MIT Spokes cycles across the country to teach STEM classes

Some students have jobs or internships over the summer. But members of MIT Spokes rode their bicycles 3,800 miles across the country to teach STEM classes at learning festivals, centers for adjudicated youth, summer schools, libraries, summer camps, and after-school programs. 

Spokes members Robert Henning ’20, Mariela Perez-Cabarcas ’22, Simone Lassar ’22, Maxwell Yun ’22, Franklin Zhang ’22, and Sophia Fang ’22 rode from Washington to San Francisco in just 80 days. Teammate and  senior Keaten Clarno was scheduled but unable to ride, so they helped with planning and logistics.

At various locations along their route, the riders taught middle school and high school students STEM classes in color and chromatography, 3D printing, the physics of music, circuitry, and solar cell technology. They showed students how to make electricity using just light and a tart mixture of raspberry and blackberry juices. They explained the structural color that makes peacock feathers so bright. And they taught students how to print 3D parts, including “Among Us” characters and a rocket. They also held workshops on music with a focus on the physics of melody, harmony, timbre, and rhythm. They showed students how electricity works and had them build their own circuits to change electricity into light while explaining the fundamental laws of electricity. Spokes riders also made a tradition of ending each day with local ice cream recommended by their students before moving on to their next waypoint. 

Graydon Hoskins from Hazard, Kentucky, who attended the solar cell workshop, says, “I am thankful for MIT for showing how it is done. I loved every part about this. I have learned so much and met amazing people — the kids, the high-schoolers, and the teachers. I want to tell MIT how perfect [a job] they did for all of the students, and I love how they would come to a small town to show so much support!” 

Spokes team member Simone Lassar observes, “The students were exuberant, curious, and excited. We had conversations with them ranging from what type of shoes we would be if we were shoes to how they could apply to MIT if they wanted to. The MIT brand carries a lot of weight and prestige, especially in some of these small towns that we passed through. It has been awesome to connect with students and show them MIT is not an unreachable place, but a collection of curious and excited individuals like themselves.”

The MIT Regional Entrepreneurship Acceleration Program formed a new nonprofit in partnership with AccelerateKY and helped arrange the Spokes team’s travels across Kentucky and schedule their appearance at the Bowling Green Learning Festival in June.  

“I had the pleasure of seeing the MIT Spokes team working up close through the learning festival they held in Bowling Green at the Innovation Campus at Western Kentucky University,” says Sam Ford SM '07, who completed a master’s in comparative media studies in 2007 and is executive director of AccelerateKY.  

“The team’s interdisciplinary curiosity and fascination not only with the topics they taught, but with the culture of the places they visited, really engaged the kids in dreaming about what’s possible for their futures,” says Ford. 

The group rode between 40 and 113 miles every day, averaging around 60, and admit the early climb up the Blue Ridge Parkway in Virginia taxed their leg muscles. The struggles of riding uphill were more than made up for with the amazing scenery and the opportunity to fly down the hills in the aero tuck position. 

“The views were absolutely stunning. We biked into the city of Black Hawk, Colorado early one morning. The backdrop of coniferous forests, with soft morning light, and a steep downhill was maybe the most beautiful view I’ve seen in my life!” says Lassar. 

Even on their days off, Spokes riders were still busy — sightseeing, swimming, and hiking in national parks such as Bryce Canyon and Capitol Reef. They also spent time climbing through narrow metal structures at the City Museum in St. Louis, Missouri.

The trek finished at the Golden Gate Bridge in San Francisco, where the riders were greeted by family members and friends. The Spokes riders thank all of their sponsors — especially the MIT Edgerton Center, the Experimental Study Group at MIT, and Mayor Jerry Fultz of Wayland, Kentucky. The cyclists were welcomed by the mayor and his wife in this tiny town of just 400 people.   

The group is already talking about their next adventure — a possible bike ride across Europe next summer. In October, they plan to cycle from Boston to Provincetown, Massachusetts, together — because they consider each other “family” now. And, just like their website says — “Engineers and scientists can be athletes, too.”

