Reality check on technologies to remove carbon dioxide from the air

In 2015, 195 nations plus the European Union signed the Paris Agreement and pledged to undertake plans designed to limit the global temperature increase to 1.5 degrees Celsius. Yet in 2023, the world exceeded that target for most, if not all of, the year — calling into question the long-term feasibility of achieving that target.

To do so, the world must reduce the levels of greenhouse gases in the atmosphere, and strategies for achieving levels that will “stabilize the climate” have been both proposed and adopted. Many of those strategies combine dramatic cuts in carbon dioxide (CO₂) emissions with the use of direct air capture (DAC), a technology that removes CO₂ from the ambient air. As a reality check, a team of researchers in the MIT Energy Initiative (MITEI) examined those strategies, and what they found was alarming: The strategies rely on overly optimistic — indeed, unrealistic — assumptions about how much CO₂ could be removed by DAC. As a result, the strategies won’t perform as predicted. Nevertheless, the MITEI team recommends that work to develop the DAC technology continue so that it’s ready to help with the energy transition — even if it’s not the silver bullet that solves the world’s decarbonization challenge.

DAC: The promise and the reality

Including DAC in plans to stabilize the climate makes sense. Much work is now under way to develop DAC systems, and the technology looks promising. While companies may never run their own DAC systems, they can already buy “carbon credits” based on DAC. Today, a multibillion-dollar market exists on which entities or individuals that face high costs or excessive disruptions to reduce their own carbon emissions can pay others to take emissions-reducing actions on their behalf. Those actions can involve undertaking new renewable energy projects or “carbon-removal” initiatives such as DAC or afforestation/reforestation (planting trees in areas that have never been forested or that were forested in the past). 

DAC-based credits are especially appealing for several reasons, explains Howard Herzog, a senior research engineer at MITEI. With DAC, measuring and verifying the amount of carbon removed is straightforward; the removal is immediate, unlike with planting forests, which may take decades to have an impact; and when DAC is coupled with CO₂ storage in geologic formations, the CO₂ is kept out of the atmosphere essentially permanently — in contrast to, for example, sequestering it in trees, which may one day burn and release the stored CO₂.

Will current plans that rely on DAC be effective in stabilizing the climate in the coming years? To find out, Herzog and his colleagues Jennifer Morris and Angelo Gurgel, both MITEI principal research scientists, and Sergey Paltsev, a MITEI senior research scientist — all affiliated with the MIT Center for Sustainability Science and Strategy (CS3) — took a close look at the modeling studies on which those plans are based.

Their investigation identified three unavoidable engineering challenges that together lead to a fourth challenge — high costs for removing a single ton of CO₂ from the atmosphere. The details of their findings are reported in a paper published in the journal One Earth on Sept. 20.

Challenge 1: Scaling up

When it comes to removing CO₂ from the air, nature presents “a major, non-negotiable challenge,” notes the MITEI team: The concentration of CO₂ in the air is extremely low — just 420 parts per million, or roughly 0.04 percent. In contrast, the CO₂ concentration in flue gases emitted by power plants and industrial processes ranges from 3 percent to 20 percent. Companies now use various carbon capture and sequestration (CCS) technologies to capture CO₂ from their flue gases, but capturing CO₂ from the air is much more difficult. To explain, the researchers offer the following analogy: “The difference is akin to needing to find 10 red marbles in a jar of 25,000 marbles of which 24,990 are blue [the task representing DAC] versus needing to find about 10 red marbles in a jar of 100 marbles of which 90 are blue [the task for CCS].”

Given that low concentration, removing a single metric ton (tonne) of CO₂ from air requires processing about 1.8 million cubic meters of air, which is roughly equivalent to the volume of 720 Olympic-sized swimming pools. And all that air must be moved across a CO₂-capturing sorbent — a feat requiring large equipment. For example, one recently proposed design for capturing 1 million tonnes of CO₂ per year would require an “air contactor” equivalent in size to a structure about three stories high and three miles long.

Recent modeling studies project DAC deployment on the scale of 5 to 40 gigatonnes of CO₂ removed per year. (A gigatonne equals 1 billion metric tonnes.) But in their paper, the researchers conclude that the likelihood of deploying DAC at the gigatonne scale is “highly uncertain.”

Challenge 2: Energy requirement

Given the low concentration of CO₂ in the air and the need to move large quantities of air to capture it, it’s no surprise that even the best DAC processes proposed today would consume large amounts of energy — energy that’s generally supplied by a combination of electricity and heat. Including the energy needed to compress the captured CO₂ for transportation and storage, most proposed processes require an equivalent of at least 1.2 megawatt-hours of electricity for each tonne of CO₂ removed.

The source of that electricity is critical. For example, using coal-based electricity to drive an all-electric DAC process would generate 1.2 tonnes of CO₂ for each tonne of CO₂ captured. The result would be a net increase in emissions, defeating the whole purpose of the DAC. So clearly, the energy requirement must be satisfied using either low-carbon electricity or electricity generated using fossil fuels with CCS. All-electric DAC deployed at large scale — say, 10 gigatonnes of CO₂ removed annually — would require 12,000 terawatt-hours of electricity, which is more than 40 percent of total global electricity generation today.

Electricity consumption is expected to grow due to increasing overall electrification of the world economy, so low-carbon electricity will be in high demand for many competing uses — for example, in power generation, transportation, industry, and building operations. Using clean electricity for DAC instead of for reducing CO₂ emissions in other critical areas raises concerns about the best uses of clean electricity.

