Two blind spots torture physicists: the birth of the universe and the center of a black hole. The former may feel like a moment in time and the latter a point in space, but in both cases the normally interwoven threads of space and time seem to stop short. These mysterious points are known as singularities.

Singularities are predictions of Albert Einstein’s general theory of relativity. According to this theory, clumps of matter or energy curve the space-time fabric toward themselves, and this curvature induces the force of gravity. Pack enough stuff into a small enough spot, and Einstein’s equations seem to predict that space-time will curve infinitely steeply there, such that gravity grows infinitely strong.

Most physicists don’t believe, however, that Einstein’s theory says much about what really happens at these points. Rather, singularities are widely seen as “mathematical artifacts,” as Hong Liu, a physicist at the Massachusetts Institute of Technology, put it, not objects that “occur in any physical universe.” They are where general relativity malfunctions. The singularities are expected to vanish in a more fundamental theory of gravity that Einstein’s space-time picture merely approximates—a theory of quantum gravity.

But as physicists take steps toward that truer and more complete theory by merging general relativity and quantum physics, singularities are proving hard to erase. The British mathematical physicist Roger Penrose won the Nobel Prize in Physics for proving in the 1960s that singularities would inevitably occur in an empty universe made up entirely of space-time. More recent research has extended this insight into more realistic circumstances. One paper established that a universe with quantum particles would also feature singularities, although it only considered the case where the particles don’t bend the space-time fabric at all. Then, earlier this year, a physicist proved that these blemishes exist even in theoretical universes where quantum particles do slightly nudge space-time itself—that is, universes quite a bit like our own.

This trilogy of proofs challenges physicists to confront the possibility that singularities may be more than mere mathematical mirages. They hint that our universe may in fact contain points where space-time frays so much that it becomes unrecognizable. No object can pass, and clocks tick to a halt. The singularity theorems invite researchers to grapple with the nature of these points and pursue a more fundamental theory that can clarify what might continue if time truly stops.

Space-Time’s Fatal Flaws

Karl Schwarzschild first discovered an arrangement of space-time with a singularity in 1916, just months after Einstein published general relativity. The bizarre features of the “Schwarzschild solution” took years for physicists to understand. Space-time assumes a shape analogous to a whirlpool with walls that swirl more and more steeply as you go farther in; at the bottom, the curvature of space-time is infinite. The vortex is inescapable; it has a spherical boundary that traps anything falling inside, even light rays.

It took decades for physicists to accept that these inconceivable objects, eventually dubbed black holes, might actually exist.

J. Robert Oppenheimer and Hartland Snyder calculated in 1939 that if a perfectly spherical star gravitationally collapses to a point, its matter will become so dense that it will stretch space-time into a singularity. But real stars bubble and churn, especially while imploding, so physicists wondered whether their nonspherical shapes would stop them from forming singularities.

Penrose eliminated the need for geometric perfection in 1965. His landmark proof relied on two assumptions. First, you need a “trapped surface” inside of which light can never escape. If you cover this surface in light bulbs and switch them on, their light rays will fall inward faster than they can travel outward. Crucially, this shell of light will shrink regardless of whether it started out as a perfect sphere, a dimpled golf ball, or something more misshapen.

Second, space-time should always curve in such a way that light rays bend toward each other but never diverge. In short, gravity should be attractive, which is the case so long as energy is never negative.

With these two stipulations, Penrose proved the mortality of at least one of the trapped light rays. Its otherwise eternal journey through space and time must terminate in a singularity, a point where the space-time fabric ceases to exist, where there is no future for the light ray to travel into. This was a new definition of a singularity, distinct from the infinite curvature of the Schwarzschild solution. Its generality enabled Penrose to prove in three scant pages of math that, under his two assumptions, singularities will inevitably form.

“Penrose’s paper was probably the most important paper in general relativity ever written, other than Einstein’s original paper,” said Geoff Penington, a physicist at the University of California, Berkeley.

Stephen Hawking soon extended Penrose’s argument to the early universe, proving that a cosmos described by general relativity must have sprung from a singular point during the Big Bang. This cosmological singularity resembles a black hole in that, if you imagine rewinding the history of the universe, light rays will run into a wall at the beginning of time.

Over the years, physicists have accumulated heaps of evidence that black holes exist, and that the universe began with an event that looks very much like a Big Bang. But do these phenomena truly represent space-time singularities?

Many physicists find the actual existence of such points unthinkable. When you try to calculate the fate of a particle approaching the singularity, general relativity glitches and gives impossible, infinite answers. “The singularity means a lack of predictability,” Liu said. “Your theory just breaks down.”

But the particle in the real world must have a fate of some sort. So a more universal theory that can predict that fate—very likely a quantum theory—must take over.

General relativity is a classical theory, meaning that space-time takes on one, and only one, shape at every moment. In contrast, matter is quantum mechanical, meaning it can have multiple possible states at once—a feature known as superposition. Since space-time reacts to the matter in it, theorists expect that any matter particles in a superposition of occupying two different locations should force space-time into a superposition of two distortions. That is, space-time and gravity should also follow quantum rules. But physicists haven’t yet worked out what those rules are.

Into the Onion

Theorists approach their quest for a quantum theory of gravity the way they might peel an onion: layer by layer. Each layer represents a theory of a universe that imperfectly approximates the real one. The deeper you go, the more of the interplay between quantum matter and space-time you can capture.

The German physicist-soldier Karl Schwarzschild calculated the shape that space-time takes around a 

massive point. Years later, physicists realized that this geometry contains a singularity.

COURTESY OF PUBLIC DOMAIN

 

Penrose worked in the outermost layer of the onion. He used the general theory of relativity and ignored quantumness entirely. In effect, he proved that the space-time fabric has singularities when it is completely devoid of any quantum matter.

Physicists aspire to someday reach the onion’s core. In it, they’ll find a theory describing both space-time and matter in all their quantum glory. This theory would have no blind spots—all calculations should yield meaningful results.

But what about the middle layers? Could physicists resolve Penrose’s singularities by moving to something a little more quantum, and therefore a little more realistic?

“It was the obvious speculation, that somehow quantum effects should fix the singularity,” Penington said.

They first tried to do so in the late 2000s. The assumption that had confined Penrose’s proof to the outermost layer was that energy is never negative. That’s true in everyday, classical situations, but not in quantum mechanics. Energy goes negative, at least momentarily, in quantum phenomena such as the Casimir effect, where (experiments show) two metal plates attract each other in a vacuum. And negative energies play a role in the way black holes are thought to radiate particles, eventually “evaporating” entirely. All the deeper, quantum layers of the onion would feature this exotic energetic behavior.

The physicist who peeled the top layer was Aron Wall, then based at the University of Maryland and now at the University of Cambridge. To cut into the quantum realm and abandon Penrose’s energy assumption, Wall latched on to a theoretical discovery made in the 1970s by Jacob Bekenstein.

Bekenstein knew that for any given region of space, the contents of the region grow more mixed up as time goes on. In other words, entropy, a measure of this mixing, tends to increase, a rule known as the second law of thermodynamics. While considering a region that contains a black hole, the physicist realized that the entropy comes from two sources. There’s the standard source—the number of ways that quantum particles in the space around the black hole could be arranged. But the black hole has entropy too, and the amount depends on the black hole’s surface area. So the total entropy of the region is a sum: the surface area of the black hole plus the entropy of nearby quantum stuff. This observation became known as the “generalized” second law.

Wall “made it his mission to understand the generalized second law,” said Raphael Bousso, a physicist at Berkeley. “He was thinking about it in much clearer and much better ways than everybody else on the planet.”

Reaching the quantum layers of the onion would mean accommodating negative energy and the presence of quantum particles. To do so, Wall reasoned that he could take any surface area in general relativity and add to it the entropy of those particles, as the generalized second law suggested. Penrose’s proof of his singularity theorem had involved the trapped surface. So Wall upgraded it to a “quantum trapped surface.” And when he reworked Penrose’s singularity theorem in this way, it held. Singularities form even in the presence of quantum particles. Wall published his findings in 2010.

In 2010, Aron Wall, now at the University of Cambridge, revamped Penrose’s proof to show that singularities 

exist in a world where space-time has no quantum properties but is filled with particles that do.

PHOTOGRAPH: NICOLE WALL

“Aron’s paper was a seminal breakthrough in combining quantum mechanics and gravity in a more precise way,” Penington said.

Having peeled back the classical outer layer of the onion, where energy is always positive, Wall reached a lightly quantum layer—a context physicists call semiclassical. In a semiclassical world, space-time guides the journeys of quantum particles, but it cannot react to their presence. A semiclassical black hole will radiate particles, for instance, since that’s a consequence of how particles experience a space-time warped into a black hole shape. But the space-time—the black hole itself—will never actually shrink in size even as the radiation leaks energy into the void for all eternity.

That’s almost, but not exactly, what happens in the real universe. You could watch a black hole radiate particles for a century without seeing it shrink a single nanometer. But if you could watch for longer—many trillions upon trillions of years—you would see the black hole waste away to nothing.

The next onion layer beckoned.

Dialing Up the Quantumness

Bousso recently revisited Wall’s proof and found that he could cut a little deeper. What about the world where black holes shrink as they radiate? In this scenario, the space-time fabric can react to quantum particles.

Using more refined mathematical machinery developed by Wall and others since 2010, Bousso found that, despite the intensified quantumness of his scenario, singularities continue to exist. He posted his paper, which has not yet been peer-reviewed, in January.

The world of Bousso’s new theorem still departs from our universe in notable ways. For mathematical convenience, he assumed that there’s an unlimited variety of particles—an unrealistic assumption that makes some physicists wonder whether this third layer matches reality (with its 17 or so known particles) any better than the second layer does. “We don’t have an infinite number of quantum fields,” said Edgar Shaghoulian, a physicist at the University of California, Santa Cruz.

Still, for some experts, Bousso’s work delivers a satisfying denouement to the Penrose and Wall singularity story, despite its unrealistic abundance of particles. It establishes that singularities can’t be avoided, even in space-times with mild reactions to quantum matter. “Just by adding small quantum corrections, you can’t prevent the singularity,” Penington said. Wall and Bousso’s work “answers that pretty definitively.”

The Real Singularity

But Bousso’s theorem still doesn’t guarantee that singularities must form in our universe.

Some physicists hold out hope that the dead ends do somehow go away. What seems like a singularity could actually connect to somewhere else. In the case of a black hole, perhaps those light rays end up in another universe.

And a lack of a Big Bang singularity might imply that our universe began with a “Big Bounce.” The idea is that a previous universe, as it collapsed under the pull of gravity, somehow dodged the formation of a singularity and instead bounced into a period of expansion. Physicists who are developing bounce theories often work in the second layer of the onion, using semiclassical physics that exploits negative-energy quantum effects to get around the singularity required by the Penrose and Hawking theorems. In light of the newer theorems, they will now need to swallow the uncomfortable truth that their theories violate the generalized second law as well.

One physicist pursuing bounces, Surjeet Rajendran of Johns Hopkins University, says he is undaunted. He points out that not even the generalized second law is gospel truth. Rejecting it would make singularities avoidable and continuations of space-time possible.

Singularity skeptics can also appeal to the theory at the core of the onion, where space-time behaves in truly quantum ways, such as taking on superpositions. There, nothing can be taken for granted. It becomes hard to define the concept of area, for instance, so it’s not clear what form the second law should take, and therefore the new theorems won’t hold.

Bousso and like-minded physicists, however, suspect that a highly quantum arena with no notion of area is tantamount to a dead-end for a light ray, and therefore that something Penrose would recognize as a singularity should persist in the core theory and in our universe. The beginning of the cosmos and the hearts of black holes would truly mark edges of the map where clocks can’t tick and space stops.

“Inside of black holes, I am positive there is some notion of singularity,” said Netta Engelhardt, a physicist at MIT who has worked with Wall.

In that case, the still-unknown fundamental theory of quantum gravity would not kill singularities but demystify them. This truer theory would allow physicists to ask questions and calculate meaningful answers, but the language of those questions and answers would change dramatically. Space-time quantities like position, curvature and duration might be useless for describing a singularity. There, where time ends, other quantities or concepts might have to take their place. “If you had to make me guess,” Penington said, “whatever quantum state describes the singularity itself does not have a notion of time.”

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Posted Monday 21 July 2025 at 4:08 am AEST (my time).

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They use ultrasound, which is normally inaudible to people, along with something called acoustic metasurfaces—tiny lenses that can bend sound in specific directions. By combining two ultrasound beams that travel in curved paths and meet at a single point, they’re able to make sound audible only at that intersection. As Jing explained, “The person standing at that point can hear sound, while anyone standing nearby would not. This creates a privacy barrier between people for private listening.”

To make this happen, the system includes two ultrasonic speakers and the metasurface lenses, which were 3D printed by Xiaoxing Xia from Lawrence Livermore National Lab. Each beam has a slightly different frequency, and when they meet, a local reaction makes the sound audible. Neither beam is loud on its own—the sound only forms at that shared point.

Jia-Xin “Jay” Zhong, one of the researchers, shared how they tested the idea: “We used a simulated head and torso dummy with microphones inside its ears to mimic what a human being hears at points along the ultrasonic beam trajectory, as well as a third microphone to scan the area of intersection. We confirmed that sound was not audible except at the point of intersection, which creates what we call an enclave.”

One of the biggest advantages of their approach is that it works across a wide range of sound frequencies—between 125 Hz and 4 kHz, which covers most of what people can hears. Even in rooms where sound usually bounces around, their system held up well. And it’s surprisingly compact too: the whole setup measures about 16 centimeters, roughly the size of a pencil case.

“We essentially created a virtual headset,” Zhong said. In practice, it means that someone standing in the audible enclave can hear what’s being played clearly, while everyone else around hears nothing at all. That could be especially useful in shared spaces like cars, classrooms, or open offices.

Right now, the sound can travel about one meter and hits around 60 decibels which is similar to regular talking volume. The team believes they can push those limits further by using stronger ultrasound.

All this might seem futuristic, but it’s grounded in solving a basic problem: how to direct sound only where it’s needed. If you’re into tech and sound design, this could open up a whole new world of personalized audio experiences.

Source: Penn State, PNAS | Image via Depositphotos

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Posted Sunday 20 July 2025 at 4:27 pm AEST (my time).

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This new device, called an Atmospheric Water Harvesting Window (AWHW), uses a unique hydrogel panel that looks like black bubble wrap. These dome-shaped bubbles soak up water vapor from the air, especially at night when humidity is higher. During the day, sunlight makes the vapor inside evaporate. That vapor then condenses on a glass surface and drips down through a tube, turning into drinkable water.

The AWHW doesn’t rely on power sources like batteries or solar panels. It’s completely passive, meaning it works on its own. The team tested a meter-sized panel in Death Valley, California, one of the driest places in North America, and got between 57.0 and 161.5 milliliters of water per day even with humidity as low as 21 percent. That’s more than what other similar passive devices have managed.

“We have built a meter-scale device that we hope to deploy in resource-limited regions, where even a solar cell is not very accessible,” said Xuanhe Zhao, a professor at MIT. “It’s a test of feasibility in scaling up this water harvesting technology. Now people can build it even larger, or make it into parallel panels, to supply drinking water to people and achieve real impact.”

Another cool part of the design is how they kept the water safe to drink. Usually, these kinds of hydrogels use salts like lithium chloride to absorb more vapor but that can lead to salt leaking into the water, which isn’t ideal. To solve this, MIT’s team mixed in glycerol, a compound that helps keep salt locked inside the gel. In testing, the lithium ion concentration in the harvested water stayed below 0.06 ppm (parts per million), which is way below the safe limit.

The hydrogel domes also give the material more surface area, letting it collect more vapor. The outer glass panel is coated with a special polymer film that helps cool the glass, making it easier for vapor to condense.

“This is just a proof-of-concept design, and there are a lot of things we can optimize,” said lead author Chang Liu, now a professor at the National University of Singapore. “For instance, we could have a multipanel design. And we’re working on a next generation of the material to further improve its intrinsic properties.”

