Studying physics can so busy and stressful that deciding what you should do after graduating is probably the last thing on any student’s mind. Here to help you work out what to do next are four careers experts, who took part in an episode of Physics World Live earlier this year. They all studied physics or engineering – and have thought long and hard about the career opportunities available for physics graduates.

The four experts are:

• Crystal Bailey, director of programmes and inclusive practices at the American Physical Society (APS);
• Tamara Clelford, a physics consultant currently leading a review of the Chartered Physicist standard at the Institute of Physics, which publishes Physics World;
• Araceli Venegas-Gomez, chief executive and founder of quantum education company QURECA;
• Tushna Commissariat, features editor of Physics World, who edits the annual Physics World Careers and APS Careers guides.

The career options for physicists are wide but can also seem overwhelming – so what advice do you have for people starting out on their career journey today?

Crystal Bailey: Finding a fulfilling career means trying to find something that matches your values. I don’t just mean what you’re interested in or what you like – but who you are as a person. So the first step always starts with self-assessment and self exploration, exploring what it is you really want from your life.

Do you want a job that has good work-life balance? Do you want something with a flexible schedule? Or do you want to make money? Making money is a very righteous and noble thing to want to do it – there’s nothing wrong with that. But when I give careers talks and ask the audience if they’ve asked themselves those questions, almost nobody raises their hand

So I encourage you to reflect on a time when’ve you been really happy and fulfilled. I don’t just mean were you doing, say, a quantum-mechanics problem, but were you with other people? Were you alone? Were you doing something with your hands, building something? Or was it something theoretical? You need to understand what will be a good match for you.

After you’ve done that self-assessment and understand what you need, I advise you do “informational interviews”, which basically involves getting in touch with somebody – online or in person – to ask them what they do day-to-day. What advice do they have? Where’s their sector going?

You’ll get real insider knowledge and, more importantly, it’ll help you build your network – especially if you follow-up, say, every six months to thank them for advice and update them of your situation. It’ll keep that relationship fresh and serve you later when you’re actually looking for jobs in a more targeted way.

Tamara Clelford: You need to understand what it is you enjoy. Are you a leader or do you like to be managed? Do you prefer to be told what to do? Do you like working in a team or working alone? Are you theoretical or more experimental? Do you prefer research or the real world? Maybe you just want to work with, say, aeroplanes, which is a perfectly valid reason to do so.

You also need to ask yourself where you want to work. Do you want to work in a big company, a medium-sized firm, or a small start up? I began in a large defence company, where I could easily switch jobs if something wasn’t the right fit. But in a big firm you often get taken off work as priorities change, so I now work for myself, which is fabulous.

Araceli Venegas-Gomez: The hardest thing is finding out what you like. Your long-term goal might be to get rich or have your own company. Once you work that out, you’ll need a short-term plan. It’ll probably change but having a plan is a great start. Then ask yourself: are you good at it? That self-assessment – understanding your skills and talents – is really important.

Next, find out what companies are there. Create a LinkedIn profile. Talk to people. Expand your network. Go to careers events. Do mock interviews – maybe not for your dream job but to help you learn how to do them. Learn how to do a CV and apply for jobs. Use all the resources available to you.

Tushna Commissariat: My advice is don’t leave your job search until just before you graduate. Start looking at internships and summer jobs as early as you can. I recall interviewing one physicist who sent an e-mail to NASA and got an internship at the age of 15. But on the other hand, remember that even if you land your perfect job, it might not work out, and its always okay to change your mind.

What is the number one skill – over and above technical knowledge – that physicists have that will help them in their career?

Crystal Bailey: Physicists often go into well-paid jobs that have “engineering” in the title, working alongside other STEM graduates. In fact, physicists have many of the same scientific and technical skills that make engineers and computer scientists so attractive to employers. But what sets physicists apart is a confidence that they can teach themselves whatever they need to know to go to the next step.

It’s a kind of “intellectual fearlessness” that is part of being a physicist. You’re used to marching up to the edge of what is known about the universe and taking that next step over to discover new knowledge. You might not know the answer, but you know you can teach yourself how to find the answer – or find somebody who can help you get there.

