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.

© Image: Amelia Bruno

WPR spoke with three church leaders in the state to see what they are experiencing in their growing congregations after a recent report from the Hartford Institute for Religion Research found church attendance across the country increased for the first time in 25 years.

The post Wisconsin church leaders see rising interest in faith, reflecting national trends appeared first on WPR.

Budget constraints are forcing liberal-leaning states that spend their own money on healthcare for noncitizens to scale back that aid, as they grapple with federal Medicaid cuts and the expiration of federal subsidies that helped people buy Obamacare plans.

Under federal law, immigrants who are in the country illegally are not eligible for federally funded health coverage.

But as of last month, six states — California, Colorado, Illinois, New York, Oregon and Washington — plus the District of Columbia were spending state dollars to cover some income-eligible noncitizen adults regardless of their immigration status. A total of 14 states plus the district provide state-funded coverage to noncitizen children whether they are here legally or not. And three states — Colorado, New Jersey and Vermont — cover pregnant women regardless of their immigration status.

In addition, 40 states have taken up options in Medicaid and the Children’s Health Insurance Program, known as CHIP, to provide coverage to lawfully present children and/or pregnant women who are not citizens.

But the sweeping tax and spending bill President Donald Trump signed into law last summer cuts federal spending on Medicaid, the joint federal-state health insurance program for low-income people. It also places new eligibility restrictions on lawfully present immigrants, including refugees and asylees, who are enrolled in a variety of government-subsidized health programs, including Medicaid, CHIP, Medicare and plans available on the insurance marketplaces created under the Affordable Care Act, better known as Obamacare.

And Congress at the end of last year failed to renew federal subsidies that helped people buy Obamacare plans.

With less federal money to provide health benefits, at least five states (California, Colorado, Illinois, Minnesota and Washington) plus the District of Columbia have already scaled back or announced plans to scale back state-funded health benefits for immigrants. Other states also may have to pull back as budget pressures continue.

“The federal government shifted much more of the financial burden of providing those services to states. And so states are taking a holistic view at their healthcare budgets and trying to figure out where they can cut,” said Medha Makhlouf, a law professor and the founding director of the Medical-Legal Partnership Clinic at Penn State Dickinson Law, who studies immigrants’ access to healthcare.

“Historically and currently, as we’re seeing, immigrants are going to be the first to be cut, for a variety of reasons. They don’t have political power in the same way citizens do.”

Drishti Pillai, director of immigrant health policy at KFF, a health policy research group, warned that the state cuts, combined with the federal changes, “will likely increase uninsured rates and reduce access to care among immigrants and their children, most of whom are U.S. citizens.

“Over the long-term, these changes could lead to worse health outcomes that could be more complex and expensive to treat,” Pillai said.

But Cooper Smith, director of homeland security and immigration at the America First Policy Institute, a conservative think tank that has worked on policy development with the current Trump administration, said that when budgets tighten, policymakers should prioritize U.S. citizens.

“Taxpayers pay into a system,” Smith said. “I think it’s reasonable to expect that those who have paid into the system should be the primary beneficiaries of public benefit.”

California has traditionally provided some of the most generous benefits. But last June, Democratic Gov. Gavin Newsom signed a state budget that barred immigrants who are here illegally from newly enrolling in the state’s Medicaid program, known as Medi-Cal. In addition, current enrollees between the ages of 19 and 59 will have to pay a new $30 monthly premium beginning in July 2027. And this July, the state will eliminate dental care for noncitizens.

Newsom’s budget plan for next year proposes scaling back Medi-Cal coverage for some immigrants living in the country lawfully, including an estimated 200,000 asylees, refugees, and others with certain immigration statuses.

California Democratic state Sen. María Elena Durazo is pushing legislation this session that would undo the enrollment freeze and restore access to full-scope Medi-Cal coverage for adults living in the U.S. illegally.

“California immigrants are not going to go away,” Durazo said. “We need them. They’re agricultural workers, they’re food workers, they’re construction workers.

“Are we going to not provide the minimal basic healthcare coverage and think that somehow it’s not going to come back to haunt us through emergency rooms and other counties and public hospitals?”

Hannah Orbach-Mandel, a policy analyst at the nonprofit California Budget and Policy Center, said the state should find alternatives to the cuts, such as raising corporate taxes. She said scaling back coverage puts immigrants “in a really vulnerable position that ultimately can result in people dying.”

Colorado made a similar choice.

Using state money, Colorado’s SilverEnhanced Savings program allows immigrants who are here illegally to buy Obamacare plans with zero premiums. But budget constraints prompted the state to lower the enrollment cap for the program to 6,700 from 12,000.

Now the state is poised to downsize another program. Last year, the state launched Cover All Coloradans to provide state-funded health coverage for low-income children and pregnant women who would be eligible for CHIP or Medicaid if not for their immigration status. But a bill the legislature sent last month to Democratic Gov. Jared Polis would scale back some of the benefits available under the program and cap enrollment to help close a roughly $1 billion state budget gap driven in part by ballooning Medicaid costs.

When the law creating the program was enacted in 2022, financial analysts estimated it would cost $14.7 million this fiscal year and cover almost 3,700 children and pregnant women. Instead, the program ended up serving almost 28,000 people at an estimated cost of $104.5 million.

Colorado Republican state Rep. Rick Taggart, a member of the Joint Budget Committee, called the changes to the program “a painful compromise.”

“It’s impossible to separate the human side from the financial side in this area,” Taggart said in a phone interview. “We are talking about children, and we’re talking about pregnant women, and they have very real needs … the children, in most cases, didn’t have anything to do with the decision about immigrating to the U.S. and to Colorado.”

But other Colorado lawmakers said providing services to children who are here illegally ends up depriving the children of legal residents.

“When we come up here compassionately talking about kids, let’s talk about all the kids in our state,” Republican state Rep. Brandi Bradley said during debate on the House floor last month. “There’s plenty of kids whose parents are working a ton of jobs to just keep up with inflation and the price of groceries in the state, while we continue to grow programs like this.”

According to a 2001 court ruling, the New York Constitution bars the state from distinguishing between citizens and legal immigrants in providing Medicaid. Legal immigrants include people who have temporary and humanitarian status or might be here under the Deferred Action for Childhood Arrivals program, known as DACA, and would be income-eligible for Medicaid.

But even New York has had to make changes. Because of federal funding cuts, the state says, it is narrowing the income eligibility rules for its state-run Essential Plan, which provides zero-premium coverage for people who are here legally but do not qualify for Medicaid.

