Here’s how the game works:

1. 1. Enter a word guess – in this game the word has six letters.
   2. After submitting your guess, each letter in the guessed word is coloured to provide feedback:
      • Green: The letter is correct and is in the correct position in the target word.
      • Yellow: The letter is correct but is in the wrong position in the target word.
      • Grey: The letter is not in the target word at all.
   3. Using this colour feedback, refine your next guess.
   4. Continue guessing until you correctly identify the hidden word(s) or run out of attempts.

If you want a clue to the answer, read the article here.

Fancy some more? Check out our puzzles page.

The post Word wave puzzle no.6 appeared first on Physics World.

 

Join us for an in‑depth conversation with Simon Cherry, former editor‑in‑chief of Physics in Medicine & Biology (PMB), as he shares his expert perspective on the evolving landscape of medical physics research, molecular imaging, and non-invasive imaging technologies. Drawing on decades of leadership in biomedical imaging and academic publishing, Simon will reflect on major advances shaping the field, define the characteristics of high‑impact imaging research, and highlight how PMB continues to support rigorous, influential scientific work through its editorial standards and peer‑review practices.

This session will additionally spotlight the research of Xinchen (Alison) Deng, a finalist of the PMB 2024 Early Career Award. Attendees will gain insights into her contributions to the advanced medical imaging community, such as photon-counting CT for clinical applications and how her work supports broader advances in medical physics and X-ray/CT imaging research.

Simon will also discuss what makes early‑career research stand out and share strategies for researchers aiming to increase the visibility, reach, and long‑term impact of their publications within competitive scientific journals.

Moderator

Carol Clark, Publisher, Physics in Medicine & Biology

Speakers

Simon R Cherry, PhD received his BSc (Hons) in physics with astronomy from University College London in 1986 and a PhD in medical physics from the Institute of Cancer Research, University of London in 1989. Simon is currently Distinguished Research Professor in the Departments of Biomedical Engineering and Radiology at UC Davis and Professor of Total-Body Imaging at King’s College London. Simon’s research interests centre around biomedical imaging. His major contributions have been in developing systems for positron emission tomography (PET), including designing the microPET scanner for small-animal imaging, the earliest examples of simultaneous PET and MRI studies and co-leading the EXPLORER consortium that developed the world’s first total-body PET scanner. Simon is a Fellow of the Royal Society and Member of the U.S. National Academy of Engineering and National Academy of Inventors. He has received several international awards including the Society of Nuclear Medicine and Molecular Imaging Cassen Prize in 2022. He served as editor-in-chief of the journal Physics in Medicine & Biology from 2011–2020. Simon is the author of more than 350 peer-reviewed journal articles, review articles and book chapters in the field of biomedical imaging. He is also lead author of the widely-used textbook Physics in Nuclear Medicine.

Xinchen (Alison) Deng, PhD, is a diagnostic imaging physics resident at The Ohio State University. Her research focuses on advancing photon-counting CT for clinical applications, including multi-contrast imaging, early disease detection, detector optimization, and AI-based improvements in image quality. Before beginning the residency, Xinchen (Alison) was a postdoctoral fellow in medical physics at the University of Victoria, where she worked with Magdalena Bazalova-Carter and collaborated with Redlen Technologies on the optimization of photon-counting detectors and the clinical applications of spectral CT. She received her PhD in medical physics from the University of British Columbia, where she applied Raman spectroscopy, machine learning, and explainable AI to study radiation treatment response in cancer cells and tissues. Her broader research background spans photon-counting CT, spectral X-ray imaging, Raman spectroscopy, radiotherapy response assessment, and AI-driven image and data analysis. Her photon-counting CT research has been featured in the Physics in Medicine & Biology Early Career Researcher Focus Collection. She is a finalist for the PMB Early Career Researcher Award.

About this journal

Physics in Medicine & Biology. The international journal of biomedical physics and engineering, published by IOP Publishing on behalf of the Institute of Physics and Engineering in Medicine (IPEM)

Editor-in-chief: Katia Parodi, Ludwig-Maximilians University, Munich, Germany

 

 

The post Shaping the field of medical imaging: a fireside chat with Simon R Cherry and Xinchen Deng appeared first on Physics World.

German technology start-up Akhetonics is reimagining the future of high-performance computing (HPC), deploying its proprietary know-how in photonic-design automation and photonic integrated circuits (PICs) to realize the first all-optical digital computation devices. The end-game: an all-optical digital processor – what Akhetonics calls its reasoning processing unit (RPU) – that will fundamentally transform the way that data is processed, transmitted and stored at every level of the HPC stack.

While the anticipated upsides of photonic computing are compelling – think bandwidth scaling, energy-efficiency and reduced latency – the translation of Akhetonics’ R&D effort and early-stage PIC designs into at-scale implementation is still in its infancy. What’s evident even now, though, is that the start-up’s progress is being accelerated by collaboration, interdisciplinary expertise and innovation spanning photonic system design and precision-motion and control technologies.

Collaboration, co-development, customization

A case study in this regard is Akhetonics’ partnership with SmarAct, a German manufacturer and developer of specialist products for nano- and picometre-precise positioning, metrology and automated assembly. Both companies are shaping the future of photonics, albeit along complementary tracks: Akhetonics with its vision of a full-stack photonic computing architecture; SmarAct with a portfolio of enabling technologies that’s helping to make next-generation optical computing a practical proposition.

The two vendors began working together after an initial meeting four years ago at the European Conference on Optical Communication (ECOC 22) in Basel, Switzerland. Top of the Akhetonics shopping list: a versatile optical test system to characterize its advanced PICs.

“There are off-the-shelf PIC characterization systems on the market, but they all impose constraints on our design options,” explains Leonardo Del Bino, co-founder and chief technology officer (CTO) at Akhetonics. “Instead, we teamed up with SmarAct to co-develop a compact and modular test architecture built to our exacting specifications. The resulting set-up gives us the flexibility to evaluate and optimize all sorts of innovative PIC designs.”

At the heart of the Akhetonics optical testing workstation sit two discrete nanopositioning subsystems: an XY-Rz chip platform (itself comprising three nanopositioning stages offering horizontal translation along the X and Y axes and rotation limited to the Z axis) and a Z-Rx-Ry “pivot” for an optical probe (where translation occurs only along the Z axis, with simultaneous angular rotation around the X and Y axes). The test set-up (which also incorporates a digital microscope for PIC inspection and alignment) can be used without restriction in every degree of freedom, with the two subsystems working in tandem to ensure precise multi-axis positioning for optical coupling and electro-optic characterization tasks.

On a more granular level, each nanopositioning subsystem is based on SmarAct positioners equipped with integrated optical encoders (SmarAct’s METIRIO® sensors with a resolution of 1 nm) along all six axes of motion. The first subsystem provides three degrees of freedom for lateral alignment (with 49 mm travel in both X and Y, 360° rotation around Rz and a unidirectional positional repeatability of ±40 nm per linear axis). The second subsystem provides vertical and angular alignment (with 31 mm travel in Z, ±5° tilt around Ry, ±45° rotation around Rz and a unidirectional positional repeatability of ±40 nm for the linear Z axis).

To evaluate new PIC designs, Akhetonics’ optical test routines focus primarily on measuring and minimizing insertion losses between input/output fibres and on-chip planar waveguides. Typical measurements include wavelength- and polarization-dependent coupling losses (for example, when a fibre array couples laser light into a series of on-chip grating couplers or when light is coupled from a planar waveguide back into an optical fibre). The subsequent integration of electrical probes into the test system (using two separate XYZ probe towers) supports electro-optic characterization of key on-chip building blocks like optical modulators and photodetectors.

“The requirements for the electro-optical testing are nothing crazy,” says Del Bino. On-chip electrical contacts are in the ballpark of 100 μm x 100 μm, so Akhetonics engineers need positional and alignment precision an order-of-magnitude better than that for their electrical probes. “On-chip optical testing is a different story,” he adds. “A fibre-waveguide mismatch of 1 μm means significant coupling losses, so we need positional accuracy and repeatability of tens of nm or less.”

