At the center of most galaxies lies a supermassive black hole, with a mass millions to billions of times that of our sun and some of the most extreme gravity in the universe.

Some stars that venture too close to such black holes live to tell the tale. Rather than being completely destroyed, they survive to make repeated close passes, producing a new flare of light each time.

These repeating partial tidal disruption events (rpTDEs) give astronomers the opportunity to watch the same star and black hole interaction unfold again and again, thanks to wide-field time-domain surveys that repeatedly scan large areas of the sky for objects that change in brightness.

But in several cases, researchers have noticed a puzzling pattern: The successive flares grow progressively dimmer. For years, theoretical models couldn’t explain why.

Now, a team of astrophysicists at Syracuse University has shown that the answer may lie in a previously overlooked factor—the star’s spin.¹

When stars meet black holes

In a typical tidal disruption event (TDE), a black hole’s tidal force—the difference in gravitational pull across a nearby star—tears the star completely apart.

As the disrupted stellar debris falls toward, or “accretes” onto, the black hole, it loses energy that is emitted in the form of light over the course of days to months.

While black holes themselves emit no light, TDEs provide a short-lived supply of fuel that lights up the surrounding region, allowing astronomers to study these otherwise invisible objects indirectly.

If a star orbiting a black hole does not come close enough to be completely ripped apart, it can, however, lose a fraction of its mass, resulting in a partial TDE. In a repeating partial TDE, the surviving core continues orbiting the black hole, losing more material with each new close pass, a few months to several years apart.

The dimming mystery

How much material a star loses during repeated encounters depends partly on its internal structure. Bandopadhyay compares a low-mass star to a fluffy meringue, which can become increasingly vulnerable to the black hole’s tidal forces. By contrast, a higher-mass star has a more centrally concentrated, onion-like star and can shed its outer layers while its dense core remains relatively unaffected, losing decreasing amounts of mass over time.

Those differences can help explain why rpTDEs don’t all behave the same way. But one pattern in particular has mystified researchers. Of the roughly 10 repeating systems identified to date, four have produced flares that grow progressively dimmer.

Decreasing mass loss might seem like an obvious explanation. But previous hydrodynamical simulations showed that, surprisingly, even as the material lost decreased with each encounter, the predicted flares retained roughly the same brightness.

“We were puzzled by this for two years,” Bandopadhyay says.

Their previous work had revealed another effect of the black hole’s tidal forces.In addition to stripping material from the star, they exert a torque that causes the star to spin faster with each close encounter. As a result, although less material falls back toward the black hole, it returns over a shorter period of time, helping to keep the predicted flare at roughly the same brightness.

To reproduce the dimming astronomers were actually observing, the researchers needed what Bandopadhyay called “a new ingredient”—a star that was already spinning rapidly before its first encounter with the black hole.

The new study found that this initial rotation prevents the star from being significantly spun up during each passage. Without that additional spin-up, the timescale over which the stripped material falls back remains relatively constant. As the star loses less material with each encounter, the peak fallback rate—and the predicted brightness of the flare—can finally decline.

Tracing the star’s past

But why would the star already be spinning so rapidly?

“It is also extremely difficult to ‘bind’ a star to a supermassive black hole so tightly that it orbits the black hole in a matter of months, and yet they seem to do so in rpTDEs,” Coughlin says.

The so-called Hills mechanism may explain both. Under this scenario, a pair of closely orbiting stars passes near a supermassive black hole, which tears the binary apart, ejecting one star and capturing the other.

In a close binary, the stars can become tidally locked, causing each to rotate on its axis at the same rate that the pair orbits each other. The tighter the binary, the shorter that orbital period and the faster a tidally locked star spins. The binaries capable of leaving a captured star on the short orbit observed in rpTDEs would have to be extremely tight—also leaving a tidally locked star spinning rapidly before its capture.

“Ananya’s work demonstrates that each of these peculiarities can be explained by the same underlying phenomenon: the tidal destruction of a binary system and the capture of one of the stars,” Coughlin says. “From a theoretical standpoint, this is a major step forward in our understanding of the physics at play in these systems.”

Zooming out, Coughlin notes that Hills capture may also have produced some of the stars orbiting Sagittarius A*, the supermassive black hole at the center of the Milky Way. The new findings could therefore help explain some of the properties of stars in what he calls “our own cosmological backyard.”

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A 2025 study by South Korean researchers caught the attention of the astronomy community when it suggested that the evidence behind dark energy could be wrong. Dark energy makes up about 70% of the universe but nobody knows what it is. This makes it a frequent target for scepticism.

Scientists had long known that the universe was expanding. But in the late 1990s, researchers discovered that this expansion was accelerating. Astrophysicists concluded that something must be driving the acceleration. They named this unknown quantity “dark energy”.

The 2025 re-analysis, by a team at Yonsei University in Seoul, suggested that the expansion of the universe was not in fact accelerating.

Our team at the University of Southampton has now gone through the Yonsei team’s findings. Our results, published in Monthly Notices of the Royal Astronomical Society (MNRAS), suggest that the results were a false alarm.

However, the Yonsei University team have said they stand by their results and that a follow up study supports their finding that the universe may not be accelerating.

What is dark energy?

In the late 1990s, cosmology was upended by a startling discovery. Scientists knew the universe was expanding, but they had assumed that gravity was gradually slowing down this expansion.

However, by observing incredibly bright, thermonuclear explosions of white dwarf stars, two independent teams of astronomers found the opposite. Because these explosions, known as Type Ia supernovae, have almost the same intrinsic brightness, comparing how bright they appear gives a precise distance.

In 1998, data revealed that distant supernovae were fainter than they should have been in a slowing universe. Because faintness implies distance, the observations placed these supernovae farther away than expected, and implied that the cosmic expansion was not slowing.

In fact, when the calculations were run, it was clear that the expansion of the universe was actually speeding up.

There was no convincing physical explanation for this “cosmic acceleration”. Physicists labelled the mysterious, repulsive force that causes the acceleration “dark energy”.

Cosmic calibration

To understand the Yonsei University group’s claims, we must understand how astronomers use supernovae as “standard candles” to measure cosmic distances. Type Ia supernovae are remarkably alike, but they are not identical.

Cosmologists correct for these differences. They use subtle calibrations to account for relationships between the brightness of the supernovae and measurements like their colour, the duration of their light curves, and the type of galaxies they explode in.

One of those corrections is well known but subtle. It links the brightness of a supernova to the size of the galaxy it exploded in. Big galaxies hold more stars, and so more mass. After the standard corrections, supernovae in those big (massive) galaxies come out a few per cent brighter than ones in small galaxies. Nobody knows why.

