Tag: Physical Review Letters

• Quasicrystal, heal thyself

  Scientists have uncovered a remarkable self-healing property in a strange class of materials known as quasicrystals, revealing their ability to grow into a perfect, single structure even when faced with obstacles. The discovery challenges a long-held understanding of crystal formation and opens the door to creating large, strong materials free of defects for a new generation of applications.

  Imagine you’re tiling a massive bathroom floor. You could use identical square tiles, laying them in a simple, repeating grid. This is analogous to a regular crystal. The pattern is predictable and repeats perfectly. When the arrangement of atoms repeats at fixed intervals, called periodicity, you have a conventional crystal.

  But as you’re laying these tiles, you come across a pipe sticking out of the floor. To get around it, you would have to cut tiles into awkward shapes, breaking your perfect pattern. The lines where the mismatched tiles meet would also create a permanent scar on your floor. In the world of materials, these ‘scars’ are called defects or grain boundaries.

  These defects are often the weakest points in a material, making it more prone to breaking or corrosion. Say you’ve a chair that’s starting to crack: the odds are the crack would’ve originated at a grain boundary. For many decades, a major goal for materials scientists has been to create large single crystals: materials with no grain boundaries and thus maximum strength and performance.

  Now, what if instead of squares, you were tiling with a special set of tiles, say a mix of two different diamond shapes (see below). You could cover the entire floor without gaps, creating a pattern that looks ordered and intricate. But then you notice the catch: the pattern never exactly repeats. This is the essence of a quasicrystal. It has long-range order—its atoms are arranged in a pattern you can predict—but that pattern doesn’t repeat.

  

  This fundamental difference led scientists to ask a critical question: what happens when a growing quasicrystal encounters an obstacle like that pipe in the floor? Does it also form messy, weakening defects or does its unique, more flexible structure give it a way to grow around the disruption and ‘fix’ itself on the other side?

  In a new study in Physical Review Letters, researchers from the University of Michigan, Ann Arbor, hypothesised that quasicrystals possess a unique “self-healing” ability. They’ve written that this ability stems from special atomic rearrangements, known as phasons, that are only possible in quasicrystals. To test this, they decided to watch a specific type of quasicrystal, a decagonal quasicrystal, grow around a common, bubble-shaped obstacle found in metal alloys called shrinkage pores.

  To watch this microscopic drama unfold, the researchers used a two-part strategy in which they combined a real-world experiment with a sophisticated computer simulation.

  For the experiment, they used a technique called synchrotron X-ray microtomography. Think of it as a super-powered CT scan for materials. They took a small cylinder of an aluminium-cobalt-nickel alloy, melted it, and then carefully cooled it down. As the alloy solidified, quasicrystals began to form and grow, eventually running into the shrinkage pores that were already present in the material. The X-rays allowed the scientists to capture rapid, 3D “movies” of this process in real-time, tracking the crystal’s growth as it navigated the porous landscape.

  To understand the atom-level mechanics behind what they were seeing, the team ran molecular dynamics simulations. This is like creating a virtual universe in a computer, where they could build a model quasicrystal atom by atom. They then programmed a virtual pore in its path and let the simulation run to watch exactly how the individual atoms rearranged themselves as the crystal grew and enveloped the obstacle.

  The results were astonishing: both the live-action X-ray movies and the computer simulations showed the quasicrystal growing around the pores without forming any permanent defects.

  As the growing front of the quasicrystal met a pore, it momentarily distorted its shape to flow around the void. Then when the two fronts met on the far side of the pore, they merged together perfectly. The crystal continued to grow as if the pore had never been there, resulting in a single, flawless quasicrystal that had simply swallowed the obstacle.

  The key evidence from the experiments was the smoothness of the final quasicrystal’s surface. In a normal crystal, if a defect like a grain boundary had formed where the two fronts met, it would have created a permanent groove on the surface. The fact that the quasicrystal’s surface healed into a smooth, convex shape confirmed that no such defect was present.

  

  The computer simulations revealed the secret to this ability: the phasons. A phason flip is one pattern of tiles changing into another without disrupting the overall order. When the growing crystal collided with the pore, it created localised stress. But through a cascade of these phason flips, the quasicrystal was able to distribute this stress and seamlessly stitch its structure back together. The scientists described this as an err-and-repair mechanism because the quasicrystal makes a temporary “mistake” at the collision point, then quickly corrects it. The result: perfect structure.

  The finding has profound implications for materials science. First, it suggests that creating large, defect-free single crystals—currently a notoriously difficult and expensive process for conventional materials—could be dramatically easier with quasicrystals. Their built-in fault tolerance means they might be grown perfectly using simpler and cheaper methods.

  The ability to easily create large, single-grain quasicrystals could also better unlock their incredible potential. Lacking the weak points of grain boundaries, quasicrystals could be used to create exceptionally strong, hard, and lightweight alloys for aerospace and industrial applications. Their unique structure also gives them interesting properties like low friction and low adhesion, making them ideal as durable nonstick coating.

