Glioblastoma is the most aggressive brain cancer and the hardest to treat, as it spreads and invades healthy brain tissue in a diffuse, microscopic way. Surgical treatment calls for a fine balance between excising all cancerous tissues and removing as little healthy brain tissue as possible. To help neurosurgeons more accurately remove glioblastoma, an international research collaboration has developed an optical imaging probe that identifies microscopic cancer cells in the margins of tumour-resected cavities in the brain.

The imaging probe works by exploiting the significantly increased fatty acid (FA) metabolism exhibited by glioblastoma cells. FA metabolism plays a key role in tumour progression and proliferation and is central to cancer immunity. To enable real-time, non-invasive imaging of FA absorption, the researchers – from Erasmus University Medical Center (Erasmus MC) in The Netherlands and the University of Missouri in the USA – covalently linked a long-chain saturated FA with the clinically approved near-infrared (NIR) dye indocyanine green (ICG).

ICG has intrinsic low autofluorescence, enables deep tissue imaging and exhibits a high signal-to-noise ratio compared with visible fluorophores. The team hypothesized that a probe combining ICG with a FA might specifically accumulate in tumours and enable efficient intraoperative visualization of tumour margins. Importantly, the spectral characteristics of ICG make it compatible with many existing intraoperative cameras and surgical microscopes.

The researchers initially investigated the uptake of the FA-ICG probe in living cells, confirming that the dye’s physiological uptake resembles that of natural FAs. They then used fluorescence imaging to assess FA-ICG uptake in mice with implanted glioblastoma, observing high accumulation in the brain tumours.

Comparing the fluorescence signal from mice administered with equivalent doses of FA-ICG and ICG revealed that the average radiance from FA-ICG was approximately 2.2 times higher than that from IGC. At 12 and 24 h post-injection, retention of the probe in the brain was approximately two to three times higher in the tumour-bearing than the non-tumour-bearing hemisphere.

Next, lead authors Meedie Ali and Pavlo Khodakivskyi and their colleagues investigated the application of FA-ICG as a preclinical imaging agent in a patient-derived model of glioblastoma. They showed that the probe could successfully image tumour growth at different time points in several mice.

“This finding is of importance for preclinical research since patient-derived xenograph models of glioblastoma are characterized by an unpredictable growth pattern and low tumour implantation rates,” explains principal investigator Elena Goun from the University of Missouri. “Thus, monitoring of tumour status by sensitive, non-invasive in vivo fluorescence imaging would be of high value as the introduction of optical imaging of reporter genes [an alternative monitoring approach] is known to result in tumour phenotypic alterations.”

Fluorescence-guided surgery

The researchers also demonstrated the feasibility of FA-ICG as a contrast agent for NIR image-guided cancer surgery, performing surgery on tumour-bearing mice using a standard NIR camera approved for use in surgical suites. Not only did the FA-ICG probe successfully image glioblastoma in the animals’ brains, but the brains also exhibited a considerably higher fluorescence signal than seen from similar mice injected with an ICG-only dye.

Subsequently, the team employed the probe during surgical resection of veterinarian-diagnosed symptomatic canine mastocytoma (a skin cancer) in a pet dog. Ten hours after injection with FA-ICG, the dog underwent surgery, with image-guided surgery performed successfully using an open-air NIR surgical camera.

If the probe transitions to routine clinical use, it could prove be of great benefit to neurosurgeons. If they can identify cancer cells, which are microscopic and resemble healthy brain tissue, outside the surgical margins, follow-up chemotherapy and radiation treatments should be more effective and cancer recurrence may be delayed. The probe also offers the practical features of a workable surgical procedure, an appropriate half-life and fluorescence that can be seen under normal operating room lights.

“Our results demonstrate that FA metabolism represents an excellent target for tumour imaging, leading to significantly enhanced uptake of the FA-ICG probe in tumours,” the researchers write. “[The probe] represents a promising candidate for a wide range of applications in the fields of metabolic imaging, drug development and most notably for translation in image-guided surgery.”

The researchers are now planning a Phase I clinical trial to examine the safety and efficacy of the probe. Specifically, they aim to determine how well patients tolerate the probe, what side effects may occur at an effective dose, and how the probe’s performance compares to existing optical imaging surgical tools.

“The upside of fluorescence-guided surgery is that you can make little remnants much more visible using the light emitting properties of these tumour cells when you give them a dye,” says Rutger Balvers, a neurosurgeon at Erasmus MC who is expected to lead the human clinical trials, in a press statement. “And we think that the upside of FA-ICG compared to what we have now is that it’s more select in targeting tumour cells. The visual properties of the probe are better than what we’ve used before.”

The study is described in npj Imaging.

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As a mother of two, I’ve read a lot of children’s books. While there are some so good that even parents don’t mind reading them again and again, it’s also very easy for them to miss the mark and end up “accidentally” hidden behind other books. They’ve not only got to have an exciting story, but also easy wording, a rhythmic pace, flowing language and captivating pictures.

Great non-fiction kids’ books are especially hard to find as they need to add in yet another ingredient: facts. As a result, they can often struggle to portray educational topics in an accessible and engaging way without being boring. So when I saw the ever impressive Jess Wade had published her second children’s book about physics, Light: the Extraordinary Energy That Illuminates Our World, I was intrigued.

Wade is a woman of many talents. She’s an accomplished physicist at Imperial College London, a trailblazing advocate for equality in science, and an enthusiastic science communicator. Her first book, Nano: the Spectacular Science of the Very (Very) Small, won the 2022 UK Literary Association (UKLA) Book Award for information books (3–14+ years).

And now, with the help of beautiful illustrations by Argentinian artist Ana Sanfelippo, Wade has created a clear, concise explanation of light, how it behaves and how we use it. The book starts by describing where light comes from and why we need it, and goes on to more complex topics like reflection, scattering and dispersion, the electromagnetic spectrum, and technologies that use light.

The language is clear, the sentences are simple, and there is a flow to the narrative that makes up for the lack of a story. Wade makes the science relatable for children by bringing in real-world examples – such as how your shadow changes length during the day, and how apples reflect red light so look red. And throughout, Sanfelippo’s gorgeous illustrations fill the pages with colourful images of a girl and her dog exploring the concepts discussed, keeping the content bright and cheerful.

Cats and secrets

Now obviously I am not the target audience for Light. So, as my own children are too young (the age range listed is 7–12 years), I asked my eight-year old-niece, Katie, to take a look.

Instantly, Katie loved the illustrations, which helped keep her engaged with the content as she read – her favourite was one of a cat using a desk lamp to create a shadow. She was intrigued by how fast light is – “you’d have to run seven and a half times around Planet Earth in a single second” – and liked being “let in on a secret” when Wade explains that white light actually contains a rainbow.

But as the book went on, she found some bits confusing, like the section on the electromagnetic spectrum. “It’s definitely a book someone Katie’s age should read with a grown up, and maybe in two sittings, because it’s very information heavy (in a good way),” said her mum, Nicci. Indeed, there are a couple of page spreads that stand out as being particularly busy and wordy, and these dense parts somewhat interrupt the book’s flow. “But overall, she found the topic very interesting, and it provoked a lot of questions,” Nicci continued. “I enjoyed sharing it with her!”

I think it’s safe to say that Wade can add another success to her list of many accomplishments. Light is beautiful and educational, and personally, I wouldn’t hesitate to give it as gift or keep it at the front of the bookshelf.

• 2025 Walker Books 32pp £12.99hb

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Superconductors are materials that, below a certain critical temperature, exhibit zero electrical resistance and completely expel magnetic fields, a phenomenon known as the Meissner effect. They can be categorized into two types.

Type-I superconductors are what we typically think of as conventional superconductors. They entirely repel magnetic fields and abruptly lose their superconducting properties when the magnetic field exceeds a certain threshold, known as the critical field, which depends on both magnetic field strength and temperature.

In contrast, Type-II superconductors have two critical field values. As the magnetic field increases, the material transitions through different states. At low magnetic fields below the first critical field, magnetic flux is completely excluded. Between the first and second critical fields, some magnetic flux enters the material. Above the second critical field, superconductivity is destroyed.

In Type-II superconductors, when magnetic flux enters the material, it does so at discrete points, forming quantized vortices. These vortices repel each other and self-organize into a regular pattern known as the Abrikosov lattice. This effect has also been observed in Bose-Einstein condensates (bosons at extremely low temperatures) and chiral magnets (magnetic materials with spirally aligned magnetic moments). Interestingly, similar vortex self-organization is seen in liquid crystals, offering deeper insights into the underlying physics.

In this study, the researchers investigate vortex behaviour within a liquid crystal droplet, revealing a novel phenomenon termed Abrikosov clusters, which parallels the structures seen in Type-II superconductors. They examine the transition from an isotropic liquid phase to a chiral liquid phase upon cooling. Through a combination of experimental observations and theoretical modelling, the study demonstrates how chiral domains, in other words topological defects, cluster due to the interplay between vortex repulsion and the spatial confinement imposed by the droplet.

To model this behaviour, the researchers use a mathematical framework originally developed for superconductivity called the Ginzburg-Landau equation, which helps identify how certain vortex patterns emerge by minimizing the system’s energy. An interesting observation is that light passing through the chiral domains of the droplet can resultingly obtain chirality. This suggests that the research may offer innovative ways to steer and shape light, making it valuable for both data communication and astronomical imaging.