But bigger galaxies typically have older stars that are made up of heavier elements, which could in turn influence the properties of the supernovae that some of those stars become. The authors of the 2025 study proposed a far bigger evolutionary effect. They argued that the brightness of Type Ia supernovae changes significantly as the universe ages.

Specifically, they claimed that older white dwarf stars, which are more common in the nearby, present-day universe, produce brighter explosions. That would mean today’s supernovae are inherently much brighter than their distant, early-universe counterparts.

If true, the faintness we observe in distant supernovae would not be a result of their being further away, a relationship that is in turn driven by dark energy, but rather an evolutionary trait of the stars themselves. This claim threatened to dismantle almost three decades of progress in astronomy.

Addressing the claims

Extraordinary claims need careful testing. At the University of Southampton we embarked on an audit of the data, using observations from the Dark Energy Survey (DES), an astronomy project designed to constrain dark energy’s properties, alongside the same dataset used by the authors of the 2025 work. In doing so, we sought to replicate and test their conclusions.

Our re-analysis revealed what we saw as two problems in the Yonsei study: a technical omission and a flawed assumption about stellar populations. Once corrected, the data fell back in line with standard cosmological results, including those of DES.

Crucially, the 2025 study did not account for how supernovae in big galaxies come out a few per cent brighter than ones in small galaxies. When our team reapplied this correction to the same dataset, the correlation between a supernova’s brightness and galaxy age – presented by the Yonsei team in their paper – became far weaker.

This doesn’t mean that the age of a white dwarf star has no effect on the brightness of a resulting supernova. Indeed, most researchers in the field would likely accept that the brightnesses of supernovae are more affected by the age of the stars that explode than by the mass of the galaxy that it is in.

However, measuring a galaxy’s mass is far less expensive in terms of telescope time and requires fewer technical assumptions than measurements of the ages of galaxies, which are challenging. What we showed is that the galaxy mass calibration is adequate to the accuracy required to constrain dark energy.

Secondly, the 2025 analysis relied on a flawed proxy, incorrectly assuming that the overall age of a host galaxy is identical to the specific age of the star that exploded. But galaxies are not uniform like this: even large, ancient galaxies contain localised pockets of young stars.

By overestimating the difference in age between the stars that explode nearby and those that explode in the distant universe, by a factor of three to five, the Yonsei University study overestimated the amount by which the age-brightness calibration could bias supernova distance measurements.

Once these omissions were corrected, the data behaved as expected under the standard model of cosmology. As such, evidence for an accelerating universe remains secure.

Science friction

While the 2025 study turned out to be incorrect, the challenge it posed was valuable. Challenging accepted ideas and rigorously testing our observations is fundamental to scientific progress.

Their claims provided an opportunity for cosmology teams to return to their baseline data, interrogate their core assumptions, and test different ways of calibrating the measurements. When measuring distances, however, the age effect and the mass effect are highly correlated, and therefore we (the Southampton team) believe it is incorrect to make a full age-based adjustment to distances that have already been corrected for the mass effect.

The Southampton team was invited, among others, to a workshop at Yonsei in June 2026, where we discussed our methods and talked about ways to include both stellar mass and stellar ages into the brightness corrections.

In a follow up study, recently accepted by MNRAS, the Yonsei University team maintain that the ages of stars have an important effect on the supernovae they produce. They say that two methods used in earlier studies have suppressed the importance of this phenomenon.

When these effects are accounted for consistently, they explain, it leads to results similar to those in the 2025 study. “We therefore continue to find evidence that stellar-population age is an important factor in Type Ia supernova standardisation,” authors Junhyuk Son and Hyejeon Cho, told The Conversation.

“Because the typical ages of supernova host populations change with cosmic time, understanding this effect is important for precision measurements of the Universe’s expansion history.”

Future measurements will depend on understanding supernova explosions better. It is a job we hope the community can take on together. But none of this removes the need for dark energy: it is still there, and still unexplained.

Ultimately, we have shown that existing measurements of dark energy are robust. Rather than debating whether cosmic acceleration exists, the astrophysical community can return to the exciting task of figuring out what dark energy actually is and how we best measure it.

Over the next 10 years, the Vera C. Rubin Observatory in Chile will carry out a project known as the Legacy Survey of Space and Time, which will produce a sample of supernovae 20 times larger than the DES sample used in our analysis. There is no better time to be investigating the mysteries of dark energy.

 

This article is republished from The Conversation under a Creative Commons license. Original article.

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The team discovered the new object using NASA’s James Webb Space Telescope (JWST). The telescope spotted the bright red dot in the very early universe, just a few hundred million years after the Big Bang. The scientists conclude ¹ that the most likely explanation for the strange red dot is that it is a mashup of a black hole and a star — a combination that has never been observed until now. The object is likely a hugely dense cloud of gas, powered not by standard nuclear fusion, but by a central black hole.

“Our picture of this object is evolving very rapidly,” says lead author Rohan Naidu, a NASA Hubble Fellow and Pappalardo Fellow at MIT’s Kavli Institute for Astrophysics and Space Research (MKI). “We think there is a central black hole that is 100,000 times as massive as the sun. And around this black hole, there would be this very extended envelope of gas that looks like a star the size of the solar system. It’s huge.”

If the bright red dot is indeed a black hole star, it would help to solve the identity of other mysterious “little red dots” that have appeared in nearly every deep space image JWST has taken to date.

“These little red dots seem to be everywhere in the early universe but essentially disappear by the present day,” Naidu says. “What exactly these objects are has been one of the most debated topics of the JWST era.”

The study’s MIT co-authors are MKI Director Robert Simcoe, the Bruno B. Rossi Professor of Experimental Physics; and Wendy Sun ’26, along with collaborators from multiple other institutions.

A singular source

Naidu and his colleagues didn’t intend to find a black hole star. They were looking for the most distant, earliest galaxies, as part of a survey that they named “Mirage or Miracle” (MoM). The team used the JWST to look into deep space, back when the universe was a few hundred million years old. Their goal was to look for galaxies that actually formed at those early times.

“There’s been this puzzle of many bright galaxies showing up at extremely early times,” Naidu says. “What we found was that what looks like an extremely bright early galaxy, aka a ‘miracle,’ in some cases actually could be a ‘mirage.’”

As they looked through JWST’s images for intriguing sources to target with their survey, they noticed a feature that stood out from the rest: a dot that was very red, and very bright.