Many studies assume that a DAC unit could also get energy from “waste heat” generated by some industrial process or facility nearby. In the MITEI researchers’ opinion, “that may be more wishful thinking than reality.” The heat source would need to be within a few miles of the DAC plant for transporting the heat to be economical; given its high capital cost, the DAC plant would need to run nonstop, requiring constant heat delivery; and heat at the temperature required by the DAC plant would have competing uses, for example, for heating buildings. Finally, if DAC is deployed at the gigatonne per year scale, waste heat will likely be able to provide only a small fraction of the needed energy.

Challenge 3: Siting

Some analysts have asserted that, because air is everywhere, DAC units can be located anywhere. But in reality, siting a DAC plant involves many complex issues. As noted above, DAC plants require significant amounts of energy, so having access to enough low-carbon energy is critical. Likewise, having nearby options for storing the removed CO₂ is also critical. If storage sites or pipelines to such sites don’t exist, major new infrastructure will need to be built, and building new infrastructure of any kind is expensive and complicated, involving issues related to permitting, environmental justice, and public acceptability — issues that are, in the words of the researchers, “commonly underestimated in the real world and neglected in models.”

Two more siting needs must be considered. First, meteorological conditions must be acceptable. By definition, any DAC unit will be exposed to the elements, and factors like temperature and humidity will affect process performance and process availability. And second, a DAC plant will require some dedicated land — though how much is unclear, as the optimal spacing of units is as yet unresolved. Like wind turbines, DAC units need to be properly spaced to ensure maximum performance such that one unit is not sucking in CO₂-depleted air from another unit.

Challenge 4: Cost

Considering the first three challenges, the final challenge is clear: the cost per tonne of CO₂ removed is inevitably high. Recent modeling studies assume DAC costs as low as $100 to $200 per ton of CO₂ removed. But the researchers found evidence suggesting far higher costs.

To start, they cite typical costs for power plants and industrial sites that now use CCS to remove CO₂ from their flue gases. The cost of CCS in such applications is estimated to be in the range of $50 to $150 per ton of CO₂ removed. As explained above, the far lower concentration of CO₂ in the air will lead to substantially higher costs.

As explained under Challenge 1, the DAC units needed to capture the required amount of air are massive. The capital cost of building them will be high, given labor, materials, permitting costs, and so on. Some estimates in the literature exceed $5,000 per tonne captured per year.

Then there are the ongoing costs of energy. As noted under Challenge 2, removing 1 tonne of CO₂ requires the equivalent of 1.2 megawatt-hours of electricity. If that electricity costs $0.10 per kilowatt-hour, the cost of just the electricity needed to remove 1 tonne of CO₂ is $120. The researchers point out that assuming such a low price is “questionable,” given the expected increase in electricity demand, future competition for clean energy, and higher costs on a system dominated by renewable — but intermittent — energy sources.

Then there’s the cost of storage, which is ignored in many DAC cost estimates.

Clearly, many considerations show that prices of $100 to $200 per tonne are unrealistic, and assuming such low prices will distort assessments of strategies, leading them to underperform going forward.

The bottom line

In their paper, the MITEI team calls DAC a “very seductive concept.” Using DAC to suck CO₂ out of the air and generate high-quality carbon-removal credits can offset reduction requirements for industries that have hard-to-abate emissions. By doing so, DAC would minimize disruptions to key parts of the world’s economy, including air travel, certain carbon-intensive industries, and agriculture. However, the world would need to generate billions of tonnes of CO₂ credits at an affordable price. That prospect doesn’t look likely. The largest DAC plant in operation today removes just 4,000 tonnes of CO₂ per year, and the price to buy the company’s carbon-removal credits on the market today is $1,500 per tonne.

The researchers recognize that there is room for energy efficiency improvements in the future, but DAC units will always be subject to higher work requirements than CCS applied to power plant or industrial flue gases, and there is not a clear pathway to reducing work requirements much below the levels of current DAC technologies.

Nevertheless, the researchers recommend that work to develop DAC continue “because it may be needed for meeting net-zero emissions goals, especially given the current pace of emissions.” But their paper concludes with this warning: “Given the high stakes of climate change, it is foolhardy to rely on DAC to be the hero that comes to our rescue.”

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A bioinspired capsule can pump drugs directly into the walls of the GI tract

Inspired by the way that squids use jets to propel themselves through the ocean and shoot ink clouds, researchers from MIT and Novo Nordisk have developed an ingestible capsule that releases a burst of drugs directly into the wall of the stomach or other organs of the digestive tract.

This capsule could offer an alternative way to deliver drugs that normally have to be injected, such as insulin and other large proteins, including antibodies. This needle-free strategy could also be used to deliver RNA, either as a vaccine or a therapeutic molecule to treat diabetes, obesity, and other metabolic disorders.

“One of the longstanding challenges that we’ve been exploring is the development of systems that enable the oral delivery of macromolecules that usually require an injection to be administered. This work represents one of the next major advances in that progression,” says Giovanni Traverso, director of the Laboratory for Translational Engineering and an associate professor of mechanical engineering at MIT, a gastroenterologist at Brigham and Women’s Hospital, an associate member of the Broad Institute, and the senior author of the study.

Traverso and his students at MIT developed the new capsule along with researchers at Brigham and Women’s Hospital and Novo Nordisk. Graham Arrick SM ’20 and Novo Nordisk scientists Drago Sticker and Aghiad Ghazal are the lead authors of the paper, which appears today in Nature.