Do you want to learn more about this topic?

Vortex dynamics and mutual friction in superconductors and Fermi superfluids by N B Kopnin (2002)

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The quantum tangent kernel method is a mathematical approach used to understand how fast and how well quantum neural networks can learn. A quantum neural network is a machine learning model that runs on a quantum computer. Quantum tangent kernels help predict how the model will behave, particularly as it becomes very large – this is known as the infinite-width limit. This allows researchers to assess a model’s potential before training it, helping them design more efficient quantum circuits tailored to specific learning tasks.

A major challenge in quantum machine learning is the barren plateau problem, where the optimization landscape becomes flat, hiding the location of the minimum energy state. Imagine hiking in the mountains, searching for the lowest valley, but standing on a huge, flat plain. You wouldn’t know which direction to go. This is similar to trying to find the optimal solution in a quantum model when the learning signal disappears.

To address this, the researchers introduce the concept of quantum expressibility, which describes how well a quantum circuit can explore the space of possible quantum states. In the hiking analogy, quantum expressibility is like the detail level of your map. If expressibility is too low, the map lacks enough detail to guide you. If it’s too high, the map becomes overly complex and confusing.

The researchers investigate how quantum expressibility influences the value concentration of quantum tangent kernels. Value concentration refers to the tendency of kernel values to cluster around zero, which contributes to barren plateaus. Through numerical simulations, the authors validate their theory and show that quantum expressibility can help predict and understand the learning dynamics of quantum models.

In machine learning, loss functions measure the difference between predicted outputs and actual target values. These can relate to a global optimum (the best possible value across the entire system) or a local optimum (the best value within a small region or subset of qubits). The study shows that high expressibility can drastically reduce quantum tangent kernel values for global tasks, though this effect can be partially mitigated for local tasks.

The study establishes the first rigorous analytical link between the expressibility of quantum encodings and the behaviour of quantum neural tangent kernels. It offers valuable insights for improving quantum learning algorithms and supports the design of better quantum models, especially large, powerful quantum circuits, by showing how to balance expressiveness and learnability.

Do you want to learn more about this topic?

A comprehensive review of quantum machine learning: from NISQ to fault tolerance by Yunfei Wang and Junyu Liu (2024)

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Physicists have taken a major step toward unlocking the mysteries of antimatter by being the first to perform coherent spin spectroscopy on a single antiproton. Done by researchers on CERN’s BASE collaboration, the experiment measures the magnetic properties of antimatter with record-breaking precision. As a result, it could help us understand why there is much more matter than antimatter in the universe,

“The level of control the authors have achieved over an individual antimatter particle is unprecedented,” says Dmitry Budker, a physicist at the University of California, Berkeley, who was not involved in the study. “This opens the path to much more precise tests of fundamental symmetries of nature.”

In theory, the universe should have been born with equal amounts of matter and antimatter. Yet all the visible structures we see today – including stars, galaxies, planets and people – are made almost entirely of matter. This cosmic imbalance remains one of the biggest open questions in physics and is known as the baryon asymmetry problem.

“The general motivation for studying antiprotons is to test fundamental symmetries and our understanding of them,” says Stefan Ulmer, a senior member of BASE and head of the Ulmer Fundamental Symmetries Laboratory at RIKEN in Japan. “What we know about antimatter is that it appears as a symmetric solution to quantum mechanical equations – there’s no obvious reason why the universe should not contain equal amounts of matter and antimatter.”

This mystery can be probed by doing very precise comparisons of properties of matter and antimatter particles – in this case, the proton and the antiproton. For example, the Standard Model says that protons and antiprotons should have identical masses but equal and opposite electrical charges. Any deviations from the Standard Model description could shed light on baryon asymmetry.

Leap in precision

Now, the BASE (Baryon Antibaryon Symmetry Experiment) team has focused on coherent spectroscopy, which is a quantum technique that uses microwave pulses to manipulate the spin states of a single antiproton.

“We were doing spectroscopy on the spin of a single trapped antiproton, stored in a cryogenic Penning trap system,” Ulmer explains. “It is significant because this is of highest importance in studying the fundamental properties of the particle.”

By applying microwave radiation at just the right frequency, the team induced Rabi oscillations –periodic flipping of the antiproton’s spin – and observed the resulting resonances. The key result was a resonance peak 16 times narrower than in any previous antiproton measurements, meaning the team could pinpoint the transition frequency with much greater accuracy. Combined with a 1.5-fold improvement in signal-to-noise ratio, the measurement paves the way for at least a tenfold increase in the precision of antiproton magnetic moment measurements.“In principle, we could reduce the linewidth by another factor of ten if additional technology is developed,” says Ulmer.

Budker described the measurement as unprecedented, adding, “This is a key to future precise tests of CPT invariance and other fundamental-physics experiments”.

Deeply held principle

CPT symmetry – the idea that the laws of physics remain unchanged if charge, parity, and time are simultaneously reversed – is one of the most deeply held principles in physics. Testing it to higher and higher precision is essential for identifying any cracks in the Standard Model.

Ulmer says the team observed antiproton spin coherence times of up to 50 s. Coherence here refers to the ability of the antiproton’s quantum spin state to remain stable and unperturbed over time, which is essential for achieving high-precision measurements.

Measuring magnetic moments of nuclear particles is already notoriously difficult, but doing so for antimatter pushes the limits of experimental physics.

“These measurements require the development of experiments that are about three orders of magnitude more sensitive than any other apparatus developed before,” says Ulmer. “You need to build the world’s most sensitive detectors for single particles, the smallest Penning traps, and superimpose ultra-extreme magnetic gradients.”

The BASE team started development in 2005 and had early successes in proton measurements by 2011. Antiproton studies began in earnest in 2017, but achieving coherent spin control – as in the current work – required further innovations including ultra-homogeneous magnetic fields, cryogenic temperatures, and the exquisite control of noise.

Toward a deeper understanding

These improvements could also make other experiments possible. “This will also allow more precise measurements of other nuclear magnetic moments, and paves a path to better measurements in proton–antiproton mass comparisons,” Ulmer notes.

There may even be distant connections to quantum computing. “If coherence times for matter and antimatter are identical – something we aim to test – then the antimatter qubit might have applications in quantum information,” he says. “But honestly, operating an antimatter quantum computer, if you could do the same with matter, would be inefficient.”

More realistically, the team hopes to use their transportable trap system, BASE STEP, to bring antiprotons to a dedicated offline laboratory for even higher-resolution studies.

“The BASE collaboration keeps a steady course on increasing the precision of fundamental symmetry tests,” says Budker. “This is an important step in that direction.”

The research is described in Nature.

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In this work, Al and W are compared as individual dopants as well as co-dopants to arrive to an optimal cathode active material design. The objective is to improve the energy density of the materials without compromising cycle life; a feat which was previously thought unattainable for Ni-rich, Co-free layered oxide materials.

The findings emphasize the importance of understanding the effect of chemical composition and synthesis conditions on the morphology of the material particles. In turn, this morphology plays a determinant role in the cycling performance of the electrode.

In addition to conventional material characterization methods (such as x-ray diffraction, scanning electron microscopy, incremental capacity analysis, etc.), measurements of the particles’ strength were also analyzed to provide better insight on how the material will perform in an expanding-contracting electrode. Mechanical resilience if often overlook when studying and designing cathode materials, however, particularly in materials that are prone to microcracking, this information provides an important piece of the puzzle to understand the degradation mechanisms of the electrode.

This led to the development of a Co-free cathode material which can provide a capacity of 260 mAh/g on the first cycle while retaining 95% capacity after 50 cycles in half cells cycled to 4.3 V. At a lower upper-cutoff voltage of 4.06 V, this material delivers 220 mAh/g with no observable capacity loss after 100 cycles.

Ines Hamam has obtained her PhD in materials engineering (in 2024) and her MSc in physics (in 2020) from the University of Dalhousie under the supervision of world-renowned battery expert Dr Jeff Dahn. She is now a technologist at BMW furthering the world effort of transport electrification.

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To paraphrase Jane Austen, it is a truth universally acknowledged that a research project in possession of large datasets must be in want of artificial intelligence (AI).

The first time I really became aware of AI’s potential was in the early 2000s. I was one of many particle physicists working at the Collider Detector at Fermilab (CDF) – one of two experiments at the Tevatron, which was the world’s largest and highest energy particle collider at the time. I spent my days laboriously sifting through data looking for signs of new particles and gossiping about all things particle physics.

CDF was a large international collaboration, involving around 60 institutions from 15 countries. One of the groups involved was at the University of Karlsruhe (now the Karlsruhe Institute of Technology) in Germany, and they were trying to identify the matter and antimatter versions of a beauty quark from the collider’s data. This was notoriously difficult – backgrounds were high, signals were small, and data volumes were massive. It was also the sort of dataset where for many variables, there was only a small difference between signal and background.

In the face of such data, Michael Feindt, a professor in the group, developed a neural-network algorithm to tackle the problem. This type of algorithm is modelled on the way the brain learns by combining information from many neurons, and it can be trained to recognize patterns in data. Feindt’s neural network, trained on suitable samples of signal and background, was able to more easily distinguish between the two for the data’s variables, and combine them in the most effective way to identify matter and antimatter beauty quarks.

