MIT engineers are testing a new propulsion system that combines the power and speed of conventional chemical thrusters with the precision and fuel-efficiency of electrical thrusters. 

The system could enable the design of nimbler, more flexible small satellites, which could perform both fast, powerful maneuvers and slower, precise adjustments, depending on the mission and moment at hand.

The key to the new system is a special propellant that can power both chemical and electrical thrusters, which traditionally have required separate, bulky fuel sources. 

“If you can have chemical and electrical propulsion in one small package, it’s the best of both worlds,” says Amelia Bruno, a former postdoc in MIT’s Department of Aeronautics and Astronautics (AeroAstro). “This opens the door for small satellites to do even more science, more observations, and more interesting missions, all on a smaller and cheaper platform.” 

Bruno is the lead author of a study appearing this week in the Journal of Propulsion and Power showing that a type of “green monopropellant” originally developed by the U.S. Air Force for use in chemical propulsion in space can also effectively power tiny “electrospray” thrusters. Electrospray thrusters are dime-sized rockets that use electric fields to charge up a liquid propellant’s particles, which are then shot into space as a thrust-generating spray.

Electrospray thrusters are extremely fuel-efficient and can perform slow and precise maneuvers, such as pushing a small spacecraft bit by bit through a long, interplanetary journey. Chemical thrusters, in contrast, require a large fuel supply to perform short and fast bursts, for instance to quickly ascend and descend, or speed up and slow down. 

Now that the MIT group has found a propellant that can fuel both chemical and electrospray thrusters, they see big potential for small spacecraft. The team is working with NASA to launch the Green Propulsion Dual Mode mission — a briefcase-sized CubeSat that will carry a chemical thruster and four electrospray thrusters, all fueled by a single propellant tank. The mission will be the first to test such a two-in-one propulsion system for small spacecraft. If it is successful, Bruno says the mission could pave the way for small satellites to explore beyond Earth’s orbit. 

“We could send CubeSats to Mars, or the asteroid belt, where they could make the journey slowly, using electrospray thrusters,” says study co-author Paulo Lozano, the Miguel Alemán Velasco Professor of Aeronautics and Astronautics at MIT. “You could then use your chemical thrusters to quickly move to look at interesting features. You could have a lot more flexibility to do a lot more things.”

The study’s co-authors also include Matthew Corrado SM ’22, PhD ’26.

A sea of ions

Lozano’s group at MIT designs, fabricates, and tests electrospray thrusters for use in satellites that range from the size of a lunchbox to a small carry-on suitcase. Compared to conventional satellites, these microsatellites are significantly smaller and cheaper to launch into space.

But smaller spacecraft require smaller everything else, including propulsion systems. In that respect, electrospray thrusters are a good fit. The thrusters Lozano develops are about the size of a thumbnail. Each thruster sits atop a small reservoir of ionic liquid propellant. When the reservoir is connected to a battery, the battery supplies some amount of voltage that electrically charges a corresponding amount of ions in the liquid. The charged particles are then channeled out of the reservoir, through the thruster’s tips and into space as a thrust-inducing spray. 

Over the past decade, Lozano has tested many thruster designs, under varying conditions, and with various types of ionic liquid propellant — a fuel that is essentially made from salts that can remain in liquid form. 

“Ionic liquids are very stable and can even remain a liquid in space, which not a lot of materials can do,” Bruno says. “And it’s basically a sea of ions, which is why we base our technology around it, so we can pull those ions out into an electrospray.”

Bruno and Lozano have collaborated with the U.S. Air Force, which synthesized a new kind of ionic liquid propellant — the Advanced SpaceCraft Energetic Non-Toxic propellant (ASCENT) — which was being tested in chemical thrusters. Chemical thrusters are high-force propulsion systems typically associated with launching rockets and performing hard and fast maneuvers once in space. ASCENT was designed as a “green,” less toxic alternative to hydrazine, which has been the traditional fuel source for chemical propulsion and is extremely hazardous to handle. 