  The err-and-repair mechanism is also a form of self-healing at the most fundamental level. Understanding this process could inspire the design of a new class of “smart” materials. For instance, imagine a composite that when cracked or damaged can automatically absorb the defect and ‘repair’ its own atomic structure, maintaining its integrity and performance. The principles revealed in this study could be applied to other fields, such as creating more durable catalysts for the chemical industry that can resist degradation from internal pores.

  Overall, this research reshapes our understanding of how ordered matter can form and provides a blueprint for a new generation of resilient materials built from the atom up.

  2025.10.21

• What does a quantum Bayes’s rule look like?

  Bayes’s rule is one of the most fundamental principles in probability and statistics. It allows us to update our beliefs in the face of new evidence. In its simplest form, the rule tells us how to revise the probability of a hypothesis once new data becomes available.

  A standard way to teach it involves drawing coloured balls from a pouch: you start with some expectation (e.g. “there’s a 20% chance I’ll draw a blue ball”), then you update your belief depending on what you observe (“I’ve drawn a red ball, so the actual chance of drawing a blue ball is 10%”). While this example seems simple, the rule carries considerable weight: physicists and mathematicians have described it as the most consistent way to handle uncertainty in science, and it’s a central part of logic, decision theory, and indeed nearly every field of applied science.

  There are two well-known ways of arriving at Bayes’s rule. One is the axiomatic route, which treats probability as a set of logical rules and shows that Bayesian updating is the only way to preserve consistency. The other is variational, which demands that updates should stay as close as possible to prior beliefs while remaining consistent with new data. This latter view is known as the principle of minimum change. It captures the intuition that learning should be conservative: we shouldn’t alter our beliefs more than is necessary. This principle explains why Bayesian methods have become so effective in practical statistical inference: because they balance a respect for new data with loyalty to old information.

  A natural question arises here: can Bayes’s rule be extended into the quantum world?

  Quantum theory can be thought of as a noncommutative extension of probability theory. While there are good reasons to expect there should be a quantum analogue of Bayes’s rule, the field has for a long time struggled to identify a unique and universally accepted version. Instead, there are several competing proposals. One of them stands out: the Petz transpose map. This is a mathematical transformation that appears in many areas of quantum information theory, particularly in quantum error correction and statistical sufficiency. Some scholars have even argued that it’s the “correct” quantum Bayes’s rule. Still, the situation remains unsettled.

  In probability, the joint distribution is like a big table that lists the chances of every possible pair of events happening together. If you roll a die and flip a coin, the joint distribution specifies the probability of getting “heads and a 3”, “tails and a 5”, and so on. In this big table, you can also zoom out and just look at one part. For example, if you only care about the die, you can add up over all coin results to get the probability of each die face. Or if you only care about the coin, you can add up over all die results to get the probability of heads or tails. These zoomed-out views are called marginals.

  The classical Bayes’s rule doesn’t just update the zoomed-out views but the whole table — i.e. the entire joint distribution — so the connection between the two events also remains consistent with the new evidence.

  In the quantum version, the joint distribution isn’t a table of numbers but a mathematical object that records how the input and output of a quantum process are related. The point of the new study is that if you want a true quantum Bayes’s rule, you need to update that whole object, not just one part of it.

  A new study by Ge Bai, Francesco Buscemi, and Valerio Scarani in Physical Review Letters has taken just this step. In particular, they’ve presented a quantum version of the principle of minimum change by showing that when the measure of change is chosen to be quantum fidelity — a widely used measure of similarity between states — this optimisation leads to a unique solution. Equally remarkably, this solution coincided with the Petz transpose map in many important cases. As a result, the researchers have built a strong bridge between classical Bayesian updating, the minimum change principle, and a central tool of quantum information.

  The motivation for this new work isn’t only philosophical. If we’re to generalise Bayes’s rule to include quantum mechanics as well, we need to do so in a way that respects the structural constraints of quantum theory without breaking away from its classical roots.

  The researchers began by recalling how the minimum change principle works in classical probability. Instead of updating only a single marginal distribution, the principle works at the level of the joint input-output distribution. Updating then becomes an optimisation problem, i.e. finding the subsequent distribution that’s consistent with the new evidence but minimally different from the evidence from before.

  In ordinary probability, we talk about stochastic processes. These are rules that tell us how an input is turned into an output, with certain probabilities. For example if you put a coin into a vending machine, there might be a 90% chance you get a chips packet and a 10% chance you get nothing. This rule describes a stochastic process. This process can also be described with a joint distribution.

  In quantum physics, however, it’s tricky. The inputs and outputs aren’t just numbers or events but quantum states, which are described by wavefunctions or density matrices. This makes the maths much more complex. The resulting stochastic processes also become sequences of events called completely positive trace-preserving (CPTP) maps.

  A CPTP map is the most general kind of physical evolution allowed: it takes a quantum state and transforms it into another quantum state. And in the course of doing so, it needs to follow two rules: it shouldn’t yield any negative probabilities and it should ensure the total probability adds up to 1. That is, your chance of getting a chips packet shouldn’t be –90% nor should it be 90% plus a 20% chance of getting nothing.