At the time, this work was interesting simply because it was a new way of trying to extract a small signal from a very large background. But the neural network turned out to be a key development that underpinned many of CDF’s physics results, including the landmark observation of a B_s meson (a particle formed of an antimatter beauty quark and a strange quark) oscillating between its matter and antimatter forms.

Versions of the algorithm have since been used elsewhere, including by physicists on three of the four main experiments at CERN’s Large Hadron Collider (LHC). In every case, the approach allowed researchers to extract more information from less data, and in doing so, accelerated the pace of scientific advancement.

What was even more interesting is that the neural-network approach didn’t just benefit particle physics. There was a brief foray applying the network to hedge fund management and predicting car insurance rates. A company Phi-T (later renamed Blue Yonder) was spun out from the University of Karlsruhe and applied the algorithm to optimizing supply-chain logistics. After a few acquisitions, the company is now award-winning and global. The neural network, however, remained free for particle physicists to use.

From lab to living room

Many types of neural networks and other AI approaches are now routinely used to acquire and analyse particle physics data. In fact, our datasets are so large that we absolutely need their computational help, and their deployment has moved from novelty to necessity.

To give you a sense of just how much information we are talking about, during the next run period of the LHC, its experiments are expected to produce about 2000 petabytes (2 × 10¹⁸ bytes) of real and simulated data per year that researchers will need to analyse. This dataset is almost 10 times larger than a year’s worth of videos uploaded to YouTube, 30 times larger than Google’s annual webpage datasets, and over a third as big as a year’s worth of Outlook e-mail traffic. These are dataset sizes very much in want of AI to analyse.

Particle physics may have been an early adopter, but AI has now spread throughout physics. This shouldn’t be too surprising. Physics is data-heavy and computationally intensive, so it benefits from the step up in speed and computational complexity to analyse datasets, simulate physical systems, and automate the control of complicated experiments.

For example, AI has been used to classify gravitational-lensing images in astronomical surveys. It has helped researchers interpret the resulting distributions of matter they infer to be there in terms of different models of dark energy. Indeed, in 2024 it improved Dark Energy Survey results equivalent to quadrupling their data sample (see box “An AI universe”).

AI has even helped design new materials. In 2023 Google DeepMind discovered millions of new crystals that could power future technologies, a feat estimated to be equivalent to 800 years of research. And there are many other advances – AI is a formidable tool for accelerating scientific progress.

But AI is not limited to complex experiments. In fact, we all use it every day. AI powers our Internet searches, helps us understand concepts, and even leads us to misunderstand things by feeding us false facts. Nowadays, AI pervades every aspect of our lives and presents us with challenges and opportunities whenever it appears.

Physicists have their say

It’s this mix of challenge and opportunity that makes now the right time to examine the relationship between physics and AI, and what each can do for the other. In fact, the Institute of Physics (IOP) has recently published a “pathfinder” study on this very subject, on which I acted as an adviser. Pathfinder studies explore the landscape of a topic, identifying the directions that a subsequent, deeper and more detailed “impact” study should explore.

This current pathfinder study – Physics and AI: a Physics Community Perspective – is based on an IOP member survey that examined attitudes towards AI and its uses, and an expert workshop that discussed future potential for innovation. The resulting report, which came out in April 2025, revealed just how widespread the use of AI is in physics.

About two thirds of the 700 people who replied to the survey said they had used AI to some degree, and every physics area contained a good fraction of respondents who had at least some level of familiarity with it. Most often this experience involved different machine-learning approaches or generative AI, but respondents had also worked with AI ethics and policy, computer vision and natural language processing. This is a testament to the many uses we can find for AI, from very specific pattern recognition and image classification tasks, to understanding its wider implications and regulatory needs.

Proceed with caution

Although it is clear that AI can really accelerate our research, we have to be careful. As many respondents to the survey pointed out, AI is a powerful aid, but simply using it as a black box and imagining it does the right thing is dangerous. AI tools and the challenges we put them to are complex – we need to ensure we understand what they are doing and how well they are doing it to have confidence in their answers.

There are any number of cautionary tales about the consequences of using AI badly and obtaining a distorted outcome. A 2017 master’s thesis by Joy Adowaa Buolamwini from Massachusetts Institute of Technology (MIT) famously analysed three commercially available facial-recognition technologies, and uncovered gender and racial bias by the algorithms due to incomplete training sets. The programmes had been trained on images predominantly consisting of white men, which led to women of colour being misidentified nearly 35% of the time, while white men were correctly classified 99% of the time. Buolamwini’s findings prompted IBM and Microsoft to revise and correct their algorithms.

Even estimating the uncertainty associated with the use of machine learning is fraught with complication. Training data are never perfect. For instance, simulated data may not perfectly describe equipment response in an experiment, or – as with the example above – crucial processes occurring in real data may be missed if the training dataset is incomplete. And the performance of an algorithm is never perfect; there may be uncertainties associated with the way the algorithm was trained and its parameters chosen.

Indeed, 69% of respondents to the pathfinder survey felt that AI poses multiple risks to physics, and one of the main concerns was inaccuracy due to poor or bad training data (figure 1). It’s bad enough getting a physics result wrong and discovering a particle that isn’t really there, or missing a new particle that is. Imagine the risks if poorly understood AI approaches are applied to healthcare decisions when interpreting medical images, or in finance where investments are made on the back of AI-driven model suggestions. Yet despite the potential consequences, the AI approaches in these real-world cases are not always well calibrated and can have ill-defined uncertainties.

New approaches are being considered in physics that try to separate out the uncertainties associated with simulated training data from those related to the performance of the algorithm. However, even this is not straightforward. A 2022 paper by Aishik Ghosh and Benjamin Nachman from Lawrence Berkeley National Laboratory in the US (Eur. Phys. J. C 82 46) notes that devising a procedure to be insensitive to the uncertainties you think are present in training data is not the same as having a procedure that is insensitive to the actual uncertainties that are really there. If that’s true, not only is measurement uncertainty underestimated but, depending on the differences between training data and reality, false results can be obtained.

The moral is that AI can and does advance physics, but we need to invest the time to use it well so that our results are robust. And if we do that, others can benefit from our work too.

How physics can help AI

Physics is a field where accuracy is crucial, and we are as rigorous as we can be about understanding bias and uncertainty in our results. In fact, the pathfinder report highlights that our methodologies to quantify uncertainty can be used to advance and strengthen AI methods too. This is critical for future innovation and to improve trust in AI use.

Advances are already under way. One development, first introduced in 2017, is physics-informed neural networks. These impose consistency with physical laws in addition to using training data relevant to their particular applications. Imposing physical laws can help compensate for limited training data and prevents unphysical solutions, which in turn improves accuracy. Although relatively new, it’s a rapidly developing field, finding applications in sectors as diverse as computational fluid dynamics, heat transfer, structural mechanics, option pricing and blood pressure estimation.

Another development is in the use of Bayesian neural networks, which incorporate uncertainty estimates into their predictions to make results more robust and meaningful. The approach is being trialled in decision-critical fields such as medical diagnosis and stock market prediction.

But this is not new to physics. The neural network developed at CDF in the 2000s was an early Bayesian neural network, developed to be robust against outliers in data, avoid issues in training caused by statistical fluctuations, and to have a sound probabilistic basis to interpret results. All the features, in fact, that make the approach invaluable for analysing many other systems outside physics.

So physics benefits from AI and can drive advances in it too. This is a unique relationship that needs wider recognition, and this is a good moment to bring it to the fore. The UK government has said it sees AI as “the defining opportunity of our generation”, driving growth and innovation, and that it wants the UK to become a global AI superpower. Action plans and strategies are already being implemented. Physics has a unique perspective to offer help and make this happen. It’s time for us to include it in the conversation.

In the words of the pathfinder report, we need to articulate and showcase what AI can do for physics and what physics can do for AI. Let’s make this the start of putting physics on the AI map for everyone.

The post How AI can help (and hopefully not hinder) physics appeared first on Physics World.

A new photodetector made up of vertically stacked perovskite-based light absorbers can produce real photographic images, potentially challenging the dominance of silicon-based technologies in this sector.  The detector is the first to exploit the concept of active optical filtering, and its developers at ETH Zurich and Empa in Switzerland say it could be used to produce highly sensitive, artefact-free images with much improved colour fidelity compared to conventional sensors.

The human eye uses individual cone cells in the retina to distinguish between red, green and blue (RGB) colours. Imaging devices such as those found in smartphones and digital cameras are designed to mimic this capability. However, because their silicon-based sensors absorb light over the entire visible spectrum, they must split the light into its RGB components. Usually, they do this using colour-filter arrays (CFAs) positioned on top of a monochrome light sensor. Then, once the device has collected the raw data, complex algorithms are used to reconstruct a colour image.

Although this approach is generally effective, it is far from ideal. One drawback is the presence of “de-mosaicing” artefacts from the reconstruction process. Another is large optical losses, as pixels for red light contain filters that block green and blue light, while those for green block red and blue, and so on. This means that each pixel in the image sensor only receives about a third of the incident light spectrum, greatly reducing the efficacy of light capture.

No need for filters

A team led by ETH Zurich materials scientist Maksym Kovalenko has now developed an alternative image sensor based on lead halide perovskites. These crystalline semiconductor materials have the chemical formula APbX₃, where A is a formamidinium, methylammonium or caesium cation and X is a halide such as chlorine, bromine or iodine.

