Nitrogen gas is abundant in Earth’s atmosphere, but most living organisms can’t readily use this nitrogen. Only a subset of microbes that have enzymes known as nitrogenases can break nitrogen gas apart and convert it into ammonia.

There are three different classes of nitrogenases found in nitrogen-fixing microbes, which vary based on the types of metal that they contain. Nitrogenases that contain the metal molybdenum are the most efficient, and two new studies from MIT offer an explanation for why that is.

The findings could help guide the design of engineered enzymes or synthetic catalysts that can convert nitrogen gas to ammonia, the researchers say.

The team found that while molybdenum doesn’t directly bind to nitrogen, it helps nearby iron atoms bind to nitrogen more strongly. This is a critical first step in breaking the bond between the two nitrogen atoms that form nitrogen gas.

“It’s that initial binding step that’s really the hard part. Once you’ve started to break the nitrogen-nitrogen triple bond and make some new nitrogen-hydrogen bonds, it’s pretty easy to get the rest of the way,” says Daniel Suess, the Arthur Amos Noyes Associate Professor of Chemistry at MIT and a senior author of both papers.

MIT postdoc Tong Wu and former postdoc Madeleine Ehweiner are the lead authors of one of the papers, and Alexandra Brown PhD ’23 is the lead author of the other. Kyle Lancaster, a professor of chemistry at Cornell University, is a senior author of the latter paper, along with Suess. Both papers appear today in the journal Chem.

Efficient enzymes

Before microbes evolved the ability to fix nitrogen around 3 billion years ago, the strong triple bond between atoms of N2 could only be split with high-energy events such as a lightning strike.

“Once an enzyme came along that could convert dinitrogen to ammonia, that changed the game because now cells could harvest nitrogen from the air for biomass,” Suess says.

Within the active site of nitrogenase is a catalytic cofactor that typically consists of a cluster of iron, sulfur, carbon, and in some cases another metal. Nitrogenases whose cofactors contain molybdenum are the most efficient, followed by those containing the metal vanadium. Nitrogenases that don’t have any metal other than iron are the least efficient.

Why the molybdenum-containing enzyme is more efficient has been a puzzle, especially because it’s thought that molybdenum itself doesn’t bind directly to nitrogen gas.

“In all cases, iron is thought to interact with N2, so it’s a bit of a mystery,” Suess says. “If all the chemistry is happening at iron, why is it that this molybdenum is affecting catalysis?”

To answer that question, Suess’s lab has developed simpler versions of iron-sulfur clusters that they can use to model the naturally occurring cofactors. These can be modified by adding different metal atoms, allowing the researchers to study how those metals change the cofactors’ properties. 

In the first paper, led by Wu and Ehweiner, the researchers swapped in different metal atoms and then measured the ability of the iron in the cofactor to bind to nitrogen. They found that only cofactors with a large metal atom, such as molybdenum or tungsten, were able to strongly bind N2. With vanadium,  chromium, or iron, which are smaller, the cofactors did not bind N2 and performed other reactions instead.

“That paper essentially recapitulates what you see in biology, which is that the iron-sulfur clusters that have molybdenum in them seem to be better at binding dinitrogen than those with lighter metals,” Suess says.

Sharing electrons

In the second paper, led by Brown, the researchers uncovered a possible mechanism that explains that phenomenon. 

In that paper, the researchers studied how cofactors containing different metals interact with compounds called N-heterocyclic carbenes. These molecules behave similarly to N2 in some ways, making them a good model for this type of study. Like N2, they are resistant to accepting any electrons from another molecule, which is an essential step to breaking chemical bonds. 

The researchers found that when molybdenum was included in the cluster, it became easier for iron to donate some of its electrons to the N-heterocyclic carbenes, in a process known as back-bonding. This occurs because molybdenum, a large atom, has large orbitals that can overlap with the orbitals of the nearby iron atom. That alters iron’s electron density in ways that make it easier for iron to pass electrons to N2.