“When we see something very red in the universe, we often assume that it is surrounded by dust, like soot or ash,” Simcoe explains. “The same way that the wildfire smoke from Canada recently made the sky in Boston look bright red, astronomical objects can also appear redder than their intrinsic color when you see them through a veil of dust.”

But there were other signatures in the light that didn’t quite match up with what physicists expect from dust. The team also observed another strange pattern: The dot’s light was extremely bright, except below certain wavelengths, where the light completely disappeared.

This spectral drop-off is known as a “Balmer break” — a signature traditionally associated with dense gas soaking up photons in the atmospheres of stars that are a few hundred millions of years old. Vega, one of the brightest stars in the night sky shows exactly this pattern.

“The break we observed in this object is the deepest break we have ever observed in any object, ruling out ‘ordinary’ stars as the source,” Naidu says. “But it made us wonder if we were seeing a new kind of ‘stellar atmosphere,’ but on a spectacular scale.”

What’s more, the red dot’s light contained almost no signature of metals or any elements other than hydrogen and helium. “It was truly singular in so many ways,” Naidu says.

Pure light

To puzzle out what the source of the red dot could be, the team ran simulations of different scenarios to see what combination of astrophysical features could produce the red dot’s distinctive color.

“We started to ask: Could you make something that red using just hydrogen, without any dust?” Simcoe says. “To our surprise, it turns out you can, if you have an extremely dense screen of hydrogen, so dense that it looks more like the surface of an enormous star than a wispy interstellar nebula.”

Their simulations pointed to the red dot possibly being some powerful enshrouded energy source, surrounded by an extremely dense cocoon of hydrogen. If this were the case, it would explain the light-blocking Balmer break and the lack of anything other than hydrogen and helium that the astronomers observed. But it still wouldn’t explain the object’s extreme brightness.

“You have something that looks a bit like a star but is 100 billion times brighter,” Naidu says. “That means you can’t be powering this by nuclear fusion, which is the energy source that sits at the heart of all the stars we have.”

Black holes, however, routinely produce energy at the scales the team observed. Naidu and his colleagues incorporated an active, accreting black hole into their simulations of the hydrogen-cocooned star and varied the black hole’s mass, along with other parameters. They then compared the resulting brightness of the simulated “black hole star” with the brightness that JWST observed from the red dot.

From these simulations, they found the closest match, and concluded that the most likely scenario to explain the red dot, is a black hole star. Specifically, the object likely contains a central black hole that is about 100,000 times as massive as the sun. This powerful core is surrounded by a dense, star-like cocoon of hydrogen that is roughly the size of the solar system.

The team has named the object MoM-BH*-1, after the survey that detected it, as well as the moniker “black hole star – one,” which implies that the object is the first of others. The researchers suspect that black hole stars could explain many of the other little red dots that appear in JWST images. Those objects are not as bright as MoM-BH*-1.

“Every little red dot is consistent with being a black hole star, embedded in a generic early galaxy,” Naidu says. “But what is special about MoM-BH*-1 is, the black hole star is essentially completely outshining its surrounding host galaxy, such that we’re seeing pure black hole star light.”

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Some weeks ago, artificial intelligence company OpenAI announced ten advances in mathematics and computer science made with their as-yet unreleased model Astra. The discoveries cover a wide range of mathematical fields, including geometry, cryptography and coding theory.

These ten are just the latest in a rapidly growing list of mathematical breakthroughs made by generative artificial intelligence (AI) systems.

OpenAI accompanied the announcement with a statement on “responsibility to the mathematical community”. Here, the company acknowledges concerns about attribution, accountability for the correctness of AI-assisted results, and the changing nature of mathematical discovery.

The statement reflects a growing understanding that the remarkable technical advances due to AI are only part of a bigger story about what mathematics means, and what its future will look like.

AI is raising big questions

Large language models (LLMs) such as ChatGPT and Claude are creating disruptions in all parts of the mathematical community.

University departments are grappling with the ethical implications of these tools in their research and teaching. Editorial boards of many peer-reviewed journals are being inundated with AI-written papers and must decide how to assess and disclose the use of generative AI. The arXiv preprint repository has seen a sharp increase in mathematical submissions in recent months.

Math papers uploaded to @arxiv per month. Jan 1992 to Jul 2026. Source: https://t.co/nrqjhJBtij pic.twitter.com/pmHvtbYK2X

— Pablo Groisman 🎲 (@pgroisma) August 3, 2026

Funding agencies are starting to develop policies governing acceptable use of AI in applications and assessment.

Broader philosophical questions about how and why mathematics is done in the first place are hard to avoid.

If an LLM can discover a proof, construct an example, or formulate a new question, is there anything uniquely valuable about human mathematical creativity? Is mathematics mostly about producing new theorems, or is it about developing understanding? When a machine contributes to a discovery, who should get the credit?

Growing concern – but little consensus

Among working mathematicians, there is little consensus about the answers. Some have genuine anxiety about the future of the discipline. One mathematician wrote that the advance of AI-assisted mathematics had triggered a “profound spiritual crisis”.

Others have tried to articulate principles for integrating AI into research in a responsible way. The recent Leiden Declaration, signed by thousands of mathematicians from around the world, argues AI should augment rather than replace human mathematical creativity. At the same time, it emphasises transparency, accountability, and proper attribution.

Two weeks ago at the International Congress of Mathematicians, the largest and most prestigious meeting in the field, Terence Tao spoke about “the age of AI”. The famous Fields medallist urged mathematicians to think ahead. In his view, the question is how AI systems might be incorporated into research to strengthen the culture and values of mathematics.

Two attitudes to AI

Two collaborations from my own research over the past few weeks show just how varied these attitudes have become.

My colleague Saul Freedman and I announced an answer to the “semiregularity problem” about highly symmetric networks. This had been the subject of dozens of peer-reviewed papers over several decades.

From the outset, Saul made it clear he did not want AI used in any way during our collaboration due to his concerns about the environmental and social impacts of the technology. Therefore, the work proceeded entirely without the use of these tools.

After we announced our result, two colleagues told me they had independently tried to use LLMs to solve the same problem without success.

AI-powered discovery – with human connection

In contrast, a couple of weeks ago another colleague, Aluna Rizzoli, emailed to tell me he had found an object that collaborators and I had spent over two years searching for. We had used considerable computing resources, but Aluna had used an OpenAI model and a supercomputing cluster. His computation took a mere 43 hours.

The method ChatGPT had generated, working within the intriguingly named “Monster group”, relied on a much more intricate version of an existing algorithm. It would have taken my collaborators and me months to develop this generalisation.