Beginning in July, the program will no longer cover households making between 200% and 250% of the federal poverty level. The change will end coverage for an estimated 450,000 New Yorkers.

“Our priority continues to be protecting coverage for as many New Yorkers as possible and ensuring people have information and assistance during this transition,” said Danielle De Souza, a spokesperson for the New York State Department of Health.

Stateline reporter Shalina Chatlani can be reached at schatlani@stateline.org

The federal government’s probe into the 2020 election has reached Wisconsin, with several current and former election officials, including multiple people in Milwaukee, confirming they have been interviewed or approached by the FBI.

The exact nature of the investigation remains unclear, though it appears to be at least somewhat centered around the 2020 election. The agency’s election investigations elsewhere in the country have featured subpoenas for ballots and other election records, but legal experts still say it won’t be easy for the federal government to convince a court to give it access to ballots.

Milwaukee County officials are nonetheless preparing for that possibility, in part because they still retain ballots from the 2020 election, though they declined to discuss those preparations or comment on the record. Those ballots contain identifying information that could, in some cases, allow otherwise unidentifiable absentee ballots to be matched to the voters who cast them. Milwaukee is one of the few jurisdictions in Wisconsin that still has ballots from that election, and the city has long been a target of voter fraud accusations and related attacks from the political right.

Elsewhere in Wisconsin — in communities whose elections have faced less scrutiny and in the vast majority of municipalities where 2020 ballots were destroyed according to the standard retention schedules in state law — election officials are less alarmed and are instead focused on preparing for the midterm elections.

Still, news of the FBI interest has created confusion and some fear on the part of voters and election officials.

What happened?

So far, the FBI has contacted multiple current and former election officials in Wisconsin.

The FBI interviewed Wisconsin Elections Commission deputy administrator Robert Kehoe within the last few weeks. The news of the interview was first reported by the Milwaukee Journal Sentinel. The interview focused on the 2020 election, with agents asking Kehoe to explain how Wisconsin elections operate.

The agency has also attempted to contact Milwaukee County Election Director Michelle Hawley. An agent left a business card at Hawley’s home when she was not there. Milwaukee County Clerk George Christensen criticized the agency for approaching Hawley at her home rather than through the county.

“While we cooperate with all legitimate law enforcement actions, we will defend against any attack on our democracy and will defend the rights of voters of Milwaukee County,” Christensen said in a statement.

Agents also left a card for, called and texted a former Milwaukee election official, who confirmed the contact to Votebeat but requested anonymity because of personal safety concerns. That official declined to say whether they responded to the FBI.

Milwaukee Mayor Cavalier Johnson confirmed the FBI has reached out to city employees about the probe.

“The president for whatever reason cannot seem to let it go that he lost an election,” Johnson told a WISN 12 reporter.

Wisconsin Elections Commission spokeswoman Emilee Miklas declined to comment for this story. Other officials declined to speak on the record, and an FBI spokesperson didn’t answer Votebeat questions about the probe.

David Becker, the executive director of the nonpartisan nonprofit Center for Election Innovation and Research and a former Justice Department voting section attorney, said the federal government’s actions appeared more to be aimed at intimidating election officials than producing actionable criminal cases.

He pointed to FBI Director Kash Patel’s public statements in April suggesting arrests related to the 2020 election were coming, as well as federal officials discussing potential cases on social media before they’re brought before courts.

“If you think you’re going to bring charges and prosecute individuals, you don’t do anything that the federal government has done over the last few months,” he said.

Becker also noted that any potential federal crimes connected to the 2020 election are “well beyond the statute of limitations for any potential federal jurisdiction or crimes,” adding, “This is a problem for any investigation relating to 2020.”

Even so, Becker said election officials’ worries were justified. He said the Election Official Legal Defense Network, which he leads, has received more requests for legal assistance from election officials than ever before “even though all of these efforts indicate that the federal government knows it’s got nothing.”

How do the events in Wisconsin relate to probes elsewhere?

It’s unclear how the FBI interviews in Wisconsin relate to the agency’s scrutiny of the 2020 election in other states.

In January the FBI raided a Fulton County, Georgia, election office seeking records tied to the 2020 election. About a month later, the agency subpoenaed records related to the audit of the 2020 election in Maricopa County, Arizona, which includes Phoenix.

Separately, the U.S. Justice Department has sought access to 2024 ballots in Wayne County, Michigan, home to Detroit.

Those jurisdictions share several characteristics with Milwaukee County.  All are located in highly competitive swing states won by former President Joe Biden in 2020, and all became central targets of President Donald Trump, who repeatedly challenged the election results despite court rulings, audits and reviews repeatedly reaffirming his loss.

Fulton, Wayne, Maricopa, and Milwaukee County are the largest and most heavily scrutinized election jurisdictions in their respective states. Each has been the subject of persistent conspiracy theories about the 2020 election, many of which remain prevalent on social media, even after extensive investigations found no evidence of widespread fraud.

“What’s really disconcerting,” said former longtime Wisconsin election chief Kevin Kennedy, “is the fact that there is a clear pattern here to try and continue to stir up issues that were resolved in every single opportunity there was to review them, whether it was a court case, an independent audit or the actual certification and review process that exists.”

What comes next?

The short answer is that nobody really knows.

Officials have been considering the possibility that the federal government may seize the city’s 2020 ballots, which contain personally identifiable information.

Kennedy said recent actions by the Trump administration offer “no reason to think that information that should be protected is going to be protected.”

Kennedy said Wisconsin’s decentralized election system was intentionally designed to distribute authority among local jurisdictions — both to keep election administration accountable at the community level and to limit the amount of sensitive voter information concentrated in any one place.

“You put that at the national level,” he said, “and it only takes one bad actor — and we’ve got evidence there’s more than one of those already in the federal government — to totally disrupt the process when you consolidate that kind of information that’s protected through the various state and local laws and practices.”

Becker said it will be an uphill battle for the federal government to successfully obtain Milwaukee’s ballots. But he said the mere possibility that federal officials could theoretically identify how individual people voted is deeply troubling.

“That is not the way a democratic society works,” he said. “Now, I don’t think they’re likely going to be able to do that. I think that’s going to be incredibly difficult. It’s not impossible, but the fact that they seem to engender this fear is troubling enough.”

Alexander Shur is a reporter for Votebeat based in Wisconsin. Contact Shur at ashur@votebeat.org.

Votebeat is a nonprofit news organization reporting on voting access and election administration across the U.S. Sign up for Votebeat’s free national newsletter here.