Published in Nature Water, the study says the AWHW could last at least a year and shows promise for making safe, sustainable water in places with harsh climates. The researchers believe an array of vertical panels could one day supply water to individual households, especially in remote or off-grid locations.

Source: MIT News, Nature

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Posted Sunday 20 July 2025 at 4:26 pm AEST (my time).

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On August 2, 2027, a remarkable total solar eclipse will darken the skies over parts of Europe, North Africa, and the Middle East. For up to 6 minutes and 23 seconds, the Moon will completely obscure the Sun, casting a dramatic shadow over Earth in what is being called the “Great North African Eclipse.” This event will be the longest total solar eclipse visible from land between 1991 and 2114. With such an extended period of totality and broad visibility, this awe-inspiring celestial phenomenon will be a once-in-a-lifetime experience for millions of people across continents, skywatchers, scientists, and photographers alike.

Clear skies are expected in many parts of North Africa, especially in Libya and Egypt, which are known for dry, sunny weather in August. These favourable conditions enhance the likelihood of an unobstructed view, making the region ideal for eclipse observation, scientific research, and astronomical tourism opportunities.

The total solar eclipse will occur on Monday, August 2, 2027, during the early to mid-afternoon hours, depending on your location.

Exact timings may vary by a few minutes depending on the observer’s precise location along the path of totality. The full eclipse duration, from first contact (partial eclipse begins) to final contact (partial eclipse ends), will last approximately 2.5 to 3 hours, with totality lasting up to 6 minutes and 23 seconds in select areas like Luxor, Egypt.

Three rare astronomical conditions will occur simultaneously to produce such a long eclipse:

These combined factors will result in a total solar eclipse lasting over six minutes, significantly longer than most eclipses, which typically range from two to three minutes.

The eclipse will begin over the Atlantic Ocean and move eastward across Europe, Africa, and the Middle East. The path of totality, which is the narrow band where the eclipse will be fully visible, will be approximately 258 to 275 kilometers wide.

The eclipse will exit over the Indian Ocean and continue past the Chagos Archipelago before fading.

The 2027 eclipse will not be visible in many parts of the world, including:

Observers in these regions may not witness any part of the eclipse or will only experience a minor partial eclipse.

The upcoming 2027 eclipse is not the longest in history, but it is the most significant of this century. The longest recorded total solar eclipse occurred on June 15, 743 BC, lasting 7 minutes and 28 seconds. However, the 2027 event will be the longest visible from land between 1991 and 2114. The next similarly long eclipse will not take place until August 23, 2114.

This event is significant not only because of its rare duration but also because of its wide visibility across multiple countries. Scientists, astronomers, and casual observers alike will benefit from the opportunity to study the Sun’s corona and other solar phenomena during the extended period of totality.

For those living near the eclipse path, it is a truly once-in-a-century opportunity. The combination of scientific importance and natural beauty makes this a date worth marking in calendars worldwide.

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Posted Sunday 20 July 2025 at 6:39 am AEST (my time).

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When you crack open your mailbox, it’s almost as if your letters just appear. Long before the days of speedy, overnight mail deliveries, postal service workers meticulously sorted through letters by hand and transported mail on horseback. For more than 250 years, the US Postal Service has worked behind the scenes to build a faster delivery network, and this mission has quietly pushed it to the forefront of technology.

“Most people treat the Postal Service like a black box,” USPS spokesperson Jim McKean tells The Verge. “You take your letter, you put it in a mailbox, and then it shows up somewhere in a couple of days. The truth is that that piece of mail gets touched by a lot of people and machines and transported in that period of time — it’s a modern marvel.”

One of its big breakthroughs took place in 1918 with the introduction of airmail. The USPS worked with the Army Signal Corps to use leftover World War I aircraft to launch the service, and the planes were as barebones as they could get. An excerpt from a 1968 issue of Postal Life called the early aircraft “a nervous collection of whistling wires” with “linen stretched over wooden ribs, all attached to a wheezy, water-cooled engine.”

JR-1B mail planes were the first used by the USPS (1918).

Photo: National Archives and Records Administration

 

At the time, pilots literally risked their lives delivering mail — 34 of them died between 1918 and 1927. “There was no commercial aviation, no airports. There was no radio. There was no navigation,” USPS historian Stephen Kochersperger says. “The Postal Service had to develop all of those things just for getting the mail delivered.”

Once the USPS established that it could reliably deliver mail by plane, Congress allowed it to contract airmail service to commercial aviation companies, laying the groundwork for the major airlines that we know today, like American Airlines and United Airlines. Along with getting paid for delivering mail, contractors found that they could make even more money by carrying passengers with their cargo. “That was where commercial aviation took off,” Kochersperger says.

Airmail routes gradually began to expand internationally, first to Canada and then to Cuba. But a couple decades later, the USPS experimented with a novel form of delivery: mail-by-missile. In 1959, the USPS and the US Navy loaded a Regulus I missile with two mail containers that had 3,000 letters in total. The missile traveled 100 miles in around 23 minutes, successfully landing at a Navy base in Mayport, Florida, with the help of a parachute. Despite its success, the idea never took off. It turns out missiles just can’t carry that much mail. And overall, this rather ridiculous demonstration was more of a stunt to show force during the Cold War, according to the Smithsonian.

The Regulus I missle carried 3,000 pieces of mail (1959).

Photo: Collection of United States Postal Service

 

Back on the ground, the USPS set its sights on improving the speed of mail processing. Though it began experimenting with a mail canceling machine in the 1920s, which put a mark on used postage, it wasn’t until the 1950s that it deployed an electromechanical sorting machine. Instead of manually sorting mail using the “pigeonhole” method, in which workers would insert pieces of mail into different compartments inside the post office depending on the address, the machine could do that for them.

The Transorma multi-position letter sorting machine measured 13 feet high and was split across two levels. It carried mail on a conveyor belt from its lower level to a group of five postal workers at the upper level. The clerks would then use a keyboard to enter information about their destination. Based on the inputted information, the machine would then transport letters to different trays and drop them into chutes that brought them back to the lower level. But as the volume of mail increased in the years after World War II — going from 33 billion pieces of mail per year to 66.5 billion between 1943 and 1962 — the USPS needed a way to keep up.

For years, the USPS had depended on clerks to memorize dozens of delivery schemes that they would use to sort letters, preparing them for carriers to distribute throughout town. “That changed dramatically in 1963, [with] probably the biggest innovation the Postal Service has ever rolled out, called the ZIP code,” Kochersperger says. “For the first time, mailing lists could be digitized in computers and sorted in new ways.”

The ZIP code — short for Zone Improvement Plan — uses its first digit to indicate which region of the US a parcel is headed, the second and third to signal a nearby major city, and the final two to indicate a specific delivery area. The pace of innovation at the USPS ramped up following the introduction of the ZIP code, with many subsequent innovations building on its foundation.

The “Mr. Zip” character helped the USPS promote the ZIP code (1968).

Image: The United States Postal Service

 

That includes the USPS’s adoption of optical character recognition (OCR), a widely used technology that converts written or printed words into machine-readable text. In 1965, the USPS began to send large volumes of mail through OCR machines, allowing a “digital eye” to recognize addresses and automatically sort letters. If the machine couldn’t make out a person’s handwriting, the USPS would send an image to a remote encoding center (REC) for human review.

At one point, the USPS had as many as 55 RECs, but now only one remains in Salt Lake City, Utah. “As our computer systems have gotten better at recognizing handwriting, we’ve gotten to the point where it’s significantly reduced the number of letters that have to go to remote coding,” McKean says. Today, the USPS’s OCR technology can read handwritten mail at nearly 98 percent accuracy, while machine-printed addresses bump its accuracy to 99.5 percent.

That’s thanks to advances in machine learning, which the USPS, too, has been using in the background for more than 20 years; it first started using a handwriting recognition tool in 1999. The USPS is currently in the middle of a 10-year modernization plan, which includes investments in technology, such as AI. However, the plan has faced criticism for raising the price of stamps and causing service disruptions in some areas.

“The Postal Service is a driver of technological change,” McKean says. “It’s hard to overstate the amount of technology that the Postal Service has been involved in either popularizing or innovating over the last 250 years.”

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Posted Sunday 20 July 2025 at 6:26 am AEST (my time).

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For many years, scientists have tried using bacteria to repair these cracks automatically. But most of these methods need an external supply of nutrients to keep the bacteria working. Jin points out this major hurdle, saying, “Microbe-mediated self-healing concrete has been extensively investigated for more than three decades, but it still suffers from one important limitation—none of the current self-healing approaches are fully autonomous since they require an external supply of nutrients for the healing agents to continuously produce repair materials.”

Her solution takes cues from nature by recreating lichens, which are simple organisms made of fungi and cyanobacteria that survive on nothing more than air, sunlight, and water. Jin’s team designed a synthetic version using diazotrophic cyanobacteria, which absorb carbon dioxide and nitrogen from the air, and filamentous fungi, which help collect calcium ions and make calcium carbonate (CaCO₃), a mineral that can fill in concrete cracks.

They tested three microbe pairings: Trichoderma reesei with Anabaena inaequalis, T. reesei with Nostoc punctiforme, and T. reesei with both A. inaequalis and N. punctiforme. All three combinations grew well in a lab setup that had only air and light—no added nutrients. To see how well the microbes performed, the team used five methods: optical density to check light absorption, dry weight of biomass, resazurin assay for metabolic activity, fungal plating on selective media, and a phycocyanin test to check cyanobacteria health.

Results showed that the paired microbes were healthier and more productive than when grown alone. They were able to form CaCO₃ even in concrete samples, pointing to real-world potential. What makes this approach stand out is its ability to repair cracks without human assistance, which could one day reduce the need for expensive manual inspection and maintenance.

Jin is also working with social scientists at Texas A&M University to understand how the public feels about using "living" organisms in buildings, and to explore ethical and legal questions involved. The research, funded by DARPA’s Young Faculty Award program, brings biology and engineering together to solve a practical problem that affects millions of people.

Source: Texas A&M University, ScienceDirect

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Posted Sunday 20 July 2025 at 6:25 am AEST (my time).

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So, you’re driving a car at half the speed of light. (Both hands on the wheel, please.) You turn on the headlights. How fast would you see this light traveling? What about a person standing by the road? Would they see the light beam moving at 1.5 times the speed of light? But that’s impossible, right? Nothing is faster than light.

Yes, it seems tricky. The problem is, our ideas about the world are based on our experiences, and we don’t have much experience going that fast. I mean, the speed of light is 3 x 10⁸ meters per second, a number we represent with the letter c. That’s 670 million miles per hour, friend, and things start to get weird at extreme speeds.

It turns out that both the driver and the person on the road would measure the light as traveling at the same speed, c. The motion of the light source (the car) and the relative motion of the observers make no difference. Albert Einstein predicted this in 1905, and it’s one of the two main postulates behind his theory of special relativity.

Oh, it doesn’t sound so “special” to you? Well, what he then showed is that if the speed of light is a universal constant, then time is relative. The faster you move through space, the slower you move through time. The clock on a hyper-speed spaceship would literally tick slower, and if you were in that ship, you would age more slowly than your friends back home. That’s called time dilation.

A Commonsense Example

The idea that everyone sees light traveling at the same speed seems like common sense. But let’s look at a more familiar situation, and you’ll see that it’s not how things usually work. Say you’re driving at 10 meters per second, and someone in the car takes a tennis ball and throws it forward with a speed of 20 m/s. A bystander who happens to have a radar gun measures the speed of the ball. What reading do they get ?

Nope, NOT 20 m/s. To them the ball is moving at 30 m/s (i.e., 10 + 20). So much for common sense. The difference arises from the fact that they are measuring from different “reference frames,” one moving, the other stationary.

It’s all good, though; everyone agrees on the outcome. If the ball hits the person, the miscreants and the bystander would calculate the same time of impact. Yes, the people in the car see the ball moving at a slower speed, but they also see the bystander moving toward them (from their perspective), so it works out the same in the end.

This is the other main postulate of special relativity: The physics are the same for all reference frames—or to be specific, for all “inertial,” or non-accelerating, frames. Observers can be moving at different velocities, but those velocities have to be constant.

Anyway, now maybe you can see why it’s actually quite bizarre that the speed of light is the same for all observers, regardless of their motion.

Waves in an Empty Sea

How did Einstein get this crazy idea ? I’m going to show you two reasons. The first is that light is an electromagnetic wave. Physicists had long known that light behaved like a wave. But waves need a medium to “wave” in. Ocean waves require water; sound waves require air. Remove the medium and there is no wave.

But then, what medium was sunlight passing through as it traveled through space? In the 1800s, many physicists believed there must be a medium in space, and they called it the luminiferous aether because that’s fun to say.

In 1887, Albert Michelson and Edward Morley devised a clever experiment to detect this aether. They built a device called an interferometer, which split a beam of light in half and sent the halves along two paths of equal length, bouncing off mirrors, and merging again at a detector, like this:

Obviously they didn’t have a laser, but they had a similar light source. Now, if the Earth was moving through an aether as it circled the sun, that aether would change the speed of light, depending on whether the light was moving in the direction of Earth’s motion or at a right angle to that motion.

And here’s the genius part: They didn’t have to actually measure the speed of light, they only had to see if the two beams arrived at the detector at the same time. If there was any change in speed, the beams would be out of sync and would cancel each other when recombined. That interference would show up as a dark spot on the detector. If they moved at exactly the same speed, the sinusoidal waves would align and you’d see a bright spot.

They ran this experiment at all different times of year to get different angles with respect to the sun, but the result was always the same. There was no change in speed—which meant, sadly, that people had to stop saying “luminiferous aether.” Evidently, light waves could travel through a vacuum!

Maxwell’s Equations and Reference Frames

The reason for this, as proven by Heinrich Hertz, is that light is an electromagnetic wave—an oscillation of electric and magnetic fields perpendicular to each other. The changing electric field creates a magnetic field, and the changing magnetic field creates an electric field, and this endless cycle makes light self-propagating. It can travel through empty space because it's two waves in one.

Now for the rough part (mathematically). We know the relationship between the electric and magnetic fields—it’s described in Maxwell's famous four equations. If you use some math stuff (full details here), it's possible to write the following equations for the electric field (E) and the magnetic field (B). (If all these Greek symbols are Greek to you, just skip over this.)

All you need to know is that, together, these equations describe an electromagnetic wave. But wait! That's not all. If we plug in the values of μ₀ and ε₀—the fundamental magnetic and electric constants, respectively—you get a wave speed (v for velocity) that is exactly the speed of light:

Einstein used this to postulate that the speed of light was the same for all observers. How? Well, since we accepted that any one inertial reference frame is as valid as another, Maxwell's equations must work in both. That means the speed of light is the same in both reference frames—even if they’re in motion relative to one another. UNLIKE the tennis ball scenario above!

Time Dilation

Finally, imagine we build a clock to measure time. Not one of your grandfather’s clocks with a swinging pendulum, which would be a problem in zero gravity. Our clock is cooler than that. Basically we get two parallel mirrors and bounce a pulse of light back and forth between them.

If we know the distance between the mirrors (s) and the speed of the light (we do, it's c), then we can calculate the time for one tick.

Now assume our clock is in a spaceship with a big window, like in the movies. This spaceship is moving with a constant velocity that is half the speed of light (c/2) with respect to some nearby planet. Someone on that planet uses a telescope to look through the spaceship window and peek at the light clock. Here's what that planet person would see:

Notice that since the spaceship is moving, the light has to travel at an angle in order to hit the other spot on the opposite mirror. If we continued this, it would be a series of zigzags. Take a minute to think about that.

It’s like if you were riding in a bus and tossed a ball straight up and then caught it without moving your hand. In your reference frame, the ball just moves straight up and down. But to that guy on the street, the ball would trace out an arc, moving up and down but also forward.

In our light clock, since the light has to travel at an angle to hit the correct spot, it travels a farther distance. Oh, but that light still travels at the speed of light, so it takes more time to reach the other mirror. And if the spaceship is moving at a speed of c/2, that would be a lot more time. Result? As seen from the person on the planet, the spaceship clock ticks slower. There you have it: time dilation.

