthermore, far from being confined to SARS-CoV-2, wastewater surveillance has already been adapted for use in combating other viral pathogens. Another paper from September 2021 described an advance in the development of effective wastewater surveillance for dengue, Zika, and yellow fever viruses, with SMART researchers successfully measuring decay rates of these medically significant arboviruses in wastewater. This was followed by another review paper by SMART published in July 2022 that explored current progress and future challenges and opportunities in wastewater surveillance for arboviruses. These developments represent an important first step toward establishing arbovirus wastewater surveillance, which would help policymakers in Singapore and beyond make better informed and more targeted public health measures in controlling arbovirus outbreaks such as dengue, which is a significant public health concern in Singapore.

    “Our learnings from using wastewater surveillance as a key tool over the course of Covid-19 will be crucial in helping researchers develop similar methods to monitor and tackle other viral pathogens and future pandemics,” says Lee Wei Lin, first author of the latest SMART paper and research scientist at SMART AMR. “Wastewater surveillance has already shown promising utility in helping to fight other viral pathogens, including some of the world’s most prevalent mosquito-borne diseases, and there is significant potential for the technology to be adapted for use against other infectious viral diseases.”

    The research is carried out by SMART and its collaborators at SCELSE, NTU, and NUS, co-led by Professor Eric Alm (SMART and MIT) and Associate Professor Janelle Thompson (SCELSE and NTU), and is supported by Singapore’sNational Research Foundation (NRF) under its Campus for Research Excellence And Technological Enterprise (CREATE) program. The research is part of an initiative funded by the NRF to develop sewage-based surveillance for rapid outbreak detection and intervention in Singapore.

    SMART was established by MIT in partnership with the NRF in 2007. SMART is the first entity in CREATE developed by NRF and serves as an intellectual and innovation hub for research interactions between MIT and Singapore, undertaking cutting-edge research projects in areas of interest to both Singapore and MIT. SMART currently comprises an Innovation Centre and five interdisciplinary research groups: AMR, Critical Analytics for Manufacturing Personalized-Medicine, Disruptive & Sustainable Technologies for Agricultural Precision, Future Urban Mobility, and Low Energy Electronic Systems.

    The AMR IRG is a translational research and entrepreneurship program that tackles the growing threat of antimicrobial resistance. By leveraging talent and convergent technologies across Singapore and MIT, they tackle AMR head-on by developing multiple innovative and disruptive approaches to identify, respond to, and treat drug-resistant microbial infections. Through strong scientific and clinical collaborations, our goal is to provide transformative, holistic solutions for Singapore and the world. More

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    These neurons have food on the brain

    A gooey slice of pizza. A pile of crispy French fries. Ice cream dripping down a cone on a hot summer day. When you look at any of these foods, a specialized part of your visual cortex lights up, according to a new study from MIT neuroscientists.

    This newly discovered population of food-responsive neurons is located in the ventral visual stream, alongside populations that respond specifically to faces, bodies, places, and words. The unexpected finding may reflect the special significance of food in human culture, the researchers say. 

    “Food is central to human social interactions and cultural practices. It’s not just sustenance,” says Nancy Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience and a member of MIT’s McGovern Institute for Brain Research and Center for Brains, Minds, and Machines. “Food is core to so many elements of our cultural identity, religious practice, and social interactions, and many other things that humans do.”

    The findings, based on an analysis of a large public database of human brain responses to a set of 10,000 images, raise many additional questions about how and why this neural population develops. In future studies, the researchers hope to explore how people’s responses to certain foods might differ depending on their likes and dislikes, or their familiarity with certain types of food.

    MIT postdoc Meenakshi Khosla is the lead author of the paper, along with MIT research scientist N. Apurva Ratan Murty. The study appears today in the journal Current Biology.

    Visual categories

    More than 20 years ago, while studying the ventral visual stream, the part of the brain that recognizes objects, Kanwisher discovered cortical regions that respond selectively to faces. Later, she and other scientists discovered other regions that respond selectively to places, bodies, or words. Most of those areas were discovered when researchers specifically set out to look for them. However, that hypothesis-driven approach can limit what you end up finding, Kanwisher says.