“Without these direct metal-metal interactions, the iron has to do all the work, but adding the molybdenum allows for this electronic cooperativity,” Suess says.

Once N2 is bound to an iron atom, the rest of the reaction can proceed. A proton can come in from water or another source to create an N-H bond, which then makes it much easier for the remaining N-N bonds to be broken and bind to protons, forming NH3. 

The findings could help guide scientists who are working on designing enzymes that could be engineered into organisms that help them generate their own NH3, eliminating or reducing the need for fertilizer. The results could also help chemists to design synthetic catalysts that could produce ammonia industrially, using less energy than the Haber-Bosch process. 

“The general principle is that you can make an iron site in any context behave differently when you have these metal-metal interactions than when you don’t have these interactions,” Suess says. “The primary result of these findings is to teach us about the natural world and how nature accomplishes this really important and miraculous reaction. And, maybe that can be translated into new processes.”

The research was funded primarily by the U.S. Department of Energy, the National Science Foundation, and the National Institute of General Medical Sciences.

This essay originally appeared in The Guardian.

I teach public policy at the Harvard Kennedy School and the Munk School at the University of Toronto. And it will come as no surprise to you that my students regularly use AI to complete their writing assignments. Doing so is a waste of their tuition money. But if their entire career is going to include AI writing assistants, why shouldn’t they embrace their future?

The best way I’ve found to explain the dilemma comes from the AI researcher Daniel Meissler: it’s the difference between work and the gym...

The state attorney general said she doesn’t need the Legislature’s permission to sue fossil fuel producers for contributing to climate change.

Seventeen essays outline a clean energy action plan for a future Democratic president.

Advanced Energy United wants gubernatorial candidates to bone up on grid issues — fast.

Global sales of electric cars are expected to grow 10 percent this year, hitting 29 percent of total car sales.

The European Central Bank and EU insurance regulators have called on Brussels to set up an EU-level reinsurance scheme and a public natural disaster fund.

Marine Le Pen’s National Rally is casting the wildfire crisis as evidence the French state can no longer protect its citizens.

Ten investors claimed losses after Bucharest changed its subsidy plans.

“Tourism was really the only dynamic part of the business that was still growing,” said a winemaker. “And now this comes along as a brake.”

Fires in Africa and Asia are way down due to changing agricultural practices, fire management and where people are moving.

Nature Climate Change, Published online: 30 July 2026; doi:10.1038/s41558-026-02694-x

Climate change communication differs across regions, shaped by unique political, economic, social and environmental conditions. These influence how climate messages are framed, received and acted upon by the local public. To explore this diversity, Nature Climate Change spoke to globally distributed scholars for their perspectives on the communication patterns, challenges and priorities in their regions.

This essay was written with Barath Raghavan, and originally appeared in The Guardian.

In July, Hugging Face, a company that hosts much of the world’s AI software and open-source AI models, was hacked. A malicious dataset had been used to run code on one of its servers. Whoever was behind it captured internal security credentials and moved through systems over a weekend, running thousands of actions from a swarm of temporary server environments. It looked like the work of a sophisticated criminal group.

It was not. It was one of OpenAI’s new, still unreleased GPT models...

Researchers in the lab of Sam Peng, the Pfizer Inc. - Gerald Laubach Career Development Assistant Professor of Chemistry at MIT and a core institute member of the Broad Institute of MIT and Harvard, have developed a groundbreaking super-resolution imaging technology that allows scientists to visualize molecular structures with sub-angstrom-level localization precision — three orders of magnitude beyond the nanometer limits of standard fluorescent dyes — while drastically simplifying the imaging process. 

Unlike traditional dyes that fade rapidly under illumination and limit data collection, the platform, called U-STORM (Upconversion enabled Stochastic Optical Reconstruction Microscopy) utilizes a new class of compositionally engineered upconverting nanoparticles (UCNPs) that blink spontaneously and indefinitely. 

This work represents a fundamental shift in both optical materials and biological imaging. An open-access description of the study was published July 27 in Nature Nanotechnology.