Inspired by cephalopods

Drugs that consist of large proteins or RNA typically can’t be taken orally because they are easily broken down in the digestive tract. For several years, Traverso’s lab has been working on ways to deliver such drugs orally by encapsulating them in small devices that protect the drugs from degradation and then inject them directly into the lining of the digestive tract.

Most of these capsules use a small needle or set of microneedles to deliver drugs once the device arrives in the digestive tract. In the new study, Traverso and his colleagues wanted to explore ways to deliver these molecules without any kind of needle, which could reduce the possibility of any damage to the tissue.

To achieve that, they took inspiration from cephalopods. Squids and octopuses can propel themselves by filling their mantle cavity with water, then rapidly expelling it through their siphon. By changing the force of water expulsion and pointing the siphon in different directions, the animals can control their speed and direction of travel. The siphon organ also allows cephalopods to shoot jets of ink, forming decoy clouds to distract predators.

The researchers came up with two ways to mimic this jetting action, using compressed carbon dioxide or tightly coiled springs to generate the force needed to propel liquid drugs out of the capsule. The gas or spring is kept in a compressed state by a carbohydrate trigger, which is designed to dissolve when exposed to humidity or an acidic environment such as the stomach. When the trigger dissolves, the gas or spring is allowed to expand, propelling a jet of drugs out of the capsule.

In a series of experiments using tissue from the digestive tract, the researchers calculated the pressures needed to expel the drugs with enough force that they would penetrate the submucosal tissue and accumulate there, creating a depot that would then release drugs into the tissue.

“Aside from the elimination of sharps, another potential advantage of high-velocity columnated jets is their robustness to localization issues. In contrast to a small needle, which needs to have intimate contact with the tissue, our experiments indicated that a jet may be able to deliver most of the dose from a distance or at a slight angle,” Arrick says.

The researchers also designed the capsules so that they can target different parts of the digestive tract. One version of the capsule, which has a flat bottom and a high dome, can sit on a surface, such as the lining of the stomach, and eject drug downward into the tissue. This capsule, which was inspired by previous research from Traverso’s lab on self-orienting capsules, is about the size of a blueberry and can carry 80 microliters of drug.

The second version has a tube-like shape that allows it to align itself within a long tubular organ such as the esophagus or small intestine. In that case, the drug is ejected out toward the side wall, rather than downward. This version can deliver 200 microliters of drug.

Made of metal and plastic, the capsules can pass through the digestive tract and are excreted after releasing their drug payload.

Needle-free drug delivery

In tests in animals, the researchers showed that they could use these capsules to deliver insulin, a GLP-1 receptor agonist similar to the diabetes drug Ozempic, and a type of RNA called short interfering RNA (siRNA). This type of RNA can be used to silence genes, making it potentially useful in treating many genetic disorders.

They also showed that the concentration of the drugs in the animals’ bloodstream reached levels on the same order of magnitude as those seen when the drugs were injected with a syringe, and they did not detect any tissue damage.

The researchers envision that the ingestible capsule could be used at home by patients who need to take insulin or other injected drugs frequently. In addition to making it easier to administer drugs, especially for patients who don’t like needles, this approach also eliminates the need to dispose of sharp needles. The researchers also created and tested a version of the device that could be attached to an endoscope, allowing doctors to use it in an endoscopy suite or operating room to deliver drugs to a patient.

“This technology is a significant leap forward in oral drug delivery of macromolecule drugs like insulin and GLP-1 agonists. While many approaches for oral drug delivery have been attempted in the past, they tend to be poorly efficient in achieving high bioavailability. Here, the researchers demonstrate the ability to deliver bioavailability in animal models with high efficiency. This is an exciting approach which could be impactful for many biologics which are currently administered through injections or intravascular infusions,” says Omid Veiseh, a professor of bioengineering at Rice University, who was not involved in the research.

The researchers now plan to further develop the capsules, in hopes of testing them in humans.

The research was funded by Novo Nordisk, the Natural Sciences and Engineering Research Council of Canada, the MIT Department of Mechanical Engineering, Brigham and Women’s Hospital, and the U.S. Advanced Research Projects Agency for Health.

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Undergraduates with family income below $200,000 can expect to attend MIT tuition-free starting in 2025

Undergraduates with family income below $200,000 can expect to attend MIT tuition-free starting next fall, thanks to newly expanded financial aid. Eighty percent of American households meet this income threshold.

And for the 50 percent of American families with income below $100,000, parents can expect to pay nothing at all toward the full cost of their students’ MIT education, which includes tuition as well as housing, dining, fees, and an allowance for books and personal expenses.

This $100,000 threshold is up from $75,000 this year, while next year’s $200,000 threshold for tuition-free attendance will increase from its current level of $140,000.

These new steps to enhance MIT’s affordability for students and families are the latest in a long history of efforts by the Institute to free up more resources to make an MIT education as affordable and accessible as possible. Toward that end, MIT has earmarked $167.3 million in need-based financial aid this year for undergraduate students — up some 70 percent from a decade ago.

“MIT’s distinctive model of education — intense, demanding, and rooted in science and engineering — has profound practical value to our students and to society,” MIT President Sally Kornbluth says. “As the Wall Street Journal recently reported, MIT is better at improving the financial futures of its graduates than any other U.S. college, and the Institute also ranks number one in the world for the employability of its graduates.” 

“The cost of college is a real concern for families across the board,” Kornbluth adds, “and we’re determined to make this transformative educational experience available to the most talented students, whatever their financial circumstances. So, to every student out there who dreams of coming to MIT: Don’t let concerns about cost stand in your way.”