Does this mean that time goes slower for the people on the spaceship? Nope. In their reference frame the light just bounces up and down and time is normal.

Yes, it seems very weird, but it's not. It only seems weird because we never travel anywhere near the speed of light. In fact, time slows down in any moving vehicle—even when you get in your car and drive to work—but at normal speeds the effect is so tiny that it’s imperceptible.

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Posted Saturday 19 July 2025 at 6:17 am AEST (my time).

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This week, a federal court in Miami started hearing a wrongful death case involving Tesla's crash-prone Autopilot driver assistance system. It's not the first time that Tesla Autopilot has been implicated in fatal traffic crashes, but it is the first time that a federal court has heard such a case.

Until now, the most high-profile court case involving Tesla Autopilot was probably the California trial over the death of Walter Huang, who was killed in 2018 when his Tesla Model X steered itself into a concrete highway divider. Huang's family took Tesla to court in April 2024 but quickly settled with the automaker under terms that have been kept secret.

And earlier this week, Tesla settled another Autopilot lawsuit concerning the death of Jeremy Banner in 2019. In that case, the Tesla's sensors failed to recognize a tractor-trailer crossing the highway and collided with it, shearing the top off the car and killing Banner.

So far, Tesla has been unable to settle with the plaintiffs in the federal case, which also concerns a fatal crash in 2019 in Florida. In this case, in April 2019, a Tesla being driven by George McGee sped through a stop sign in the Florida Keys and struck Naibel Benavides and Dillon Angulo, who were standing near their car and stargazing. Benavides was killed, and Angulo was left with a brain injury.

"I feel like we were experimented on, and this technology was out on the road before it was safe," Angulo told an NBC interviewer in 2023.

What did the experts say?

Missy Cummings, a former fighter pilot and expert on autonomous systems, pulled few punches. Cummings, who received torrents of abuse from Tesla fans after being appointed to a senior advisor position at the National Highway Traffic Safety Administration in 2021, told the court that "it is my professional opinion that Tesla's Autopilot is defective because Tesla knowingly allows the car to be operated in operational domains for which it is explicitly not designed for."

Tesla's cockpit monitoring of drivers to ensure they remain alert "is simply not sufficient as keeping the driver engaged," Cummings told the court. And Tesla has a long history of "promoting the abuse and misuse of Autopilot." Cummings' testimony detailed her work at NHTSA investigating other fatal Tesla crashes, like the 2016 death of Joshua Brown, and detailed some of the interactions she and her agency had had with the car company.

For example, she said Tesla "clearly recognized that mode confusion is an issue—this is where people, for example, think the car is in Autopilot and don't understand that the Autopilot has disengaged," she told the court.

Cummings also referred to the deposition of Tesla autopilot firmware engineer Ajshay Phatak. Phatak's deposition told the court that the company did not keep good track of Autopilot crashes prior to 2018, and Cummings pointed out that "it was clear they knew that they had a big problem with people ignoring the warnings. Ignoring the hands-on requests. And...as you know, prior to this accident. It was known to Tesla that they were having problems with people ignoring their warnings."

Tesla's abuse of statistics to make misleading claims about safety is nothing new: In 2017, Ars found out that Tesla's claim about Autopilot reducing crashes was not at all backed by data, which, in fact, showed the driver assist increased crash rates.

Mendel Singer, a statistician at Case Western University School of Medicine, was very unimpressed with Tesla's approach to crash data statistics in his testimony. Singer noted that he was "not aware of any published study, any reports that are done independently... where [Tesla] actually had raw data and could validate it to see does it tend to make sense" and that the car company was not comparing like with like.

"Non-Teslas crashes are counted based on police reports, regardless of safety system deployment," Singer said. Further, Tesla kept misleading claims about safety on its website for years, Singer pointed out. When asked whether he would have accepted a paper for peer review from Tesla regarding its reports, "that would have been a really quick and easy rejection," he said.

While it's possible that Tesla will still settle this case, we may also see the trial carried out to its conclusion.

"The plaintiffs in this instance have already received compensation from the driver of the Tesla in question, apparently in a decent amount. My understanding is that this makes them much less likely to take the kinds of offers Tesla has been making for settlements, and this is more about the justice," said Edward Niedermeyer, author and long-time Tesla-watcher.

"That said, the judge in the case has made some frustrating rulings around confidentiality on key issues, so it's possible that may be in Tesla's favor. They could also just up their settlement offer enough to be impossible to refuse," Niedermeyer said.

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Posted Saturday 19 July 2025 at 6:16 am AEST (my time).

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Welcome to Edition 8.03 of the Rocket Report! We are at an interesting stage in Europe, with its efforts to commercialize spaceflight. Finally, it seems the long-slumbering continent is waking up to the need to leverage private capital to drive down the costs of space access, and we are seeing more investment flow into European companies. But it is critical that European policymakers make strategic investments across the industry or companies like PLD Space, which outlined big plans this week, will struggle to get off the launch pad.

As always, we welcome reader submissions, and if you don't want to miss an issue, please subscribe using the box below (the form will not appear on AMP-enabled versions of the site). Each report will include information on small-, medium-, and heavy-lift rockets, as well as a quick look ahead at the next three launches on the calendar.

Avio celebrates freedom from Arianespace. Representatives from Italy, Germany, and France met at the European Space Agency headquarters last week to sign the Launcher Exploitation Declaration, which officially began the transfer of Vega C launch operation responsibilities from Arianespace to the rocket’s builder, Avio, European Spaceflight reports. "It is a historic step that reinforces our nation's autonomy in access to space and assigns us a strategic responsibility towards Europe," said Avio CEO Giulio Ranzo. "We are ready to meet this challenge with determination, and we are investing in technologies, expertise, and infrastructure to ensure a competitive service."

A breaking of long-term partnerships ... In addition to securing control over the full exploitation of the Vega launch vehicle family, Italy, through Avio, is also investing in what comes next. The country has committed more than 330 million euros to the development of the MR60 methalox rocket engine and two demonstrator vehicles. These, along with the MR10 engine being developed under the Vega E programme, will support Avio’s preparation of a future reusable launch vehicle. Historically, France, Germany, and Italy have worked together on European launch vehicles. This appears to be another step in breaking up that long-term partnership toward more nationalistic efforts.

PLD Space outlines grand ambitions. PLD Space, Spain’s sole contestant in the European Launcher Challenge, unveiled its long-term strategy at the company’s Industry Days event this week, Payload reports. The company is targeting a production rate of 32 Miura 5 launchers annually by 2030. To achieve this output, PLD plans to deepen its vertical integration, consolidate its supplier network, and begin to serialize its manufacturing process beginning in 2027.

Building up the supply chain ... The company’s production plans also call for the parallel development of Miura Next, a heavy-lift vehicle capable of bringing 13 tons to orbit. However, the company will start with the Miura 5 vehicle, which PLD expects to launch for the first time from French Guiana in 2026. Since the beginning of 2024, PLD has invested a total of 50 million euros in its Miura 5 supply chain, consisting of 397 industrial partners, many of which are located in Spain and other European countries.  These plans are great, but sooner or later, the 14-year-old company needs to start putting rockets in space. (submitted by EllPeaTea)

New consortium will study space plane. A UK-based space and defense consultant group, Frazer-Nash, will lead a program to design a vehicle and its integrated systems with the goal of building and flying a Mach 5-capable aircraft at the edge of space by early 2031. This so-called INVICTUS program was funded with a 7 million-euro grant from the European Space Agency and is seen as a stepping stone toward developing a reusable space plane that takes off and lands horizontally from a runway.

Seeking to lead a new era of flight ... Over 12 months, INVICTUS has been tasked to deliver the concept and elements of the preliminary design of the full flight system. It will attempt to demonstrate the efficacy of hydrogen-fueled, precooled air-breathing propulsion at hypersonic speeds, technology that will ultimately enable horizontal take-off. "With INVICTUS, Europe is seizing the opportunity to lead in technologies that will redefine how we move across the planet and reach beyond it," said Tommaso Ghidini, head of the Mechanical Department at the European Space Agency. (submitted by Jid)

ESA backs North Sea launch site. A private company developing a launch site in the North Sea, EuroSpaceport, has secured support from the European Space Agency. The company, founded five years ago, is developing a sea-based launch platform built on a repurposed offshore wind turbine service vessel, European Spaceflight reports. Rockets are envisioned to launch from a position 50 to 100 km offshore from the port of Esbjerg, in Denmark.

Seeing the forest for the trees ... On Wednesday, EuroSpaceport announced that it had signed an agreement with the European Space Agency and Polish rocket builder SpaceForest to support the first launch from its Spaceport North Sea platform. The company will receive support from the agency through its Boost! Program. SpaceForest has been a recipient of Boost! funding, receiving 2.4 million euros in October 2024. SpaceForest said the mission will be used to verify the launch procedures of its Perun rocket under nominal suborbital conditions. (submitted by EllPeaTea)

Amazon and SpaceX, best frenemies? Maybe not, but for the time being, they appear to be friends of convenience. A Falcon 9 rocket launched from Florida's Space Coast early on Wednesday with a batch of Internet satellites for Amazon's Project Kuiper network, thrusting a rival one step closer to competing with SpaceX's Starlink broadband service. With this launch, Amazon now has 78 Kuiper satellites in orbit, Ars reports. The full Kuiper constellation will consist of 3,232 satellites to provide broadband Internet service to most of the populated world, bringing Amazon in competition with SpaceX's Starlink network.

Launch is not cheap ... Kuiper is an expensive undertaking, estimated at between $16.5 billion and $20 billion by the industry analytics firm Quilty Space. Quilty has concluded that Amazon is spending $10 billion on launch alone, exceeding the company's original cost estimate for the entire program. Amazon has booked more than 80 launches to deploy the Kuiper constellation, but the company didn't turn to SpaceX until it had to. A shareholder lawsuit filed in 2023 accused Amazon founder Jeff Bezos and the company's board of directors of breaching their "fiduciary duty" by not considering SpaceX as an option for launching Kuiper satellites. The plaintiffs in the lawsuit alleged Amazon didn't consider the Falcon 9 due to an intense and personal rivalry between Bezos and SpaceX founder Elon Musk. Amazon bowed to the allegations and announced a contract with SpaceX for three Falcon 9 launches in December 2023 to provide "additional capacity" for deploying the Kuiper network.

NASA targets end of July for Crew-11. NASA said Monday that it and SpaceX were targeting July 31 for the flight of SpaceX’s Crew-11 mission to the orbiting outpost, Spaceflight Now reports. The mission is led by NASA astronaut Zena Cardman. She will be flying along with fellow NASA astronaut Mike Fincke, Japan Aerospace Exploration Agency (JAXA) astronaut Kimiya Yui and Roscosmos cosmonaut Oleg Platonov.

Pushing Dragon reuse ... The mission was moved up from its previously planned August launch window to create more room in the manifest for the arrival of the Cargo Dragon flying the CRS-33 mission. That Dragon will perform a boost of the space station as a demonstration of some of the capabilities SpaceX will use on its US Deorbit Vehicle currently in work. Crew-11 will fly to the orbiting outpost on Crew Dragon Endeavour, which will be its sixth trip to the ISS. This will be the first Crew Dragon spacecraft to fly for a sixth time.

SpaceX won't use Johnston Atoll for rocket cargo tests. Johnston Atoll, an unincorporated US territory and Pacific island wildlife refuge with a complicated military history, will no longer become a SpaceX reusable rocket test site, Popular Science reports. "The Department of the Air Force has elected to hold the preparation of the Johnston Atoll Environmental Assessment for a proposed rocket cargo landing demonstration on Johnston Atoll in abeyance while the service explores alternative options for implementation," Air Force spokesperson Laurel Falls said.

Taking a toll on the atoll ... Located roughly 860 miles southwest of Hawaii, Johnston Atoll has served as a base for numerous US military operations for over 90 years. Despite this, the atoll remains a home for 14 tropical bird species as part of the Pacific Remote Islands Marine National Monument. The site had been under consideration for tests as part of a military program to deliver cargo around the planet, using suborbital missions on rocket such as SpaceX's Starship vehicle. The Johnston Atoll plans included the construction of two landing pads that were met with public backlash from wildlife experts and indigenous representatives. (submitted by Tfargo04)

Blue Origin confirms ESCAPADE is up next. On Thursday, Blue Origin said on social media that the second launch of its New Glenn rocket will carry NASA's ESCAPADE mission as its primary payload. This launch will support ESCAPADE’s science objectives as the twin spacecraft progress on their journey to the Red Planet. Also onboard is a technology demonstration from @Viasat in support of @NASASpaceOps' Communications Services Project.

Left unsaid was when the launch will occur ... The social media post confirms a report from Ars in June, which said the ESCAPADE spacecraft was up next on New Glenn. Previously, the company has said this second launch will take place no earlier than August 15. However, that is less than one month away. Late September is probably the earliest realistic launch date, with October or November more likely for the second flight of the company's large rocket.

Next three launches

July 19: Falcon 9 | Starlink 17-3 | Vandenberg Space Force Base, California | 03:44 UTC

July 21: Falcon 9 | O3b mPOWER 9 & 10 | Cape Canaveral Space Force Station, Florida | 21:00 UTC

July 22: Falcon 9 | NASA's Tandem Reconnection and Cusp Electrodynamics Reconnaissance Satellites | Vandenberg Space Force Base, California | 18:05 UTC

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Posted Saturday 19 July 2025 at 6:15 am AEST (my time).

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Sixty thousand years ago, two groups of Neanderthals lived just a stone’s throw apart in what’s now northern Israel. But they had very different cultures when it came to food, according to a recent study. Archaeologist Anaëlle Jallon of Hebrew University of Jerusalem and her colleagues examined dozens of animal bones from both sites, looking for clues about Neanderthal meal prep. It turns out that something as mundane as the cut marks left by butchering an animal can reveal differences in ancient people’s way of life.

What did Neanderthals eat? It depends.

The Neanderthals who lived around the Sea of Galilee between 70,000 and 50,000 years ago had their pick of meat entrees on the hoof. The area was home to several species of deer, from tiny roe deer to larger red deer, along with gazelles, wild goats, boar, and larger game like aurochs and relatives of modern horses. For Neanderthal hunters equipped with wood and stone hunting tools, the place was a veritable buffet. And you might expect that one group of Neanderthals would eat pretty much the same things as any others in the area.

However, what Jallon and her colleagues found in their recent study looks more like the Pleistocene version of New York and Chicago having very different styles of pizza: same ingredients, different ways of using them.

One group of Neanderthals lived in a cave now called Kebara on the western flanks of Mount Carmel, while the other lived in a cave now called Amud on a steep cliff overlooking the valley floor. They may not have lived at the same moment; the range of dates for both sites spans about 20,000 years. But both were just a few kilometers from the Sea of Galilee, living in very similar environments populated by the same plants and wildlife, and they used very similar stone tool technology.

Jallon and her colleagues looked at the piles of animal bones archaeologists have unearthed at both sites. They noted which bones, from which types of game, tended to show up with cut marks more often. They also noted how many cut marks the bones tended to bear and what those marks looked like. And their study revealed that Neanderthals just 70 kilometers apart were hunting slightly different prey and choosing different cuts of meat—and one group apparently liked their meat a little fresher than the other.

Gazelle prepared “a la Amud,” or “a la Kebara”?

Neanderthals at Kebara had pretty broad tastes in meat. The butchered bones found in the cave were mostly an even mix of small ungulates (largely gazelle) and medium-sized ones (red deer, fallow deer, wild goats, and boar), with just a few larger game animals thrown in. And it looks like the Kebara Neanderthals were “use the whole deer” sorts of hunters because the bones came from all parts of the animals’ bodies.

On the other hand (or hoof), at Amud, archaeologists found that the butchered bones were almost entirely long bone shafts—legs, in other words—from gazelle. Apparently, the Neanderthal hunters at Amud focused more on gazelle than on larger prey like red deer or boar, and they seemingly preferred meat from the legs.