The rewards of reciprocity

Operationally, the custom test system is the result of ongoing collaboration between the engineering team at Akhetonics and the product development team at SmarAct. After firming up performance requirements, the two vendors iterated back-and-forth with 3D CAD drawings before converging on a final agreed system design.

“We supplied the nanopositioning stages that form the precision-motion core of the workstation,” explains Max Trippel, SmarAct’s business development manager for photonic applications. Akhetonics then integrated these modules into its own test environment, including the fibre and fibre-array-unit holders; the required optical and electrical measurement equipment; and the software routines for photonic characterization. “This approach gives Akhetonics the flexibility to build exactly the test workflow it needs, while relying on a stable and repeatable positioning platform,” Trippel adds.

Crucially, the SmarAct software “ecosystem” and Python interface make it easy to integrate the nanopositioning stages within Akhetonics’ PIC test routines. That software application platform, known as the SmarAct Control and Process Environment (SCoPE), also creates opportunities for continuous process improvement and product innovation (for example, the ability to develop custom routines for fast calibration and alignment on all axes).

In this way, SCoPE will be a natural fit as Akhetonics transitions its PIC testing programme from an R&D setting through small-batch production runs and, ultimately, to volume manufacturing capability (see “SCoPE in brief”, below). “Our goal with SCoPE is to provide customers with easy access for system development, whether that’s for the testing or assembly of photonic components,” says Trippel. “SCoPE is a living platform and we’re constantly integrating novel functionalities and application-specific plug-ins as well as features to facilitate control of third-party devices.”

Strategically, the SmarAct-Akhetonics partnership is shaping up as a win-win for the long term, with the latter’s growth trajectory and evolving platform technologies feeding into SmarAct’s product development roadmap. “Our customers’ success is our success,” Trippel concludes. “Working in this way with Akhetonics and its peers means we have line-of-sight on ‘what’s next’ in photonic computing. Watch this space: the last word has not been spoken.”

The post Nanopositioning, customization and control: opening new frontiers in optical computing appeared first on Physics World.

I am writing from Argentina as a researcher working within the country’s national scientific system. While discussions about science funding are often framed in terms of budgets and economic indicators, the reality is experienced in laboratories, offices and research facilities where scientific knowledge is produced.

Today, many of us in Argentina are witnessing a progressive deterioration of Argentine science, both in terms of infrastructure and human capital.

I am based at the Comisión Nacional de Energía Atómica (CNEA), where uncertainty has become routine. Hundreds of employees remain on temporary contracts that can be terminated without warning. On 1 July 2026, a total of 61 colleagues lost their jobs, with armed military police officers occupying the building to contain a peaceful employee protest. Beyond the immediate human cost, these dismissals are steadily dismantling scientific capabilities that have taken decades to build.

The impact became tangible when my colleague Paula Alderete, an experienced electron microscope specialist, lost her position. Alderete, who has published two papers with me, was responsible not only for operating a highly specialized instrument, but also for maintaining it and supporting dozens of research projects.

Without Paula Alderete’s expertise, our lab’s electron microscope has become largely unusable. It is a quiet reminder that scientific infrastructure depends as much on people as on expensive equipment.

Faramarz S Gard

Without her expertise, the microscope has become largely unusable. Every morning I pass her empty desk on my way to the laboratory. It has become a quiet reminder that scientific infrastructure depends as much on people as on expensive equipment.

The deterioration extends beyond personnel. In my laboratory, an X-ray photoelectron spectroscopy (XPS) instrument worth more than $1m must sometimes be protected with plastic sheets because rainwater leaks through the roof.

Equipment designed to operate under tightly controlled environmental conditions is now exposed to circumstances that threaten its long-term reliability. Similar situations are becoming increasingly common across Argentina’s universities and research institutes as maintenance budgets shrink and infrastructure ages.

Argentina has a scientific tradition that reaches far beyond its borders. The country has produced five Nobel laureates, including three in the sciences, and has made internationally recognized contributions to nuclear technology, medicine, agriculture, astronomy and materials science. Institutions such as CNEA, CONICET (Argentina’s national research council) and the national universities have trained generations of researchers whose work has strengthened international collaborations and advanced scientific knowledge worldwide.

Scientific systems rarely collapse through a single dramatic event. They decline incrementally: one technician dismissed, one laboratory left unrepaired, one instrument taken out of service, one young scientist deciding to build a career elsewhere. These individual losses accumulate into a weakening of scientific capacity that may take decades to rebuild.

When a country loses the expertise required to operate advanced laboratories or maintain unique scientific infrastructure, the consequences extend beyond national borders.

Faramarz S Gard

This should concern more than Argentina. Modern science depends on international networks of researchers, shared facilities and long-term collaboration. When a country loses the expertise required to operate advanced laboratories or maintain unique scientific infrastructure, the consequences extend beyond national borders.

Knowledge is lost, collaborations weaken, and opportunities for discovery diminish.scientific infrastructure is more than buildings and instruments; it is the people whose expertise gives those resources value. Once that expertise disappears, replacing it is far more difficult than preserving it.

The empty desk of a single scientist may seem insignificant, but multiplied across laboratories and institutions, it becomes a warning of how a research system is quietly dismantled. Argentina’s experience is therefore not only a national concern but also a reminder that scientific capacity, wherever it exists, is a global resource that is remarkably difficult to rebuild once lost.

I write this letter not only to describe a difficult situation but also to call attention to the broader consequences of sustained disinvestment in science. The international scientific community should recognize that the erosion of research systems in countries such as Argentina represents a loss not only for those nations but for global science itself.

Science is built by people, sustained by institutions, and enabled by infrastructure. Today, all three are under increasing strain in Argentina.

The post Letter from Argentina: ‘an empty desk and an idle electron microscope signal the erosion of the country’s science’ appeared first on Physics World.

To play, click or drag one or more tiles to move them into the empty space. Repeat until you have reconstructed the image.

Image courtesy: iStock/vasantytf; iStock/extravagantni

Fancy some more? Check out our puzzles page.

The post The science of being ‘cultured’: a slide puzzle appeared first on Physics World.

As I was strolling through Belfast’s Titanic Quarter last summer, I discovered with delight a road called Bell’s Theorem Crescent. It’s named in honour of the Belfast-born physicist John Bell, best known for his eponymous theorem. Despite not being as embedded in popular culture as Schrödinger’s cat, his theorem will certainly ring a bell (forgive the pun) with most physicists.

The story goes back to 1935 when Albert Einstein, Boris Podolsky and Nathan Rosen (EPR) came up with their now-famous thought experiment, which was designed to show that quantum mechanics was, as a description of nature, incomplete. In the EPR scenario, non-local influences can affect the measurement outcomes performed on spatially separated particles.

Given how odd that seemed, some physicists invoked what are known as “hidden variables” to get rid of these non-local correlations and restore local realism. It was in 1964, while working at the CERN particle-physics lab, that Bell showed that no local hidden-variable theory can be compatible with the predictions of quantum mechanics.

This result – Bell’s theorem – is something I was familiar with but I had paid little attention to where it had originally appeared. As I discovered, it was published in a paper called “On the Einstein Podolsky Rosen paradox” in the journal Physics (3 195) in 1964. Also known as Physics Physique Физика, this little-known publication existed only from 1964 to 1968. In fact, Bell’s theorem is the most important result published in it.

The journal was set up by two physicists at Bell Laboratories in the US – Bernd Matthias and future Nobel laureate Philip Anderson. According to A Mind Over Matter – Andrew Zangwill’s 2021 biography of Anderson – they were not too happy with conventional physics journals. “The referees,” Anderson said, “often did a bad job, the editors took too long to make decisions, and too much wrong physics got published.”

Those are complaints you’ll still hear in coffee breaks at most physics conferences today, and they prompted Matthias to contact the media magnate Robert Maxwell, who’d founded Oxford-based publishing house Pergamon Press. Maxwell had figured out that scientific publishing could be a very lucrative business so he helped the two physicists to set up the journal.

A publishing experiment

In their first editorial, Anderson and Matthias called Physics an “experimental journal”, which they hoped would solve the problem of there being “far more good physics written than any physicist can read”. Their solution was to pick only those papers worth the attention of all physicists, which the editors did by reviewing – then accepting or rejecting – every submission themselves. They even paid authors 10 cents a word if their paper appeared in print.