Crucially, the composition of these materials determines which wavelengths of light they will absorb. For example, when they contain more iodide ions, they absorb red light, while materials containing more bromide or chloride ions absorb green or blue light, respectively. Stacks of these materials can thus be used to absorb these wavelengths selectively without the need for filters, since each material layer remains transparent to the other colours.

The idea of vertically stacked detectors that filter each other optically has been discussed since at least 2017, including in early work from the ETH-Empa group, says team member Sergey Tsarev. “The benefits of doing this were clear, but the technical complexity discouraged many researchers,” Tsarev says.

To build their sensor, the ETH-Empa researchers had to fabricate around 30 functional thin-film layers on top of each other, without damaging prior layers. “It’s a long and often unrewarding process, especially in today’s fast-paced research environment where quicker results are often prioritized,” Tsarev explains. “This project took us nearly three years to complete, but we chose to pursue it because we believe challenging problems with long-term potential deserve our attention. They can push boundaries and bring meaningful innovation to society.”

The team’s measurements show that the new, stacked sensors reproduce RGB colours more precisely than conventional silicon technologies. The sensors also boast high external quantum efficiencies (defined as the number of photons produced per electron used) of 50%, 47% and 53% for the red, green and blue channels respectively.

Machine vision and hyperspectral imaging

Kovalenko says that in purely technical terms, the most obvious application for this sensor would be in consumer-grade colour cameras. However, he says that this path to commercialization would be very difficult due to competition from highly optimized and cost-effective conventional technologies already on the market. “A more likely and exciting direction,” he tells Physics World, “is in machine vision and in so-called hyperspectral imaging – that is, imaging at wavelengths other than red, green and blue.”

Perovskite sensors are particularly interesting in this context, explains team member Sergi Yakunin, because the wavelength range they absorb over can be precisely controlled by defining a larger number of colour channels that are clearly separated from other. In contrast, silicon’s broad absorption spectrum means that silicon-based hyperspectral imaging devices require numerous filters and complex computer algorithms.

“This is very impractical even with a relatively small number of colours,” Kovalenko says. “Hyperspectral image sensors based on perovskite could be used in medical analysis or in automated monitoring of agriculture and the environment, for example, or in other specialized imaging systems that can isolate and enhance particular wavelengths with high colour fidelity.”

The researchers now aim to devise a strategy for making their sensor compatible with standard CMOS technology. “This might include vertical interconnects and miniaturized detector pixels,” says Tsarev, “and would enable seamless transfer of our multilayer detector concept onto commercial silicon readout chips, bringing the technology closer to real-world applications and large-scale deployment.”

The study is detailed in Nature.

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In atom-based quantum technologies, motion is seen as a nuisance. The tiniest atomic jiggle or vibration can scramble the delicate quantum information stored in internal states such as the atom’s electronic or nuclear spin, especially during operations when those states get read out or changed.

Now, however, Manuel Endres and colleagues at the California Institute of Technology (Caltech), US, have found a way to turn this long-standing nuisance into a useful feature. Writing in Science, they describe a technique called erasure correction cooling (ECC) that detects and corrects motional errors without disturbing atoms that are already in their ground state (the ideal state for many quantum applications). This technique not only cools atoms; it does so better than some of the best conventional methods. Further, by controlling motion deliberately, the Caltech team turned it into a carrier of quantum information and even created hyper-entangled states that link the atoms’ motion with their internal spin states.

“Our goal was to turn atomic motion from a source of error into a useful feature,” says the paper’s lead author Adam Shaw, who is now a postdoctoral researcher at Stanford University. “First, we developed new cooling methods to remove unwanted motion, like building an enclosure around a swing to block a chaotic wind. Once the motion is stable, we can start injecting it programmatically, like gently pushing the swing ourselves. This controlled motion can then carry quantum information and perform computational tasks.”

Keeping it cool

Atoms confined in optical traps – the basic building blocks of atom-based quantum platforms – behave like quantum oscillators, occupying different vibrational energy levels depending on their temperature. Atoms in the lowest vibrational level, the motional ground state, are especially desirable because they exhibit minimal thermal motion, enabling long coherence times and high-fidelity control over quantum states.

Over the past few decades, scientists have developed various methods, including Sisyphus cooling and Raman sideband cooling, to persuade atoms into this state. However, these techniques face limitations, especially in shallow traps where motional states are harder to resolve, or in large-scale systems where uniform and precise cooling is required.

ECC builds on standard cooling methods to overcome these challenges. After an initial round of Sisyphus cooling, the researchers use spin-motion coupling and selective fluorescence imaging to pinpoint atoms still in excited motional states without disturbing the atoms already in the motional ground state. They do this by linking an atom’s motion to its internal electronic spin state, then shining a laser that only causes the “hot” (motionally excited) atoms to change the spin state and light up, while the “cold” ones in the motional ground state remain dark. The “hot” atoms are then either re-cooled or replaced with ones already in the motional ground state.

This approach pushed the fraction of atoms in the ground motional state from 77% (after Sisyphus cooling alone) to over 98% and up to 99.5% when only the error-free atoms were selected for further use. Thanks to this high-fidelity preparation, the Caltech physicists further demonstrated their control over motion at the quantum level by creating a motional qubit consisting of atoms in a superposition of the ground and first excited motional states.

Cool operations

Unlike electronic superpositions, these motional qubits are insensitive to laser phase noise, highlighting their robustness for quantum information processing. Further, the researchers used the motional superposition to implement mid-circuit readout, showing that quantum information can be temporarily stored in motion, protected during measurement, and recovered afterwards. This paves the way for advanced quantum error correction, and potentially other applications as well.

“Whenever you find ways to better control a physical system, it opens up new opportunities,” Shaw observes. Motional qubits, he adds, are already being explored as a means of simulating systems in high-energy physics.

A further highlight of this work is the demonstration of hyperentanglement, or entanglement across both internal (electronic) and external (motional) degrees of freedom. While most quantum systems rely on a single type of entanglement, this work shows that motion and internal states in neutral atoms can be coherently linked, paving the way for more versatile quantum architectures.

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The webinar will present the overall experience of a radiotherapy department that utilized RTsafe’s QA solutions in preparation for achieving ISRS certification. The session will focus on the use of RTsafe’s Prime phantom in combination with film remote dosimetry services, demonstrating how this approach enables End-to-End QA testing and supports accurate, reproducible film dosimetry audits. Attendees will gain insights into how these tools can be employed to validate the entire SRS treatment workflow, from imaging and planning to dose delivery, while aligning with the rigorous standards required for ISRS certification.

Serenella Russo is senior medical physicist and Reference MPE at the Radiation Oncology Unit, Santa Maria Annunziata Hospital, Florence. She brings expertise in external beam radiation therapy dosimetry, with a focus on small field measurements and detector characterization, as well as clinical implementation and planning of VMAT/IMRT, SRS/SBRT techniques. Russo is responsible for the Italian Association of Medical Physics (AIFM) audit service for radiotherapy megavoltage photons beams. Coordinator of (AIFM) SBRT Working Group and responsible for the Italian multi-center project “Inter-comparison on small field dosimetry ” proposed by the SBRT WG.

Professor of Radiotherapy Dosimetry at the Medical Physics Specialization School, University of Florence and serves as editor for Physica Medica. Author and Co-author of numerous scientific publications about SRS/SBRT and small field dosimetry.

Silvia Scoccianti is head of Radiation Oncology at Santa Maria Annunziata Hospital and Azienda USL Toscana Centro, Italy.  She brings expertise in Linac-based radiosurgery, stereotactic radiotherapy and gamma knife radiosurgery for brain metastases, recurrent gliomas, intercranial benign tumors, AVM, and trigeminal neuralgia.  Head of the Italian Association of Radiotherapy and Clinical Oncology (AIRO) Brain Tumor Group. Chief of the multidisciplinary tumor board for CNS a multi-hospital network of Azienda USL Toscana Centro. Study director and Principal investigator for multicenter neuro-oncological trials.

Scoccianti co-authored Italian national CNS tumor guidelines published by the Italian Association of Medical Oncology (AIOM). Author and Co-author of numerous scientific publications about primary and secondary brain tumors.

The post Preparation for ISRS certification using RTsafe’s solutions. An overall experience appeared first on Physics World.

What skills do you use every day in your job?

I’m thankful every day that my physics background helps me quickly understand information – even outside my areas of expertise – and fit it into the larger puzzle of what’s valuable and/or critical for our company, business, products, team and technology. I also believe it’s under-appreciated how difficult it is to communicate clearly – especially on technical topics or across large teams – and the challenge scales with the size of the team. Crafting clear communication is therefore something that I try to give extra time and attention to myself. I also encourage the wider team to follow that example and do themselves as they develop our technology and products.

What do you like best and least about your job?

The best thing for me is that every day, every task and action, no matter how small, helps bit-by-bit to build a world that is safer and more secure against the backdrop of dramatic changes in autonomy. What’s also great are the remarkable people I work with – on my team and across the company. They’re dedicated, intelligent, and each exemplary in their own unique ways. My least favourite part of the job is PowerPoint, which to me is the least effective and most time-consuming means of communicating ever created. In the business world, however, you have to accept and accommodate your customers’ preferences – and that means using PowerPoint.

What do you know today, that you wish you knew when you were starting out in your career?

