More than 90 papers from China have been recognized with a top-cited paper award for 2024 from IOP Publishing, which publishes Physics World. The prize is given to corresponding authors who have papers published in both IOP Publishing and its partners’ journals from 2021 to 2023 that are in the top 1% of the most cited papers.
Among them are quantum physicist Xin Wang from Xi’an Jiaotong University and environmental scientist Huijuan Cui from the Institute of Geographic Sciences and Natural Resources Research.
Cui, who carries out research into climate change, says that China’s carbon neutrality goal has attracted attention all over the world, which may be a reason why the paper, published in Environmental Research Letters, garnered so many citations. “As the Chinese government pays more attention on sustainability issues like climate change…we see growing activities and influence from Chinese researchers,” she says.
A similar impact can be seen in Wang’s work in “chiral quantum networks”, which is published in Quantum Science and Technology, and is equally seen as an area that is quickly gaining traction.
Citations have an important role in Chinese research, and they can also highlight a research topic’s growing impact. “They indicate that what we are studying is a mainstream research field,” Wang says. “Our peers agree with our results and judgement of the field’s future.” Cui, meanwhile, says that citations reflect a “a positive acceptance and recognition of the quality of the research”.
Wang, however, notes that citations and impact doesn’t necessarily happen overnight and that researchers must not base their work’s impact on instantly generating citations.
He adds that some pioneering papers are not well-cited initially with researchers only beginning to realize their value after several years. “If we are confident that our findings are important, we should not be upset with its bad citation but keep on working,” he says. “It is the role of the researcher to stick with their gut to uncover their key research questions. Citations will come afterwards.”
Language barriers
When it comes to Chinese researchers getting their research cited internationally, Wang says that the language barrier is one of the greatest challenges. “The readability of a paper has a close relation with its citation,” adds Wang. “Most highly cited papers not only have an insight into scientific problems, but also are well-written.”
He adds that non-native speakers tend to avoid using “snappy” expressions, which often leads to a conservative and uninspiring tone. “These expressions are grammatically correct but awkward to native speakers,” Wang states.
Despite the potential difficulties with slow citations and language barriers, Cui says that success can be achieved through determination and focussing on important research questions. “Constant effort yields success,” adds Cui. “Keep digging into interesting questions and keep writing high-quality papers.”
That view is backed by Wang. “If your research is well-cited, congratulations,” adds Wang. “However, please do not be upset with a paper with few citations – it still might be pioneering work in its field.”
For the full list of top-cited papers from China for 2024, see here. Xin Wang’s and Huijuan Cui’s award-winning research can be read here and here, respectively
Glioblastoma, the most common primary brain cancer, is treated with surgical resection where possible followed by chemoradiotherapy. Researchers at the University of Miami’s Sylvester Comprehensive Cancer Center have now demonstrated that delivering the radiotherapy on an MRI-linac could provide an early warning of tumour growth, potentially enabling rapid adaptation during the course of treatment.
The Sylvester Comprehensive Cancer Center has been treating glioblastoma patients with MRI-guided radiotherapy since 2017. While standard clinical practice employs MRI scans before and after treatment (roughly three months apart) to monitor a patient’s response, the MRI-linac enables daily imaging. The research team, led by radiation oncologist Eric Mellon, proposed that such daily scans could reveal any changes in the tumour volume or resection cavity far earlier than the standard approach.
To investigate this idea, Mellon and colleagues studied 36 patients with glioblastoma undergoing chemoradiotherapy on a 0.35 T MRI-linac. During 30 radiotherapy fractions, delivered over six weeks, they imaged patients daily on the MRI-linac to assess the volumes of lesions and surgical resection cavities (the site where the tumour was removed).
The researchers then compared the non-contrast MRI-linac images to images recorded pre- (one week before) and post- (one month after) treatment using a standalone 3T MRI with gadolinium contrast. Detailing their findings in the International Journal of Radiation Oncology – Biology – Physics, they report that in general, lesion and cavity volumes seen on non-contrast MRI-linac scans correlated strongly with volumes measured using standalone contrast MRI.
Of the patients in this study, eight had a cavity in the brain, 12 had a lesion and 16 had both cavity and lesion. From pre- to post-radiotherapy, 18 patients exhibited lesion growth, while 11 had cavity shrinkage. In 74% of the cases, changes in lesion volume (growth, shrinkage or no change) assessed on the MR-linac matched those seen on contrast MRI.
“If MRI-linac lesion growth did occur, which was in 60% of our patients [with lesions], there is a 57% chance that it will correspond with tumour growth on standalone post-contrast imaging,” said first author Kaylie Cullison, who shared the study findings at the recent ASTRO Annual Meeting.
In the other 26% of cases, contrast MRI suggested lesion shrinkage while the MRI-linac scans showed lesion growth. Cullison suggested that this may be partly due to radiation-induced oedema, which is difficult to distinguish from tumour on the non-contrast MRI-linac images.