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New endowment fund will power student innovation at MIT Sandbox

Through a $1 million gift from Daniel Gilbert ’91 and his wife, Judy, the MIT Sandbox Innovation Fund Program gained a permanent source of funding with the establishment of the MIT Sandbox Endowment Fund. The new fund allows MIT Sandbox to expand its entrepreneurship education offerings to MIT undergraduate and graduate students, bringing to life the Gilberts’ dream to transform MIT Sandbox into a permanent program at the Institute, bringing technologies, ideas, and business concepts to life in ways beyond the imagination.

“The Gilberts’ generous gift exemplifies the ethos of the MIT Sandbox Program, which is to learn through doing,” says Anantha P. Chandrakasan, dean of the MIT School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “I’m grateful to the Gilberts for their vision and support of MIT Sandbox through this foundational gift.”

Launched in 2016, MIT Sandbox has established a reputation as a top-notch program that equips students with lessons they can apply in life, as well as the classroom. MIT Sandbox currently supports 500 teams of student entrepreneurs annually, offering mentorship from seasoned business leaders and non-dilutive seed funding of up to $25,000 to support the development of nascent technologies, the launch of early-stage companies, or the germination of ideas.

“As one of the first endowment funders for MIT Sandbox, Dan is not only bringing life to the program financially, but he is also championing this one-of-a-kind organization to peers inside and outside of the Institute, paving the way for others to see that their financial support has immediate real-world impact by making MIT student innovation possible through world-class education,” says MIT Vice Chancellor for Undergraduate and Graduate Education Ian A. Waitz, the founding faculty director of MIT Sandbox.

MIT Sandbox focuses on tailored education and mentoring and equips students with practical skills and the know-how to launch and run a business. Many teams spend 12 to 18 months working with MIT Sandbox mentors and evolving their ideas through market research, customer discovery, and prototyping.

“Our program is not a competition. Our goal is to encourage all MIT undergraduate and graduate students to see an entrepreneurial experience in the same way they see a research experience — as an important opportunity to experiment and learn,” explains MIT Sandbox Executive Director Jinane Abounadi.

Entrepreneurship education broadens students’ perspective

This ethos is shared by Dan Gilbert, who says his experience as an MIT student was life-changing. He learned first-hand the transformative power of innovation, which developed into a life-long passion for early-stage startups. Through his gift to MIT Sandbox, Gilbert hopes the program provides a forum for MIT students to learn and experience entrepreneurship — cultivating a unique spark of innovation and an appreciation for the speed of disruption that comes only from the startup environment.

Now an early-stage investor in the San Francisco Bay Area, Gilbert was one of the founding funders of MIT Sandbox and remains a dedicated mentor.

“I believe that starting a company is a unique way for students to think about how their coursework and research has application beyond the MIT campus,” says Gilbert. “Thinking about potential users of their technology or service ideas encourages students to take a broader view — it gives them a different lens.”

MIT Chancellor Melissa Nobles says the MIT Sandbox experience represents impactful educational opportunities for students.

“MIT Sandbox helps make possible the dreams of brilliant MIT minds through the practice of inquiry and hands-on experimentation undertaken in the program,” says Nobles. “Innovation is what drives MIT, and I’m pleased that we can offer our students a place to tackle hard problems, devise creative solutions, and make real-world impact and change through the MIT Sandbox program.”    

Scaling MIT Sandbox = scaling student entrepreneurs

Since 2016, hundreds of MIT Sandbox teams have launched startups, many of which have gone on to raise multimillion-dollar funding rounds — a scale that Gilbert says is breathtaking, and a testament to student enthusiasm for the program and a real thirst for an entrepreneurial experience.

One success story of the program is Lightmatter. The founder and chief scientist of Lightmatter, Darius Bunandar ’19, shares that MIT Sandbox was what made the difference for the venture.

