High‑voltage transmission systems are a key part of power grids, transporting electricity from where it is generated to where it is used. Electricity is moved at high voltage and low current to reduce losses and improve efficiency. These systems are essential for grid stability, integrating renewable energy, and enabling long‑distance power transfer. There are two main high‑voltage direct current (HVDC) technologies: line‑commutated converters (LCC) and voltage‑source converters (VSC). LCCs are an older technology that use high‑power semiconductor switches called thyristors and are suited to very large power transfers. VSCs are a newer technology that use insulated‑gate bipolar transistors (IGBTs), allowing faster control of power flow, better stability, and more compact converter stations.
In this study, the researchers interviewed thirteen leading experts to understand which HVDC technology is likely to dominate in the future, how semiconductor devices may evolve, and what cost or supply issues might arise. The experts agreed that thyristors used in LCCs are a mature technology with limited room for improvement, and that demand for LCC systems is declining in North America and Europe, though they will remain important in regions requiring very high‑capacity transmission such as China and India. In contrast, IGBTs used in VSC systems are expected to continue improving, particularly in reliability, packaging, and voltage capability, reflecting the growing use of VSCs in Europe and North America. Some experts even suggested that VSC converter stations may now be comparable in cost to, or cheaper than, LCC stations, and that further improvements in IGBT cost and performance could reduce VSC system costs further.
There was debate about whether silicon‑carbide (SiC) MOSFETs could eventually replace IGBTs in VSC systems. While SiC devices offer advantages in high‑frequency applications, they currently cannot handle the very high currents required for HVDC, and challenges remain in packaging and long‑term reliability. Experts also noted that although global demand for power electronics is rising, this is unlikely to constrain HVDC development; instead, shortages of other components, particularly high‑voltage transformers, may pose greater risks. Overall, this research clarifies which power‑electronic technologies are poised to shape the next generation of HVDC systems and highlights why future grids are expected to rely increasingly on VSC converters and advanced semiconductor devices.
For Keamogetswe Ramonaheng, physics was never just about equations – it was about clarity. “From a young age, I was attracted to mathematics and science as a way of understanding complex phenomena through a structured approach,” she says. “Physics was the area that spoke to me the most because it is the foundation for the fundamental principles that govern the natural world.”
Ramonaheng’s academic journey began at the University of the Free State (UFS), where she completed her undergraduate and honours studies before starting an internship at Universitas Academic Hospital in Bloemfontein. There she saw how a rigorous physics training can lead to tangible, clinical benefits. “The ability to comprehend and harness the interaction between radiation and matter in the human body demonstrated the power and relevance of scientific inquiry,” she recalls.
In many ways, nuclear medicine found me
Keamogetswe Ramonaheng
Thanks to a fellowship from the International Atomic Energy Agency (IAEA), Ramonaheng completed a clinical placement at Royal North Shore Hospital in Sydney, Australia. She later continued her postgraduate studies at UFS, becoming the first Black South African woman to earn a PhD in medical physics for nuclear medicine. “In many ways, nuclear medicine found me,” says Ramonaheng, who is grateful to the encouragement of various senior staff members who saw her potential and guided her into the field.
Multifaceted role
Following a spell as an independent medical physicist and manager at Universitas Academic Hospital and lecturer at UFS, Ramonaheng joined NuMeRI in 2024 and the University of Pretoria. Along with the team of scientists she leads, Ramonaheng oversees the safe and effective use of ionizing radiation at NuMeRI used to treat and diagnose disease in a safe and effective manner.
It’s a varied role, which stretches from providing patient-focused clinical services to carrying out applied research. “We integrate research with operations,” says Ramonaheng. “That requires careful planning and rigorous quality assurance, ensuring that innovation does not compromise safety.”
Among her duties, Ramonaheng carries out dosimetry calculations for innovative radiopharmaceuticals, works on new forms of quantitative imaging, and helps to develop novel radionuclide therapies, including using alpha particles to treat cancer. She also uses gamma-ray cameras equipped with highly sensitive cadmium-zinc-telluride detectors, which allow radiopharmaceuticals to be quantified and imaged more precisely.
Ramonaheng is particularly interested in “theranostics” – a form of “precision medicine” that combines therapy with diagnostics. It involves giving a patient a tumour-targeting molecule labelled with a radionuclide. This allows the tumour to be visualized using techniques such as positron emission tomography (PET) or single-photon emission computerized tomography (SPECT). The same molecule – or one similar to it – is then used to deliver a therapeutic radionuclide directly to the tumour.
Daily challenges
For Ramonaheng, a typical day is fast-paced. Mornings often begin with her overseeing radiation-safety protocols and ensuring that radiation imaging and counting equipment are working as well as possible, such that they meet quality assurance standards. Through the day, Ramonaheng also oversees all operational medical-physics activities and carries out her duties as chair of NuMeRI’s radiation protection committee.
