Macrobiological features have been cited as irreducibly complex, including animal joints and the trap mechanisms of carnivorous plants, even the cell itself.

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The first study of its kind shows that ecosystems experience far more cumulative heat than standard measurements reveal. A marine heatwave may have an official beginning and end, but the warm water affecting an ecosystem often does not. Like a pot that continues absorbing heat over time, coastal waters can remain unusually warm for weeks [...]

Bentham Science Launches Two New Journals to Accelerate Research in Wireless Communications and Engineering

Bentham Science Publishers has announced the launch of two new peer-reviewed journals designed to capture the rapid transformation of modern technology: Wireless and Communication Letters and Current Engineering Letters and Reviews. Both publications are now open for manuscript submissions, offering researchers an international platform to share emerging discoveries in communication systems, engineering technologies, and interdisciplinary innovation. The launches arrive as advances in artificial intelligence, sixth-generation wireless networks, satellite systems, advanced materials, and sustainable engineering continue to reshape scientific research and industrial development.

Wireless and Communication Letters is focused on the technologies that enable today’s increasingly connected world. The journal will publish original letters, full-length research articles, comprehensive reviews, mini-reviews, and guest-edited thematic issues addressing both theoretical developments and practical applications. Its scope covers the complete communication chain, from antennas and radio-frequency hardware to signal processing, network architecture, cybersecurity, and intelligent software. By emphasizing the efficient dissemination of research, the journal aims to provide a rapid channel for findings that could influence the next generation of wireless infrastructure.

The technical challenges addressed by the journal are central to the future of digital connectivity. Fifth-generation networks have already introduced high-speed mobile broadband, ultra-reliable low-latency communication, and massive machine-type connectivity, but researchers are now investigating 6G systems capable of supporting even greater data rates, extremely precise sensing, and seamless integration with artificial intelligence. These systems may combine terahertz frequencies, reconfigurable intelligent surfaces, distributed antennas, edge computing, and advanced machine-learning algorithms. Such technologies require new approaches to spectrum allocation, energy efficiency, network coordination, and data security.

The journal also welcomes research on the Internet of Things and machine-to-machine communication, areas in which billions of sensors and devices are expected to exchange data with minimal human intervention. In these environments, communication protocols must operate reliably despite limited power, unpredictable radio conditions, and enormous numbers of connected nodes. Research into low-power wide-area networks, intelligent scheduling, network virtualization, and secure device authentication could help support applications ranging from industrial automation and smart cities to remote healthcare and environmental monitoring.

A further component of the journal’s scope involves the physical science of communication. Antenna design, electromagnetic propagation, microwave engineering, optical links, and satellite communication determine how information travels through real environments. Engineers must account for obstacles, atmospheric conditions, interference, polarization, bandwidth limitations, and the movement of users or spacecraft. Developments in compact antennas, beamforming, optical wireless communication, and satellite-terrestrial integration could improve connectivity in rural regions, disaster zones, transportation systems, and future space missions.

Wireless and Communication Letters is led by Editor-in-Chief Dr. Nasimuddin of the Antenna and Optical Department at the Institute for Infocomm Research in Singapore. Supported by an international editorial board, the journal states that it will apply a rigorous and objective peer-review process. This is particularly important in a field where theoretical claims must often be tested through simulations, prototypes, channel measurements, or performance comparisons. Research involving modulation, coding, information theory, cognitive radio, software-defined networking, artificial intelligence, privacy, and communication protocols will be considered within the journal’s broad technological remit.

Alongside the communications title, Bentham Science has introduced Current Engineering Letters and Reviews, an interdisciplinary journal intended to connect research across the full spectrum of engineering sciences. Announced on 3 August 2026, the journal will publish research articles, short communications, comprehensive reviews, mini-reviews, and thematic issues. Its mission is to bring together discoveries that are often separated into specialist fields, reflecting the reality that many modern technologies emerge from collaboration among mechanical, electrical, materials, chemical, biomedical, environmental, and computer engineers.

