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.

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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 surge in autonomous weapons. 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 ways 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!

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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.

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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.

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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.

A total of 139 employees at the US Environmental Protection Agency (EPA) have been suspended after signing a “declaration of dissent” accusing Donald Trump’s administration of “undermining” the agency’s mission. The letter, dated 1 July, stated that the signatories “stand together against the current administration’s focus on harmful deregulation, mischaracterization of previous EPA activities, and disregard for scientific expertise”.

Addressed to EPA administrator Lee Zeldin, the letter was signed by a total of more than 400 EPA workers, of whom 170 put their names to the document, with the rest choosing to remain anonymous. Zeldin suspended the employees on 3 July, with EPA officials telling them to provide contact information so the agency could be in touch with them while they are on leave.

Copied to leaders of the US Senate and House of Representatives, the letter was organized by the Stand Up For Science pressure group. The letter states that “EPA employees join in solidarity with employees across the Federal government in opposing this administration’s policies, including those that undermine the EPA mission of protecting human health and the environment.”

The document lists five “primary concerns”, including the scientific consensus being ignored to benefit polluters, and undermining public trust by EPA workers being distracted from protecting public health and the environment through objective science-based policy.

The letter adds that the EPA’s progress in the US’s most vulnerable communities is being reversed through the cancellation of environmental justice programmes, while budget cuts to the Office of Research and Development, which helps support the agency’s rules on environmental protection and human health, mean it cannot meet the EPA’s science needs. The letter also points to a culture of fear at the EPA, with staff being forced to choose between their livelihood and well-being.

In response to the letter, Zeldin said he had a “ZERO tolerance policy for agency bureaucrats unlawfully undermining, sabotaging and undercutting the agenda of this administration”. An EPA statement, sent to Physics World, notes that the letter “contains information that misleads the public about agency business”, adding that the letter’s signatories “represent a small fraction of the thousands of [agency] employees”. On 18 July Zeldin then announced a plan to eliminate the EPA’s Office of Research and Development, which could lead to more than 1000 agency scientists being sacked.

Climate concerns

In late July, more than 280 NASA employees signed a similar declaration of dissent protesting against staff cuts at the agency as well as calling on the acting head of NASA not to make the budget cuts Trump proposed. Another example of the tension in US science took place in May when hundreds of staff from the National Science Foundation (NSF) gathered in front of NSF headquarters for a photo marking the agency’s 75th birthday. NSF officials, who had been criticized for seeking to cut the agency’s budget and staff, and slash the proportion of scientific grants’ costs allowed for ancillary expenses, refused to support the event with an official photographer.

Staff then used their own photographer, but they could only take a shot from a public space at the side of the building. In late June, the administration announced that the NSF will have to quit the building, which it has occupied since 2017. No new location for the headquarters has been announced, with NSF spokesperson Michelle Negrón declining to comment on the issue. The new tenant will be the Department of Housing and Urban Development.

The Department of Energy, meanwhile, has announced that it will hire three scientists who have expressed doubts about the scientific consensus on climate change – although details of the trio’s job descriptions remain unknown. They are Steven Koonin, a physicist at Stanford University’s Hoover Institution, along with atmospheric scientist John Christy, director of the Earth System Science Center at the University of Alabama in Huntsville, and Alabama meteorologist Roy Spencer.

The appointments come as the administration is taking steps to de-emphasize government research on climate and weather science. The proposed budget for financial year 2026 would close 10 labs belonging to the National Oceanic and Atmospheric Administration (NOAA). The NOAA’s National Weather Service has already lost 600 of its 4200 employees this year, while NASA has announced that it will no longer host the National Climate Assessment website globalchange.gov.

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When lockdown hit, school lab technician Emanual Wallace started posting videos of home science experiments on social media. Now, as Big Manny, he’s got over three million followers on Instagram and TikTok; won TikTok’s Education Creator of the Year 2024; and has created videos with celebrities like Prince William and Brian Cox. Taking his science communication beyond social media, he’s been on CBBC’s Blue Peter and Horrible Science; has made TV appearances on shows like This Morning and BBC Breakfast; and has even given talks at Buckingham Palace and the Houses of Parliament.