Tamara Clelford: It might not help us narrow down where we want to work, but physicists are capable of solving a huge range of problems. We can root around a problem, look for its fundamental aspects, and use mathematical and experimental skills to solve it. Whether it’s a hardware problem, a software problem or the need to derive an equation, we can do all that.

As physicists, we have the ability to upskill, to improve and to solve whatever problem we want.

Tamara Clelford

If we’re not an expert in a particular area, we know we can go and get the relevant expertise. As physicists, we know where our limits are. We’re not going to make stuff up to sound better than we are. We have the ability to upskill, to improve and to solve whatever problem we want.

Araceli Venegas-Gomez: As physicists, we have a multidisciplinarity that we often don’t realize we have. If you’re, say, a marine engineer, you’re going to work in marine engineering. But as a physicist, you can work anywhere there’s a job for you. What’s more, physicists don’t only solve problems; we also want to know why they exist. It might take us a bit longer to find a solution, but we look at it in a way that engineers might not.

Tushna Commissariat: One of the brilliant things about physicists is that they’re absolutely confident that they can come in and fix a problem. You see physicists going into biology and saying “Oh cancer, I can do that”. There are physicists who’ve gone into politics and into sport. I’ve even seen physicists improving nappies for babies.

At the same time, there’s almost a joy in failure: if something doesn’t work or goes wrong, it means something exciting and interesting is about to happen. I remember Rolf-Dieter Heuer, who was then director-general of CERN, saying it’ll be more exciting if we don’t find the Higgs boson because it would have meant the Standard Model of particle physics is broken – which would open up a wealth of possibilities.

What do you know today that you wish you’d known at the start of your career?

Crystal Bailey: When I went to grad school, I liked physics and thought “I’m good at it and I want to keep doing physics”. But I didn’t really having a clear reason for staying in academia. I was just doing what I thought was expected of me and didn’t even want a career in academia. So I wish I had had more of a sense of ownership and a little more confidence about my career.

Don’t doubt yourself. Don’t let anybody tell you that you can’t do something.

Crystal Bailey

The key message is: don’t doubt yourself. Don’t let anybody tell you that you can’t do something. It’s your life – and what you want is the most important thing. I just wish I had been given a little more encouragement and a little more confidence to go in new directions.

Tamara Clelford: In life, your priorities change and it’s very difficult to project into the future. At any particular time, you have certain experience and knowledge, on which you make the best decision you can make. But if, in five or 10 years’ time, you realize things aren’t working, then change and do something else. Trust your instincts – and change when you need to change.

Araceli Venegas-Gomez: I wish I’d known at the start of my career that everything’s going to be okay and there’s no need to panic. If you’re doing a PhD and you don’t finish it, that’s fine – I don’t think I’ve ever met a single physicist who’s ended up jobless. There are millions of options so remind yourself that everything is going to be okay.

Tushna Commissariat: When you’re studying, it’s easy to feel you’re in a kind of bubble universe of exams, practicals or labs. Set backs can feel like the end of the world when they really aren’t: your marks on a particular test won’t determine your entire future. Remember that you gain so many useful skills while studying, whether it’s working with other people or doing outreach work, which might seem a waste of time but are great for your CV.

• This article is based on the 9 April 2025 episode of Physics World Live, which you can watch on demand here.
• For more information, check out Physics World Jobs, the Physics World Careers guide, and the careers services offered by the Institute of Physics.

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The engines in everyday devices such as cars, vacuum cleaners and fans rely on a classical understanding of heat, energy and work. In recent years, scientists have designed (and in some cases built) new types of engines that incorporate unique quantum features. In addition to boosting performance, these features allow quantum engines to perform tasks that classical machines cannot.

