Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.

MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. 

The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.

The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.

“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.

Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.

Overcoming the limits

In an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.

But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.

To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.

So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.

“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.

The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.

Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”

“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.

They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.

To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.

“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.

Leveraging magnetism

This lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.

They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.

The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.

The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.

A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.

“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.

Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.

This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities.

Last week, national security agencies from the Five Eyes—that’s the rich, English-language-speaking countries club—jointly released a statement warning of the increasing cyber risks of AI models: in particular, their ability to autonomously hack into systems and networks. The statement was more measured than some of the breathless headlines about it, and the advice they gave is pretty much the standard advice everyone gives—albeit with newfound urgency.

Internet risks are nothing new, and cyberattacks—both large and small—have been a significant issue since long before the current crop of generative AI models...

The move deepens what Democrats say is the president's politicization of federal emergency management decisions.

The American Clean Power Association is assessing the policy and tax landscape for renewable energy sources after Republican rollbacks.

If his deregulatory actions survive judicial scrutiny, Aaron Szabo will have played a key role in reshaping EPA’s regulatory power.

The EU’s plan to support livestock farmers recasts a climate problem as a strategic asset.

They say the European Commission should “strengthen the EU ETS and make it future-proof to ensure long-term investment predictability and regulatory stability."

Heat-related damage tends to be less straightforward to measure than losses caused by floods and wildfires, the European Banking Authority said.

Mounting death tolls in the U.S., Australia, Chile and other countries over the past decade have revealed how unprepared many areas are for such infernos.

The figures are the latest evidence of the deadly toll of recent extreme temperatures across Europe.

The Regional Greenhouse Gas Initiative is delivering a windfall that states can use to cut planet-warming emissions — or lower bills.

The state's largest electric utility got approval to adjust power prices in line with a first-in-the-nation law.

Nature Climate Change, Published online: 08 July 2026; doi:10.1038/s41558-026-02690-1

Carbon markets reward the climate mitigation benefits of nature-based solutions but rarely value their broader societal impacts. Creating markets for verified co-benefits, such as coastal protection and biodiversity, could redirect investment towards projects that deliver larger gains for both people and climate.

If you are crossing an unfamiliar room in the dark, you may grope around a bit to get a sense of your space.

But for many animals, feeling out a space comes more naturally. A mouse, for instance, can efficiently navigate in the dark just by grazing its whiskers against walls and other obstacles.

Fan Wang, a professor of brain and cognitive sciences and an investigator at the McGovern Institute for Brain Research at MIT, has discovered how neurons in a mouse’s brainstem use signals from the animal’s touch-sensitive whiskers to estimate an object’s distance from the face.

Her team’s findings, published June 25 in the journal Neuron, unlock key circuitry the brain uses to represent the space immediately surrounding the body.

Mapping space

The circuit the team discovered is part of the brain’s system for creating an egocentric map of space — that is, understanding where things are relative to one’s own body. Neuroscientists know that the brain calls on specialized circuits to understand space in this way, which are different from its system for mapping space using external landmarks.

In their study, Wang and her team explored how the brain maps the space closest to the body, known as the peripersonal space. This is the space in which we move, and it is vital that we understand where things are in relationship to our bodies so we can reach, step, avoid hazards, and otherwise interact effectively with our environment.

Wang says mice were an appealing model for investigating how the brain understands objects’ distance within the peripersonal space, because a rodent’s whiskers seem so much like a built-in set of rulers. These whiskers, which vary in length, are swept back and forth as the animals explore their environment. As whiskers bend and vibrate, the mechanical sensations are relayed to the brain by sensory neurons at their base. Those neurons fire more when a whisker bends close to the face than they do in response to contact near the whisker’s tip, communicating information about the proximity of the touch.

Wang’s team wanted to know if the brain uses these signals to build an internal ruler-like representation of distance more precise than “near” or “far.” To find out, graduate student Wenxi Xiao and Research Scientist Kyle Severson monitored neural activity in a small sensory-processing region in the brainstem where tactile signals from the whiskers first arrive in the brain. They studied what happened there as mice walked on a treadmill while brushing their whiskers against a wall that passed by at different distances.