MIT is one of only nine colleges in the US that does not consider applicants’ ability to pay as part of its admissions process and that meets the full demonstrated financial need ⁠for all undergraduates. MIT does not expect students on aid to take loans, and, unlike many other institutions, MIT does not provide an admissions advantage to the children of alumni or donors. Indeed, 18 percent of current MIT undergraduates are first-generation college students.

“We believe MIT should be the preeminent destination for the most talented students in the country interested in an education centered on science and technology, and accessible to the best students regardless of their financial circumstances,” says Stu Schmill, MIT’s dean of admissions and student financial services.

“With the need-based financial aid we provide today, our education is much more affordable now than at any point in the past,” adds Schmill, who graduated from MIT in 1986, “even though the ‘sticker price’ of MIT is higher now than it was when I was an undergraduate.”

Last year, the median annual cost paid by an MIT undergraduate receiving financial aid was $12,938⁠, allowing 87 percent of students in the Class of 2024 to graduate debt-free. Those who did borrow graduated with median debt of $14,844. At the same time, graduates benefit from the lifelong value of an MIT degree, with an average starting salary of $126,438 for graduates entering industry, according to MIT’s most recent survey of its graduating students.

MIT’s endowment — made up of generous gifts made by individual alumni and friends — allows the Institute to provide this level of financial aid, both now and into the future.

“Today’s announcement is a powerful expression of how much our graduates value their MIT experience,” Kornbluth says, “because our ability to provide financial aid of this scope depends on decades of individual donations to our endowment, from generations of MIT alumni and other friends. In effect, our endowment is an inter-generational gift from past MIT students to the students of today and tomorrow.”

What MIT families can expect in 2025

As noted earlier: Starting next fall, for families with income below $100,000, with typical assets, parents can expect to pay nothing for the full cost of attendance, which includes tuition, housing, dining, fees, and allowances for books and personal expenses.

For families with income from $100,000 to $200,000, with typical assets, parents can expect to pay on a sliding scale from $0 up to a maximum of around $23,970, which is this year’s total cost for MIT housing, dining, fees, and allowances for books and personal expenses.

Put another way, next year all MIT families with income below $200,000 can expect to contribute well below $27,146, which is the annual average cost for in-state students to attend and live on campus at public universities in the US, according to the Education Data Initiative. And even among families with income above $200,000, many still receive need-based financial aid from MIT, based on their unique financial circumstances. Families can use MIT’s online calculators to estimate the cost of attendance for their specific family.

This past summer, MIT’s faculty-led Committee on Undergraduate Admissions and Financial Aid was publicly charged by President Kornbluth with undertaking a review of the Institute’s admissions and financial aid policies, to ensure that MIT remains as fully accessible as possible to all students, regardless of their financial circumstances. The steps announced today are the first of these recommendations to be reviewed and adopted.

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A model of virtuosity

A crowd gathered at the MIT Media Lab in September for a concert by musician Jordan Rudess and two collaborators. One of them, violinist and vocalist Camilla Bäckman, has performed with Rudess before. The other — an artificial intelligence model informally dubbed the jam_bot, which Rudess developed with an MIT team over the preceding several months — was making its public debut as a work in progress.

Throughout the show, Rudess and Bäckman exchanged the signals and smiles of experienced musicians finding a groove together. Rudess’ interactions with the jam_bot suggested a different and unfamiliar kind of exchange. During one duet inspired by Bach, Rudess alternated between playing a few measures and allowing the AI to continue the music in a similar baroque style. Each time the model took its turn, a range of expressions moved across Rudess’ face: bemusement, concentration, curiosity. At the end of the piece, Rudess admitted to the audience, “That is a combination of a whole lot of fun and really, really challenging.”

Rudess is an acclaimed keyboardist — the best of all time, according to one Music Radar magazine poll — known for his work with the platinum-selling, Grammy-winning progressive metal band Dream Theater, which embarks this fall on a 40th anniversary tour. He is also a solo artist whose latest album, “Permission to Fly,” was released on Sept. 6; an educator who shares his skills through detailed online tutorials; and the founder of software company Wizdom Music. His work combines a rigorous classical foundation (he began his piano studies at The Juilliard School at age 9) with a genius for improvisation and an appetite for experimentation.

Last spring, Rudess became a visiting artist with the MIT Center for Art, Science and Technology (CAST), collaborating with the MIT Media Lab’s Responsive Environments research group on the creation of new AI-powered music technology. Rudess’ main collaborators in the enterprise are Media Lab graduate students Lancelot Blanchard, who researches musical applications of generative AI (informed by his own studies in classical piano), and Perry Naseck, an artist and engineer specializing in interactive, kinetic, light- and time-based media. Overseeing the project is Professor Joseph Paradiso, head of the Responsive Environments group and a longtime Rudess fan. Paradiso arrived at the Media Lab in 1994 with a CV in physics and engineering and a sideline designing and building synthesizers to explore his avant-garde musical tastes. His group has a tradition of investigating musical frontiers through novel user interfaces, sensor networks, and unconventional datasets.

The researchers set out to develop a machine learning model channeling Rudess’ distinctive musical style and technique. In a paper published online by MIT Press in September, co-authored with MIT music technology professor Eran Egozy, they articulate their vision for what they call “symbiotic virtuosity:” for human and computer to duet in real-time, learning from each duet they perform together, and making performance-worthy new music in front of a live audience.