    “There could be other things that we might not think to look for,” she says. “And even when we find something, how do we know that that’s actually part of the basic dominant structure of that pathway, and not something we found just because we were looking for it?”

    To try to uncover the fundamental structure of the ventral visual stream, Kanwisher and Khosla decided to analyze a large, publicly available dataset of full-brain functional magnetic resonance imaging (fMRI) responses from eight human subjects as they viewed thousands of images.

    “We wanted to see when we apply a data-driven, hypothesis-free strategy, what kinds of selectivities pop up, and whether those are consistent with what had been discovered before. A second goal was to see if we could discover novel selectivities that either haven’t been hypothesized before, or that have remained hidden due to the lower spatial resolution of fMRI data,” Khosla says.

    To do that, the researchers applied a mathematical method that allows them to discover neural populations that can’t be identified from traditional fMRI data. An fMRI image is made up of many voxels — three-dimensional units that represent a cube of brain tissue. Each voxel contains hundreds of thousands of neurons, and if some of those neurons belong to smaller populations that respond to one type of visual input, their responses may be drowned out by other populations within the same voxel.

    The new analytical method, which Kanwisher’s lab has previously used on fMRI data from the auditory cortex, can tease out responses of neural populations within each voxel of fMRI data.

    Using this approach, the researchers found four populations that corresponded to previously identified clusters that respond to faces, places, bodies, and words. “That tells us that this method works, and it tells us that the things that we found before are not just obscure properties of that pathway, but major, dominant properties,” Kanwisher says.

    Intriguingly, a fifth population also emerged, and this one appeared to be selective for images of food.

    “We were first quite puzzled by this because food is not a visually homogenous category,” Khosla says. “Things like apples and corn and pasta all look so unlike each other, yet we found a single population that responds similarly to all these diverse food items.”

    The food-specific population, which the researchers call the ventral food component (VFC), appears to be spread across two clusters of neurons, located on either side of the FFA. The fact that the food-specific populations are spread out between other category-specific populations may help explain why they have not been seen before, the researchers say.

    “We think that food selectivity had been harder to characterize before because the populations that are selective for food are intermingled with other nearby populations that have distinct responses to other stimulus attributes. The low spatial resolution of fMRI prevents us from seeing this selectivity because the responses of different neural population get mixed in a voxel,” Khosla says.

    “The technique which the researchers used to identify category-sensitive cells or areas is impressive, and it recovered known category-sensitive systems, making the food category findings most impressive,” says Paul Rozin, a professor of psychology at the University of Pennsylvania, who was not involved in the study. “I can’t imagine a way for the brain to reliably identify the diversity of foods based on sensory features. That makes this all the more fascinating, and likely to clue us in about something really new.”

    Food vs non-food

    The researchers also used the data to train a computational model of the VFC, based on previous models Murty had developed for the brain’s face and place recognition areas. This allowed the researchers to run additional experiments and predict the responses of the VFC. In one experiment, they fed the model matched images of food and non-food items that looked very similar — for example, a banana and a yellow crescent moon.

    “Those matched stimuli have very similar visual properties, but the main attribute in which they differ is edible versus inedible,” Khosla says. “We could feed those arbitrary stimuli through the predictive model and see whether it would still respond more to food than non-food, without having to collect the fMRI data.”

    They could also use the computational model to analyze much larger datasets, consisting of millions of images. Those simulations helped to confirm that the VFC is highly selective for images of food.

    From their analysis of the human fMRI data, the researchers found that in some subjects, the VFC responded slightly more to processed foods such as pizza than unprocessed foods like apples. In the future they hope to explore how factors such as familiarity and like or dislike of a particular food might affect individuals’ responses to that food.

    They also hope to study when and how this region becomes specialized during early childhood, and what other parts of the brain it communicates with. Another question is whether this food-selective population will be seen in other animals such as monkeys, who do not attach the cultural significance to food that humans do.