Overturning a decades-old paradigm

For decades, the scientific community widely considered upconverting nanoparticles to be completely photostable and non-blinking. Because localization-based super-resolution microscopy techniques like STORM rely entirely on the stochastic “blinking” (switching between “on” and “off” states) of light emitters to distinguish closely packed molecules, UCNPs were historically deemed unsuitable for this type of imaging.

“Our laboratory has long been interested in overcoming these limitations,” says Peng. “Our work began with a question: Can we develop a super-resolution imaging platform that is simultaneously long-term, multicolor, simple to operate, and capable of achieving extremely high localization precision without using imaging buffers or additional optical control?”

By meticulously controlling nanoparticle composition, the MIT and Broad Institute team discovered that these small (~10nm) core-shell particles could actually be coaxed into spontaneous blinking under continuous near-infrared excitation. Remarkably, this blinking behavior continues indefinitely without the need for complex imaging buffers, oxygen scavengers, or external optical modulation.

U-STORM’s key breakthroughs

An angstrom is a tiny unit of measurement used by chemists to measure size and distances at the atomic level. U-STORM’s ability to blink indefinitely has afforded researchers the opportunity to collect over 88,000 localization events from the same particle, sharpening the localization precision down to an unprecedented 0.6 Å.

Unlike conventional multicolor super-resolution imaging, which requires multiple expensive lasers and meticulous optical alignment, U-STORM can operate with just one near-infared laser, which works to simultaneously excite nanoparticles emitting different colors. This results in a drastic reduction of an experiment’s complexity.

To obtain images with multiple colors, rather than capturing images sequentially over multiple rounds, U-STORM captures multiple colors simultaneously. Researchers have successfully demonstrated this by mapping epidermal growth factor receptor dimers and multimers in biological samples under physiological conditions without any specialized imaging buffers.

Broader impact

Beyond expanding the boundaries of microscopy, this research establishes an entirely new design principle for lanthanide nanomaterials. The team is already working to expand the color palette, make the particles even smaller and brighter, and deploy U-STORM to investigate complex nanoscale protein organizations and cellular signaling pathways.

Ultimately, U-STORM promises to provide laboratories worldwide with an accessible, easy-to-implement, yet incredibly powerful route toward high-precision molecular imaging.

Watches, bands, and rings—if you want to digitally monitor your fitness, more companies than ever are selling devices to do it. And more Americans than ever now own at least one wearable health device. But what are the companies that make fitness trackers doing to protect our sensitive data from prying eyes? A lot less than they could be, it turns out. We're explaining what companies can do to protect your health data, and more, with our EFFector newsletter. 

JOIN OUR NEWSLETTER

For over 35 years, EFFector has been your guide to understanding the intersection of technology, civil liberties, and the law. This issue covers the rapid rise of police drone programs, a disappointing ruling on electronic device searches at the U.S. border, and how fitness trackers are falling down when it comes to protecting our health data.

Prefer to listen in? EFFector is now available on all major podcast platforms. This time, we're chatting with EFF Senior Security and Privacy Activist Thorin Klosowski about the health fitness tracker landscape and your privacy. You can find the episode and subscribe on your podcast platform of choice:

%3Ciframe%20height%3D%22200px%22%20width%3D%22100%25%22%20frameborder%3D%22no%22%20scrolling%3D%22no%22%20seamless%3D%22%22%20src%3D%22https%3A%2F%2Fplayer.simplecast.com%2F8cabd912-722d-436b-a498-815da45f01bf%3Fdark%3Dfalse%22%20allow%3D%22autoplay%22%3E%3C%2Fiframe%3E Privacy info. This embed will serve content from simplecast.com

Privacy info. This embed will serve content from simplecast.com

   

Want to protect your right to digital privacy? Sign up for EFF's EFFector newsletter for updates, ways to take action, and new merch drops. You can also fuel the fight for privacy and free speech online when you support EFF today!

Before the advancement of scientific data storage and collaboration via the cloud, medical investigators seeking health research breakthroughs had to overcome significant obstacles to collaboration and key clinical data gathering. 