Many neurons in the region were sensitive to the whisker bending triggered by the wall. Some behaved similarly to the sensory neurons they were getting their information from, firing more when the wall was closer to the face and thus serving as a proximity-based distance code. But other cells were tuned in to discrete distances, firing only when the distance of the wall the whiskers had touched was within a specific range.

The whiskers rule

For some neurons, activity peaked when the wall was 23 millimeters away from the face, near the tips of the longest whiskers. Others responded most when the wall was at intermediate distances. “Each of these neurons represents a specific distance, and together they span the full range reached by the longest whisker, like tick marks on the ruler,” Wang explains. “We call that the map code.”

The team wanted to know how the brain converts proximity signals from different whiskers into accurate map code of object’s distances from the head. “You cannot just listen to individual whisker neurons, because a contact at the tip of a short whisker would be in the middle of a long whisker. You need a brain circuit to build a unified distance map,” Wang says.

Through computational modeling and by exploring what happened when they manipulated neural signaling in specific ways, Wang’s team showed how distances can be calculated by comparing inputs from different sensory neurons. Their findings suggest that each brainstem neuron that makes up the map code receives both direct excitatory inputs from proximity-sensitive whisker neurons and inhibitory inputs from neurons driven by proximity-dependent whisker touch signals.

“Essentially, the inhibitory pathway allows the brainstem to compare two inputs by subtraction,” Wang explains. “If one input signals ‘this is how far it is’ and the other signals ‘this is how far I estimate it to be,’ subtracting one from the other yields an intermediate value. We think it’s a simple and elegant way to transform tactile input into a representation of discrete distance.”

Wang notes that despite their importance, the brain’s body-centered representations of space have so far received little attention from neuroscientists, who know much more about how we understand locations in space relative to landmarks (an allocentric map). She is eager to investigate how the egocentric map code her team discovered is integrated with other brain systems to guide movement, social interactions, and other behavior, and hopes the findings will further exploration from other groups.

The study was funded by grants from the National Institutes of Health.

In today's world, artificial intelligence chatbots such as ChatGPT and Claude can perform many functions, such as composing work emails and planning travel itineraries. These chatbots are systems built around large vision-language models (VLMs): AI trained on a massive dataset that includes books, websites, code, and images. 

The AI algorithms are then refined on massive amounts of human-generated feedback to follow instructions and avoid harmful or unwanted output, and use that "knowledge" to produce text or images based on input from a user. Although chatbots have clear limitations, they can be very helpful for a wide range of tasks, including in some areas that traditionally require specialized skills, like computer programming.

As part of a project for the U.S. Department of the Air Force–MIT AI Accelerator's Phantom Program, U.S. Air Force cadet Joshua Lynch — with the help of his mentor, Laura Niss, a technical staff member in the Embedded and AI Systems Group at MIT Lincoln Laboratory — wanted to determine if, as a complete novice to coding, he could develop a fully functional program. He used a process called "vibe-coding," in which a user relies entirely on prompts to guide a generative AI chatbot to write and refine code. 

His motivation was to empower anyone familiar with the military problem space, regardless of their technical background, to advance their ideas for useful software applications, essentially bypassing the time and cost constraints of the traditional military software development pipeline. Lynch aimed to build his own application while Niss monitored his experience with the technology.

"The Phantom student wanted to see if he could create a useful application through self-identified vibe-coding, without any previous experience," Niss says. "Within this project, I wanted to understand how his perception of AI changed over time with use. We both wanted to understand better where and how AI could be used by nontechnical users in the military."

Lynch set out to see if, starting with no coding skills and using chatbots, he could create an application specific to his type of tactical team to help reduce collateral damage while enhancing survivability in the broader mission. This application would offer capabilities including AI-assisted target recognition; modular intelligence, surveillance, and reconnaissance; autonomous striking; and communication management on the battlefield. 

During the project, Lynch completed several professional development courses in AI and familiarized himself with both military and nonmilitary uses of the technology. For the basis for his code generation, he used the paid models of three AI chatbots: Anthropic's Claude, OpenAI's ChatGPT, and Google's Gemini. Most of this work was done only through the chatbots' main chat function on a web browser, not as an integrated system within a development environment, as is standard now. The final application was produced using Google AI Studio App, which can create applications that interface with the Gemini application programming interface and has AI integrated in the development environment. 