“ASCENT happens to be an ionic liquid mixture,” Bruno says. “And we said, hey, that’s the stuff we typically use. Theoretically, this should work. Let’s go figure out how.”

Spray and spin

In their new study, Bruno, Lozano, and Corrado tested the performance of electrospray thrusters that they fueled with ASCENT. Each thruster they used was attached to a small cube-shaped reservoir about the size of a Lego brick. They filled each reservoir with 1 gram of ASCENT, a liquid that has a viscosity similar to baby oil. They then attached a thruster to opposite sides of a CubeSat, which they set on a MagLev stand — a custom testbed that is designed to magnetically levitate a sample or device. The MagLev in Lozano’s lab is installed inside a large vacuum chamber, which the researchers can tune to mimic the conditions in space.

Over multiple experiments, the team remotely applied varying levels of voltage to activate the thrusters, which in turn produced a spray that spun the CubeSat around, like a floating, spinning top. The researchers measured the amount of thrust produced with each trial, and calculated ASCENT’s fuel efficiency as they ran the thrusters continuously over periods lasting up to 100 hours. 

In the end, they found that ASCENT was able to successfully fuel each electrospray thruster. What’s more, the propellant, which was originally intended for chemical propulsion, was just as efficient as other, conventional ionic liquids at propelling electric thrusters.

“Compared to our normal electrospray propellants, ASCENT can provide similar performance in terms of thrust,” Bruno says. “Now that we know our thrusters work with ASCENT, we can start thinking of all the ways we can make them even better.” 

Now that ASCENT has been proven to work in both chemical and electrical propulsion, she and Lozano say that a single tank of the fuel can be used to power both types of thrusters, all in a compact, two-in-one system that could fit within a small CubeSat. The team will test the idea with NASA’s Green Propulsion Dual Mode mission, which is scheduled to launch in November. 

“This will be the first time that a satellite will have a shared propellant tank,” says Lozano, who notes that in addition to long, exploratory interplanetary missions, small satellites equipped with both chemical and electrical propulsion could also be useful for missions closer to Earth, such as for weather and climate observations. 

“Say there’s a storm coming, and you’d want to deploy your constellation of small satellites to observe over one location,” he says. “You could choose to send them quickly or slowly depending on the nature of the observation. And the only way to do that is if you have two propulsion systems, which is now possible.”

This research is supported, in part, by NASA.

Hydrogen has the potential to be a climate-friendly fuel since it doesn’t release carbon dioxide when used as an energy source. Currently, however, most methods for producing hydrogen involve fossil fuels, making hydrogen less of a “green” fuel over its entire life cycle.

A new process developed by MIT engineers could significantly shrink the carbon footprint associated with making hydrogen.

Last year, the team reported that they could produce hydrogen gas by combining seawater, recycled soda cans, and caffeine. The question then was whether the benchtop process could be applied at an industrial scale, and at what environmental cost.

Now, the researchers have carried out a “cradle-to-grave” life cycle assessment, taking into account every step in the process at an industrial scale. For instance, the team calculated the carbon emissions associated with acquiring and processing aluminum, reacting it with seawater to produce hydrogen, and transporting the fuel to gas stations, where drivers could tap into hydrogen tanks to power engines or fuel cell cars. They found that, from end to end, the new process could generate a fraction of the carbon emissions that is associated with conventional hydrogen production.

In a study appearing today in Cell Reports Sustainability, the team reports that for every kilogram of hydrogen produced, the process would generate 1.45 kilograms of carbon dioxide over its entire life cycle. In comparison, fossil-fuel-based processes emit 11 kilograms of carbon dioxide per kilogram of hydrogen generated.

The low-carbon footprint is on par with other proposed “green hydrogen” technologies, such as those powered by solar and wind energy.

“We’re in the ballpark of green hydrogen,” says lead author Aly Kombargi PhD ’25, who graduated this spring from MIT with a doctorate in mechanical engineering. “This work highlights aluminum’s potential as a clean energy source and offers a scalable pathway for low-emission hydrogen deployment in transportation and remote energy systems.”