I wish I’d known that anyone who believes a hardware start-up will only take three or four years to develop a product has to be kidding. But jokes aside, I believe that learning things is often more valuable than knowing things – and the past 11 years have been an amazing journey of learning. If I had a time machine would I go back and tweak what I did early on? Absolutely! But would I hand myself a cheat-sheet that let me skip all the learning? Absolutely not!

The post Ask me anything: Tom Driscoll – ‘It’s under-appreciated how difficult it is to communicate clearly’ appeared first on Physics World.

A tiny neodymium particle suspended inside a superconducting trap could become a powerful new platform in the search for dark matter, say physicists at Rice University in the US and Leiden University in the Netherlands. Although they have not detected any dark matter signals yet, they note that their experiment marks the first time that magnetic levitation technology has been tested in this context, making it an important proof of concept.

“By showing what current technology can already achieve, we open the door to a promising experimental path to solving one of the biggest mysteries in modern physics,” says postdoctoral researcher Dorian Amaral, who co-led the project with his Rice colleague Christopher Tunnell, as well as Dennis Uitenbroek and Tjerk Oosterkamp in Leiden.

Dark matter is thought to make up most of the matter in our universe. However, since it has only ever been observed through its gravitational effects, we know very little about it, including whether it interacts (either with itself or with other particles) via forces other than gravity. Other fundamental properties, such as its mass and spin, are equally mysterious. Indeed, various theories predict dark matter particle masses that range from around 10⁻¹⁹ eV/c² to a few times the mass of our own Sun – a staggering 90 orders of magnitude.

The B‒L model

The theory that predicts masses at the lower end of this range is known as the ultralight dark matter (ULDM) model. Some popular ULDM candidates include the QCD axion, axion-like particles and vector particles.

In their present work, Amaral and colleagues concentrated on vector particles. This type of dark-matter particle, they explain, can “communicate”, or interact, via charges that are different from those found in ordinary electromagnetism. Their goal, therefore, was to detect the forces arising from these so-called dark interactions.

To do this, the team focused on interactions that differ based on the baryon (B) and lepton (L) numbers of a particle. Several experiments, including fifth-force detectors such as MICROSCOPE and Eöt-Wash as well as gravitational wave interferometers such as LIGO/Virgo and KAGRA, likewise seek to explore interactions within this so-called B‒L model. Other platforms, such as torsion balances, optomechanical cavities and atomic interferometers, also show promise for making such measurements.

Incredibly sensitive setup

The Rice-Leiden team, however, chose to explore an alternative that involves levitating magnets with superconductors via the Meissner effect. “Levitated magnets are excellent force and acceleration sensors, making them ideal for detecting the minuscule signatures expected from ULDM,” Amaral says.

Such detectors also have a further advantage, he adds. Because they operate at ultralow temperatures, they are much less affected by thermal noise than is the case for detectors that rely on optical or electrical levitation. This allows them to levitate much larger and heavier objects, making them more sensitive to interactions such as those expected from B‒L model dark matter.

In their experiment, which is called POLONAISE (Probing Oscillations using Levitated Objects for Novel Accelerometry In Searches of Exotic physics), the Rice and Leiden physicists levitated a tiny magnet composed of three neodymium-iron-boron cubes inside a superconducting trap cooled to nearly absolute zero. “This setup was incredibly sensitive, enabling us to detect incredibly small motions caused by tiny external forces,” Amaral explains. “If ultralight dark matter exists, it would behave like a wave passing through the Earth, gently tugging on the magnet in a predictable, wave-like pattern. Detecting such a motion would be a direct signature of this elusive form of dark matter.”

An unconventional idea

The Rice-Leiden collaboration began after Oosterkamp and Tunnell met at a climate protest and got to chatting about their scientific work. After over a decade working on some of the world’s most sensitive dark matter experiments – with no clear detections to show for it – Tunnell was eager to return to the drawing board in terms of detector technologies. Oosterkamp, for his part, was exploring how quantum technologies could be applied to fundamental questions in physics. This shared interest in cross-disciplinary thinking, Amaral remembers, led them to the unconventional idea at the heart of their experiment. “From there, we spent a year bridging experimental and theoretical worlds. It was a leap outside our comfort zones – but one that paid off,” he says.

“Although we did not detect dark matter, our result is still valuable – it tells us what dark matter is not,” he adds. “It’s like searching a room and not finding the object you are looking for: now you know to look somewhere else.”

The team’s findings, which are detailed in Physical Review Letters, should help physicists refine theoretical models of dark matter, Amaral tells Physics World. “And on the experimental side, our work advises the key improvements needed to turn magnetic levitation into a world-leading tool for dark matter detection.”

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Deep learning algorithms have been trained to classify different types of biological tissue, based purely on the tissue’s natural optical responses to laser light. The work was done by researchers led by Travis Sawyer at the University of Arizona in US, who hope that their new approach could be used in the future to diagnose diseases using optical microscopy.

Precision medicine is a fast-growing field whereby medical treatments are tailored to individual patients – taking factors like genetics and lifestyle into account. A key part of this process is phenotyping, which involves identifying the molecular characteristics of diseased tissues.

Previously, phenotyping most often involved labelling tissues with fluorescent biomarkers, which allowed clinicians to create clear medical images using optical microscopy. However, the process of labelling tissues is often invasive, expensive and time-consuming, limiting its accessibility in practical treatments.

More recently, advances have been made in label-free imaging, which can phenotype tissues by observing how they interact with laser light. This is difficult, however, because tissues will often display complex nonlinear responses in the light they emit, which are deeply intertwined with their surrounding molecular environments. As Sawyer explains, this creates a whole new set of challenges.

Altering abundance

“In general, the potential of label-free imaging has been limited by a lack of specificity in understanding what is producing the measured signal,” he says. “This is because many different high-level disease processes can lead to an altering abundance of downstream measurable biomarkers.”

Sawyer’s team addressed these challenges by exploring how deep learning algorithms could be trained to recognize these nonlinear optical responses, and identify them in microscopy images.

To do this, they used a technique called spatial transcriptomics, which maps out variations in RNA levels across tissue samples. RNA molecules carry copies of the instructions stored in DNA, offering a snapshot of gene activity in different regions of tissue.

Alongside transcriptomics data from six different types of tissue, the team also probed the samples with two different optical microscopy techniques. These are autofluorescence, which detects the specific frequencies of molecules excited by a laser, providing details on the tissue’s composition; and second harmonic generation, which detects highly ordered structures (such as collagen) by capturing photons they emit at twice the frequency of a laser probe.

One-to-one matching

The researchers then co-registered these label-free microscopy images with their spatial transcriptomics data. “This allowed us to match one-to-one the transcriptomic signature of a small area of tissue with a surrounding image region capturing the microenvironment of the tissue,” Sawyer explains. “The transcriptomic signature was used to generate tissue and disease phenotypes.”

Based on these simultaneous measurements, the team developed a deep learning algorithm that could accurately predict the unique phenotypes of each tissue. Once trained, the model could classify tissues using only the label-free microscopy images, without any need for transcriptomics data from the samples being studied. “Using deep learning, we were able to accurately predict tissue phenotypes defined by the transcriptomic signature to almost 90% accuracy using label-free microscopy images,” Sawyer says.

Compared with classical image analysis algorithms, the team’s deep learning approach was vastly more reliable in predicting tissue characteristics. This showcased the need to account for the influence of tissues’ surrounding environments on their optical responses.

For now, the technique is still in its early stages, and will require assessments with far larger groups of patients, and with other types of tissue and diseases before it can be applied clinically. Still, the team’s results are a promising step towards label-free imaging, which could have important implications for precision medicine.

“This could lead to transformative technology that could have major clinical impact by enabling precision medicine approaches, in addition to basic science applications by allowing minimally invasive and longitudinal measurement of biological signatures,” Sawyer explains.

The technique is described in Biophotonics Discovery.

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Cephalopods such as squid and octopus can rapidly change the colour of their skin, but the way they do it has been something of a mystery – until now. Using a microscopy technique known as holotomography, scientists in the US discovered that the tuneable optical properties of squid skin stem from winding columns of platelets in certain cells. These columns have sinusoidal-wave refractive index profiles, and they function as Bragg reflectors, able to selectively transmit and reflect light at specific wavelengths.

“Our new result not only helps advance our understanding of structural colouration in cephalopods skin cells, it also provides new insights into how such gradient refractive index distributions can be leveraged to manipulate light in both biological and engineered systems,” says Alon Gorodetsky of the University of California, Irvine, who co-led this research study together with then-PhD student Georgii Bogdanov.

Stacked and winding columns of platelets

In their study, Gorodetsky, Bogdanov and colleagues including Roger Hanlon of the Marine Biological Laboratory (MBL) in Woods Hole, Massachusetts, examined the iridescent cells (iridophores) and cell clusters (splotches) responsible for producing colours in longfin inshore squids (Doryteuthis pealeii). To do this, they used holotomography, which creates three-dimensional images of individual cells and cell clusters by measuring subtle changes in a light beam as it passes through a sample of tissue. From this, they were able to map out changes in the sample’s refractive index across different structures.

The holotomography images revealed that the iridophores comprise stacked and winding columns of platelets made from a protein known as reflectin, which has a high refractive index, alternating with a low-refractive-index extracellular space. These Bragg-reflector-like structures are what allow tissue in the squid’s mantle to switch from nearly transparent to vibrantly coloured and back again.