“MIT Sandbox helped prepare us with the tools to solve some of the challenging aspects of starting a business by making meaningful connections to mentors like Dan Gilbert,” says Bunandar. “Dan helped us navigate fundraising procedures, from understanding our first term sheet to conducting the negotiation process and framing the technology as a product for our customers. When bringing an early-stage technology to the market, it is not only about solving hard problems but also about evangelizing the technology or the solutions to customers. The investment of time and expertise from MIT Sandbox helped us build a successful foundation.”

“This kind of success story can be re-told by many hundreds of teams for which MIT Sandbox has served as a launchpad,” says Abounadi. “With our new Endowment Fund ramping up, we’re all tremendously excited to see what growth experiences MIT Sandbox can bring to the students of today and tomorrow.”

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AI system makes models like DALL-E 2 more creative

The internet had a collective feel-good moment with the introduction of DALL-E, an artificial intelligence-based image generator inspired by artist Salvador Dali and the lovable robot WALL-E that uses natural language to produce whatever mysterious and beautiful image your heart desires. Seeing typed-out inputs like “smiling gopher holding an ice cream cone” instantly spring to life clearly resonated with the world. 

Getting said smiling gopher and attributes to pop up on your screen is not a small task. DALL-E 2 uses something called a diffusion model, where it tries to encode the entire text into one description to generate an image. But once the text has a lot of more details, it's hard for a single description to capture it all. Moreover, while they're highly flexible, they sometimes struggle to understand the composition of certain concepts, like confusing the attributes or relations between different objects. 

To generate more complex images with better understanding, scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) structured the typical model from a different angle: they added a series of models together, where they all cooperate to generate desired images capturing multiple different aspects as requested by the input text or labels. To create an image with two components, say, described by two sentences of description, each model would tackle a particular component of the image.  

The seemingly magical models behind image generation work by suggesting a series of iterative refinement steps to get to the desired image. It starts with a “bad” picture and then gradually refines it until it becomes the selected image. By composing multiple models together, they jointly refine the appearance at each step, so the result is an image that exhibits all the attributes of each model. By having multiple models cooperate, you can get much more creative combinations in the generated images. 

Take, for example, a red truck and a green house. The model will confuse the concepts of red truck and green house when these sentences get very complicated. A typical generator like DALL-E 2 might make a green truck and a red house, so it'll swap these colors around. The team's approach can handle this type of binding of attributes with objects, and especially when there are multiple sets of things, it can handle each object more accurately.

“The model can effectively model object positions and relational descriptions, which is challenging for existing image-generation models. For example, put an object and a cube in a certain position and a sphere in another. DALL-E 2 is good at generating natural images but has difficulty understanding object relations sometimes,” says MIT CSAIL PhD student and co-lead author Shuang Li, “Beyond art and creativity, perhaps we could use our model for teaching. If you want to tell a child to put a cube on top of a sphere, and if we say this in language, it might be hard for them to understand. But our model can generate the image and show them.”

Making Dali proud 

Composable Diffusion — the team's model — uses diffusion models alongside compositional operators to combine text descriptions without further training. The team's approach more accurately captures text details than the original diffusion model, which directly encodes the words as a single long sentence. For example, given “a pink sky” AND “a blue mountain in the horizon” AND “cherry blossoms in front of the mountain,” the team's model was able to produce that image exactly, whereas the original diffusion model made the sky blue and everything in front of the mountains pink. 

“The fact that our model is composable means that you can learn different portions of the model, one at a time. You can first learn an object on top of another, then learn an object to the right of another, and then learn something left of another,” says co-lead author and MIT CSAIL PhD student Yilun Du. “Since we can compose these together, you can imagine that our system enables us to incrementally learn language, relations, or knowledge, which we think is a pretty interesting direction for future work.”

While it showed prowess in generating complex, photorealistic images, it still faced challenges since the model was trained on a much smaller dataset than those like DALL-E 2, so there were some objects it simply couldn't capture. 