This article first appeared on Wisconsin Watch and is republished here under a Creative Commons Attribution-NoDerivatives 4.0 International License. To republish, go to the original and consult the Wisconsin Watch republishing guidelines.

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Despite regional variability in climate, electricity sources, congestion, and the wide variation in individual driving patterns, electric vehicles generate less greenhouse gas emissions and do not cost more than comparable gas-powered vehicles for drivers and vehicle fleet owners in most parts of the United States, according to a new study by MIT researchers.

The team’s approach captures many key factors that contribute to regional and individual differences in the life-cycle emissions and ownership cost of electric vehicles, including meteorological data, the distance and duration of trips, and fuel prices.

To paint a fuller picture of emissions and costs than was previously available, the researchers sourced data from thousands of U.S. zip codes and drilled down to the level of individual drivers within those locations. Their study considers time-averaged fuel prices so as not to be overly influenced by fluctuations in prices at any one point in time. They finalized their analysis at the end of 2024 and early 2025.

Their results indicate that a person’s driving behaviors can matter as much as regional factors like the local electricity mix when it comes to the emissions savings of an electric vehicle, compared to a similar gas-powered vehicle. In most locations, a battery-electric vehicle reduces emissions between 40 and 60 percent, with larger impacts in urban areas. 

They also found that colder climates do not reduce overall emission benefits as much as some media reports assume.

The researchers utilized this detailed analysis to update a public tool they previously developed, carboncounter.com, which enables individuals to compare the life-cycle emissions and total ownership costs of nearly any car on the market. A new version of carboncounter.com is also being released today.

“There are a lot of statements being thrown around, like that electric vehicles don’t reduce emissions very much in cool climates, and we wanted to analyze these factors systematically and evaluate these statements against one another simultaneously. Rather than simply asking, ‘Are EVs better?’, this paper helps answer ‘better for whom, and under what conditions?’” says Marco Miotti PhD ’20, a senior researcher at ETH Zurich who completed this research while a graduate student in the Institute for Data, Systems, and Society (IDSS) at MIT. 

He is joined on the paper by senior author Jessika Trancik, a professor in IDSS. The research appears today in Environmental Research Letters.

A holistic approach

Many prior studies that compare emissions and costs of electric vehicles (EVs) to combustion-engine vehicles cover a few factors, like the amount of renewable energy in the grid and how gas prices impact affordability, Miotti says.

“To our knowledge, there have been few efforts so far that bring all these factors together. But if someone wants to buy a car and have a better understanding of the factors that affect emissions and costs, this holistic approach is important,” he adds.

The researchers focused on two types of EVs: battery-electric vehicles, which only operate on electricity, and plug-in hybrid electric vehicles, which also have a combustion engine that works in tandem with the battery to optimize fuel savings.

The team expanded and improved a set of previously developed vehicle cost and emissions models to incorporate a wider variety of factors and data types.

For instance, they refined an existing model that estimates energy use and gas mileage so it could capture more nuances of local climate variability. 

“But the real effort was not just in extending these different models, but in bringing together all these different data and making them work with the models in a consistent manner,” Miotti says.

The team sourced data on a wide variety of factors for each U.S. zip code, such as typical drive cycles, the amount of traffic, local gas and electricity prices, makeup of the regional electricity mix, meteorological profiles, and more. They used statistical approaches to amalgamate different types of data. 

For example, the team used a probabilistic matching technique to combine data on how often people drive, which was drawn from nationwide travel surveys, with more detailed GPS data that includes factors like drivers’ acceleration patterns and the distance they usually drive on each day of the week.

The researchers designed their analysis to focus on the spatial picture of emissions and costs, based on U.S. zip codes, while simultaneously considering the impact of the size and features of each specific vehicle model.

“At the end of the day, it’s the vehicle and fleet owners who make decisions about vehicle purchases. So, we wanted to make sure to consider their wide-ranging individual perspectives rather than simply performing a region-by-region comparison,” says Trancik.

Lower emissions, comparable costs

In the end, their modeling framework revealed that all factors they analyzed matter about equally in determining emissions-reduction potential of EVs compared to internal combustion vehicles. 

EVs reduce emissions the most in areas with a cleaner electricity mix, denser traffic, higher annual travel distances, and a mild climate, in decreasing order of importance. In each area, emission reductions increase for drivers who drive more often, drive larger vehicles, and are more frequently stuck in traffic. 

In a colder area like North Dakota, fuel economy of battery-electric vehicles might be reduced by as much as 50 percent on a particularly frigid night, but the effect on annual emission benefits is minimal. 

“We even did a sensitivity study to see if the range is reduced in very cold climates, and we found that, even in the most unfavorable conditions, EVs still reduce emissions by a substantial amount,” Miotti says.

On the cost side, the models show that, in most places across the U.S., EVs are competitive with comparable combustion-engine vehicles in terms of lifetime ownership cost, even without clean vehicle tax credits. And in areas where electricity is relatively affordable, battery-electric vehicles tend to cost less than their plug-in hybrid or combustion-engine counterparts.

In the future, the researchers want to expand this analysis to include a temporal dimension, so the framework also considers how changes in vehicle, fuel, and electricity prices affect emissions and costs over time. 

“While we found that the electricity mix is a big driver of the spatial variation in emissions savings of EVs, the electricity grid is decarbonizing everywhere. As that happens, emissions savings across space will become more homogenous for EVs, but the differences across one driver to another will remain,” Miotti says.

They could also use the framework to explore regions outside the United States or incorporate data on hybrid-electric vehicles that cannot be plugged in.

This work was funded, in part, by the MIT Martin Family Society of Fellows for Sustainability.

© Credit: iStock

The U.S. Forest Service’s plan to close scores of research stations could threaten the nation’s wildfire readiness, many foresters fear, and erode decades of work to understand timber production, soil health, pests and diseases, watersheds and wildlife.

Late last month, the Forest Service announced plans to close 57 of its 77 research stations, located across 31 states, merging them into a single organization in Fort Collins, Colorado.

The agency described the move as a way to consolidate, not cut, the agency’s scientific work, and “unify research priorities.”

It’s unclear how many scientists will be affected by the transition, but it comes as part of a larger agency reorganization that is expected to move roughly 5,000 employees to new outposts. Forest Service leaders have framed the closures as a way to reduce the agency’s real estate footprint, citing a facilities budget Congress has shrunk, as opposed to curtailing its scientific work.

But many longtime foresters fear the closures will threaten vital research that has been the backbone of forest management for state agencies, timber companies and tribes. Many of the research stations slated for closure study fire behavior, forecast smoke dispersal and help inform evacuation decisions.