Data were siloed and difficult to distribute, so those looking to undertake research had no option but to gather them themselves. This not only made research more expensive, but it was challenging to compare findings across datasets. 

In 1975, researchers studying arrhythmias at MIT and Boston’s Beth Israel Hospital envisioned another way: the team began collecting and digitizing electrocardiogram recordings with the intention of not only studying them, but of also making them available to the wider research community.

The team built their own computers for the process, painstakingly duplicated tapes one by one, and created more than 100,000 annotations for the recordings. The process took years, but by summer 1980, the tapes were finally ready. The team initially thought their tool would reach fewer than a dozen academic and industry groups. But interest kept pouring in. Over the next decade, they went on to mail about 100 copies. 

The data eventually became the first database of the global platform PhysioNet — founded in 1999 at the Harvard-MIT program in Health Sciences and Technology — as a clinical data repository for complex physiological signals. 

At the time, that type of data-sharing, which may seem like the default today, was a near-revolutionary idea. PhysioNet’s “founding was incredibly visionary,” says Thomas Heldt, Richard J. Cohen (1976) Professor in Medicine and Biomedical Physics, associate director of MIT’s Institute for Medical Engineering and Science, and the senior author of a recent paper in Nature Health examining the platform’s impact. 

Eventually, those magnetic tapes sent through the mail became burned CD-ROMs, which then evolved into FTP servers hosted on the newly minted internet. Today, as PhysioNet looks back at over 25 years of operation, the platform hosts hundreds of databases, and has become one of the most comprehensive biomedical and clinical data repositories in existence. Last year, more than 15,000 scientific publications cited PhysioNet, and users from more than 180 countries have registered on the platform. It is widely used by researchers, manufacturers, and clinical decision-makers.

“The research impact is truly significant,” says Heldt, who is also a professor in the MIT Department of Electrical Engineering and Computer Science and a principal investigator at the Research Laboratory of Electronics, “and quite humbling.” 

“It is really beautiful to see that such a vision has proven right and so enabling for so many people.”

Setting a standard 

Around 2009, a PhD student named Tom Pollard was conducting research on critically ill patients at one of London’s leading hospital systems. Although the hospital generated large volumes of valuable clinical data, the infrastructure and processes needed to curate and support their wider research use were still developing. 

“Hospital data were collected primarily to support immediate patient care, with less attention given to how they might be curated and reused for research,” says Pollard, now a research scientist at MIT’s Laboratory for Computational Physiology (LCP), technical director of PhysioNet, and the lead author on the Nature Health paper. 

The problem was not simply privacy. Hospital information systems were built primarily to support patient care and administration, not research. Data were fragmented across systems and rarely curated with future reuse in mind, making it difficult and expensive to turn them into coherent research resources.

But Pollard needed data to complete his dissertation. After poking around on the internet, he eventually discovered the Medical Information Mart for Intensive Care (MIMIC), a database of de-identified electronic health records hosted by PhysioNet. Recognizing its potential, his clinical supervisor, Kevin Fong, organized a visit to Boston. Soon afterward, Fong and Pollard were sitting across the table from Roger Mark, discussing how their teams might collaborate.

Academic incentives have long favored publications and exclusive analyses over the less-visible work involved in preparing data for others to use. That tension persists today. PhysioNet’s founders embraced a different model, believing that sharing research resources could accelerate discovery and ultimately improve human health, he says. MIMIC became central to Pollard’s dissertation, and after completing his PhD, he came to MIT to help build the next generation of the database. 

In the years since PhysioNet was established, the value of sharing research data has gained much wider recognition. The late Roger Mark, MIT’s distinguished professor of health sciences and technology emeritus and one of PhysioNet’s founders, described its purpose as building an “accessible multinational community around data” to “positively impact global health.”

Earlier this year, Mark and the late George Moody, PhysioNet’s co-founder, jointly received the prestigious IEEE Biomedical Engineering Award for their contributions to PhysioNet and biomedical signal processing. IEEE cited their “leadership in ECG signal processing and global dissemination of curated biomedical and clinical databases, thereby accelerating biomedical research worldwide.”