  These complications mean that, while the joint distribution in classical Bayesian updating is a simple table, the one in quantum theory is more sophisticated. It uses two mathematical tools in particular. One is purification, a way to embed a mixed quantum state into a larger ‘pure’ state so that mathematicians can keep track of correlations. The other is Choi operators, a standard way of representing a CPTP map as a big matrix that encodes all possible input-output behaviour at once.

  Together, these tools play the role of the joint distribution in the quantum setting: they record the whole picture of how inputs and outputs are related.

  Now, how do you compare two processes, i.e. the actual forward process (input → output) and the guessed reverse process (output → input)?

  In quantum mechanics, one of the best measures of similarity is fidelity. It’s a number between 0 and 1. 0 means two processes are completely different and 1 means they’re exactly the same.

  In this context, the researchers’ problem statement was this: given a forward process, what reverse process is closest to it?

  To solve this, they looked over all possible reverse processes that obeyed the two rules, then they picked the one that maximised the fidelity, i.e. the CPTP map most similar to the forward process. This is the quantum version of applying the principle of minimum change.

  In the course of this process, the researchers found that in natural conditions, the Petz transpose map emerges as the quantum Bayes’s rule.

  In quantum mechanics, two objects (like matrices) commute if the order in which you apply them doesn’t matter. That is, A then B produces the same outcome as B then A. In physical terms, if two quantum states commute, they behave more like classical probabilities.

  The researchers found that when the CPTP map that takes an input and produces an output, called the forward channel, commutes with the new state, the updating process is nothing but the Petz transpose map.

  This is an important result for many reasons. Perhaps foremost is that it explains why the Petz map has shown up consistently across different parts of quantum information theory. It appears it isn’t just a useful tool but the natural consequence of the principle of minimum change applied in the quantum setting.

  The study also highlighted instances where the Petz transpose map isn’t optimal, specifically when the commutativity condition fails. In these situations, the optimal updating process depends more intricately on the new evidence. This subtlety departs clearly from classical Bayesian logic because in the quantum case, the structure of non-commutativity forces updates to depend non-linearly on the evidence (i.e. the scope of updating can be disproportionate to changes in evidence).

  Finally, the researchers have shown how their framework can recover special cases of practical importance. If some new evidence perfectly agrees with prior expectations, the forward and reverse processes become identical, mirroring the classical situation where Bayes’s rule simply reaffirms existing beliefs. Similarly, in contexts like quantum error correction, the Petz transpose map’s appearance is explained by its status as the optimal minimal-change reverse process.

  But the broader significance of this work lies in the way it unifies different strands of quantum information theory under a single conceptual roof. By proving that the Petz transpose map can be derived from the principle of minimum change, the study has provided a principled justification for its widespread use rather than being restricted to particular contexts. This fact has immediate consequences for quantum computing, where physicists are looking for ways to reverse the effects of noise on fragile quantum states. The Petz transpose map has long been known to do a good job of recovering information from these states after they’ve been affected by noise. Now that physicists know the map embodies the smallest update required to stay consistent with the observed outcomes, they may be able to design new recovery schemes that exploit the structure of minimal change more directly.

  The study may also open doors to extending Bayesian networks into the quantum regime. In classical probability, a Bayesian network provides a structured way to represent cause-effect relationships. By adapting the minimum change framework, scientists may be able to develop ‘quantum Bayesian networks’ where the way one updates their expectations of a particular outcome respects the peculiar constraints of CPTP maps. This could have applications in quantum machine learning and in the study of quantum causal models.

  There are also some open questions as well. For instance, the researchers have noted that if different measures of divergence other than fidelity are used, e.g. the Hilbert-Schmidt distance or quantum relative entropy, the resulting quantum Bayes’s rules may be different. This in turn indicates that there could be multiple valid updating rules, each suited to different contexts. Future research will need to map out these possibilities and determine which ones are most useful for particular applications.

  In all, the study provides both a conceptual advance and a technical tool. Conceptually, it shows how the spirit of Bayesian updating can carry over into the quantum world; technically, it provides a rigorous derivation of when and why the Petz transpose map is the optimal quantum Bayes’s rule. Taken together, the study’s finding strengthens the bridge between classical and quantum reasoning and offers a deeper understanding of how information is updated in a world where uncertainty is baked into reality rather than being due to an observer’s ignorance.

  2025.10.04

• Rescuing superconductivity

  From a paper in Nature Reviews Physics, December 19, 2024:

  One of the forefront fields of modern superconductivity research is that on hydrides at high pressures. Over the past few years, this research has attracted considerable publicity, of which a substantial fraction has been negative. Scientific fraud has been committed and exposed, and arguments continue about specific aspects of data presented in some other papers. Among all the noise that is being generated, one might lose sight of the big-picture question of whether the field is on solid foundations or not, that is, whether high-pressure hydrides host superconductivity at all. Here, we readdress this central issue. We select and critically examine what we identify as six key papers on the topic. We have all spent substantial portions of our careers working on superconductivity, so hope that the conclusions that we reach will carry at least some weight. We also decided to include among our authorship team only people who have never worked directly on hydride superconductivity, so that our examination of the scientific facts can be as impartial as possible. We conclude that it is overwhelmingly probable that the phenomenon of hydride superconductivity is genuine.