And not too fresh, apparently—the bones at Kebara showed fewer cut marks, and the marks that were there tended to be straighter. Meanwhile, at Amud, the bones were practically cluttered with cut marks, which crisscrossed over each other and were often curved, not straight. According to Jallon and her colleagues, the difference probably wasn’t a skill issue. Instead, it may be a clue that Neanderthals at Amud liked their meat dried, boiled, or even slightly rotten.

That’s based on comparisons to what bones look like when modern hunter-gatherers butcher their game, along with archaeologists’ experiments with stone tool butchery. First, differences in skill between newbie butchers and advanced ones don’t produce the same pattern of cut marks Jallon and her colleagues saw at Amud. But “it has been shown that decaying carcasses tend to be more difficult to process, often resulting in the production of haphazard, deep, and sinuous cut marks,” as Jallon and her colleagues wrote in their recent paper.

So apparently, for reasons unknown to modern archaeologists, the meat on the menu at Amud was, shall we say, a bit less fresh than that at Kebara. Said menu was also considerably less varied. All of that meant that if you were a Neanderthal from Amud and stopped by Kebara for dinner (or vice versa) your meal might seem surprisingly foreign.

Each group’s table manners may have been a little surprising to the other, too. Most of the animal bones at Amud were broken into fragments and appeared to have been burned, while most of the ones at Kebara were intact and unburned. That may suggest differences in cooking, or it may suggest differences in what people did with the bones after they’d cut all the meat off: tossing them in a corner of the cave instead of tossing them in the fire.

Again, the whole experience is perhaps a little like what New Yorkers must feel when they order a pizza in Chicago, or the other way around, or what Texans experience when they order brisket anywhere else.

Local tools for local cuisine

So two groups of Neanderthals managed to have strikingly different food cultures over a distance of just a few miles. And stone tools found in both caves suggest the cultural differences weren’t limited to eating habits.

Microscopic images of the cuts on butchered animal bones at both sites look similar, which suggests that the work was done with the same basic types of tools. And that’s mostly true; Neanderthals in both places used broadly similar techniques to make similar tools: triangular flakes and points, knapped from flint. Archaeologists who study stone tools, though, noticed some subtle differences that suggest Neanderthals at each site developed their own signature styles.

“The stone tools from Kebara and Amud show some technological variations interpretable as local traditions or accumulated through social learning,” Jallon and her colleagues wrote in their recent paper.

In other words, Neanderthals weren’t a cultural monolith (or a monolithic culture—not sorry). Their cultures were more complex and diverse than we’ve previously realized, and that shows up in subtle ways at different sites. It’s fascinating to speculate about how much more we could see about these extinct cultures if things like textiles, leather, and plants had survived better over millennia.

Frontiers in Environmental Archaeology, 2023 DOI: 10.3389/fearc.2025.1575572; (About DOIs).

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Posted Saturday 19 July 2025 at 6:14 am AEST (my time).

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A new drug usually starts with a tragedy.

Peter Ray knows that. Born in what is now Zimbabwe, the child of a mechanic and a radiology technician, Ray fled with his family to South Africa during the Zimbabwean War of Liberation. He remembers the journey there in 1980 in a convoy of armored cars. As the sun blazed down, a soldier taught 8-year-old Ray how to fire a machine gun. But his mother kept having to stop. She didn’t feel well.

Doctors in Cape Town diagnosed her with cancer. Ray remembers going to her radiation treatments with her, the hospital rooms, the colostomy bags. She loved the beach, loved to walk along the line where the water met the land. But it got harder for her to go. Sometimes she came home from the hospital for a while and it seemed like things would get better. Ray got his hopes up. Then things would fall apart again. Surgery, radiation, chemotherapy—the treatments that were on the table in the 1980s—were soon exhausted. As she lay dying, he promised her he was going to make a difference, somehow. He was 13 years old.

Ray studied to become a medicinal chemist, first in South Africa, taking out loans to fund his studies, then at the University of Liverpool. He worked at drug companies across the UK, on numerous projects. Now, at 53, he is one of the lead drug designers at a pharmaceutical company called Recursion. He thinks about that promise to his mom a lot. “It’s lived with me my whole life,” he says. “I need to get drugs on the market that impact cancer.”

The desire to stop your own tragedies from happening to someone else may be a strong motivator. But the process of drug discovery has always been grindingly, gruelingly slow. First, chemists like Ray zero in on their target—usually a protein, a long string of amino acids coiled and folded upon itself. They call up a model of it on their computer screen and watch it turn in a black void. They note the curves and declivities in its surface, places where a molecule, sailing through the darkness like a spaceship, could dock. Then, atom by atom, they try to build the spaceship.

When the new molecule is ready, the chemists pass it along to the biologists, who test it on living cells in warm rooms. More tragedy: Many cells die, for reasons that are not always clear. Biology is complex, and the new drug doesn’t work as expected. The chemists will have to create another, and another, tweaking, adjusting, often for years. One biologist, Keith Mikule of Insilico Medicine, told me of his experience at a different drug company. After five years of work, their best molecule had unforeseen, dangerous side effects that meant they could take it no further. “There was a large team of chemists, a large team of biologists, thousands of molecules made, and no real progress,” he said.

If a team is very lucky, they get a molecule that, in mice, does what it’s supposed to. They get a chance to give it to a small group of healthy human volunteers, a phase I trial. If the volunteers stay healthy, then they give it to more people, including those with the disease in question, in a phase II. If the sick people don’t get sicker, they get a chance—phase III—to give it to more sick people, as many as they can find, as diverse a group as possible.

At each stage, for reasons few people understand and fewer can predict, great rafts of drugs drop out. More than 90 percent of hopefuls fail along the way. When you meet drug hunters, you might ask them, cautiously, tenderly, if they’ve ever had a drug make it. “It’s very rare,” says Mikule, who has one drug (niraparib, for ovarian cancer) to his name. “We’re unicorns.”

But Mikule, Ray, and other chemists and biologists are trying a new approach. When I talk to Ray, he’s excited to show me a molecule he and his colleagues at Recursion have been working on. It’s a so-called MALT1 inhibitor, designed to interfere with the growth of blood cancer cells. On his screen, REC-3565 is a series of rings and lines, another skeletal spaceship floating in the void. But it exists in the real world too: Just a few weeks before my chat with Ray, the first phase I volunteers swallowed it in a little pill. What’s special about this molecule, Ray says, isn’t just that it has survived the gauntlet thus far. It’s that REC-3565 “wouldn’t have come by human design.” Ray’s team, he believes, would not have made the logical leaps required to reach this point without using artificial intelligence.

As the world’s pharma giants get caught up on AI, Recursion is among a group of startups betting everything on the technology. Founded 12 years ago by academics in Utah, the company made its name by taking snapshots of cells under various conditions, creating a vast database of pictures, and turning AI on them to identify potential new targets. Last year, Recursion acquired another decade-old startup—Ray’s former employer, Exscientia—which pioneered the use of AI to design small molecules. There are others, including Mikule’s employer Insilico, which was founded in 2014. Just last year, Xaira Therapeutics launched with $1 billion in venture capital—the biggest biotech funding round in years. (The only other new startup that pulled in as much in 2024 was Safe Superintelligence, cofounded by a former top OpenAI researcher.)

Pipetting robots at work at Recursion’s automated lab in Oxford, England.

Courtesy of Recursion

 

There are no drugs on the market designed using AI. But both Recursion and Insilico have gotten candidates through phase II clinical trials, which means they’re safe in patients. REC-994 is for cerebral cavernous malformation, a disease that causes brain lesions, and ISM001-055 is for idiopathic pulmonary fibrosis, a progressive, fatal lung condition. More AI-linked drug candidates are in development, from Insilico, Recursion, and other companies, including the one Ray showed me.

All of these molecules, right now, are like cards lying face down on the table. Can AI help make drugs that actually work, faster and cheaper than usual, or are the drug hunters about to be dealt another losing hand?

In the summer of 1981, a headline on the cover of Fortune magazine proclaimed that the age of digital drug discovery was at hand. The story explored how scientists were using computer visualization to select the best molecules to try in cells, hoping to break through the gridlock. Derek Lowe, a medicinal chemist who writes the long-running blog In the Pipeline, recalls that the Fortune article made some drug hunters at the time nervous. At the pharmaceutical company Schering-Plough, where he worked, there was a room labeled “Computer-Aided Drug Discovery (CADD),” packed with expensive equipment. “The medicinal chemists across the hall didn’t think too much of that,” Lowe told me, “so they put a sign over their door that said ‘BADD: Brain-Assisted Drug Discovery.’”

Computers did revolutionize everything. But the hard problems of drug discovery didn’t evaporate with the touch of a cursor. Seasoned drug hunters refer in a jaundiced tone to combinatorial chemistry, an attempt to stumble across new kinds of drugs by assembling molecular pieces in random order. (It didn’t work, in part because the costs of such a wildly democratic approach were crippling.) Computational chemistry, which allows scientists to simulate how a target and a molecule will interact, gained grudging acceptance—but its success depends on accurate models of the target and the candidates, and for that you need old-fashioned elbow grease in the lab.

If anything, the hard problems have grown harder as the full complexity of biology has come into focus. “We have more things to worry about than we used to,” says Lowe. Cancers with different mutations driving them respond to different therapies. Drugs that attached to a certain receptor were linked to heart problems, and thus any new drug candidate, no matter how promising, must be removed from the running if it shows affinity for that receptor.

Karen Billeci, a principal biologist at Recursion, still remembers one of the first times she heard a drug hunter mention artificial intelligence. One dawn in 1993, Billeci was walking across her company’s parking lot on the edge of the San Francisco Bay with a couple of other employees. They worked at a scrappy startup called Genentech (later acquired by Roche for $47 billion). Billeci’s programmer friends were exploring whether neural networks—a form of machine learning—could be used to find patterns in patient information and help reveal why some responded to a drug and others didn’t. “These great drugs would go into humans, and they would fail,” Billeci says. They talked in the parking lot about whether, someday, there would be software that could learn to see patterns they couldn’t. “We didn’t say ‘train,’” Billeci recalls. “We didn’t have the words for that yet.”

It gradually became clear, over the next several decades, that AI might do more than pick out patterns in patient data. In 2020, something happened that crystallized what might be possible. In a global competition that fall, an AI built by Alphabet’s DeepMind showed it could correctly predict how a protein would fold up into its final form—a canonical hard problem in biology and a key task for drug hunters. DeepMind’s AI easily beat out all the other contestants. David Baker, a biochemist at the University of Washington, was inspired to dig deeper into using AI to design new drug proteins, work that later won him the 2024 Nobel Prize for Chemistry. “It didn’t take us long to develop methods that surpassed the ones we had been developing before,” he says. (Baker is one of the founders of Xaira.)

After that, what else might be possible with AI? What if it were shown all the drugs that have ever existed, with all the data about how they work, and then set loose on a database of untried molecules to identify others to explore? What if—and this is where the discussion around machine learning has gotten to now, in 2025—the software could take in a decent chunk of all the information about biology generated by humankind and, in an act both spooky and profound, suggest entirely new things?

Sometimes, humanity is learning, AI produces things that look good at first glance but turn out to be whimsical potpourris of words or thoughts, mere nothingburgers. The fact that drug discovery involves extensive real-life testing makes it unlikely that such suggestions would survive the process. The biggest risk of AI hallucinations might be wasted time and resources. But the failure rate of new drugs is already so high that scientists at these startups think the risk is worth taking.

Peter Ray looks at the MALT1 inhibitor floating in the void. “If I get a drug to market, I would feel I had fulfilled my promise,” he says. He points out where the AI revealed a way to remove a section of the molecule that could cause toxicity. It was a reaction that had not occurred to any of the humans involved.

The real question is whether molecules designed using AI are any better at getting to market. The last few stages of the process are the most expensive, the most unpredictable. In any clinical trial, it’s hard to find the right people, says Carol Satler, the vice president of clinical development at Insilico. It’s slow. She worries about it—hopes she has made the right choices, contacted the right doctors, excluded the people who would not benefit, included those who might, to see what the drug can do. By the time a drug reaches trials, it represents a billion dollars and a decade in the lives of hundreds, if not thousands, of scientists. One patient signs up. Then two. Months pass. Time crawls. “The meter is always running,” Satler says. “It’s so expensive.”

Late last year, soon after Recursion finalized its acquisition of Exscientia, 300-odd drug hunters from both companies converged on an event space in London.

The pink-lit conference hall buzzed with news of an announcement made just days before by Recursion’s chief scientific officer. Molecule REC-617, developed by Exscientia, had been given to 18 patients whose terminal cancers had stopped responding to other treatments. The phase I clinical trial was designed to see both whether patients could tolerate the drug candidate and whether it had any effect. One patient—a woman with ovarian cancer that had come back three times—surprised everyone: She lived. She was still alive after six months of the treatment. Because the trial is blinded, no one at Recursion or Exscientia has any idea who this woman is and whether she is still alive today. But in that room, she seemed to radiate with life.

The announcement contained another noteworthy detail. Because Exscientia used AI to narrow down the number of candidate molecules before any of them were made, it was not thousands but a mere 136 that were finally manufactured and tested in cells. (Ray’s MALT1 inhibitor involved making only 344, also a tiny fraction of what would have happened in a traditional setting.) Chris Gibson, Recursion’s cofounder and CEO, underscored that number in his talk to the assembled crowd, emphasizing the savings in time and resources. By failing faster, goes the logic—by using AI not only to invent new molecules but also to rule most of them out in advance—it might be possible to bring down the cost of the first stages of this extremely costly process.

In the center’s lobby, a breakout group with David Mauro, Recursion’s chief medical officer, Jakub Flug, an Exscientia medicinal chemist, and a handful of others stood in a circle. The employees were having what amounted to an enormous blind date. They were meeting people they’d never seen in person, telling their stories, trying to see how they would all fit together. They took turns introducing themselves and saying why they had chosen to join these companies. One person said: I’m here to have fun. Another said: I’m here because I was tired of doing something that I didn’t believe in anymore. Another: I am here because I want to actually release a drug onto the market. Everyone nodded at this one.

Downstairs, in a basement room, Gibson was thinking about the future too. His hope is that Recursion is laying the groundwork for what drug discovery will someday be like across the industry, starting with the eight drugs that have advanced to clinical trials and the handful behind them, in the preclinical stage. “If we’re doing this right, if we’re building a learning system, the next 10 drugs after that have a higher probability of success. Next 10 drugs after that, higher probability of success. We keep refining this thing,” he said.

I asked him about his claim, last summer, that there would soon be information about 10 or so different candidates. This critical mass, with information going public in a large bolus, is a calculated goal, he said: If around 90 percent of drugs fail, then Recursion needs to show results of about 10 different programs just to see if they are doing what they hope. “At the end of the day, it’ll be fair to judge us by the first 10,” Gibson says. “That’s enough of an n.” Enough of a sample size, in other words, to see what this approach can do.

Inside Recursion’s lab in Oxford, England.

Courtesy of Recursion

 

One cold morning late last year, I went to see one of Recursion’s discovery engines. Patrick Collins, the director of automation, and Su Jerwood, a principal scientist in pharmacology, showed me into a room the size of a small supermarket with aisles of machines in plate-glass cases. White lamps like halos hung above them. “We’ve got biology on one side, chemistry on the other,” Collins said. A magnetic railway threaded through the machines, connecting pipetting robots to incubator chambers. “It’s about design, make, test, learning, loop,” Collins said. He indicated cases of bottles and powders, “all the building blocks, reagents and things.” Humans keep the machines topped up.

These machines, Jerwood explained, dispense molecules created from raw atoms, molecules that AI systems have already tested and explored in virtual spaces. The candidate drugs drip onto trays of cells, and the system evaluates their effects. It’s new, and there are kinks to be worked out. Some parts of the automated process still need humans to move them along, Collins said, and Recursion is figuring out how to streamline the flow of information to and from the AI. But when it works, scientists will have the results of thousands of tests glowing on their screens. The automated system has been up and running for about a year, so it wasn’t involved in making the candidates currently in clinical trials. But it is helping to make future drugs.