But he’s not stopped there. Wallace has also recently published a second book in his Science is Lit series, Awesome Electricity and Mad Magnets, which is filled with physics experiments that children can do at home. He talks to Sarah Tesh about becoming the new face of science communication, and where he hopes this whirlwind journey will go next.

What sparked your interest in science?

I’ve always been really curious. Ever since I was young, I had a lot of questions. I would, for example, open up my toys just so I could see what was inside and how they worked. Then when I was in year 8 I had a science teacher called Mr Carter, and in every lesson he was doing experiments, like exciting Bunsen burner ones. I would say that’s what ignited my passion for science. And naturally, I just gravitated towards science because it answered all the questions that I had.

Growing up, what were the kind of science shows that you were really interested in?

When I was about 11 the show that I used to love was How it’s Made? And there’s a science creator called Nile Red – he creates chemistry videos, and he inspired me a lot. I used to watch him when I was growing up and then I actually got to meet him as well. He’s from Canada so when he came over, he came to my house and we did some experiments. To be inspired by him and then to do experiments with him, that was brilliant. I also used to watch a lot of Brian Cox when I was younger, and David Attenborough – I still watch Attenborough’s shows now.

You worked in a school for a while after your degrees at the University of East London – what made you go down that path rather than, say, staying in academia or going into industry?

Well, my bachelor’s and master’s degrees are in biomedical science, and my aspiration was to become a biomedical scientist working in a hospital lab, analysing patient samples. When I came out of university, I thought that working as a science technician at a school would be a great stepping stone to working as a biomedical scientist because I needed to gain some experience within a lab setting. So, the school lab was my entry point, then I was going to go into a hospital lab, and then work as a biomedical scientist.

But my plans have changed a bit now. To become a registered biomedical scientist you need to do nine months in a hospital lab, and at the moment, I’m not sure if I can afford to take nine months off from my work doing content creation. I do still want to do it, but maybe in the future, who knows.

What prompted you to start making the videos on social media?

When I was working in schools, it was around the time of lockdown. There were school closures, so students were missing out on a lot of science – and science is a subject where to gain a full understanding, you can’t just read the textbook. You need to actually do the experiments so you can see the reactions in front of you, because then you’ll be more likely to retain the information.

I started to notice that students were struggling because of all the science that they had missed out on. They were doing a lot of Google classrooms and Zoom lessons, but it just wasn’t having the full impact. That’s when I took it upon myself to create science demonstration videos to help students catch up with everything they’d missed. Then the videos started to take off.

How do you come up with the experiments you feature in your videos?  If you’re hoping to help students, do you follow the school curriculum?

I would say right now there’s probably three main types of videos that I make. The first includes experiments that pertain to the national curriculum – the experiments that might come up in, say, the GCSE exams. I focus on those because that’s what’s going to be most beneficial to young people.

Secondly, I just do fun experiments. I might blow up some fruit or use fire or blow up a hydrogen balloon. Just something fun and visually engaging, something to get people excited and show them the power of science.

And then the third type of video that I make is where I’m trying to promote a certain message. For example, I did a video where I opened up a lithium battery, put it into water and we got an explosion, because I wanted to show people the dangers of not disposing of batteries correctly. I did another one where I showed people the effects of vaping on the lungs, and one where I melted down a knife and I turned it into a heart to persuade people to put down their knives and spread love instead.

Who would you say is your primary audience?

Well, I would say that my audience is quite broad. I get all ages watching my videos on social media, while my books are focused towards primary school children, aged 8 to 12 years. But I’ve noticed that those children’s parents are also interested in the experiments, and they might be in their 30s. So it’s quite a wide age range, and I try to cater for everyone.

In your videos, which of the sciences would you say is the easiest to demonstrate and which is the hardest?

I’d say that chemistry is definitely the easiest and most exciting because I can work with all the different elements and show how they react and interact with each other. I find that biology can sometimes be a bit tricky to demonstrate because, for example, a lot of biology involves the human body – things like organ systems, the circulatory system and the nervous system are all inside the body, while cells are so small we can’t really see them. But there’s a lot that I can do with physics because there’s forces, electricity, sound and light. So I would say chemistry is the easiest, then physics, and then biology is the hardest.

Do you have a favourite physics experiment that you do?