The significant anatomic changes seen during daily imaging of glioblastoma patients suggest that adaptation could play an important role in improving their treatment. In cases where lesions or surgical resection cavities shrink, for example, treatment margins could be reduced to spare normal brain tissue from irradiation. Conversely, for patients with growing lesions, radiotherapy margins could be expanded to ensure complete tumour coverage.
Importantly, there were no cases in this study where patients showed a decrease in their MRI-linac lesion volumes and an increase in their standalone MRI volumes from pre- to post-treatment. In other words, the MR-linac did not miss any cases of true tumour growth. “You can use the MRI-linac non-contrast imaging as an early warning system for potential tumour growth,” said Cullison.
Based on their findings, the researchers propose an adaptive workflow for glioblastoma radiotherapy. For resection cavities, which are clearly visible on non-contrast MRI-linac images, adaptation to shrinkage seen on weekly (standalone or MRI-linac) non-contrast MR images is feasible. Alongside, if an MRI-linac scan shows lesion progression during treatment, gadolinium contrast could be administered (for standalone MRI or MRI-linac scans) to confirm this growth and define adaptive target volumes.
An additional advantage of this workflow is it reduces the use of contrast. Glioblastoma evolution is typically evaluated using contrast-enhanced MRI. However, potential gadolinium deposition with repeated contrast scans is a concern among patients, and the US Food & Drug Administration advises that gadolinium contrast studies should be minimized where possible. This new adaptive approach meets this requirement by only requiring contrast when non-contrast MRI shows an increase in lesion size.
Cullison tells Physics World that the team will next conduct an adaptive radiation therapy trial using the proposed workflow, to determine whether it improves patient outcomes. “We also plan further exploration and analysis of our data, including multiparametric MRI from the MRI-linac, in a larger patient cohort to try to predict patient outcomes (tumour growth; true progression versus pseudo-progression; survival times, etc) earlier than current methods allow,” she explains.
With a rapidly growing interest in magnetic materials for unconventional computing, data storage, and sensor applications, active research is needed not only on material synthesis but also characterization of their properties. In addition to structural and integral magnetic characterizations, imaging of magnetization patterns, current distributions and magnetic fields at nano- and microscale is of major importance to understand the material responses and qualify them for specific applications.
In this webinar, four experts will present on some of the key magnetic imaging technologies for the upcoming decade:
Scanning SQUID microscopy
Nanoscale magnetic resonance imaging
Coherent X-ray magnetic imaging
Scanning electron microscopy with polarization analysis
The webinar will run for two hours, with time for audience Q&A after each speaker.
Left to right: Katja Nowack, Christian Degen, Claire Donnelly, Mathias Kläui
Katja Nowack received her PhD in physics at Delft University of Technology in 2009, focussing on controlling and reading out the spin of single electrons in electrostatically defined quantum dots for spin-based quantum information processing. During her postdoc at Stanford University, she shifted to low-temperature magnetic imaging using scanning superconducting quantum interference devices (SQUIDs). In 2015, she joined the Department of Physics at Cornell University, where her lab develops magnetic imaging techniques to study quantum materials and devices, including topological material, unconventional superconductors and superconducting circuits.
Christian Degen joined ETH Zurich in 2011 after positions at MIT, Leiden University and IBM Research, Almaden. His background includes a PhD in magnetic resonance (Beat Meier) and postdoctoral training in scanning force microscopy (Dan Rugar). Since 2009, he has led a research group on quantum sensing and nanomechanics. He is a co-founder of the microscopy start-up QZabre.
Claire Donnelly. Following her MPhys at the University of Oxford, Claire went to Switzerland to carry out her PhD studies at the Paul Scherrer Institute and ETH Zurich. She was awarded her PhD in 2017 for her work on 3D systems, in which she developed X-ray magnetic tomography, work that was recognized by a number of awards. After a postdoc at the ETH Zurich, she moved to the University of Cambridge and the Cavendish Laboratory as a Leverhulme Early Career Research Fellow, where she focused on the behaviour of three-dimensional magnetic nanostructures. Since September 2021 she is a Lise Meitner Group Leader of Spin3D at the Max Planck Institute for Chemical Physics of Solids in Dresden, Germany. Her group focuses on the physics of three-dimensional magnetic and superconducting systems, and developing synchrotron X-ray-based methods to resolve their structure in 3D.
Mathias Kläui is professor of physics at Johannes Gutenberg-University Mainz and adjunct professor at the Norwegian University of Science and Technology. He received his PhD at the University of Cambridge, after which he joined the IBM Research Labs in Zürich. He was a junior group leader at the University of Konstanz and then became associate professor in a joint appointment between the EPFL and the PSI in Switzerland before moving to Mainz. He has published more than 400 articles and given more than 250 invited talks, is a Fellow of the IEEE, IOP and APS and has been awarded a number of prizes and scholarships.
About this journal
JPhys Materials is a new open access journal highlighting the most significant and exciting advances in materials science.
Editor-in-chief: Stephan Roche is ICREA professor at the Catalan Institute of Nanosciences and Nanotechnology (ICN2) and the Barcelona Institute of Science and Technology.