As I examined the machines in their pristine chambers, I wondered about what it means, now, to be the kind of human who loves to think about molecules, loves to make them, whose joy comes from understanding how they work. I asked Collins this. He thought back to the moment when he first crystallized a protein by hand, first saw a drug molecule clasped against it. “I was hooked for life,” he said. Those traditional tools still have their place. But perhaps it is not here, where the focus is getting something to the clinic as fast as possible, something that works. “We’re all trying to think about patients,” Collins said.

Jerwood gave her answer: “I am so hungry for something new all the time.” Standing there, above the automated lab, she imagined the regions of chemistry where no one has yet gone, structures and reactions that lie on the far side of unknown processes. The sun was just pulling itself over the horizon. She thought of all the things the machines might do, all the things that she will do no longer. “It’s down to the untouched space, yeah? Because then I will have time to look into that space,” she said. “I will have time to take that risk.”

For some pharmaceutical researchers, though, the promise of AI goes beyond pushing scientific boundaries or even treating disease. Alex Zhavoronkov, the CEO and cofounder of Insilico, says the company favors targets that are implicated in both illness and aging. Its drug candidate for idiopathic pulmonary fibrosis, for example, is designed to prevent scarring of the lungs by dampening certain biological pathways, but it also may slow the aging of healthy cells. Zhavoronkov hopes to bring new drugs to the clinic, perhaps faster and cheaper, even as he uncovers new treatments for aging-related disease and decline.

When I speak with Zhavoronkov, he’s at a company-wide retreat in Chongqing, China. “In 20 years, I’m going to be 66,” he says. “I saw my dad when he was 66, and it’s not pretty.” He is frank about having high expectations, about his desire for speed in an industry where speed isn’t always readily available. He shows me a video of an automated lab in Suzhou, China. “We built it during Covid,” he says, explaining that some of the laboratory scientists on the project worked around the clock, sleeping in the facility, to get it up and running.

There is something vaguely science-fictional about the setup, and about Zhavoronkov’s particular form of pragmatism. Zhavoronkov has scars on his arm where he’s had skin removed to make induced pluripotent stem cells, which can be reprogrammed to grow into many types of tissue. “If you want to buy my IPSC, give us a call. We’ll ship it to you,” he says. “The more data there is about you in the public domain, the higher chances you have to get a real good treatment when you get sick, especially with cancer.”

In the lab video, the camera glides through a black hallway, then through an anteroom, past a wall of glass. The glass can be dimmed, if the work going on behind it is confidential. Behind the wall are machines loaded with trays of reagents and cells, with arms that swivel as they move components around. Humans are rarely needed.

Sooner or later, in some form, AI tools will be standard in drug discovery, suspects Derek Lowe, the medicinal chemist and blogger. He calls himself a short-term pessimist, long-term optimist about these things. It’s happened again and again in the industry: New strategies arrive, ride a wave of hype, crash and burn. Then some of them, in some form, rise again and quietly become part of what’s normal. Already, big pharmaceutical companies—the behemoths of drug discovery—are starting their own AI-related research groups. Recursion, meanwhile, is exploring the use of AI not only to dream up and test new molecules but also to find trial participants, speeding along those last, costliest steps to market.

The transformation isn’t going to be without casualties. “These techniques, both the automation part and the software, are going to make more and more things slide into that ‘humans don’t do that kind of grunt work’ category,” Lowe says. Large numbers of jobs held by human chemists will wink out of existence. Those “who know how to use the machines are going to replace the ones who don’t,” Lowe says. Even Peter Ray no longer feels it’s accurate to describe him as a medicinal chemist. “I’m something else,” he muses. “I don’t know what to call it, to be honest.”

In the months since the blind date in London, Recursion has announced two drug candidates entering clinical trials, the MALT1 inhibitor and a molecule for lung cancer. A drug for a digestive disease is already in trials. Insilico is in the process of trying to advance to a phase III trial for its idiopathic pulmonary fibrosis drug, with Carol Satler on the phone to doctors. The cards are being turned over, one by one. Ray goes running sometimes, through his neighborhood near Dundee, Scotland, and thinks of his mother.

Gibson reflected on the long game he sees Recursion playing. The way it’s tinged with urgency. Yes, they want to change the world. And personally, he thinks it’s been too long in coming. “There’s a lot of people here who have lost a loved one or multiple loved ones to a specific disease,” he said. “They’re pissed off. They’re here because they want to get revenge on the lack of opportunity that that family member, or friend, or child, had.” The meter is ticking, numbering the days, as drugs move through trials and everyone waits to see what happens.

Time is the thing we are all running out of. Some of us faster than others.

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Posted Friday 18 July 2025 at 4:26 am AEST (my time).

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Per- and polyfluoroalkyl substances (PFAS)—valued for their exceptional thermal stability, chemical inertness, low dielectric permittivity, and oil/water repellency—are crucial to critical technology sectors. In semiconductor fabrication, PFAS-based photoresists, etchants, and vapour-deposition chamber linings enable nanometre-scale patterning. Aerospace and defence employ PFAS hydraulic fluids, fuel-line coatings, and sealants to withstand extreme temperatures and pressures.

In high-frequency electronics, PFAS insulators boost signal integrity. Aqueous film-forming foams (AFFF) depend on PFAS for rapid fire suppression at airports and petrochemical facilities. Automotive applications include fluid-pump films and brake-system seals. Renewable energy systems—from photovoltaic modules to wind-turbine surface coatings—leverage PFAS for weather resistance, anti-reflectivity, and corrosion protection. Persistent environmental and health impacts have prompted stringent U.S. EPA and European regulations, driving research into fluorine-free materials and PFAS remediation technologies

The key to PFAS' staying power is the carbon–fluorine (C–F) bond, one of the toughest chemical links out there. Breaking it usually needs high temperatures or strong chemicals, but this new method works at room temperature, and without using expensive or toxic metals like platinum or iridium.

The breakthrough centers around a boron-based structure called 9,10-dihydro-9,10-diboraanthracene (DBA). When two electrons are added to DBA, it becomes reactive enough to attack PFAS-like molecules. The team tested this on fluorobenzenes (C₆FₙH₆₋ₙ) in a solvent called THF (Tetrahydrofuran), using versions with anywhere from 1 to 6 fluorine atoms.

Their studies showed the catalyst works in two main ways:

• When there are fewer fluorines, it behaves like a boron-based nucleophile, attacking the molecule in an SNAr-type reaction that helps break covalent bonds like that of carbon with a halogen (like Chlorine in this case).
• When there are more fluorines, it acts as a reducing agent, donating electrons and pulling off hydrogen atoms.

Doctoral researcher Christoph Buch put it simply: “To break C–F bonds, we need electrons, which our catalyst transfers with exceptional efficiency. So far, we’ve been using alkali metals like lithium as the electron source, but we’re already working on switching to electrical current instead. That would make the process both much simpler and more efficient.”

The team also sees promise beyond PFAS cleanup. Many medicines include fluorine to make them last longer or work better. Professor Matthias Wagner explained: “With this catalyst, we now have a tool that allows us to precisely control the degree of fluorination in such compounds.”

This discovery could offer a safer and more flexible way to deal with PFAS pollution—and may help fine-tune the design of future pharmaceuticals.

Source: Goethe University Frankfurt, American Chemical Society

This article was generated with some help from AI and reviewed by an editor. Under Section 107 of the Copyright Act 1976, this material is used for the purpose of news reporting. Fair use is a use permitted by copyright statute that might otherwise be infringing.

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Posted Thursday 17 July 2025 at 1:30 pm AEST (my time).

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The new battery uses radioactive carbon-14 nanoparticles and quantum dots (14CNP/CQD) as electrodes. These were embedded into the device along with a perovskite film treated with dual chlorine-based additives: methylammonium chloride (MACl) and cesium chloride (CsCl). These additives helped strengthen the crystal structure of the perovskite, making it more stable and better at moving electric charges. Compared to older designs, the team recorded a roughly 56,000-fold improvement in electron mobility and a maximum continuous operation of nine hours during testing.

“This study represents the first successful integration of perovskite into a betavoltaic cell, pioneering perovskite betavoltaic cells,” the researchers stated.

Betavoltaic cells work by turning beta particles—emitted during radioactive decay—into electricity. Since beta rays can’t penetrate human skin and can be blocked by materials like aluminum, the technology is considered biologically safe. Professor In explained, “I decided to use a radioactive isotope of carbon because it generates only beta rays.” Carbon-14 is also a by-product from nuclear reactors, making it cheap, widely available, and recyclable. Because it breaks down very slowly, it could power devices for hundreds or even thousands of years.

To raise energy conversion efficiency—the measure of how well a battery turns electrons into usable power—the team turned to a titanium dioxide semiconductor, often found in solar cells, and enhanced it with a ruthenium-based dye. The bond between the dye and titanium dioxide was made stronger through a citric acid treatment. When beta rays from radiocarbon hit the dye, they triggered a chain of electron reactions known as an avalanche. These electrons were then captured by the titanium dioxide and sent through a circuit to generate electric current.

The battery was also designed with radiocarbon in both the anode and cathode, increasing the amount of beta radiation and reducing energy loss over distance. This approach helped boost its energy conversion efficiency from 0.48% in older models to 2.86%.

Still, the system only converts a small portion of radioactive energy into electricity, meaning its output remains lower than standard lithium-ion batteries. Professor In suggests improving the shape of the beta emitter and finding better absorbers could further raise power output.

“This research marks the world’s first demonstration of the practical viability of betavoltaic cells,” said In. “We plan to accelerate commercialization of next-generation power supply technologies for extreme environments and pursue further miniaturization and technology transfer.”

Doctoral student Junho Lee added, “Although this research involves daily challenges that often seem impossible, we are driven by a strong sense of mission, knowing that the future of our nation is closely tied to energy security.”

The team believes that, with further development, these radiocarbon-powered batteries could be used in areas ranging from pacemakers to space probes and drones. As In puts it, “We can put safe nuclear energy into devices the size of a finger.”

Source: DGIST, American Chemical Society, Royal Society of Chemistry

This article was generated with some help from AI and reviewed by an editor. Under Section 107 of the Copyright Act 1976, this material is used for the purpose of news reporting. Fair use is a use permitted by copyright statute that might otherwise be infringing.

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Posted Thursday 17 July 2025 at 1:28 pm AEST (my time).

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General Motors and Redwood Materials are joining forces yet again, this time with the intent to build energy storage units made out of new batteries and recycled EV packs.

The two companies signed a non-binding memorandum of understanding to build energy storage out of US-manufactured batteries, as well as “second-life” EV packs from GM’s vehicles. The announcement comes on the heels of Redwood’s decision to move more aggressively into the energy storage business with the creation of a new division. The company’s first project will be building a storage system for an AI development center in California.

Battery storage systems play a crucial role in balancing energy for the grid. These systems can store energy from a variety of sources, including renewables like wind and solar, releasing it when needed, which helps save power during periods of low demand.

The rise of AI is putting increasing pressure on the grid, in the US and globally. The steepest rise in global electricity demand is coming from new data centers in the US and China, as well as the manufacturing of electric vehicles, batteries, solar panels, and semiconductors.

GM has a preexisting partnership with Redwood to recycle scrap from its battery manufacturing facilities in Warren, Ohio, and Spring Hill, Tennessee, as well as end-of-life EV batteries . The automaker says this new deal will help power its ambitions to expand beyond EV batteries and into grid management and energy storage. GM has its own energy division that sells power banks, charging equipment, solar panels, and management software to residential and commercial customers.

“The market for grid-scale batteries and backup power isn’t just expanding, it’s becoming essentialinfrastructure,” said Kurt Kelty, GM’s VP of batteries, propulsion, and sustainability, in a statement. “Electricity demand is climbing, and it’s only going to accelerate. To meet that challenge, the U.S. needs energy storage solutions that can be deployed quickly, economically, and made right here at home. GM batteries can play an integral role.”

Redwood Materials was founded in 2017 by Tesla’s former chief technologist JB Straubel. In addition to breaking down scrap from Tesla’s battery-making process with Panasonic, Redwood recycles batteries from Ford, Toyota, Nissan, Specialized, Amazon, Lyft, Rad Power Bikes, Lime, stationary storage facilities, and others. The company also produces cathodes at a facility in Nevada, and eventually at its under-construction site in South Carolina.

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Posted Thursday 17 July 2025 at 5:09 am AEST (my time).

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To paraphrase Mean Girls, "stop trying to make hydrogen happen."

For some years now, detractors of battery electric vehicles have held up hydrogen as a clean fuel panacea. That sometimes refers to hydrogen combustion engines, but more often, it's hydrogen fuel cell electric vehicles, or FCEVs. Both promise motoring with only water emitted from the vehicles' exhausts. It's just that hydrogen actually kinda sucks as a fuel, and automaker Stellantis announced today that it is ending the development of its light-, medium- and heavy-duty FCEVs, which were meant to go into production later this year.

Hydrogen's main selling point is that it's faster to fill a tank with the stuff than it is to recharge a lithium-ion battery. So it's a seductive alternative that suggests a driver can keep all the convenience of their gasoline engine with none of the climate change-causing side effects.

But in reality, that's pretty far from true.

It's not nearly as fast as using room-temperature gasoline or diesel—when Toyota raced a hydrogen-powered car several years ago, it took up to seven minutes to fill the Corolla's tanks with gas pressurized to 70 MPa. When it tried again a few years later, it switched to cryogenic fuel, which had to be kept at a chilly -253° C. Neither sounds particularly practical.

Hydrogen is also much less energy-dense by volume, and making the stuff is far from efficient, even when you use entirely renewable electricity. And of course, the vast majority of commercial hydrogen is not so-called blue hydrogen, which was made with renewables but is instead mostly produced via steam reformation from hydrocarbon stocks. That's an energy-intensive process and one that is very far from carbon-neutral.

Finally, there's virtually no infrastructure for hydrogen road vehicles to refuel.

The vehicles are inefficient, and the fuel is expensive, difficult to store, and hard to find. So it's perhaps no wonder that someone at Stellantis finally saw sense. Between the high development costs and the fact that FCEVs only sell with strong incentives, the decision was made to cancel the production of hydrogen vans in France and Poland.

Stellantis says there will be no job losses at its factories and that R&D staff will be put to work on other projects.

"In a context where the Company is mobilizing to respond to demanding CO₂ regulations in Europe, Stellantis has decided to discontinue its hydrogen fuel cell technology development program," said Jean-Philippe Imparato, chief operating officer for Enlarged Europe. "The hydrogen market remains a niche segment, with no prospects of mid-term economic sustainability. We must make clear and responsible choices to ensure our competitiveness and meet the expectations of our customers with our electric and hybrid passenger and light commercial vehicles offensive."

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Posted Thursday 17 July 2025 at 5:08 am AEST (my time).

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For a star, its initial mass is everything. It determines how quickly it burns through its hydrogen and how it will evolve once it starts fusing heavier elements. It's so well understood that scientists have devised a "main sequence" that acts a bit like a periodic table for stars, correlating their mass and age with their properties.

The main sequence, however, is based on an assumption that's almost always true: All of the energy involved comes from the gravity-driven fusion of lighter elements into heavier ones. However, three astrophysicists consider an alternative source of energy that may apply at the very center of our galaxy— energy released when dark matter particles and antiparticles collide and annihilate. While we don't even know that dark matter can do that, it's a hypothetical with some interesting consequences, like seemingly immortal stars, and others that move backward along the main sequence path.

Dark annihilations

We haven't figured out what dark matter is, but there are lots of reasons to think that it is comprised of elementary particles. And, if those behave like all of the particles we understand well, then there will be both regular and antimatter versions. Should those collide, they should annihilate each other, releasing energy in the process. Given dark matter's general propensity not to interact with anything, these collisions will be extremely rare except in locations with very high dark matter concentrations.

The only place that's likely to happen is at the very center of our galaxy. And, for a while, there was an excess of radiation coming from the galactic core that people thought might be due to dark matter annihilations, although it eventually turned out to have a more mundane explanation.

At the extreme densities found within a light year of the supermassive black hole at the center of our galaxy, concentrations are high enough that these collisions could be a major source of energy. And so astronomers have considered what all that energy might do to stars that end up in a black hole's orbit, finding that under the right circumstances, dark matter destruction could provide more energy to a star than fusion.