Now that Composable Diffusion can work on top of generative models, such as DALL-E 2, the scientists want to explore continual learning as a potential next step. Given that more is usually added to object relations, they want to see if diffusion models can start to “learn” without forgetting previously learned knowledge — to a place where the model can produce images with both the previous and new knowledge.

“This research proposes a new method for composing concepts in text-to-image generation not by concatenating them to form a prompt, but rather by computing scores with respect to each concept and composing them using conjunction and negation operators,” says Mark Chen, co-creator of DALL-E 2 and research scientist at OpenAI. “This is a nice idea that leverages the energy-based interpretation of diffusion models so that old ideas around compositionality using energy-based models can be applied. The approach is also able to make use of classifier-free guidance, and it is surprising to see that it outperforms the GLIDE baseline on various compositional benchmarks and can qualitatively produce very different types of image generations.”

“Humans can compose scenes including different elements in a myriad of ways, but this task is challenging for computers,” says Bryan Russel, research scientist at Adobe Systems. “This work proposes an elegant formulation that explicitly composes a set of diffusion models to generate an image given a complex natural language prompt.”

Alongside Li and Du, the paper's co-lead authors are Nan Liu, a master's student in computer science at the University of Illinois at Urbana-Champaign, and MIT professors Antonio Torralba and Joshua B. Tenenbaum. They will present the work at the 2022 European Conference on Computer Vision.

The research was supported by Raytheon BBN Technologies Corp., Mitsubishi Electric Research Laboratory, and DEVCOM Army Research Laboratory.

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Studies of autism tend to exclude women, researchers find

In recent years, researchers who study autism have made an effort to include more women and girls in their studies. However, despite these efforts, most studies of autism consistently enroll small numbers of female subjects or exclude them altogether, according to a new study from MIT.

The researchers found that a screening test commonly used to determine eligibility for studies of autism consistently winnows out a much higher percentage of women than men, creating a “leaky pipeline” that results in severe underrepresentation of women in studies of autism.

This lack of representation makes it more difficult to develop useful interventions or provide accurate diagnoses for girls and women, the researchers say.

“I think the findings favor having a more inclusive approach and widening the lens to end up being less biased in terms of who participates in research,” says John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology and a professor of brain and cognitive sciences at MIT. “The more we understand autism in men and women and nonbinary individuals, the better services and more accurate diagnoses we can provide.”

Gabrieli, who is also a member of MIT’s McGovern Institute for Brain Research, is the senior author of the study, which appears in the journal Autism Research. Anila D’Mello, a former MIT postdoc who is now an assistant professor at the University of Texas Southwestern, is the lead author of the paper. MIT Technical Associate Isabelle Frosch, Research Coordinator Cindy Li, and Research Specialist Annie Cardinaux are also authors of the paper.

Screening out females

Autism spectrum disorders are diagnosed based on observation of traits such as repetitive behaviors and difficulty with language and social interaction. Doctors may use a variety of screening tests to help them make a diagnosis, but these screens are not required.

For research studies of autism, it is routine to use a screening test called the Autism Diagnostic Observation Schedule (ADOS) to determine eligibility for the study. This test, which assesses social interaction, communication, play, and repetitive behaviors, provides a quantitative score in each category, and only participants who reach certain scores qualify for inclusion in studies.

While doing a study exploring how quickly the brains of autistic adults adapt to novel events in the environment, scientists in Gabrieli’s lab began to notice that the ADOS appeared to have unequal effects on male and female participation in research. As the study progressed, D’Mello noticed some significant brain differences between the male and female subjects in the study.

To investigate these differences further, D’Mello tried to find more female participants using an MIT database of autistic adults who have expressed interest in participating in research studies. However, when she sorted through the subjects, she found that only about half of the women in the database had met the ADOS cutoff scores typically required for inclusion in autism studies, compared to 80 percent of the males.