I would say my favourite physics experiment is the one with the Van de Graff generator. I love that one – how the static electricity makes your hair stand up and then you get a little electric shock, and you can see the little electric sparks. 

You’re becoming a big name in science communication – what does an average day look like for you now?

On an average day, I’m doing content creation. I will research some ideas, find some potential experiments that I might want to try. Then after that I will look at buying the chemicals and equipment that I need. From there, I’ll probably do some filming, which I normally just do in my garden. Straight after, I will edit all the clips together, add the voiceover, and put out the content on social media. One video can easily take the whole day – say about six or seven hours – especially if the experiment doesn’t go as planned and I need to tweak the method or pop out and get extra supplies.

In your videos you have a load of equipment and chemicals. Have you built up quite a laboratory of kit in your house now?

Yeah, I’ve got a lot of equipment. And some of it is restricted too, like there’s some heavily regulated substances. I had to apply for a licence to obtain certain chemicals because they can be used to make explosives, so I had to get clearance.

What are you hoping to achieve with your work?

I’ve got two main goals at the moment. One of them is bringing science to a live audience. Most people, they just see my content online, but I feel like if they see it in person and they see the experiments live, it could have an even bigger impact. I could excite even more people with science and get them interested. So that’s one thing that I’m focusing on at the moment, getting some live science events going.

I also want to do some longer-form videos because my current ones are quite short – they’re normally about a minute long. I realize that everyone learns in different ways. Some people like those short, bite-sized videos because they can gain a lot of information in a short space of time. But some people like a bit more detail – they like a more lengthy video where you flesh out scientific concepts. So that’s something that I would like to do in the form of a TV science show where I can present the science in more detail.

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Researchers in Australia say that they have created the first CMOS chip that can control the operation of multiple spin qubits at ultralow temperatures. Through an advanced approach to generating the voltage pulses needed to control the qubits, a team led by David Reilly at the University of Sydney showed that control circuits can be integrated with qubits in a heterogeneous chip architecture. The design is a promising step towards a scalable platform for quantum computing.

Before practical quantum computers can become a reality, scientists and engineers must work out how to integrate large numbers (potentially millions) of qubits together – while preserving the quantum information as it is processed and exchanged. This is currently very difficult because the quantum nature of qubits (called coherence) tends to be destroyed rapidly by heat and other environmental noise.

One potential candidate for integration are the silicon spin qubits, which have advantages that include their tiny size, their relatively long coherence times, and their compatibility with large-scale electronic control circuits.

To operate effectively, however, these systems need to be cooled to ultralow temperatures. “A decade or more ago we realized that developing cryogenic electronics would be essential to scaling-up quantum computers,” Reilly explains. “It has taken many design iterations and prototype chips to develop an approach to custom silicon that operates at 100 mK using only a few microwatts of power.”

Heat and noise

When integrating multiple spin qubits onto the same platform, each of them must be controlled and measured individually using integrated electronic circuits. These control systems not only generate heat, but also introduce electrical noise – both of which are especially destructive to quantum logic gates based on entanglement between pairs of qubits.

Recently, researchers have addressed this challenge by separating the hot, noisy control circuits from the delicate qubits they control. However, when the two systems are separated, long cables are needed to connect each qubit individually to the control system. This creates a dense network of interconnects that would prove extremely difficult and costly to scale up to connect millions of qubits.

For over a decade, Reilly’s team have worked towards a solution to this control problem. Now, they have shown that the voltage pulses needed to control spin qubits can be generated directly on a CMOS chip by moving small amounts of charge between closely spaced capacitors. This is effective at ultralow temperatures, allowing the on-board control of qubits.

CMOS chiplet

“We control spin qubits using a tightly integrated CMOS chiplet, addressing the interconnect bottleneck challenge that arises when the control is not integrated with qubits,” Reilly explains. “Via careful design, we show that the qubits hardly notice the switching of 100,000 transistors right next door.“

The result is a two-part chip architecture that, in principle, could host millions of silicon spin qubits. As a benchmark, Reilly’s created two-qubit entangling gates on their chip. When they cooled their chip to the millikelvin temperatures required by the qubits, its control circuits carried out the operation just as flawlessly as previous systems with separated control circuits.

While the architecture is still some way from integrating millions of qubits onto the same chip, the team believes that this goal is a step closer.