The study’s MIT co-authors are Brooke Bao, Enoch Ellis, and professor of mechanical engineering Douglas Hart.

Gas bubble

Dropping an aluminum can in water won’t normally cause much of a chemical reaction. That’s because when aluminum is exposed to oxygen, it instantly forms a shield-like layer. Without this layer, aluminum exists in its pure form and can readily react when mixed with water. The reaction that occurs involves aluminum atoms that efficiently break up molecules of water, producing aluminum oxide and pure hydrogen. And it doesn’t take much of the metal to bubble up a significant amount of the gas.

“One of the main benefits of using aluminum is the energy density per unit volume,” Kombargi says. “With a very small amount of aluminum fuel, you can conceivably supply much of the power for a hydrogen-fueled vehicle.”

Last year, he and Hart developed a recipe for aluminum-based hydrogen production. They found they could puncture aluminum’s natural shield by treating it with a small amount of gallium-indium, which is a rare-metal alloy that effectively scrubs aluminum into its pure form. The researchers then mixed pellets of pure aluminum with seawater and observed that the reaction produced pure hydrogen. What’s more, the salt in the water helped to precipitate gallium-indium, which the team could subsequently recover and reuse to generate more hydrogen, in a cost-saving, sustainable cycle.

“We were explaining the science of this process in conferences, and the questions we would get were, ‘How much does this cost?’ and, ‘What’s its carbon footprint?’” Kombargi says. “So we wanted to look at the process in a comprehensive way.”

A sustainable cycle

For their new study, Kombargi and his colleagues carried out a life cycle assessment to estimate the environmental impact of aluminum-based hydrogen production, at every step of the process, from sourcing the aluminum to transporting the hydrogen after production. They set out to calculate the amount of carbon associated with generating 1 kilogram of hydrogen — an amount that they chose as a practical, consumer-level illustration.

“With a hydrogen fuel cell car using 1 kilogram of hydrogen, you can go between 60 to 100 kilometers, depending on the efficiency of the fuel cell,” Kombargi notes.

They performed the analysis using Earthster — an online life cycle assessment tool that draws data from a large repository of products and processes and their associated carbon emissions. The team considered a number of scenarios to produce hydrogen using aluminum, from starting with “primary” aluminum mined from the Earth, versus “secondary” aluminum that is recycled from soda cans and other products, and using various methods to transport the aluminum and hydrogen.

After running life cycle assessments for about a dozen scenarios, the team identified one scenario with the lowest carbon footprint. This scenario centers on recycled aluminum — a source that saves a significant amount of emissions compared with mining aluminum — and seawater — a natural resource that also saves money by recovering gallium-indium. They found that this scenario, from start to finish, would generate about 1.45 kilograms of carbon dioxide for every kilogram of hydrogen produced. The cost of the fuel produced, they calculated, would be about $9 per kilogram, which is comparable to the price of hydrogen that would be generated with other green technologies such as wind and solar energy.

The researchers envision that if the low-carbon process were ramped up to a commercial scale, it would look something like this: The production chain would start with scrap aluminum sourced from a recycling center. The aluminum would be shredded into pellets and treated with gallium-indium. Then, drivers could transport the pretreated pellets as aluminum “fuel,” rather than directly transporting hydrogen, which is potentially volatile. The pellets would be transported to a fuel station that ideally would be situated near a source of seawater, which could then be mixed with the aluminum, on demand, to produce hydrogen. A consumer could then directly pump the gas into a car with either an internal combustion engine or a fuel cell.

The entire process does produce an aluminum-based byproduct, boehmite, which is a mineral that is commonly used in fabricating semiconductors, electronic elements, and a number of industrial products. Kombargi says that if this byproduct were recovered after hydrogen production, it could be sold to manufacturers, further bringing down the cost of the process as a whole.

“There are a lot of things to consider,” Kombargi says. “But the process works, which is the most exciting part. And we show that it can be environmentally sustainable.”