“We realized then that there’s a discrepancy and that the ADOS is essentially screening out who eventually participated in research,” D’Mello says. “We were really surprised at how many males we retained and how many females we lost to the ADOS.”

To see if this phenomenon was more widespread, the researchers looked at six publicly available datasets, which include more than 40,000 adults who have been diagnosed as autistic. For some of these datasets, participants were screened with ADOS to determine their eligibility to participate in studies, while for others, a “community diagnosis” — diagnosis from a doctor or other health care provider — was sufficient.

The researchers found that in datasets that required ADOS screening for eligibility, the ratio of male to female participants ended up being around 8:1, while in those that required only a community diagnosis the ratios ranged from about 2:1 to 1:1.

Previous studies have found differences between behavioral patterns in autistic men and women, but the ADOS test was originally developed using a largely male sample, which may explain why it often excludes women from research studies, D’Mello says.

“There were few females in the sample that was used to create this assessment, so it might be that it’s not great at picking up the female phenotype, which may differ in certain ways — primarily in domains like social communication,” she says.

Effects of exclusion

Failure to include more women and girls in studies of autism may contribute to shortcomings in the definitions of the disorder, the researchers say.

“The way we think about it is that the field evolved perhaps an implicit bias in how autism is defined, and it was driven disproportionately by analysis of males, and recruitment of males, and so on,” Gabrieli says. “So, the definition doesn't fit as well, on average, with the different expression of autism that seems to be more common in females.”

This implicit bias has led to documented difficulties in receiving a diagnosis for girls and women, even when their symptoms are the same as those presented by autistic boys and men.

“Many females might be missed altogether in terms of diagnoses, and then our study shows that in the research setting, what is already a small pool gets whittled down at a much larger rate than that of males,” D’Mello says.

Excluding girls and women from this kind of research study can lead to treatments that don’t work as well for them, and it contributes to the perception that autism doesn’t affect women as much as men.

“The goal is that research should directly inform treatment, therapies, and public perception,” D’Mello says. “If the research is saying that there aren’t females with autism, or that the brain basis of autism only looks like the patterns established in males, then you’re not really helping females as much as you could be, and you’re not really getting at the truth of what the disorder might be.”

The researchers now plan to further explore some of the gender and sex-based differences that appear in autism, and how they arise. They also plan to expand the gender categories that they include. In the current study, the surveys that each participant filled out asked them to choose male or female, but the researchers have updated their questionnaire to include nonbinary and transgender options.

The research was funded by the Hock E. Tan and K. Lisa Yang Center for Autism Research, the Simons Center for the Social Brain at MIT, and the National Institutes of Mental Health.

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

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

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

A widespread problem 

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

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

A solution with societal and economic benefits

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

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

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

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Turning carbon dioxide into valuable products

Carbon dioxide (CO₂) is a major contributor to climate change and a significant product of many human activities, notably industrial manufacturing. A major goal in the energy field has been to chemically convert emitted CO₂ into valuable chemicals or fuels. But while CO₂ is available in abundance, it has not yet been widely used to generate value-added products. Why not?

The reason is that CO₂ molecules are highly stable and therefore not prone to being chemically converted to a different form. Researchers have sought materials and device designs that could help spur that conversion, but nothing has worked well enough to yield an efficient, cost-effective system.

Two years ago, Ariel Furst, the Raymond (1921) and Helen St. Laurent Career Development Professor of Chemical Engineering at MIT, decided to try using something different — a material that gets more attention in discussions of biology than of chemical engineering. Already, results from work in her lab suggest that her unusual approach is paying off.

The stumbling block

The challenge begins with the first step in the CO₂ conversion process. Before being transformed into a useful product, CO₂ must be chemically converted into carbon monoxide (CO). That conversion can be encouraged using electrochemistry, a process in which input voltage provides the extra energy needed to make the stable CO₂ molecules react. The problem is that achieving the CO₂-to-CO conversion requires large energy inputs — and even then, CO makes up only a small fraction of the products that are formed.