Vijit Nautiyal from the University of New England, Armidale, New South Wales, Australia has now proposed a new type of quantum engine that exchanges not only heat, but also particles, with thermal reservoirs. The advantage of Nautiyal’s proposed quantum thermochemical engine, as described in Physical Review E, is that it combines near-maximum efficiency with high power output. “It’s equivalent to driving a Ferrari at the running cost of a Toyota,” Nautiyal explains. “You enjoy the thrill of high power while saving on fuel efficiency.”

Classical and quantum engines

Car engines typically operate in a four-stroke (Otto) cycle. In the intake stroke, the piston moves downwards, drawing air and fuel into a cylinder. The compression stroke then causes the piston to move upwards, compressing the mixture and increasing its temperature and pressure adiabatically (that is, without losing or gaining heat). Next comes the expansion stroke, when heat is added in the form of an igniting spark, causing the gas to expand adiabatically and performing work on the piston. Finally, during the exhaust stroke, the piston moves up, expelling the spent exhaust gases out of the cylinder.

Nautiyal’s proposed quantum engine replaces the fuel in a car engine with a weakly interacting one-dimensional Bose-Einstein condensate, or Bose gas, in a harmonic trap. Here, the ignition and exhaust (thermalization) strokes are equivalent to coupling the Bose gas to a surrounding cloud of thermal atoms that serves as a hot or cold reservoir. Because the Bose gas (the working fluid) can exchange both heat and particles with this reservoir, the setup can be considered an open quantum system. During the two work strokes (compression and expansion), the gas is instead treated as an isolated quantum many-body system.

The piston in this quantum engine is the strength of inter-atomic interactions in the gas. To  move the piston, Nautiyal’s scheme calls for abruptly increasing this interaction strength during the compression stroke and abruptly decreasing it during the expansion stroke.

Engine operations

When Nautiyal’s system exchanges only heat with the hot and cold reservoirs, it cannot operate as an engine because its beneficial output work is less than the input work. However, if it also exchanges particles with the reservoirs, it operates as a thermochemical engine with output work greater than the input, compensating for any quantum friction experienced during the process.

Like the classical Otto engine cycle, Nautiyal’s quantum engine experiences a trade-off between power and efficiency. In classical engines, operating the cycle at a faster speed increases engine power; however, it also typically decreases efficiency because dissipative effects such as heat and friction increase irreversible losses. Similarly, in quantum engines, driving the system faster during the work stroke produces losses in the form of non-adiabatic energy excitations.

These excitations can be suppressed if the work strokes are performed extremely slowly (a quasi-static quench), leading to maximum efficiency. However, this comes at the cost of null power output due to extremely long driving time. Optimizing this trade-off between power and efficiency is thus one of the main goals of this field of finite-time quantum thermodynamics.

The upper bound on the work and efficiency produced by Nautiyal’s thermochemical engine is set by an adiabatic quantum thermochemical engine operating at zero temperature. Remarkably, this engine can operate at near maximum efficiencies while maintaining high power output even in the sudden quench, out-of-equilibrium regime. This is because instead of increasing efficiency by extending cycle time, one can increase it by boosting the flow of particles from the hot reservoir, which raises the internal energy of the working fluid. The additional energy can then be converted into mechanical work during the expansion stroke.

Asked about possible applications of his quantum engine, Nautiyal referred to “quantum steampunk”. This term, which was coined by the physicist Nicole Yunger Halpern at the US National Institute of Standards and Technology and the University of Maryland, encapsulates the idea that as quantum technologies advance, the field of quantum thermodynamics must also advance in order to make such technologies more efficient. A similar principle, Nautiyal explains, applies to smartphones: “The processor can be made more powerful, but the benefits cannot be appreciated without an efficient battery to meet the increased power demands.” Conducting research on quantum engines and quantum thermodynamics is thus a way to optimize quantum technologies.

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Dear Physics World readers, I’m going to let you in on a secret. I get anxious every time I see the word “networking” on a meeting or conference agenda. I’m nervous whether anyone will talk to me and – if they do – what I’ll say in reply. Will I end up stuck in a corner fiddling on my phone to make it seem like I want to join in but have something more important to do?

If you feel this way – or even if you don’t – please read on because I have some something important to say for anyone who attends or organizes scientific events.