Anderson and Matthias admitted that many physicists had already written to them, criticizing their proposed selection model. They’d been told that their “judgements will be fallible”, that they might be “overimpressed by showiness”, and that they’d end up rejecting excellent work. In response, the pair promised to try their best, accepting they would make mistakes and that their “criteria will be, not better, but different”.

The journal’s criteria clearly succeeded, in that Anderson and Matthias identified the merits of Bell’s paper, which was far from mainstream

These criteria clearly succeeded, in that they identified the merits of Bell’s paper, which was far from mainstream. Keen to find out more about the events surrounding its publication, I contacted Zangwill, who had interviewed Anderson several times for his book. Zangwill shared the following quote, published here for the first time.

“One day,” Anderson recalled, “a paper came in from a guy neither of us had heard of – John Bell. It was about hidden variables in quantum mechanics. Bernd had no clue, but I accepted it with the hope that its publication would spell the death of Bohmian trajectory quantum mechanics. It’s a very famous paper today.”

In 1967 the quantum physicist John Clauser found Bell’s paper after stumbling upon Physics Physique Физика, attracted by its unusual title. Clauser, who shared the 2022 Nobel Prize in Physics, carried out the first Bell test in 1972 with Stuart Friedmann. Their work triggered a long series of experiments that would deepen our understanding of quantum mechanics and catalyse the development of quantum technologies.

Over its short life, Physics did publish work from other notable physicists including Murray Gell-Mann, Leonard Susskind and Leo Kadanoff. In fact, the journal was ahead of its time, with the model of in-house editorial selection for broad interest returning in the 2000s with the Nature series of journals. The inclusive, multi-language title was also well ahead of its day.

Sadly, Physics Physique Физика was discontinued in 1968 after just three volumes

Sadly, Physics Physique Физика was discontinued in 1968 after just three volumes. According to what Anderson told Zangwill, it stopped “not because it lacked submissions, but because they had promised contributors rapid publication and Maxwell kept increasing the time between the appearances of successive issues”. Maxwell’s aggressive pursuit of profit might also have played a role.

The journal has since been archived by the American Physical Society and is freely accessible online. One can only wonder what might have happened if Anderson and Matthias had picked a different name for their journal.

The post John Bell and the most obscure journal in physics appeared first on Physics World.

 

Environmental Research: Climate was launched in 2022, with the aim of uniting multidisciplinary communities. With a scope that is intentionally broad, the mission of the journal is to publish innovative research examining the causes, consequences, and solutions of climate variability and change. Since our launch, we have published more than 270 articles covering physical and biogeochemical processes that shape Earth’s climate across spatial and temporal scales; the human and ecological dimensions that shape climate-related risks and impacts; and the design, analysis and evaluation of climate mitigation, adaptation, and risk-management strategies and solutions. This webinar will explore the current state of climate research, including changes in the field over the last five years since the launch of the journal, and direct us toward the future.

Moderator: Noah Diffenbaugh, Stanford University
Noah Diffenbaugh is the William Wrigley Professor and Kimmelman Family Senior Fellow in Stanford’s Doerr School of Sustainability, and the Olivier Nomellini Family University Fellow in Undergraduate Education. His research is focused on understanding what aspects of the climate system most directly and acutely impact people and ecosystems. Noah has served as a Lead Author for the Intergovernmental Panel on Climate Change (IPCC), and as editor-in-chief of the peer-review journals Geophysical Research Letters and Environmental Research: Climate. He has provided testimony and scientific expertise to federal, state and local officials. Noah is an elected Fellow of the American Geophysical Union (AGU), a recipient of the James R. Holton Award and William Kaula Award from the AGU, and has been recognized as a Kavli Fellow by the U.S. National Academy of Sciences.

Katharine Mach, University of Miami
Katharine Mach is professor and chair of the Department of Environmental Science and Policy at the University of Miami Rosenstiel School of Marine, Atmospheric, & Earth Science. During spring 2025, she was the Coleman P. Burke Distinguished Visiting Professor at the Yale School of Environment. Her research assesses climate-related risks and response options to address increased flooding, extreme heat, wildfire and other hazards. Through innovative approaches to integrating evidence, she informs effective adaptations to the risks.

Katharine was the 2020 recipient of the Piers Sellers Prize for world-leading contribution to solution-focused climate research. She has served as a chapter author or lead in the Fourth through Sixth US National Climate Assessments and the IPCC Sixth Assessment Report and as a member of the National Academies Climate Security Roundtable. She serves as co-editor-in-chief for Climate Risk Management, an editorial board member for Oxford Open Climate Change and Environmental Research: Climate, and an advisory committee member for the Aspen Global Change Institute. Across all of her research projects, she engages in relevant policy processes and explores and discusses climate risk and adaptation with the media, the private sector, nongovernmental organizations and communities.

Andrew Pershing, Climate Central
As chief programme officer, Andrew Pershing, PhD, leads Climate Central’s climate science activity. His role includes bridging primary research and media analysis to amplify critical work on climate change and climate impacts and make it accessible to audiences around the world. As the director of attribution science and climate fingerprinting, Andrew is developing tools like the Climate Shift Index to quantify how climate change has increased the likelihood or severity of conditions in the atmosphere and ocean. He works closely with communications experts at Climate Central and our partners to help the media and others to incorporate this information into their content.

Andrew has led interdisciplinary research teams to study the impact of global warming on marine ecosystems in the northwest Atlantic. More recently, his work has focused on how climate trends interact with decisions that people make and on how marine ecosystems store and process carbon. As a communicator, Andrew has deep experience working with journalists to present climate science on air and in print.

Inês Azevedo, Stanford University
Inês M Lima Azevedo is a professor in the Energy Science & Engineering department at Stanford University. She is a senior fellow at both Stanford Woods Institute for the Environment and for Precourt Institute for Energy. She has a BSc in environmental engineering (2004) and a MSc in engineering policy and management of technology from IST-Portugal, and a PhD in engineering and public policy from Carnegie Mellon University (2009). Inês is passionate about solving problems that include environmental, technical, economic and policy issues, where traditional engineering approaches play an important role but cannot provide a complete answer. In particular, she is interested in assessing how energy systems are likely to evolve, which requires comprehensive knowledge of the technologies that can address future energy needs and the decision-making process followed by various agents in the economy.

About this journal

Environmental Research: Climate explores the causes, consequences, and solutions of climate variability and change, by uniting research communities across the entire spectrum of the climate system.

Editor-in-chief: Noah Diffenbaugh Stanford University, USA

 

The post Environmental Research: Climate, fireside chat appeared first on Physics World.

Sugars are a vital ingredient in the recipe for life, responsible for a range of biological processes and a key component of nucleic acids such as DNA and RNA. The question of how these essential biomolecules were produced on primitive Earth is a pressing concern for origin-of-life researchers, with laboratory experiments suggesting that primordial conditions would not yield sufficient quantities.

In a study led by Izaskun Jiménez-Serra at the Centro de Astrobiología (CAB, CSIC-INTA) in Spain, researchers have observed a four-carbon sugar, erythrulose, in the interstellar medium for the first time. Such sugar could have contributed to the resources available for early biological processes, providing insight into how life began on Earth.

Sugars in space

Based on studies of asteroid samples, researchers suggest that key building blocks for life may have had an extraterrestrial origin. Previous analysis of samples from the asteroid Bennu has demonstrated the presence of the sugar glucose, a primary energy source for biological processes, and ribose, a sugar that contributes to the development of nucleic acids.

The idea is that meteorites and comets containing these important chemical components impacted Earth and contributed to the resources available to develop life. This latest research looks backwards in this process to question whether these molecules may have formed in the clouds of dust and gas that parent these astronomical bodies.

Detailed in Nature Astronomy, this first observation of sugar in the interstellar medium suggests that the ingredients for life may indeed form in dust clouds as previously proposed.

Detecting erythrulose

To detect these molecules, Jiménez-Serra and colleagues used the Yebes 40 m and IRAM 30 m telescopes in Spain. Ultrasensitive, broadband spectral surveys allowed them to identify the signature of erythrulose inside G+0.693−0.027, a cloud of gas close to the centre of the Milky Way.