“This work now opens a path to scaling up spin qubits since control systems can now be tightly integrated,” Reilly says. “The complexity of the control platform has previously been a major barrier to reaching the scale where these machines can be used to solve interesting real-world problems.”

The research is described in Nature.

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A large, low density region of space surrounding the Milky Way may explain one of the most puzzling discrepancies in modern cosmology. Known as the Hubble tension, the issue arises from conflicting measurements of how fast the universe is expanding. Now, a new study suggests that the presence of a local cosmic void could explain this mismatch, and significantly improves agreement with observations compared to the Standard Model of cosmology.

“Numerically, the local measurements of the expansion rate are 8% higher than expected from the early universe, which amounts to over six times the measurement uncertainty,” says Indranil Banik, a cosmologist at the University of Portsmouth and a collaborator on the study. “It is by far the most serious issue facing cosmology.”

The Hubble constant describes how fast the universe is expanding and it can be estimated in two main ways. One method involves looking far into the past by observing the cosmic microwave background (CMB). This is radiation that was created shortly after the Big Bang and permeates the universe to this day. The other method relies on the observation of relatively nearby objects, such as supernovae and galaxies, to measure how fast space is expanding in our own cosmic neighbourhood.

If the Standard Model of cosmology is correct, these two approaches should yield the same result. But, they do not. Instead, local measurements suggest the universe is expanding faster than the expansion given by early-universe data. Furthermore, this disagreement is too large to dismiss as experimental error.

Local skewing

One possible explanation is that something about our local environment is skewing the results. “The idea is that we are in a region of the universe that is about 20% less dense than average out to a distance of about one billion light years,” Banik explains. “There is actually a lot of evidence for a local void from number counts of various kinds of sources across nearly the whole electromagnetic spectrum, from radio to X-rays.”

Such a void would subtly affect how we interpret the redshifts of galaxies. This is the stretching of the wavelength of galactic light that reveals how quickly a galaxy is receding from us. In an underdense (of relatively low density) region, galaxies are effectively pulled outward by the gravity of surrounding denser areas. This motion adds to the redshift caused by the universe’s overall expansion, making the local expansion rate appear faster than it actually is.

“The origin of such a [void] would trace back to a modest underdensity in the early universe, believed to have arisen from quantum fluctuations in density when the universe was extremely young and dense,” says Banik. However, he adds, “A void as large and deep as observed is not consistent with the standard cosmological model. You would need structure to grow faster than it predicts on scales larger than about one hundred million light–years”.

Testing the theory

To evaluate whether the void model holds up against data, Banik and his collaborator Vasileios Kalaitzidis at the UK’s University of St Andrews compared it with one of cosmology’s most precise measurement tools: baryon acoustic oscillations (BAOs). These are subtle ripples in the distribution of galaxies that were created by sound waves in the early universe and then frozen into the large-scale structure of space as it cooled.

Because these ripples provide a characteristic distance scale, they can be used as a “standard ruler” to track how the universe has expanded over time. By comparing the apparent size of this ruler as observed at different distances, cosmologists can map the universe’s expansion history. Crucially, if our galaxy lies inside a void, that would alter how the ruler appears locally, in a way that can be tested.

The researchers compared the predictions of their model with twenty years of BAO observations, and the results are striking. “BAO observations over the last twenty years show the void model is about one hundred million times more likely than the Standard Model of cosmology without any local void,” says Banik. “Importantly, the parameters of all these models were fixed without considering BAO data, so we were really just testing the predictions of each model.”

What lies ahead

While the void model appears promising, Banik says that more data are needed. “Additional BAO observations at relatively short distances would help a lot because that is where a local void would have the greatest impact.” Other promising avenues include measuring galaxy velocities and refining galaxy number counts. “I would suggest that it can be essentially confirmed in the next five to ten years, since we are talking about the nearby universe after all.”

Banik is also analysing supernovae data to explore whether the Hubble tension disappears at greater distances. “We are testing if the Hubble tension vanishes in the high-redshift or more distant universe, since a local void would not have much effect that far out,” he says.

Despite the challenges, Banik remains optimistic. With improved surveys and more refined models, cosmologists may be closing in on a solution to the Hubble tension.

The research is described in Monthly Notices of the Royal Astronomical Society.