    The research was funded by the National Institutes of Health, the National Eye Institute, and the National Science Foundation through the MIT Center for Brains, Minds, and Machines. More

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    Helping cassava farmers by extending crop life

    The root vegetable cassava is a major food staple in dozens of countries across the world. Drought-resistant, nutritious, and tasty, it has also become a major source of income for small-scale, rural farmers in places like West Africa and Southeast Asia.

    But the utility of cassava has always been limited by its short postharvest shelf life of two to three days. That puts millions of farmers who rely on the crop in a difficult position. The farmers can’t plant more than they can sell quickly in local markets, and they’re often forced to sell below market prices because buyers know the harvest will spoil rapidly. As a result, cassava farmers are among the world’s poorest people.

    Now the startup CassVita is buying cassava directly from farmers and applying a patent-pending biotechnology to extend its shelf life to 18 months. The approach has the potential to transform economies in rural, impoverished regions where millions of families rely on the crop for income.

    CassVita tells farmers how much cassava the company will buy each month, and processes the cassava at a manufacturing facility in Cameroon. It currently sells the first version of its product as a powdered food to people in Cameroon and to West African immigrants in the U.S.

    But CassVita founder and CEO Pelkins Ajanoh ’18 says the future of the company will revolve around its next product: a cassava-based flour that can act as a direct substitute for wheat. The wheat substitute would dramatically broaden CassVita’s target market to include the fast-growing, trillion-dollar healthy food market.

    Ajanoh says CassVita is currently able to increase farmers’ incomes by about 400 percent through its purchases.

    “Our objective is to leverage proprietary technology to offer a healthier and better-tasting alternative to wheat while creating prosperity for local farmers,” Ajanoh says. “We’re hoping to tap into this huge market while empowering farmers, all by minimizing spoilage and incentivizing farmers to plant more.”

    Gaining confidence to help a community

    While growing up in Cameroon, Ajanoh’s parents always emphasized the importance of education for him and his three siblings. But Ajanoh lost his father when he was 13, and his mother moved to the U.S. a year later to help provide for the family. During that time, Ajanoh lived with his grandmother, a cassava farmer. For many years, Ajanoh watched his grandmother harvest cassava without making any lasting financial gains. He remembers feeling powerless as his grandmother struggled to pay for things like diabetes medication.

    Then Ajanoh earned the top marks on the national exams that Cameroonian students take before college. After high school, he joined his mother in the U.S. and came to MIT to study mechanical engineering. Once on campus, Ajanoh says he had lunch with new people all the time to learn from them.

    “I’d never had this community of intellectuals — and they were from all over the world — so I soaked up as much as I could,” Ajanoh says. “That sparked an interest in entrepreneurship, because MIT is super entrepreneurial. Everyone’s thinking of starting something cool.”

    Ajanoh also got a confidence boost during an internship in the summer after his junior year, when he created self-driving technology for General Motors that was eventually patented.

    “It made me realize I could do something really valuable for the world, and by the end of that internship I was thinking, ‘Now I want to solve a problem in my community,’” he says.

    Returning to the crop he knew well, Ajanoh received a series of grants from the MIT Sandbox Innovation Fund to experiment with ways to extend the shelf life of cassava. In the summer of 2018, the MIT-Africa program sponsored three MIT students to fly to Cameroon with him to participate in internships with the company.

    Today CassVita partners with development banks to help farmers get loans to buy the cassava sticks used for planting. Ajanoh says CassVita decided on a powdered food for its first product because it requires less marketing to sell to West Africans, who are familiar with the dish. Now the company is working on a cassava flour that it will market to all consumers looking for healthy alternatives to wheat that can be used in pastries and other baked goods.

    “Cassava makes sense as a global substitute to wheat because it’s gluten free, grain free, nut free, and it also helps with glucose regulation, to normalize blood sugar levels, to lower triglycerides, so the health benefits are exciting,” Ajanoh says. “But the farmers were still living in poverty, so if we could solve the shelf-life problem then we could empower these farmers to offer healthier wheat alternatives to the global market.”