To explore opportunities for improving this process, Furst and her research group focused on the electrocatalyst, a material that enhances the rate of a chemical reaction without being consumed in the process. The catalyst is key to successful operation. Inside an electrochemical device, the catalyst is often suspended in an aqueous (water-based) solution. When an electric potential (essentially a voltage) is applied to a submerged electrode, dissolved CO₂ will — helped by the catalyst — be converted to CO.

But there’s one stumbling block: The catalyst and the CO₂ must meet on the surface of the electrode for the reaction to occur. In some studies, the catalyst is dispersed in the solution, but that approach requires more catalyst and isn’t very efficient, according to Furst. “You have to both wait for the diffusion of CO₂ to the catalyst and for the catalyst to reach the electrode before the reaction can occur,” she explains. As a result, researchers worldwide have been exploring different methods of “immobilizing” the catalyst on the electrode.

Connecting the catalyst and the electrode

Before Furst could delve into that challenge, she needed to decide which of the two types of CO₂ conversion catalysts to work with: the traditional solid-state catalyst or a catalyst made up of small molecules. In examining the literature, she concluded that small-molecule catalysts held the most promise. While their conversion efficiency tends to be lower than that of solid-state versions, molecular catalysts offer one important advantage: They can be tuned to emphasize reactions and products of interest.

Two approaches are commonly used to immobilize small-molecule catalysts on an electrode. One involves linking the catalyst to the electrode by strong covalent bonds — a type of bond in which atoms share electrons; the result is a strong, essentially permanent connection. The other sets up a non-covalent attachment between the catalyst and the electrode; unlike a covalent bond, this connection can easily be broken.

Neither approach is ideal. In the former case, the catalyst and electrode are firmly attached, ensuring efficient reactions; but when the activity of the catalyst degrades over time (which it will), the electrode can no longer be accessed. In the latter case, a degraded catalyst can be removed; but the exact placement of the small molecules of the catalyst on the electrode can’t be controlled, leading to an inconsistent, often decreasing, catalytic efficiency — and simply increasing the amount of catalyst on the electrode surface without concern for where the molecules are placed doesn’t solve the problem.

What was needed was a way to position the small-molecule catalyst firmly and accurately on the electrode and then release it when it degrades. For that task, Furst turned to what she and her team regard as a kind of “programmable molecular Velcro”: deoxyribonucleic acid, or DNA.

Adding DNA to the mix

Mention DNA to most people, and they think of biological functions in living things. But the members of Furst’s lab view DNA as more than just genetic code. “DNA has these really cool physical properties as a biomaterial that people don’t often think about,” she says. “DNA can be used as a molecular Velcro that can stick things together with very high precision.”

Furst knew that DNA sequences had previously been used to immobilize molecules on surfaces for other purposes. So she devised a plan to use DNA to direct the immobilization of catalysts for CO₂ conversion.

Her approach depends on a well-understood behavior of DNA called hybridization. The familiar DNA structure is a double helix that forms when two complementary strands connect. When the sequence of bases (the four building blocks of DNA) in the individual strands match up, hydrogen bonds form between complementary bases, firmly linking the strands together.

Using that behavior for catalyst immobilization involves two steps. First, the researchers attach a single strand of DNA to the electrode. Then they attach a complementary strand to the catalyst that is floating in the aqueous solution. When the latter strand gets near the former, the two strands hybridize; they become linked by multiple hydrogen bonds between properly paired bases. As a result, the catalyst is firmly affixed to the electrode by means of two interlocked, self-assembled DNA strands, one connected to the electrode and the other to the catalyst.

Better still, the two strands can be detached from one another. “The connection is stable, but if we heat it up, we can remove the secondary strand that has the catalyst on it,” says Furst. “So we can de-hybridize it. That allows us to recycle our electrode surfaces — without having to disassemble the device or do any harsh chemical steps.”