Rudess contributed the data on which Blanchard trained the AI model. Rudess also provided continuous testing and feedback, while Naseck experimented with ways of visualizing the technology for the audience.

“Audiences are used to seeing lighting, graphics, and scenic elements at many concerts, so we needed a platform to allow the AI to build its own relationship with the audience,” Naseck says. In early demos, this took the form of a sculptural installation with illumination that shifted each time the AI changed chords. During the concert on Sept. 21, a grid of petal-shaped panels mounted behind Rudess came to life through choreography based on the activity and future generation of the AI model.

“If you see jazz musicians make eye contact and nod at each other, that gives anticipation to the audience of what’s going to happen,” says Naseck. “The AI is effectively generating sheet music and then playing it. How do we show what’s coming next and communicate that?”

Naseck designed and programmed the structure from scratch at the Media Lab with assistance from Brian Mayton (mechanical design) and Carlo Mandolini (fabrication), drawing some of its movements from an experimental machine learning model developed by visiting student Madhav Lavakare that maps music to points moving in space. With the ability to spin and tilt its petals at speeds ranging from subtle to dramatic, the kinetic sculpture distinguished the AI’s contributions during the concert from those of the human performers, while conveying the emotion and energy of its output: swaying gently when Rudess took the lead, for example, or furling and unfurling like a blossom as the AI model generated stately chords for an improvised adagio. The latter was one of Naseck’s favorite moments of the show.

“At the end, Jordan and Camilla left the stage and allowed the AI to fully explore its own direction,” he recalls. “The sculpture made this moment very powerful — it allowed the stage to remain animated and intensified the grandiose nature of the chords the AI played. The audience was clearly captivated by this part, sitting at the edges of their seats.”

“The goal is to create a musical visual experience,” says Rudess, “to show what’s possible and to up the game.”

Musical futures

As the starting point for his model, Blanchard used a music transformer, an open-source neural network architecture developed by MIT Assistant Professor Anna Huang SM ’08, who joined the MIT faculty in September.

“Music transformers work in a similar way as large language models,” Blanchard explains. “The same way that ChatGPT would generate the most probable next word, the model we have would predict the most probable next notes.”

Blanchard fine-tuned the model using Rudess’ own playing of elements from bass lines to chords to melodies, variations of which Rudess recorded in his New York studio. Along the way, Blanchard ensured the AI would be nimble enough to respond in real-time to Rudess’ improvisations.

“We reframed the project,” says Blanchard, “in terms of musical futures that were hypothesized by the model and that were only being realized at the moment based on what Jordan was deciding.”

As Rudess puts it: “How can the AI respond — how can I have a dialogue with it? That’s the cutting-edge part of what we’re doing.”

Another priority emerged: “In the field of generative AI and music, you hear about startups like Suno or Udio that are able to generate music based on text prompts. Those are very interesting, but they lack controllability,” says Blanchard. “It was important for Jordan to be able to anticipate what was going to happen. If he could see the AI was going to make a decision he didn’t want, he could restart the generation or have a kill switch so that he can take control again.”

In addition to giving Rudess a screen previewing the musical decisions of the model, Blanchard built in different modalities the musician could activate as he plays — prompting the AI to generate chords or lead melodies, for example, or initiating a call-and-response pattern.

“Jordan is the mastermind of everything that’s happening,” he says.

What would Jordan do

Though the residency has wrapped up, the collaborators see many paths for continuing the research. For example, Naseck would like to experiment with more ways Rudess could interact directly with his installation, through features like capacitive sensing. “We hope in the future we’ll be able to work with more of his subtle motions and posture,” Naseck says.

While the MIT collaboration focused on how Rudess can use the tool to augment his own performances, it’s easy to imagine other applications. Paradiso recalls an early encounter with the tech: “I played a chord sequence, and Jordan’s model was generating the leads. It was like having a musical ‘bee’ of Jordan Rudess buzzing around the melodic foundation I was laying down, doing something like Jordan would do, but subject to the simple progression I was playing,” he recalls, his face echoing the delight he felt at the time. “You're going to see AI plugins for your favorite musician that you can bring into your own compositions, with some knobs that let you control the particulars,” he posits. “It’s that kind of world we’re opening up with this.”

Rudess is also keen to explore educational uses. Because the samples he recorded to train the model were similar to ear-training exercises he’s used with students, he thinks the model itself could someday be used for teaching. “This work has legs beyond just entertainment value,” he says.

The foray into artificial intelligence is a natural progression for Rudess’ interest in music technology. “This is the next step,” he believes. When he discusses the work with fellow musicians, however, his enthusiasm for AI often meets with resistance. “I can have sympathy or compassion for a musician who feels threatened, I totally get that,” he allows. “But my mission is to be one of the people who moves this technology toward positive things.”

“At the Media Lab, it’s so important to think about how AI and humans come together for the benefit of all,” says Paradiso. “How is AI going to lift us all up? Ideally it will do what so many technologies have done — bring us into another vista where we’re more enabled.”

“Jordan is ahead of the pack,” Paradiso adds. “Once it’s established with him, people will follow.”

Jamming with MIT

The Media Lab first landed on Rudess’ radar before his residency because he wanted to try out the Knitted Keyboard created by another member of Responsive Environments, textile researcher Irmandy Wickasono PhD ’24. From that moment on, “It's been a discovery for me, learning about the cool things that are going on at MIT in the music world,” Rudess says.