  It’s intriguing such an exercise had to be undertaken. It’s yet another reminder that practising science isn’t simply a matter of following the facts. Science is part of the world, not separate from it, and is affected by what others think of it, especially based on perceptions of trustworthiness, self-correctability, and integrity. Self-correctability in particular went out the window the moment the holes in the Dias/Salamat saga became clear, followed by integrity. Imagine discovering a groundbreaking new natural phenomenon: usually such things revitalise fields looking for a breakthrough, but here, the field became marred by a slew of bad papers that shrunk funding opportunities and rendered young researchers trying to enter or already in the field nervous about their future.

  In fact the self-correctability and integrity issues were compounded by the actions of the journals that published the problem papers. Nature and Physical Review Letters both have submissions peer-reviewed. The process of peer review is designed to check whether the data provided match the conclusion provided, not the integrity of the data. However, the data the journals reviewed before publishing the papers was also the data independent experts reviewed to find flaws, consequently leading to the retractions. What explains this? Further, one of the papers, purporting to show superconductivity in LuNH and published in Nature in March 2023, didn’t contain enough evidence to support the conclusion, which the journal’s review missed as well. A Nature news feature reported in September that year:

  Critiques started appearing as soon as the Nature paper was published. One major line of criticism is that the Rochester team didn’t provide enough evidence to show that resistance does go to zero in its material. Dias and his colleagues state in the paper that they removed “small residual resistance” from some of their electrical measurements, but critics argue that it should not be necessary to remove background for these types of measurements, given clean readings of both a sample’s current and voltage. The problem with removing a background, says Sven Friedemann, a physicist at the University of Bristol, UK, is that it implies that the raw data do not go to zero — and therefore don’t show superconductivity.

  The same feature also quoted two scientists saying Nature’s retraction of a carbonaceous sulphur hydride paper in 2022 was “not strong enough”.

  The names of many of the authors of the review should be familiar to people who have been following the Dias/Salamat saga, including Peter Hirschfeld, Steven Kivelson, Andrew Mackenzie, and Subir Sachdev. The review reportedly began with the two possible outcomes — hydrides display superconductivity versus hydrides don’t — being equally probable and concluded in favour of the former after assessing the results reported by multiple groups. While the nominal definition of superconductivity alludes only to the fact that a material’s electrical resistance drops to zero, condensed-matter physicists perform four tests looking for different features. One is zero electrical resistance; another is that the material’s magnetisation varies through a particular pattern. On this count the reviewers assessed data from only one group, that of Mikhail Eremets & co. in 2022.

  Yet another familiar name, Jorge Hirsch, has already expressed his disapproval towards the review. “I was surprised and disappointed to see this. I speculate [they wrote] it because hydrides being superconductors would establish the validity of BCS theory, in which they firmly believe,” he told Physics. A bit of relevant background here is that Hirsch is a detractor of the popular BCS theory of superconductivity and a proponent of his own holes theory. While Physics writes that he’s already flagged some problems with the Eremets et al. paper, it doesn’t say the Eremets et al. paper raised significant doubts about the validity of his holes theory — which is to say both the study and Hirsch’s idea could be flawed rather than the study alone. Overall, if science is to remain trustworthy, scientists need to undertake exercises like this, conducting — while being seen to be conducting — impartial reviews of the prevailing evidence and considering whether it makes sense to continue working in fields beleaguered by the influence of some dishonest exponents.

  I only hope reviewers will also take a closer look at the roles journals and their misguided incentives — and the still largely blind trust the global scientific community places in them — play in sustaining scandals in science.

  2024.12.21

• Unexpected: Magnetic regions in metal blow past speed limit

  You’re familiar with magnetism, but do you know what it looks like at the smallest scale? Take a block of iron, for example. It’s ferromagnetic, which means if you place it near a permanent magnet – like a refrigerator magnet – the block will also become magnetic to a large extent, larger than materials that aren’t ferromagnetic.

  If you zoom in to the iron atoms, you’ll see a difference between areas that are magnetised and areas that aren’t. Every subatomic particle has four quantum numbers, sort of like its Aadhaar or social security ID. No two electrons in the same system can have the same ID, i.e. one, some or all of these numbers differ from one electron to the next. One of these numbers is the spin quantum number, and it can have one of two values, or states, at any given time. Physicists refer to these states as ‘up’ and ‘down’. In the magnetised portions, in the iron block, you’ll see that electrons in the iron atoms will either all be pointing up or all down. This is a defining feature of magnetism.