As the day progresses, she might find herself reviewing clinical theranostics dosimetry workflows to carrying out patient-specific dose calculations or evaluating quantitative imaging metrics from SPECT/CT and PET/CT systems. Other tasks include reviewing research protocols for cancer theranostics, mentoring postgraduate students at the University of Pretoria, and examining clinical trials.
Innovation accelerates when silos are dismantled
Keamogetswe Ramonaheng
Ramonaheng works in a highly interdisciplinary environment, collaborating with radiographers, nurses, radiochemists, radiopharmacists, medical physicists and clinicians to address live issues in real time. “Innovation accelerates when silos are dismantled,” she says.
The work is not without its challenges. Funding for postgraduate training is a persistent concern. Clinical physics is also a highly specialized field, which means it can be hard to recruit people with the right skills, who might be drawn to better-paid industry jobs. In addition, NuMeRI is an operationally complex mix of advanced imaging systems, radiopharmaceuticals and clinical regulations, which requires good project-management and planning skills.
But Ramonaheng, who recently won two awards at the 8th Theranostics World Congress in Cape Town, feels the benefits outweigh the challenges. “It is very fulfilling to see the translation of research into clinical application,” she says. Just as gratifying, she adds, is watching her students move from their studies to publications and clinical applications. “You see the entire process of scientific advancement.”
A more promising future
Looking ahead, Ramonaheng envisages a growing use of artificial intelligence (AI) in her work. She also collaborates with national and international partners to automate workflows and enhance efficiency, precision and patient-centred care. Another ambition for Ramonaheng is to further strengthen NuMeRI as an Africa-wide hub for research, clinical service and training – a vision reinforced by the IAEA recently naming NuMeRI as one of 18 global “anchor centres” for its work in radiotherapy and medical imaging.
Ramonaheng believes medical physics will grow rapidly in Africa over the next 10 years, fuelled by an expansion of theranostics and precision medicine. Her hope is to guide this growth through mentorship and leadership, ensuring that Africa develops its own talent pool of medical physicists who can address the continent’s unique healthcare needs.
Africa suffers, for example, from limited access to advanced imaging and targeted therapies. Ramonaheng’s aim is to optimize personalized and precision medicine for cancer patients, ultimately improving treatment outcomes and quality of life. Eventually, she hopes, medical physics will be recognized as a profession across the continent. “We are building not only research outputs but human capital.”
Leadership is not only about the creation of paths, but the creation of paths where there were no paths previously
Keamogetswe Ramonaheng
Being a pioneer in the field has required resilience on her part. “Competence must be coupled with confidence,” says Ramonaheng, who has had to learn the unwritten rules of a world dominated by men. As a mentor, her guiding principle is the African concept of motho ke motho ka batho babang – a person is a person only through others. “Leadership is not only about the creation of paths,” she says, “but the creation of paths where there were no paths previously.”
Her message to young physicists – particularly women and those from other underrepresented groups – is clear. “Medical physics is a dynamic and impactful field at the intersection of physics, medicine and technology,” she says. “ It allows you to see the direct translation of science to patients.” Medical physics requires resilience, curiosity and commitment, but for Ramonaheng its beauty is that equations don’t stay on paper – they become a tool for healing.
With a PhD in nuclear physics, Paul Howarth has had a long career in the nuclear sector, working on the European Fusion Programme and at British Nuclear Fuels, as well as co-founding the Dalton Nuclear Institute at the University of Manchester. He was a non-executive board director of the National Physical Laboratory and served as chief executive officer of the National Nuclear Laboratory.
Howarth became president-elect of the Institute of Physics (IOP) in September 2025. In February he became IOP president after space physicist Michele Dougherty stepped aside from the role to avoid any conflicts of interest given her position as executive chair of the Science and Technology Facilities Council. Howarth is set to be IOP president until 2029. Physics World recently caught up with Howarth to find out more about his career and vision for physics.
What originally sparked your interest in physics?
I think it probably came from my father. He was a research chemist. We lived in Cheshire near the Jodrell Bank Observatory and its iconic Lovell Telescope. I was fascinated by that and it captivated my interest in astronomy and so I did a degree in physics and astrophysics at the University of Birmingham.
You stayed at Birmingham to do a PhD in nuclear fusion. What attracted you to that field?
It goes back to my interest in astronomy and the ability to use mathematics to describe the universe. Yet by the end of the degree, I was fascinated by nuclear fusion as an energy source and a sustainable means of clean energy for society. During my PhD, I got to work on the JET tokamak in Oxfordshire, which was wonderful. It was when JET was doing its first deuterium-tritium plasma shot, which was an exciting time.