During two visits to Cambridge last spring (assisted by his wife, theater and music producer Danielle Rudess), Rudess reviewed final projects in Paradiso’s course on electronic music controllers, the syllabus for which included videos of his own past performances. He brought a new gesture-driven synthesizer called Osmose to a class on interactive music systems taught by Egozy, whose credits include the co-creation of the video game “Guitar Hero.” Rudess also provided tips on improvisation to a composition class; played GeoShred, a touchscreen musical instrument he co-created with Stanford University researchers, with student musicians in the MIT Laptop Ensemble and Arts Scholars program; and experienced immersive audio in the MIT Spatial Sound Lab. During his most recent trip to campus in September, he taught a masterclass for pianists in MIT’s Emerson/Harris Program, which provides a total of 67 scholars and fellows with support for conservatory-level musical instruction.

“I get a kind of rush whenever I come to the university,” Rudess says. “I feel the sense that, wow, all of my musical ideas and inspiration and interests have come together in this really cool way.”

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Can robots learn from machine dreams?

For roboticists, one challenge towers above all others: generalization — the ability to create machines that can adapt to any environment or condition. Since the 1970s, the field has evolved from writing sophisticated programs to using deep learning, teaching robots to learn directly from human behavior. But a critical bottleneck remains: data quality. To improve, robots need to encounter scenarios that push the boundaries of their capabilities, operating at the edge of their mastery. This process traditionally requires human oversight, with operators carefully challenging robots to expand their abilities. As robots become more sophisticated, this hands-on approach hits a scaling problem: the demand for high-quality training data far outpaces humans’ ability to provide it.

Now, a team of MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers has developed a novel approach to robot training that could significantly accelerate the deployment of adaptable, intelligent machines in real-world environments. The new system, called “LucidSim,” uses recent advances in generative AI and physics simulators to create diverse and realistic virtual training environments, helping robots achieve expert-level performance in difficult tasks without any real-world data.

LucidSim combines physics simulation with generative AI models, addressing one of the most persistent challenges in robotics: transferring skills learned in simulation to the real world. “A fundamental challenge in robot learning has long been the ‘sim-to-real gap’ — the disparity between simulated training environments and the complex, unpredictable real world,” says MIT CSAIL postdoc Ge Yang, a lead researcher on LucidSim. “Previous approaches often relied on depth sensors, which simplified the problem but missed crucial real-world complexities.”

The multipronged system is a blend of different technologies. At its core, LucidSim uses large language models to generate various structured descriptions of environments. These descriptions are then transformed into images using generative models. To ensure that these images reflect real-world physics, an underlying physics simulator is used to guide the generation process.

The birth of an idea: From burritos to breakthroughs

The inspiration for LucidSim came from an unexpected place: a conversation outside Beantown Taqueria in Cambridge, Massachusetts. ​​“We wanted to teach vision-equipped robots how to improve using human feedback. But then, we realized we didn’t have a pure vision-based policy to begin with,” says Alan Yu, an undergraduate student in electrical engineering and computer science (EECS) at MIT and co-lead author on LucidSim. “We kept talking about it as we walked down the street, and then we stopped outside the taqueria for about half-an-hour. That’s where we had our moment.”

To cook up their data, the team generated realistic images by extracting depth maps, which provide geometric information, and semantic masks, which label different parts of an image, from the simulated scene. They quickly realized, however, that with tight control on the composition of the image content, the model would produce similar images that weren’t different from each other using the same prompt. So, they devised a way to source diverse text prompts from ChatGPT.

This approach, however, only resulted in a single image. To make short, coherent videos that serve as little “experiences” for the robot, the scientists hacked together some image magic into another novel technique the team created, called “Dreams In Motion.” The system computes the movements of each pixel between frames, to warp a single generated image into a short, multi-frame video. Dreams In Motion does this by considering the 3D geometry of the scene and the relative changes in the robot’s perspective.

“We outperform domain randomization, a method developed in 2017 that applies random colors and patterns to objects in the environment, which is still considered the go-to method these days,” says Yu. “While this technique generates diverse data, it lacks realism. LucidSim addresses both diversity and realism problems. It’s exciting that even without seeing the real world during training, the robot can recognize and navigate obstacles in real environments.”

The team is particularly excited about the potential of applying LucidSim to domains outside quadruped locomotion and parkour, their main test bed. One example is mobile manipulation, where a mobile robot is tasked to handle objects in an open area; also, color perception is critical. “Today, these robots still learn from real-world demonstrations,” says Yang. “Although collecting demonstrations is easy, scaling a real-world robot teleoperation setup to thousands of skills is challenging because a human has to physically set up each scene. We hope to make this easier, thus qualitatively more scalable, by moving data collection into a virtual environment.”

Who's the real expert?

The team put LucidSim to the test against an alternative, where an expert teacher demonstrates the skill for the robot to learn from. The results were surprising: Robots trained by the expert struggled, succeeding only 15 percent of the time — and even quadrupling the amount of expert training data barely moved the needle. But when robots collected their own training data through LucidSim, the story changed dramatically. Just doubling the dataset size catapulted success rates to 88 percent. “And giving our robot more data monotonically improves its performance — eventually, the student becomes the expert,” says Yang.

“One of the main challenges in sim-to-real transfer for robotics is achieving visual realism in simulated environments,” says Stanford University assistant professor of electrical engineering Shuran Song, who wasn’t involved in the research. “The LucidSim framework provides an elegant solution by using generative models to create diverse, highly realistic visual data for any simulation. This work could significantly accelerate the deployment of robots trained in virtual environments to real-world tasks.”