    The project has taken on additional urgency now that the war in Ukraine is limiting that country’s wheat and grain exports, raising prices, and heightening food insecurity in regions around the globe.

    Showing the value of helping farmers

    Ajanoh says the majority of people farming cassava are women, and he says the challenges related to cassava’s shelf life have contributed to gender inequities in many communities. In fact, of the roughly 500 farmers CassVita works with in Cameroon, 95 percent are women.

    “That has always excited me because I was raised by women, so working on something that could empower women in their communities and give them authority is fulfilling,” Ajanoh says.

    Ajanoh has already heard from farmers who have been able to send their children to school for the first time because of improved financial situations. Now, as CassVita continues to scale, Ajanoh wants to stay focused on the technology that enables these new business models.

    “We’re evolving into a food technology company,” Ajanoh says. “We prefer to focus on leveraging technology to impact lives and improve outcomes in these communities. Right now, we’re going all the way to consumers because this is an opportunity the Nestles and the Unilevers of the world won’t pick up because the market doesn’t make sense to them yet. So, we have to build this company and show them the value.” More

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    Could used beer yeast be the solution to heavy metal contamination in water?

    A new analysis by researchers at MIT’s Center for Bits and Atoms (CBA) has found that inactive yeast could be effective as an inexpensive, abundant, and simple material for removing lead contamination from drinking water supplies. The study shows that this approach can be efficient and economic, even down to part-per-billion levels of contamination. Serious damage to human health is known to occur even at these low levels.

    The method is so efficient that the team has calculated that waste yeast discarded from a single brewery in Boston would enough to treat the city’s entire water supply. Such a fully sustainable system would not only purify the water but also divert what would otherwise be a waste stream needing disposal.

    The findings are detailed today in the journal Nature Communications Earth and Environment, in a paper by MIT Research Scientist Patritsia Statathou; Brown University postdoc and MIT Visiting Scholar Christos Athanasiou; MIT Professor Neil Gershenfeld, the director of CBA; and nine others at MIT, Brown, Wellesley College, Nanyang Technological University, and National Technical University of Athens.

    Lead and other heavy metals in water are a significant global problem that continues to grow because of electronic waste and discharges from mining operations. In the U.S. alone, more than 12,000 miles of waterways are impacted by acidic mine-drainage-water rich in heavy metals, the country’s leading source of water pollution. And unlike organic pollutants, most of which can be eventually broken down, heavy metals don’t biodegrade, but persist indefinitely and bioaccumulate. They are either impossible or very expensive to completely remove by conventional methods such as chemical precipitation or membrane filtration.

    Lead is highly toxic, even at tiny concentrations, especially affecting children as they grow. The European Union has reduced its standard for allowable lead in drinking water from 10 parts per billion to 5 parts per billion. In the U.S., the Environmental Protection Agency has declared that no level at all in water supplies is safe. And average levels in bodies of surface water globally are 10 times higher than they were 50 years ago, ranging from 10 parts per billion in Europe to hundreds of parts per billion in South America.

    “We don’t just need to minimize the existence of lead; we need to eliminate it in drinking water,” says Stathatou. “And the fact is that the conventional treatment processes are not doing this effectively when the initial concentrations they have to remove are low, in the parts-per-billion scale and below. They either fail to completely remove these trace amounts, or in order to do so they consume a lot of energy and they produce toxic byproducts.”

    The solution studied by the MIT team is not a new one — a process called biosorption, in which inactive biological material is used to remove heavy metals from water, has been known for a few decades. But the process has been studied and characterized only at much higher concentrations, at more than one part-per-million levels. “Our study demonstrates that the process can indeed work efficiently at the much lower concentrations of typical real-world water supplies, and investigates in detail the mechanisms involved in the process,” Athanasiou says.