Other natural Bragg reflectors

Squids aren’t the only animals that use Bragg reflectors for structural colouration, Gorodetsky notes. The scales of Morpho butterflies, for example, get their distinctive blue colouration from nanostructured Bragg gratings made from alternating high-refractive-index lamellae and low-refractive-index air gaps. Another example is the panther chameleon. The skin cells of this famously colour-changing reptile contain reconfigurable photonic lattices consisting of high-refractive-index nanocrystals within a low-refractive-index cytoplasm. These structures allow the animal to regulate its temperature as well as change its colour.

Yet despite these previous findings, and extensive research on cephalopod colouration, Gorodetsky says the question of how squid splotch iridophores can change from transparent to colourful , while maintaining their spectral purity, had not previously been studied in such depth. “In particular, the cells’ morphologies and refractive index distributions in three dimensions had not been previously resolved,” he explains. “Overcoming the existing knowledge gap required the development and application of combined experimental and computational approaches, including advanced imaging, refractive index mapping and optical modelling.”

Extending to infrared wavelengths

After using advanced computational modelling to capture the optical properties of the squid cells, the researchers, who report their work in Science, built on this result by designing artificial nanomaterials inspired by the natural structures they discovered. While the squid iridophores only change their visible appearance in response to neurophysiological stimuli, the researchers’ elastomeric composite materials (which contain both nanocolumnar metal oxide Bragg reflectors and nanostructured metal films) also change at infrared wavelengths.

Composite materials like the ones the UC Irvine-MBL team developed could have applications in adaptive camouflage or fabrics that adjust to hot and cold temperatures. They might also be used to improve multispectral displays, sensors, lasers, fibre optics and photovoltaics, all of which exploit multilayered Bragg reflectors with sinusoidal-wave refractive index profiles, says Gorodetsky.

The researchers now plan to further explore how gradient refractive index distributions contribute to light manipulation in other biological systems. “We also hope to refine our engineered multispectral composite materials to enhance their performance for specific practical applications, such as advanced camouflage and other wearable optical technologies,” Gorodetsky tells Physics World.

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This episode of the Physics World Weekly podcast is a conversation with two physicists, Ada Altieri and Silvia De Monte, who are using their expertise in statistical physics to understand the behaviour of ecological communities.

A century ago, pioneering scientists such as Alfred Lotka and Vito Volterra showed that statistical physics techniques could explain – and even predict – patterns that ecologists observe in nature. At first, this work focused on simple ecosystems containing just one or two species (such as rabbits and foxes), which are relatively easy to model.

Nowadays, though, researchers such as Altieri and De Monte are turning their attention to far more complex communities. One example is the collection of unicellular organisms known as protists that live among plankton in the ocean. Another, closer to home, is the “microbiome” in the human gut, which may contain hundreds or even thousands of species of bacteria.

Modelling these highly interconnected communities is hugely challenging. But as Altieri and De Monte explain, the potential rewards – from identifying “tipping points” in fragile ecosystems to developing new treatments for gut disorders such as irritable bowel syndrome and Crohn’s disease – are great.

This discussion is based on a Perspective article that Altieri (an associate professor at the Laboratory for Matter and Complex Systems at the Université Paris Cité, France) and De Monte (a senior research scientist at the Institute of Biology in the École Normale Supérieure in Paris and the Max Planck Institute for Evolutionary Biology in Ploen, Germany) wrote for the journal EPL, which sponsors this episode of the podcast.

The post From rabbits and foxes to the human gut microbiome, physics is helping us understand the natural world appeared first on Physics World.

A shortage of data is hampering efforts to establish the role of climate change in extreme-weather events in the tropics and global south. So say an international team of scientists, who claim the current situation is a “scientific injustice” and call for more investment in climate science and weather monitoring in poorer countries.

The researchers, who are part of World Weather Attribution, have made the call after analysing the role of climate change in an episode of torrential rain in June that triggered a landslide in Columbia. It killed 27 people and triggered devastating floods in Venezuela that displaced thousands.

Their study reported that the Colombian Andes were unusually wet from April to June, while the part of Venezuela where the floods occurred experienced its five wettest days of the year. In the current climate, such weather events would be expected every 10 years in Colombia and every three years in Venezuela.

According to the researchers, there is a high level of uncertainty in the study due to a lack of long-term observational data in the region and high uncertainties in global climate models when assessing the tropics. Colombia and Venezuela have complex tropical climates that are under-researched, with some data even suggesting that rainfall in the region is becoming less intense.

But the group says that the possibility of heavier rainfall linked to climate change should not be ruled out in the region, particularly on shorter, sub-daily timescales, which they could not investigate. They add that Colombia and Venezuela are almost certainly facing increased heatwave, drought and wildfire risk.

Mariam Zachariah at the Centre for Environmental Policy at Imperial College London, who was involved with the work, says that the combination of mountains, coasts, rainforests and complex-weather systems in many tropical countries means “rainfall is varied, intense and challenging to capture in climate models”.

“Many countries with tropical climates have limited capacity to do climate science, meaning we don’t have a good understanding of how they are being affected by climate change,” says Zachariah. “Our recent study on the deadly floods in the Democratic Republic of Congo in May is another example. Once again, our results were inconclusive.”

Climate scientist Paola Andrea Arias Gómez at the Universidad of Antioquia in Colombia, who was also involved in the study, says that extreme weather is “non-stop” in Colombia and Venezuela. “One year we face devastating flash floods; the next, severe droughts and wildfires,” she adds. “Unfortunately, extreme weather is not well understood in northern South America. We urgently need more investment in climate science to understand shifting risks and prepare for what’s ahead. More science will save lives.”

The post Scientists decry ‘scientific injustice’ over lack of climate data in developing regions appeared first on Physics World.

New experimental evidence for a quantum spin liquid – a material with spins that remain in constant fluctuation at extremely low temperatures – has been unveiled by an international team of scientists. The researchers used neutron scattering to reveal photon-like collective spin excitations in a crystal of cerium zirconate.

When most magnetic materials are cooled to nearly absolute zero, their spin magnetic moments will align into an ordered pattern to minimize the system’s energy. Yet in 1973, the future Nobel laureate Philip Anderson proposed an alternative class of magnetic materials in which this low temperature order does not emerge.

Anderson considered the spins of atoms that interact with each other in an antiferromagnetic way. This is when the spin of each atom seeks to point in the opposite direction of its nearest neighbours. If the spins in a lattice are able to adopt this orientation, the lowest energy state is an ordered antiferromagnet with zero overall magnetism.

Geometrical frustration

In a tetrahedral lattice, however, the geometrical arrangement of nearest neighbours means that it is impossible for spins to arrange themselves in this way. This is called frustration, and the result is a material with multiple low-energy spin configurations, which are disordered.

So far, this behaviour has been observed in materials called spin ices – where one of the many possible spin configurations is frozen into place at ultralow temperatures. However, Anderson envisioned that a related class of materials could exist in a more exotic phase that constantly fluctuates between different, equal-energy states, all the way down to absolute zero.

Called quantum spin liquids (QSLs), such materials have evaded experimental confirmation – until now. “They behave like a liquid form of magnetism – without any fixed ordering,” explains team member Silke Bühler-Paschen at Austria’s Vienna University of Technology. “That’s exactly why a real breakthrough in this area has remained elusive for decades.” “We studied cerium zirconate, which forms a three-dimensional network of spins and shows no magnetic ordering, even at temperatures as low as 20 mK.”. This material was chosen because it has a pyrochlore lattice, which is based on corner-sharing tetrahedra.

Collective magnetic excitations

The team looked for collective magnetic excitations that are predicted to exist in QSLs. These excitations are expected to have linear energy–momentum relationships, which is similar to how conventional photons propagate. As a result, these particle-like excitations are called emergent photons.

The team used polarized neutron scattering experiments to search for evidence of emergent photons. When neutrons strike a sample, they can exchange energy and momentum with the lattice. This exchange can involve magnetic excitations in the material and the team used scattering experiments to map-out the energy and momenta of these excitations at temperatures in the 33–50 mK range.

“For the first time, we were able to detect signals that strongly indicate a 3D quantum spin liquid – particularly, the presence of so-called emergent photons,” Bühler-Paschen says. “The discovery of these emergent photons in cerium zirconate is a very strong indication that we have indeed found a QSL.”

As well as providing evidence for Anderson’s idea, the research pave the way for the further exploration of other potential QSLs and their applications. “We plan to conduct further experiments, but from our perspective, cerium zirconate is currently the most convincing candidate for a quantum spin liquid,” Bühler-Paschen says.

The research could have important implications for our understanding of high-temperature superconductivity. In his initial theory, Anderson predicted that QSLs could be precursors to high-temperature superconductors.

The research is described in Nature Physics.

The post Hints of a 3D quantum spin liquid revealed by neutron scattering appeared first on Physics World.

In September 2023, seismic detectors around the world began picking up a mysterious signal. Something – it wasn’t clear what – was causing the entire Earth to shake every 90 seconds. After a period of puzzlement, and a second, similar signal in October, theoretical studies proposed an explanation. The tremors, these studies suggested, were caused by standing waves, or seiches, that formed after landslides triggered huge tsunamis in a narrow waterway off the coast of Greenland.