That prompted three astrophysicists (Isabelle John, Rebecca Leane, and Tim Linden) to try to look at things in an organized fashion, modeling a "dark main sequence" of stars as they might exist within a close proximity to the Milky Way's center.

The intense gravity and radiation found near the galaxy's core mean that stars can't form there. So, anything that's in a tight orbit had formed somewhere else before gravitational interactions had pushed it into the gravitational grasp of the galaxy's central black hole. The researchers used a standard model of star evolution to build a collection of moderate-sized stars, from one to 20 solar masses at 0.05 solar mass intervals. These are allowed to ignite fusion at their cores and then shift into a dark-matter-rich environment.

Since we have no idea how often dark matter particles might run into each other, John, Leane, and Linden use two different collision frequencies. These determine how much energy is imparted into these stars by dark matter, which the researchers simply add as a supplement to the amount of fusion energy the stars are producing. Then, the stars are allowed to evolve forward in time.

(The authors note that stars that are thrown into the grasp of a supermassive black hole tend to have very eccentric orbits, so they spend a lot of time outside the zone where dark matter collisions take place with a significant frequency. So, what they've done is the equivalent of having these stars experience the energy input given their average orbital distance from the galaxy's core. In reality, a star would spend some years with higher energy input and some years with lower input as it moves about its orbit.)

Achieving immortality

The physics of what happens is based on the same balance of forces that govern fusion-powered stars, but produces some very strange results. Given only fusion power, a star will exist at a balance point. If gravity compresses it, fusion speeds up, more energy is released, and that energy causes the star to expand outward again. That causes the density drop, slowing fusion back down again.

The dark matter annihilations essentially provide an additional source of energy that stays constant regardless of what happens to the star's density. At the low end of the mass range the researchers considered, this can cause the star to nearly shut off fusion, essentially looking like a far younger star than it actually is. That has the effect of causing the star to move backward along the main sequence diagram.

The researchers note that even lighter stars could essentially get so much additional energy that they can't hold together and end up dissipating, something that's been seen in models run by other researchers.

As the mass gets higher, stars reach the point where they essentially give up on fusion and get by with nothing but dark matter annihilations. They have enough mass to hold together gravitationally, but end up too diffused for fusion to continue. And they'll stay that way as long as they continue to get additional injections of energy. "A star like this might look like a young, still-forming star," the authors write, "but has features of a star that has undergone nuclear fusion in the past and is effectively immortal."

John, Leane, and Linden find that the higher mass stars remain dense enough for fusion to continue even in proximity to the galaxy's black hole. But the additional energy kept that fusion happening at a moderate rate. They proceeded through the main sequence, but at a pace that was exceptionally slow, so that running the simulation for a total of 10 billion years didn't see them change significantly.

The other strange thing here is that all of this is very sensitive to how much dark matter annihilation is taking place. A star that's "immortal" at one average distance will progress slowly through the main sequence if its average distance is a light year further out. Similarly, stars that are too light to survive at one location will hold together if they are a bit further from the supermassive black hole.

Is there anything to this?

The big caution is that this work only looks at the average input from dark matter annihilation. In reality, a star that might be immortal at its average distance will likely spend a few years too hot to hold together, and then several years cooling off in conditions that should allow fusion to reignite. It would be nice to see a model run with this sort of pulsed input, perhaps basing it on the orbits of some of the stars we've seen that get close to the Milky Way's central black hole.

In the meantime, John, Leane, and Linden write that their results are consistent with some of the oddities that are apparent in the stars we've observed at the galaxy's center. These have two distinctive properties: They appear heavier than the average star in the Milky Way, and all seem to be quite young. If there is a "dark main sequence," then the unusual heft can be explained simply by the fact that lower mass stars end up dissipating due to the additional energy. And the model would suggest that these stars simply appear to be young because they haven't undergone much fusion.

The researchers suggest that we could have a clearer picture if we were able to spend enough time observing the stars at our galaxy's core with a large enough telescope, allowing us to understand their nature and orbits.

Physical Review D, 2025. DOI: Not yet available  (About DOIs).

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Posted Thursday 17 July 2025 at 5:08 am AEST (my time).

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More and more, we're seeing imaging technologies and machine learning showing up in automotive applications. It's usually to diagnose some kind of problem like quality control, although not always—the camera-based system by UVeye that we wrote about a few years ago made news recently after Hertz started using it to charge renters for things like scuffs on hubcaps. I have fewer concerns about customer abuse with General Motors' use of CT scanning, which simply seems like a clever adaptation of medical technology into another industry.

Ignore, if you can, GM's business decisions. Maybe you're upset because it killed your favorite brand,  changed the shape of the Corvette headlights, or abandoned Apple CarPlay. There are many valid reasons, but none change the fact that the company's engineers are quite creative. (That's probably why it stings so much when the company starts hacking things up.)

GM first turned to X-rays as a way of doing two-dimensional quality control on castings during the development process, according to Ed Duby, manufacturing engineering executive director at GM. "Much like the application to people, when you think about X-ray and CT scan, it's really trying to diagnose something without having to go into surgery. We kind of want to do the same thing with our castings," Duby told me.

"What we're trying to do is use this tool to look at where the defects are, to change our processes so that when we build it, we don't have to deal with the defects and then the ramp plan that comes with it," Duby said. Destructive testing used to be the name of the game—slicing a casting into bits to see if there were any problems in it.

But that was a laborious and time-consuming process. Using CT scanners to 3D image parts during development has improved first-time quality by 90 percent, Duby told me, and has cut development time by a third.

The datasets have value beyond quality control checks. "The less we have to do physically, the more that we can do with math modeling, the more that we can do with simulation, the more data that you collect," said Mike Trevorrow, senior vice president of global manufacturing at GM. "We're a company who's been doing this for 100 years. We wish we would have collected all the data of all the things we've tried, which would then enable newer technologies like AI and other things to give us even better predictive capabilities."

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Posted Wednesday 16 July 2025 at 1:39 pm AEST (my time).

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NASA's New Horizons spacecraft got a fleeting glimpse of Pluto 10 years ago, revealing a distant world with a picturesque landscape that, paradoxically, appears to be refreshing itself in the cold depths of our Solar System.

The mission answered numerous questions about Pluto that have lingered since its discovery by astronomer Clyde Tombaugh in 1930. As is often the case with planetary exploration, the results from New Horizons' flyby of Pluto on July 14, 2015, posed countless more questions. First and foremost, how did such a dynamic world come to be so far from the Sun?

For at least the next few decades, the only resources available for scientists to try to answer these questions will be either the New Horizons mission's archive of more than 50 gigabits of data recorded during the flyby, or observations from billions of miles away with powerful telescopes on the ground or space-based observatories like Hubble and James Webb.

That fact is becoming abundantly clear. Ten years after the New Horizons encounter, there are no missions on the books to go back to Pluto and no real prospects for one.

A mission spanning generations

In normal times, with a stable NASA budget, scientists might get a chance to start developing another Pluto mission in perhaps 10 or 20 years, after higher-priority missions like Mars Sample Return, a spacecraft to orbit Uranus, and a probe to orbit and land on Saturn's icy moon Enceladus. In that scenario, perhaps a new mission could reach Pluto and enter orbit before the end of the 2050s.

But these aren't normal times. The Trump administration has proposed cutting NASA's science budget in half, jeopardizing not only future missions to explore the Solar System but also threatening to shut down numerous operating spacecraft, including New Horizons itself as it speeds through an uncharted section of the Kuiper Belt toward interstellar space.

The proposed cuts are sapping morale within NASA and the broader space science community. If implemented, the budget reductions would affect more than NASA's actual missions. They would also slash NASA's funding available for research, eliminating grants that could pay for scientists to analyze existing data stored in the New Horizons archive or telescopic observations to peer at Pluto from afar.

The White House maintains funding for newly launched missions like Europa Clipper and an exciting mission called Dragonfly to soar through the skies of Saturn's moon Titan. Instead, the Trump administration's proposed budget, which still must be approved by Congress, suggests a reluctance to fund new missions exploring anything beyond the Moon or Mars, where NASA would focus efforts on human exploration and bankroll an assortment of commercial projects.

In this environment, it's difficult to imagine the development of a new Pluto mission to begin any time in the next 20 years. Even if Congress or a future presidential administration restores NASA's planetary science budget, a Pluto mission wouldn't be near the top of the agency's to-do list.

The National Academies' most recent decadal survey prioritized Mars Sample Return, a Uranus orbiter, and an Enceladus "Orbilander" mission in their recommendations to NASA's planetary science program through 2032. None of these missions has a realistic chance to launch by 2032, and it seems more likely than not that none of them will be in any kind of advanced stage of development by then.

The panel of scientists participating in the latest decadal survey—released in 2022—determined that a second mission to Pluto did not merit a technical risk and cost evaluation report, meaning it wasn't even shortlisted for consideration as a science priority for NASA.

There's a broad consensus in the scientific community that a follow-up mission to Pluto should be an orbiter, and not a second flyby. New Horizons zipped by Pluto at a relative velocity of nearly 31,000 mph (14 kilometers per second), flying as close as 7,750 miles (12,500 kilometers).

At that range and velocity, the spacecraft's best camera was close enough to resolve something the size of a football field for less than an hour. Pluto was there, then it was gone. New Horizons only glimpsed half of Pluto at decent resolution, but what it saw revealed a heart-shaped sheet of frozen nitrogen and methane with scattered mountains of water ice, all floating on what scientists believe is likely a buried ocean of liquid water.

Pluto must harbor a wellspring of internal heat to keep from freezing solid, something researchers didn't anticipate before the arrival of New Horizons.

So, what is Pluto's ocean like? How thick are Pluto's ice sheets? Are any of Pluto's suspected cryovolcanoes still active today? And, what secrets are hidden on the other half of Pluto?

These questions, and more, could be answered by an orbiter. Some of the scientists who worked on New Horizons have developed an outline for a conceptual mission to orbit Pluto. This mission, named Persephone for the wife of Pluto in classical mythology, hasn't been submitted to NASA as a real proposal, but it's worth illustrating the difficulties in not just reaching Pluto, but maneuvering into orbit around a dwarf planet so far from the Earth.

Nuclear is the answer

The initial outline for Persephone released in 2020 called for a launch in 2031 on NASA's Space Launch System Block 2 rocket with an added Centaur kick stage. Again, this isn't a realistic timeline for such an ambitious mission, and the rocket selected for this concept doesn't exist. But if you assume Persephone could launch on a souped-up super heavy-lift SLS rocket in 2031, it would take more than 27 years for the spacecraft to reach Pluto before sliding into orbit in 2058.

Another concept study led by Alan Stern, also the principal investigator on the New Horizons mission, shows how a future Pluto orbiter could reach its destination by the late 2050s, assuming a launch on an SLS rocket around 2030. Stern's concept, called the Gold Standard, would reserve enough propellant to leave Pluto and go on to fly by another more distant object.

Persephone and Gold Standard both assume a Pluto-bound spacecraft can get a gravitational boost from Jupiter. But Jupiter moves out of alignment from 2032 until the early 2040s, adding a decade or more to the travel time for any mission leaving Earth in those years.

It took nine years for New Horizons to make the trip from Earth to Pluto, but the spacecraft was significantly smaller than an orbiter would need to be. That's because an orbiter has to carry enough power and fuel to slow down on approach to Pluto, allowing the dwarf planet's weak gravity to capture it into orbit. A spacecraft traveling too fast, without enough fuel, would zoom past Pluto just like New Horizons.

The Persephone concept would use five nuclear radioisotope power generators and conventional electric thrusters, putting it within reach of existing technology. A 2020 white paper authored by John Casani, a longtime project manager at the Jet Propulsion Laboratory who died last month, showed the long-term promise of next-generation nuclear electric propulsion.

A relatively modest 10-kilowatt nuclear reactor to power electric thrusters would reduce the flight time to Pluto by 25 to 30 percent, while also providing enough electricity to power a radio transmitter to send science data back to Earth at a rate four times faster, according to the mission study report on the Persephone concept.

However, nuclear electric propulsion technologies are still early in the development phase, and Trump's budget proposal also eliminates any funding for nuclear rocket research.

A rocket like SpaceX's Starship might eventually be capable of accelerating a probe into the outer Solar System, but detailed studies of Starship's potential for a Pluto mission haven't been published yet. A Starship-launched Pluto probe would have its own unique challenges, and it's unclear whether it would have any advantages over nuclear electric propulsion.

How much would all of this cost? It's anyone's guess at this point. Scientists estimated the Persephone concept would cost $3 billion, excluding launch costs, which might cost $1 billion or more if a Pluto mission requires a bespoke launch solution. Development of a nuclear electric propulsion system would almost certainly cost billions of dollars, too.

All of this suggests 50 years or more might elapse between the first and second explorations of Pluto. That is in line with the span of time between the first flybys of Uranus and Neptune by NASA's Voyager spacecraft in 1986 and 1989, and the earliest possible timeline for a mission to revisit those two ice giants.

So, it's no surprise scientists are girding for a long wait—and perhaps taking a renewed interest in their own life expectancies—until they get a second look at one of the most seductive worlds in our Solar System.

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Tesla is on trial in Miami today in a case that accuses Elon Musk’s company of liability in a fatal crash involving Autopilot. The driver-assist system has come under scrutiny in the past for a number of fatal incidents, but Tesla has only rarely faced a jury trial over the question of whether Autopilot was at fault for someone’s death.

The trial comes at a particularly risky moment for Tesla, which is currently forging ahead with its plan to introduce robotaxis to more cities. The company is also experiencing a monthslong backlash for Musk’s hard-right turn and his work with Donald Trump’s administration.

Autopilot, which can control steering and braking functions, as well as perform automatic lane changes while on certain highways, has come under increased scrutiny from federal regulators. And it has been at the center of several lawsuits, some of which Tesla has settled and others of which have been dismissed.

The case in question involves an inattentive driver of a Tesla Model S and a couple who were out stargazing late at night. Naibel Benavides, 20, was killed in 2019 when George McGee’s Model S rammed into a stationary SUV parked next to a T-intersection. McGee was using Autopilot, but had dropped his phone and was inattentive at the time of the crash. Benavides and her boyfriend, Dillon Angulo, who was seriously injured, were standing outside of the SUV when McGee’s Tesla plowed into it.

The case, which is being heard in the US District Court for the Southern District of Florida, was filed by Angulo and the family of Benavides.

Tesla plans to argue that the company isn’t at fault because Autopilot was not fully in control of the vehicle at the time of the crash, citing data that shows that McGee overrode the driver assist by pressing the accelerator at the time of the crash. Also, Tesla has long argued that drivers bear responsibility when crashes occur involving Autopilot. On its website, the company says that its driver-assistance systems ”require active driver supervision and do not make the vehicle autonomous.”

The plaintiffs will argue that the system bears some responsibility for failing to warn the driver that a crash was imminent. The vehicle ignored a stop sign before the crash, and the automatic emergency braking should have worked even if Autopilot was not engaged.

Still, it will be tough for the plaintiffs to convince a jury that Tesla was at fault. In Florida automobile liability cases, the standard is “whether the car manufacturer exhibited a reckless disregard for human life equivalent to manslaughter by designing and marketing the vehicle,” the court notes.

Indeed, in another case involving a crash from 2019, Tesla was found not to be liable for the death of a Model 3 owner whose vehicle crashed while driving in Autopilot. And in another case, a jury ruled against plaintiff Justine Hsu, who sued Tesla after her vehicle hit a median while using Autopilot.

Tesla has managed to dodge responsibility for fatal crashes involving its products for a long time. The company was forced to issue several recalls after a federal investigation into dozens of crashes involving Tesla vehicles with Autopilot, but it has never been criminally indicted.

In 2023, Musk laughed off a question from investors as to whether his company would accept legal liability for its self-driving vehicles in the future. “There’s a lot of people who assume we have legal liability,” Musk said, “judging by the lawsuits.”

Still, the stakes are incredibly high for Tesla — but then again, when are they not? The presiding judge in the Florida case has ruled that the plaintiffs may seek punitive damages from Tesla. And because Tesla has refused to impose geographic limits on Autopilot, despite evidence that the system was ill-equipped to handle some situations, the judge said that a jury could reasonably rule against Tesla.