It came as a bolt from the blue for many Nobel watchers. This year’s Nobel Prize for Physics went to John Hopfield and Geoffrey Hinton for their “foundational discoveries and inventions that enable machine learning and artificial neural networks”.
In this podcast I explore the connections between artificial intelligence (AI) and physics with the author Anil Ananthaswamy – who has written the book Why Machines Learn: The Elegant Maths Behind Modern AI. We delve into the careers of Hinton and Hopfield and explain how they laid much of the groundwork for today’s AI systems.
We also look at why Hinton has spoken out about the dangers of AI and chat about the connection between this year’s physics and chemistry Nobel prizes.
SmarAct proudly supports Physics World‘s Nobel Prize coverage, advancing breakthroughs in science and technology through high-precision positioning, metrology and automation. Discover how SmarAct shapes the future of innovation at smaract.com.
Determining the surface structure of an insulating material is a difficult task, but it is important for understanding its chemical and physical properties. A team of researchers in Austria has now succeeded in doing just this for the technologically important insulator aluminium oxide (Al2O3). The team’s new images – obtained using non-contact atomic force microscopy (AFM) – not only reveal the material’s surface structure but also explain why a simple cut through a crystal is not energetically favourable for the material and leads to a complex rearrangement of the surface.
Al2O3 is an excellent insulator and is routinely employed in many applications, for example as a support material for catalysts, as a chemically resistant ceramic and in electronic components. Characterizing how the surface atoms arrange themselves in this material is important for understanding, among other things, how chemical reactions occur on it.
A technique that works for all materials
Atoms in the bulk of a material arrange themselves in an ordered crystal lattice, but the situation is very different on the surface. The more insulating a material is, the more difficult it is to analyse its surface structure using conventional experimental techniques, which typically require conductivity.
Researchers led by Jan Balajka and Johanna Hütner at TU Wien have now used non-contact AFM to study the basal (0001) plane of Al2O3. This technique works – even for completely insulating materials – by scanning a sharp tip mounted on a quartz tuning fork at a distance of just 0.1 nm above a sample’s surface. The frequency of the fork varies as the tip interacts with the surface atoms and by measuring these changes, an image of the surface structure can be generated.
The problem is that while non-contact AFM can identify where the atoms are located, it cannot distinguish between the different elements making up a compound. Balajka, Hütner and colleagues overcame this problem by modifying the tip and attaching a single oxygen atom to it. The oxygen atoms on the surface of the sample being studied repel this oxygen atom, while its aluminium atoms attract it.
“Mapping the local repulsion or attraction enabled us to visualize the chemical identity of each surface atom directly,” explains Hütner. “The complex three-dimensional structure of the subsurface layers was then determined computationally with novel machine learning algorithms using the experimental images as input,” adds Balajka.
Surface restructuring
According to their analyses, which are detailed in Science, when a cut is made on the Al2O3 surface, it restructures so that the aluminium in the topmost layer is able to penetrate deeper into the material and chemically bond with the oxygen atoms therein. This reconstruction energetically stabilizes the structure, but it remains stoichiometrically the same.
“The atomic structure is a foundational attribute of any material and is reflected in its macroscopic properties,” says Balajka. “The surface structure governs any surface chemistry, such as chemical reactions in catalytic processes.”
Balajka says that the challenges the team had to overcome in this work were threefold: “The first was the strongly insulating character of the material; the second, the lack of chemical sensitivity in (conventional) scanning probe microscopy; and the third, the structural complexity of the alumina surface, which leads to a large configuration of possible structures.”
As an enigmatic insulator, alumina has posed significant challenges for experimental studies and its surface structure has evaded precise determination since 1960s, Balajka tells Physics World. Indeed, it was listed as one of the “three mysteries in surface science” in the late 1990s.
The new findings provide a fundamental piece of knowledge: the detailed surface structure of an important material, and pave the way for advancement in catalysis, materials science and many other fields, he adds. “The experimental and computational approaches we employed in this study can be applied to study other materials that have been too complex or inaccessible to conventional techniques.”
The enigmatic Ξ(2030) particle, once thought to consist of three quarks, may actually be a molecular pentaquark – an exotic hadron comprising five quarks. That is the conclusion of Chinese physicists Cai Cheng and Jing-wen Feng at Sichuan Normal University and Yin Huang at Southwest Jiaotong University. They employed a simplified strong interaction theory to calculate the decay rate of the exotic hadron, concluding that it comprises five quarks.
This composition aligns more closely with experimental data than does the traditional three-quark model for Ξ(2030). While other pentaquarks have been identified in accelerator experiments to date, these particles are still considered exotic and are poorly understood compared to two-quark mesons and three-quark baryons. As a result, this latest work is a significant step towards understanding pentaquarks.
The Ξ(2030) is named for its mass in megaelectronvolts and was first discovered at Fermilab in 1977. At that time, the idea of exotic hadrons that did not fit into the conventional meson–baryon classification was not widely accepted. Conventionally, a meson comprises a quark and an antiquark and a baryon contains three quarks.