Engineers at the University of Oxford, UK, have now confirmed this hypothesis. Using satellite altimetry data from the Surface Water Ocean Topography (SWOT) mission, the team constructed the first images of the seiches, demonstrating that they did indeed originate from landslide-triggered mega-tsunamis in Dickson Fjord, Greenland. While events of this magnitude are rare, the team say that climate change is likely to increase their frequency, making continued investments in advanced satellite missions essential for monitoring and responding to them.

An unprecedented view into the fjord

Unlike other altimeters, SWOT provides two-dimensional measurements of sea surface height down to the centimetre across the entire globe, including hard-to-reach areas like fjords, rivers and estuaries. For team co-leader Thomas Monahan, who studied the seiches as part of his PhD research at Oxford, this capability was crucial. “It gave us an unprecedented view into Dickson Fjord during the seiche events in September and October 2023,” he says. “By capturing such high-resolution images of sea-surface height at different time points following the two tsunamis, we could estimate how the water surface tilted during the wave – in other words, gauge the ‘slope’ of the seiche.”

The maps revealed clear cross-channel slopes with height differences of up to two metres. Importantly, these slopes pointed in opposite directions, showing that water was moving backwards as well as forwards across the channel. But that wasn’t the end of the investigation. “Finding the ‘seiche in the fjord’ was exciting but it turned out to be the easy part,” Monahan says. “The real challenge was then proving that what we had observed was indeed a seiche and not something else.”

Enough to shake the Earth for days

To do this, the Oxford engineers approached the problem like a game of Cluedo, ruling out other oceanographic “suspects” one by one. They also connected the slope measurements with ground-based seismic data that captured how the Earth’s crust moved as the wave passed through it. “By combining these two very different kinds of observations, we were able to estimate the size of the seiches and their characteristics even during periods in which the satellite was not overhead,” Monahan says.

Although no-one was present in Dickson Fjord during the seiches, the Oxford team’s estimates suggest that the event would have been terrifying to witness. Based on probabilistic (Bayesian) machine-learning analyses, the team say that the September seiche was initially 7.9 m tall, while the October one measured about 3.9 m.

“That amount of water sloshing back and forth over a 10-km-section of fjord walls creates an enormous force,” Monahan says. The September seiche, he adds, produced a force equivalent to 14 Saturn V rockets launching at once, around 500 GN. “[It] was literally enough to shake the entire Earth for days,” he says.

What made these events so powerful was the geometry of the fjord, Monahan says. “A sharp bend near its outlet effectively trapped the seiches, allowing them to reverberate for days,” he explains. “Indeed, the repeated impacts of water against fjord walls acted like a hammer striking the Earth’s crust, creating long-period seismic waves that propagated around the globe and that were strong enough to be detected worldwide.”

Risk of tsunamigenic landslides will likely grow

As for what caused the seiches, Monahan suggests that climate change may have been a contributing factor. As glaciers thin, they undergo a process called de-buttressing wherein the loss of ice removes support from the surrounding rock, leading it to collapse. It was likely this de-buttressing that caused two enormous landslides in Dickson Fjord within a month, and continued global warming will only increase the frequency. “As these events become more common, especially in steep, ice-covered terrain, the risk of tsunamigenic landslides will likely grow,” Monahan says.

The researchers say they would now like to better understand how the seiches dissipated afterwards. “Although previous work successfully simulated how the megatsunamis stabilized into seiches, how they decayed is not well understood,” says Monahan. “Future research could make use of SWOT satellite observations as a benchmark to better constrain the processes behind disputation.”

The findings, which are detailed in Nature Communications, show how top-of-the-line satellites like SWOT can fill these observational gaps, he adds. To fully leverage these capabilities, however, researchers need better processing algorithms tailored to complex fjord environments and new techniques for detecting and interpreting anomalous signals within these vast datasets. “We think scientific machine learning will be extremely useful here,” he says.

The post Earth-shaking waves from Greenland mega-tsunamis imaged for the first time appeared first on Physics World.

Microrobots provide a promising vehicle for precision delivery of therapeutics into the body. But there’s a fine balance needed between optimizing multifunctional cargo loading and maintaining efficient locomotion. A research collaboration headed up at the University of Oxford and the University of Michigan has now developed permanent magnetic droplet-derived microrobots (PMDMs) that meet both of these requirements.

The PMDMs are made from a biocompatible hydrogel incorporating permanent magnetic microparticles. The hydrogel – which can be tailored to each clinical scenario – can carry drugs or therapeutic cells, while the particles’ magnetic properties enable them to self-assemble into chains and perform a range of locomotion behaviours under external magnetic control.

“Our motivation was to design a microrobot system with adaptable motion capabilities for potential applications in drug delivery,” explains Molly Stevens from the University of Oxford, experimental lead on this study. “By using self-assembled magnetic particles, we were able to create reconfigurable, modular microrobots that could adapt their shape on demand – allowing them to manoeuvre through complex biological terrains to deliver therapeutic payloads.”

Building the microrobots

To create the PMDMs, Stevens and collaborators used cascade tubing microfluidics to rapidly generate ferromagnetic droplets (around 300 per minute) from the hydrogel and microparticles. Gravitational sedimentation of the 5 µm-diameter microparticles led to the formation of Janus droplets with distinct hydrogel and magnetic phases. The droplets were then polymerized and magnetized to form PMDMs of roughly 0.5 mm in diameter.

The next step involved self-assembly of the PMDMs into chains. The researchers demonstrated that exposure to a precessing magnetic field caused the microrobots to rapidly assemble into dimers and trimers before forming a chain of eight, with their dipole moments aligned. Exposure to various dynamic magnetic fields caused the chains to move via different modalities, including walking, crawling, swinging and lateral movement.

The microrobots were able to ascend and descend stairs, and navigate obstacles including a 3-mm high railing, a 3-mm diameter cylinder and a column array. The reconfigurable PMDM chains could also adapt to confined narrow spaces by disassembling into shorter fragments and overcome tall obstacles by merging into longer chains.

Towards biomedical applications

By tailoring the hydrogel composition, the researchers showed that the microrobots could deliver different types of cargo with controlled dosage. PMDMs made from rigid polyethylene glycol diacrylate (PEGDA) could deliver fluorescent microspheres, for example, while soft alginate/gelatin hydrogels can be used for cell delivery.

PMDM chains also successfully transported human mesenchymal stem cell (hMSC)-laden Matrigel without compromising cell viability, highlighting their potential to deliver cells to specific sites for in vivo disease treatment.

To evaluate intestinal targeting, the researchers delivered PMDMs to ex vivo porcine intestine. Once inside, the microrobots assembled into chains and exhibited effective locomotion on the intestine surface. Importantly, the viscous and unstructured tissue surface did not affect chain assembly or motion. After navigation to the target site, exposing the PMDMs to the enzyme collagenase instigated controlled cargo release. Even after full degradation of the hydrogel phase, the chains retained integrity and locomotion capabilities.

The team also demonstrated programmable release of different cargoes, using hybrid chains containing rigid PEGDA segments and degradable alginate/gelatin segments. Upon exposure to collagenase, the cargo from the degradable domains exhibited burst release, while the slower degradation of PEGDA delayed the release of cargo in the PEGDA segments.

In another potential clinical application, the researchers delivered microrobots to 3D-printed human cartilage with an injury site. This involved catheter-based injection of PMDMs followed by application of an oscillating magnetic field to assemble the PMDMs into a chain. The chains could be navigated by external magnetic fields to the targeted injury site, where the hydrogel degraded and released the drug cargo.

After drug delivery, the team guided the microrobots back to the initial injection site and retrieved them using a magnetic catheter. This feature offers a major advantage over traditional microrobots, which often struggle to retrieve magnetic particles after cargo release, potentially triggering immune responses, tissue damage or other side effects.

“For microrobots to be clinically viable, they must not only perform their intended functions effectively but also do so safely,” explains co-first author Yuanxiong Cao from the University of Oxford. “The ability to retrieve the PMDM chains after they completed the intended therapeutic delivery enhances the biosafety of the system.”

Cao adds that while the focus for the intestine model was to demonstrate navigation and localized delivery, the precise control achieved over the microrobots suggests that “extraction is also feasible in this and other biomedically relevant environments”.

Predicting PMDM performance

Alongside the experiments, the team developed a computational platform, built using molecular dynamics simulations, to provide further insight into the collective behaviour of the PMDMs.

“The computational model was instrumental in predicting how individual microrobot units would self-assemble and respond to dynamic magnetic fields,” says Philipp Schoenhoefer, co-first author from the University of Michigan. “This allowed us to understand and optimize the magnetic interactions between the particles and anticipate how the robots would behave under specific actuation protocols.”

The researchers are now using these simulations to design more advanced microrobot structures with enhanced multifunctionality and mechanical resilience. “The next-generation designs aim to handle the more challenging in vivo conditions, such as high fluid shear and irregular tissue architectures,” Sharon Glotzer from the University of Michigan, simulation lead for the project, tells Physics World.

The microrobots are described in Science Advances.

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What happens when you put a visual artist in the middle of a quantum physics lab? This month’s Physics World Stories podcast explores that very question, as host Andrew Glester dives into the artist-in-residence programme at the Yale Quantum Institute in the US.

Each year, the institute welcomes an artist to explore the intersections of art and quantum science, bridging the ever-fuzzy boundary between the humanities and the sciences. You will hear from the current artist-in-residence Serena Scapagnini, a visual artist and art historian from Italy. At Yale, she’s exploring the nature of memory, both human and quantum, through her multidisciplinary projects.