Perhaps the most striking aspect is what happened next. Rather than “scooping” our ongoing work, Aluna invited us to write a joint paper based on the discovery, which appeared on arXiv last week.

Working out where AI fits

Each of these collaborations produced important mathematical advances, yet they embody starkly contrasting attitudes to generative AI.

One deliberately excluded the technology on ethical grounds, while the other embraced it as an indispensable research partner, without abandoning the etiquette of mathematical research. Neither approach appears inherently incompatible with doing excellent mathematics.

That is perhaps the defining feature of the current moment. Rather than converging on a single view, mathematicians are still working out where AI fits within the research process.

The conversation is no longer about whether LLMs are capable of contributing to mathematics research. Instead, we are talking about how their use can be reconciled with the values of collaboration, transparency, and intellectual integrity that form the foundation of our discipline.

 

This article is republished from The Conversation under a Creative Commons license.  Original article.

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The study ¹ introduces IceBoost v2.0, a machine-learning model developed by Niccolò Maffezzoli, physicist and researcher at Ca’ Foscari University of Venice and affiliated with CNR-ISP. IceBoost v2.0 was trained on more than 7 million ice-thickness measurements collected from glaciers across the globe. The model combines these observations with 26 physical and geometrical variables (including topographic slope and curvature, ice velocity, and temperature) to reconstruct on a point-by-point basis the ice thickness, and thus the volume, of every glacier included in the Randolph Glacier Inventory (RGI), the world’s most up-to-date global glacier inventory, excluding the polar ice sheets.

An interactive web-app allows users to explore the data from Maffezzoli’s model for every glacier worldwide.

According to the new estimates, the world’s glaciers contain approximately 150,000 cubic kilometres of ice, equivalent to 32.3 centimetres of global mean sea-level rise if they were to melt completely (excluding Antarctica and Greenland). While this figure is consistent with previous global estimates, IceBoost v2.0 provides a far more realistic estimation of each glacier’s distribution, matching field observations up to 40% more accurately.

One of the most striking examples is the Geikie Plateau in eastern Greenland, where the glacier is up to 2 kilometres thick. In this area, IceBoost v2.0 estimates nearly twice as much ice as previously reported. “The distribution of glacier ice thickness is a fundamental variable for glaciological and climate models,” explains Niccolò Maffezzoli. “To predict how glaciers will evolve by 2100 and quantify their contribution to sea-level rise, we first need the most detailed possible picture of their current state. Researchers involved in the Glacier Model Intercomparison Project (GlacierMIP4), who are producing the next generation of glacier simulations to inform the IPCC’s assessments of glacier evolution through to 2100, will use IceBoost v2.0 as their sole representation of present-day situation.”

The new dataset also provides valuable guidance for future field campaigns and scientific investments. In fact, IceBoost v2.0 maps identify regions where model estimates are most reliable, as well as areas where additional observations are most urgently needed, including the Himalaya and the Karakoram ranges, and the major Patagonian ice fields. The research also has significant implications for freshwater management. Glaciers sustain rivers, ecosystems, agriculture, and local communities, supporting the livelihoods of around 1.9 billion people worldwide. Better estimates of glacier thickness and ice volume make it possible to produce more reliable projections of future freshwater availability, particularly in increasingly arid and desertifying regions such as parts of South America.

“Glaciers are highly complex systems. Although the governing physics is well understood, many of the parameters involved in the equations remain unknown, poorly constrained or  extremely difficult to measure,” Maffezzoli concludes. “Machine-learning models offer an alternative approach: they learn directly from data, generating predictions without imposing a predefined physical description. When sufficient data is available, this approach often proves highly effective. The future lies in hybrid models, where physical modelling and learning from experimental measurements work together to produce even more accurate estimates. We must move quickly: glaciers at mid-latitudes, including those in the Alps, are expected to disappear within the next few decades.”

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Arctic terrestrial permafrost ecosystems store around 1,300 gigatonnes of carbon from organic sources, such as from the remains of plants. Sediments in oceans and river deltas contain a further 400 gigatonnes. Global warming, however, is taking its toll on this natural freezer: temperatures in the Arctic are rising faster than anywhere else on our planet, with the result that permafrost in the region is thawing rapidly. The carbon stored here can then enter the Arctic Ocean via rivers and eroding coastlines. “Consequently, up to 0.02 gigatonnes are entering the sea each year, and according to forecasts, this outflow could rise by 70 to 150 per cent by the year 2100,” says Dr Manuel Ruben, lead author of the study from the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI). “However, how much of this is released back into the atmosphere as a greenhouse gas and how much is stored in the seabed has, until now, been largely unknown.” Yet, this knowledge is essential for assessing the climate impact of thawing permafrost.

To get to the bottom of this unknown, the researchers retrieved and analysed sediment cores taken at various intervals off the coast of the Canadian island of Herschel Island ¹. The cores contain deposits spanning around 50 years. Analysis of the sediments revealed something surprising: “Although the sea here carries away huge quantities of organic carbon from the coast, surprisingly little of it ends up in the ocean’s active carbon cycle,” says Manuel Ruben. “Microorganisms convert around ten per cent of the organic carbon from the sediments into gases, which rise into the water and can then enter our atmosphere.” The major share, however, is stored in the seabed.

In conducting their analysis, the researchers first examined the composition of the sediment cores in detail and also investigated how rapidly permafrost deposits accumulate on the seabed. To this end, they measured how much inorganic dissolved carbon accumulates in tiny cavities within the sediment layers – known as pore water. This provides an indication of how much CO₂ microorganisms have ‘exhaled’ after ‘digesting’ the organic carbon. The isotopic composition of the pore water provides insights into which organic material from which source has been broken down. ”Carbon isotopes represent our atomic indicators that can identify the food source of the microorganisms,” says Prof. Gesine Mollenhauer, a geochemist at the AWI and co-spokesperson for the ‘The Ocean Floor – Earth’s Unexplored Interface’ cluster of Excellence. “The ¹³C isotope, for example, tells us whether they have consumed carbon from land or from the sea. By way of the ¹⁴C isotope, we were able to determine whether the single-celled organisms preferred old organic carbon from permafrost or fresh organic carbon from algae remains.”

One explanation for this lies in the eating habits of these tiny organisms: “The sediment is home to ‘gourmet’ bacteria that apparently prefer fresh carbon stemming from, for example, more recent algal remains over the ‘old’ carbon from permafrost deposits,” explains Gesine Mollenhauer. This suggests that the organic carbon entering the sea from land contributes less to the amount of greenhouse gases in the atmosphere than originally feared. “However, we do need further research here. This is because some of the organic carbon from the permafrost may already have been broken down before it reaches the seabed.”