“A reasonable jury could find that Tesla acted in reckless disregard of human life for the sake of developing their product and maximizing profit,” she wrote.

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Physicists with the LIGO/Virgo/KAGRA collaboration have detected the gravitational wave signal (dubbed GW231123) of the most massive merger between two black holes yet observed, resulting in a new black hole that is 225 times more massive than our Sun. The results were presented at the Edoardo Amaldi Conference on Gravitational Waves in Glasgow, Scotland.

The LIGO/Virgo/KAGRA collaboration searches the universe for gravitational waves produced by the mergers of black holes and neutron stars. LIGO detects gravitational waves via laser interferometry, using high-powered lasers to measure tiny changes in the distance between two objects positioned kilometers apart. LIGO has detectors in Hanford, Washington, and in Livingston, Louisiana. A third detector in Italy, Advanced Virgo, came online in 2016. In Japan, KAGRA is the first gravitational-wave detector in Asia and the first to be built underground. Construction began on LIGO-India in 2021, and physicists expect it will turn on sometime after 2025.

To date, the collaboration has detected dozens of merger events since its first Nobel Prize-winning discovery. Early detected mergers involved either two black holes or two neutron stars.  In 2021, LIGO/Virgo/KAGRA confirmed the detection of two separate "mixed" mergers between black holes and neutron stars.

A tour of Virgo. Credit: EGO-Virgo

LIGO/Virgo/KAGRA started its fourth observing run in 2023, and by the following year had announced the detection of a signal indicating a merger between two compact objects, one of which was most likely a neutron star. The other had an intermediate mass—heavier than a neutron star and lighter than a black hole. It was the first gravitational-wave detection of a mass-gap object paired with a neutron star and hinted that the mass gap might be less empty than astronomers previously thought.

Until now, the most massive back hole merger was GW190521, detected in 2020. It produced a new black hole with an intermediate mass—about 140 times as heavy as our Sun. Also found in the fourth run, GW231123 dwarfs the prior merger. According to the collaboration, the two black holes that merged were about 100 and 140 solar masses, respectively. It took some time to announce the discovery because the objects were spinning rapidly, near the limits imposed by the general theory of relativity, making the signal much more difficult to interpret.

The discovery is also noteworthy because it conflicts with current theories about stellar evolution. The progenitor black holes are too big to have formed from a supernova. Like its predecessor, GW190521, GW231123 may be an example of a so-called "hierarchical merger," meaning the two progenitor black holes were themselves each the result of a previous merger before they found each other and merged.

“The discovery of such a massive and highly spinning system presents a challenge not only to our data analysis techniques but will have a major effect on the theoretical studies of black hole formation channels and waveform modeling for many years to come," said Ed Porter of CNRS in Paris.

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Ice is a key component in the universe. There are frozen water molecules on comets, moons, exoplanets, and in your drink as you cool off from the summer heat. However, under the microscope, not all ice is the same, even though it is made of the same components.

The internal structure of Earth’s ice is a cosmological oddity. Its molecules are arranged in geometric structures, usually hexagons that repeat each other. Ice on Earth forms this way due to the temperature and pressure of the our planet: water here freezes slowly, and this allows its molecules to arrange themselves into crystals.

But ice that forms in space is different because of the conditions—the water exists in a vacuum and is subject to extreme temperatures. Space ice, as a result, is believed to be amorphous, lacking a distinct organizational structure like on Earth.

This presents a challenge for scientists trying to understand the formation of planets and the generation of life. Not fully understanding the dynamics of amorphous ice in space has knock-on effects. For instance, not knowing exactly how space water freezes makes it difficult to estimate the proportion of water in other solar systems.

Researchers are therefore studying space ice to gain a better understanding of how frozen water behaves away from Earth. Ice samples from comets, asteroids, and other solar system debris would be helpful, but until these can be captured, scientists are trying to understand space ice with computer models and simulations of ice on Earth. The more they study it, the more surprises it reveals.

A recent report, published in the journal Physical Review B, posits that the amorphous ice that abounds in the universe does have some kind of order. The paper theorizes it is likely made up of structured fragments—crystallized regions, as on Earth, but only about 3 nanometers wide—surrounded by chaos.

To reach this conclusion, the team first ran computer models of water molecules subjected to temperature changes at different rates, simulating the creation of ice in space. They then compared this with the results of lab experiments to produce actual amorphous ice. Water vapor was passed over an extremely cold slab to become ice, with no liquid state occurring in between, a process similar to what happens in a planetary system at birth. A partially amorphous material was produced, whose structure most closely matched a simulation from the models that comprised 20 percent crystalline material and 80 percent amorphous ice.

“We now have a good idea of what the most common form of ice in the universe looks like at the atomic level,” said Michael B. Davies, part of the ICE Group at the University of Cambridge and a coauthor of the study, in a statement.

Knowing the structure of space ice is important for interrogating the speculative idea of panspermia, a hypothesis that life on Earth originated through compounds or “seeds” of life arriving on our planet from space. If space ice is amorphous and of low density, then building blocks for life could potentially have been carried inside. If, instead, there are lots of crystalline parts, then there is less likelihood (because of less space) of this having occurred.

This story originally appeared on WIRED en Español and has been translated from Spanish.

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To solve these problems, researchers from the Korea Institute of Science and Technology (KIST) and Korea University are looking to ultrasound. Unlike RF waves, ultrasound is less absorbed by human tissue and causes less interference, making it more suitable for charging medical implants and skin-worn devices.

A team led by Dr. Sunghoon Hur and Professor Hyun-Cheol Song built a flexible ultrasonic receiver using advanced piezoelectric materials. This receiver works even when bent and can stick closely to curved surfaces like skin. Tests showed it could wirelessly deliver 20 milliwatts of power through 3 cm of water and 7 milliwatts through 3 cm of skin—enough to run small devices like wearable sensors or implants.

The team also showed that the receiver could be used to charge batteries, opening the door to longer-lasting implants that don’t need frequent surgery for battery replacement. Dr. Hur stated, “Through this research, we have demonstrated that wireless power transmission technology using ultrasound can be applied practically. We plan to conduct further research for miniaturization and commercialization to accelerate the practical application of the technology.”

Meanwhile, scientists are also studying ultrasound-powered triboelectric nanogenerators (US-TENGs). These devices can send power through the skin without surgery, but they’ve struggled with low output and stiffness. To improve this, a new version called the dielectric-ferroelectric boosted US-TENG (US-TENGDF-B) was developed. It uses a special design to produce more power with gentler ultrasound and from farther away.

This upgraded device reached about 26 volts and delivered 6.7 milliwatts when charging a battery from 35 mm away. It stayed stable even when bent, making it useful for curved parts of the body or implants like artificial hearts. Researchers say it’s effective for short-term wireless charging deep inside the body, especially in flexible systems.

Together, these technologies show real potential for powering low-energy electronics safely, both in water and inside the human body. They could help make future devices like pacemakers, neurostimulators, underwater sensors, and drones run longer and more reliably—without needing frequent charging or replacements.

Source: KIST, Wiley Online Library | Image via Depositphotos

This article was generated with some help from AI and reviewed by an editor. Under Section 107 of the Copyright Act 1976, this material is used for the purpose of news reporting. Fair use is a use permitted by copyright statute that might otherwise be infringing.

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On July 2, NASA revealed the existence of 3I/ATLAS, only the third ever interstellar object observed in the universe. These are objects that exist in interstellar space—the areas between stars—and which are not gravitationally bound to any star. The two other interstellar objects discovered to date are the comets 1I/ʻOumuamua and 2I/Borisov.

3I/ATLAS was discovered on July 1, when its existence was reported by a telescope at Rio Hurtado in Chile, operated by the Asteroid Terrestrial Impact Alert System. Known commonly as ATLAS, this is a NASA-funded system developed and operated by the University of Hawaii to detect asteroids that could potentially hit Earth. It uses four telescopes—two in Hawaii, one in Chile, and one in South Africa—to automatically scan the entire sky several times each night to monitor celestial movements.

An illustration of 1I/ʻOumuamua, which was the first ever interstellar object discovered in October 2017. 

It is thought to be up to 400 meters long and cigar-shaped.

Illustration: ESA/Hubble, NASA, ESO, M. Kornmesser

 

An image of 2I/Borisov, the second interstellar object discovered in August 2019. 

It is thought to be about 975 meters in diameter and moving at 177,000 km/h.

PHOTOGRAPH: NASA/ESA/D. JEWITT (UCLA)

 

Archived data collected in the preceding weeks by ATLAS' three other telescopes, as well as by the Zwicky Sky Facility at the Palomar Observatory, operated by the California Institute of Technology, confirmed the discovery. Additional observations of 3I/ATLAS were then made by numerous telescopes around the world, gradually revealing more details about it.

3I/ATLAS is estimated to be, at most, about 20 kilometers in size. It is currently located about 670 million kilometers from the sun and is approaching our star from the direction of Sagittarius at a speed of about 61 km per second. Its speed is expected to increase as it approaches the sun.

When astronomers studied its orbit, they found that 3I/ATLAS was moving too fast to be bound by the sun’s gravity and so will head straight through the solar system and into interstellar space, never to be seen again.

Generally, celestial bodies are named after their discoverers, but in the case of 3I/ATLAS, it was named after the ATLAS research team. The “I” stands for “interstellar,” indicating that the object came from outside the solar system; the “3” was added to the name because it is the third interstellar object discovered.

The object was was uncovered because ATLAS initially identified it as something that might be on a possible collision path with Earth, but NASA says there is no risk of the object hitting our planet. Even when 3I/ATLAS is closest to Earth, it will be about 240 million kilometers away.

3I/ATLAS will be visible using ground-based telescopes until September. It will then enter the inner orbit of Mars in late October and will be hidden in the sun’s shadow when it passes our star at its closest point, making it impossible to observe from Earth. However, it will reappear from the sun’s shadow in early December and become visible again.

3I/ATLAS is an active comet, which means that as it gets close to the sun and heats up, the ice in its nucleus could sublimate and form a nebula-like cloud of gas and dust called a coma—otherwise known as a tail.

Because 3I/ATLAS is an object that came from outside the solar system, it may provide valuable data about objects that exist in the further reaches of space. For this reason, astronomers around the world are now paying close attention to it.

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The original version of this story appeared in Quanta Magazine.

One July afternoon in 2024, Ryan Williams set out to prove himself wrong. Two months had passed since he’d hit upon a startling discovery about the relationship between time and memory in computing. It was a rough sketch of a mathematical proof that memory was more powerful than computer scientists believed: A small amount would be as helpful as a lot of time in all conceivable computations. That sounded so improbable that he assumed something had to be wrong, and he promptly set the proof aside to focus on less crazy ideas. Now, he’d finally carved out time to find the error.

That’s not what happened. After hours of poring over his argument, Williams couldn’t find a single flaw.

“I just thought I was losing my mind,” said Williams, a theoretical computer scientist at the Massachusetts Institute of Technology. For the first time, he began to entertain the possibility that maybe, just maybe, memory really was as powerful as his work suggested.

Over the months that followed, he fleshed out the details, scrutinized every step, and solicited feedback from a handful of other researchers. In February, he finally posted his proof online, to widespread acclaim.

“It’s amazing. It’s beautiful,” said Avi Wigderson, a theoretical computer scientist at the Institute for Advanced Study in Princeton, New Jersey. As soon as he heard the news, Wigderson sent Williams a congratulatory email. Its subject line: “You blew my mind.”

Time and memory (also called space) are the two most fundamental resources in computation: Every algorithm takes some time to run and requires some space to store data while it’s running. Until now, the only known algorithms for accomplishing certain tasks required an amount of space roughly proportional to their run time, and researchers had long assumed there’s no way to do better. Williams’ proof established a mathematical procedure for transforming any algorithm—no matter what it does—into a form that uses much less space.

What’s more, this result—a statement about what you can compute given a certain amount of space—also implies a second result, about what you cannot compute in a certain amount of time. This second result isn’t surprising in itself: Researchers expected it to be true, but they had no idea how to prove it. Williams’ solution, based on his sweeping first result, feels almost cartoonishly excessive, akin to proving a suspected murderer guilty by establishing an ironclad alibi for everyone else on the planet. It could also offer a new way to attack one of the oldest open problems in computer science.

“It’s a pretty stunning result, and a massive advance,” said Paul Beame, a computer scientist at the University of Washington. “It’s a little bit less of a surprise that it’s Ryan doing this.”

Space to Roam

Williams, 46, has an open, expressive face and a hint of gray in his hair. His office, which looks out over the colorful spires of MIT’s Stata Center, is another illustration of the creative use of space. A pair of yoga mats have transformed a window ledge into a makeshift reading nook, and the desk is tucked into an oddly shaped corner, freeing up room for a couch facing a large whiteboard brimming with mathematical scribblings.

MIT is a long way from Williams’ childhood home in rural Alabama, where there was no shortage of space. He grew up on a 50-acre farm and first saw a computer at age 7, when his mother drove him across the county for a special academic enrichment class. He recalled being transfixed by a simple program for generating a digital fireworks display.

“It was taking a random color and sending it in a random direction from the middle of the monitor,” Williams said. “You couldn’t have predicted what picture you’re going to get.” The world of computers seemed a wild and wonderful playground, full of infinite possibilities. Young Williams was hooked.

“I was writing programs to myself, on paper, to be run on a future computer,” he said. “My parents didn’t really know what to do with me.”

Williams’ office, like his new result, makes creative use of space.

Photograph: Katherine Taylor for Quanta Magazine

 

As he grew older, Williams advanced from imaginary computers to real ones. For his last two years of high school, he transferred to the Alabama School of Math and Science, a prestigious public boarding school, where he first encountered the theoretical side of computer science.

“I realized that there was a wider world of things out there, and that there was a way to think mathematically about computers,” he said. “That’s what I wanted to do.”

Williams was especially drawn to a branch of theoretical computer science called computational complexity theory. It deals with the resources (such as time and space) that are needed to solve computational problems such as sorting lists or factoring numbers. Most problems can be solved by many different algorithms, each with its own demands on time and space. Complexity theorists sort problems into categories, called complexity classes, based on the resource demands of the best algorithms for solving them—that is, the algorithms that run fastest or use the least amount of space.

But how do you make the study of computational resources mathematically rigorous? You won’t get far if you try to analyze time and space by comparing minutes to megabytes. To make any progress, you need to start with the right definitions.

Getting Resourceful

Those definitions emerged from the work of Juris Hartmanis, a pioneering computer scientist who had experience making do with limited resources. He was born in 1928 into a prominent Latvian family, but his childhood was disrupted by the outbreak of World War II. Occupying Soviet forces arrested and executed his father, and after the war Hartmanis finished high school in a refugee camp. He went on to university, where he excelled even though he couldn’t afford textbooks.

“I compensated by taking very detailed notes in lectures,” he recalled in a 2009 interview. “There is a certain advantage to [having] to improvise and overcome difficulties.” Hartmanis immigrated to the United States in 1949, and worked a series of odd jobs—building agricultural machinery, manufacturing steel and even serving as a butler—while studying mathematics at the University of Kansas City. He went on to a spectacularly successful career in the young field of theoretical computer science.

In 1960, while working at the General Electric research laboratory in Schenectady, New York, Hartmanis met Richard Stearns, a graduate student doing a summer internship. In a pair of groundbreaking papers they established precise mathematical definitions for time and space. These definitions gave researchers the language they needed to compare the two resources and sort problems into complexity classes.

One of the most important classes goes by the humble name “P.” Roughly speaking, it encompasses all problems that can be solved in a reasonable amount of time. An analogous complexity class for space is dubbed “PSPACE.”

The relationship between these two classes is one of the central questions of complexity theory. Every problem in P is also in PSPACE, because fast algorithms just don’t have enough time to fill up much space in a computer’s memory. If the reverse statement were also true, the two classes would be equivalent: Space and time would have comparable computational power. But complexity theorists suspect that PSPACE is a much larger class, containing many problems that aren’t in P. In other words, they believe that space is a far more powerful computational resource than time. This belief stems from the fact that algorithms can use the same small chunk of memory over and over, while time isn’t as forgiving—once it passes, you can’t get it back.