Experimental investigation

To explore that idea, Furst and her team — postdocs Gang Fan and Thomas Gill, former graduate student Nathan Corbin PhD ’21, and former postdoc Amruta Karbelkar — performed a series of experiments using three small-molecule catalysts based on porphyrins, a group of compounds that are biologically important for processes ranging from enzyme activity to oxygen transport. Two of the catalysts involve a synthetic porphyrin plus a metal center of either cobalt or iron. The third catalyst is hemin, a natural porphyrin compound used to treat porphyria, a set of disorders that can affect the nervous system. “So even the small-molecule catalysts we chose are kind of inspired by nature,” comments Furst.

In their experiments, the researchers first needed to modify single strands of DNA and deposit them on one of the electrodes submerged in the solution inside their electrochemical cell. Though this sounds straightforward, it did require some new chemistry. Led by Karbelkar and third-year undergraduate researcher Rachel Ahlmark, the team developed a fast, easy way to attach DNA to electrodes. For this work, the researchers’ focus was on attaching DNA, but the “tethering” chemistry they developed can also be used to attach enzymes (protein catalysts), and Furst believes it will be highly useful as a general strategy for modifying carbon electrodes.

Once the single strands of DNA were deposited on the electrode, the researchers synthesized complementary strands and attached to them one of the three catalysts. When the DNA strands with the catalyst were added to the solution in the electrochemical cell, they readily hybridized with the DNA strands on the electrode. After half-an-hour, the researchers applied a voltage to the electrode to chemically convert CO₂ dissolved in the solution and used a gas chromatograph to analyze the makeup of the gases produced by the conversion.

The team found that when the DNA-linked catalysts were freely dispersed in the solution, they were highly soluble — even when they included small-molecule catalysts that don’t dissolve in water on their own. Indeed, while porphyrin-based catalysts in solution often stick together, once the DNA strands were attached, that counterproductive behavior was no longer evident.

The DNA-linked catalysts in solution were also more stable than their unmodified counterparts. They didn’t degrade at voltages that caused the unmodified catalysts to degrade. “So just attaching that single strand of DNA to the catalyst in solution makes those catalysts more stable,” says Furst. “We don’t even have to put them on the electrode surface to see improved stability.” When converting CO₂ in this way, a stable catalyst will give a steady current over time. Experimental results showed that adding the DNA prevented the catalyst from degrading at voltages of interest for practical devices. Moreover, with all three catalysts in solution, the DNA modification significantly increased the production of CO per minute.

Allowing the DNA-linked catalyst to hybridize with the DNA connected to the electrode brought further improvements, even compared to the same DNA-linked catalyst in solution. For example, as a result of the DNA-directed assembly, the catalyst ended up firmly attached to the electrode, and the catalyst stability was further enhanced. Despite being highly soluble in aqueous solutions, the DNA-linked catalyst molecules remained hybridized at the surface of the electrode, even under harsh experimental conditions.

Immobilizing the DNA-linked catalyst on the electrode also significantly increased the rate of CO production. In a series of experiments, the researchers monitored the CO production rate with each of their catalysts in solution without attached DNA strands — the conventional setup — and then with them immobilized by DNA on the electrode. With all three catalysts, the amount of CO generated per minute was far higher when the DNA-linked catalyst was immobilized on the electrode.

In addition, immobilizing the DNA-linked catalyst on the electrode greatly increased the “selectivity” in terms of the products. One persistent challenge in using CO₂ to generate CO in aqueous solutions is that there is an inevitable competition between the formation of CO and the formation of hydrogen. That tendency was eased by adding DNA to the catalyst in solution — and even more so when the catalyst was immobilized on the electrode using DNA. For both the cobalt-porphyrin catalyst and the hemin-based catalyst, the formation of CO relative to hydrogen was significantly higher with the DNA-linked catalyst on the electrode than in solution. With the iron-porphyrin catalyst they were about the same. “With the iron, it doesn’t matter whether it’s in solution or on the electrode,” Furst explains. “Both of them have selectivity for CO, so that’s good, too.”