Deviation from three-quark model
Consequently, based on its properties, the scientific community classified the particle as a baryon, similar to protons and neutrons. However, further investigations at CERN, SLAC, and Fermilab revealed that the particle’s interaction properties deviated significantly from what the three-quark model predicted, leading scientists to question its three-quark nature.
To address this issue earlier this year, Yin Huang and colleague Hao Hei proposed that the Ξ(2030) could be a molecular pentaquark, suggesting that it consists of a meson and a baryon loosely bound together by the strong nuclear force. In the present study, Cheng, Feng, and Huang elaborated on this idea, analysing a model where the particle is composed of a K meson, which contains a strange antiquark and a light quark (either up or down), alongside a Σ baryon that comprises a strange quark and two light quarks.
To do the study, the team had to use a simplified approach to calculating strong interactions. This is because quantum chromodynamics, the comprehensive theory describing such interactions, is too complex for detailed calculations of hadronic properties. Their approach focuses on hadrons rather than the fundamental quarks and gluons that make up hadrons. They calculated the probabilities of the Ξ(2030) decaying into various strongly interacting particles, including π and K mesons, as well as Σ and Λ baryons.
“It is confirmed that this particle is a hadron molecular state, and its core is primarily composed of K and Σ components,” explains Feng. “The main decay channels are K+Σ and K+Λ, which are consistent with the experimental results. This conclusion not only deepens our understanding of the internal structure of the Ξ(2030), but also further supports the applicability of the concept of hadronic molecular state in particle physics.”
Extremely short lifetime
The Ξ(2030) particle has an extremely short lifetime of about 10-23 s , making it challenging to study experimentally. As a result, measuring its properties can be imprecise. The uncertainty surrounding these measurements means that comparisons with theoretical results are not always conclusive, indicating that further experimental work is essential to validate the team’s claims regarding the interaction between the meson and baryons that make up the Ξ(2030).
“However, experimental verification still needs time, involving multi-party cooperation and detailed planning, and may also require technological innovation or experimental equipment improvement,” said Huang.
Despite the challenges, the researchers are not pausing their theoretical investigations. They plan to delve deeper into the structure of the Ξ(2030) because the particle’s complex nature could provide valuable insights into the subatomic strong interaction, which remains poorly understood due to the intricacies of quantum chromodynamics.
“Current studies have shown that although the theoretically calculated total decay rate of Ξ(2030) is basically consistent with the experimental data, the slight difference reveals the complexity of the particle’s internal structure,” concluded Feng. “This important discovery not only reinforces the hypothesis of Ξ(2030) as a meson–baryon molecular state, but also suggests that the particle may contain additional components, such as a possible triquark configuration.”
Moreover, the very conclusion regarding the molecular pentaquark structure of Ξ(2030) warrants further scrutiny. The effective theory employed by the authors draws on data from other experiments with strongly interacting particles and includes a fitting parameter not derived from the foundational principles of quantum chromodynamics. This raises the possibility of alternative structures for Ξ(2030).
“Maybe Ξ(2030) is a molecular state, but that means explaining why K and Σ should stick together – [Cheng and colleagues] do provide an explanation but their mechanism is not validated against other observations so it is impossible to evaluate its plausibility,” said Eric Swanson at University of Pittsburgh, who was not involved in the study.
Baker bagged half the prize “for computational protein design” and Hassibis and Jumper share the other half for “for protein structure prediction”.
Baker is a biochemist based at the University of Washington in Seattle. Hassibis did a PhD in cognitive neuroscience at University College London and is CEO and co-founder of UK-based Google DeepMind. Also based at Google DeepMind, Jumper studied physics at Vanderbilt University and the University of Cambridge before doing a PhD in chemistry at the University of Chicago.
Entirely new protein
In 2003 Baker was the first to create an entirely new protein from its constituent amino acids – and his research group has since created many more new proteins. Some of these molecules have found use in sensors, nanomaterials, vaccines and pharmaceuticals.
In 2020 Jumper and Hassibis created AlphaFold2, which is an artificial-intelligence model that can predict the structure of a protein based on its amino-acid sequence. A protein begins as a linear chain of amino acids that folds itself to create a complicated 3D structure.
These structures can be determined experimentally using techniques including X-ray crystallography, electron microscopy and nuclear magnetic resonance. However this is time-consuming and expensive.
Used by millions
AlphaFold2 was trained using many different protein structures and went on to successfully predict the structures of nearly all of the 200,000 known proteins. It has been used by millions of people around the world and could boost our understanding of a wide range of biological and chemical processes including bacterial resistance to antibiotics and the decomposition of plastics.
SmarAct proudly supports Physics World‘s Nobel Prize coverage, advancing breakthroughs in science and technology through high-precision positioning, metrology and automation. Discover how SmarAct shapes the future of innovation at smaract.com.
A sensible crew cut, a chic bob, an outrageous mullet. You can infer a lot about a person by how they choose to style their hair. But it might surprise you to know that it is possible to learn more about some objects in the natural world from their “hair” – be it the “quantum hair” that can reveal the deepest darkest secrets of what happens within a black hole, or glassy hair that emerges from the depths of our planet, via a volcano.