The team had to overcome various challenges, including the spectral signatures of different molecules overlapping and blending into one another. To ensure the validity of their observations, they identified over 180 molecular species to better understand any possible interference with the emission of erythrulose. This extensive characterization allowed the researchers to obtain very high levels of confidence in their observations, they say.

The team also analysed how the erythrulose may have been formed in the extreme low temperatures and vacuum conditions of interstellar space. The abundance of erythrulose is eight times that of three-carbon sugars which are currently undetected by these incredibly sensitive observations. This led the team to model the formation of erythrulose from simpler and more readily available two-carbon aldehydes and alcohols – a process that occurs on the surface of grains of dust in space, with the potential to then contribute to chemical systems on Earth.

Extraterrestrial building blocks

On Earth, erythrulose is found in fruits such as melons and raspberries. In aqueous environments, such as bodies of water on Earth, erythrulose easily changes its configuration into the sugar threose. Similar to ribose in RNA, threose appears in threose nucleic acid (TNA). One of the simplest nucleic acids, TNA may be one of the first involved in the early development of life.

According to Jiménez-Serra: “The discovery of erythrulose in the interstellar medium opens up the possibility for these key organic compounds to form in other molecular clouds in the Galaxy where stars and planets are forming”.

The team’s future plans include searching for larger sugars such as the five-carbon ribose found in RNA. “By combining astronomical observations with laboratory experiments and theoretical calculations, we will explore the chemistry of these sugars and of their related compounds, which could have played a key role in the origin of life,” shared Jiménez-Serra.

The work is being completed as part of the ERC consolidator grant OPENS. This highly interdisciplinary project draws from a range of research methods and scientific fields to approach the question of how life begun on earth. By continuing this research, the team hopes to learn more about the very earliest stage of our journey on this planet.

The post Scientists discover sugar in interstellar space appeared first on Physics World.

What happens when a cosmologist points their tools at the dinner plate instead of the night sky? One person who has done just that is Sarah Bridle, who studied natural sciences at the University of Cambridge in the UK and then stayed on to do a PhD in cosmology, graduating in 2000. Having spent two decades mapping dark matter and building large collaborations around open data, Bridle is now chair in food, climate and society at the University of York, applying that same quantitative, physics based mindset to food.

Food manufacturing is a huge sector: in the UK, it employs 400,000 people and contributes £28bn to the economy. Physics underpins much in the field – everything from productivity, food safety and food security to health and nutrition, waste reduction and environmental impact. Bridle talks to Gleb Yakubov, a food physicist at the University of Leeds, about what inspired their switch from cosmology to food physics and tips for those looking to follow suit. Yakubov is a committee member of the food physics group of the Institute of Physics (IOP), which bridges industry and academia, raises the profile of physics in food, and represents the community to policymakers.

What attracted you to food physics?

It began when I heard that the late David MacKay, who was working at the University of Cambridge, was ill. He worked on error-correction codes, education and artificial intelligence, before pivoting to sustainability and became chief scientific adviser to the UK energy department. I started thinking hard about why we do what we do, and what would happen without him doing the fantastic work he led on energy and climate.

I began watching his talks and learning about climate change – something I hadn’t really engaged with while immersed in astrophysics. In one of his penultimate presentations, he highlighted food as having huge potential to influence climate change through diets and how we produce food. From that moment on I wanted to understand this area – it was compelling, timely and I got obsessed.

You’ve called land use the “dark matter” of climate solutions – what do you mean by this?

Most people aren’t aware that food is responsible for up to a third of global greenhouse gas emissions and, in the UK, about 70% of land is used for agriculture. Many climate solutions involve land – forests for sequestration, biofuels, solar – and people often say “we can’t spare land, we need it for food”. But the way we use land for food can be extremely inefficient, which blocks those climate solutions. That’s a huge, underappreciated lever. 

When you started working in food, what surprised you most?

As a data geek, I expected it to be all about technical solutions but what surprised me was how dominant social and cultural factors are too. You see versions of the so-called “Jevons paradox”: introduce an efficient technology and people just use more of it, cancelling the gains. Unless, that is, you pair it with social and cultural change. Food systems aren’t just complicated; they’re a complex mix of people, incentives, norms and feedback loops.

How did you shift that approach, given that people don’t always behave rationally?

I don’t think people are random; they just use different decision criteria than many models assume. For example, if you raise the price of meat to reduce consumption, some people may see it as more of a luxury and desire it more. You can get perverse responses unless you understand the social context. On top of that, my recent work looks at the potential for civil unrest and even societal collapse – uncertainties we wouldn’t have anticipated five years ago. It pushes us to combine quantitative modelling with deeper social insight.

Can you share an example where astrophysics tools didn’t work in a food context?

Early on I tried to repurpose multispectral satellite-imaging techniques to detect blackgrass in UK wheat fields. Blackgrass is a serious weed that hits yields and this felt like a neat transfer – I’d been analysing astronomical imaging, so why not fields? But it turned out to be too difficult with satellite data alone. Variables like soil moisture, rockiness and other field conditions confounded the signals. That experience nudged me away from a pure imaging approach toward broader system modelling – and, later, back to data with more contextual integration.

Tell us about your “future food calculator”?

About five years ago we started building a quantitative model of the food system to compare interventions on a common footing: on-farm practices such as fertiliser, irrigation, crop management; consumer dietary shifts; and land-use choices. We turned it into a web-based dashboard inspired by MacKay’s energy calculator. Users can move sliders, explore UK production and consumption, greenhouse gas emissions, self-sufficiency and land use, and ask, “Do these choices get us to net zero, and with what trade-offs?”

How do you handle the “people” side – demographics and messaging – in such models and outreach?

You have to meet people where they are. Older generations, on average, are more focused on health than climate. Children and younger people tend to feel invincible about health but are very concerned about climate. If you’re trying to encourage dietary change, those differences matter for messaging and for assumptions in models.

What have you learned about information versus behaviour change?

From obesity research we know that giving people information often doesn’t change behaviour. But it does increase receptiveness to policy. You couldn’t have a sugary drinks tax without enough people accepting that sugary drinks contribute to type 2 diabetes and put pressure on the National Health Service.

I think the same is true for climate and food: we need systemic changes that make sustainable choices easy by default, and public understanding helps make those changes legitimate. Reformulation and choice architecture are good examples – you can reduce beef content in a lasagne by adding pulses, or put low-impact options first in canteens. Many people won’t even notice, but outcomes improve.

While space has an intrinsic “wow” factor, how do you create that for the food industry?

For many people there’s a complete disconnect between food and how it’s produced. Experience helps more than pictures so community garden projects, for example, let people see how hard it is to grow food and how weather drives yields. That fosters respect for food, reduces waste, increases interest in the natural world and builds practical resilience.

You’ve also advised on environmental labelling – what should a good label do?

People already face a lot of labels, and shopping may not be when they’re most receptive. Studies show labels don’t shift individual purchasing as much as you might expect. But labels do change production. In pilot programmes, adding climate labels led operators to change what they offered once they saw the differences. In Canada, when authorities announced a tighter threshold for “red” sugar labels, manufacturers reformulated to avoid red. So the big impact is upstream – on what gets produced.

What’s inside the “digital twin” of UK food you’re building and what can it tell policymakers?

We take UK production and consumption, feed use and losses, and then we test interventions and see how they affect greenhouse-gas emissions and self-sufficiency. An example is soil carbon management practices such as how to reduce tillage – the mechanical manipulation of soil to prepare seedbeds – manage crop residues and control weeds. Another is dietary and consumer preference shifts, such as reducing red meat consumption. The twin helps interrogate trade-offs and unintended consequences, and assess whether a bundle of changes can hit net-zero targets while maintaining resilience.

Herbicides like glyphosate illustrate those trade-offs. How do you manage that complexity?

Exactly. No-till – an agricultural technique that plants crops without disturbing the soil through traditional ploughing or tilling – can reduce emissions but may increase reliance on herbicides if weed pressure rises. The point is to quantify those interactions at the system level and compare pathways transparently, rather than assuming any single intervention is universally better in isolation.