    The team studied the use of a type of yeast widely used in brewing and in industrial processes, called S. cerevisiae, on pure water spiked with trace amounts of lead. They demonstrated that a single gram of the inactive, dried yeast cells can remove up to 12 milligrams of lead in aqueous solutions with initial lead concentrations below 1 part per million. They also showed that the process is very rapid, taking less than five minutes to complete.

    Because the yeast cells used in the process are inactive and desiccated, they require no particular care, unlike other processes that rely on living biomass to perform such functions which require nutrients and sunlight to keep the materials active. What’s more, yeast is abundantly available already, as a waste product from beer brewing and from various other fermentation-based industrial processes.

    Stathatou has estimated that to clean a water supply for a city the size of Boston, which uses about 200 million gallons a day, would require about 20 tons of yeast per day, or about 7,000 tons per year. By comparison, one single brewery, the Boston Beer Company, generates 20,000 tons a year of surplus yeast that is no longer useful for fermentation.

    The researchers also performed a series of tests to determine that the yeast cells are responsible for biosorption. Athanasiou says that “exploring biosorption mechanisms at such challenging concentrations is a tough problem. We were the first to use a mechanics perspective to unravel biosorption mechanisms, and we discovered that the mechanical properties of the yeast cells change significantly after lead uptake. This provides fundamentally new insights for the process.”

    Devising a practical system for processing the water and retrieving the yeast, which could then be separated from the lead for reuse, is the next stage of the team’s research, they say.

    “To scale up the process and actually put it in place, you need to embed these cells in a kind of filter, and this is the work that’s currently ongoing,” Stathatou says. They are also looking at ways of recovering both the cells and the lead. “We need to conduct further experiments, but there is the option to get both back,” she says.

    The same material can potentially be used to remove other heavy metals, such as cadmium and copper, but that will require further research to quantify the effective rates for those processes, the researchers say.

    “This research revealed a very promising, inexpensive, and environmentally friendly solution for lead removal,” says Sivan Zamir, vice president of Xylem Innovation Labs, a water technology research firm, who was not associated with this research. “It also deepened our understanding of the biosorption process, paving the way for the development of materials tailored to removal of other heavy metals.”

    The team also included Marios Tsezos at the National Technical University of Athens, in Greece; John Gross at Wellesley College; Camron Blackburn, Filippos Tourlomousis, and Andreas Mershin at MIT’s CBA; Brian Sheldon, Nitin Padture, Eric Darling at Brown University; and Huajian Gao at Brown University and Nanyang Technological University, in Singapore. More

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    Engineers use artificial intelligence to capture the complexity of breaking waves

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

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

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

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

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

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

    Learning tank

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

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

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

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

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

    Safe harbor

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

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

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

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

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

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

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

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    From seawater to drinking water, with the push of a button

    MIT researchers have developed a portable desalination unit, weighing less than 10 kilograms, that can remove particles and salts to generate drinking water.

    The suitcase-sized device, which requires less power to operate than a cell phone charger, can also be driven by a small, portable solar panel, which can be purchased online for around $50. It automatically generates drinking water that exceeds World Health Organization quality standards. The technology is packaged into a user-friendly device that runs with the push of one button.

    Unlike other portable desalination units that require water to pass through filters, this device utilizes electrical power to remove particles from drinking water. Eliminating the need for replacement filters greatly reduces the long-term maintenance requirements.

    This could enable the unit to be deployed in remote and severely resource-limited areas, such as communities on small islands or aboard seafaring cargo ships. It could also be used to aid refugees fleeing natural disasters or by soldiers carrying out long-term military operations.

    “This is really the culmination of a 10-year journey that I and my group have been on. We worked for years on the physics behind individual desalination processes, but pushing all those advances into a box, building a system, and demonstrating it in the ocean, that was a really meaningful and rewarding experience for me,” says senior author Jongyoon Han, a professor of electrical engineering and computer science and of biological engineering, and a member of the Research Laboratory of Electronics (RLE).

    Joining Han on the paper are first author Junghyo Yoon, a research scientist in RLE; Hyukjin J. Kwon, a former postdoc; SungKu Kang, a postdoc at Northeastern University; and Eric Brack of the U.S. Army Combat Capabilities Development Command (DEVCOM). The research has been published online in Environmental Science and Technology.