“The intuition is just so simple,” Williams said. “You can reuse space, but you can’t reuse time.”

But intuition isn’t good enough for complexity theorists: They want rigorous proof. To prove that PSPACE is larger than P, researchers would have to show that for some problems in PSPACE, fast algorithms are categorically impossible. Where would they even start?

A Space Odyssey

As it happened, they started at Cornell University, where Hartmanis moved in 1965 to head the newly established computer science department. Under his leadership it quickly became a center of research in complexity theory, and in the early 1970s, a pair of researchers there, John Hopcroft and Wolfgang Paul, set out to establish a precise link between time and space.

Hopcroft and Paul knew that to resolve the P versus PSPACE problem, they’d have to prove that you can’t do certain computations in a limited amount of time. But it’s hard to prove a negative. Instead, they decided to flip the problem on its head and explore what you can do with limited space. They hoped to prove that algorithms given a certain space budget can solve all the same problems as algorithms with a slightly larger time budget. That would indicate that space is at least slightly more powerful than time—a small but necessary step toward showing that PSPACE is larger than P.

With that goal in mind, they turned to a method that complexity theorists call simulation, which involves transforming existing algorithms into new ones that solve the same problems, but with different amounts of space and time. To understand the basic idea, imagine you’re given a fast algorithm for alphabetizing your bookshelf, but it requires you to lay out your books in dozens of small piles. You might prefer an approach that takes up less space in your apartment, even if it takes longer. A simulation is a mathematical procedure you could use to get a more suitable algorithm: Feed it the original, and it’ll give you a new algorithm that saves space at the expense of time.

Algorithm designers have long studied these space-time trade-offs for specific tasks like sorting. But to establish a general relationship between time and space, Hopcroft and Paul needed something more comprehensive: a space-saving simulation procedure that works for every algorithm, no matter what problem it solves. They expected this generality to come at a cost. A universal simulation can’t exploit the details of any specific problem, so it probably won’t save as much space as a specialized simulation. But when Hopcroft and Paul started their work, there were no known universal methods for saving space at all. Even saving a small amount would be progress.

The breakthrough came in 1975, after Hopcroft and Paul teamed up with a young researcher named Leslie Valiant. The trio devised a universal simulation procedure that always saves a bit of space. No matter what algorithm you give it, you’ll get an equivalent one whose space budget is slightly smaller than the original algorithm’s time budget.

“Anything you can do in so much time, you can also do in slightly less space,” Valiant said. It was the first major step in the quest to connect space and time.

But then progress stalled, and complexity theorists began to suspect that they’d hit a fundamental barrier. The problem was precisely the universal character of Hopcroft, Paul and Valiant’s simulation. While many problems can be solved with much less space than time, some intuitively seemed like they’d need nearly as much space as time. If so, more space-efficient universal simulations would be impossible. Paul and two other researchers soon proved that they are indeed impossible, provided you make one seemingly obvious assumption: Different chunks of data can’t occupy the same space in memory at the same time.

“Everybody took it for granted that you cannot do better,” Wigderson said.

Paul’s result suggested that resolving the P versus PSPACE problem would require abandoning simulation altogether in favor of a different approach, but nobody had any good ideas. That was where the problem stood for 50 years—until Williams finally broke the logjam.

First, he had to get through college.

Complexity Classes

In 1996, the time came for Williams to apply to colleges. He knew that pursuing complexity theory would take him far from home, but his parents made it clear that the West Coast and Canada were out of the question. Among his remaining options, Cornell stood out to him for its prominent place in the history of his favorite discipline.

“This guy Hartmanis defined the time and space complexity classes,” he recalled thinking. “That was important for me.”

Williams was admitted to Cornell with generous financial aid and arrived in the fall of 1997, planning to do whatever it took to become a complexity theorist himself. His single-mindedness stuck out to his fellow students.

“He was just super-duper into complexity theory,” said Scott Aaronson, a computer scientist at the University of Texas, Austin, who overlapped with Williams at Cornell.

But by sophomore year, Williams was struggling to keep up in courses that emphasized mathematical rigor over intuition. After he got a middling grade in a class on the theory of computing, the teacher suggested he consider other careers. But Williams wouldn’t give up his dream. He doubled down and enrolled in a graduate theory course, hoping that a stellar grade in the harder class would look impressive on his grad school applications. The professor teaching that graduate course was Hartmanis, by then an elder statesman in the field.

Williams began attending Hartmanis’ office hours every week, where he was almost always the only student who showed up. His tenacity paid off: he earned an A in the course, and Hartmanis agreed to advise him on an independent research project the following semester. Their weekly meetings continued throughout Williams’ time in college. Hartmanis encouraged him to cultivate an individual approach to complexity research and gently steered him away from dead ends.

“I was deeply influenced by him then,” Williams said. “I guess I still am now.”

But despite earning a coveted graduate research fellowship from the National Science Foundation, Williams was rejected by every doctoral program he applied to. On hearing the news, Hartmanis phoned a colleague, then turned around and congratulated Williams on getting accepted into a yearlong master’s program at Cornell. A year later Williams again applied to various doctoral programs, and with that extra research experience under his belt, he found success.

Williams continued working in complexity theory in grad school and the years that followed. In 2010, four years after receiving his doctorate, he proved a milestone result—a small step, but the largest in decades, toward solving the most famous question in theoretical computer science, about the nature of hard problems. That result cemented Williams’ reputation, and he went on to write dozens of other papers on different topics in complexity theory.

P versus PSPACE wasn’t one of them. Williams had been fascinated by the problem since he first encountered it in college. He’d even supplemented his computer science curriculum with courses in logic and philosophy, seeking inspiration from other perspectives on time and space, to no avail.

“It’s always been in the back of my mind,” Williams said. “I just couldn’t think of anything interesting enough to say about it.”

Last year, he finally found the opportunity he’d been waiting for.

Squishy Pebbles

The story of Williams’ new result started with progress on a different question about memory in computation: What problems can be solved with extremely limited space? In 2010, the pioneering complexity theorist Stephen Cook and his collaborators invented a task, called the tree evaluation problem, that they proved would be impossible for any algorithm with a space budget below a specific threshold. But there was a loophole. The proof relied on the same commonsense assumption that Paul and his colleagues had made decades earlier: Algorithms can’t store new data in space that’s already full.

For over a decade, complexity theorists tried to close that loophole. Then, in 2023, Cook’s son James and a researcher named Ian Mertz blew it wide open, devising an algorithm that solved the tree evaluation problem using much less space than anyone thought possible. The elder Cook’s proof had assumed that bits of data were like pebbles that have to occupy separate places in an algorithm’s memory. But it turns out that’s not the only way to store data. “We can actually think about these pebbles as things that we can squish a little bit on top of each other,” Beame said.

Cook and Mertz’s algorithm roused the curiosity of many researchers, but it wasn’t clear that it had any applications beyond the tree evaluation problem. “Nobody saw how central it is to time versus space itself,” Wigderson said.

Ryan Williams was the exception. In spring 2024, a group of students gave a presentation about the Cook and Mertz paper as their final project in a class he was teaching. Their enthusiasm inspired him to take a closer look. As soon as he did, an idea jumped out at him. Cook and Mertz’s method, he realized, was really a general-purpose tool for reducing space usage. Why not use it to power a new universal simulation linking time and space—like the one designed by Hopcroft, Paul and Valiant, but better?

That classic result was a way to transform any algorithm with a given time budget into a new algorithm with a slightly smaller space budget. Williams saw that a simulation based on squishy pebbles would make the new algorithm’s space usage much smaller—roughly equal to the square root of the original algorithm’s time budget. That new space-efficient algorithm would also be much slower, so the simulation was not likely to have practical applications. But from a theoretical point of view, it was nothing short of revolutionary.

For 50 years, researchers had assumed it was impossible to improve Hopcroft, Paul and Valiant’s universal simulation. Williams’ idea—if it worked—wouldn’t just beat their record—it would demolish it.

“I thought about it, and I was like, ‘Well, that just simply can’t be true,’” Williams said. He set it aside and didn’t come back to it until that fateful day in July, when he tried to find the flaw in the argument and failed. After he realized that there was no flaw, he spent months writing and rewriting the proof to make it as clear as possible.

At the end of February, Williams finally put the finished paper online. Cook and Mertz were as surprised as everyone else. “I had to go take a long walk before doing anything else,” Mertz said.

Valiant got a sneak preview of Williams’ improvement on his decades-old result during his morning commute. For years, he’s taught at Harvard University, just down the road from Williams’ office at MIT. They’d met before, but they didn’t know they lived in the same neighborhood until they bumped into each other on the bus on a snowy February day, a few weeks before the result was public. Williams described his proof to the startled Valiant and promised to send along his paper.

“I was very, very impressed,” Valiant said. “If you get any mathematical result which is the best thing in 50 years, you must be doing something right.”

PSPACE: The Final Frontier

With his new simulation, Williams had proved a positive result about the computational power of space: Algorithms that use relatively little space can solve all problems that require a somewhat larger amount of time. Then, using just a few lines of math, he flipped that around and proved a negative result about the computational power of time: At least a few problems can’t be solved unless you use more time than space. That second, narrower result is in line with what researchers expected. The weird part is how Williams got there, by first proving a result that applies to all algorithms, no matter what problems they solve.

“I still have a hard time believing it,” Williams said. “It just seems too good to be true.”

Williams used Cook and Mertz’s technique to establish a stronger link between space and time—the first 

progress on that problem in 50 years.

Photograph: Katherine Taylor for Quanta Magazine

 

Phrased in qualitative terms, Williams’ second result may sound like the long-sought solution to the P versus PSPACE problem. The difference is a matter of scale. P and PSPACE are very broad complexity classes, while Williams’ results work at a finer level. He established a quantitative gap between the power of space and the power of time, and to prove that PSPACE is larger than P, researchers will have to make that gap much, much wider.

That’s a daunting challenge, akin to prying apart a sidewalk crack with a crowbar until it’s as wide as the Grand Canyon. But it might be possible to get there by using a modified version of Williams’ simulation procedure that repeats the key step many times, saving a bit of space each time. It’s like a way to repeatedly ratchet up the length of your crowbar—make it big enough, and you can pry open anything. That repeated improvement doesn’t work with the current version of the algorithm, but researchers don’t know whether that’s a fundamental limitation.

“It could be an ultimate bottleneck, or it could be a 50-year bottleneck,” Valiant said. “Or it could be something which maybe someone can solve next week.”

If the problem is solved next week, Williams will be kicking himself. Before he wrote the paper, he spent months trying and failing to extend his result. But even if such an extension is not possible, Williams is confident that more space exploration is bound to lead somewhere interesting—perhaps progress on an entirely different problem.

“I can never prove precisely the things that I want to prove,” he said. “But often, the thing I prove is way better than what I wanted.”

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The Curiosity rover was sent up the Mount Sharp, the biggest sediments stack on Mars. On the way, it collected samples that indicated a portion of carbon dioxide in the Martian atmosphere might have been sequestered in the sedimentary rocks, just as it happens with limestone on Earth. This would have drawn carbon dioxide out of the atmosphere, reducing the greenhouse effect that warmed the planet.

Based on these findings, a team of scientists led by Benjamin Tutolo, a researcher at the University of Calgary, used this data to conclude Mars had a carbon cycle that could explain the presence of liquid water on its surface. Building on that earlier work, a team led by Edwin Kite, a professor of planetary science at the University of Chicago (and member of the Curiosity science team) has now built the first Martian climate model that took these new results into account. The model also included Martian topography, the luminosity of the Sun, latest orbital data, and many other factors to predict how the Martian conditions and landscape evolved over the span of 3.5 billion years.

Their results mean that any Martian life would have had a rough time of it.

Martian seas

The model Kite’s team built was unique in that it captured evolution of the Martian landscape and climate over an extremely long time. “This has been under development for the last couple of years, along with the paper on the carbonate rocks,” says Tutolo, who was the second author of Kite’s study.

Earlier models we used to simulate long-term climatic changes on Mars lacked spatial resolution, basically treated the entire planet as a single pixel and evolving this pixel over billions of years. On the other hand, models that had high spatial resolution—taking into account all the hills, mountains, and riverbeds—could only be run over relatively short time spans. “To the best of my knowledge, we built the first spatially resolved, long-term climate evolution model for Mars,” Kite said. His team started the run of this new model 3.5 billion years ago, when the planet was at the onset of a period Kite calls the “era of salts.”

“Very early in Mars' history, maybe 4 billion years ago, the planet was warm enough to support lakes and river networks,” Kite told Ars. “There were seas, and some of those seas were as big as the Caspian Sea, maybe bigger. It was a wet place.” This wet period, though, didn’t last long—it was too short to make the landscape deeply weathered and deeply eroded.

Kite’s team used their model to focus on what happened as the planet got colder, when the era of salts started. “Big areas of snowmelts created huge salt flats, which eventually built up over time, accumulating into a thick sedimentary deposit Curiosity rover is currently exploring,” Kite said. But the era of salts did not mark the end of liquid water on the Martian surface.

Flickering habitability

The landscape turned arid, judging by Earth’s standards, roughly 3.5 billion years ago. “There were long periods when the planet was entirely dry,” Kite said. During these dry periods, Mars was almost as cold as it is today. But once in a while, small areas with liquid water appeared on the Martian surface like oases amidst an otherwise unwelcoming desert. It was a sterile planet with flickering, transient habitable spots with water coming from melted snow.

This rather bleak picture of the Martian landscape’s evolution makes questions about our chances for finding traces of life in there tricky.

“You can do a thought experiment where you take a cup of water from the Earth’s ocean and pour it into one of those transient lakes on Mars,” Kite said. “Some microbes in this cup of water would do fine in such conditions.” The bigger question, he thinks, is whether life could originate (rather than just survive) on ancient Mars. And, perhaps more critically, whether hypothetical life that originated even before the salts era, when the planet was warm and wet, could persist in the oases popping up in the Kite’s model.

The answer, sadly, is probably not.

The dry periods were very long, and the lifespan of the oases was geologically short—about one hundred thousand years. “I would say these conditions were harsh enough to extinguish any surface life,” Kite said. Consequently, all the oases in his model were considered uninhabited.

All these predictions about Martian climate should hold if the Kite’s model proves right. But that’s still uncertain.

Journey uphill

Kite admits the Curiosity’s discovery of carbonate rocks on Mount Sharp laid the foundation for the model his team built. “The biggest limitation of this model is that most of the data it’s based on comes from one place and one rover,” Kite acknowledged. If it turned out Mount Sharp was unusually rich in carbonates and thus not representative of Mars as a whole, the model wouldn’t accurately capture its past. Even Curiosity itself, which discovered the carbonate rocks that laid the foundation for Kite’s work with its discovery of carbonate rocks, could still undercut this model with future findings. “What if there is a cessation in carbonate disposition? Looking at the mineralogy in the Mount Sharp itself as we go upwards can put a constraint on that,” Tutolo says.

Another limitation is that the model says nothing about the times before the salts dominated. There was significantly more surface water; the planet must have been much warmer, and we still don’t know what was causing it. “To explain that, we need a non-carbon dioxide warming agent, and I’m trying to figure out what that might be,” Kite said.

Ultimately, the model doesn’t bring us much closer to answering the question about the possibility of life developing on Mars. Kite thinks the chances are still slim, but slightly above zero. He speculates the only place where life could possibly make it through the salt era on Mars is subsurface waters. Potential microbes could survive the dry periods there and move up to the surface to live in the lakes during brief periods of habitability.

“I think we can’t completely close the door on Martian life just yet,” Kite said. He thinks we need to look for biologically produced molecules in ancient Martian rocks. “Curiosity recently encountered long-chain alkanes. On Earth, we’d say this molecule was probably produced by life, which is intriguing. I am very much looking forward to bringing such samples back to Earth for further analysis.”

Nature, 2025.  DOI: 10.1038/s41586-025-09161-1

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