Over three months, Lynch worked with these models to build his application, called the Remote Operating Modular Augmentation Device (ROMAD-AI). During this time, he learned several methods to improve the code output. For example, he often encountered difficulties with the AI chatbots lacking hierarchical focus and modifying unrelated code sections. He discovered it was important to break problems into small parts, frame questions clearly, and steer conversations back on topic when they stray too far from the objective. 

Learning to recognize the chatbots' limitations and effectively work around them took up most of the project timeline. As Lynch gained more experience with the chatbots, limitations in the AI capabilities and time for development caused him to re-scope the project, moving it from an application that could assist on the battlefield to one that could perform basic document processing, such as analyzing tactical maps of battlefields and generating mission-planning documents through an interface with a VLM-powered chatbot. While the resulting prototype did not perform all capabilities Lynch originally set out to include (and in its current iteration was not secure for the desired use case), it proved the capability and usefulness of such an application for service members.

"I was quite impressed with this final product, and it showed me how powerful these systems can be at prototyping designs from nonexperts," Niss says. "I'm now of the opinion that these can be powerful tools for nontechnical experts to convey problems and possible solutions to technical experts, and aid in communicating desired outcomes."

Niss observed the change in Lynch's perspective of AI language models during his experience. After starting with an impressive goal, Lynch gained understanding of the capabilities of current technology and significantly scoped down his expectations by the end of the project period. Measures of his perceptions of the different AI systems over time and across system updates were particularly interesting to Lynch and Niss, with Claude showing more stability than ChatGPT across traits such as likeability, anthropomorphism, and perceived intelligence. Lynch found AI to be a helpful tutor, but noted its inaccuracies on topics he knew well.

The project showed that AI chatbots can empower nontechnical service members to produce viable software applications for their unique problems, although it works better as a prototyping assistant than as a full production tool when handling sensitive information and for critical applications. Improper vetting of code may lead to security risks, as demonstrated by an instance where Lynch didn't realize that the final application was sending the input documents to a Gemini AI model to analyze, rather than parsing the documents locally on his computer. Although AI can generate significant amounts of functional code, code review remains a bottleneck in this space.

"For me, this project reinforced the expanse between experts in different fields," Niss says. "No matter how good AI gets, I think we'll always need to collaborate to get to the best solutions for the most important problems."

Research was sponsored by the Department of the Air Force Artificial Intelligence Accelerator and was accomplished under Cooperative Agreement Number FA8750-19-2-1000.

This blog post is part 1 of a 2-part series. The second part will set out recommendations for companies and policymakers.

Six years ago—one month into a global pandemic—we argued that the automated moderation processes many platforms were rapidly adopting should be highly transparent, easily appealable, and temporary. We warned that "protocols adopted in times of crisis often persist when the crisis is over."

That warning proved prescient. The use of automation and artificial intelligence (AI) to identify, flag, and moderate content has become the new norm—a permanent feature of how platforms govern speech online. In this two part series, we’re take stock of this new norm, and considering what platforms can and should do to ensure that AI serves online expression rather than stifling it.

A brief history of automated content moderation

From spam filtering and keyword blacklists to the hash-matching technologies used to identify child sexual abuse material and terrorist content, automated technologies have been used in commercial content moderation for many years. While these tools have long posed risks to freedom of expression, their use was, for quite some time, relatively limited in scope.

Then, in 2017, a blog post published by Facebook (now Meta) described the company's "fairly recent" use of artificial intelligence to identify, classify, and remove violent extremist content. At the same time, Facebook emphasized caution, noting that it did not want to suggest there was "any easy technical fix."

Just one year later, Mark Zuckerberg appeared before the U.S. Senate's Commerce and Judiciary Committees and disclosed that "99 percent of the ISIS and Al Qaida content" removed by Facebook was flagged by AI "before any human sees it." He also stated that Facebook was "developing A.I. tools that can identify certain classes of bad activity proactively and flag it for our team at Facebook." At the time, we raised concerns about the ethical implications of using AI in this manner.

Then came 2020. The sudden reduction of the human moderation workforce, combined with a dramatic increase in social media use—and with it, a surge in misinformation—created the perfect conditions for platforms to expand their reliance on AI-driven moderation. It quickly became apparent that companies'—and particularly Meta's—approach to moderation during the pandemic represented a backslide in transparency, freedom of expression, and access to remedy. The increased reliance on automation was a significant factor.