Now, we all know there are many benefits to networking. It’s a good way to meet like-minded people, tell others about what you’re doing, and build a foundation for collaboration. Networking can also boost your professional and personal development – for example, by identifying new perspectives and challenges, finding a mentor, connecting with other organizations, or developing a tailor-made support system.

However, doing this effectively and efficiently is not necessarily easy. Networking can also soak up valuable time. It can create connections that lead nowhere. It can even be a hugely exploitative and one-sided affair where you find yourself under pressure to share personal and/or professional information that you didn’t intend to.

Top tips

Like most things in life, what you get from networking depends on what you put in. To make the most of such events, try to think about how others are feeling in the same situation. Chances are that they will be a bit nervous and apprehensive about opening the conversation. So there’s no harm in you going first.

A good opening gambit is to briefly introduce yourself, say who you are, where you work and what you do, and seek similar information from the other person. Preparing a short “elevator pitch” about yourself makes it easier to start a conversation and reduces the need to think on the spot. (Fun fact: elevator pitch gets its name from US inventor Elisha Otis, who needed a concise way of explaining his device to catch a plummeting elevator.)

Make an effort to remember other people’s names. I am not brilliant at this and have found that double checking and using people’s names in conversation is a good way to commit them to memory. Some advance preparation also helps. If possible, study the attendee list, so you know who else might be there and where they’re from. Be yourself and try to be an active listener – listen to what others are saying and ask thoughtful questions.

Don’t feel the need to stick with one person or group of people for the whole the time. Five minutes or so is polite and then you can move on and mingle further. Obviously, if you are making a good connection then it’s worth spending a bit more time. But if you are genuinely engaged, making plans to follow up post event should be straightforward.

Decide the best way to share your contact details. It could be an iPhone air drop, taking a photo of someone’s name badge, sending an e-mail, or swapping business cards (seems a bit unecological these days). If there are people you want to meet, don’t be afraid to seek them out. It’s always a nice compliment to approach someone and say: “Ah, I was hoping to speak to you today; I’ve heard a lot about you.”

On the flip side, avoid hanging out with your cronies, by which I mean colleagues from the same company or organization or people you already know well. Set yourself a challenge to meet people you’ve never met before. Remember few of us like being left out so try to involve others in a conversation. That’s especially true if someone’s listening but not getting the chance to speak; think of a question to bring that person into the discussion.

Of course, if someone you meet doesn’t seem to be relevant to you, don’t be afraid to admit it. I’m sure they won’t be offended if you don’t follow up after the meeting. And to those who are already comfortable with networking, remember not to hog all the limelight and to encourage others to participate.

A message to organizers

Let me end with a message to organizers, which – I’ll be honest – is the main reason I’m writing this article. I have recently attended conferences and events where the music is so loud that people, myself included, have gone with the smokers to the perishing cold outside simply so we can hear each other speak. Am I getting old or is this defeating the object of networking? Please, no more loud music!

I also urge event organizers to have places where people can connect, including tables and seating areas where you can put your plates and drinks down. There’s nothing worse than trying to talk while juggling cutlery to avoid a quiche collapsing down the front of your shirt. Buffets are always better than formal sit-down dinners as it provides more opportunity for people to mix. But remember that long queues for food can arise.

So what has networking ever done for me? Over the years the benefits have changed, but most recently I have met some great peer mentors, people whom I can share cross-industry experience and best practice with. And, if I hadn’t been at a certain Institute of Physics networking event last year and met Matin Durrani, the editor of Physics World, then I wouldn’t be writing this article for you today.

I’ll let you, though, be the judge of whether that was a success. [Editor’s note: it certainly was…]

The post Honor Powrie: ‘So what has networking ever done for me?’ appeared first on Physics World.

Evaluating electrocardiogram (ECG) traces using a new deep-learning model known as EchoNext looks set to save lives by flagging patients at high risk of structural heart disease (SHD) who might otherwise be missed.

