Human T cell leukemia virus type 1, the first human retrovirus ever identified, infects an estimated 5 to 10 million people worldwide, a figure extrapolated from surveys covering only about one-seventh of the global population. The virus inserts a DNA copy of its RNA genome into the chromosomes of host T cells, establishing a lifelong infection that can drive a spectrum of diseases, including adult T cell leukemia/lymphoma, HTLV-1-associated myelopathy, and HTLV-1-associated uveitis, a chronic inflammation inside the eye that is particularly common in endemic regions such as Japan. Despite the scale of the problem, coordinated global action has been slow; the World Health Organization only published technical reports aimed at reducing HTLV-1-related morbidity and mortality in 2021, and no broadly effective intervention has emerged in decades of research.

Current treatment for the ocular disease relies heavily on corticosteroids, delivered as topical drops, injections into the eye, or oral prednisone. This approach has serious shortcomings. Studies indicate that 30 to 60 percent of patients with HTLV-1-associated uveitis experience recurrent episodes, and long-term steroid exposure can trigger cataracts, glaucoma, macular edema, epiretinal membranes, retinal degeneration, optic nerve atrophy, and bullous keratopathy. Most importantly, corticosteroids suppress inflammation without reducing the number of virus-infected cells or halting disease progression. Newer molecular targeted agents, including the anti-CCR4 antibody mogamulizumab and the immunomodulatory drug lenalidomide, have been explored, but treatment-related inflammation and adverse effects remain significant obstacles. This therapeutic gap motivated a team led by Yaru Zou and Koju Kamoi of Tokyo Medical and Dental University to investigate whether an epigenetic drug already approved for blood cancer could address the root of the ocular inflammation.

The drug in question is valemetostat, marketed as EZHARMIA, a dual inhibitor of the enzymes EZH1 and EZH2 that was approved in 2022 for adult T cell leukemia/lymphoma. Both enzymes are the catalytic components of polycomb repressive complex 2, a molecular machine that silences genes by adding three methyl groups to lysine 27 on histone H3, producing the repressive chromatin mark known as H3K27me3. Previous work had shown that EZH1 and EZH2 are overexpressed, and H3K27me3 deposition increased, in ATL cells and in cells expressing the viral oncoprotein Tax. Dual EZH1/2 inhibitors have also been reported to curb the spontaneous proliferation and exaggerated immune responses of HTLV-1-infected cells in patients with HTLV-1-associated myelopathy. What remained unknown was whether such inhibition could benefit the inflamed eye.

To find out, the researchers built a series of laboratory models centered on ARPE-19 cells, a human retinal pigment epithelial cell line chosen because the retinal pigment epithelium forms the outer blood-retinal barrier and governs retinal immune homeostasis. They exposed these ocular cells to three kinds of T cells: MT-2 cells, which carry transcriptionally active HTLV-1 and express the viral protein Tax; TL-Om1 cells, an ATL-derived line in which the virus is largely silent and Tax is low or absent; and Jurkat cells, which are uninfected and served as controls. In parallel, they treated the T cell lines themselves. Western blotting and quantitative PCR revealed that HTLV-1 infection drove significant upregulation of both EZH1 and EZH2, along with elevated H3K27me3, in infected retinal pigment epithelial cells and in MT-2 cells, with EZH1 protein rising roughly 2.5-fold in infected ocular cells and six-fold in MT-2 cells. This was the first demonstration that HTLV-1 infection induces polycomb-mediated epigenetic changes in retinal cells.

The team then treated the models with valemetostat at 0.01, 0.1, and 1 micromolar for 96 hours, concentrations selected in preliminary experiments because they harmed infected T cells while leaving normal ocular cells, including primary human trabecular meshwork cells, unaffected below 10 micromolar. The results were strikingly dependent on the model. In co-cultures of retinal cells with Tax-positive MT-2 cells, and in retinal cells directly infected with HTLV-1, the drug produced modest but statistically significant reductions in cell viability at the highest dose, accompanied by small increases in late-stage apoptosis. No such effects appeared in co-cultures with TL-Om1 or Jurkat cells. Crucially, the absolute magnitude of apoptosis remained limited, typically only 1 to 3 percent above baseline, indicating that valemetostat is not broadly toxic to retinal cells, an important safety consideration given their role in maintaining vision.

The virological findings were equally nuanced. Valemetostat did not significantly reduce the HTLV-1 proviral load in co-culture systems or in directly infected retinal pigment epithelial cells. However, in MT-2 cells themselves, 1 micromolar valemetostat significantly lowered proviral load and suppressed the messenger RNA levels of both Tax and HBZ, the two hallmark transcripts of the virus. Because MT-2 cells represent the transcriptionally active, highly infectious state of HTLV-1, while TL-Om1 represents latency, the results suggest that the drug’s antiviral effect is gated by the transcriptional status of the infected cell. Within the four-day treatment window, the authors conclude, valemetostat’s benefit to the ocular environment is likely indirect, acting primarily by quieting the most virally active cells rather than by eradicating provirus everywhere.