You’ll also hear from Florian Carle, managing director of the institute and the co-ordinator of the residency. Once a rocket scientist, Carle has always held a love of theatre and the arts alongside his scientific work. He believes art–science collaborations open new possibilities for engaging with quantum ideas, and that includes music – which you’ll hear in the episode.

Discover more about quantum art and science in the free-to-read Physics World Quantum Briefing 2025

The post Entangled expressions: where quantum science and art come together appeared first on Physics World.

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In an era where video games often prioritize fast-paced action and instant gratification, Exographer offers a refreshing change. With a contemplative journey that intertwines the realms of particle physics and interactive storytelling, this beautifully pixelated game invites players to explore a decaying alien civilization through the lens of scientific discovery while challenging them with dexterity and intellect.​

Exographer was developed by particle physicist and science-fiction author Raphaël Granier de Cassagnac and his video-game studio SciFunGames. At its core, it is a puzzle-platformer – where the player’s character has to move around an environment using platforms while solving puzzles. The character in question is Ini, an alien explorer who discovers a multifunctional camera in the opening scenes of the game’s narrative. Stranded on a seemingly deserted planet, Ini is tasked with unlocking the mystery of the world’s fallen civilization.

The camera quickly becomes central to gameplay, allowing for environmental analysis, teleportation to previously visited locations and, most intriguingly, the discovery of subatomic particles through puzzles inspired by Feynman diagrams. These challenges require players to match particle trajectories using various analytical tools, mirroring the investigative processes of real-world physicists. ​

It is in these games where the particle physics really shines through. Beamlines have to be tracked and redirected to unveil greater understanding of the particles that make up this strange land and, with that, Ini’s abilities to understand the world.

As you crack one puzzle, a door opens and off you pootle to another blockage or locked door. Players will doubtless, as I did, find themselves wandering around areas pondering how to unlock it. A tip for those a little stuck: use the camera wherever a background seems a little different. In most circumstances, clues and cues will be waiting there.

Pixels and particles

The game’s environments are meticulously crafted, drawing inspiration from actual laboratories and observatories. I played the game on Nintendo Switch, but it is also available on several other platforms – including PS5, Xbox and Steam – and it looks pretty much identical on each. The pixel art style is not merely a visual choice but a thematic one, symbolizing the fundamental “pixels” of the universe of elementary particles. As players delve deeper, they encounter representations of particles including electrons, gluons and muons, each unlocking new abilities that alter gameplay and exploration. ​

Meanwhile, the character of Ini moves in a smooth and – for those gamers among us with a love of physics – realistic way. There is even a hint of lighter gravity as you hold down the button to activate a longer jump.

What sets Exographer apart is its ability to educate without compromising entertainment. The integration of scientific concepts is seamless, offering players a glimpse into the world of particle physics without overwhelming them. However, it’s worth noting that some puzzles may present a steep learning curve, potentially posing challenges for those less familiar with scientific reasoning.

Complementing the game’s visual and intellectual appeal is its atmospheric soundtrack, composed by Yann Van Der Cruyssen, known for his work on the game Stray. As with Stray – where you take the role of a stray cat with a backpack – the music enhances the sense of wonder and discovery, underscoring the game’s themes of exploration and scientific inquiry. ​

Exographer is more than just a game; it’s an experience that bridges the gap between science and (pixelated) art. It challenges players to think critically, to explore patiently, and to appreciate the intricate beauty of the universe’s building blocks. For those willing to engage with its depth, Exographer offers a rewarding journey that lingers after the console is turned off.

• 2024 SciFunGames and Abylight Studios Nintendo Switch £17.99; PS5 £15.99; Xbox £16.74; PC £16.75

The post <em>Exographer</em>: a scientific odyssey in pixel form appeared first on Physics World.

Researchers in Japan have directly visualized the formation and evolution of quasiparticles known as excitons in carbon nanotubes for the first time. The work could aid the development of nanotube-based nanoelectronic and nanophotonic devices.

Carbon nanotubes (CNTs) are rolled-up hexagonal lattices of carbon just one atom thick. When exposed to light, they generate excitons, which are bound pairs of negatively-charged electrons and positively-charged “holes”. The behaviour of these excitons governs processes such as light absorption, emission and charge carrier transport that are crucial for CNT-based devices. However, because excitons are confined to extremely small regions in space and exist for only tens of femtoseconds (fs) before annihilating, they are very difficult to observe directly with conventional imaging techniques.

Ultrafast and highly sensitive

In the new work, a team led by Jun Nishida and Takashi Kumagai at the Institute for Molecular Science (IMS)/SOKENDAI, together with colleagues at the University of Tokyo and RIKEN, developed a technique for imaging excitons in CNTs. Known as ultrafast infrared scattering-type scanning near-field optical microscopy (IR s-SNOM), it first illuminates the CNTs with a short visible laser pulse to create excitons and then uses a time-delayed mid-infrared pulse to probe how these excitons behave.

“By scanning a sharp gold-coated atomic force microscope (AFM) tip across the surface and detecting the scattered infrared signal with high sensitivity, we can measure local changes in the optical response of the CNTs with 130-nm spatial resolution and around 150-fs precision,” explains Kumagai. “These changes correspond to where and how excitons are formed and annihilated.”

According to the researchers, the main challenge was to develop a measurement that was ultrafast and highly sensitive while also having a spatial resolution high enough to detect a signal from as few as around 10 excitons. “This required not only technical innovations in the pump-probe scheme in IR s-SNOM, but also a theoretical framework to interpret the near-field response from such small systems,” Kumagai says.

The measurements reveal that local strain and interactions between CNTs (especially in complex, bundled nanotube structures) govern how excitons are created and annihilated. Being able to visualize this behaviour in real time and real space makes the new technique a “powerful platform” for investigating ultrafast quantum dynamics at the nanoscale, Kumagai says. It also has applications in device engineering: “The ability to map where excitons are created and how they move and decay in real devices could lead to better design of CNT-based photonic and electronic systems, such as quantum light sources, photodetectors, or energy-harvesting materials,” Kumagai tells Physics World.

Extending to other low-dimensional systems

Kumagai thinks the team’s approach could be extended to other low-dimensional systems, enabling insights into local dynamics that have previously been inaccessible. Indeed, the researchers now plan to apply their technique to other 1D and 2D materials (such as semiconducting nanowires or transition metal dichalcogenides) and to explore how external stimuli like strain, doping, or electric fields affect local exciton dynamics.

“We are also working on enhancing the spatial resolution and sensitivity further, possibly toward single-exciton detection,” Kumagai says. “Ultimately, we aim to combine this capability with in operando device measurements to directly observe nanoscale exciton behaviour under realistic operating conditions.”

The technique is detailed in Science Advances.

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Topological insulators are materials that behave as insulators in their interior but support the flow of electrons along their edges or surfaces. These edge states are protected against weak disorder, such as impurities, but can be disrupted by strong disorder. Recently, researchers have explored a new class of materials known as topological Anderson insulators. In these systems, strong disorder leads to Anderson localization, which prevents wave propagation in the bulk while still allowing robust edge conduction.

The Fermi energy is the highest energy an electron can have in a material at absolute zero temperature. If the Fermi energy lies in a conductive region, the material will conduct; if it lies in a ‘gap’, the material will be insulating. In a conventional topological insulator, the Fermi energy sits within the band gap. In topological Anderson insulators, it sits within the mobility gap rather than the conventional band gap, making the edge states highly stable. Electrons can exist in the mobility gap (unlike in the band gap), but they are localized and trapped. Until now, the transition from a topological insulator to a topological Anderson insulator has only been achieved through structural modifications, which limits the ability to tune the material’s properties.

In this study, the authors present both theoretical and experimental evidence that this phase transition can be induced by applying heat. Heating introduces energy exchange with the environment, making the system non-Hermitian. This approach provides a new way to control the topological state of a material without altering its structure. Further heating prompts a second phase transition, from a topological Anderson insulator to an Anderson insulator, where all electronic states become localized, and the material becomes fully insulating with no edge conduction.

This research deepens our understanding of how disorder influences topological phases and introduces a novel method for engineering and tuning these phases using thermal effects. It also provides a powerful tool for modulating electron conductivity through a simple, non-invasive technique.

Do you want to learn more about this topic?

Interacting topological insulators: a review by Stephan Rachel (2018)

The post A new path to robust edge states using heat and disorder appeared first on Physics World.

Lepton flavour universality is a principle in particle physics that concerns how all leptons (electrons, muons and taons) should interact with the fundamental forces of nature. The only difference between these interactions should be due to the different masses of the three particles.

This idea is a crucial testable prediction of the Standard Model and any deviations might suggest new physics beyond it.

Although many experimental results have generally supported this claim, some recent experimental results have shown tensions with its predictions.

Therefore the CMS collaboration at CERN set out to analyse data from proton-proton collisions, this time using a special high-rate data stream, designed for collecting around 10 billion proton decays.

They looked for signs of the decay of B mesons (a bottom quark and an up antiquark) into electron-positron or muon-antimuon pairs.

If lepton flavour universality is true, the likelihood of these two outcomes should be almost equal.

The authors found exactly that. To within their experimental uncertainty, there was no evidence of one decay being more likely than the other.

These results provide further support for this principle and suggest that different avenues ought to be studied to seek physics beyond the Standard Model.

The post Another win for lepton flavour universality appeared first on Physics World.