In December 2017 University of Oxford volcanologist Tamsin Mather travelled to Nicaragua to visit an “old friend”: the Masaya volcano, some 20 km south of the country’s capital of Managua. Recent activity had created a small, churning lava lake in the centre of the volcano’s active crater, one whose “mesmerising” glow at night attracted a stream of enchanted tourists.
For those who could draw their eyes away from the roiling lava, however, another treat awaited: a gossamer carpet of yellow fibres strung across the downwind crater’s edge. Known to geologists as “Pele’s hair”, Mather describes these beautiful deposits as like “glistening spiders’ webs”, shiny and glass-like, looking like “fresh cut grass after some dew”.
These glassy strands, often blown along by the wind, have been found in the vicinity of volcanoes across the globe – not only Masaya, but also Mount Etna in Italy, Erta Ale in Ethiopia, and across Iceland, where they are instead dubbed nornahár, or “witches’ hair”. They have even been found produced by underwater volcanoes at depths of up to 4.5 km below sea level. However, Pele’s hair is arguably most associated with Hawaii, from whose religion (not the footballer) the deposits take their name (see box “The legend of Pele”).
Lava fountains and candy floss
Although you might hardly guess it from its fine nature, Pele’s hair has quite the violent birth. It forms when droplets of molten rock are flung into the air from lava fountains, cascades, particularly vigorous flows or even bursting gas bubbles. This material is then stretched out into long threads as the air (or, in some cases, water) quenches them into a volcanic glass. Pele’s hair can be both thicker and finer than its human counterpart, ranging from around 1 to 300 µm thick (Jour. Research US Geol. Survey5 93). While the strands are typically around 5–15 cm in length, some have been recorded to reach a whopping 2 m long.
Fringe science A handful of Pele’s hair – collected in Hawaii in 2022 – viewed under a microscope. The golden-brown glass strands are intertwined, whereas the thicker dark glass is rigid. The shape of the darker glass reflects the shape of the lava droplets as they quenched. (Courtesy: USGS)
Katryn Wiese – an earth scientist at the College of San Mateo in California – explains that the hairs form in the same way that glass blowers craft their wares. “Melt a silica-rich material like beach sand and as it cools down, blow air through it to elongate it and stretch it out,” she says. Key to the formation of Pele’s hair, Wiese notes, is that the molten lava does not have time to crystallize as it cools. “Pele’s hair is really no different than ash. Ash is basically small beads of microscopic glass, whereas Pele’s hair is a strung-out thin line of glass.”
Go to a funfair and you’ll see this same process at play at the candy floss stall. “Sugar is melted by a heat coil in the centre of a cotton candy machine and then the liquid melted sugar is blown outwards while the device spins,” Wiese explains, to produce “thin threads of liquid that freeze into non-crystalline sugar or glass”.
Just as there is a fine art to spinning cotton candy, so too does the formation of Pele’s hair require very specific conditions to be met. First, the lava has to cool slowly enough so it can stretch out into thin strands. Second, the lava must be sufficiently fluid, rather than being more viscous. That’s why Pele’s hair is only formed by so-called basaltic eruptions, where the magma has a relatively low silica content of around 45–52%.
The composition of the initial lava is also a factor in the colour of the hairs, which can range from a golden yellow to a dark brown. “Hawaiian glasses are classically amber coloured,” notes Wiese. She explains that basalts from Hawaii are primarily made up of silica and aluminium oxides (a mix of iron, magnesium and calcium oxides), as well as trace amounts of other elements and gases. “The gases often contribute to oxidation of the elements and can also lead to different colours in the glass – the same process as blown glass in the art world.”
The legend of Pele
Elemental spirits According to legend, the Halema‘uma‘u pit crater of the volcano Kīlauea is the final resting place of Pele, the Hawaiian goddess of fire and volcanoes. (CC BY-SA 3.0 Tim Bray)
Both Pele’s hair and Pele’s tears take their name from the Hawaiian goddess of volcanoes and fire: Pelehonuamea, “She who shapes the sacred land”, who is believed to reside beneath the summit of the volcano Kīlauea on the Big Island – the current eruptive centre of the Hawaiian hotspot.
Many ancient legends of Pele depict the deity as having a fiery personality. According to one account, it was this temperament that brought her to Hawaii in the first place, having been born on the island of Tahiti. As the story goes, Pele seduced the husband of her sister Nāmaka, the water goddess. This led to a fight between the siblings that proved the final straw for their father, who sent Pele into exile.
Accepting a great canoe from her brother, the king of the sharks, Pele voyaged across the seas – trying to light her fires on every island she reached – pursued by the vengeful Nāmaka. Mirroring how the Hawaiian islands were erupted in sequence as the Earth’s crust moved relative to the underlying hotspot, Pele moved along the chain repeatedly trying to dig a fiery crater in which to live, only for each to be extinguished by Nāmaka.