  Scientists have used it to make hard-disk drives that are used in computers. Each drive stores information by encoding it in electrons’ spins using a magnetic field, where, say, ‘1’ is up and ‘0’ is down, so a series of 1s and 0s become a series of ups and downs.

  In the iron block, the parts that are magnetised are called domains. They demarcate regions of uniform electron spin in three dimensions in the block’s bulk. For a long time, scientists believed that the ‘walls’ of a domain – i.e. the imaginary surface between areas of uniform spin and areas of dis-uniform spin – could move at up to around 0.5 km/s. If they moved faster, they could destabilise and collapse, allowing a kind of magnetic chaos to spread within the material. They arrived at this speed limit from their theoretical calculations.

  The limit matters because it says how fast the iron block’s magnetism can be manipulated, to store or modify data for example, without losing that data. It also matters for any other application that takes advantage of the properties of ferromagnetic materials.

  In 2020, a group of researchers from the Czech Republic, Germany, and Sweden found that if you stacked up a layer of ferromagnets, the domain walls could move much faster – as much as 14 km/s – without collapsing. Things can move fast in the subatomic realm, yet 14 km/s was still astonishing for ferromagnetic materials. So scientists set about testing it.

  A group from Italy, Sweden, and the US reported in a paper published in Physical Review Letters on December 19 (preprint here) that they were able to detect domain walls moving in a composite material at a stunning 66 km/s – greater than the predicted speed. Importantly, however, existing theories that explain a material’s magnetism at the subatomic scale don’t predict such a high speed, so now physicists know their theories are missing something.

  In their study, the group erected a tiny stack of the following elements, in this order: tantalum, copper, a cobalt-iron compound, nickel, the cobalt-iron compound, copper, and tantalum. Advanced microscopy techniques revealed that the ferromagnetic nickel layer (just a nanometre wide) had developed domains of two shapes: some were like stripes and some formed a labyrinth with curved walls.

  The researchers then tested the domain walls using the well-known pump-probe technique: a blast of energy first energises a system, then something probes it to understand how it’s changed. The pump here was an extremely short pulse of infrared radiation and the probe was a similarly short pulse of ultraviolet (UV) radiation.

  The key is the delay between the pump and probe pulses: the smaller the delay, the greater the detail that comes to light. (Three people won the physics Nobel Prize this year for finding ways to make this delay as small as possible.) In the study it was 50 femtoseconds, or 500 trillionths of a second.

  The UV pulse was diffracted by the electrons in nickel. A detector picked up the diffraction patterns and the scientists ‘read’ them together with computer simulations of the domains to understand how they changed.

  How did the domains change? The striped walls were practically unmoved but the curved walls of the labyrinthine pattern did move, by about 17-23 nanometres. The group made multiple measurements. When they finally calculated an average speed (which is equal to distance divided by time), they found it to be 66 km/s, give or take 20 km/s.

  

  The observation of extreme wall speed under far-from-equilibrium conditions is the … most significant result of this study,” they wrote in their paper. This is true: even though the researchers found that the domain-wall speed limit in a multilayer ferromagnetic material is much higher than 0.5 km/s – as the 2020 group predicted – they also found it to be a lot higher than the expected 14 km/s. Of course, it’s also stunning because the curved domain walls moved at more than 10-times the speed of sound in that material – and the more curved a portion was, the faster it seemed to move.

  The researchers concluded that “additional mechanisms are required to fully understand these effects” – as well as that they could be “important” to explain “ultrafast phenomena in other systems such as emerging quantum materials”.

  This is my second recent post about scientists finding something they didn’t expect to, but in settings more innocuous than in the vast universe or at particle smashers. Read the first one, about the way paint dries, here.

  2023.12.21

• You can do worse than watching paint dry – ask physics

  I live in Chennai, a city whose multifaceted identity includes its unrelenting humidity. Its summers are seldom hotter than those in Delhi but they are more unbearable because it leaves people sweaty, dehydrated, and irritated. Delhi’s heat doesn’t have the same effect because when people sweat there, the droplets evaporate into the air, whose low relative humidity allows it to ‘accommodate’ moisture. But in Chennai, the air is almost always humid, more so during the summer, and the sweat on people’s skin doesn’t evaporate. Yet their bodies continue to sweat because it’s one of the few responses they have to the heat.

  Paint, fortunately, has a different story to tell. Fresh paint on a wall doesn’t dry faster or slower depending on how humid the air is. This is because pain is made of water plus some polymers whose molecules are much larger than those of water. At first, water does begin to escape the paint and evaporate from the surface. This pulls the polymer molecules to the surface in a process called advection. On the surface, the polymer molecules form a dense layer that prevents the water below from interacting directing with the air, or its humidity. So the rate of evaporation slows until it reaches a constant low value. This is why, even in dry weather, paint takes its time to dry.

  Scientists have verified that this is the case in a new study, in which they also reported that their findings can be used to understand the behaviour of little respiratory droplets in which viruses travel through the air. (Some studies – like this and this – have suggested that a virus’s viability may depend on the relative humidity and how quickly the droplet dries, among other factors. Since the relative humidity varies by season, a link could explain why some viral outbreaks are more seasonal.)