The group is continuing to develop the process. They recently designed a small reactor, about the size of a water bottle, that takes in aluminum pellets and seawater to generate hydrogen, enough to power an electric bike for several hours. They previously demonstrated that the process can produce enough hydrogen to fuel a small car. The team is also exploring underwater applications, and are designing a hydrogen reactor that would take in surrounding seawater to power a small boat or underwater vehicle.

This research was supported, in part, by the MIT Portugal Program.

There’s a lot of untapped potential in our homes and vehicles that could be harnessed to reinforce local power grids and make them more resilient to unforeseen outages, a new study shows.

In response to a cyber attack or natural disaster, a backup network of decentralized devices — such as residential solar panels, batteries, electric vehicles, heat pumps, and water heaters — could restore electricity or relieve stress on the grid, MIT engineers say.

Such devices are “grid-edge” resources found close to the consumer rather than near central power plants, substations, or transmission lines. Grid-edge devices can independently generate, store, or tune their consumption of power. In their study, the research team shows how such devices could one day be called upon to either pump power into the grid, or rebalance it by dialing down or delaying their power use.

In a paper appearing this week in the Proceedings of the National Academy of Sciences, the engineers present a blueprint for how grid-edge devices could reinforce the power grid through a “local electricity market.” Owners of grid-edge devices could subscribe to a regional market and essentially loan out their device to be part of a microgrid or a local network of on-call energy resources.

In the event that the main power grid is compromised, an algorithm developed by the researchers would kick in for each local electricity market, to quickly determine which devices in the network are trustworthy. The algorithm would then identify the combination of trustworthy devices that would most effectively mitigate the power failure, by either pumping power into the grid or reducing the power they draw from it, by an amount that the algorithm would calculate and communicate to the relevant subscribers. The subscribers could then be compensated through the market, depending on their participation.

The team illustrated this new framework through a number of grid attack scenarios, in which they considered failures at different levels of a power grid, from various sources such as a cyber attack or a natural disaster. Applying their algorithm, they showed that various networks of grid-edge devices were able to dissolve the various attacks.

The results demonstrate that grid-edge devices such as rooftop solar panels, EV chargers, batteries, and smart thermostats (for HVAC devices or heat pumps) could be tapped to stabilize the power grid in the event of an attack.

“All these small devices can do their little bit in terms of adjusting their consumption,” says study co-author Anu Annaswamy, a research scientist in MIT’s Department of Mechanical Engineering. “If we can harness our smart dishwashers, rooftop panels, and EVs, and put our combined shoulders to the wheel, we can really have a resilient grid.”

The study’s MIT co-authors include lead author Vineet Nair and John Williams, along with collaborators from multiple institutions including the Indian Institute of Technology, the National Renewable Energy Laboratory, and elsewhere.

Power boost

The team’s study is an extension of their broader work in adaptive control theory and designing systems to automatically adapt to changing conditions. Annaswamy, who leads the Active-Adaptive Control Laboratory at MIT, explores ways to boost the reliability of renewable energy sources such as solar power.

“These renewables come with a strong temporal signature, in that we know for sure the sun will set every day, so the solar power will go away,” Annaswamy says. “How do you make up for the shortfall?”

The researchers found the answer could lie in the many grid-edge devices that consumers are increasingly installing in their own homes.

“There are lots of distributed energy resources that are coming up now, closer to the customer rather than near large power plants, and it’s mainly because of individual efforts to decarbonize,” Nair says. “So you have all this capability at the grid edge. Surely we should be able to put them to good use.”

While considering ways to deal with drops in energy from the normal operation of renewable sources, the team also began to look into other causes of power dips, such as from cyber attacks. They wondered, in these malicious instances, whether and how the same grid-edge devices could step in to stabilize the grid following an unforeseen, targeted attack.

Attack mode

In their new work, Annaswamy, Nair, and their colleagues developed a framework for incorporating grid-edge devices, and in particular, internet-of-things (IoT) devices, in a way that would support the larger grid in the event of an attack or disruption. IoT devices are physical objects that contain sensors and software that connect to the internet.