The costs and benefits of AI content moderation

We knew in 2020 that the use of AI to moderate content would present problems for online freedom of expression. Today, those problems are well-documented. A 2025 joint declaration by special rapporteurs and representatives of the United Nations (UN), Organization for Security and Co-operation in Europe (OSCE), Organization of American States (OAS), and African Commission on Human and Peoples’ Rights (ACHPR) states:

“The use of AI content moderation can lead to over-removal, discrimination and censorship. Reliance on inherently biased datasets and opaque training processes can amplify pre-existing inequalities, risking homogenisation of expression, and erasure of linguistic and cultural diversity.”

EFF and many of our allies have documented these impacts. For example, our 2019 paper co-authored with Witness and Syrian Archive examined the impact of extremist content regulations—and their implementation through automation and AI—on human rights documentation. A 2020 report from Human Rights Watch highlighted the consequences of these removals, noting: "There is no way of knowing how much potential evidence of serious crimes is disappearing without anyone's knowledge."

The Center for Democracy and Technology's recent series on content moderation in the Global South demonstrates persistent inequities in content moderation of four “low-resource” languages—so-called because the relative scarcity of training data makes it more difficult to develop equitable and accurate AI models for them. 

Content moderation often disproportionately impacts vulnerable and historically marginalized groups, and AI content moderation is no different. GLAAD recognizes the role AI plays in scaling content moderation but notes that “when moderation systems lack nuance, transparency, and human oversight, they can fail to curb harassment and wrongly suppress legitimate LGBTQ content.”

These failures are not incidental. They are a predictable consequence of deploying automated systems to make complex judgments about language, culture, context, and identity at scale.

All of that said, automated content moderation can offer important benefits. The primary one: helping to spare human content moderators who must review content that varies from whimsical to horrific, often for little pay and with devastating mental health consequences. Outsourcing this work to the bots can offer some relief—though it’s worth noting that the humans hired to train the AI models face a similar dynamic.

In addition, AI models could potentially be trained over time to be more precise, accurate, and dynamic, helping to mitigate over-censorship and disinformation. The jury is still out on whether this potential will be realized; what we do know is that new approaches to the persistent problem of over and under-enforcement are desperately needed.

Automated moderation is no longer an experiment

Getting the balance between real costs and potential benefits depends a lot on the details: how automated systems are designed, trained, implemented, and audited.  

Despite advances in the sophistication and scale of automated moderation systems, many of the transparency, accountability, and due process safeguards advocated by civil society, researchers, and human rights experts have yet to be fully realized. At the same time, automated systems have become increasingly central to how platforms enforce their rules and govern online speech.

The question today is not whether companies will use AI to moderate content, but under what conditions they should do so. And now as ever, the answer is not that the public should just trust that platforms’ deployment of increasingly powerful systems will serve, rather than inhibit online expression. In fact, as automated systems become more sophisticated and more deeply embedded in platform governance, the need for transparency and accountability becomes more urgent. 

In the global race to build and dominate the AI industry, it can sure seem like the interests of ordinary people sit last on the agenda. It's just the opposite for EFF. While companies furiously jam AI tools into their veins and your eyeballs, EFF’s technologists, activists, and attorneys have been meticulously cutting through the hype to ensure AI can serve your privacy and free expression. Technology has leaned into a new era, and this summer you can help EFF fight for the people.

JOIN EFF

Over the next two weeks, we’re encouraging you to support the cause as an EFF member for as little as $10 each month. You can get great member swag every year like our privacy puffy stickers, Claw Back t-shirt, and Privacy Badger Crewneck.

Fight mass surveillance! Pictured: Claw Back member t-shirt and Privacy Badger Crewneck.

AI tools—beyond their marketing fluff—demonstrate both incredible potential and real danger. With the support of members around the world, EFF detangles the possibilities from the anxieties and threats with the care and nuance it deserves. In recent months, EFF:

• Mobilized people against the GUARD Act, which would require problematic age verifications systems for AI companions.
• Joined civil society partners to call out a General Services Administration proposal that would make AI tools less safe and less useful.
• Sued for answers under the Freedom of Information Act to uncover how the government is using AI to evaluate requests for medical care.
• Testified before the U.S. Congress Homeland Security Subcommittee on Cybersecurity and Infrastructure Protection.