The new engineering journal will cover aerospace, civil, structural, transportation, manufacturing, energy, robotics, automation, nanotechnology, artificial intelligence, and digital engineering. It will also address sustainable engineering, renewable energy systems, smart materials, advanced manufacturing, and biomedical technologies. These subjects are increasingly connected: an electric vehicle, for example, depends simultaneously on materials science, power electronics, software, communications, manufacturing, and transportation engineering. An interdisciplinary publication can provide a forum for examining such systems as integrated technological platforms rather than isolated components.

The scientific importance of this approach is becoming more visible as engineers confront challenges involving climate change, resource efficiency, resilient infrastructure, and automation. Renewable energy systems require improved storage, power conversion, and grid management; smart materials can respond to changes in temperature, pressure, or electromagnetic fields; and artificial intelligence can optimize industrial processes by identifying patterns in large streams of sensor data. At the same time, engineering research must demonstrate reliability, scalability, safety, and economic feasibility if laboratory innovations are to produce measurable benefits in society and industry.

Both journals invite contributions from researchers, engineers, scientists, academics, and industry professionals worldwide. Bentham Science says that information about open-access policies, article processing charges, waiver eligibility, editorial policies, and complaints and appeals procedures is available through the respective journal websites. With their emphasis on rapid communication, technical rigor, and interdisciplinary exchange, the two publications seek to become new hubs for research at the intersection of connectivity, intelligent systems, sustainable technology, and engineering innovation.

Subject of Research: Wireless communications, communication engineering, interdisciplinary engineering research, emerging technologies, artificial intelligence, sustainable engineering, and advanced technological systems.

Article Title: Bentham Science Launches Two New Journals to Accelerate Research in Wireless Communications and Engineering

News Publication Date: 3 August 2026 (for the announcement of Current Engineering Letters and Reviews; no publication date is provided for Wireless and Communication Letters).

Keywords

Wireless communications, 5G, 6G, Internet of Things, satellite communications, antenna design, signal processing, artificial intelligence, engineering research, sustainable engineering, robotics, renewable energy, smart materials, advanced manufacturing.

The spots on a leopard, the stripes on a zebra, and the mask on a raccoon are all types of coloration in mammal fur, referred to as pelage. Scientists have struggled for a long time to understand how these colorations evolved. A team of researchers recently discovered how to reconstruct the colors of ancient mammal fur using cell structures that contain the chemical pigment melanin, known as melanosomes. They hope to use this understanding of ancient mammal pelage to determine how they lived and what their environments were like.

To decipher the color of ancient mammal fur, the researchers constructed a statistical computer model to predict their fur colors based on the shape of their melanosomes. First, they needed to understand how the shapes of melanosomes control fur color in modern mammals. To measure the length and width of the modern melanosomes, the scientists used a beam of electrons like a flashlight to map them in great detail, a technique called scanning electron microscopy.

They measured the length and width of 2,615 melanosomes from the fur of 116 different living mammal species, including a Red Panda, a German Shepard, and one or more species of most types in the mammalian classification. Then they recorded the wavelengths of light reflected off each melanosome using a microspectrophotometer. These wavelengths represented the exact color of hair from each melanosome they measured. This means they now had a quantitative measurement of the color of each hair to compare with its corresponding melanosome length and width.

When they examined their data, the researchers found that spherical melanosomes produced brighter fur colors, like reds and oranges, while elongated melanosomes produced darker fur colors, like browns and grays. The researchers plotted the microspectrophotometric measurements for each melanosome as a graph showing how much light it absorbed across each possible wavelength, creating reflectance curves. Based on these data, the researchers used computer software to create a statistical model that could predict reflectance curves, and therefore color, from melanosome shape alone.

Once the researchers created a model, they needed to find fossilized mammal melanosomes to predict their fur color. Mammal fossils are rare, and pieces of cells are only preserved in the most immaculate fossil deposits. In fact, these researchers only found 6 fossils of this caliber, from Mesozoic rocks deposited around 250 million years ago, soon after mammals evolved. 