For their new framework, named EUREICA (Efficient, Ultra-REsilient, IoT-Coordinated Assets), the researchers start with the assumption that one day, most grid-edge devices will also be IoT devices, enabling rooftop panels, EV chargers, and smart thermostats to wirelessly connect to a larger network of similarly independent and distributed devices. 

The team envisions that for a given region, such as a community of 1,000 homes, there exists a certain number of IoT devices that could potentially be enlisted in the region’s local network, or microgrid. Such a network would be managed by an operator, who would be able to communicate with operators of other nearby microgrids.

If the main power grid is compromised or attacked, operators would run the researchers’ decision-making algorithm to determine trustworthy devices within the network that can pitch in to help mitigate the attack.

The team tested the algorithm on a number of scenarios, such as a cyber attack in which all smart thermostats made by a certain manufacturer are hacked to raise their setpoints simultaneously to a degree that dramatically alters a region’s energy load and destabilizes the grid. The researchers also considered attacks and weather events that would shut off the transmission of energy at various levels and nodes throughout a power grid.

“In our attacks we consider between 5 and 40 percent of the power being lost. We assume some nodes are attacked, and some are still available and have some IoT resources, whether a battery with energy available or an EV or HVAC device that’s controllable,” Nair explains. “So, our algorithm decides which of those houses can step in to either provide extra power generation to inject into the grid or reduce their demand to meet the shortfall.”

In every scenario that they tested, the team found that the algorithm was able to successfully restabilize the grid and mitigate the attack or power failure. They acknowledge that to put in place such a network of grid-edge devices will require buy-in from customers, policymakers, and local officials, as well as innovations such as advanced power inverters that enable EVs to inject power back into the grid.

“This is just the first of many steps that have to happen in quick succession for this idea of local electricity markets to be implemented and expanded upon,” Annaswamy says. “But we believe it’s a good start.”

This work was supported, in part, by the U.S. Department of Energy and the MIT Energy Initiative.

Car design is an iterative and proprietary process. Carmakers can spend several years on the design phase for a car, tweaking 3D forms in simulations before building out the most promising designs for physical testing. The details and specs of these tests, including the aerodynamics of a given car design, are typically not made public. Significant advances in performance, such as in fuel efficiency or electric vehicle range, can therefore be slow and siloed from company to company.

MIT engineers say that the search for better car designs can speed up exponentially with the use of generative artificial intelligence tools that can plow through huge amounts of data in seconds and find connections to generate a novel design. While such AI tools exist, the data they would need to learn from have not been available, at least in any sort of accessible, centralized form.

But now, the engineers have made just such a dataset available to the public for the first time. Dubbed DrivAerNet++, the dataset encompasses more than 8,000 car designs, which the engineers generated based on the most common types of cars in the world today. Each design is represented in 3D form and includes information on the car’s aerodynamics — the way air would flow around a given design, based on simulations of fluid dynamics that the group carried out for each design.

Each of the dataset’s 8,000 designs is available in several representations, such as mesh, point cloud, or a simple list of the design’s parameters and dimensions. As such, the dataset can be used by different AI models that are tuned to process data in a particular modality.

DrivAerNet++ is the largest open-source dataset for car aerodynamics that has been developed to date. The engineers envision it being used as an extensive library of realistic car designs, with detailed aerodynamics data that can be used to quickly train any AI model. These models can then just as quickly generate novel designs that could potentially lead to more fuel-efficient cars and electric vehicles with longer range, in a fraction of the time that it takes the automotive industry today.

“This dataset lays the foundation for the next generation of AI applications in engineering, promoting efficient design processes, cutting R&D costs, and driving advancements toward a more sustainable automotive future,” says Mohamed Elrefaie, a mechanical engineering graduate student at MIT.

Elrefaie and his colleagues will present a paper detailing the new dataset, and AI methods that could be applied to it, at the NeurIPS conference in December. His co-authors are Faez Ahmed, assistant professor of mechanical engineering at MIT, along with Angela Dai, associate professor of computer science at the Technical University of Munich, and Florin Marar of BETA CAE Systems.