The scope of AI, both the good and the bad, multiplies every day. If we want the AI-powered benefits of efficiency, scientific discovery, and greater accessibility to knowledge, then we also need strong protections against surveillance, harms to creativity and innovation online, perpetuating systemic bias, and privacy violations now.

With AI taking over the public consciousness, you can be assured that EFF will never stop advocating for you. Together, we can ensure that technology supports freedom, justice, and innovation for all people.

Join EFF

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When a star dies, a black hole is born. This has been the textbook origin story for most black holes. At the end of a massive star’s life, its outer layers blast away in a brilliant supernova, and its core collapses into a gravitationally tight and dense region, forming a black hole.

Recent discoveries from gravitational-wave detectors have revealed hundreds of merging black holes across the universe. Many of them have been thought to come directly from exploding stars. But black holes can also come from other, smaller black holes. The products of previous black hole mergers can, in principle, merge again, creating a more massive black hole. This alternative, black-holes-birthing-black-holes pathway is known as “hierarchical merging.”

Now MIT scientists are finding that a good number of merging black holes may have indeed merged before. They carried out a new analysis of recent data from the LIGO, Virgo, and KAGRA observatories, containing 155 pairs of binary black holes, and found about 14 percent of merging black holes in the universe may in fact be second-generation black holes that formed from the previous merging of two smaller black holes. 

The results, which the team reports this week in Physical Review Letters, suggest that repeated hierarchical merging is a significant pathway by which black holes form. 

“We’re finding that, for some of these merging black holes, it’s not their first rodeo,” says the study’s first author, Cailin Plunkett, a graduate student in MIT’s Department of Physics. “Overall in the universe, black holes are merging all the time. The question of how often are they repeatedly merging was pretty uncertain. Now we’re seeing a relatively consistent picture where there’s a decent percentage of black holes that are coming from this repeated pathway.”

The study’s co-authors are Salvatore Vitale, associate professor of physics at MIT; Thomas Callister of Williams College; and Michael Zevin of Adler Planetarium and Northwestern University.

Lopsided pairs

When a massive star collapses and dies, the resulting black hole should have very little spin. In addition to losing a huge amount of mass when it explodes, the star should also lose much of its inherent spin, or angular momentum. The black hole left over should then have little to no spin. 

In contrast, when two black holes merge, the collision should create a new, wildly spinning second-generation black hole. 

“They would be spinning very fast, at about 70 percent their maximum possible spin,” Vitale says. 

Scientists suspect that hierarchical mergers occur in dense stellar environments, where stars are so tightly packed together that multiple neighboring stars could die and collapse to form black holes that are then close enough to merge with each other to form second-generation black holes. 

“You might have a ton of stars whizzing around each other, and if some are massive and explode, they become black holes. The black holes continue to whizz around, and can capture each other and merge,” Plunkett says. “This process can repeat potentially ad infinitum, by virtue of the fact that you have a ton of stars and black holes in this really dense environment.”

One sign of a hierarchical merger is that one black hole in a pair of merging black holes has a much higher spin, and higher mass, than the other. Such a lopsided duo would signal that at least one of the black holes came from the collision of two previous black holes. 

In 2024, scientists detected two such lopsided mergers in signals recorded by the LIGO, Virgo, and KAGRA observatories. The observatories detect incoming gravitational waves — incredibly small wobbles in the fabric of space and time — that are the reverberations from distant cosmic phenomena, such as colliding black holes. 

The observatories detected two gravitational-wave signals, labeled GW241011 and GW241110, each of which likely contain a black hole spinning much faster than its partner. The hierarchical mergers were discovered by analyzing each signal in detail to tease out the specific masses and spins of the black holes involved in each merger.

That work inspired Plunkett and Vitale to do a search of similar hierarchical mergers using all the gravitational-wave signals that the observatories have captured to date. 

A pattern of wobbles

For their new study, the team analyzed the LIGO-Virgo-KAGRA Gravitational Wave Transient Catalog 4.0 (GWTC-4.0), which comprises gravitational-wave detections from the observatories’ fourth observing run. Rather than analyze each gravitational-wave signal one by one, which is what scientists did for GW241011 and GW241110, Plunkett and Vitale searched for a characteristic pattern of hierarchical mergers across the data overall, to see if any matching signals popped out.