In addition to the impact of land-ocean carbon transport on atmospheric greenhouse gas levels, further effects are possible. For instance, this transport influences the biogeochemistry of coastal waters, which also play an important role in providing food for the local population. This is because the sediments alter the amount of sunlight available: on the one hand, the freshly broken-off fragments cloud the coastal ocean, while on the other hand, the organic carbon they contain discolours the water as it dissolves into it. Single-celled organisms such as algae, however, need light to convert this into biomass and oxygen. This primary production, in turn, forms the foundation for marine life such as fish, crustaceans and seals. The researchers aim to investigate these complex interrelationships, among other things, as part of the international ‘Arctic Pulse’ campaign planned for 2027. Through coordinated measurement campaigns aboard the Polarstern research icebreaker, using the AWI’s research aircraft and on land, they will investigate how rapid environmental change is altering ecosystems in the Arctic.

“Our study shows, more precisely than ever before, how much carbon is safely stored in the seabed – and just how much of the decomposed material actually originates from the old permafrost,” says Manuel Ruben. “This provides an important foundation for climate models that can predict the consequences of permafrost thawing for the global climate.”

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Moiré collapse

Most of this research has focused on materials whose crystal structure looks roughly the same in every direction, such as graphene, where the interesting physics shows up only at very small twist angles, close to perfect alignment. Now, a team of researchers instead looks at a strongly directional material: bilayer black phosphorus, whose atomic structure behaves very differently along two perpendicular directions in the plane, somewhat like a fabric that stretches easily one way but not the other. That built-in directionality turns out to change the whole logic of moiré physics, producing an effect the authors call moiré collapse. ¹

In black phosphorus, the two layers start out rotated 90 degrees relative to each other. As that angle is dialed away from 90 degrees, the moiré pattern does something unusual. Rather than staying a two-dimensional pattern, like a slowly shifting checkerboard, it stretches more and more along one direction until, at a specific angle that depends only on the material’s own geometry, it effectively stops repeating in the other direction altogether. What remains behaves like a one-dimensional crystal, even though the material is still built from ordinary two-dimensional atomic sheets. For twisted bilayer black phosphorus, the calculations place this collapse at a twist of about 71.6 degrees, or equivalently about 18.4 degrees away from the initial 90-degree arrangement.

Pure geometry

This transformation has nothing to do with how electrons repel or attract each other; it is pure geometry. The mathematical description of the moiré pattern, expressed in terms of the directions in which its periodicity repeats, is squeezed from two independent directions down to one. In ordinary space, this means the pattern keeps a well-defined repeat distance along one axis while stretching out indefinitely along the other, with no repeating pattern left in that direction at all.

Such a dramatic structural change reshapes the electrons riding on top of it. At the untwisted 90-degree configuration, the anisotropy of one layer happens to cancel out the anisotropy of the other, so electrons move about equally well in every in-plane direction. As the twist angle approaches the collapse point, that cancellation breaks down. The electron energy bands become sharply directional, and electrons increasingly travel along one preferred line rather than spreading across the plane. Their energy spectrum starts to show the telltale fingerprints of one-dimensional systems, including sharp spikes in the density of available electron states known as one-dimensional van Hove singularities.

Luttinger liquid physics

One-dimensional conductors behave very differently from ordinary metals. In a three-dimensional metal, electrons can mostly be treated as independent particles that only weakly notice one another. Confine them to a single dimension, however, and that picture stops working: electrons cannot slip past each other, so they are forced to move collectively, in a coordinated wave-like fashion. The framework that describes this collective behavior, called Luttinger liquid theory, traces back to work by Sin-Itiro Tomonaga in 1950 and Joaquin Luttinger in 1963, and it has since been confirmed experimentally in systems such as carbon nanotubes. In a Luttinger liquid, the basic excitations are not single electrons at all, but collective ripples of charge and spin, and this leads to electrical transport properties that look qualitatively different from those of a normal metal.

The calculations in this study show that moiré collapse naturally sets up the conditions Luttinger liquid physics requires. As the collapse angle is approached, the one-dimensional electronic channels that form become progressively less coupled to their neighbors, making the correlated, wave-like behavior increasingly favorable. The analysis estimates a Luttinger parameter of about 0.3 for the collapsed black phosphorus system, a value indicating strong electron-electron interactions, and it predicts distinctive collective charge oscillations and unusual, non-metallic patterns in electrical conduction that could, in principle, be measured in the laboratory.

A notable feature of this mechanism is that the electron bands stay quite dispersive even at collapse, with electron speeds exceeding 100 kilometers per second. This sets it apart from most previously studied moiré systems, including twisted graphene, where strong correlations usually arise because the electronic bands become extremely flat and electrons effectively slow to a crawl. Here, the correlated behavior instead comes from the geometric collapse of the moiré lattice itself, with fast-moving electrons confined to one dimension rather than slow ones spread over two.

Moiré collapse is not a peculiarity of one material

To test these ideas, the researchers combined simplified mathematical models with detailed first-principles calculations of the atomic and electronic structure. Beyond black phosphorus, they found the same collapse in twisted bilayer tin selenide, another layered material with strongly directional bonding. The details of the electronic structure differ somewhat because of the specific atomic orbitals involved, but the same geometric collapse into quasi-one-dimensional channels appears, suggesting the phenomenon is not a peculiarity of one material but a general consequence of twisting any sufficiently anisotropic crystal.

Taken together, the findings outline a different strategy for engineering exotic quantum matter. Instead of hunting for a special magic angle that flattens electronic bands, as in twisted graphene, this approach relies on a purely geometric collapse angle at which a two-dimensional moiré material naturally turns one-dimensional. If confirmed in experiments, it would offer a new and versatile platform for studying Luttinger liquids and other correlated electronic phases that only emerge when electrons are squeezed into moving along a single line.

Author: César Tomé López is a science writer and the editor of Mapping Ignorance

Disclaimer: Parts of this article may have been copied verbatim or almost verbatim from the referenced research paper/s.

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As the flames advanced towards the village of Bédar, in the Almería province of Andalusia, the mayor gave a desperate order to ring the church bells to warn residents. It was not enough. The July 9 Los Gallardos fire, which claimed 13 lives and burnt 7,000 hectares, was Spain’s third deadliest on record.

Most of the victims were retired foreigners living in isolated homes, who died trapped in their vehicles or fleeing on foot along dead-end paths in a landscape dominated by scrubland and rugged terrain, most of which is difficult to access.