The fossils came from 2 world-renowned deposits in Northeastern China, the Yanliao and Jehol biotas. They selected 5 previously studied fossils from these locations, including 1 specimen of Megaconus mammaliaformis, 1 specimen of Vilevolodon diplomylos, 2 specimens from the group Docodonta, and 1 specimen from the group Eutheria. All of these animals were small, rodent-like mammals. The researchers also discovered a new species of tree-dwelling mammal, Arboroharamiya fuscus.

The scientists used scanning electron microscopy to measure a total of 2,159 fossilized melanosomes to refine their color predictions. The fossilized melanosomes were all slightly elongated spheres and had far fewer differences in shape than in modern mammals. The researchers used their model to predict reflectance curves based on the shapes of the fossil melanosomes to determine the ancient mammals’ colors. They found that all the extinct mammals they examined likely had dark brown fur without any color patterns.

Based on these findings, the researchers suggested that most, if not all, early mammals were nocturnal, like many dark-colored mammals today. They suggested that this nocturnal lifestyle probably helped them survive the meteorite impact that wiped out the dinosaurs 100 million years later. While dinosaurs were unable to survive in the post-impact wasteland, these smaller, dark-colored mammals were more resilient and thrived in the aftermath. As mammals filled the ecological roles left by the dinosaurs, they evolved new color patterns, some based solely on camouflage, and others on new factors, like sexual selection and visual communication.

The researchers concluded that their model can predict fossilized mammal colors and help determine how these ancient animals lived. Notably, their data suggest that mammals likely stayed small and nocturnal throughout the Mesozoic until the dinosaurs were gone. Future researchers could use similar methods to uncover exactly how mammals evolved the colors we see today. For example, studying mammals closer to the impact event could reveal if it took a long time to develop diverse fur patterns, or if they began before the dinosaurs’ demise. Ultimately, similar studies could help determine whether the extinction of dinosaurs was necessary for the rise of mammals.

The post What colors were prehistoric mammals? appeared first on Sciworthy.

Scientists in Mexico were the first to perform ultrasounds on giant manta rays to detect pregnancies. The researchers hoped to learn more about the life cycle of the largest ray species in the world.

Have you ever wondered how researchers use artificial intelligence (AI) in environmental science? Certain AI computer models can learn from scientific data and operate without manual programming, in a process called machine learning. Machine learning models can identify patterns in environmental datasets and help scientists assess public health risks linked to environmental conditions.

One well-known public health risk is the rise in antibiotic-resistant bacteria. These bacteria carry genes called antibiotic resistance genes that protect them from antibiotic medicines. As the number of these genes increases, the medicines become less effective, and diseases spread faster.

Agricultural soils are the largest reservoirs of antibiotic resistance genes, because manure from livestock like cows and pigs often contains leftover antibiotics fed to them. When used as fertilizer, this manure transfers antibiotics into the agricultural soil, where bacteria develop antibiotic resistance genes.

To better understand how antibiotic-resistant bacteria will affect global health in the future, scientists want to develop faster ways to detect antibiotic resistance genes. However, current methods of detection are slow and fail to predict future trends. To address this problem, a group of scientists from Algeria used machine learning models to predict the prevalence of antibiotic resistance genes in soil microbes under future climate scenarios, and to identify the key environmental drivers and high-risk areas.

​The team compiled existing microbial data from 3 public databases: the National Center for Biotechnology Information’s Sequence Read Archive, the Metagenomic Rapid Annotations using Subsystems Technology database, and the Joint Genome Institute’s Integrated Microbial Genomes & Microbiomes database. Their final dataset contained about 2,000 agricultural soil samples from 67 countries across 6 continents. These samples represented a wide range of soil characteristics, climate conditions, and different types of antibiotic resistance genes. 

They integrated the microbial dataset with climate data, including global temperature and precipitation measurements from WorldClim, and land use data, including crop types, irrigation systems, and livestock density from the European Space Agency Climate Change Initiative Land Cover maps. The researchers also included data on future climate projections for 2050 and 2070 from the World Climate Research Programme based on low, medium, and high global greenhouse gas emission scenarios. 