“The research arm of the Forest Service is one of the unsung heroes in forest management around the world,” said Mike Dombeck, who served as chief of the Forest Service under President Bill Clinton and remains a vocal conservation advocate. “It is the premier forest research entity in the world, on everything from invasive species to wildland fire risk, watershed protection, basic silviculture and harvest methods.”

The Forest Service’s revamp also will relocate the agency’s headquarters from Washington, D.C., to Salt Lake City and restructure its regional management system.

The Forest Service did not grant a Stateline interview request. The agency has not said how much money it expects to save by closing the research stations.

Many Western leaders are skeptical that the consolidated operation will be able to replicate the work of the existing research stations. State officials said they’ve been given few details about how the transition will play out and whether existing research will continue.

In Washington state, the Forest Service plans to close research stations in Seattle and Wenatchee, while maintaining a facility in Olympia.

“The station in Seattle does some of the most practical-based research that we use for fire and forest management,” said Washington State Forester George Geissler. “We don’t want to lose that work. They’ve said they’ll keep Olympia open, but we don’t know what that looks like. Are they making sure we don’t lose the ongoing research?”

Forestry veterans say it’s important for the agency to continue its scientific work across a wide variety of forests and climates.

“This is research that’s been going on for decades or even a century or more,” said Kevin Hood, executive director of Forest Service Employees for Environmental Ethics, a nonprofit that advocates for agency workers. “They’re able to see how climate change impacts are playing out in a dry ponderosa forest or a humid hardwood forest. There are research plots and experimental forests that have been diligently studied for decades. This could be a loss of a lot of knowledge.”

The Pacific Wildland Fire Sciences Laboratory, for instance, plays a crucial role in issuing wildfire smoke forecasts that are relied on throughout the Northwest. After a hot, dry winter, that work could be critical as a dangerous wildfire season approaches.

In Vermont, the Burlington research station slated for closure studied maple syrup production and the effects of acid rain on different tree species, according to VTDigger.

And in Mississippi, the Southern Institute of Forest Genetics, also on the chopping block, has guided tree improvement programs that improved growth and pest resistance in Southern timber forests.

Some conservation advocates are concerned that the research station closures are aimed at suppressing studies that might show the environmental harms of logging or mining. President Donald Trump has pledged to increase timber production on federal lands. He has moved to limit environmental reviews and protections for endangered species to speed up logging projects.

In an interview with the Deseret News, Forest Service Chief Tom Schultz said that the move was designed to ensure that the Forest Service’s research “will better align with the priorities of the administration” — minerals, recreation, fire management and “active management” of forests, which can include timber harvests and thinning projects. He said the research would support not just forests but also private landowners.

“It’s not streamlining, it’s dismantling,” said Chandra Rosenthal, Western lands and Rocky Mountain advocate with Public Employees for Environmental Responsibility, a group that defends whistleblowers in the federal service. “It’s going to really impact how the Forest Service makes decisions on the ground. The way the Trump administration is trying to make a lot of decisions is gut feelings.”

In a webpage set up to respond to news coverage of the move, the Forest Service said it is a “myth” that the station closures will eliminate scientific positions or cancel research programs. But many forestry veterans said that attrition is inevitable, as researchers are asked to move their families across the country to work under a new model with few details.

“There’s concern that we’re going to see a lot of really good individuals who cannot uproot their families that we’ll lose,” said Geissler, the Washington state forester. “It’s taken a long time to develop that kind of expertise. It’s scary.”

Foresters in both conservative and liberal states said they rely heavily on the research the Forest Service provides. Most were unwilling to comment extensively about the closures without seeing more details.

“That work is absolutely important, and I sure hope it continues,” said Wyoming State Forester Kelly Norris. “I don’t think research should stop. It may need to look a little different.”

Some leaders said there may be opportunities for states, through forestry agencies and universities, to pick up the slack and ensure research continues, even if the Forest Service is no longer playing a lead role.

“This is still a little bit of an unknown area, but we’ll have to make sure that if there’s a gap there, that we’re working with our universities and (state) research centers to make sure that is still being provided,” said Utah State Forester Jamie Barnes.

Nick Smith, public affairs director with the American Forest Resource Council, a timber industry group, expressed support for the agency’s effort to consolidate its work, saying he’d had “limited interaction” with the research stations.

While some of the Forest Service’s work is controversial, agency veterans say its research program is valued by loggers and tree-huggers alike.

“Nobody was asking for this,” said Robert Bonnie, who served as undersecretary of agriculture for natural resources and environment during the Obama administration. “There was no call to do anything like this.”

Stateline reporter Alex Brown can be reached at abrown@stateline.org. 

There is growing attention on the links between artificial intelligence and increased energy demands. But while the power-hungry data centers being built to support AI could potentially stress electricity grids, increase customer prices and service interruptions, and generally slow the transition to clean energy, the use of artificial intelligence can also help the energy transition.

For example, use of AI is reducing energy consumption and associated emissions in buildings, transportation, and industrial processes. In addition, AI is helping to optimize the design and siting of new wind and solar installations and energy storage facilities.

On electric power grids, using AI algorithms to control operations is helping to increase efficiency and reduce costs, integrate the growing share of renewables, and even predict when key equipment needs servicing to prevent failure and possible blackouts. AI can help grid planners schedule investments in generation, energy storage, and other infrastructure that will be needed in the future. AI is also helping researchers discover or design novel materials for nuclear reactors, batteries, and electrolyzers.

Researchers at MIT and elsewhere are actively investigating aspects of those and other opportunities for AI to support the clean energy transition. At its 2025 research conference, MITEI announced the Data Center Power Forum, a targeted research effort for MITEI member companies interested in addressing the challenges of data center power demand.

Controlling real-time operations

Customers generally rely on receiving a continuous supply of electricity, and grid operators get help from AI to make that happen — while optimizing the storage and distribution of energy from renewable sources at the same time.

But with more installation of solar and wind farms — both of which provide power in smaller amounts, and intermittently — and the growing threat of weather events and cyberattacks, ensuring reliability is getting more complicated. “That’s exactly where AI can come into the picture,” explains Anuradha Annaswamy, a senior research scientist in MIT’s Department of Mechanical Engineering and director of MIT’s Active-Adaptive Control Laboratory. “Essentially, you need to introduce a whole information infrastructure to supplement and complement the physical infrastructure.”