After your PhD, you worked for British Nuclear Fuels. Why did you make that move and what appealed about the commercial side of physics?
In the 1990s there was quite a bit of uncertainty about the direction of nuclear fusion, but I’d always been fascinated by the huge monolith structures of nuclear power stations. So I didn’t hesitate when an opportunity arose to work at Sellafield – a huge site in north-west England with more than 200 nuclear facilities – on understanding the physics of plutonium.
You then served as chief executive officer of the UK’s National Nuclear Laboratory. How did that come about?
At British Nuclear Fuels I was working to build the case for the next generation of nuclear power plants. But in the early 2000s it was less clear that nuclear was going to be part of the UK’s energy policy. So British Nuclear Fuels was broken up into organizations such as the Nuclear Decommissioning Authority. But I was determined to continue to make the case for new nuclear build and ended up helping the UK government create a National Nuclear Laboratory to maintain sovereign nuclear capability, becoming chief executive officer in 2011.
What did that role involve?
We had contracts to support all aspects of the UK’s nuclear programme as well as build the case for future nuclear. We worked on the front end of the fuel cycle, on reactor technology, on future reactors, on legacy waste management and decommissioning. I had the responsibility for running about £2–3bn of critical nuclear real estate and infrastructure.
Many countries, not just the UK, are showing a renewed enthusiasm for nuclear – what do you attribute that to?
Yes, it’s a fascinating time for nuclear. I think things are heading now towards small modular reactors and advanced reactor systems. Larger nuclear plants are more efficient but it is possible to trade that off for smaller plants. This opens up the opportunity for others to potentially invest in nuclear. So we see, for example, individuals like Bill Gates and others who are looking at nuclear power.
That’s the challenge – to effectively support all aspects of physics. I don’t want to be in a position where we are pitching one area against another
Paul Howarth
Do you see parallels with the fusion industry and how that has grown in the past decade?
Absolutely. I think a very similar thing has happened. Of course, there’s still the engineering challenges associated with scaling up fusion but good progress is being made. And other players and entities, like Tokamak Energy and First Light Fusion, are looking at entering the market, which is great.
Having retired from the NNL in 2025, what drew you to the role of IOP president?
It was the opportunity to give something back to physics. Physics is such an important discipline that is needed across all aspects of society and through my time working in physics, I’ve seen the benefits that it brings.
What things excite you as you take up this position?
When we look across society, the impact that physics is having is massive – whether that is in data centres, artificial intelligence, net zero, medicine or even food supplies. One of the things I would like to achieve during my presidency is to qualify and quantify that impact. The role that physics can play is going to be fascinating and to be part of that journey is exciting.
What are your priorities as president?
One is to nudge the dial on getting physics recognized in society as a really valuable and important discipline. This includes making sure that schools are properly equipped and resourced for teaching physics as well as having more teachers with a physics background. This would then hopefully translate into more people studying the subject at A-level and degree level.
Machine learning The IOP’s Physics and AI Impact Pathfinder report highlighted the role of physics as both enabler and beneficiary of AI. (Courtesy: iStock/metamorworks)
Yes, it’s a challenging time at the moment. We’ve been working hard to ensure that the impact is properly assessed and that we are doing what we can to champion and support some of these critical disciplines in physics. I can understand the direction of travel from UKRI, which is the importance that the investment underpins and supports economic growth. And there are some key critical disciplines such as quantum computing, autonomous system robotics and fusion that continue to be supported and where funding has actually increased. But what we are concerned about is the potential adverse or detrimental effects of a reprioritization that may move funding away from some critical areas in physics, such as particle physics, astronomy and nuclear physics. That is a concern because they are fundamentally important disciplines.
Could there be an impact on people wanting to go into these areas?
What I worry about is the negative impact on university physics departments that work in those areas. It’s also those areas of physics that really captivate people to study the subject. But there is a knock-on effect on other areas too because many people who study physics go into engineering, which is crucial for other industry sectors – whether it’s around detectors, data systems, data acquisition, electronics, power systems, automotive, aerospace, defence or nuclear energy. So I worry that the reprioritization is not properly assessing the impact and the benefit the subjects have.
How is the IOP tackling this issue?
We need to ensure that we fight the case for those areas of physics, because they are so important. We need to find a path that ensures we maintain these critical areas but also ensure that investment is being made to support economic growth as a whole.
How do you strike that balance between being vocal about the cuts, but also needing to support emerging areas of physics?
I think that’s the challenge – to effectively support all aspects of physics. I don’t want to be in a position where we are pitching one area against another. It’s the totality of the capability, and that’s all aspects of physics and the interrelationship between those disciplines too. We should celebrate where there is growth in new and exciting areas. But equally, we must protect those areas that are fundamental pillars of physics.