After preparing the datasets, the team set up 6 machine learning models that differ in terms of their data size, model complexity, analytical speed, and customization. These models included Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), and Logistic Regression (LR). 

They ran each model using different combinations of variables from their datasets, including soil properties, microbial community metrics, climate variables, and land use characteristics. To validate the models, the team separated the full dataset into 10 random subsets. They trained the models on 9 of the subsets, then recorded the result of the 10th subset. Each model repeated this process 10 times in a different order so that each subset was evaluated once in the results, using a technique known as stratified 10-fold cross-validation. The researchers scored each model on 5 performance metrics, including precision, sensitivity, and predictive power. Based on these results, they determined that LightGBM was the most accurate model. 

They found that the LightGBM model identified soil temperature as the strongest environmental predictor of antibiotic resistance genes. At soil temperatures above 18°C (about 64°F), the number of antibiotic resistance genes increased dramatically. The model also identified soil acidity, organic carbon content, moisture, and annual precipitation as influential variables, accounting for 71% of the increase in antibiotic resistance genes under future climate scenarios. Likewise, the LightGBM model predicted that the number of high-risk areas would increase by 35% under the high-emissions scenario, with South Asia, Sub-Saharan Africa, and Mediterranean Europe as the most vulnerable regions.

Based on the LightGBM model output, the team concluded that soil conditions will change to favor antibiotic resistance genes under all future climate scenarios. They also suggested that LightGBM is a fast and effective model for environmental predictions that could be used to assess other climate change risks such as natural disasters. They recommended that future researchers explore how to apply machine learning methods to early warning systems and utilize machine learning results in environmental management decisions. 

The post Predicting microbial antibiotic resistance with AI appeared first on Sciworthy.

A newly published study reports that tropical cyclones were far more common during the Late Paleozoic Ice Age than many climate reconstructions have suggested. Led by Yan, Macarewich, Jiang, and colleagues, the work appears in Nature Communications (2026) and reframes how Earth’s cold interval coexisted with warm-water storm systems.

The researchers investigated geological signatures linked to extreme marine events, combining stratigraphic evidence with climate-model constraints. Their goal was to distinguish ordinary seasonal variability from the kind of widespread, repeated disturbances that characterize tropical cyclone belts.

A key technical advance in the study is the integration of paleoclimate boundary conditions with storm-resolving dynamical reasoning. Instead of treating cyclone activity as a simple function of temperature alone, the team considered large-scale patterns that govern where warm oceans can persist long enough to power rotating storms.

The results point to an expanded region of conditions favorable for cyclogenesis. Even though the planet was globally in an ice-age mode, ocean–atmosphere coupling—driven by uneven heat storage, changing circulation, and shifting storm tracks—likely enabled tropical systems to form across parts of the tropics more frequently than expected.

The authors describe “widespread” activity, implying that cyclone impacts were not limited to rare episodes. This interpretation aligns with the idea that cooling did not fully suppress tropical cyclone energetics; rather, it altered the geography and intensity distribution of storm-prone environments.

Using the preserved sedimentary record, the study connects storm activity to repeated depositional pulses. Those pulses, they argue, reflect basin-scale disturbance rather than local weather anomalies, strengthening the case for frequent tropical cyclone events during this interval.

From a broader perspective, the findings have implications for how we understand resilience and feedbacks in past climates. If cyclones continued to thrive under ice-age background states, then tropical extremes may persist through major global reorganizations.

The study’s viral potential is clear: it challenges an intuitive narrative that “ice ages mean calm tropics,” and replaces it with a more nuanced picture in which warm-water dynamics and circulation shifts can sustain storminess despite global cooling.

Below are the details available from the provided text:

Subject of Research: Tropical cyclone activity during the Late Paleozoic Ice Age.

Article Title: Widespread tropical cyclone activity during the Late Paleozoic Ice Age.

Article References: Yan, Q., Macarewich, S., Jiang, N. et al. Widespread tropical cyclone activity during the Late Paleozoic Ice Age. Nat Commun (2026). https://doi.org/10.1038/s41467-026-75609-1

Image Credits: AI Generated

DOI: 10.1038/s41467-026-75609-1

I didn’t want to have to depend on a weather app to know when the storms are coming.