SHD encompasses a range of conditions affecting millions worldwide, including heart failure and valvular heart disease. It is, however, currently underdiagnosed because the diagnostic test for SHD, an echocardiogram, is relatively expensive and complex and thus not routinely performed. Late diagnosis results in unnecessary deaths, reductions in patient quality-of-life and an additional burden on healthcare services. EchoNext could reduce these problems as it provides a way of determining which patients should be sent for an echocardiogram – ultrasound imaging that shows the valves and chambers and how the heart is beating – by analysing the inexpensive and commonly collected ECG traces that record electrical activity in the heart.

The EchoNext model was developed by researchers at Columbia University and NewYork-Presbyterian Hospital in the US, led by Pierre Elias, assistant professor at Columbia University Vagelos College of Physicians and Surgeons and medical director for artificial intelligence at NewYork-Presbyterian. EchoNext is a convolutional neural network, which uses the mathematical operation of convolution to generate information and make predictions. In this case, EchoNext scans through the ECG data in bite-sized segments, generating information about each segment and subsequently assigning it a numerical “weight”. From these values, the AI model then determines if a patient is showing markers of SHD and so requires an echocardiogram. EchoNext learns from retrospective data by checking the accuracy of its predictions, with more than 1.2 million ECG traces from 230,000 patients used in its initial training.

In their study, reported in Nature, the researchers describe running EchoNext on ECG data from 85,000 patients. The AI model identified 9% of those patients as being in the high-risk category for undiagnosed SHD, 55% of whom subsequently had their first echocardiogram. This resulted in a positive diagnosis in almost three-quarters of cases; double the rate of positivity normally seen in first-time echocardiograms.

EchoNext also outperformed 13 cardiologists in making diagnoses based on 3200 ECGs by correctly flagging 77% of structural heart problems while its human colleagues were only 64% accurate – a result so good that it shocked the researchers.

“The really challenging thing here was that from medical school I was taught that you can’t detect things like heart failure or valvular disease from an electrocardiogram. So we initially asked: would the model actually pick out patients with disease that we were missing? I have read more than 10,000 ECGs in my career and I can’t look at an ECG and see what an AI model is seeing,” enthuses Elias. “It’s able to pick up on different sets of patterns that are not necessarily perceptible to us.”

Elias instigated the EchoNext project after an upsetting incident in which he was unable to save a patient transferred from another hospital with critical valvular heart disease because they had been diagnosed too late. “You can’t take care of the patient you don’t know about. So we said: is there a way that we can do a better job with diagnoses?”

EchoNext is now undergoing a clinical trial, based in eight hospital emergency departments, that ends in 2026. “My number one priority is to produce the right clinical evidence that is necessary to prove this technology is safe and efficacious, can be widely adopted and has value in helping patients,” says Elias.

He stresses that it is still early days for all AI technologies, but that even in these trial phases EchoNext – which was recently designated a breakthrough technology by the US Food and Drug Administration (FDA) – is already improving patient lives.

“It’s a really wonderful thing that every week we get to meet the patients that this helped. Our goal is for this to impact as many patients as possible over the next 12 months,” states Elias, adding that since EchoNext is successfully detecting 13 types of heart disease, a similar system should be useful in other healthcare domains too. “We think these kinds of AI-augmented biomarkers can become something that is routinely ordered and used as part of clinical practice,” he concludes.

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Electron–phonon interactions in a material have been modelled by combining billions of Feynman diagrams. Using a modified form of the Monte Carlo method, Marco Bernardi and colleagues at the California Institute of Technology predicted the behaviour of polarons in certain materials without racking up significant computational costs.

Phonons are quantized collective vibrations of the atoms or molecules in a lattice. When an electron moves through certain solids, it can interact with phonons. This electromagnetic interaction creates a particle-like excitation that comprises a propagating electron surrounded by a cloud of phonons. This quasiparticle excitation is called a polaron.

By lowering the electron’s mobility, while increasing its effective mass, polarons can have a substantial impact on the electronic properties of a variety of materials – including semiconductors and high-temperature superconductors.