From the streets of Cambridge to the cutting edge of robotics research, LucidSim is paving the way toward a new generation of intelligent, adaptable machines — ones that learn to navigate our complex world without ever setting foot in it.

Yu and Yang wrote the paper with four fellow CSAIL affiliates: Ran Choi, an MIT postdoc in mechanical engineering; Yajvan Ravan, an MIT undergraduate in EECS; John Leonard, the Samuel C. Collins Professor of Mechanical and Ocean Engineering in the MIT Department of Mechanical Engineering; and Phillip Isola, an MIT associate professor in EECS. Their work was supported, in part, by a Packard Fellowship, a Sloan Research Fellowship, the Office of Naval Research, Singapore’s Defence Science and Technology Agency, Amazon, MIT Lincoln Laboratory, and the National Science Foundation Institute for Artificial Intelligence and Fundamental Interactions. The researchers presented their work at the Conference on Robot Learning (CoRL) in early November.

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Turning automotive engines into modular chemical plants to make green fuels

Reducing methane emissions is a top priority in the fight against climate change because of its propensity to trap heat in the atmosphere: Methane’s warming effects are 84 times more potent than CO2 over a 20-year timescale.

And yet, as the main component of natural gas, methane is also a valuable fuel and a precursor to several important chemicals. The main barrier to using methane emissions to create carbon-negative materials is that human sources of methane gas — landfills, farms, and oil and gas wells — are relatively small and spread out across large areas, while traditional chemical processing facilities are huge and centralized. That makes it prohibitively expensive to capture, transport, and convert methane gas into anything useful. As a result, most companies burn or “flare” their methane at the site where it’s emitted, seeing it as a sunk cost and an environmental liability.

The MIT spinout Emvolon is taking a new approach to processing methane by repurposing automotive engines to serve as modular, cost-effective chemical plants. The company’s systems can take methane gas and produce liquid fuels like methanol and ammonia on-site; these fuels can then be used or transported in standard truck containers.

"We see this as a new way of chemical manufacturing,” Emvolon co-founder and CEO Emmanuel Kasseris SM ’07, PhD ’11 says. “We’re starting with methane because methane is an abundant emission that we can use as a resource. With methane, we can solve two problems at the same time: About 15 percent of global greenhouse gas emissions come from hard-to-abate sectors that need green fuel, like shipping, aviation, heavy heavy-duty trucks, and rail. Then another 15 percent of emissions come from distributed methane emissions like landfills and oil wells.”

By using mass-produced engines and eliminating the need to invest in infrastructure like pipelines, the company says it’s making methane conversion economically attractive enough to be adopted at scale. The system can also take green hydrogen produced by intermittent renewables and turn it into ammonia, another fuel that can also be used to decarbonize fertilizers.

“In the future, we’re going to need green fuels because you can’t electrify a large ship or plane — you have to use a high-energy-density, low-carbon-footprint, low-cost liquid fuel,” Kasseris says. “The energy resources to produce those green fuels are either distributed, as is the case with methane, or variable, like wind. So, you cannot have a massive plant [producing green fuels] that has its own zip code. You either have to be distributed or variable, and both of those approaches lend themselves to this modular design.”

From a “crazy idea” to a company

Kasseris first came to MIT to study mechanical engineering as a graduate student in 2004, when he worked in the Sloan Automotive Lab on a report on the future of transportation. For his PhD, he developed a novel technology for improving internal combustion engine fuel efficiency for a consortium of automotive and energy companies, which he then went to work for after graduation.

Around 2014, he was approached by Leslie Bromberg ’73, PhD ’77, a serial inventor with more than 100 patents, who has been a principal research engineer in MIT’s Plasma Science and Fusion Center for nearly 50 years.

“Leslie had this crazy idea of repurposing an internal combustion engine as a reactor,” Kasseris recalls. “I had looked at that while working in industry, and I liked it, but my company at the time thought the work needed more validation.”

Bromberg had done that validation through a U.S. Department of Energy-funded project in which he used a diesel engine to “reform” methane — a high-pressure chemical reaction in which methane is combined with steam and oxygen to produce hydrogen. The work impressed Kasseris enough to bring him back to MIT as a research scientist in 2016.

“We worked on that idea in addition to some other projects, and eventually it had reached the point where we decided to license the work from MIT and go full throttle,” Kasseris recalls. “It’s very easy to work with MIT’s Technology Licensing Office when you are an MIT inventor. You can get a low-cost licensing option, and you can do a lot with that, which is important for a new company. Then, once you are ready, you can finalize the license, so MIT was instrumental.”

Emvolon continued working with MIT’s research community, sponsoring projects with Professor Emeritus John Heywood and participating in the MIT Venture Mentoring Service and the MIT Industrial Liaison Program.

An engine-powered chemical plant

At the core of Emvolon’s system is an off-the-shelf automotive engine that runs “fuel rich” — with a higher ratio of fuel to air than what is needed for complete combustion.

“That’s easy to say, but it takes a lot of [intellectual property], and that’s what was developed at MIT,” Kasseris says. “Instead of burning the methane in the gas to carbon dioxide and water, you partially burn it, or partially oxidize it, to carbon monoxide and hydrogen, which are the building blocks to synthesize a variety of chemicals.”

The hydrogen and carbon monoxide are intermediate products used to synthesize different chemicals through further reactions. Those processing steps take place right next to the engine, which makes its own power. Each of Emvolon’s standalone systems fits within a 40-foot shipping container and can produce about 8 tons of methanol per day from 300,000 standard cubic feet of methane gas.