The pair had their final confrontation on Maui, with Nāmaka defeating Pele and tearing her apart at the hill known today as Ka Iwi o Pele – “the bones of Pele”. Her spirit, meanwhile, flew to Kīlauea, finding its eternal home in the Halema‘uma‘u pit crater.
Tears and hairs – volcanic insights
Another important factor in the formation of Pele’s hair is the velocity at which magma is “spurted” out during an eruption, according to Japanese volcanologist Daisuke Shimozuru, who was studying Pele’s hair and tears in the 1990s.
Based on experiments involving jets of ink released from a nozzle at different speeds, Shimozuru concluded that thread-like expulsions like Pele’s hair are only formed when the eruption velocity is sufficiently high (Bulletin of Volcanology56 217). At lower speeds, the molten material is instead quenched without being stretched, forming glassy droplets, referred to as Pele’s tears, sometimes with a hair or two attached.
Glassy droplets Some Pele’s tears from Volcanoes National Park in Hawaii. (Courtesy: Shutterstock/Cata Hula)
According to Kenna Rubin – a volcanologist at the University of Rhode Island – studying the shape of these black globules can shine a light on the properties of the lava that formed them. They can provide information not only about the ejection speed, but also related parameters such as the temperature, viscosity and the distance they travelled in the atmosphere before solidifying.
Furthermore, the tears can preserve tiny bubbles of volcanic gases within themselves, trapped in cavities known as “vesicles”. Analysing these gases can reveal many details of the chemical composition of the magma that released them. These can be a useful tool to shine a light on the exact nature of the hazard posed by such eruptions.
In a similar fashion, Pele’s hair can also offer valuable insights to volcanologists about the nature of the eruptions that formed them – thereby helping to inform models of the hazards that future volcanoes may pose to nearby life and property.
Window within, and to the past
“Pele’s hair and tears are a subset of the pantheon of particles ejected by a volcano when they erupt,” notes Rubin. By examining the particles that come out over time, as well as studying the geophysical activity at a volcano, such as seismicity and gas ejection, researchers “can then make inferences about the conditions that were extant in past eruptions”. In turn, she adds, “This allows us to look at old eruption deposits that we didn’t witness erupting, and infer the same kinds of conditions.”
While Pele’s hair and tears are both relatively rare volcanic products, when they do exist they can help to constrain the eruption conditions – offering a window into not only recent but also past eruptions when so-called “fossil” samples have been preserved.
Blown away The Masaya volcano just outside of Managua, Nicaragua, is frequently active. Collecting samples of volcanic material such as Pele’s hair produced in different eruptions allows scientists to analyse how the volcano’s interior is changing over time. (Courtesy: Shutterstock/Christian Hartmann)
Alongside the composition of the glasses (and any trapped gases within such), the shape of hairs and tears can shine a light on the various forces that affected them as they were flying through the air cooling. In fact, the presence of the hair around a volcano is itself a sign that the lava is of the least viscous type, and is undergoing some form of fountaining or bubbling.
There are, of course, many other types of material or fragments of rock that get ejected into the air when volcanoes erupt. But the great thing about Pele’s hair is that, having cooled from lava to a glass, it represents the lava’s bulk composition. As Wiese notes, “We can quickly determine the composition of the lavas that are erupting from just a single sample.”
For example, Mather collected samples of Pele’s hair from Masaya during a 2001 return visit to her cherished Nicaraguan haunt, enabling Mather and her colleagues to determine the composition of the lava erupting from Masaya’s vent in terms of both major elements and lead isotopes (Journal of Atmospheric Chemistry46 207; Atmospheric Environment37 4453). As Mather says, “With other measurements we can think about how this composition changes with time and also compare it with the gas and particles that are dispersed in the plume.”
Pele’s curse
Hairy hazard Prevailing winds blow much of the Pele’s hair from Kīlauea Volcano’s summit lava lake into the Ka‘ū Desert, where it accumulates against gully walls. (Courtesy: USGS)
There is an urban legend on the islands that anything native to Hawaii – whether it be sand, rock or even volcanic glass – cannot be removed without being cursed by Pele herself. Despite invoking Hawaii’s ancient volcano goddess, the myth is believed to actually be quite recent in origin. According to one account, it was dreamt up by a frustrated park ranger who were frustrated by tourists taking rocks from the island as souvenirs. Another attributes it to tour drivers, who tired of tourists bringing said rocks onto their buses, and leaving dirt behind.
Either way, the story has taken hold as if it were an ancient Hawaiian taboo, one that some take extremely seriously. Volcanologist Kenna Rubin, for one, often receives returned rocks at her office at the University of Hawaii. “Tourists and visitors find my contact details online and return the lava rocks, or Pele’s hair,” she explains. “They apologise for taking the items as they feel they have been cursed by the goddess.”
The legend of Pele’s curse may be fictitious, but the hazards presented by Pele’s hair are very real, both to the unwitting visitor to Hawaii, and also the state’s permanent residents. Like fibreglass – which the hairs closely resemble – broken slivers of the hair can gain sharp ends that easily puncture the skin (or, worse, the eye) and break into smaller pieces as people try to remove them.