The pattern they searched for represents a range of orbital “wobbles.” Just before they merge, two black holes spiral toward each other in a disk-like, orbital plane. When the spins of the pair are perpendicular to the plane, this remains relatively steady. But when one or both spins are not perpendicular to the plane, the disk will wobble. The degree to which the whole plane wobbles, or “precesses,” can tell scientists about the balance of masses and spins between the two spiraling black holes. 

Plunkett and Vitale developed a model for the range of wobbling that should be a sign of a hierarchical merger, specifically between a first-generation and a second-generation black hole. 

The team applied the model to the entire GWTC-4.0 catalog, which comprises gravitational-wave signals from 153 black hole mergers, in addition to the signals from GW241011 and GW241110. Their analysis revealed that a number of mergers fit the pattern for orbital wobbling that was likely caused by the colliding of first- and second-generation black holes. 

Specifically, they found that roughly 14 percent of merging black holes in the universe may have merged before, and that these second-generation black holes had very particular masses: Black holes of around 10 solar masses (10 times the mass of the sun) and 30 solar masses were run-of-the-mill star-born black holes, while second-generation black holes had masses of around 20 solar masses or 40 solar masses and above. 

“One of the reasons why the 40-and-above regime is interesting is, stellar evolution theory predicts you shouldn’t be able to form black holes in that mass range at all from just a supernova,” Plunkett says. “We think supernovae from really massive stars end up being so violent that they leave no black holes at all above roughly 45 solar masses. Yet we have seen black holes that are that massive. And the question is: Where did they come from?”

The team’s new analysis provides support for the idea that black holes can form from the repeated merging of other black holes, and that this alternate origin story could explain some of the curious black holes that we can detect today. 

This work was supported, in part, by the National Science Foundation, and the Brinson Foundation.

Many experts refer to hydrogen as “the fuel of the future.” It is expected to help decarbonize the global economy in two main ways: burning it or feeding it into a fuel cell produces storable energy with no carbon emissions, just water. And it can be used in place of fossil fuels or as a chemical feedstock in hard-to-decarbonize industrial processes such as steel and cement production.

But for hydrogen to realize its potential, two challenges must be overcome. Researchers worldwide are now working to address the first: finding a method of producing pure hydrogen that’s both cheap and low in carbon emissions.

Just as critical is finding a good means of transporting and storing hydrogen. A team led by researchers at the MIT Energy Initiative (MITEI) has been tackling that less-discussed but important challenge. The location where the pure hydrogen is produced is likely to be far away from where it will be used, so moving it will be critical — and difficult.

The problem stems from two characteristics of hydrogen: It’s the lightest gas there is, and it has low energy density per volume. Therefore, delivering a given amount of energy requires a large volume of hydrogen and a container that’s sealed so tightly that the hydrogen molecules can’t escape. Suffice it to say, moving a liquid fuel such as gasoline is easier. And without a good means of storing and transporting hydrogen, it can’t fulfill its promise as the world’s clean fuel of the future.

In 2024, with funding provided by ExxonMobil Technology and Engineering Co. through MITEI, a team of MITEI researchers and their Exxon colleagues began examining various approaches to transporting hydrogen. The researchers have now concluded that there’s no single answer; the cost and carbon emissions from a given transportation method will vary from one location to another. Therefore, instead of presenting a table showing the “best” outcome, the team created a tool that enables users to understand the various options and choose the best option for their particular use case. 

The researchers present their study and the tool they developed in a new paper published in the journal Fuel.    

The study was led by former MITEI postdocs Gasim Ibrahim, now an R&D engineer/scientist at Honeywell, and Guiyan Zang, former MITEI group lead who is now an associate professor at Washington State University. Additional MIT co-authors include former postdocs Bosong Lin, Jacqueline Garrido, Woojae Shin, and Haoxiang Lai.

The hydrogen challenge and hydrogen “carriers” that can help

The team’s starting assumption was that for hydrogen to become a viable fuel for the world, it would need to be transported over long distances — specifically, overseas, across continents, or across large water bodies. Given the properties of hydrogen gas, it would be best to convert it to some liquid form before shipping.