However, physicists have struggled to model polarons and it would be extremely helpful for them to represent polarons using Feynman diagrams. These are a mainstay of particle physics, which are used to calculate the probabilities of certain particle interactions taking place. This has been challenging because polarons emerge from a superposition of infinitely many higher-order interactions between electrons and phonons. With each successive order, the complexity of these interactions steadily increases – along with the computational power required to represent them with Feynman diagrams.

Higher-order trouble

Unlike some other interactions, each higher order becomes more and more important in representing the polaron as accurately as possible. As a result, calculations cannot be simplified using standard perturbation theory – where only the first few orders of interaction are required to closely approximate the overall process.

“If you can calculate the lowest order, it’s very likely that you cannot do the second order, and the third order will just be impossible,” Bernardi explains. “The computational cost typically scales prohibitively with interaction order. There are too many diagrams to compute, and the higher-order diagrams are too computationally expensive. It’s basically a nightmare in terms of scaling.”

Bernardi’s team – which also included Yao Luo and Jinsoo Park  – approached the problem with the Monte Carlo method. This involves taking repeated random samples within a space of all possible events contributing to a process, then adding them together. It allows researchers to build up a close approximation of the process, without accounting for every possibility.

The team generated a series of Feynman diagrams spanning the full range of possible electron–phonon interactions. Then, they combined the diagrams to gain precise descriptions of the dynamic and ground-state properties of polarons in real materials.

Statistical noise

One issue with a fully-random Monte Carlo approach is the sign problem, which arises from statistical noise that can emerge as electrons scatter between different energy bands during electron–phonon interactions. Since different bands can contribute positively or negatively to the interaction probabilities represented by Feynman diagrams, these contributions can cancel each other out when added together.

To avoid this, Bernardi’s team adapted the Monte Carlo method to evaluate each band contribution in a structured, non-random way – preventing sign cancellations. In addition, the researchers applied a matrix compression approach. This vastly reduced the size and complexity of the electron–phonon interaction data, without sacrificing accuracy. Altogether, this enabled them to generate billions of diagrams without significant computational costs.

“The clever diagram sampling, sign problem removal, and electron–phonon matrix compression are the three key pieces of the puzzle that have enabled this paradigm shift in the polaron problem,” Bernardi explains.

The trio hopes that its technique will help us understand polaron behaviours. “The method we developed could also help study strong interactions between light and matter, or even provide the blueprint to efficiently add up Feynman diagrams in entirely different physical theories,” Bernardi says. In turn, it could help to provide deeper insights into a variety of effects where polarons contribute – including electrical transport, spectroscopy, and superconductivity.

The research is described in Nature Physics.

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A young gas giant exoplanet appears to be causing its host star to emit energetic outbursts. This finding, which comes from astronomers at the Netherlands Institute for Radio Astronomy (ASTRON) and collaborators in Germany, Sweden and Switzerland, is the first evidence of planets actively influencing their stars, rather than merely orbiting them.

“Until now, we had only seen stars flare on their own, but theorists have long suspected that close-in planets might disturb their stars’ magnetic fields enough to trigger extra flares,” explains Maximilian Günther, a project scientist with the European Space Agency’s Cheops (Characterising ExOPlanet Satellite) mission. “This study now offers the first observational hint that this might indeed be happening.”

Stars with flare(s)

Most stars produce flares at least occasionally. This is because as they spin, they build up magnetic energy – a process that Günther compares to the dynamos on Dutch bicycles. “When their twisted magnetic field lines occasionally snap, they release bursts of radiation,” he explains. “Our own Sun regularly behaves like this, and we experience its bursts of energy as part of space weather on Earth.” The charged particles that follow such flares, he adds, are responsible for the aurorae at our planet’s poles.

The flares the ASTRON team spotted came from a star called HIP 67522. Although classified as a G dwarf star like our own Sun, HIP 67522 is much younger, being 17 million years old rather than 4.5 billion. It is also slightly larger and cooler, and astronomers had previously used data from NASA’s Transiting Exoplanet Survey Satellite (TESS) to identify two planets orbiting it. Denoted HIP 67522 b and HIP 67522 c, both are located near their host, but HIP 67522 b is especially close, completing an orbit in just seven Earth days.