Not only can an active lava lake produce enough of the hair to carpet the surrounding area, but strands are easily picked up by the wind. From Kīlauea Volcano, for example, the US Geological Survey notes that prevailing winds tend to blow much of the Pele’s hair that is produced south to the Ka‘ū Desert, where it builds up in drifts against gully walls (see photo). In fact, hairs have been known to be carried up to tens of kilometres from the originating volcanic vent – and it is not uncommon on Hawaii to find Pele’s hair snagged on trees, utility poles and the like.
Hair in the catchment
Wind-blown Pele’s hair also poses a threat to the many locals who collect rainwater for drinking. “As ash, laze [“lava haze” – a mix of glass shards and acid released when basaltic lava enters the ocean] and Pele’s hair have been found to contain various metals and are hazardous to ingest, catchment users should avoid accumulating it in their water tanks,” the Hawaii State Department of Health advises in the event of volcanic activity.
However, even though Pele’s hair has the potential to harm humans, there are some residents of Hawaii who do benefit from it – birds. Collecting the strands like the bits of straw they resemble, our avian friends have been known to use the volcanic deposits to feather their nests; in fact, one made entirely from Pele’s hair has been preserved for posterity in the collections of the Hawaii Volcanoes National Park.
Pele’s tears can also serve as a proxy for the severity of eruptions. In a study published this March, geologist Scott Moyer and environmental scientist Dork Sahagian showed that the diameter of vesicles preserved in Pele’s tears from Hawaii is related to the height of the lava fountains that formed them (Frontiers in Earth Science12 10.3389/feart.2024.1379985). Fountain height, in turn, is constrained by the separated gas content of the source magma, which controls eruption intensity.
It’s clear that Pele’s hair and tears are far more than a beautiful natural curiosity. Thanks to the tools and techniques of geoscience, we can use them to unravel the mysteries of Earth’s hidden interior.
John Hopfield and Geoffrey Hinton share the 2024 Nobel Prize for Physics for their “foundational discoveries and inventions that enable machine learning and artificial neural networks”. Known to some as the “godfather of artificial intelligence (AI)”, Hinton, 76, is currently based at the University of Toronto in Canada. Hopfield, 91, is at Princeton University in the US.
Ellen Moons from Karlstad University, who chairs the Nobel Committee for Physics, said at today’s announcement in Stockholm: “This year’s laureates used fundamental concepts from statistical physics to design artificial neural networks that function as associative memories and find patterns in large data sets. These artificial neural networks have been used to advance research across physics topics as diverse as particle physics, materials science and astrophysics.”
Speaking on the telephone after the prize was announced, Hinton said, “I’m flabbergasted. I had no idea this would happen. I’m very surprised”. He added that machine learning and artificial intelligence will have a huge influence on society that will be comparable to the industrial revolution. However, he pointed out that there could be danger ahead because “we have no experience dealing with things that are smarter than us.”
“Two kinds of regret”
Hinton admitted that he does have some regrets about his work in the field. “There’s two kinds of regret. There’s regrets where you feel guilty because you did something you knew you shouldn’t have done. And then there are regrets where you did something that you would do again in the same circumstance but it may in the end not turn out well. That second kind of regret I have. I am worried the overall consequence of this might be systems more intelligent than us that eventually take control.”
Hinton spoke to the Nobel press conference from the West Coast of the US, where it was about 3 a.m. “I’m in a cheap hotel in California that doesn’t have a very good Internet connection. I was going to get an MRI scan today but I think I’ll have to cancel it.”
Hopfield began his career as a condensed-matter physicist before making the shift to neuroscience. In a 2014 perspective article for the journal Physical Biology called “Two cultures? Experiences at the physics–biology interface”, Hopfield wrote, “Mathematical theory had great predictive power in physics, but very little in biology. As a result, mathematics is considered the language of the physics paradigm, a language in which most biologists could remain illiterate.” Hopfield saw this as an opportunity because the physics paradigm “brings refreshing attitudes and a different choice of problems to the interface”. However, he was not without his critics in the biology community and wrote that one must have “have a thick skin”.
In the early 1980s, Hopfield developed his eponymous network, which can be used to store patterns and then retrieve them using incomplete information. This is called associative memory and an analogue in human cognition would be recalling a word when you only know the context and maybe the first letter or two.
A Hopfield network is layer of neurons (or nodes) that are all connected together such that the state, 0 or 1, of each node is affected by the states of its neighbours (see above). This is similar to how magnetic materials are modelled by physicists – and a Hopfield network is reminiscent of a spin glass.
When an image is fed into the network, the strengths of the connections between nodes are adjusted and the image is stored in a low-energy state. This minimization process is essentially learning. When an imperfect version of the same image is input, it is subject to an energy-minimization process that will flip the values of some of the nodes until the two images resemble each other. What is more, several images can be stored in a Hopfield network, which can usually differentiate between all of them. Later networks used nodes that could take on more than two values, allowing more complex images to be stored and retrieved. As the networks improved, evermore subtle differences between images could be detected.