There are known ways to do that, but what would be best for shipping? How much would various methods cost, and how much would they add to the carbon intensity of the delivered hydrogen?

“There hasn’t been a lot of attention paid to addressing those questions,” Ibrahim says. While some studies have been done, their conclusions are inconsistent and many uncertainties remain, both because the cost and carbon emissions will differ from place to place and because there’s not a lot of data to inform how the large-scale transportation of hydrogen will work.

“So we decided the best thing to do was to develop an adaptive tool that would enable users to perform their own assessments — a tool that could be updated very easily,” Ibrahim explains. “And we would make it open source, so anyone can see and update the numbers that we used in formulating and testing it. As the industry develops, and as scale becomes more a factor, the assumptions made in [our initial] assessments of the economics and the carbon intensity [of different shipping methods] will need to be updated.”

To focus on the transportation and storage issues, their model — called the Hydrogen Carrier Analysis Tool, or HyCAT — doesn’t consider how the starting hydrogen is produced, or how the hydrogen is used after it’s delivered. HyCAT focuses on determining the costs and carbon emissions incurred as the hydrogen is transported and delivered. In addition, while a full life-cycle assessment would include all environmental impacts, HyCAT focuses on emissions of greenhouse gases (GHGs).

The tool is easy to use, says Ibrahim. Built into it is a user interface with drop-down menus for inputting assumptions, and results from an analysis are presented in simple bar charts that include links to tables presenting the details.

Ibrahim clarifies that, while HyCAT has a well-defined boundary — “incoming hydrogen to outgoing hydrogen” — in an analysis of a specific situation, the user will input various factors about the local situation, including the carbon intensity and cost associated with production of the incoming hydrogen. “So that will inform the final values that come out of a HyCAT analysis,” says Ibrahim, and in part explains why the results vary from place to place.

Based on the user’s assumptions, HyCAT calculates the cost and GHG emissions at five steps in the “supply chain”:

• converting the hydrogen into liquid form at the “export” terminal;
• storing the hydrogen-rich liquid;
• shipping it when an empty tanker becomes available;
• storing it at the “import” terminal; and
• releasing the hydrogen as a gas suitable for burning or being fed into a pipeline for distribution.  

Options for liquifying hydrogen gas

The main decision in analyzing the cost and emissions of a proposed hydrogen transport plan is how to convert the gaseous hydrogen to a liquid, and then how to recover the hydrogen gas at the end.

One approach is to simply change the gaseous hydrogen into an easily transportable liquid. But turning hydrogen gas into a liquid requires making it very, very cold. Indeed, notes Ibrahim, “you would need to consume about a third of the energy content of the hydrogen to make the gaseous hydrogen cold enough to liquify.” A further problem arises as the liquified hydrogen is being stored and moved. Unless the vessel containing the liquid hydrogen is properly insulated, the liquid hydrogen can re-gasify and escape. The upside of hydrogen liquefaction is that no chemical reactions are required.

Other options involve using a hydrogen “carrier.” Some liquid chemical compounds will absorb hydrogen atoms under certain conditions, and under other conditions will release them. Therefore, one approach to solving the hydrogen transportation problem is to make a carrier compound absorb the hydrogen where it’s made and then release it when it reaches its destination. This approach therefore involves two chemical reactions — one to bind the hydrogen to the carrier and the other to release it.  

In their demonstration runs, the researchers looked at the hydrogen carriers involving three potential compounds, each of which has known advantages and disadvantages.

One of those carriers is produced by adding hydrogen to toluene. That chemical reaction hasn’t been studied a lot, but there’s one known drawback: the source of toluene is typically the oil and gas industry, so the toluene itself has a relatively high carbon intensity when it picks up the hydrogen. Moreover, over time some of the toluene is lost, so more toluene must be added.
    
The researchers also looked at “synthetic methane,” which is made by reacting hydrogen with carbon dioxide. That reaction has been known for some time. Ibrahim notes that making synthetic methane actually consumes carbon dioxide, often captured from the atmosphere. On the negative side, however, one of the products of the reaction is water, so some of the hydrogen is lost each time the reaction occurs.