Are there any opportunities even in this difficult situation?
As we continue to engage government and other stakeholders on these funding changes, there is an opportunity to define physics’ impact as a benefit to society as well as big opportunities for science-driven growth arising from increased investment in key areas. I believe that a developed nation like the UK, which has a very good international standing, should continue to invest in all aspects of the discipline.
What other challenges lie ahead?
It is really important that we remain an inclusive discipline and we also need to get our heads around the impact of AI on physics. The IOP has already done some work with the community in this area with the Physics and AI Impact Pathfinder report, which highlighted the role of physics as both enabler and beneficiary of AI, and also explored the discipline-specific views physicists hold regarding AI in science and society. I am interested in us understanding more about what AI means for physics and being a physicist, how we embed AI in the training of physicists so physicists can use it and become better physicists. I would be keen for the IOP to carry out more work to understand the impact it’s clearly going to have.
How do you see the subject evolving over the coming decade?
I think that society is embracing what science and technology, and in particular physics, can do. We need to help ensure that the next generation of physicists are being appropriately trained to become good physicists. In fundamental physics, there are some fascinating things developing like bringing together cosmology and quantum physics, understanding quantum gravity, the nature of time and what’s happening down at the particle physics level. It feels as if something’s coming together. I’d love to be around when physics can finally pull all of that together and go “we’ve got it – the light bulb’s gone on”.
Researchers in Japan have succeeded in measuring the temperature inside living cells with high precision using a new class of biocompatible quantum nanosensor – something that has been difficult to do until now even. If improved, the nanosensor could be used to characterize a wide range of biological phenomena and so help in disease diagnosis, they say.
Recent years have seen the advent of a new generation of nanoscale quantum sensors that can detect the tiny magnetic fields of biological systems. Some of these sensors rely on photons and others on electrons or spin defects – typically diamond specially engineered with nitrogen–vacancy (NV) defects. This material is made by removing two carbon atoms from the diamond lattice and replacing one with a nitrogen atom. The other “hole” is left empty, thereby creating a vacancy or defect. The spin state of the defect is influenced by the local magnetic field that can be “read out” from the way it fluoresces.
While a powerful tool, and biocompatible, this type of quantum sensor does suffer from certain limits. For one, it can be structurally inhomogeneous, which affects how it detects temperature and other physical or chemical parameters inside biological cells.
A more homogenous structure
Even though the new molecular quantum nanosensor (MoQN) works in the same way as these conventional devices, it does not suffer from this problem, explain Nobuhiro Yanai of the University of Tokyo and Hitoshi Ishiwata of the National Institutes for Quantum Science and Technology (QST), who led this research effort. This is because it has a more homogenous structure and does not contain any defects. Instead, it is made by embedding molecular spin qubits, in this case fabricated from pentacene, in nanocrystals of para-terphenyl. This design makes the structure uniform on a molecular scale and preserves the quantum coherence of the spin qubits. It is then coated with Pluronic F127, which is a biocompatible surfactant.
By detecting the spin direction of the “excited triplet state” of the pentacene qubits using a technique known as optically detected magnetic resonance (OMDR), the researchers can precisely determine the temperature of the qubits’ surroundings from the OMDR peak position. When they tested their method inside the cytoplasm of cancer cells in vivo, they found that the intracellular temperature was consistently higher than the surrounding medium.
Yanai says he embarked on this study after reading about the work of Sam Bayliss’ group at the UK’s University of Glasgow, and Ashok Ajoy’s group at the University of California, Berkeley in the US on OMDR in pentacene-doped para-terphenyl crystals. He says he immediately got the idea that nanocrystals of this material could be used for quantum sensing inside cells. This was because his group had already developed such nanocrystals for a different purpose in previous research.
Ensuring biocompatibility
“I then spoke with Hitoshi Ishiwata, who is an expert in quantum sensing using NV centres,” he recalls. “While many molecular qubits have been developed to date, there had been no examples demonstrating their sensing ability within living cells.”
The project required materials science expertise, he tells Physics World, and in particular, finding out how to reduce the material to the nanoscale and ensuring it was biocompatible.
“We already knew that nanodiamonds are good quantum sensors for temperature measurements, but I had noticed a practical limitation: their ODMR spectra often vary significantly from particle to particle,” he says. “This spectral dispersion can introduce errors, especially when trying to perform precise measurements at the single-particle level.”
Replacing hydrogen with deuterium
The researchers thought they had overcome this problem during the first run of their experiments because they found that different particles showed identical OMDR spectra. However, their joy quickly waned when they observed that the spectra were still broadened by hyperfine interactions between the pentacene-doped para-terphenyl molecules’ electron spins and hydrogen nuclear spins.