How do the field’s data practices compare with astrophysics – and what would you change tomorrow?

Food physics feels like astrophysics did 20–30 years ago. In astrophysics today everybody codes, collaborations are large and data and code are open. When we released Python code on GitHub for food-system models and invited contributions, basically no-one engaged because the skills weren’t there. Many models live in Excel rather than in transparent, version-controlled code.

We need standards, shared datasets, open code and co-ordinated efforts

Sarah Bridle

Even at the policy interface there are gaps: you might get a major report with spreadsheets but not the underlying code or assumptions, which we then have to reverse-engineer. With a problem as urgent as climate change, that level of opacity isn’t defensible. We need standards, shared datasets, open code and co-ordinated efforts.

What advice do you have for physicists or mathematicians who want to follow in your footsteps?

Quantitative modelling, open-source practices and Python-based tooling are highly sought after right now. Help build transparent models and shared datasets, and join or create collaborative efforts that mirror modern astrophysics consortia. Also, find your people. The best advice I got was to “adopt your own mentors”. I worked with a career coach and asked people for 30-minute calls. Many were lukewarm, but a few were transformative and became long-term collaborators. It was painful but made all the difference.

What is a concrete step someone could take now?

One is to bring open-science habits to food-system work: publish code and assumptions, contribute quantitative skills to cross-disciplinary teams, help build shared datasets and reproducible models, and translate findings into accessible tools – like interactive calculators – for policymakers and the public.

What motivates you now?

My motivation is about doing what I can to help with the crisis we’re walking into. It’s a way to channel worries about the future into practical improvements.

Looking a decade ahead, what would success look like for the UK food system?

Success would mean that UK production and consumption are aligned with net-zero targets and that land is used more efficiently so other climate solutions can scale. Through product reformulation and choice, I hope we can make low-impact diets easy by default. I also hope that self-sufficiency and resilience improve – and that open, quantitative modelling with transparent code and assumptions is embedded in policymaking.

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New insights into the “reverse sprinkler” problem popularized by Richard Feynman have been provided by US researchers using modified rotary sprinklers. The results, which indicate that the reverse sprinkler is driven by the angular momentum induced at its centre by water being drawn in, could shed light on the fluid dynamics of open systems.

Rotary lawn sprinklers are ubiquitous devices that eject water at an angle to the head, thereby generating the torque to rotate and water a 360° area. The puzzle is how – or whether – sprinklers rotate if the problem is inverted and they suck the fluid in rather than blowing it out. A lawn sprinkler will, of course, draw in air, but Feynman conducted experiments with a submerged sprinkler that sucked water in and arrived at conflicting results.

Applied mathematician Leif Ristroph of New York University says that the asymmetry of the problem is not in itself surprising. “You can blow out a candle but you can’t suck it out,” he points out. “When you blow out a fluid through an orifice at a high enough flow rate, it forms a concentrated jet…When you reverse the system and pull in fluid at the same flow rate, the flow does not reverse – it pulls in fluid from all directions. That comes from the irreversibility of the Navier-Stokes equation.”

The correct way to model the system, however, is less clear. Some researchers have argued that the problem should be best solved by considering the total angular momentum of the system. Others focused on the torque exerted on the outside of the structure as fluid enters the nozzles, or the angular momentum that builds up at the centre due to the fluid that flows in from the outside of the arms.

Attempting to disentangle these explanations, Ristroph and colleagues constructed a set of specially designed sprinklers. They submerged the devices before either drawing water out of the centre of each one or feeding it in. Each device’s geometry was designed either to amplify or nullify one of the effects previously proposed to determine the torque and thereby the rotation rate. For example, they tested the impact of the overall angular momentum by comparing devices with “spiral” arms (in which water travelled 360° before escaping) to those with S-shaped “hookback” arms (in which it doubled back). This did not explain the observed results.

There was one factor, however, that correlated with torque and rotation rate irrespective of the geometry of the arms. “If you measure the angular momentum flux from any one of these designs it’s quantitatively one-to-one with the torque on the solid [in the forward case],” says Ristroph. “The beautiful thing is the same thing works in the reverse case, except now you should look at the centre where these arms begin, and there are very subtle asymmetries that inject angular momentum to the core of the device…That’s the common unifying principle: in all cases there are jets generated, but in the reverse case those jets are pointing in.” The lower torque at the centre makes the sprinkler much slower in reverse.

Mechanical engineer Earl Dowell of Duke University says that “the study in the paper is largely experimental,” although he acknowledges that “the experiments appear to have been carried out competently and the results presented in a well-organized manner”.

“What the authors call theories are notional ideas based upon highly simplified concepts that tend to be favoured by some physicists,” Dowell adds. “An expert in fluid mechanics…would attack this problem with well-established computational models for the flow field and rigid body dynamics for the sprinkler per se.” He believes that this is not, to his knowledge, being done because neither experiments nor computational simulations are expected to reveal any fundamental new concepts in fluid mechanics.

“I have a hard time thinking this going to generate a practical device,” concedes Ristroph. However, he says that entirely new methods were needed to conduct these experiments and the researchers are now developing new computer simulations of fluid dynamics, in which fluids enter and escape from the system, to apply to it. “This is a beautiful problem to test your experimental methods, your computational methods, modelling, theory…This is a place to give it the best test it can possibly have.”

The research is published in Proceedings of the National Academy of Sciences.

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How did you get on?

9 words Warming up nicely

13 words Getting hot, hot, hot

17 words Top dog!

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In 2008 the US author Neal Stephenson published a speculative fiction novel called Anathem. Set on the fictional planet of Arbre, the story follows an order of monks, mostly isolated from the rest of the world, who every so often open up their doors to receive information on the state of the outside world.

In the book, Stephenson explores a Dark Age of the Internet, in which companies flood the network with plausible-sounding-but-false information in a ploy to force consumers to buy the firms’ own tools for distinguishing truth from falsehood.

Initially, this “crap”, as Stephenson calls it, had to be made by humans, but eventually the process is run by a so-called “Artificial Inanity”, which is able to create hundreds or thousands of bogus versions, or “bogons”, for every legitimate document.

Today, we face our own version of Stephenson’s Dark Age as our information landscape increasingly fills with AI slop, now going beyond AI-generated videos of uncanny kittens or images depicting politicians and celebrities in outlandish situations.

With the rise of chatbots powered by large language models (LLMs), our search results, social-media feeds and even scientific journals are getting polluted with data that, at first glance, appears relevant and plausible but contains no value beneath its gilded veneer.

As LLM-generated slop infects our previously trusted knowledge base, we face an information landscape that is quickly rotting beneath us.

As scientists, we must position our work in relation to other recent research. Yet that task gets harder when we have to sort out the real from the hallucinated. As LLM-generated slop infects our previously trusted knowledge base, we face an information landscape that is quickly rotting beneath us.

Where we could once track an idea to a paper with human authors (who bore consequences for acts of fraud), we face a future where we have to question whether an article’s authors, institutions or experiments ever existed, on top of evaluating its scientific merits.

This is not a hypothetical danger. In 2024 a team led by medical researcher Almira Osmanovic Thunström at the University of Gothenburg invented a fictitious medical condition called “bixonimania” and publicized it through posts on Medium and two pre-print papers.

Their goal was to test whether AI chatbots would pick up and propagate word of the fake disease, which they duly did. The researchers even tried to insert information that made the work obviously fake. This included acknowledging a colleague from Starfleet Academy, thanking the University of Fellowship of the Ring for funding, and even writing early on in the article that “this entire paper is made up”.

Trapped in a bog

As physicists, we are not immune to the dangers of information rot. A flood of LLM-generated articles prompted arXiv in May to announce consequences for authors whose submissions have “incontrovertible evidence that the authors did not check the results of LLM generation”, including having “hallucinated references”. Those consequences include a one-year ban from arXiv and the requirement, thereafter, that any submitted paper must already be accepted at a “reputable peer-reviewed venue”.

I initially welcomed the news. Harsh consequences are one way to discourage LLM usage, especially for sloppy shortcuts. But the more I reflect on it, the more I realize that arXiv’s consequences could eventually fall even on those who are not using LLMs to write their papers.