In the latest work, which is detailed in Nature, ASTRON’s Ekaterina Ilin and colleagues used Cheops’ precision targeting to make more detailed observations of the HIP 67522 system. These observations revealed a total of 15 flares, and Ilin notes that almost all of them appeared to be coming towards us as HIP 67522 b transited in front of its host as seen from Earth. This is significant, she says, because it suggests that the flares are being triggered by the planet, rather than by some other process.

“This is the first time we have seen a planet influencing its host star, overturning our previous assumptions that stars behave independently,” she says.

Six times more flaring

The ASTRON team estimate that HIP 67522 b is exposed to around six times as many flares as it would be if it wasn’t triggering some of them itself. This is an unusually high level of radiation, and it may help explain recent observations from the James Webb Space Telescope (JWST) that show HIP 67522 b losing its atmosphere faster than expected.

“The new study estimates that the planet is cutting its own atmosphere’s life short by half,” Günther says. “It might lose its atmosphere in the next 400‒700 million years, compared to the 1 billion years it would otherwise.”

If such a phenomenon turns out to be common, he adds, “it could help explain why some young planets have inflated atmospheres or evolve into smaller, denser worlds. And it could inform how we see the demography of ‘adult planets’.”

Astrobiology implications

One big unanswered question, Günther says, is whether the slightly more distant planet HIP 67522 c shows similar interactions with its host. “Comparing the two would be incredible, not only doubling the sampling size, but revealing how distance from the star affects magnetic interactions.”

The ASTRON researchers say they also want to understand the magnetic field of HIP 67522 b itself. More broadly, they plan to look for other such systems, hoping to find out how common they really are.

For Günther, who was not directly involved in the present study, even a single example is already important. “I have worked on exoplanets and stellar flares myself for many years, mostly inspired by the astrobiology implications, but this discovery opens a whole new window into how stars and planets can influence each other,” he says. “It is a wake-up call to me that planets are not just passive passengers; they actively shape their environments,” he tells Physics World. “That has big implications for how we think about planetary atmospheres, habitability and the evolution of worlds across the galaxy.”

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Many of us will have careers with three distinct eras: education, work and retirement. While the first two tend to be regimented, the third age offers the possibility of pursuing a wide range of interests.

Our guest in this episode of the Physics World Weekly podcast is the retired particle physicist Michael Albrow, who is scientist emeritus at Fermilab in the US. He has just published his book Space Times Matter: One Hundred Short Stories About The Universe, which is a collection of brief essays and poems related to science.

Much of the book comes from a newspaper column that Albrow wrote earlier in his retirement and he has also been involved in collaborations with visual and musical artists. In this podcast he talks about this third age of his career as a physicist and gives some tips for your retirement.

The post Third age careers for physicists: writing and the arts beckon appeared first on Physics World.

Ever since the Fukushima Daiichi nuclear power plant accident that caused the discharge of radionuclides from the power plant into the ocean, operators at the Tokyo Electric Power Company (TEPCO) have been implementing measures to reduce groundwater inflow into the damaged reactor buildings. TEPCO has also been pumping water into the reactors since the accident to cool them.

The cooled water is then treated using the Advanced Liquid Processing System (ALPS), which removes all radioactive materials from the water except for tritium – which is very difficult to remove and has a half-life of 12.32 ±0.02 years. This treated water ended up being accumulated and stored at the site, with limited space to store it.

To combat this storage issue, the Japanese government implemented a new policy in 2021 focused on discharging the ALPS-treated water into the ocean using a 1 km long tunnel. The release of the treated water (containing tritium) began on 24th August 2023, and the plan is to continue releasing it until 2050. The government set a threshold for tritium suspension levels of 700 Bq/L in the discharge outlet vicinity and 30 Bq/L in the ocean. If the concentrations exceed these thresholds, then the discharging must stop immediately.