To improve the spectral resolution, Ishiwata says he suggested chemically modifying the molecule by replacing the hydrogen in it with deuterium. And the technique worked: “the hyperfine broadening was strongly suppressed, allowing us to determine the OMDR spectra much more precisely.”
These findings, which are detailed in Science Advances, show that MoQNs are a chemically versatile platform for quantum sensing in living cells and that they can operate directly inside them while maintaining the precision needed for absolute thermometry, he says. Their appeal also lies in in the fact that their structures can be easily modified.
It will not all be plain sailing, however, adds Yanai. MoQNs cannot yet target specific organelles within cells, so endowing them with this targeting capability is an important future challenge. “What is more, their size has been limited to around 200 nm so far, so creating smaller MoQN particles will be crucial,” he says.
News that large language models (LLM) have made major advances in solving Erdős problems – a set of problems formulated by the renowned 20th-century mathematician Paul Erdős – has created an amalgamation of uproar and interest among mathematicians. The past month alone has seen two significant LLM-generated solutions. The first relates to prime sets, a generalization of prime numbers, and was solved after Liam Price, an amateur mathematician from the US, fed the problem statement into GPT-5.4 Pro without other information. The second came last week when the company behind ChatGPT, OpenAI, announced that it had used artificial intelligence to disprove Erdős’ planar unit distance conjecture.
LLMs have solved Erdős problems before, but the one Price chose wasn’t just any Erdős problem. It was one that human mathematicians had worked on for 60 years without success. The nature of the solution was also unusual. While previous LLM mathematical proofs used standard techniques, this one took an entirely different approach. Rather than starting from Erdős’ original probability-theory-based framing of the problem, as human mathematicians had, the LLM found an alternative route – one that led naturally, in less than a page, to a correct proof.
“Paul Erdős had a concept of ‘Proofs from The Book’, meaning that the argument is so compact and elegant that this is the proof God would’ve written down in ‘The Book’,” Jared Lichtman, a mathematician at Stanford University in the US, wrote on the social media site X after the proof was announced. “After reading the GPT5.4 proof of Erdős #1196, I would say this is a Book Proof of the result.”
The planar unit distance conjecture, meanwhile, concerns a deceptively simple question: if you have n points in a plane, how many pairs of points can be exactly one distance unit apart? Erdős thought the limit was n1+C/log log(n) where C is a positive constant, but OpenAI’s model identified a higher bound. What’s more, the company claims it did so by coming up with a counter example, and in doing so, a whole family of objects that mathematicians had so far not considered. This counter example was generated using sophisticated ideas from algebraic number theory.
Some members of the mathematics community have greeted these proofs, and the advent of AI in mathematics in general, with enthusiasm. OpenAI’s announcement quotes Arul Shankar, a number theorist at the University of Toronto, Canada, as saying that the new proof “demonstrates that current AI models go beyond just helpers to human mathematicians – they are capable of having original ingenious ideas, and then carrying them out to fruition”.
Others, however, are more cautious. David Bessis, a mathematician-turned-science writer who previously worked on algebra, geometry and topology, claims that the programme of trying to automate mathematics stems from a misconception of mathematics as a logically direct process of churning out theorems, given some rules. Writing in his Substack newsletter, Bessis argues that the method used to verify AI-generated proofs, which involves a computer program called Lean, may reduce the benefit the mathematics community gains from proofs. Notably, proofs that are verifiable in Lean are not always parse-able by humans, which detracts from (and in certain cases removes) the insights researchers typically get from new proofs.
How AI is being used in mathematics…
To evaluate the merits of these arguments, it’s useful to understand how AI is currently used within mathematics research. The first strategy is the one Price used to solve Erdős #1196: directly prompting an LLM. “Large language models have proven their worth at literature search: finding similar instances of a problem, or a proof, in past literature,” notes François Charton, an AI engineer at the California-based start-up AxiomMath, which is using AI to accelerate mathematics research.
The second strategy is to use AI models trained on other types of data. According to Charton, these models are especially good at spotting “weak signals and correlations” and thereby uncovering patterns in data that might be too laborious or convoluted for humans to identify.
Both methods have shown promise for generating new results, but they are not universal – at least, not yet. “It [AI] seems to do a lot better at certain types of maths than others,” says Thomas Bloom, a mathematician at the University of Manchester, UK, who maintains a webpage that tracks solutions to Erdős problems. In particular, Bloom says that to the best of his knowledge, AI “hasn’t done anything interesting in category theory” – a field whose reputation for abstraction is only matched by its track record of bridging supposedly distinct areas of mathematics.