Relying on shortcuts is easy. Say you’re writing a literature review and paper A, which you’ve read and trust, has a fact you want to cite – but you notice that paper A cites paper B for this fact. It’s simple to scroll down to the reference section and copy paper B’s citation into your own citations (I would be lying if I said I’ve never done this myself).

But as the information landscape rots, this shortcut could run you afoul of policies like arXiv’s. What appears to be a valid citation – maybe even containing the names of authors you know personally – can be hallucinated.

We must double down on the skills that make us experts – our scientific judgement, information literacy, communication skills and careful scholarship.

Or it could be entirely fake, as a team of researchers who cited one of the “bixonimania” pre-prints in a peer-reviewed article discovered. Their paper was justifiably retracted by the journal editor, over their objections. By simply copying a reference without double-checking it, you can unintentionally join ranks alongside LLM users. Worse, you open yourself and all of your co-authors to being banned from arXiv.

I am not suggesting that arXiv’s policy is flawed. Rather, I argue that those who intend their writing to be AI-free must be vigilant. We must double down on the skills that make us experts – our scientific judgement, information literacy, communication skills and careful scholarship. We should lean into practising these skills – and teaching them – even though they are precisely the skills that AI companies claim LLMs can replace.

In Anathem, a character simply hand-waves away their Internet full of bogons, but we have no such solution. Instead, as our information landscape rots, we find ourselves trapped in a bog. To get out, we must plan each step carefully and take none for granted. What appears to be solid ground may well prove treacherous. Moving safely is a slow process, full of checking and double-checking as we painstakingly build out paths and tools we can trust.

Simply choosing not to use an LLM when writing is no longer enough. We have only our own scholarly skills to sort the bogons from the ground truth. We will all have to slow down if we want out of the bog.

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The Bullet Cluster of galaxies might only have half as much dark matter than scientists thought, or perhaps even none at all, according to a new look at observations of this immense collision of galaxies from the James Webb Space Telescope (JWST).

“Our results indicate that the required amount of additional unseen mass – dark matter – is reduced,” astrophysicist Dong Zhang of the University of Bonn, who led the study, tells Physics World.

The Bullet Cluster is a central battleground for proponents of alternative models of gravity seeking to usurp dark-matter theory. The cluster itself is actually two clusters of hundreds of galaxies that have collided head-on. In 2006 the Hubble Space Telescope mapped how the Bullet Cluster’s mass was warping space and creating gravitational lensing, and from this it was possible to infer where the dark matter in the cluster was relative to the galaxies themselves and the hot, X-ray emitting gas. While the galaxies and hot gas remained in the middle, the dark matter from both colliding clusters had passed through unhindered and is now on opposite sides – exactly as dark-matter theory predicted.

In 2025 the JWST observed the Bullet Cluster and the initial analysis, led by Sangjun Cha of Yonsei University, found that while the distribution of mass in the cluster is more complex than expected, it still supports the dark-matter model.

“Those JWST observations of the Bullet Cluster have sparked a lot of interest,” says astrophysicist Richard Massey of Durham University, who was not involved in the new study.

Counting stellar remnants

Astrophysicists led by Zhang have now made their own analysis of the JWST data and arrived at a very different conclusion, as they report in Physical Review D.

Zhang’s team aimed to precisely measure the total mass of not just the stars, but also of dark stellar remnants – white dwarfs, neutron stars and black holes formed when stars die – to see if these objects could account for the extra gravity attributed to dark matter. They found that there would be enough stellar remnants to reduce the amount of dark matter required by half, at least. Furthermore, applying MOND (modified Newtonian dynamics) to the cluster potentially removes the need for dark matter entirely.

They utilized a model called the integrated galaxy-wide initial mass function (IGIMF), which is a variation on the standard IMF that describes how a collapsing molecular cloud fragments into clumps that produce stars. The standard IMF favours low-mass over high-mass stars.

However, variations in chemical compositions, stellar radiation fields and the rate at which gas cools in a given environment mean that the IMF varies from galaxy to galaxy. The IGIMF is designed to get around this by integrating over the full stellar population of a galaxy, or in this case, an entire cluster of galaxies.

“The IGIMF framework was developed independently of MOND and is motivated by observational constraints related to star formation and stellar populations,” says Zhang.

One of the quirks of the IGIMF is that it predicts that in the early universe, more massive stars formed in giant elliptical galaxies than form in galaxies today, consequently producing more stellar remnants than what the standard IMF predicts. Such giant galaxies exist in the core of clusters such as the Bullet. Since massive stars leave behind massive remnants when they go supernova, the gravity of these remnants could account for a large amount of the dark matter. This is supported, says Zhang, by the abundance of heavy elements in some galaxies that’s hard to explain without invoking an early population of very massive stars.

When the quantity of stellar remnants predicted by the IGIMF is applied to the standard model of cosmology “it does not appear sufficient to account for the strong lensing mass in the central regions of the Bullet Cluster,” says Zhang. It is only when applying MOND that things begin to align, he adds.

Questioning the mass function

However, not everyone is convinced by the choice to use the IGIMF over the standard IMF.

“It’s kind of a neat theoretical framework, in which aspects that could produce a total IMF are broken down into separate mathematical terms that can be modelled individually,” says Massey. “But it’s also kind of irrelevant for this sort of empirical analysis, since mainstream analyses already assume that the IMF varies between galaxies, and even in different regions of a galaxy.”

Traditionally, estimating the total stellar mass of a galaxy is accomplished by taking spectra or multi-band photometry and employing a technique called “stellar population synthesis” that best-fits a galaxy’s light to a particular distribution of stellar masses, Massey explains.

“However, the emitted light can be dominated by the brightest, most massive stars,” he says, meaning that the far greater number of less massive stars has to be extrapolated from a galaxy’s stellar mass function, which is derived from the IMF while factoring in the galaxy’s age.

Stellar remnants are even more difficult to detect directly, hence the dependency on the IMF or IGIMF.

“Stellar remnants cannot generally be detected in external galaxies beyond the Milky Way and its satellites,” says Kathy Romer, an astrophysicist at the University of Sussex who studies galaxy clusters. “Exceptions would be via gravitational-wave events or ultra-luminous X-ray binaries, but those are only the tip of the iceberg, so estimates of the total contribution of stellar remnants will always be an extrapolation of what we measure in the Milky Way and Magellanic Clouds.”

Like Massey, who describes IGIMF as being “less mainstream”, Romer is also sceptical about the new results, but acknowledges that “there are some strong aspects to their paper” and that “bringing fresh perspectives and challenging the orthodoxy is essential to keep science moving forward”.

Massey also highlights recent work from a team led by Gregor Rihtarŝiĉ and including Douglas Clowe of Ohio University who led the original Hubble observations of the Bullet Cluster, and who are running simulations to try and refine their maps of dark matter in the cluster.

The post Does the Bullet Cluster contain a lot less dark matter than we thought? appeared first on Physics World.

Radioactive materials have become a vital tool in our modern society, enabling the delivery of powerful medical treatments, the detection of toxic substances, and the generation of energy on a massive scale. But they also pose significant risks to human health, which means that organizations handling radioactive isotopes must have robust procedures in place to manage and monitor the dose received by their workforce and the wider public.

Dosimetry badges provide a visible and straightforward way to measure external exposure to radiation, but they are unable to determine the amount of radioactive particulates that have entered the body. Quantifying these intakes requires internal dosimetry, which typically uses measurements of urine or faecal samples – or less commonly in vivo radiological counts – to estimate the intake of specific radioisotopes. While the internal dose cannot be measured directly, monitoring data from these bioassays can be combined with biokinetic models to assess the likely impact of an individual inhaling, ingesting or being injected with a radioactive substance.

But internal dosimetry is not an exact science, and organizations with limited experience of radiation safety can struggle to translate regulatory requirements and the associated guidance into monitoring programmes that provide appropriate protection for their employees. Luckily, help is at hand. Dade Moeller & Associates, a wholly owned subsidiary of NV5, is a consultancy company specializing in radiation safety and dosimetry monitoring. Dade Moeller’s team of senior and certified health physicists can help organizations to balance worker protection with regulatory compliance.