Progress and plans

Furst and her team have now demonstrated that their DNA-based approach combines the advantages of the traditional solid-state catalysts and the newer small-molecule ones. In their experiments, they achieved the highly efficient chemical conversion of CO₂ to CO and also were able to control the mix of products formed. And they believe that their technique should prove scalable: DNA is inexpensive and widely available, and the amount of catalyst required is several orders of magnitude lower when it’s immobilized using DNA.

Based on her work thus far, Furst hypothesizes that the structure and spacing of the small molecules on the electrode may directly impact both catalytic efficiency and product selectivity. Using DNA to control the precise positioning of her small-molecule catalysts, she plans to evaluate those impacts and then extrapolate design parameters that can be applied to other classes of energy-conversion catalysts. Ultimately, she hopes to develop a predictive algorithm that researchers can use as they design electrocatalytic systems for a wide variety of applications.

This research was supported by a grant from the MIT Energy Initiative Seed Fund.

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

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MIT students contribute to success of historic fusion experiment

For more than half a century, researchers around the world have been engaged in attempts to achieve fusion ignition in a laboratory, a grand challenge of the 21st century. The High-Energy-Density Physics (HEDP) group at MIT’s Plasma Science and Fusion Center has focused on an approach called inertial confinement fusion (ICF), which uses lasers to implode a pellet of fuel in a quest for ignition. This group, including nine former and current MIT students, was crucial to an historic ICF ignition experiment performed in 2021; the results were published on the anniversary of that success.

On Aug. 8, 2021, researchers at the National Ignition Facility (NIF), Lawrence Livermore National Laboratory (LLNL), used 192 laser beams to illuminate the inside of a tiny gold cylinder encapsulating a spherical capsule filled with deuterium-tritium fuel in their quest to produce significant fusion energy. Although researchers had followed this process many times before, using different parameters, this time the ensuing implosion produced an historic fusion yield of 1.37 megaJoules, as measured by a suite of neutron diagnostics. These included the MIT-developed and analyzed Magnetic Recoil Spectrometer (MRS). This result was published in Physical Review Letters on Aug. 8, the one-year anniversary of the ground-breaking development, unequivocally indicating that the first controlled fusion experiment reached ignition.

Governed by the Lawson criterion, a plasma ignites when the internal fusion heating power is high enough to overcome the physical processes that cool the fusion plasma, creating a positive thermodynamic feedback loop that very rapidly increases the plasma temperature. In the case of ICF, ignition is a state where the fusion plasma can initiate a “fuel burn propagation” into the surrounding dense and cold fuel, enabling the possibility of high fusion-energy gain.

“This historic result certainly demonstrates that the ignition threshold is a real concept, with well-predicted theoretical calculations, and that a fusion plasma can be ignited in a laboratory” says HEDP Division Head Johan Frenje.

The HEDP division has contributed to the success of the ignition program at the NIF for more than a decade by providing and using a dozen diagnostics, implemented by MIT PhD students and staff, which have been critical for assessing the performance of an implosion. The hundreds of co-authors on the paper attest to the collaborative effort that went into this milestone. MIT’s contributors included the only student co-authors.

“The students are responsible for implementing and using a diagnostic to obtain data important to the ICF program at the NIF, says Frenje. “Being responsible for running a diagnostic at the NIF has allowed them to actively participate in the scientific dialog and thus get directly exposed to cutting-edge science.”

Students involved from the MIT Department of Physics were Neel Kabadi, Graeme Sutcliffe, Tim Johnson, Jacob Pearcy, and Ben Reichelt; students from the Department of Nuclear Science and Engineering included Brandon Lahmann, Patrick Adrian, and Justin Kunimune.

In addition, former student Alex Zylstra PhD ’15, now a physicist at LLNL, was the experimental lead of this record implosion experiment.

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