High temperatures and extremely dry weather turn scrub, grasslands and bushes into highly flammable fuel. This, combined with complex terrain that makes fire spread faster and unpredictably, poses an enormous challenge for firefighting efforts.

Andalusian legislation requires local emergency plans for forest fires in high-risk areas, and self-protection plans for detached buildings and housing estates. However, there is a widespread problem with compliance and implementation of these measures.

Fires are changing

The disaster in Los Gallardos shows that this problem will only get worse, unless we take decisive and effective action that goes beyond the current dominant paradigm of simply extinguishing and resisting fire.

Where, when and how fires occur is changing. Highly destructive fires are on the rise, housing is expanding into the urban-forest interface. This exposes more homes to fire, and the the risk is increasing most quickly in scrubland and grassland areas, which are not typically associated with fire risk.

This is a major problem for communities that do not know how to respond to a risk they have not often been exposed to. We therefore need to urgently rethink how fire risk is managed.

Improving fire resilience

It is unsustainable to respond only after a disaster has happened. What we need are strategies based on preparedness, mitigation and adaptation. The following eight points are particularly important in achieving this.

1. Manage social vulnerability

Wildfires exacerbate the existing social vulnerabilities that affect people’s ability to anticipate, cope with, withstand and recover from them. In high-risk areas the population may consist of elderly people, people living alone in isolated homes, with financial constraints, or who do not know the local area well.

Fires also pose a risk to local economies. Businesses – including those that provide public services and infrastructure – can be vulnerable for a range of reasons, including flammable facilities, their location, or disruptions to supply chains, essential services and staff mobility.

Targeted planning can identify and address all of these vulnerabilities.

2. Adopt nature-based solutions

Nature-based solutions have not been fully integrated into fire risk management, even though they are widely recognised as essential parts of climate change adaptation and nature restoration.

Nature-based solutions include controlled grazing, the creation of less flammable habitats, green firebreaks, and the restoration of water networks to increase ambient humidity and create areas that slow the spread of fire. These measures also bring many additional benefits for the local population and economy.

3. Create (and update) emergency, self-protection and training plans

Local authorities must draw up emergency plans that the public can access. Isolated dwellings and housing estates must have their own coordinated plans. The plans should include fire hydrants and water supply points, accessible warning systems, access and evacuation routes, and protocols for vulnerable people.

But plans alone are not enough without training. There is also a need for ongoing programmes, tailored to each community, that train people to safely maintain their homes and surroundings, familiarise them with meeting points and shelter areas, and teach them to distinguish between lockdown and evacuation.

Drills are essential. Evacuation is not the same as fleeing, and emergency response must be planned and practised.

4. Promote collective action

Risk reduction is most effective when it is community-based, as this increases overall collective resilience. This means public authorities, community organisations, and the public and private sector must all work alongside one another.

The public should also be actively involved in the design of emergency plans. This builds legitimacy, improves the capacity to respond to fires, and fosters trust and a sense of shared responsibility for preventing fires.

5. Promote integrated and coordinated governance

Fire risk management cannot be the sole responsibility of forestry or firefighting services – it needs an integrated system. All agencies that act in the area must take fire risk into account when making decisions.

In practice, this means avoiding new construction and establishing specific building regulations in high-risk areas, creating buffer zones between different land uses, and promoting uses that help to maintain fire-resilient landscapes.

6. Incentivise risk management

Economic tools and behavioural insights can be leveraged to make self-protection measures and land management the easiest and most rewarding options. This includes nudges: interventions that are designed to encourage but not force people to make certain decisions.

One example of this is payments for environmental services, which financially incentivise land management practices that reduce wildfire risk.

The role of insurance is also evolving in the face of natural and climate-related risks. Instruments are being designed based on a proactive model that incentivises individual and collective risk-reduction behaviour.

7. Improve monitoring and advance warning systems

Up-to-date information on a fire’s development is essential. Real-time monitoring systems, sensors, satellite imagery, predictive models of fire behaviour and alert applications providing detailed information can all improve response capabilities and enable safer evacuations. Integrating these tools into local management plans is a necessary investment.

8. Encourage public and private investment in long-term preparedness

Investments in prevention and preparedness provide significant returns. Growing requirements – both regulatory and voluntary – for the private sector to disclose information on nature-related risks and dependencies are generating new demand for investment products (carbon and biodiversity credits, habitat banks, environmental impact bonds, and so on).

This growing private investment must now be channelled towards measures that strengthen both people’s and the landscape’s wildfire resilience. This is to supplement, not replace public investment.

The Los Gallardos blaze serves as a reminder that we need to fundamentally change the way we understand and manage fire risk. Investment in preparedness and coordinated planning between public authorities, the private sector and the general public could be the key to ensuring that, should the alarm bells ring again, the population is ready to respond.

 

This article is republished from The Conversation under a Creative Commons license.  Original article.

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Physicists and materials scientists have long assumed substrates do not react to electrical stimulus, but new research from the University of California San Diego Alex Frañó’s lab and a team of collaborators has shown that substrates are not inert after all. The discovery ¹ has the potential to help engineers build the dense, three-dimensional, brain-inspired computer chips needed for more energy-efficient computing.

In a human brain, a vast network of neurons works across different regions, enabling every thought and action we take. Yet thoughts that are quick and simple for humans — recognizing faces, differentiating a cat from a dog or solving a captcha — can be devilishly hard and energy-intensive for computers. Quantum materials may provide a solution.

It was this quest to develop new materials that led Frañó’s lab to study vanadium dioxide thin-film devices. When a voltage is applied to the thin film, an electric filament forms — something similar happens when the Earth and atmosphere create a voltage strong enough to result in lightning. The filament in the thin film drives the device’s electrical spiking, much like the signals in our neurons.

This reaction has been studied countless times in labs around the world, but Frañó’s lab was implementing a new technique, developed by graduate student Elliot Kisiel, called dark-field X-ray microscopy. Kisiel, who spent most of his graduate studies at Argonne National Laboratory and is now a Mayer Postdoctoral Fellow there, developed the tool to combine the best of two worlds: electron microscopy and X-ray diffraction.

“Dark-field X-ray microscopy gives us the ability to look at an entire device in one image, which not only greatly improves how fast we can perform these measurements, but also allows us to study the areas surrounding the devices with high fidelity,” he stated.

A changing substrate

Since the lenses for X-rays are often thick and, therefore, absorb most of the X-rays that pass through them, the team needed to look at something that provided more signal, which is why they decided to look at the substrate during some of their preliminary testing with this new technique.

What they saw was completely unexpected: not only was there a change in the thin film, but there was also a change in the substrate, showing for the first time that the two parts were sharing energy or “coupled.” This discovery went against everything scientists had assumed about substrates for decades.