Fit for purpose

“We encourage people to think about what they are monitoring for,” says Dr Brett Rosenberg, a certified health physicist at Dade Moeller with expertise in internal dosimetry. “The dosimetry programme must be tailored to the activity and half-life of the materials being handled, how those particular radionuclides interact with the body, and the maximum dose that an individual is likely to receive.” All of these considerations are important for prescribing a monitoring programme that does not become too onerous in costs and implementation while still providing an accurate assessment of occupational exposures.

In low-dose environments, for example, Rosenberg would advise organizations to run a confirmatory programme, in which samples from a subset of the workforce are used to check for any unexpected intake that would warrant further investigation. In contrast, routine internal dosimetry becomes a necessary requirement when there is a greater risk of workers receiving doses that exceed monitoring criteria – or even limits – set by the regulations.

Such situations are likely to arise, for example, in facilities that develop and manufacture medical radioisotopes, such as highly volatile iodine-131 for treating thyroid conditions. Radionuclides with the potential to generate higher doses are also produced throughout the fuel cycle for nuclear power generation, from extracting and refining the uranium – which creates radioactive dust that is easily inhaled – through to the management of nuclear waste.

In the US, such commercial operations are overseen by the Nuclear Regulatory Commission (NRC), which sets maximum limits both for the total radiation dose and the intake that a worker can receive. Companies must demonstrate compliance with these limits, but the NRC does not dictate the methodologies that are used for internal monitoring, or for estimating the intake from lab-based measurements. “Programme development and design is not based on regulation, it’s based on guidance,” says Rosenberg.

Flexibility leads to variability

That leaves organizations with significant flexibility in how they design their monitoring strategies, which allows programmes to be tailored to operational realities rather than forced into a rigid template. However, it also leads to variability in monitoring approaches and in the interpretation of results. Annual audits required by the NRC offer an opportunity for experts like Rosenberg not just to verify compliance, but also to identify opportunities for improvement. “I look for technical shortfalls in internal dosimetry, and check that the measures in place are effective and appropriate,” he says.

Many companies value the input they receive through these regular evaluations. Dan Sowers, for example, is the Global Radiation Protection Manager for Westinghouse, a nuclear services company that, among other things, fabricates uranium fuel and maintains the inner workings of nuclear reactors when they are not being operated. “My priority is the health of our workers,” he says. “Ultimately, we want our internal dosimetry programme to demonstrate that workplace controls are effective.”

Regular input from external advisors helps Sowers to ensure that robust procedures are in place for contamination control, and for assigning a dose to an employee when there is an intake of radioactivity. “The experts at Dade Moeller have enough real-world experience to know what’s right, which can go beyond the guidance we get from the regulators,” says Sowers. “They bring a fresh set of eyes to our operation and share best practice from other sites.”

The team at Dade Moeller also provides expert input to the US Department of Energy (DOE), which runs the national labs as well as facilities for nuclear security. Unlike the NRC, the DOE prescribes structured programmes for internal dosimetry that are tailored to the radioactive materials being handled at each site. It mandates lower dose thresholds than specified by the NRC for workers to undergo routine internal dosimetry checks, and uses a newer set of recommendations for assessing intake and estimating the dose received by different parts of the body. (However, notes Rosenberg, neither the NRC nor DOE use the latest set of recommendations, which are being adopted internationally.)

Creating consistency

Many of those recommendations come from the International Commission on Radiological Protection (ICRP), an independent body that provides guidance on dose limits and monitoring methodologies. The ICRP also develops and publishes the biokinetic models that are used by dosimetrists to translate the radiation counts from bioassays into estimates of intake and effective dose. Specific models have been developed to assess the impact of inhaling or ingesting different radioactive species, but the results are open to interpretation and depend on the version of the model being used.

To avoid these ambiguities, national regulators in Europe have combined forces to create a unified framework for internal dosimetry. Based on the outcomes from a co-ordinated research project in the early 2000s, this framework defines a standard set of methods and assumptions to ensure that different organizations calculate similar dose estimates from the same monitoring data. However, no such consistency exists in the US, where the NRC does not specify the details of the monitoring programme or the modelling methods a company should use, or between regulators in other parts of the world.

That lack of consistency can create difficulties for international companies like Westinghouse, which operates across 21 countries in different geographic regions. “Our internal dosimetry programmes are slightly different in each location,” says Sowers. “Those differences don’t affect the fundamentals of how we protect the health and safety of the worker, but the fine print of the regulations often requires us to do things slightly differently to make sure we are compliant.”

Dealing with uncertainty

To improve consistency, Rosenberg is involved in both national and international standards initiatives that aim to define best practice. However, he says, even the most carefully designed monitoring programme cannot eliminate the inherent uncertainty in estimating the effective dose from lab-based measurements of excreted samples.

“Even the most up-to-date models may not be very accurate,” says Rosenberg. “While the bioassay results are taken from a real individual, the model assigns the dose to a reference person who weighs 70 kg, excretes 135 g of faeces per day, and has both ovaries and a prostate.” As a result, the effective dose calculations based on excreted samples can carry errors of 300% when considering the anatomy and physiology of an “average worker”, and the practical limitations around counting statistics and sampling. “For practitioners, the challenge is not to remove uncertainty, but to understand and manage it,” says Rosenberg.

Given these intrinsic uncertainties, Rosenberg’s mission is to help regulators and commercial organizations to deploy the most effective methodologies for improving the standard of care. As part of these efforts, he has developed a week-long training course that aims to demystify the process of internal dosimetry for managers and regulators who need to understand the guidance from regulatory and international bodies like the ICRP.

“We do see uncertainty associated with inappropriate dosimetry practices, and to advance the field both the regulators and the practitioners need to stay current with the latest recommendations and models,” he says. “It’s easy to follow NRC regulations, but putting structured monitoring programmes in place and using the most appropriate models will help companies to produce more accurate evaluations and improve the safety of their workers.”

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To play, click or drag one or more tiles to move them into the empty space. Repeat until you have reconstructed the image.

Image courtesy: iStock/Galeanu Mihai

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The fast-charging performance of lithium-ion batteries is strongly influenced by the structure of porous electrodes and its evolution during battery operation. In such electrodes, ionic transport is governed by the electrolyte conductivity as well as by the porosity, tortuosity and thickness of the electrode. The latter parameters undergo both reversible and irreversible changes, induced by the lithiation and delithiation of graphite particles and by the formation of the solid–electrolyte interphase (SEI).

We will discuss how SEI formation alters electrode thickness, porosity and tortuosity. Particular emphasis will be placed on how these structural changes depend on the initial electrode porosity and how SEI formation can be quantitatively linked to the evolution of transport properties.

Second, we will address state-of-charge–dependent effects. Graphite particle expansion during lithiation changes the pore structure, influencing electrode porosity, thickness and pore volume. It will be shown how these changes affect ionic resistance and electrolyte transport, and how they connect to macroscopic effects such as the recently described electrolyte-motion–induced salt inhomogeneity (EMSI).

Together, these insights provide a coherent picture of how SEI formation and state-of-charge-dependent expansion alter ionic transport in porous graphite anodes, offering guidance for electrode design and improved methodologies for realistic parameter determination used in battery modelling and fast-charging optimisation.

Lennart Reuter received his PhD from the Technical University of Munich (TUM) in June 2025 under the supervision of Hubert Gasteiger and is now a postdoctoral researcher at Uppsala University. His research centres on identifying and quantifying material and electrolyte decomposition processes in lithium-ion batteries. He implemented OEMS for gas analysis in the presence of volatile commercial electrolytes and developed a novel three-electrode cell for the operando detection of parasitic side reactions. In addition, he investigated structural and transport changes in porous electrodes during operation, including the characterisation of porous transport properties by electrochemical impedance spectroscopy (EIS).

Jonas Dickmanns is a PhD candidate at the Chair of Technical Electrochemistry, supervised by Hubert Gasteiger at the Technical University of Munich (TUM), focusing on advanced lithium-ion battery cell chemistry and anode materials. He received his MSc in chemistry from TUM, where he worked on the electrochemical characterisation of graphite. He later expanded his research to other anode materials, including microscale silicon and silicon/carbon composites. His work investigates key electrode processes such as solid–electrolyte interphase (SEI) formation, structural evolution of anodes, and electrolyte interactions. The goal of his research is to enhance the performance, stability and lifetime of next-generation lithium-ion batteries.