A technique currently used to track whale vocalizations could be adapted to pick up the hydrodynamic pressure fields created as they swim, researchers in Norway have demonstrated. Through analysis of backscattered light in an undersea optical fibre, Robin Rørstadbotnen and Martin Landrø at the Norwegian University of Science and Technology (NTNU) showed how silent whales can be detected as they swim close to the fibre. The duo hopes the discovery could ultimately provide a valuable new tool for whale conservation.

With the combined disruption of climate change and human activity, whales are rapidly being forced into new habitats and migration patterns. To understand these profound shifts in behaviour, conservationists widely rely on a technique named distributed acoustic sensing (DAS), which detects acoustic waves via the strain they induce on optical fibres.

When coherent laser pulses are passed through a fibre, a small portion of laser light is inevitably scattered back to the source by small fluctuations in the fibre’s structure and density. If the fibre is strained by an acoustic wave, the oscillating pressure field imprints a phase shift on this backscattered light, which varies in proportion to the degree of strain.

So far, this approach has been used to pick up whale vocalizations: the loudest sounds produced by any animal, which can travel for thousands of kilometres through the ocean. By analysing backscattered light across the global network of undersea optical fibres, researchers have gathered valuable information about how the habitats and migration paths of these marine mammals are changing.

“At NTNU, we started activity within distributed acoustic sensing in 2016,” Rørstadbotnen describes. “When the Center for Geophysical Forecasting was launched in 2021, this technology became a key field of interest.”

Despite the value of data that can be gathered through whale vocalizations, the approach leaves conservationists with a significant blind spot for the majority of the time when whales are silent. To address this limitation, Rørstadbotnen and Landrø considered how DAS could be adapted to pick up the hydrodynamic pressure and velocity fields constantly being created by the whales moving through the water, which generates ultralow-frequency signals similar to those created by the movement of ships.

To test this idea, the duo revisited scattering data gathered from a seabed optical fibre off the coast of Svalbard in the Norwegian Arctic. After filtering the data for a selected band of frequencies, they carried out frequency–wavenumber analysis to determine how rapidly the signals varied along the length of the fibre. Just as they hoped, “we found that this new approach could be applied to detect silent whales, given that they are swimming close to the fibre optic cable,” Rørstadbotnen says.

Through this analysis, the researchers were able to identify ships crossing the fibre from depths of up to 413 m, and at a maximum distance of 550 m from the fibre. They also managed to determine the vessel speeds and roughly estimate their sizes. Crucially, they were also able to distinguish the speeds and approximate sizes of silent whales swimming less than 40 m away from the fibre, as well as estimating their depths.

“Since we also recorded the acoustic signals in the frequency range from 5 to 100 Hz, we could identify whales when they were vocalizing closer to the sea surface,” Rørstadbotnen describes. “Thereby, we could use this information as a strong indication that the low-frequency signal observed a bit later in time was most likely generated by the whale moving closer to the fibre.”

Having demonstrated this possibility, Rørstadbotnen and Landrø now hope that their technique could provide researchers with a new tool for tracking whales around the world, even when they aren’t vocalizing. “In addition, this method can potentially detect toothed whales that vocalize in the high-frequency band outside what is normally recorded for distributed acoustic sensing,” Rørstadbotnen adds.

Beyond its potential for whale conservation, the duo is also hopeful that the technique could help researchers build up a more extensive picture of marine mammals more generally, along with other vital elements of fast-changing marine ecosystems.

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

The post Undersea optical fibres detect the motion of silent whales appeared first on Physics World.

Pollinating insects form a vital part of any ecosystem, enabling the biodiversity that we see on Earth today. However, biodiversity is in rapid decline around the world, and monitoring insect species is a difficult task that often requires some insects to be killed. To support the conservation of biodiversity, which is critical to ensure the sustainability of human civilization, more robust monitoring is required. In a study published in PNAS Nexus, researchers have developed a new method to identify and classify individual insects, based on radar imaging and machine learning.