Researchers at the University of Tokyo have now collaborated with Fukushima University to investigate the effects of discharging tritium into the local ocean environment, and whether the discharging of this treated water is actually having an adverse impact. The study used an ocean general circulation model known as COCO4.9 to look at the influence of climate conditions – such as long-term global warming – on the discharge scenarios of tritium from the power plant. The researchers examined multiple discharge scenarios (based on the amount of tritium released) up until 2099.

Previously, no modelling had been performed looking at long-term impacts relating to the changing environmental conditions of the planet. In a press release from the University of Tokyo, lead author Alexandre Cauqouin states that: “In our global ocean simulations, we could investigate how ocean circulation changes due to the global warming and representation of fine-scale ocean eddies influence the temporal and spatial distribution of tritium originating from these treated-water releases”.

It is important to find out how fast and far the tritium discharge spreads because both climate change and eddies in water currents can speed up the movement of tritium through the ocean.

The study revealed that in all but one of the modelled scenarios (and at the release location, which has a much higher concentration because the treated water hasn’t dissipated yet), the tritium concentration in the ocean remained almost the same, and at a very low concentration. This was true for both long- and short-term scenarios – showing that the discharge from the Fukushima Daiichi nuclear power plant has an almost negligible impact on the ocean.

Other than the worst-case scenario, the model discovered that the increase in tritium from the treated water is 0.1% or less of the tritium background concentration of 0.03–0.2 Bq/L within 25 km of the discharge site in the Pacific Ocean. This is well below detection limits – such a small amount that the presence of the added tritium from the treated water cannot be measured directly in the seawater. The results are also far below the safety standards of 10,000 Bq/L set by the World Health Organization and consistent with physical seawater monitoring being performed today.

Even in the worst-case scenario, the levels of tritium still fell well below the detection limits, but the model did find that in such a high-CO₂ emission scenario, there would be an increased concentration of tritium in the south of Japan due to the Kuroshio current – which could theoretically reach the western coast of the US, but in insufficient concentrations to have any adverse effects throughout the Pacific Ocean.

Overall, the study showed that the long-term safety threshold won’t be exceeded under the current treated water release plans. The study could also help with building future models to better understand how tritium moves through both water vapour and ocean water – as tritium could be used in the future as a chemical tracer to track atmospheric and oceanic circulation, precipitation patterns, river catchments, moisture sources and groundwater flow.

The research is published in Marine Pollution Bulletin.

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

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

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

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

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

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

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

Fluorescence-guided surgery

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

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

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

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

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

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

The study is described in npj Imaging.

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

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

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

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

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

Cats and secrets

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

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

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

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

• 2025 Walker Books 32pp £12.99hb

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

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

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

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

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

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

Do you want to learn more about this topic?

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

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

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

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

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

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

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

Do you want to learn more about this topic?

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

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

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

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

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

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

Leap in precision

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

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

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

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

Deeply held principle

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

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

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

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

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

Toward a deeper understanding

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

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

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

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

The research is described in Nature.

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

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

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

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

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

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

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

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

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

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

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

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

From lab to living room

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

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

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

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

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

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

Physicists have their say

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

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

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

Proceed with caution

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

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

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

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

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

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

How physics can help AI

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

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

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

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

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

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

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

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

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

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

No need for filters

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

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

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

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

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

Machine vision and hyperspectral imaging

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

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

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

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

The study is detailed in Nature.

The post Stacked perovskite photodetector outperforms conventional silicon image sensors appeared first on Physics World.

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.

The post Physicists turn atomic motion from a nuisance to a resource appeared first on Physics World.

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

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

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

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

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

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

What skills do you use every day in your job?

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

What do you like best and least about your job?

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

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

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

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

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

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

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

The B‒L model

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

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

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

Incredibly sensitive setup

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

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

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

An unconventional idea

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

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

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

The post New experiment uses levitated magnets to search for dark matter appeared first on Physics World.

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.

The post Deep learning classifies tissue for precision medicine appeared first on Physics World.

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.

The post Squid use Bragg reflectors in their skin to change colour appeared first on Physics World.

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.