Monumental thinker: The grave of Paul Erdős (Erdős Pál) in Budapest, Hungary. (Courtesy: Varga József, CC-BY-SA 3.0)
Another challenge is that with AI systems churning out new proofs at scale, there are simply not enough people with the skills needed to check them. A process called autoformalization could solve this problem by turning human proofs into what Bessis calls “bulletproof, machine-verifiable logical derivations” expressed in Lean or other specialized languages. At that point, AI-generated proofs could be checked automatically. The question is, could humans lose out on some knowledge in the process?
For doubters like Bessis, who refers to AI-driven autoformalization (at least as practiced by certain firms) as “AI slop”, the answer is very little. But within the AI camp, there is considerable interest in what AI brings to autoformalization. “I see autoformalization as the bridge in both directions, as important as proving itself,” Charton argues. “We can use Lean to translate between these two languages so that a Lean proof can be reverse-translated into a sketch, lemmas or natural language a human mathematician can engage with. That bidirectional translation preserves and extends mathematical knowledge at scale.”
…and how it isn’t
In the 18th century, when Leonhard Euler began arranging the logical thought processes of mathematics into theorems, definitions and proofs, mathematicians were primarily interested in solving problems with underpinnings in the physical world: questions of volume and distance, and, more generally, geometry and counting. Since then, though, mathematics has become a discipline that is at least as concerned with coming up with interesting problems as it is with solving them.
Two aspects of this change seem relevant to debates over AI’s utility. The first is that posing problems requires a broader skillset than solving them. The second is that solving posed problems sometimes requires mathematicians to invent new structures, tools or objects. Fermat’s Last Theorem, which posits that there are no three positive integers a, b, and c that satisfy the equation an + bn = cn for any integer value of n greater than 2, is a good example. At face value, this nearly 400-year-old theorem seems simple. However, proving it was the life’s work of a modern mathematician, Andrew Wiles, who won the Abel Prize in 2016 for developing the numerous new tools required, as well as for the proof itself.
Coming up with such tools – or indeed whole new frameworks – is a challenging and hugely creative endeavour. There are no rules as to the kinds of objects you are allowed to create, and unlike a proof (which is either correct or incorrect), there is no finality, either. If the new framework is a good one, it will crop up frequently and naturally in various branches of mathematics, and other mathematicians will incorporate it into their own work. If it isn’t, they won’t.
Currently, not even AI enthusiasts like Charton think machines are capable of such leaps. “Theory building is completely out of reach right now,” he tells Physics World. “Models, especially generative models, can provide a mathematician with interesting examples, or discover surprising relations that may bring a theoretical breakthrough, but the breakthrough still depends on the mathematician. I believe this will remain the case for some time.”
A new tool for scientists and mathematicians alike
In many areas of science, AI works in a way that is entirely distinct from human thinking. In physics, for example, machine learning algorithms are trained to analyse large amounts of data, find patterns and use them to infer underlying laws. This strategy could advance our understanding of some of the most fundamental questions in physics, but it is very different from how a human scientist would do it, and therefore perhaps more likely to be seen as a welcome new tool.
On the theorem-proving side of mathematics, the distinction between methods a human might use and those an algorithm might use is more blurred. Yet in some ways, Bloom thinks incorporating AI into mathematics could bring the field closer to other sciences. In particle physics, for example, “you don’t go in and take these individual recordings [of data]. It’s all automated,” he tells Physics World. “Until now, there has been no equivalent for maths. It takes time and attention to prove theorems, and maybe this had been a bottleneck.”
AxiomMath’s Charton agrees. “Every new math tool in history has automated something that used to be the work of a human mathematician – from the abacus all the way to symbolic algebra,” he says. “With each new tool, the role of the mathematician evolved rather than disappeared. Tasks got automated, and problems that felt impossible became trivial – but mathematicians just keep moving up the stack to the next set of questions. I see AI as the latest shift rather than a categorical break from history.”
Data are at the core of science, but traditional journal articles normally deliver a distillation of the raw data gathered by the authors. While the movement towards open access to data is widely supported by researchers and funding agencies, a 2024 study by IOP Publishing revealed that many scientists still encounter a wide range of practical, ethical and technical barriers when it comes to sharing their data.
As a result, the publisher has launched a free online course that aims to give early-career researchers the practical skills and confidence they need to share and manage research data effectively.
To talk about the course and IOP Publishing’s open data policy I am joined by Laura Feetham-Walker, who is head of publishing strategy and performance at IOP Publishing.
IOP Publishing is a wholly owned subsidiary of the Institute of Physics and it publishes Physics World.
John Hill has become director of the Brookhaven National Laboratory in Long Island, New York, after serving as interim lab director since September. Hill will now oversee Brookhaven’s 3000-strong team of scientists, engineers and technicians as well as manage the lab’s annual $900m budget.