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    Filter-free technology

    Commercially available portable desalination units typically require high-pressure pumps to push water through filters, which are very difficult to miniaturize without compromising the energy-efficiency of the device, explains Yoon.

    Instead, their unit relies on a technique called ion concentration polarization (ICP), which was pioneered by Han’s group more than 10 years ago. Rather than filtering water, the ICP process applies an electrical field to membranes placed above and below a channel of water. The membranes repel positively or negatively charged particles — including salt molecules, bacteria, and viruses — as they flow past. The charged particles are funneled into a second stream of water that is eventually discharged.

    The process removes both dissolved and suspended solids, allowing clean water to pass through the channel. Since it only requires a low-pressure pump, ICP uses less energy than other techniques.

    But ICP does not always remove all the salts floating in the middle of the channel. So the researchers incorporated a second process, known as electrodialysis, to remove remaining salt ions.

    Yoon and Kang used machine learning to find the ideal combination of ICP and electrodialysis modules. The optimal setup includes a two-stage ICP process, with water flowing through six modules in the first stage then through three in the second stage, followed by a single electrodialysis process. This minimized energy usage while ensuring the process remains self-cleaning.

    “While it is true that some charged particles could be captured on the ion exchange membrane, if they get trapped, we just reverse the polarity of the electric field and the charged particles can be easily removed,” Yoon explains.

    They shrunk and stacked the ICP and electrodialysis modules to improve their energy efficiency and enable them to fit inside a portable device. The researchers designed the device for nonexperts, with just one button to launch the automatic desalination and purification process. Once the salinity level and the number of particles decrease to specific thresholds, the device notifies the user that the water is drinkable.

    The researchers also created a smartphone app that can control the unit wirelessly and report real-time data on power consumption and water salinity.

    Beach tests

    After running lab experiments using water with different salinity and turbidity (cloudiness) levels, they field-tested the device at Boston’s Carson Beach.

    Yoon and Kwon set the box near the shore and tossed the feed tube into the water. In about half an hour, the device had filled a plastic drinking cup with clear, drinkable water.

    “It was successful even in its first run, which was quite exciting and surprising. But I think the main reason we were successful is the accumulation of all these little advances that we made along the way,” Han says.

    The resulting water exceeded World Health Organization quality guidelines, and the unit reduced the amount of suspended solids by at least a factor of 10. Their prototype generates drinking water at a rate of 0.3 liters per hour, and requires only 20 watts of power per liter.

    “Right now, we are pushing our research to scale up that production rate,” Yoon says.

    One of the biggest challenges of designing the portable system was engineering an intuitive device that could be used by anyone, Han says.

    Yoon hopes to make the device more user-friendly and improve its energy efficiency and production rate through a startup he plans to launch to commercialize the technology.

    In the lab, Han wants to apply the lessons he’s learned over the past decade to water-quality issues that go beyond desalination, such as rapidly detecting contaminants in drinking water.

    “This is definitely an exciting project, and I am proud of the progress we have made so far, but there is still a lot of work to do,” he says.

    For example, while “development of portable systems using electro-membrane processes is an original and exciting direction in off-grid, small-scale desalination,” the effects of fouling, especially if the water has high turbidity, could significantly increase maintenance requirements and energy costs, notes Nidal Hilal, professor of engineering and director of the New York University Abu Dhabi Water research center, who was not involved with this research.

    “Another limitation is the use of expensive materials,” he adds. “It would be interesting to see similar systems with low-cost materials in place.”

    The research was funded, in part, by the DEVCOM Soldier Center, the Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), the Experimental AI Postdoc Fellowship Program of Northeastern University, and the Roux AI Institute. More

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    MIT engineers introduce the Oreometer

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

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

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

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

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

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

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

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

    Confection connection

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

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

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

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

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

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

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    Cookie twist

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

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

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

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

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

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

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

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

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