The electricity grid is a complex system that requires meticulous control on time scales ranging from decades all the way down to microseconds. The challenge can be traced to the basic laws of power physics: electricity supply must equal electricity demand at every instant, or generation can be interrupted. In past decades, grid operators generally assumed that generation was fixed — they could count on how much electricity each large power plant would produce — while demand varied over time in a fairly predictable way. As a result, operators could commission specific power plants to run as needed to meet demand the next day. If some outages occurred, specially designated units would start up as needed to make up the shortfall.

Today and in the future, that matching of supply and demand must still happen, even as the number of small, intermittent sources of generation grows and weather disturbances and other threats to the grid increase. AI algorithms provide a means of achieving the complex management of information needed to forecast within just a few hours which plants should run while also ensuring that the frequency, voltage, and other characteristics of the incoming power are as required for the grid to operate properly.

Moreover, AI can make possible new ways of increasing supply or decreasing demand at times when supplies on the grid run short. As Annaswamy points out, the battery in your electric vehicle (EV), as well as the one charged up by solar panels or wind turbines, can — when needed — serve as a source of extra power to be fed into the grid. And given real-time price signals, EV owners can choose to shift charging from a time when demand is peaking and prices are high to a time when demand and therefore prices are both lower. In addition, new smart thermostats can be set to allow the indoor temperature to drop or rise —  a range defined by the customer — when demand on the grid is peaking. And data centers themselves can be a source of demand flexibility: selected AI calculations could be delayed as needed to smooth out peaks in demand. Thus, AI can provide many opportunities to fine-tune both supply and demand as needed.

In addition, AI makes possible “predictive maintenance.” Any downtime is costly for the company and threatens shortages for the customers served. AI algorithms can collect key performance data during normal operation and, when readings veer off from that normal, the system can alert operators that something might be going wrong, giving them a chance to intervene. That capability prevents equipment failures, reduces the need for routine inspections, increases worker productivity, and extends the lifetime of key equipment.

Annaswamy stresses that “figuring out how to architect this new power grid with these AI components will require many different experts to come together.” She notes that electrical engineers, computer scientists, and energy economists “will have to rub shoulders with enlightened regulators and policymakers to make sure that this is not just an academic exercise, but will actually get implemented. All the different stakeholders have to learn from each other. And you need guarantees that nothing is going to fail. You can’t have blackouts.”

Using AI to help plan investments in infrastructure for the future

Grid companies constantly need to plan for expanding generation, transmission, storage, and more, and getting all the necessary infrastructure built and operating may take many years, in some cases more than a decade. So, they need to predict what infrastructure they’ll need to ensure reliability in the future. “It’s complicated because you have to forecast over a decade ahead of time what to build and where to build it,” says Deepjyoti Deka, a research scientist in MITEI.

One challenge with anticipating what will be needed is predicting how the future system will operate. “That’s becoming increasingly difficult,” says Deka, because more renewables are coming online and displacing traditional generators. In the past, operators could rely on “spinning reserves,” that is, generating capacity that’s not currently in use but could come online in a matter of minutes to meet any shortfall on the system. The presence of so many intermittent generators — wind and solar — means there’s now less stability and inertia built into the grid. Adding to the complication is that those intermittent generators can be built by various vendors, and grid planners may not have access to the physics-based equations that govern the operation of each piece of equipment at sufficiently fine time scales. “So, you probably don’t know exactly how it’s going to run,” says Deka.

And then there’s the weather. Determining the reliability of a proposed future energy system requires knowing what it’ll be up against in terms of weather. The future grid has to be reliable not only in everyday weather, but also during low-probability but high-risk events such as hurricanes, floods, and wildfires, all of which are becoming more and more frequent, notes Deka. AI can help by predicting such events and even tracking changes in weather patterns due to climate change.

Deka points out another, less-obvious benefit of the speed of AI analysis. Any infrastructure development plan must be reviewed and approved, often by several regulatory and other bodies. Traditionally, an applicant would develop a plan, analyze its impacts, and submit the plan to one set of reviewers. After making any requested changes and repeating the analysis, the applicant would resubmit a revised version to the reviewers to see if the new version was acceptable. AI tools can speed up the required analysis so the process moves along more quickly. Planners can even reduce the number of times a proposal is rejected by using large language models to search regulatory publications and summarize what’s important for a proposed infrastructure installation.

Harnessing AI to discover and exploit advanced materials needed for the energy transition

“Use of AI for materials development is booming right now,” says Ju Li, MIT’s Carl Richard Soderberg Professor of Power Engineering. He notes two main directions.

First, AI makes possible faster physics-based simulations at the atomic scale. The result is a better atomic-level understanding of how composition, processing, structure, and chemical reactivity relate to the performance of materials. That understanding provides design rules to help guide the development and discovery of novel materials for energy generation, storage, and conversion needed for a sustainable future energy system.

And second, AI can help guide experiments in real time as they take place in the lab. Li explains: “AI assists us in choosing the best experiment to do based on our previous experiments and — based on literature searches — makes hypotheses and suggests new experiments.”

He describes what happens in his own lab. Human scientists interact with a large language model, which then makes suggestions about what specific experiments to do next. The human researcher accepts or modifies the suggestion, and a robotic arm responds by setting up and performing the next step in the experimental sequence, synthesizing the material, testing the performance, and taking images of samples when appropriate. Based on a mix of literature knowledge, human intuition, and previous experimental results, AI thus coordinates active learning that balances the goals of reducing uncertainty with improving performance. And, as Li points out, “AI has read many more books and papers than any human can, and is thus naturally more interdisciplinary.”

The outcome, says Li, is both better design of experiments and speeding up the “work flow.” Traditionally, the process of developing new materials has required synthesizing the precursors, making the material, testing its performance and characterizing the structure, making adjustments, and repeating the same series of steps. AI guidance speeds up that process, “helping us to design critical, cheap experiments that can give us the maximum amount of information feedback,” says Li.

“Having this capability certainly will accelerate material discovery, and this may be the thing that can really help us in the clean energy transition,” he concludes. “AI [has the potential to] lubricate the material-discovery and optimization process, perhaps shortening it from decades, as in the past, to just a few years.” 

MITEI’s contributions

At MIT, researchers are working on various aspects of the opportunities described above. In projects supported by MITEI, teams are using AI to better model and predict disruptions in plasma flows inside fusion reactors — a necessity in achieving practical fusion power generation. Other MITEI-supported teams are using AI-powered tools to interpret regulations, climate data, and infrastructure maps in order to achieve faster, more adaptive electric grid planning. AI-guided development of advanced materials continues, with one MITEI project using AI to optimize solar cells and thermoelectric materials.