The most clinically relevant effects emerged in the inflammatory readouts. HTLV-1-infected T cells recovered from the eyes of patients with uveitis are known to secrete copious interleukin-6, interleukin-8, and interleukin-10, all implicated in disease. In the new study, valemetostat produced a dose-dependent increase in the anti-inflammatory cytokine IL-10 in MT-2-containing conditions, significant at 1 micromolar in the co-culture system and across all doses in MT-2 cells. This fits a mechanistic expectation: blocking EZH1/2 removes H3K27me3 marks that repress the IL-10 gene, and prior genetic studies showed that loss of EZH2 directly activates IL-10 transcription in several CD4-positive T cell subsets. Meanwhile, pro-inflammatory signals fell in a context-dependent pattern. IL-12p70 and the chemokine CXCL-10 dropped in co-cultures, IL-6 and IL-8 declined mainly in MT-2 cells, and CCL-2 fell at low doses in the MT-2 co-culture, whereas the chemokine CCL-5 was unchanged in every model.

The drug’s influence on NF-kappaB, the central inflammatory pathway that Tax hijacks to drive both viral replication and cytokine production, was more subdued. Valemetostat modestly reduced NF-kappaB p65 phosphorylation in the MT-2 co-culture at the lowest dose and slightly decreased the nuclear-to-cytoplasmic ratio of p65 in retinal cells stimulated with TNF-alpha, but it did not abolish nuclear translocation. The authors attribute this to the overwhelming strength of Tax-driven NF-kappaB activation, which a 96-hour course of EZH1/2 inhibition may be insufficient to counteract, and to the fact that NF-kappaB and cytokine networks are regulated by many factors beyond polycomb complexes. Immunofluorescence confirmed that valemetostat significantly lowered H3K27me3 in retinal cells exposed to MT-2 cells or directly infected, but not in cells co-cultured with TL-Om1 or Jurkat cells, again underscoring the model dependence of the epigenetic response.

The study also carries a cautionary note about IL-10. Although the cytokine is anti-inflammatory, it can promote the survival of HTLV-1-infected T cells and facilitate immune evasion by impairing dendritic cell antigen presentation and downregulating MHC class I molecules, and patients with aggressive ATL often show abnormally elevated IL-10. The authors therefore suggest that combining EZH1/2 inhibition with IL-10 pathway blockade could prove more effective, noting evidence that IL-10 neutralization sensitizes ATL cells to standard therapies. In the TL-Om1 model of ATL-related ocular infiltration, valemetostat failed to reduce H3K27me3, proviral load, or NF-kappaB activation within the malignant cells, yet it suppressed TL-Om1-induced CXCL10 secretion and induced dose-dependent apoptosis, pointing to a benefit mediated through the inflammatory microenvironment rather than direct tumor killing.

The authors are candid about the limitations. The work relied entirely on in vitro co-cultures and established cell lines, which cannot reproduce the blood-retinal barrier, retinal architecture, or local immune regulation of a living eye, and the intraocular pharmacokinetics of valemetostat after systemic administration remain undefined. Sex as a biological variable could not be assessed because donor-defined primary cells were not used. Even so, the findings establish a proof of concept that an approved epigenetic therapy can counteract HTLV-1-induced chromatin changes in retinal cells, dampen viral transcription in the most active infected cells, and reshape the cytokine milieu that sustains uveitis. Longer treatment studies, in vivo models, and patient-derived systems will be needed to determine whether these modest, context-dependent effects can be translated into a genuine steroid-sparing therapy for one of the most burdensome manifestations of a virus that medicine has long neglected.

Subject of Research: EZH1/2-targeted epigenetic therapy with valemetostat for HTLV-1-associated uveitis

Article Title: Dual impact of EZH1/2-targeted therapy in HTLV-1-associated inflammation: Direct and indirect mechanisms

Article References: Zou, Y., Kamoi, K., Yang, M., Zong, Y., Zhang, J., & Ohno-Matsui, K. (2026). Dual impact of EZH1/2-targeted therapy in HTLV-1-associated inflammation: Direct and indirect mechanisms. iScience, 29(11), Article 117775. https://doi.org/10.1016/j.isci.2026.117775

Image Credits: AI Generated

DOI: 10.1016/j.isci.2026.117775

Keywords: HTLV-1, valemetostat, EZH1, EZH2, H3K27me3, uveitis, epigenetics, PRC2, Tax, IL-10, NF-kappaB, retinal pigment epithelium

Every time a power grid operator decides how much electricity each generator should produce, a quiet balancing act takes place. Burning less fuel saves money, but it often means allowing more pollution, and cutting emissions usually drives costs up. Engineers have long searched for a way to satisfy both goals at once, a challenge known as combined economic emission dispatch, or CEED. Now, a team of researchers has turned to one of the ocean’s most graceful feeders, the manta ray, for inspiration, and the result is an optimization algorithm that outperforms a crowded field of competitors on both cost and cleanliness.