Brookhaven opened in 1947 as one of the first three US national labs, the others being Argonne and Oak Ridge. Brookhaven carries out a wide range of research in the physical, biomedical and environmental sciences and is home to seven Nobel-prize-winning discoveries.
Brookhaven operated the Relativistic Heavy Ion Collider (RHIC) until it shut down in February. RHIC collided heavy nuclei such as gold and copper to produce a quark-gluon plasma – a state of matter thought to have been present in the very early universe.
In 2020, Brookhaven was chosen to host the next-generation Electron-Ion Collider (EIC). Costing about $2bn, the EIC will smash together electrons and protons to probe the strong nuclear force and the role of gluons in nucleons and nuclei.
Building the EIC involves revamping the RHIC accelerator as well as adding an electron ring and other components with the first experiments starting the 2030s.
As well as RHIC and the EIC, Brookhaven is also home to other big-science projects including the National Synchrotron Light Source II, which opened in 2015 at a cost of $912m.
A Brookhaven career
With a PhD in physics from the Massachusetts Institute of Technology, Hill joined Brookhaven as a postdoc in 1992 before leading the lab’s X-ray scattering group from 2001 to 2013.
He then became deputy associate laboratory director for energy and photon sciences until becoming the lab’s deputy director for science and technology from 2023 to 2025.
In September 2025 he became interim director following the resignation of the theoretical physicist JoAnne Hewitt.
In the role, Hill will also become president of Brookhaven Science Associates – a partnership between Stony Brook University and the science and tech firm Battelle – that manage and operate Brookhaven on behalf of the US Department of Energy.
Hill notes that he is “very excited” to lead the lab in the coming years. “Brookhaven is entering a defining decade, and I’m honoured to take on this role at this time,” he says. “The vision we have for our future is a powerful one, including delivering the nation’s next particle collider and advancing science across a range of critical areas.”
Pollinating insects form a vital part of any ecosystem, enabling the biodiversity that we see on Earth today. However, biodiversity is in rapid decline around the world, and monitoring insect species is a difficult task that often requires some insects to be killed. To support the conservation of biodiversity, which is critical to ensure the sustainability of human civilization, more robust monitoring is required. In a study published in PNAS Nexus, researchers have developed a new method to identify and classify individual insects, based on radar imaging and machine learning.
Radar has long been used to study migrating insects that fly at high altitudes and in large numbers, but such systems typically perform wide-area, long-range monitoring. However, thanks to a combination of millimetre-wave radar and machine learning, narrow focused identification is now possible, by detecting changes in the radar reflection of insects caused by the flapping of their wings.
“Having a background in antenna engineering, there was always the question of whether this technology can be used to address some of the environmental challenges that we’re facing,” says co-lead author Adam Narbudowicz from the Technical University of Denmark. “Some five or six years ago, we started talking with [co-author] Ian [Donohue] about those possibilities, and eventually the idea of micro-Doppler emerged, which seemed feasible from an engineering point of view and could provide some useful data on biodiversity.”
The approach taken in this study doesn’t focus on morphological features of the insects, as these are difficult to detect with radar. Instead, it uses the harmonic patterns generated by the micro-Doppler effect of an insect beating its wings as a detection strategy. Millimetre-wave radar can provide insight into biomechanical characteristics not visible with cameras, and these characteristics are encoded in the harmonic patterns of the wingbeat.
The team used machine learning to improve the accuracy of the identification and incorporated a SHAP (SHapley Additive exPlanations) analysis – an explainable AI tool that interprets and explains key outputs and prioritizes key features – to identify which signal features are the most critical for differentiating insect species. The SHAP analysed each insect across the full spectrum of micro-Doppler harmonics, extracting key features including fundamental wingbeat frequency, energy distributions, cepstral coefficients (sound signals) and how quickly an insect’s wing movement change. These data were then used to train the machine learning model.
Training the model The radar system used to collect data from insects. (Courtesy: Linta Antony)
The actual process of obtaining this data from the insects involved capturing insects at the Trinity College Dublin campus and placing them in a plastic box on top of a millimetre-wave antenna that recorded their radar signatures. The researchers then released the insects back into the wild. After data capture, the relevant micro-Doppler features were extracted from the data for model training.
The model allowed non-invasive monitoring of different insects and could distinguish between bees and wasps with 96% accuracy. The model also classified five key pollinating insect species – red-tailed bumblebee, buff-tailed bumblebee, moss carder bumblebee, western honeybee and common wasp – with an accuracy of 85%.