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How do we make good, well-informed decisions? This is the central question of author Helen Pearson’s new book Beyond Belief: How Evidence Shows What Really Works. It examines evidence-based decision-making and compares it with “conventional wisdom and questionable opinion”. Pearson is interested in how we can use scientific reasoning to empower decision-making using empirical evidence, rather than choices based on opinion or public sentiment.

Pearson uses human stories and historical narratives to outline “the evidence revolution”, in which academics, policy-makers and civilians have tried to embed evidence into decision-making. She tackles disciplines where evidence is seldom used, such as people management, or where its absence is surprising, like environmental conservation. Throughout Beyond Belief, Pearson asks us to understand what makes for good evidence, and how evidence can be better used in daily life.

An award-winning science journalist and editor at the journal Nature, Pearson is particularly good at establishing clear narratives based on a host of different scientific studies. She begins with the field of medicine, focusing on Iain Chalmers, one of the founders of the Cochrane Collaboration. Formed in 1993, it is considered to be a trailblazer in evaluating evidence-based healthcare. Indeed, the institution has influenced important changes to treatment, including the use of corticosteroids to save the lives of premature babies.

It seems obvious, Pearson suggests, that doctors would choose treatments based on rigorous evidence. Instead, she shows us that evidence-based medicine is a young field that only emerged in the 1970s. One particularly personal example she gives is the standard practice of shaving knee cartilage in meniscal tears – a procedure I nearly underwent in 2015 – which was shown to be no better than exercise therapy after a landmark trial in 2016. Beyond Belief offers a glimpse into how recent, and as-yet fragile, our reliance on evidence-based treatment is; as well as how much opposition it has faced from institutional inertia and scepticism.

The book is not just about medicine. It includes examples ranging from the roll-out of the pioneering Mexican social welfare scheme, PROGRESA, which helped to decrease poverty and improve health and education in the country; to the ineffective use of bat bridges over the UK’s A11 dual-carriageway. She also discusses using evidence to make management decisions, as opposed to relying on corporate wisdom or management consultancy. The examples are often surprising, underscoring Pearson’s central argument that evidence allows for better decisions, but getting people to use good evidence consistently is challenging.

Throughout the book, Pearson is engaging and often witty, while still discussing the complexities of implementing evidence-based best practice. Beyond Belief is highly readable too – even when navigating difficult concepts in meta-analyses. Pearson outlines the difficulties of teaching people how to use evidence, how to design studies well, and how studies can easily be misleading. She presents the enormous challenge of encouraging institutions to use evidence correctly, while remaining hopeful about our direction of travel.

There are places where tensions arise, one being in the chapter on policing. Pearson describes how evidence can be used in effective policing, for example, to support increasing patrols or stop-and-search in crime hot-spots. This reveals an interesting question about what the role of successful policing is – crime reduction, retributive justice or rehabilitation – and whose definition of justice that evidence is being used to support.

In these cases, the author’s arguments would have been strengthened if she had engaged more with the central premise of the book: that evidence always makes for better decisions. There are nuances that arise between evidence-based policy and harm reduction, and broader questions of trust that cause people to rebuke evidence entirely. A more serious conversation about these reservations might have deepened the argument in favour of empirical evidence, particularly in questions of social policy.

This discussion is not entirely absent. One particular strength of Beyond Belief is Pearson’s examination of COVID, drawn from her experience as a Nature editor during the pandemic. In this chapter, close to the book’s end, Pearson presents the difficulties of using evidence-based trials in acutely developing crises, in which scientists and public-health bodies have to adapt quickly to minimize harm. At a time when discussions about public health responses to COVID are so polarized, Beyond Belief provides a nuanced analysis of how we can arrive at a scientific conclusion under immense pressure.

At the heart of this book is Pearson’s passion and advocacy for a scientific approach to decision-making. She includes a call to action, empowering readers to be champions of evidence in their lives. Although only present in the short chapters towards the book’s end, her anecdotes about childcare, artificial intelligence and using your vote to support evidence through democracy, are useful reference tools. She also succeeds in her goal of starting a conversation about how we can best use evidence.

Ultimately, Beyond Belief sets out to demonstrate the ways in which evidence enters our lives and improves them. Pearson asks us to advocate for science in a world of increasing misinformation, and to challenge conventional wisdom while equipping us with the tools to do so. For that alone, her book is well worth reading.

• 2026 Princeton University Press 368pp £25 hb

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The frequency of extreme weather events can be predicted more accurately than presently possible by combining artificial intelligence (AI) with physical climate modelling, using a protocol called rare-event sampling. That’s the conclusion of a study from researchers in the US and France. The researchers used the approach to model extreme heat events such as the one currently roasting Europe, but they believe it could also be applicable to many other extreme events in climate science.

As the global climate warms, extreme weather is becomingly increasingly deadly and difficult to predict, and quantifying the risks of such events is important for climate change adaptation and mitigation. “Very often the rarest events carry the largest impacts,” says climate physicist Amaury Lancelin of France’s Laboratory of Dynamic Meteorology.

Full global climate model simulations will need to run for an infeasibly long time to produce sufficient data for an estimate of the precise frequencies of rare events. Alexander Wikner of the University of Chicago in Illinois gives the example of an event that, on average, occurs once every century: “Each year you’re going to flip a coin that has a one-in-a-hundred chance of coming up heads,” he says. “On average, once every hundred years, you’ll get one head, but you could very easily get no heads or two or three heads.” A meaningful estimation of the frequency therefore would traditionally require around a thousand years’ worth of simulation – which is not computationally feasible in a physical model.

Deep-learning algorithms, which ignore the underlying climate physics and simply train themselves using pattern recognition, require up to 10,000 times less computing power. However, the reliability of these in modelling extremely rare events is questionable. For example, there is evidence that an AI model will not predict more extreme cyclones than it has seen in its training data, “which is a quite unnerving idea if we imagine that tropical cyclones will be getting more extreme in the future and we want to use an AI model to forecast them,” Wikner explains. They also produce no insight into the physics of the events.

Rare-event sampling (RES) refers to techniques that preferentially allocate computing resources to specific rare events of interest, thereby avoiding modelling long periods of time in which none of those events occur. The problem, of course, is that one has to have some idea what kinds of conditions increase the probability of a rare event occurring in order to know which periods to model in more detail.

In the new research, scheduled for publication in Physical Review Letters, Lancelin, Wikner and colleagues developed the AI+RES framework. An AI algorithm runs climatic simulations repeatedly and selects those that it predicts are most likely to lead to the rare event. A full climate model then simulates only these. The researchers used this protocol to compare the frequency of heatwaves at mid-latitudes, using the predictions of a relatively coarse-grained direct numerical simulation called PlaSim as the ground truth.

They found that their technique produced similar results to PlaSim with up to 1000 times lower computational resources. The researchers now hope to apply the technique to predict other types of extreme events and apply it to more sophisticated models.

“The reason we used PlaSim is that it is computationally quite cheap compared to state-of-the-art climate models so that we can actually verify that our methodology works,” says Lancelin. “Otherwise there is no way to know whether we have made a garbage prediction or not without the ground truth.” Knowing that the AI+RES model reproduces the PlaSim results correctly, however, the researchers now hope to apply computationally expensive models in situations where it was previously impossible, with confidence that the results are likely correct.

Climate scientist Robin Noyelle of ETH Zurich believes the work marks a significant contribution to ideas that were already “floating out there”. He says that, although RES has been in use for nearly ten years, selecting the best candidates for full modelling has always proved challenging: “People were using things that we thought were reasonable – I did that during my PhD – so if I want to look at hot summers, then I select on temperature.”

This approach fails for short-term extreme events, however, because of the dynamic nature of the atmosphere. The AI model itself also failed to produce good accuracy when emulating the events, but it was “basically free” in terms of the computing power required, and provided a good starting point for the physical models. “This coupling between the two ideas is really new,” Noyelle says.

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