Radar has long been used to study migrating insects that fly at high altitudes and in large numbers, but such systems typically perform wide-area, long-range monitoring. However, thanks to a combination of millimetre-wave radar and machine learning, narrow focused identification is now possible, by detecting changes in the radar reflection of insects caused by the flapping of their wings.

“Having a background in antenna engineering, there was always the question of whether this technology can be used to address some of the environmental challenges that we’re facing,” says co-lead author Adam Narbudowicz from the Technical University of Denmark. “Some five or six years ago, we started talking with [co-author] Ian [Donohue] about those possibilities, and eventually the idea of micro-Doppler emerged, which seemed feasible from an engineering point of view and could provide some useful data on biodiversity.”

The approach taken in this study doesn’t focus on morphological features of the insects, as these are difficult to detect with radar. Instead, it uses the harmonic patterns generated by the micro-Doppler effect of an insect beating its wings as a detection strategy. Millimetre-wave radar can provide insight into biomechanical characteristics not visible with cameras, and these characteristics are encoded in the harmonic patterns of the wingbeat.

The team used machine learning to improve the accuracy of the identification and incorporated a SHAP (SHapley Additive exPlanations) analysis – an explainable AI tool that interprets and explains key outputs and prioritizes key features – to identify which signal features are the most critical for differentiating insect species. The SHAP analysed each insect across the full spectrum of micro-Doppler harmonics, extracting key features including fundamental wingbeat frequency, energy distributions, cepstral coefficients (sound signals) and how quickly an insect’s wing movement change. These data were then used to train the machine learning model.

The actual process of obtaining this data from the insects involved capturing insects at the Trinity College Dublin campus and placing them in a plastic box on top of a millimetre-wave antenna that recorded their radar signatures. The researchers then released the insects back into the wild. After data capture, the relevant micro-Doppler features were extracted from the data for model training.

The model allowed non-invasive monitoring of different insects and could distinguish between bees and wasps with 96% accuracy. The model also classified five key pollinating insect species – red-tailed bumblebee, buff-tailed bumblebee, moss carder bumblebee, western honeybee and common wasp – with an accuracy of 85%.

“I think the most impressive thing is that we can detect and classify them with such an accuracy. From a biological point of view, it’s impressive how different species beat their wings in different manner, and from an engineering point of view it’s fascinating how different wingbeats affect harmonics of radar micro-Doppler reflections,” says Narbudowicz. “Those differences are of course impossible to see just by looking at spectrograms, but it appears that a sufficiently trained machine learning algorithm can see them.”

Narbudowicz points out that the current study used precise lab-grade transceivers and a relatively controlled set-up, and that the natural next step is to move this technology to outdoor field deployment. “This requires a number of steps,” he explains. “Firstly, the device needs to be miniaturized, and battery operated; the transceiver will be less accurate than the one used in the lab, but a big problem is the ground truth verification, since in the field it can be difficult to verify exactly which species flew over the sensor.”

Despite the greater challenge with deploying the technology in the field today, the researchers suggest that this radar reflection approach could be utilized in the future in a fly-through device, which would make it much easier and cheaper to achieve non-lethal monitoring of insect biodiversity in different environments.

The post Radar identifies insect species via reflections from wingbeats appeared first on Physics World.

Dalila grew up in the Tunisian coastal city of Gabès and is accustomed to the heavy, acrid smell from the state-owned phosphate processing plant about a 10-minute walk from her home. On days when noxious fumes fill the air, Dalila rushes to tightly shut the windows and tells her children to hurry indoors. She washes them, believing it lessens harmful effects from chemicals. Emissions can last all day. Residents may get little or no advance warning. But one day in September 2025, children were at school when gases spread. Some students fell to the ground, complaining they had difficulty breathing. Others said they felt like they were suffocating. Dalila’s daughter said she felt like she was choking. Dalila rushed her to a government-run hospital. When she asked about test results or the type of substances that may have caused her difficulty breathing, Dalila said she did not get clear answers. The incident sickening the schoolchildren helped spark mass protests in the streets of Gabès, with people waving banners that read “Stop Pollution” and “Move The Plant.” It is not the first time that residents have protested water and air pollution from the factory. Dalila feels that she and others in Gabès are being ignored. They are far from alone. Fishermen report coastal waters were once abundant with fish, but, now, their nets come up empty. Scientific studies provide evidence that environmental damage poses a danger to the health of those living nearby. People take part in a demonstration against pollution…This article was originally published on Mongabay