Other MITEI researchers are developing robots that can learn maintenance tasks based on human feedback, including physical intervention and verbal instructions. The goal is to reduce costs, improve safety, and accelerate the deployment of the renewable energy infrastructure. And MITEI-funded work continues on ways to reduce the energy demand of data centers, from designing more efficient computer chips and computing algorithms to rethinking the architectural design of the buildings, for example, to increase airflow so as to reduce the need for air conditioning.

In addition to providing leadership and funding for many research projects, MITEI acts as a convenor, bringing together interested parties to consider common problems and potential solutions. In May 2025, MITEI’s annual spring symposium — titled “AI and energy: Peril and promise” — brought together AI and energy experts from across academia, industry, government, and nonprofit organizations to explore AI as both a problem and a potential solution for the clean energy transition. At the close of the symposium, William H. Green, director of MITEI and Hoyt C. Hottel Professor in the MIT Department of Chemical Engineering, noted, “The challenge of meeting data center energy demand and of unlocking the potential benefits of AI to the energy transition is now a research priority for MITEI.”

© Image: Igor Borisenko/iStock

Around 80 percent of global energy production today comes from the combustion of fossil fuels. Combustion, or the process of converting stored chemical energy into thermal energy through burning, is vital for a variety of common activities including electricity generation, transportation, and domestic uses like heating and cooking — but it also yields a host of environmental consequences, contributing to air pollution and greenhouse gas emissions.

Sili Deng, the Doherty Chair in Ocean Utilization and associate professor of mechanical engineering at MIT, is leading research to drive the transition from the heavy dependence on fossil fuels to renewable energy with storage.

“I was first introduced to flame synthesis in my junior year in college,” Deng says. “I realized you can actually burn things to make things, [and] that was really fascinating.”

Deng says she ultimately picked combustion as a focus of her work because she likes the intellectual challenge the concept offers. “In combustion you have chemistry, and you have fluid mechanics. Each subject is very rich in science. This also has very strong engineering implications and applications.”

Deng’s research group targets three areas: building up fundamental knowledge on combustion processes and emissions; developing alternative fuels and metal combustion to replace fossil fuels; and synthesizing flame-based materials for catalysis and energy storage, which can bring down the cost of manufacturing battery materials.

One focus of the team has been on low-cost, low-emission manufacturing of cathode materials for lithium-ion batteries. Lithium-ion batteries play an increasingly critical role in transportation electrification (e.g., batteries for electric vehicles) and grid energy storage for electricity that is generated from renewable energy sources like wind and solar. Deng’s team has developed a technology they call flame-assisted spray pyrolysis, or FASP, which can help reduce the high manufacturing costs associated with cathode materials.

FASP is based on flame synthesis, a technology that dates back nearly 3,000 years. In ancient China, this was the primary way black ink materials were made. “[People burned] vegetables or woods, such that afterwards they can collect the solidified smoke,” Deng explains. “For our battery applications, we can try to fit in the same formula, but of course with new tweaks.”

The team is also interested in developing alternative fuels, including looking at the use of metals like aluminum to power rockets. “We’re interested in utilizing aluminum as a fuel for civil applications,” Deng says, because aluminum is abundant in the earth, cheap, and it’s available globally. “What we are trying to do is to understand [aluminum combustion] and be able to tailor its ignition and propagation properties.”

Among other accolades, Deng is a 2025 recipient of the Hiroshi Tsuji Early Career Researcher Award from the Combustion Institute, an award that recognizes excellence in fundamental or applied combustion science research.

© Photo: John Freidah/MIT MechE

Before batteries lose power, fail suddenly, or burst into flames, they tend to produce faint sounds over time that provide a signature of the degradation processes going on within their structure. But until now, nobody had figured out how to interpret exactly what those sounds meant, and how to distinguish between ordinary background noise and significant signs of possible trouble.

Now, a team of researchers at MIT’s Department of Chemical Engineering have done a detailed analysis of the sounds emanating from lithium ion batteries, and has been able to correlate particular sound patterns with specific degradation processes taking place inside the cells. The new findings could provide the basis for relatively simple, totally passive and nondestructive devices that could continuously monitor the health of battery systems, for example in electric vehicles or grid-scale storage facilities, to provide ways of predicting useful operating lifetimes and forecasting failures before they occur.

The findings were reported Sept. 5 in the journal Joule, in a paper by MIT graduate students Yash Samantaray and Alexander Cohen, former MIT research scientist Daniel Cogswell PhD ’10, and Chevron Professor of Chemical Engineering and professor of mathematics Martin Z. Bazant.

“In this study, through some careful scientific work, our team has managed to decode the acoustic emissions,” Bazant says. “We were able to classify them as coming from gas bubbles that are generated by side reactions, or by fractures from the expansion and contraction of the active material, and to find signatures of those signals even in noisy data.”

Samantaray explains that, “I think the core of this work is to look at a way to investigate internal battery mechanisms while they’re still charging and discharging, and to do this nondestructively.” He adds, “Out there in the world now, there are a few methods that exist, but most are very expensive and not really conducive to batteries in their normal format.”

To carry out their analysis, the team coupled electrochemical testing with recording of the acoustic emissions, under real-world charging and discharging conditions, using detailed signal processing to correlate the electrical and acoustic data. By doing so, he says, “we were able to come up with a very cost-effective and efficient method of actually understanding gas generation and fracture of materials.”

Gas generation and fracturing are two primary mechanisms of degradation and failure in batteries, so being able to detect and distinguish those processes, just by monitoring the sounds produced by the batteries, could be a significant tool for those managing battery systems.

Previous approaches have simply monitored the sounds and recorded times when the overall sound level exceeded some threshold. But in this work, by simultaneously monitoring the voltage and current as well as the sound characteristics, Bazant says, “We know that [sound] emissions happen at a certain potential [voltage], and that helps us identify what the process might be that is causing that emission.”

After these tests, they would then take the batteries apart and study them under an electron microscope to detect fracturing of the materials.

In addition, they took a wavelet transform — essentially, a way of encoding the frequency and duration of each signal that is captured, providing distinct signatures that can then be more easily extracted from background noise. “No one had done that before,” Bazant says, “so that was another breakthrough.”

Acoustic emissions are widely used in engineering, he points out, for example to monitor structures such as bridges for signs of incipient failure. “It’s a great way to monitor a system,” he says, “because those emissions are happening whether you’re listening to them or not,” so by listening, you can learn something about internal processes that would otherwise be invisible.