  Generally, the human skin – as the largest outer-organ of the human body – is responsible for making sure the body doesn’t lose too much water through evaporation. Scientists think that it can adjust how much sweat is released on the skin by modifying the mix of lipids (fatty substances) in its outermost layer. If it did, it would be an example of an active process – a dynamic response to environmental and biological conditions. Paint drying, on the other hand, is a non-active process: the rate of evaporation is limited by the polymer molecules at the surface and their properties.

  In 2017, a chemical engineer at the University of Bordeaux named Jean-Baptiste Salmon predicted that an active process may not be needed at all to explain humidity-independent evaporation because it arises naturally in solutions like that of paint. The new study tested the prediction of Salmon et al. using a non-active polymer solution, i.e. one that’s incapable of developing an active response to changes in humidity.

  They filled a plastic container with polyvinyl alcohol, then drilled a small hole near the bottom and fit a glass tube there with an open end. The liquid could flow through the tube and evaporate from the end. To prevent the liquid from evaporating from its surface, they coated it with an oily substance called 1-octadecene. They placed this container on a sensitive weighing scale and the whole apparatus inside a sealed box with adjustable humidity. The researchers adjusted the humidity from 25% to 90% and each time studied the evaporation rate for more than 16 hours.

  They found that Salmon et al. were right: the evaporation rate was higher for around three hours before dropping to a lower value. This was because polymer molecules had accumulated at the layer where the liquid met the air. But in these three hours, the rate of evaporation didn’t drop even when the humidity was increased. In other words, humidity-independent evaporation begins earlier than Salmon et al. predicted.

  The researchers also reported another divergence: the evaporation rate wasn’t affected by a relative humidity of up to 80% – but beyond that, the rate fell if the humidity increased further. So what Salmon et al. said was at play but it wasn’t the full picture; some other forces were also affecting the evaporation.

  The researchers ended their paper with an idea. They took a closer look at the open end of the tube, where the polyvinyl alcohol evaporated, with a microscope. They found that the polymer layer was overlaid with a stiffer semisolid, or gel-like, layer. Such layers are known to form when there is a compressive stress, and further block evaporation. The researchers found that their equations to predict the evaporation rate roughly matched the observed value when they were modified to account for this stress. They also found that a sufficiently thick gel layer could form within one second – a short time span considering the many hours over which the rate of evaporation evolves.

  “These discrepancies motivate the search for extra physics beyond Salmon et al., which may again relate to a gelled polymer skin at the air-solution interface,” they concluded in their paper, published in the journal Physical Review Letters on December 15.

  2023.12.17

• The journal’s part in a retraction

  This is another Ranga Dias and superconductivity post, so please avert your gaze if you’re tired of it already.

  According to a September 27 report in Science, the journal Nature plans to retract the latest Dias et al. paper, published in March 2023, claiming to have found evidence of near-room-temperature superconductivity in an unusual material, nitrogen-doped lutetium hydride (N-LuH). The heart of the matter seems to be, per Science, a plot showing a drop in N-LuH’s electric resistance below a particular temperature – a famous sign of superconductivity.

  Dias (University of Rochester) and Ashkan Salamat (University of Nevada, Las Vegas), the other lead investigator in the study, measured the resistance in a noisy setting and then subtracted the noise – or what they claimed to be the noise. The problem is apparently that the subtracted plot in the published paper and the plot put together using raw data submitted by Dias and Salamat to Nature are different; the latter doesn’t show the resistance dropping to zero. Meaning that together with the noise, the paper’s authors subtracted some other information as well, and whatever was left behind suggested N-LuH had become superconducting.

  A little more than a month ago, Physical Review Letters officially retracted another paper of a study led by Dias and Salamat after publishing it last year – and notably after a similar dispute (and on both occasions Dias was opposed to having the papers retracted). But the narrative was more dramatic then, with Physical Review Letters accusing Salamat of obstructing its investigation by supplying some other data as the raw data for its independent probe.

  Then again, even before Science‘s report, other scientists in the same field had said that they weren’t bothering with replicating the data in the N-LuH paper because they had already wasted time trying to replicate Dias’s previous work, in vain.

  Now, in the last year alone, three of Dias’s superconductivity-related papers have been retracted. But as on previous occasions, the new report also raises questions about Nature‘s pre-publication peer-review process. To quote Science:

  In response to [James Hamlin and Brad Ramshaw’s critique of the subtracted plot], Nature initiated a post-publication review process, soliciting feedback from four independent experts. In documents obtained by Science, all four referees expressed strong concerns about the credibility of the data. ‘I fail to understand why the authors … are not willing or able to provide clear and timely responses,’ wrote one of the anonymous referees. ‘Without such responses the credibility of the published results are in question.’ A second referee went further, writing: ‘I strongly recommend that the article by R. Dias and A. Salamat be retracted.’