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The Cockcroft Walton lecture series is a bilateral exchange between the Institute of Physics (IOP) and the Indian Physics Association (IPA). Running since 1998, it aims to promote dialogue on global challenges through physics.

Lee Packer, who has over 25 years of experience in nuclear science and technology and is an IOP Fellow, delivered the 2025 Cockcroft Walton Lecture Series in April. Packer gave a series of lectures at the Homi Bhabha Research Centre (BARC) in Mumbai, the Institute for Plasma Research (IPR) in Ahmedabad and the Inter-University Accelerator Centre (IUAC) in Delhi.

Packer is a fellow of the UK Atomic Energy Authority (UKAEA), in which he works on nuclear aspects of fusion technology. He also works as consultant to the International Atomic Energy Agency (IAEA) in Vienna, where he is based in the physics section of the department of nuclear sciences and applications.

Packer also holds an honorary professorship at the University of Birmingham, UK, where he lectures on nuclear fusion as part of its long-running MSc course in the physics and technology of nuclear reactors.

Below, Packer talks to Physics World about the trip, his career in fusion and what advice he has for early-career researchers.

When did you first become interested in physics?

I was fortunate to have some inspiring teachers at school who made physics feel both exciting and full of possibility. It really brought home how important teachers are in shaping future careers and they deserve far more recognition than they often receive. I went on to study physics at Salford University and during that time spent a year on industrial placement at the ISIS Neutron and Muon Source based at the Rutherford Appleton Laboratory (RAL). That year deepened my interest in applied nuclear science and highlighted the immense value of neutrons across real-world applications – from materials research and medicine to nuclear energy.

Can you tell me about your career to date?

I’ve specialized in applied nuclear science throughout my career, with a particular focus on neutronics – the analysis of neutron transport – and radiation detection applied to nuclear technologies. Over the past 25 years, I’ve worked across the nuclear sector – in spallation, fission and fusion – beginning in analytical and research roles before progressing to lead technical teams supporting a broad range of nuclear programmes.

When did you start working in fusion?

While I began my career in spallation and fission, the expertise I developed in neutronics made it a natural transition into fusion in 2008. It’s important to recognize that deuterium-tritium fuelled fusion power is a neutron-rich energy source – in fact, 80% of the energy released comes from neutrons. That means every aspect of fusion technology must be developed with the nuclear environment firmly in mind.

Why do you like about working in fusion energy?

Fusion is an inherently interdisciplinary challenge and there are many interesting and difficult problems to solve, which can make it both stimulating and rewarding. There’s also a strong and somewhat refreshing international spirit in fusion – the hard challenges mean collaboration is essential. I also like working with early-career scientists and engineers to share knowledge and experience. Mentoring and teaching is rewarding, and it’s crucial that we continue building the pipelines of talent needed for fusion to succeed.

Tell me about your trip to India to deliver the Cockcroft Walton lecture series?

I was honoured to be selected to deliver the Cockcroft-Walton lecture series. Titled “Perspectives and challenges within the development of nuclear fusion energy”, the lectures explored the current global landscape of fusion R&D, technical challenges in areas such as neutronics and tritium breeding, and the importance of international collaboration. I shared some insights from activities within the UK and gave a global perspective. The reception was very positive – there’s strong enthusiasm within the Indian fusion community and they are making excellent contributions to global progress in fusion. The hosts were extremely welcoming, and I’d like to thank them for their hospitality and the fascinating technical tours at each of the institutes. It was an experience I won’t forget.

What are India’s strengths in fusion?

India has several strengths including a well-established technical community, major national laboratories such as IPR, IUAC and BARC, and significant experience in fusion through its domestic programme and direct involvement in ITER as one of the seven member states. There is strong expertise in areas such as nuclear physics, neutronics, materials, diagnostics and plasma physics.

What could India improve?

Where India might improve could be in building further on its amazing potential – particularly its broader industrial capacity and developing its roadmap towards power plants. Common to all countries pursuing fusion, sustained investment in training and developing talented people will be key to long-term success.

When do you think we will see the first fusion reactor supplying energy to the grid?