The final option they analyzed is ammonia, which forms when hydrogen reacts with nitrogen from the air. That reaction is very well-studied and is used commercially. “We’ve been producing ammonia for a long time,” says Ibrahim. And the infrastructure for transporting and storing it is well established. While Ibrahim refers to ammonia as the “most promising option,” the reaction needed to release the hydrogen has not received much attention.

Varying conclusions and future plans

Based on their sample runs, the researchers observed that the best path to follow will vary from place to place and from situation to situation. “As we developed the tool, we saw that the ‘best’ carrier was very specific to the supply chain at hand,” says Ibrahim. “It’s a function of how far you’re trying to ship your hydrogen, energy and shipping costs at your exporting and importing countries, the capital cost of building the needed facilities at both ends, and more.”

Ibrahim and his team are now planning a follow-up study in which they use HyCAT to analyze specific supply chains under certain conditions. They’ll then select assumptions that are highly uncertain and look at the range of possible values for those assumptions. “Then we’ll be able to say, ‘under these conditions, this carrier is better than that one,’ or ‘this carrier is better at cost, but worse at carbon intensity,’” says Ibrahim.

For now, the main conclusion of the study, says Ibrahim, is that “there’s no conclusion.” He warns decision-makers not to assume that anything they see in the literature can easily be generalized or extrapolated to their specific conditions. Instead, decision-makers should use HyCAT to explore the options available to them. Guided by their results and the objectives and values of their company, they will be able to optimize their supply chains and make clean-burning hydrogen a reality.

Professor Jesse Thaler has been named director of the MIT Laboratory for Nuclear Science (LNS), effective Aug. 1. He succeeds Professor Bolek Wyslouch, who directed LNS for the past decade. Thaler is a theoretical particle physicist who combines techniques from quantum field theory and machine learning to address outstanding questions in fundamental physics. 

“In his research, Jesse has done pioneering work on particle jets at the Large Hadron Collider and is a leader in combining AI and machine learning with fundamental particle physics,” says Nergis Mavalvala, dean of the MIT School of Science and the Curtis and Kathleen Marble Professor of Astrophysics. “The collaborative nature of his research programs will serve the Laboratory for Nuclear Science as science enters a new era of AI-driven discovery.”

Thaler is the William and Emma Rogers Professor of Physics in the MIT Center for Theoretical Physics — a Leinweber Institute (CTP-LI). Since 2020, he has served as inaugural director of the National Science Foundation (NSF) AI Institute for Artificial Intelligence and Fundamental Interactions, or IAIFI, which was recently renewed for another five years. Mike Williams, professor of physics, will succeed Thaler as IAIFI director. LNS is also poised to pursue new research projects through the Department of Energy’s Genesis Mission, which has a focus on AI-enabled scientific discovery.

“In my own field of particle physics, researchers are developing cutting-edge AI algorithms to handle the data deluge from collider experiments and to perform heroic theoretical calculations. This work has direct implications for discovering new physics, but the algorithms themselves turn out to be valuable well beyond our field,” says Thaler. “I’m excited to bring LNS into the next wave of discoveries supported by AI-driven capabilities.”

At IAIFI, Thaler has championed education and research activities at the intersection of physics and AI. With the MIT Institute for Data, Systems, and Society, IAIFI leadership created a doctoral program in physics, statistics, and data science. IAIFI also created dedicated postdoctoral fellowships to give early-career researchers the freedom to pursue interdisciplinary work. 

“Giving young scientists space to build connections across domains, universities, and career stages has been transformative within IAIFI,” says Thaler, who hopes to bring this type of framework to LNS. Established in 1946 to support nuclear and particle physics, LNS now encompasses research spanning cosmology, gravity, field theory, and quantum information science.

As head of LNS, Thaler will also oversee his home center of CTP-LI, which last year received a donation from the Leinweber Foundation to establish a network of theoretical physics research institutes. According to the Science Philanthropy Alliance, a nonprofit organization that promotes philanthropy for science, this constitutes the largest philanthropic commitment ever for this field.

Thaler received his PhD in physics from Harvard University in 2006, and his BS in math/physics from Brown University in 2002. From 2006 to 2009, he was a fellow at the Miller Institute for Basic Research in Science at the University of California at Berkeley. He joined the MIT faculty in 2010.