The Space Development Agency was established in 2019 to help speed up the deployment of US military space systems by sidestepping the Pentagon's traditional sluggish bureaucracy.

Seven years later, SDA is finally launching its first batches of operational satellites, just as the Pentagon plans to shutter the semi-autonomous agency and fold it back into the Space Force's procurement pipeline, newly reorganized under several program acquisition executives in a bid to streamline weapons buying.

SDA's fate is not a surprise, and lawmakers in both houses of Congress have backed the agency's closure in drafts of this year's National Defense Authorization Act.

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A new study is turning an elegant quantum idea into a practical platform for light. In research published in Light: Science & Applications, physicists report “topological Jackiw–Rebbi” states engineered inside photonic van der Waals heterostructures—systems built by stacking atomically thin materials with precisely tailored interfaces. The result is a photonic phenomenon that echoes how particles can be trapped at boundaries where the rules of their governing equations change.

The work builds on the classic Jackiw–Rebbi mechanism, originally proposed for relativistic particles. In those models, a localized mode appears when a mass term flips sign, effectively creating a domain wall. The authors translate this concept into optics by designing photonic structures where an effective “mass” characterizing light propagation changes across an interface.

What makes the platform especially compelling is the van der Waals architecture. Unlike conventional fabrication routes that can introduce disorder, the layered nature of these materials enables sharp, controllable boundaries and tunable photonic band structures. By adjusting the stacking and interlayer optical responses, the researchers create conditions in which topological modes become robust against typical imperfections.

The study emphasizes that these states are not merely conventional guided resonances. Instead, they are tied to topology: the light modes are associated with an invariant property of the band structure. As a consequence, the Jackiw–Rebbi-like photonic states are expected to persist even when the system’s details vary, so long as the relevant symmetry and band configuration remain intact.

Topological protection is more than a slogan. In photonics, it can translate into reduced sensitivity to fabrication tolerances and a pathway toward devices that rely on stable boundary transport. The paper therefore positions these localized states as building blocks for future optical components that route signals along designed interfaces.

The authors demonstrate the concept theoretically and connect it to experimental observables by focusing on how the interface should host modes localized near the domain wall region. Such localization is a hallmark of the Jackiw–Rebbi scenario, now realized in a controllable, designer photonic environment.

Beyond fundamental interest, the approach could influence the design of reconfigurable photonic circuits. Because van der Waals stacks can be combined and adjusted, the same topology-driven logic may be extended to new material systems, wavelengths, and device geometries.

Overall, the report signals a broader trend: using topological field-theory ideas to engineer light–matter behavior in atomically precise platforms, potentially enabling the next generation of robust, interface-based photonic technologies.

Subject of Research: Topological photonic states (Jackiw–Rebbi) in van der Waals heterostructures.

Article Title: Topological Jackiw-Rebbi states in photonic Van der Waals heterostructures.

Article References: Randerson, S.A., Bouteyre, P., Hu, X. et al. Light Sci Appl 15, 323 (2026). https://doi.org/10.1038/s41377-026-02392-5

Image Credits: AI Generated

DOI: 10.1038/s41377-026-02392-5

Keywords:

Known for tracking California wildfires, Watch Duty is moving beyond that mission. Millions of people are turning to it for help.

A lack of sirens and flood forecasting systems made the state’s Hill Country vulnerable to deadly floods last summer. That has changed.

Known for tracking California wildfires, Watch Duty is moving beyond that mission. Millions of people are turning to it for help.

© Joel Angel Juarez/Associated Press