  What was the difference between this review process and the one that happened before the paper was published, in which Nature‘s editors would have written to independent experts asking them for their opinions on the submitted manuscript? Why didn’t they catch the problem with the electrical resistance plot?

  One possible explanation is the sampling problem: when writing an article as a science journalist, the views expressed in the article will be a function of the scientists that I have sampled from within the scientific community. In order to obtain the consensus view, I need to sample a sufficiently large number of scientists (or a small number of representative scientists, such as those who I know are in touch with the pulse of the community). Otherwise, there’s a nontrivial risk of some view in my article being over- or under-represented.

  Similarly, during its pre-publication peer-review process, did Nature not sample the right set of reviewers? I’m unable to think of other explanations because the sampling problem accounts for many alternatives. Hamlin and Ramshaw also didn’t necessarily have access to more data than Dias et al. submitted to Nature because their criticism emerged in May 2023 itself, and was based on the published paper. Nature also hasn’t disclosed the pre-publication reviewers’ reports nor explained if there were any differences between its sampling process in the pre- and post-publication phases.

  So short of there being a good explanation, as much as we have a scientist who’s seemingly been crying wolf about room-temperature superconductivity, we also have a journal whose peer-review process produced, on two separate occasions, two different results. Unless it can clarify why this isn’t so, Nature is also to blame for the paper’s fate.

  2023.09.28

• Is Dias bringing the bus back?

  So Physical Review Letters formally retracted that paper about manganese sulphide, in the limelight for having been coauthored by Ranga P. Dias, yesterday. The retraction notice states: “Of the authors on the original paper, R. Dias stands by the data in Fig. 1(b) and does not agree to retract the Letter.” Figure 1(b) is reproduced below.

  

  The problem with the second plot is that its curves reportedly resemble some in Dias’s doctoral thesis from 2013, in which he had examined the same properties of germanium tetraselenide, a different kind of material. Curves can look the same to the extent that they can have the same overall shape; it’s a problem when they also reproduce the little variations that are a result of the specific material synthesised for a particular experiment and the measurements made on that day.

  That Dias is the only person objecting to the retraction is interesting because it means one of his coaouthors, Ashkan Salamat, agreed to it. Salamat heads a lab in the University of Nevada, Las Vegas, that’s been implicated in the present controversy. Earlier this year, well after Physical Review Letters said it was looking into the allegations against the manganese sulphide paper, Scientific American reported:

  Salamat has since responded, suggesting that even though the two data sets may appear similar, the resemblance is not indicative of copied data. “We’ve shown that if you just overlay other people’s data qualitatively, a lot of things look the same,” he says. “This is a very unfair approach.”

  Physical Review Letters also accused Salamat of attempting to obstruct its investigation after it found that the raw data he claimed to have submitted of the group’s experiments wasn’t in fact the raw data. Since then, Salamat may well have changed his mind to avoid more hassle or in deference to the majority opinion, but I’m still curious if he could have changed his mind because he no longer thought the criticisms to be unfair.

  Anyway, Dias is in the news because he’s made some claims in the past about having found room-temperature superconductors. A previous paper was retracted in September 2022, two years after it was published and independent researchers found some problems in the data. He had another paper published in March this year, reporting room-temperature but high-pressure superconductivity in nitrogen-doped lutetium hydride. This paper courted controversy because Dias et al. refused to share samples of the material so independent scientists could double-check the team’s claim.

  Following the retraction, The New York Times asked Dias what he had to say, and his reply seems to bring back the bus under which principal investigators (PIs) have liked to throw their junior colleagues at signs of trouble in the past:

  [He] has maintained that the paper accurately portrays the research findings. However, he said on Tuesday that his collaborators, working in the laboratory of Ashkan Salamat, a professor of physics at the University of Nevada, Las Vegas, introduced errors when producing charts of the data using Adobe Illustrator, software not typically used to make scientific charts.

  “Any differences in the figure resulting from the use of Adobe Illustrator software were unintentional and not part of any effort to mislead or obstruct the peer review process,” Dr. Dias said in response to questions about the retraction. He acknowledged that the resistance measurements in question were performed at his laboratory in Rochester.

  He’s saying that his lab made the measurements at the University of Rochester and sent the data to Salamat’s lab at the University of Nevada, where someone else (or elses) introduced errors using Adobe Illustrator – presumably while visualising the data, but even then Illustrator is a peculiar choice – and these errors caused the resulting plot to resemble one in Dias’s doctoral thesis. Hmm.

  The New York Times also reported that after refusing in the past to investigate Dias’s work following allegations of misconduct, the University of Rochester has now launched an investigation “by outside experts”. The university doesn’t plan to release their report of the findings, however.

  But even if the “outside experts” conclude that Dias didn’t really err and that, honestly, Salamat’s lab in Las Vegas was able to introduce very specific kinds of errors in what became figure 1(b), Dias must be held accountable for being one of the PIs of the study – a role whose responsibilities arguably include not letting tough situations devolve into finger-pointing.