A little later on in the 1980s, Hinton was exploring how algorithms could be used to process patterns in the same way as the human brain. Using a simple Hopfield network as a starting point, he and a colleague borrowed ideas from statistical physics to develop a Boltzmann machine. It is so named because it works in analogy to the Boltzmann equation, which says that some states are more probable than others based on the energy of a system.
A Boltzmann machine typically has two connected layers of nodes – a visible layer that is the interface for inputting and outputting information, and a hidden layer. A Boltzmann machine can be generative – if it is trained on a set of similar images, it can produce a new and original image that is similar. The machine can also learn to categorise images. It was realized that the performance of a Boltzmann machine could be enhanced by eliminating connections between some nodes, creating “restricted Boltzmann machines”.
Hopfield networks and Boltzmann machines laid the foundations for the development of later machine learning and artificial-intelligence technologies – some of which we use today.
Born on 6 December 1947 in London, UK, Hinton graduated with a degree in experimental psychology in 1970 from Cambridge University before doing a PhD on AI at the University of Edinburgh, which he completed in 1975. After a spell at the University of Sussex, Hinton moved to the University of California, San Diego, in 1978, before going toCarnegie-Mellon University in 1982 and Toronto in 1987.
After becoming a founding director of the Gatsby Computational Neuroscience Unit at University College London in 1998, Hinton returned to Toronto in 2001 where he has remained since. From 2014, Hinton divided his time between Toronto and Google but then resigned from Google in 2023 “to freely speak out about the risks of AI.”
Elected as a Fellow of the Royal Society in 1998, Hinton has won many other awards including the inaugural David E Rumelhart Prize in 2001 for the application of the backpropagation algorithm and Boltzmann machines. He also won the Royal Society’s James Clerk Maxwell Medal in 2016 and the Turing Award from the Association for Computing Machinery in 2018.
Hopfield was born on 15 July 1933 in Chicago, Illinois. After receiving a degree in 1954 from Swarthmore College in 1958 he completed a PhD in physics at Cornell University. Hopfield then spent two years at Bell Labs before moving to the University of California, Berkeley, in 1961.
In 1964 Hopfield went to Princeton University and then in 1980 moved to the California Institute of Technology. He returned to Princeton in 1997 where he remained for the rest of his career.
As well as the Nobel prize, Hopfield won the 2001 Dirac Medal and Prize from the International Center for Theoretical Physics as well the Albert Einstein World Award of Science in 2005. He also served as president of the American Physical Society in 2006.
Two papers written by this year’s physics laureates in journals published by IOP Publishing, which publishes Physics World, can be read here.
The Institute of Physics, which publishes Physics World, is running a survey gauging the views of the physics community on AI and physics till the end of this month. Click here to take part.
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As a young physics student, I spent the summer of 2004 toting around Roger Penrose’s The Road to Reality: A Complete Guide to the Laws of the Universe. It was one of the most challenging popular-science books I had ever come across, and I, like many others, was intrigued by Penrose’s treatise and his particular ideas about our cosmos. So I must admit that nearly a decade later, when I had the opportunity to meet the man himself at a 2015 conference hosted by Queen Mary University London, I was still somewhat starstruck.
The conference in question was one celebrating “Einstein’s Legacy: Celebrating 100 years of General Relativity”, and included scientists, writers and journalists who gave talks on everything from the “physiology of GR” to light cones and black holes. Penrose was one of the plenary speakers on a Saturday evening and I was promptly amused when he began his talk on “Light cones, black holes, infinity and beyond”, with a rather beautiful if extremely old-school transparency. Those who had attended his talks before (and indeed even to this day) already knew of this particular habit, as Penrose famously dislikes slides and prefers to give his talks with his own hand-drawn colourful sketches – in fact, I’ve never seen quite such a colourful black hole! In my blog from 2015, I described the talk as “equal parts complex, intriguing and amusing”, and I recall thoroughly enjoying it.
As any good science journalist would, I attempted to speak with him after the talk, but he was absolutely mobbed by the many students and other enthusiastic scientists at the event. So I decided to bide my time and attempt to catch him at the dinner after, where he again held court with all the QMUL students who hung on to his every word. It was only after 10 p.m. that I managed to get him alone to interview him. My colleague and I set up a camera in a quiet classroom and as we asked Penrose our first question on cosmology, a deep rumbling sound took over the room – a District and Hammersmith tube line runs past most of the classrooms at the campus.
We spent most of the interview stopping and starting and attempting to perfectly time when the next tube would rumble past. Penrose was extremely patient despite how late it was, and the fact that he had been talking for hours already. The many interruptions to filming did mean that we had the chance to chat casually with him, and though I cannot recall the exact details, the conversation was equal parts fascinating and rambling, as we went off on many tangents.
You can watch the final version of my interview with Penrose above, to learn more about who inspired him, his views on the future of cosmology, and how his career-long interest in back holes – which won him the 2020 Nobel prize – first began.
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