 Materials for a new age of computing

Thin films are so called because they are vanishingly thin — around 100 nanometers — while the substrates they sit on can be 10,000 times thicker. Despite their lopsided sizes, in this experiment, the thin film was able to push and pull on the substrate. Frañó likened the discovery to a tree on a mountaintop being able to move the entire mountain. Furthermore, the substrate also acted on the thin film, each pushing and pulling on the other.

They spent the next four years making sure what they saw wasn’t a fluke. They not only reproduced the results doing the research exactly as they had done it the first time, but they also reproduced the results when they changed certain aspects of the sample, such as the thickness and material of the substrate. They also tested their results using different instrumentation. Much of that work was done at Argonne, which has a synchrotron facility that produces high brilliance X-rays, and at Brookhaven National Laboratory, home to the world’s only all-electric ultrafast electron microscope, which is able to capture exactly how a working device behaves under realistic operating conditions.

It wasn’t a fluke.

“The assumption that substrates are inert needs to be rethought,” Frañó stated. “Going forward, we have to assume that the substrate is undergoing changes when the film is. This is a transformational notion that counters decades of previous supposition.”

This paper is a companion to a paper that was published last year in ACS Nano ². In that paper, they used dark-field X-ray microscopy to study the thin film, while this paper focuses on the substrate. Together they illustrate how one research result leads to the next research inquiry.

In Frañó’s case, the question he now asks himself is, “What can we do with this new substrate discovery? If a tree is moving the whole mountain, let’s find a way to capitalize on that.”

One idea is that the substrate can act as a medium that can couple materials on either end of the substrate. Currently, thin film devices are connected two-dimensionally, but if researchers could build on both sides of a substrate, it would allow for three-dimensionality, opening up the possibility of denser, more interconnected computer chips, and bringing us one step closer to the next generation of computing.

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In our daily life, we perceive many different events around us with different participants involved, such as a woman dancing in a theater, or a man enjoying an ice cream. To produce and understand the sentences that express these events, it is crucial to identify the number of participants involved and the role that each one plays. As in a drama, we have to identify the role that each character has to understand the story.

Agents, patients and experiencers

In some events, there are two characters involved, as in the girl kicks the ball, with the girl and the ball; in others, instead, there is only one, as in the woman dances or the man falls down, with the woman or the man. Each of these participants has a different role. For example, the woman is an agent, because she is doing the action, just the opposite of the man, who is a patient, because he is the one affected. In the case of the girl has fun, this participant has the role of experiencer, the one who experiences the enjoyment.

In psycholinguistics, the mechanisms involved in the processing of agent, patient, and experiencer roles have been investigated through different experiments such as reading sentences while participants’ brain activity or eye-movements are recorded. In these studies, it has been found that agent and patient categories play a crucial role in language comprehension and production. These two roles are used when reading, listening to, and producing sentences, and different mechanisms are involved in their processing (e.g., ¹²³⁴⁵). Therefore, they form two different categories within the event role repertoire ⁶. They are two different characters within the drama. However, as I will discuss, there is no experimental evidence for a third character, the experiencer role.

One well-established result regarding the processing of agents and patients comes from experiments where participants are asked to read sentences while their brain activity is recorded by means of electrodes, known as EEG studies. Results of these studies show that the processing of patients elicited an N400 component (a negative brain response that occurs around 400 milliseconds after a stimulus is processed) compared to the processing of agents. This effect is associated with the processing of unexpected elements. For example, when processing the day was breezy so the boy went outside to fly, it is expected to read next a kite based on the preceding context; hence, if an airplane is read instead, as this is unexpected, then an N400 effect appears ⁷. Something similar happens with the processing of agents and patients: agents are expected, whereas patients unexpected.

Across languages, speakers tend to interpret the first ambiguous noun phrase of a sentence as an agent instead of as a patient, due to the agent preference ⁸: agents are preferred and are cognitively more salient than patients. Imagine that someone reads the sentence Mary falls down. Before encountering the verb, the reader cannot know whether Mary is an agent or a patient. However, as agents are preferred over patients, the reader interprets that it is an agent by default. Once the verb is reached, there are two options: confirm that Mary is an agent (the expected role) or recategorize it as a patient (the unexpected role). In Mary falls down, it is the second option, Mary is categorized as a patient, because the verb selects this role. This recategorization is reflected in an N400 component. This result shows that agents and patients have different processing mechanisms and that they form two different categories. Furthermore, this effect has been largely found in different languages such as, Basque (Isasi-Isasmendi et al., 2024), Spanish ⁹, German (Haupt et al., 2008), Hindi (Bickel et al., 2015), or Aïwoo (Sauppe et al., 2023), supporting its robustness.

Therefore, these experimental findings show that there is a preference toward agents over patients. The agent is the protagonist of the show, while the patient plays a secondary role.

What about experiencers?

Linguists have proposed that there are other roles within the event role repertoire, for example, the experiencer role ¹⁰.This role corresponds to the participant who experiences a mental state denoted by the verb. Participants of events like loving someone or enjoying an ice cream have this role. The experiencer is the participant who loves or enjoys.

But does experimental evidence show that the experiencer role is a specific role category within the event role repertoire, as agent and patient roles do?

In Sánchez-López (2026), I addressed this question. I investigated whether speakers use a third category, the experiencer role, when processing language. In three reading eye-tracking experiments and in one EEG experiment, I asked Spanish native speakers to read sentences, while their eye-movements and their brain activity were recorded.

In the first two eye-tracking experiments, I compared de processing of sentences with two participants with different combinations of agent, experiencer, and patient roles: (i) María golpea a Juan “Mary hits John”, with an agent (María) and a patient (Juan); (ii) María asusta a Juan “Mary frightens John”, with an agent (María) and an experiencer (Juan); and (iii) María ama a Juan “Mary loves John”, with an experiencer (María) and a patient (Juan).

These two experiments revealed that Spanish native speakers produced higher fixation times on verbs selecting agent-experiencer roles (ii), than on the other two sentence types (i, iii), independently of whether the subject was animate or inanimate (e.g., María/La situación asusta a Juan “Mary/The situation frightens John”). Interestingly, no processing differences were found between sentences selecting agent-patient (i) and experiencer-patient (iii). These findings show that the combination of agent-experiencer involves higher processing cost than other combinations.

But why is the agent-experiencer combination costlier than the others?