  2023.08.16

• Gerald Guralnik (1936-2014)

  Of the six scientists who came up with the idea of a Higgs boson in the mid-1960s, independently or in collaboration with others, I’ve met all of one. Tom Kibble was at the Institute of Mathematical Science, Chennai, in January 2013 for a conference. He was 80 years old then, and looked quite frail. Every time somebody tapped his shoulder before taking a photograph, he would break into a self-effacing smile. It was clear he was surprised by the attention he was receiving. Kibble thought he didn’t deserve it.

  He, Carl Hagen and Gerald Guralnik comprised one of the three teams that conceived the mechanism to explain how some fundamental particles acquired mass in the early universe, over time making possible chemical reactions, stars, life, and many things besides. The other two teams comprised Francois Englert and Robert Brout, and Peter Higgs; Higgs’ name has today become attached to the name of the mechanism. For their work, Higgs and Englert were awarded the 2013 Nobel Prize in physics. Brout couldn’t receive the prize because he had died in 2011. Kibble, Hagen and Guralnik were left out because of limits on how many people the prize could be awarded to at a time.

  Fair share of obstacles

  On April 26, 2014, Gerald Guralnik died of a heart attack in Rhode Island after delivering a lecture at Brown University. He was 77. In those seven decades, he had become one of the world’s leading experts on theoretical particle physics, which, through the 1960s, was entering its boom time as the world would later discover. In this period, he co-scripted one of the most enduring quests in modern physics research.

  Before I started writing this, I visited the Wikipedia page for the Physical Review Letters papers published by the three groups that first called the world’s attention to their findings. In the second line, Peter Higgs is mentioned as having worked with Satyen Bose – undoubtedly the consequence of a grave misapprehension that pervaded India when the 2013 Nobel Prizes were announced. Many believed Satyen Bose had been neglected for his work, but he just hadn’t worked on the Higgs boson, only on the underlying theory that controls the lives and times of all bosons. If such are the facile issues that concern some misguided Indians today, Guralnik tackled more than a fair share in his time.

  

  For a few years after Kibble, Hagen and Guralnik published their paper, their work wasn’t taken seriously. Guralnik wrote in Huffington Post in August 2012 that, in the summer of 1965, Werner Heisenberg – the originator of the notorious uncertainty principle – thought Guralnik’s ideas were junk. The New York Times wrote that Robert Marshak, a famous theoretical physicist, told Guralnik that if he wished to survive in physics, he “must stop thinking about this sort of problem and move on,” advice that Guralnik “wisely obeyed”. According to Kibble, however, Marshak later admitted that he had been misguided.

  Deference over primacy

  Nevertheless, some other scientists had starting working on Guralnik & co.’s theories. By the 1970s, Sheldon Glashow, Abdus Salam and Steven Weinberg had succeeded in ironing out many of its inconsistencies and won the Nobel Prize for physics in 1979 for their work… even though it would be 50 more years to prove via experiment that the Higgs mechanism was for real. This is because there was no disputing that the implications of the work of Kibble, Hagen, Guralnik, Higgs, Brout and Englert were revolutionary, at least among those who were willing to accept it.

  To this end, the 1979 prizewinners and the ‘Higgs Six’ were aware of and deferential toward the contributions of others to the development of this new theory. In fact, Higgs, who has often wound up being the centre of attention when talk of his eponymous mechanism comes up, has said that he’d rather call it the ABEGHHK’tH mechanism (A denoted Phillip Warren Anderson; ‘tH, Gerardus ‘t Hooft).

  But others were less considerate, which didn’t go down well with Guralnik. As Kibble wrote in his obituary in Nature, “Guralnik came to feel that our early paper was often unfairly neglected. He gave talks and wrote papers pointing out our distinctive contribution, of which he was justifiably proud, and in which he was unquestionably the prime mover.” This doesn’t mean he went on to become a sour, old bat, of course, but only that Guralnik seemed to appreciate the gravitas of his work much more than others at the time. When  Higgs and Englert shared the 2013 Nobel Prize in physics, Guralnik told Brown Daily Herald that he was “a little hurt”, but happier for the recognition that his peers – and by extension his work – had received.

  (It is, in fact, hard to say if he is as celebrated as Higgs is today, physicists notwithstanding. Such are the consequences of asymmetric recognition, a sort of ceiling effect that silences avant garde advancements until the world is ready to hear them. This is also a complaint I’ve heard from far too many Indian scientists and whose efforts to remedy it I don’t begrudge them even if it only seems like an infantile squabble over primacy.)

  In fact, after his work in establishing the theoretical foundations of the Higgs mechanism, which itself is a cornerstone of a unified theory that describes both the electromagnetic and weak nuclear forces of nature, Guralnik proceeded to make a lot of other contributions. He worked on computational approaches to quantum field theory, quantum chromodynamics (i.e., the theory of the strong nuclear force), the application of chaos theory to particle physics, and string theory. His was a versatile genius, in part combative and in part pliant. Rest in peace.

  2014.06.05