I can’t give a definitive answer for when fusion will supply electricity to the grid as it depends on resolving some tough, complex technical challenges alongside sustained political commitment and long-term investment. There’s a broad range of views and industrial strategies being developed within the field. For example, the UK government’s recently published clean energy industrial strategy mentions the Spherical Tokamak for Energy Production programme, which aims to deliver a prototype fusion power plant by 2040 at West Burton, Nottinghamshire, at the site of a former coal power station. The Fusion Industry Association’s survey of private fusion companies reports that many are aiming for fusion-generated electricity by the late 2030s, though time projections vary.

There are others who say it may never happen?

Yes. On the other hand, some point to several critical hurdles to address and offer more cautious perspectives and call for greater realism. One such problem, close to my own interest in neutronics, is the need to demonstrate tritium-breeding blanket-technology systems and to develop lithium-6 supplies at the required scale for the industry.

What are the benefits of doing so?

The potential benefits for society are too significant to disregard on the grounds of difficulty alone. There’s no fundamental physical reason why fusion energy won’t work and the journey itself brings substantial value. The technologies developed along the way have potential for broader applications, and a highly skilled and adaptable workforce is developed with this.

What advice do you have for early-career physicists thinking about working in the field?

Fusion needs strong collaboration between people from across the board – physicists, engineers, materials scientists, modellers and more. It’s an incredibly exciting time to get involved. My advice would be to keep an open mind and seek out opportunities to work across these disciplines. Look for placements, internships, graduate or early-career positions and mentorship – and don’t be afraid to ask questions. There’s a brilliant international community in fusion, and a willingness to support those with kick-starting their careers in this field. Join the effort to develop this technology and you’ll be part of something that’s not only intellectually stimulating and technically challenging but is also important for the future of the planet.

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The UK should focus on being a “responsible, intelligent and independent leader” in space sustainability and can make a “major contribution” to the area. That’s the verdict of a new report from the Institute of Physics (IOP), which warns, however, that such a move is possible only with significant investment and government backing.

The report, published together with the Frazer-Nash Consultancy, examines the physics that underpins the space science and technology sector. It also looks at several companies that work on services such as position, navigation and timing (PNT), Earth observation as well as satellite communications.

In 2021/22 PNT services contributed over 12%, or about £280bn, to the UK’s gross domestic product – and without them many critical national infrastructures such as the financial and emergency systems would collapse. The report says, however, that while the UK depends more than ever on global navigation satellite systems (GNSS) that reliance also exposes the country to its weaknesses.

“The scale and sophistication of current and potential PNT attacks has grown (such as increased GPS signal jamming on aeroplanes) and GNSS outages could become commonplace,” the report notes. “Countries and industries that address the issue of resilience in PNT will win the time advantage.”

Telecommunication satellite services contributed £116bn to the UK in 2021/22, while Earth observation and meteorological satellite services supported industries contributing an estimated £304bn. The report calls the future of Earth observation “bold and ambitious”, with satellite data resolving “the disparities with the quality and availability of on-the-ground data, exacerbated by irregular dataset updates by governments or international agencies”.

Future growth

As for future opportunities, the report highlights “in-space manufacturing”, with companies seeing “huge advantages” in making drugs, harvesting stem cells and growing crystals through in-orbit production lines. The report says that In-Orbit Servicing and Manufacturing could be worth £2.7bn per year to the UK economy but central to that vision is the need for “space sustainability”.

The report adds that the UK is “well positioned” to lead in sustainable space practices due to its strengths in science, safety and sustainability, which could lead to the creation of many “high-value” jobs. Yet this move, the report warns, demands an investment of time, money and expertise.

“This report captures the quiet impact of the space sector, underscoring the importance of the physics and the physicists whose endeavours underpin it, and recognising the work of IOP’s growing network of members who are both directly and indirectly involved in space tech and its applications,” says Alex Davies from the Rutherford Appleton Laboratory, who founded the IOP Space Group and is currently its co-chair.

Particle physicist Tara Shears from the University of Liverpool, who is IOP vice-president for science and innovation, told Physics World that future space tech applications are “exciting and important”. “With the right investment, and continued collaboration between scientists, engineers, industry and government, the potential of space can be unlocked for everyone’s benefit,” she says. “The report shows how physics hides in plain sight; driving advances in space science and technology and shaping our lives in ways we’re often unaware of but completely rely on.”

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