With batteries, he says, “we often have a hard time interpreting the voltage and current information as precisely as we’d like, to know what’s happening inside a cell. And so this offers another window into the cell’s state of health, including its remaining useful life, and safety, too.” In a related paper with Oak Ridge National Laboratory researchers, the team has shown that acoustic emissions can provide an early warning of thermal runaway, a situation that can lead to fires if not caught. The new study suggests that these sounds can be used to detect gas generation prior to combustion, “like seeing the first tiny bubbles in a pot of heated water, long before it boils,” says Bazant.

The next step will be to take this new knowledge of how certain sounds relate to specific conditions, and develop a practical, inexpensive monitoring system based on this understanding. “Now, we know what to look for, and how to correlate that with lifetime and health and safety,” Bazant says.

One possible application of this new understanding, Samantaray says, is “as a lab tool for groups that are trying to develop new materials or test new environments, so they can actually determine gas generation or active material fracturing without having to open up the battery.”

Bazant adds that the system could also be useful for quality control in battery manufacturing. “The most expensive and rate-limiting process in battery production is often the formation cycling,” he says. This is the process where batteries are cycled through charging and discharging to break them in, and part of that process involves chemical reactions that release some gas. The new system would allow detection of these gas formation signatures, he says, “and by sensing them, it may be easier to isolate well-formed cells from poorly formed cells very early, even before the useful life of the battery, when it’s being made,” he says.

The work was supported by the Toyota Research Institute, the Center for Battery Sustainability, the National Science Foundation, and the Department of Defense, and made use of the facilities of MIT.nano.

© Photo: Alexander Cohen

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.

© Credit: Courtesy of the researchers

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.

© Credit: Courtesy of the researchers

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.

© Credit: Courtesy of Mohamed Elrefaie

As a major contributor to global carbon dioxide (CO₂) emissions, the transportation sector has immense potential to advance decarbonization. However, a zero-emissions global supply chain requires re-imagining reliance on a heavy-duty trucking industry that emits 810,000 tons of CO₂, or 6 percent of the United States’ greenhouse gas emissions, and consumes 29 billion gallons of diesel annually in the U.S. alone.

A new study by MIT researchers, presented at the recent American Society of Mechanical Engineers 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, quantifies the impact of a zero-emission truck’s design range on its energy storage requirements and operational revenue. The multivariable model outlined in the paper allows fleet owners and operators to better understand the design choices that impact the economic feasibility of battery-electric and hydrogen fuel cell heavy-duty trucks for commercial application, equipping stakeholders to make informed fleet transition decisions.

“The whole issue [of decarbonizing trucking] is like a very big, messy pie. One of the things we can do, from an academic standpoint, is quantify some of those pieces of pie with modeling, based on information and experience we’ve learned from industry stakeholders,” says ZhiYi Liang, PhD student on the renewable hydrogen team at the MIT K. Lisa Yang Global Engineering and Research Center (GEAR) and lead author of the study. Co-authored by Bryony DuPont, visiting scholar at GEAR, and Amos Winter, the Germeshausen Professor in the MIT Department of Mechanical Engineering, the paper elucidates operational and socioeconomic factors that need to be considered in efforts to decarbonize heavy-duty vehicles (HDVs).

Operational and infrastructure challenges

The team’s model shows that a technical challenge lies in the amount of energy that needs to be stored on the truck to meet the range and towing performance needs of commercial trucking applications. Due to the high energy density and low cost of diesel, existing diesel drivetrains remain more competitive than alternative lithium battery-electric vehicle (Li-BEV) and hydrogen fuel-cell-electric vehicle (H2 FCEV) drivetrains. Although Li-BEV drivetrains have the highest energy efficiency of all three, they are limited to short-to-medium range routes (under 500 miles) with low freight capacity, due to the weight and volume of the onboard energy storage needed. In addition, the authors note that existing electric grid infrastructure will need significant upgrades to support large-scale deployment of Li-BEV HDVs.

While the hydrogen-powered drivetrain has a significant weight advantage that enables higher cargo capacity and routes over 750 miles, the current state of hydrogen fuel networks limits economic viability, especially once operational cost and projected revenue are taken into account. Deployment will most likely require government intervention in the form of incentives and subsidies to reduce the price of hydrogen by more than half, as well as continued investment by corporations to ensure a stable supply. Also, as H2-FCEVs are still a relatively new technology, the ongoing design of conformal onboard hydrogen storage systems — one of which is the subject of Liang’s PhD — is crucial to successful adoption into the HDV market.

The current efficiency of diesel systems is a result of technological developments and manufacturing processes established over many decades, a precedent that suggests similar strides can be made with alternative drivetrains. However, interactions with fleet owners, automotive manufacturers, and refueling network providers reveal another major hurdle in the way that each “slice of the pie” is interrelated — issues must be addressed simultaneously because of how they affect each other, from renewable fuel infrastructure to technological readiness and capital cost of new fleets, among other considerations. And first steps into an uncertain future, where no one sector is fully in control of potential outcomes, is inherently risky. 

“Besides infrastructure limitations, we only have prototypes [of alternative HDVs] for fleet operator use, so the cost of procuring them is high, which means there isn’t demand for automakers to build manufacturing lines up to a scale that would make them economical to produce,” says Liang, describing just one step of a vicious cycle that is difficult to disrupt, especially for industry stakeholders trying to be competitive in a free market. 

Quantifying a path to feasibility

“Folks in the industry know that some kind of energy transition needs to happen, but they may not necessarily know for certain what the most viable path forward is,” says Liang. Although there is no singular avenue to zero emissions, the new model provides a way to further quantify and assess at least one slice of pie to aid decision-making.

Other MIT-led efforts aimed at helping industry stakeholders navigate decarbonization include an interactive mapping tool developed by Danika MacDonell, Impact Fellow at the MIT Climate and Sustainability Consortium (MCSC); alongside Florian Allroggen, executive director of MITs Zero Impact Aviation Alliance; and undergraduate researchers Micah Borrero, Helena De Figueiredo Valente, and Brooke Bao. The MCSC’s Geospatial Decision Support Tool supports strategic decision-making for fleet operators by allowing them to visualize regional freight flow densities, costs, emissions, planned and available infrastructure, and relevant regulations and incentives by region.

While current limitations reveal the need for joint problem-solving across sectors, the authors believe that stakeholders are motivated and ready to tackle climate problems together. Once-competing businesses already appear to be embracing a culture shift toward collaboration, with the recent agreement between General Motors and Hyundai to explore “future collaboration across key strategic areas,” including clean energy. 