Filling the data gap

Ahmed leads the Design Computation and Digital Engineering Lab (DeCoDE) at MIT, where his group explores ways in which AI and machine-learning tools can be used to enhance the design of complex engineering systems and products, including car technology.

“Often when designing a car, the forward process is so expensive that manufacturers can only tweak a car a little bit from one version to the next,” Ahmed says. “But if you have larger datasets where you know the performance of each design, now you can train machine-learning models to iterate fast so you are more likely to get a better design.”

And speed, particularly for advancing car technology, is particularly pressing now.

“This is the best time for accelerating car innovations, as automobiles are one of the largest polluters in the world, and the faster we can shave off that contribution, the more we can help the climate,” Elrefaie says.

In looking at the process of new car design, the researchers found that, while there are AI models that could crank through many car designs to generate optimal designs, the car data that is actually available is limited. Some researchers had previously assembled small datasets of simulated car designs, while car manufacturers rarely release the specs of the actual designs they explore, test, and ultimately manufacture.

The team sought to fill the data gap, particularly with respect to a car’s aerodynamics, which plays a key role in setting the range of an electric vehicle, and the fuel efficiency of an internal combustion engine. The challenge, they realized, was in assembling a dataset of thousands of car designs, each of which is physically accurate in their function and form, without the benefit of physically testing and measuring their performance.

To build a dataset of car designs with physically accurate representations of their aerodynamics, the researchers started with several baseline 3D models that were provided by Audi and BMW in 2014. These models represent three major categories of passenger cars: fastback (sedans with a sloped back end), notchback (sedans or coupes with a slight dip in their rear profile) and estateback (such as station wagons with more blunt, flat backs). The baseline models are thought to bridge the gap between simple designs and more complicated proprietary designs, and have been used by other groups as a starting point for exploring new car designs.

Library of cars

In their new study, the team applied a morphing operation to each of the baseline car models. This operation systematically made a slight change to each of 26 parameters in a given car design, such as its length, underbody features, windshield slope, and wheel tread, which it then labeled as a distinct car design, which was then added to the growing dataset. Meanwhile, the team ran an optimization algorithm to ensure that each new design was indeed distinct, and not a copy of an already-generated design. They then translated each 3D design into different modalities, such that a given design can be represented as a mesh, a point cloud, or a list of dimensions and specs.

The researchers also ran complex, computational fluid dynamics simulations to calculate how air would flow around each generated car design. In the end, this effort produced more than 8,000 distinct, physically accurate 3D car forms, encompassing the most common types of passenger cars on the road today.

To produce this comprehensive dataset, the researchers spent over 3 million CPU hours using the MIT SuperCloud, and generated 39 terabytes of data. (For comparison, it’s estimated that the entire printed collection of the Library of Congress would amount to about 10 terabytes of data.)

The engineers say that researchers can now use the dataset to train a particular AI model. For instance, an AI model could be trained on a part of the dataset to learn car configurations that have certain desirable aerodynamics. Within seconds, the model could then generate a new car design with optimized aerodynamics, based on what it has learned from the dataset’s thousands of physically accurate designs.

The researchers say the dataset could also be used for the inverse goal. For instance, after training an AI model on the dataset, designers could feed the model a specific car design and have it quickly estimate the design’s aerodynamics, which can then be used to compute the car’s potential fuel efficiency or electric range — all without carrying out expensive building and testing of a physical car.

“What this dataset allows you to do is train generative AI models to do things in seconds rather than hours,” Ahmed says. “These models can help lower fuel consumption for internal combustion vehicles and increase the range of electric cars — ultimately paving the way for more sustainable, environmentally friendly vehicles.”

“The dataset is very comprehensive and consists of a diverse set of modalities that are valuable to understand both styling and performance,” says Yanxia Zhang, a senior machine learning research scientist at Toyota Research Institute, who was not involved in the study.

This work was supported, in part, by the German Academic Exchange Service and the Department of Mechanical Engineering at MIT.