“I think the most impressive thing is that we can detect and classify them with such an accuracy. From a biological point of view, it’s impressive how different species beat their wings in different manner, and from an engineering point of view it’s fascinating how different wingbeats affect harmonics of radar micro-Doppler reflections,” says Narbudowicz. “Those differences are of course impossible to see just by looking at spectrograms, but it appears that a sufficiently trained machine learning algorithm can see them.”
Narbudowicz points out that the current study used precise lab-grade transceivers and a relatively controlled set-up, and that the natural next step is to move this technology to outdoor field deployment. “This requires a number of steps,” he explains. “Firstly, the device needs to be miniaturized, and battery operated; the transceiver will be less accurate than the one used in the lab, but a big problem is the ground truth verification, since in the field it can be difficult to verify exactly which species flew over the sensor.”
Despite the greater challenge with deploying the technology in the field today, the researchers suggest that this radar reflection approach could be utilized in the future in a fly-through device, which would make it much easier and cheaper to achieve non-lethal monitoring of insect biodiversity in different environments.
Colliding oxygen nuclei could briefly recreate one of the most extreme states of matter in the universe – according to evidence gathered by physicists working on the CMS Collaboration at the Large Hadron Collider at CERN. Their analysis suggests that when smashed together, even relatively small atomic nuclei can produce a tiny droplet of quark–gluon plasma (QGP). This is a superhot “soup” of elementary particles that is believed to have filled the universe just after the Big Bang.
Under normal conditions, quarks – the particles that make up protons and neutrons – are tightly bound together by gluons, which carry the strong nuclear force. But at extremely high temperatures, matter changes into a radically different form in which quarks and gluons move freely in a dense fluid-like state called a QGP.
Scientists believe the entire universe existed in this form for a tiny fraction of a second after the Big Bang. To recreate it here on Earth, physicists smash atomic nuclei together at nearly the speed of light.
One of the main ways researchers study this strange state of matter is by observing the fast-moving particle sprays created during the collision. In the absence of a QGP these energetic particles would travel outward freely. But if they pass through QGP, they lose energy, somewhat like a bullet slowing down in water. Physicists call this effect jet quenching.
“Jet quenching is one of the main tools we use to study the QGP,” explains Jiangyong Jia of Stony Brook University in the US, who was not involved in the CMS study. “When a high-energy collision produces a QGP droplet, energetic quarks and gluons created in the same collision have to travel through it, and they lose energy along the way.”
For many years, this energy-loss effect had only been clearly observed in collisions involving very heavy nuclei such as lead or gold. Lower mass systems, including collisions between protons and heavier nuclei, showed hints of unusual behaviour but no convincing evidence that particle jets were being slowed down.
A clear signal
The new CMS study examined collisions between oxygen nuclei, which are much smaller than lead nuclei. Oxygen contains just 16 protons and neutrons, compared with 208 in lead. This allowed researchers to investigate how small a droplet of QGP can become while still affecting energetic particles passing through it.
The collisions were performed in 2025 at an energy of about 5 TeV – the highest energy ever for oxygen ions. The CMS Collaboration measured how many high-energy particles emerged from the collisions. This was compared to simpler proton–proton collisions, which are not expected to result in jet quenching.
The physicists found a clear reduction in the number of energetic particles produced. At some energies, the suppression reached about 30%, far beyond what could be explained by random statistical fluctuations. The pattern looked remarkably similar to what researchers had previously observed in much larger lead–ion collisions, although the effect was weaker overall.
“Oxygen-16 has only 16 nucleons compared to 208 in lead, but it appears to produce a medium that absorbs jet energy in a qualitatively similar way to much heavier systems,” Jia explains. “The shape of the suppression curve in oxygen–oxygen collisions resembles what is seen in lead–lead, which suggests the underlying physics is the same.”
Understanding fireballs
The team compared its measurements with several theoretical models. Models that included energy loss caused by QGP generally matched the data better than models without it. Still, some uncertainty remains. Part of the observed effect may come not from a QGP itself, but from differences in how quarks and gluons are distributed inside oxygen nuclei before the collision even occurs.
“The main limitation right now is the nuclear parton distribution functions,” Jia says. These describe how quarks and gluons are arranged inside atomic nuclei. According to Jia, uncertainties in these distributions “can account for roughly half of the observed suppression on their own”.
Future experiments involving proton–oxygen collisions are expected to help clarify the picture. The findings may also reshape how physicists think about the minimum size needed to create QGP.
“It shows that QGP formation is not limited to heavy nuclei,” Jia says. “It can occur in collisions of nuclei as light as oxygen.”
Researchers now hope to compare oxygen with other light nuclei such as neon to understand how the properties of QGP change as the colliding systems become larger or smaller. The work could eventually help physicists build a more complete picture of how ordinary matter behaved in the universe’s earliest moments – and how the strong nuclear force operates under the most extreme conditions known in nature.