Liang believes that transitioning the transportation sector to zero emissions is just one part of an “energy revolution” that will require all sectors to work together, because “everything is connected. In order for the whole thing to make sense, we need to consider ourselves part of that pie, and the entire system needs to change,” says Liang. “You can’t make a revolution succeed by yourself.” 

The authors acknowledge the MIT Climate and Sustainability Consortium for connecting them with industry members in the HDV ecosystem; and the MIT K. Lisa Yang Global Engineering and Research Center and MIT Morningside Academy for Design for financial support.

© Photo: Bob Adams/Flickr

Charging stations for electric vehicles are essential for cleaning up the transportation sector. A new study by MIT researchers suggests they’re good for business, too.

The study found that, in California, opening a charging station boosted annual spending at each nearby business by an average of about $1,500 in 2019 and about $400 between January 2021 and June 2023. The spending bump amounts to thousands of extra dollars annually for nearby businesses, with the increase particularly pronounced for businesses in underresourced areas.

The study’s authors hope the research paints a more holistic picture of the benefits of EV charging stations, beyond environmental factors.

“These increases are equal to a significant chunk of the cost of installing an EV charger, and I hope this study sheds light on these economic benefits,” says lead author Yunhan Zheng MCP ’21, SM ’21, PhD ’24, a postdoc at the Singapore-MIT Alliance for Research and Technology (SMART). “The findings could also diversify the income stream for charger providers and site hosts, and lead to more informed business models for EV charging stations.”

Zheng’s co-authors on the paper, which was published today in Nature Communications, are David Keith, a senior lecturer at the MIT Sloan School of Management; Jinhua Zhao, an MIT professor of cities and transportation; and alumni Shenhao Wang MCP ’17, SM ’17, PhD ’20 and Mi Diao MCP ’06, PhD ’10.

Understanding the EV effect

Increasing the number of electric vehicle charging stations is seen as a key prerequisite for the transition to a cleaner, electrified transportation sector. As such, the 2021 U.S. Infrastructure Investment and Jobs Act committed $7.5 billion to build a national network of public electric vehicle chargers across the U.S.

But a large amount of private investment will also be needed to make charging stations ubiquitous.

“The U.S. is investing a lot in EV chargers and really encouraging EV adoption, but many EV charging providers can’t make enough money at this stage, and getting to profitability is a major challenge,” Zheng says.

EV advocates have long argued that the presence of charging stations brings economic benefits to surrounding communities, but Zheng says previous studies on their impact relied on surveys or were small-scale. Her team of collaborators wanted to make advocates’ claims more empirical.

For their study, the researchers collected data from over 4,000 charging stations in California and 140,000 businesses, relying on anonymized credit and debit card transactions to measure changes in consumer spending. The researchers used data from 2019 through June of 2023, skipping the year 2020 to minimize the impact of the pandemic.

To judge whether charging stations caused customer spending increases, the researchers compared data from businesses within 500 meters of new charging stations before and after their installation. They also analyzed transactions from similar businesses in the same time frame that weren’t near charging stations.

Supercharging nearby businesses

The researchers found that installing a charging station boosted annual spending at nearby establishments by an average of 1.4 percent in 2019 and 0.8 percent from January 2021 to June 2023.

While that might sound like a small amount per business, it amounts to thousands of dollars in overall consumer spending increases. Specifically, those percentages translate to almost $23,000 in cumulative spending increases in 2019 and about $3,400 per year from 2021 through June 2023.

Zheng says the decline in spending increases over the two time periods might be due to a saturation of EV chargers, leading to lower utilization, as well as an overall decrease in spending per business after the Covid-19 pandemic and a reduced number of businesses served by each EV charging station in the second period. Despite this decline, the annual impact of a charging station on all its surrounding businesses would still cover approximately 11.2 percent of the average infrastructure and installation cost of a standard charging station.

Through both time frames, the spending increases were highest for businesses within about a football field’s distance from the new stations. They were also significant for businesses in disadvantaged and low-income areas, as designated by California and the Justice40 Initiative.

“The positive impacts of EV charging stations on businesses are not constrained solely to some high-income neighborhoods,” Wang says. “It highlights the importance for policymakers to develop EV charging stations in marginalized areas, because they not only foster a cleaner environment, but also serve as a catalyst for enhancing economic vitality.”

Zheng believes the findings hold a lesson for charging station developers seeking to improve the profitability of their projects.

“The joint gas station and convenience store business model could also be adopted to EV charging stations,” Zheng says. “Traditionally, many gas stations are affiliated with retail store chains, which enables owners to both sell fuel and attract customers to diversify their revenue stream. EV charging providers could consider a similar approach to internalize the positive impact of EV charging stations.”

Zheng also says the findings could support the creation of new funding models for charging stations, such as multiple businesses sharing the costs of construction so they can all benefit from the added spending.

Those changes could accelerate the creation of charging networks, but Zheng cautions that further research is needed to understand how much the study’s findings can be extrapolated to other areas. She encourages other researchers to study the economic effects of charging stations and hopes future research includes states beyond California and even other countries.

“A huge number of studies have focused on retail sales effects from traditional transportation infrastructure, such as rail and subway stations, bus stops, and street configurations,” Zhao says. “This research provides evidence for an important, emerging piece of transportation infrastructure and shows a consistently positive effect on local businesses, paving the way for future research in this area.”

The research was supported, in part, by the Singapore-MIT Alliance for Research and Technology (SMART) and the Singapore National Research Foundation. Diao was partially supported by the Natural Science Foundation of Shanghai and the Fundamental Research Funds for the Central Universities of China.

© Image: iStock

Although critical to the nation’s food security, farm work is potentially hazardous, farmworkers receive lower wages when compared with nonsupervisory workers outside agriculture, and many hired farm workers lack legal work authorization and access to basic public services. For the United States to remain competitive as a producer of fruit, vegetables, and other labor-intensive commodities both private and government institutions will need to accelerate adaptation to a changing landscape of farm labor.

An upcoming conference on farm labor seeks to strengthen ongoing farm labor research by convening and developing a network of researchers and stakeholders. The Changing Landscape of Farm Labor Conditions in the United States: What the Future Holds and How to Prepare for It conference will take place September 17 to 19, 2024, in Santa Cruz, California.

The conference is presented by the USDA Economic Research Service and Farm Foundation. It will cover four key themes: trends in the farm labor force, including worker migration and the H-2A Temporary Agricultural Program; labor costs, farm worker conditions, and workforce development.

Visit https://farmfoundation.swoogo.com/farmlabor for more information and to register.

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