The company is starting with green methanol because it’s an ideal fuel for hard-to-abate sectors such as shipping and heavy-duty transport, as well as an excellent feedstock for other high-value chemicals, such as sustainable aviation fuel. Many shipping vessels have already converted to run on green methanol in an effort to meet decarbonization goals.

This summer, the company also received a grant from the Department of Energy to adapt its process to produce clean liquid fuels from power sources like solar and wind.

“We’d like to expand to other chemicals like ammonia, but also other feedstocks, such as biomass and hydrogen from renewable electricity, and we already have promising results in that direction” Kasseris says. “We think we have a good solution for the energy transition and, in the later stages of the transition, for e-manufacturing.”

A scalable approach

Emvolon has already built a system capable of producing up to six barrels of green methanol a day in its 5,000 square-foot headquarters in Woburn, Massachusetts.

“For chemical technologies, people talk about scale up risk, but with an engine, if it works in a single cylinder, we know it will work in a multicylinder engine,” Kasseris says. “It’s just engineering.”

Last month, Emvolon announced an agreement with Montauk Renewables to build a commercial-scale demonstration unit next to a Texas landfill that will initially produce up to 15,000 gallons of green methanol a year and later scale up to 2.5 million gallons. That project could be expanded tenfold by scaling across Montauk’s other sites.

“Our whole process was designed to be a very realistic approach to the energy transition,” Kasseris says. “Our solution is designed to produce green fuels and chemicals at prices that the markets are willing to pay today, without the need for subsidies. Using the engines as chemical plants, we can get the capital expenditure per unit output close to that of a large plant, but at a modular scale that enables us to be next to low-cost feedstock. Furthermore, our modular systems require small investments — of $1 to 10 million — that are quickly deployed, one at a time, within weeks, as opposed to massive chemical plants that require multiyear capital construction projects and cost hundreds of millions.”

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MIT physicists predict exotic form of matter with potential for quantum computing

MIT physicists have shown that it should be possible to create an exotic form of matter that could be manipulated to form the qubit (quantum bit) building blocks of future quantum computers that are even more powerful than the quantum computers in development today.

The work builds on a discovery last year of materials that host electrons that can split into fractions of themselves but, importantly, can do so without the application of a magnetic field. 

The general phenomenon of electron fractionalization was first discovered in 1982 and resulted in a Nobel Prize. That work, however, required the application of a magnetic field. The ability to create the fractionalized electrons without a magnetic field opens new possibilities for basic research and makes the materials hosting them more useful for applications.

When electrons split into fractions of themselves, those fractions are known as anyons. Anyons come in variety of flavors, or classes. The anyons discovered in the 2023 materials are known as Abelian anyons. Now, in a paper reported in the Oct. 17 issue of Physical Review Letters, the MIT team notes that it should be possible to create the most exotic class of anyons, non-Abelian anyons.

“Non-Abelian anyons have the bewildering capacity of ‘remembering’ their spacetime trajectories; this memory effect can be useful for quantum computing,” says Liang Fu, a professor in MIT’s Department of Physics and leader of the work. 

Fu further notes that “the 2023 experiments on electron fractionalization greatly exceeded theoretical expectations. My takeaway is that we theorists should be bolder.”

Fu is also affiliated with the MIT Materials Research Laboratory. His colleagues on the current work are graduate students Aidan P. Reddy and Nisarga Paul, and postdoc Ahmed Abouelkomsan, all of the MIT Department of Phsyics. Reddy and Paul are co-first authors of the Physical Review Letters paper.

The MIT work and two related studies were also featured in an Oct. 17 story in Physics Magazine. “If this prediction is confirmed experimentally, it could lead to more reliable quantum computers that can execute a wider range of tasks … Theorists have already devised ways to harness non-Abelian states as workable qubits and manipulate the excitations of these states to enable robust quantum computation,” writes Ryan Wilkinson.

The current work was guided by recent advances in 2D materials, or those consisting of only one or a few layers of atoms. “The whole world of two-dimensional materials is very interesting because you can stack them and twist them, and sort of play Legos with them to get all sorts of cool sandwich structures with unusual properties,” says Paul. Those sandwich structures, in turn, are called moiré materials.

Anyons can only form in two-dimensional materials. Could they form in moiré materials? The 2023 experiments were the first to show that they can. Soon afterwards, a group led by Long Ju, an MIT assistant professor of physics, reported evidence of anyons in another moiré material. (Fu and Reddy were also involved in the Ju work.)

In the current work, the physicists showed that it should be possible to create non-Abelian anyons in a moiré material composed of atomically thin layers of molybdenum ditelluride. Says Paul, “moiré materials have already revealed fascinating phases of matter in recent years, and our work shows that non-Abelian phases could be added to the list.”

Adds Reddy, “our work shows that when electrons are added at a density of 3/2 or 5/2 per unit cell, they can organize into an intriguing quantum state that hosts non-Abelian anyons.”

The work was exciting, says Reddy, in part because “oftentimes there’s subtlety in interpreting your results and what they are actually telling you. So it was fun to think through our arguments” in support of non-Abelian anyons.

Says Paul, “this project ranged from really concrete numerical calculations to pretty abstract theory and connected the two. I learned a lot from my collaborators about some very interesting topics.”

This work was supported by the U.S. Air Force Office of Scientific Research. The authors also acknowledge the MIT SuperCloud and Lincoln Laboratory Supercomputing Center, the Kavli Institute for Theoretical Physics, the Knut and Alice Wallenberg Foundation, and the Simons Foundation.

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