A possible explanation could be that the experiencer role is not a specific category within the event role repertoire. As Dowty ¹¹ proposed, agents and experiencers are both grouped within the same category, the one of the proto-agent. Based on this proposal, there are only two roles in the event role repertoire, proto-agent and proto-patient, and proto-agents always become subjects. Hence, in sentences like Mary frightens John, both participants are proto-agents. Since they are both possible candidates to become subject, a competition for subject assignment appears. This competition is reflected in higher processing cost compared to those sentences without competition, sentences with only one proto-agent participant (e.g., Mary hits John or Mary loves John).

To better understand this competition effect, imagine that the proto-agent is the hero of the story and the proto-patient is the villain. In a drama, if there is a hero, there is also a villain. If there are two heroes and no villain in the story, it would be unusual, and probably, it would take more time to understand the story, since this is not expected. Hence, the same happens with sentences like Mary frightens John, which have two proto-agents; this combination is unexpected and it entails higher processing cost than when there is only one proto-agent and one proto-patient.

In another reading experiment using both eye-tracking and EEG techniques, I compared the processing of the three roles, agent, experiencer, and patient again to determine whether this experiencer role behaves like the agent role or not. In these experiments, I used sentences with only one participant involved: (iv) María baila “Mary dances” with an agent, (v) María disfruta “Mary has fun” with an experiencer, and (vi) María se cae “Mary falls down” with a patient. The use of these sentences allowed a more direct comparison of the processing of these roles, since the three roles are subjects. The structure of all sentences is the same (one participant and the verb), and only the event role category changes.

Results revealed that participant looked more at sentences with agents and experiencers than at sentences with patients. Moreover, I found that participants exhibited a preference to interpret the first ambiguous noun phrase (i.e., María) as an agent/experiencer; hence, when they realized that it was a patient instead, an N400 component appeared. But this effect only appeared with patients, not with experiencers, showing that participants processed similarly agents and experiencers, and both different from patients.

What does this evidence show?

These results align with the hypothesis that there are only two role categories within the event role repertoire (Dowty, 1991): proto-agent and proto-patient. Therefore, when the sentences Mary dances or Mary has fun are processed, in both cases, Mary is interpreted as a proto-agent, whereas in Mary falls down, she is interpreted as a patient instead.

All in all, this evidence suggests that agents and experiencers belong to the same role category, the one of the proto-agent. Therefore, when we process language, it seems that we do not differentiate between agents and experiencers, rather we process them similarly. Experiencers are grouped together with agents, being the same character in the event, both proto-agents.

So, proto-agents are the protagonists of the story, the ones that spectators prefer to pay attention to, whereas proto-patients are the secondary characters who complement the story. Regarding experiencers, they do not play a different character in the story, instead, they are also the protagonists.

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Triplet-triplet annihilation

The most developed route to upconversion relies on a process called triplet-triplet annihilation. A light-absorbing molecule known as a sensitizer first captures a low-energy photon and passes that energy to a second type of molecule, the annihilator. The annihilator settles into a triplet excited state, an unusually long-lived condition that allows it to persist until it meets a second annihilator molecule in the same state. When two such molecules collide, one drops back to its ground state while the other is pushed up into a higher-energy singlet state, from which it emits a photon of higher energy than either of the photons that started the process.

A long-standing problem with this scheme is that ordinary sensitizer molecules lose a portion of the absorbed energy internally, as the initially excited state converts into the lower-energy triplet state that gets passed along. This loss caps how large an energy jump the whole system can ultimately achieve. One promising way around the problem is to replace the conventional sensitizer with an organic radical, a molecule carrying a single unpaired electron. That unpaired electron gives radicals unusual electronic and magnetic behavior and can make the conversion from absorbed light into a usable excited state considerably more efficient, since it opens transitions that are otherwise very weak in conventional molecules.

A single hydrogen to control it all

To work out why, the investigation combined light-based spectroscopy, electron spin resonance (a technique that detects unpaired electrons), and quantum chemical calculations. After the radical absorbs red light, its excitation transfers within about ten picoseconds (ten trillionths of a second) to the attached anthracene unit, creating a long-lived excited state that still carries some radical character. This internal handoff stretches the effective excited-state lifetime to more than 250 nanoseconds, roughly ten times longer than the 27 nanoseconds measured for the radical unit on its own. A longer-lived excited state gives the sensitizer far more opportunity to encounter another molecule and hand off its energy before simply decaying back to the ground state. The calculations independently pointed to the same conclusion, showing that energy transfer to a nearby annihilator proceeds far more readily through this long-lived triplet-like state than directly from the radical’s first excited state.

A mix needed

The work also tested an idea proposed in earlier research, that a single radical-containing molecule of this kind might act as both sensitizer and annihilator on its own, a so-called single-component upconversion system. The new experiments argue against this. Solutions containing only the radical molecule, across a range of concentrations, showed no detectable upconverted light, consistent with the radical’s higher excited states relaxing away too quickly for the necessary emission to compete.

Efficient upconversion instead required mixing the radical sensitizer with its hydrogen-added counterpart, or with the well-established annihilator 9,10-diphenylanthracene. Because the two molecules share nearly the same molecular framework, this two-molecule mixture is described as a “pseudo-single-component” system. Shining red light at 658 nanometres onto the radical paired with its hydrogenated twin produced blue emission at 450 nanometres, an apparent energy gain of about 0.9 electronvolts, with 7 percent of absorbed photon pairs converted into upconverted light. Pairing the same radical sensitizer with 9,10-diphenylanthracene instead produced blue emission at 437 nanometres, a slightly larger energy gain of about 1.0 electronvolt, with 12 percent efficiency. Both figures mark a large improvement over earlier all-organic radical sensitizers, which had reached efficiencies of only a few tenths of a percent, and the upconverted emission remained stable over hours of continuous illumination.

A practical strategy for photon upconversion

Beyond these specific molecules, the study illustrates how a minimal change in molecular structure, adding or removing a single hydrogen atom, can completely redirect a molecule’s photochemical role, switching it from an energy donor to an energy acceptor. Combining optical measurements, spin resonance, and computation made it possible to trace exactly why this switch occurs at the level of electronic structure, rather than simply observing that it happens.

The findings offer a clearer picture of how organic radicals can be harnessed for photon upconversion and point toward a practical strategy for building more efficient materials from radicals and their closed-shell relatives together. Such materials could eventually help capture more of the solar spectrum, drive light-powered chemical synthesis, or improve the efficiency of light-emitting technologies, by putting to use photons that would otherwise be wasted.

Author: César Tomé López is a science writer and the editor of Mapping Ignorance

Disclaimer: Parts of this article may have been copied verbatim or almost verbatim from the referenced research paper/s.

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