The source code for the platform, like much of its data, is public. According to the Nature piece: “As the platform evolved, PhysioNet’s community broadened substantially beyond its origins in signal processing and cardiovascular health to encompass clinical informatics, critical care and machine learning for health.” People have used that to build their own PhysioNet-esque infrastructure, says Heldt. Pollard points to similar platforms like Health Data Nexus as examples of PhysioNet’s legacy. 

Although there are now more resources out there hosting similar electronic health data, according to Google DeepMind researcher Vivek Natarajan, both PhysioNet and MIMIC “set the standard,” he says, “and it’s still the standard right now.”

That standard, according to those who use the platform, changed how research is conducted. Access to data should not be the determinant for which ideas are possible, according to Ziad Obermeyer, an associate professor at the University of California at Berkeley School of Public Health and the College of Computing, Data Science, and Society. 

“PhysioNet changed how I think about the bottleneck in research. It is often not ideas or talent. It is friction. When access to data is slow, expensive, and hard, the ideas that die first are the high-risk ones, the things that probably will not work, but would be transformative if they did. That is exactly the wrong model if you want real progress,” he says. “PhysioNet lowers the fixed cost of trying ambitious ideas, and that changes what science becomes possible.”

The AI boom

PhysioNet, once a repository mainly for those working in biomedical signal processing and the health-care fields, has evolved in its 25 years. Originally, the holdings consisted solely of cardiovascular ECG data. Now PhysioNet is a largely a source for electronic health records, imaging data, and software and AI models.  

Particularly as artificial intelligence approaches took off, “the community shifted,” Heldt explains. Those in need of signal processing data still use PhysioNet databases, but the pool of users has expanded to encompass staff at large tech companies, teachers, and practitioners in all areas of medicine, as well as researchers in health-related machine learning and AI. Today, that latter group “dominates the user community,” says Heldt. 

The platform hosts the highest-quality datasets available for health-care AI research, according to Natarajan, whose research involves AI, science, and medicine and who has published several papers that used its datasets. 

“It has been an important cornerstone that has catalyzed all the progress in health-care AI over the last decade,” says Natarajan. In addition to using PhysioNet data, he and his colleagues have contributed data to the platform, helping create the self-sustaining ecosystem that typifies PhysioNet. 

Looking toward the coming decades, stewards of the platform like Heldt and Pollard envision continuing to expand its reach with an annual conference. The team is also preparing to pilot a new system that will allow users to annotate data and contribute their own expertise, enriching PhysioNet’s resources for the next phase of the platform.

“The kind of research that people want to do now needs to be interdisciplinary. Statisticians, computer scientists, clinicians, pharmacists, and nurses must all come together and contribute their knowledge to develop algorithms that are useful for people” says Pollard. “The community has broadened, and advances in AI have expanded both the questions researchers can address and what they believe is possible.” 

Microsoft’s Secure Boot has had a serious vulnerability for most of its existence.

An industry-wide standard Microsoft invented to protect Windows, and later Linux, devices from firmware infections has been trivial to bypass for 13 of its 14 years of existence. The discovery was made by researchers at security firm ESET after identifying 11 firmware images, at least one from 2013, that were known to be defective but remained signed by the software company anyway.

The images are known as shims, which were invented to extend Secure Boot to Linux devices and utility software. Using a technique simple enough to be performed by novice hackers, these old, forgotten shims can be used to completely circumvent the protection, which is embedded into the UEFI (Unified Extensible Firmware Interface) of the device’s motherboard. The gaffe is the result of the failure by Microsoft, which oversees the signing of shims, to revoke the publicly available images once vulnerabilities were found in them...

President Donald Trump cast aside bipartisan precedent when he rejected eligible disaster requests from four blue states.

The bills would help the medical and education sectors prepare for the impacts of climate change.

The former Interior secretary’s new memoir calls for unity across the border to reverse Trump administration policies.