The study, published in the journal Results in Engineering by Yu-Feng Sun, Jie-Sheng Wang and colleagues, describes an enhanced version of the Manta Ray Foraging Optimization algorithm, or MRFO. The original algorithm, first introduced a few years ago, mimics the cooperative hunting strategies that manta rays use to harvest plankton. Because plankton drifts unpredictably with seasons and weather, manta rays have evolved three distinct foraging behaviors: chain foraging, in which animals line up in arcs so that each one catches food missed by the one ahead; spiral foraging, where the group coils around a dense patch of food; and somersaulting, in which individuals flip back and forth around a promising spot. Translated into mathematics, these behaviors become rules that move a population of candidate solutions around a search space, gradually converging on the best answer.

What makes the CEED problem so stubborn is its mathematical character. The relationship between a generator’s output and its fuel consumption follows a cubic curve, and the emissions of carbon dioxide, nitrogen oxides and sulfur dioxide each follow their own cubic functions. The combined objective is nonlinear, nonconvex and discontinuous, riddled with local minima that can trap classical techniques such as lambda iteration or gradient-based methods. To make matters harder, the problem is genuinely multi-objective, so the researchers converted it into a single objective by applying price penalty factors that fold the cost of each pollutant into one total figure in dollars per hour.

Constraint handling is where many algorithms stumble. Traditional approaches add a fixed penalty whenever a solution violates the power balance requirement, the equation demanding that total generation minus transmission losses equals demand. But a fixed penalty is a blunt instrument. Set it too low and the algorithm happily returns infeasible schedules; set it too high and the search becomes so restricted that it cannot explore properly. Finding the right value through trial and simulation is time-consuming and inefficient. The team’s answer was to make the penalty itself dynamic, drawing on five different mathematical functions: sinusoidal, hyperbolic tangent, arctangent, linear and hyperbolic secant. Each function is raised to an order that changes during the run, so the penalty strength adapts to both the size of the violation and the stage of the optimization. Early on, mild penalties preserve exploration; later, penalties tighten to push solutions toward feasibility.

The second major innovation targets the heart of the MRFO algorithm. In the original design, a parameter called C governs the switch between exploration, casting the net wide across the search space, and exploitation, homing in on the best solution found so far. This parameter rises monotonically from zero to one, a predictable march that can leave the algorithm poorly balanced at certain stages. The researchers replaced it with a chaotic switching factor, drawn from nine well-known chaotic maps including Chebyshev, logistic, tent, sine, Gauss and singer maps, combined with an exponential adjustment function. The result is a parameter that still trends upward but oscillates along the way, periodically reopening the search space and preventing the population from settling prematurely into a local optimum.

To find out which chaotic map worked best, the team ran all nine enhanced variants, named C1MRFO through C9MRFO, through the CEC-2022 benchmark suite of twelve test functions, using a population of thirty and five hundred iterations averaged over thirty independent runs. The Chebyshev-based variant, C1MRFO, emerged as the overall winner, ranking first among the nine and beating the original MRFO, which finished last. Wilcoxon rank-sum tests confirmed that most of the improvements were statistically significant, with p-values below the conventional 0.05 threshold. When C1MRFO was pitted against a roster of state-of-the-art metaheuristics, including the dhole optimization algorithm, evolutionary mating algorithm, dung beetle optimizer, snow geese algorithm, particle swarm optimization, grey wolf optimizer, sparrow search algorithm and marine predators algorithm, it achieved the best final rank, taking first place on many of the test functions while keeping computational times competitive.

The real test, however, came when the algorithm was applied to actual dispatch problems. The researchers simulated a six-unit power system under four load demands of 150, 175, 200 and 225 megawatts. Across all four scenarios, C1MRFO produced the lowest comprehensive total cost, which combines fuel cost with the weighted penalties for carbon dioxide, nitrogen oxide and sulfur dioxide emissions. At 150 megawatts, for example, it reached a total cost of about 10,137 dollars per hour, edging out the grey wolf optimizer and clearly beating algorithms such as the arithmetic optimization algorithm, Harris hawks optimization, whale optimization algorithm, sine cosine algorithm, particle swarm optimization and the lambda iteration method. The pattern held at every load level, with the margin over the weakest classical method growing to thousands of dollars per hour at 225 megawatts.

The dynamic penalty functions delivered a further, striking gain. When the sinusoidal penalty function was applied with the parameter order set to dy5, the total cost at 150 megawatts dropped from roughly 10,137 to 9,065.71 dollars per hour, and similar reductions appeared at the higher loads, reaching 14,621.45 dollars per hour at 225 megawatts. Comparing the five penalty shapes, the sinusoidal function consistently produced the lowest minimum costs, beating the hyperbolic tangent, arctangent, linear and hyperbolic secant alternatives by margins ranging from hundreds to nearly two thousand dollars per hour depending on the load. Notably, when the penalty order was fixed rather than dynamic, performance was worst across the board, confirming that the adaptivity of the penalty, not merely its presence, is what matters.

To verify that each improvement contributes on its own, the team conducted an ablation study on a much larger twenty-unit system with a total demand of 2,500 megawatts. The results formed a clear hierarchy: the original MRFO achieved 61,835.16 dollars per hour, the chaos-enhanced C1MRFO reached 61,826.11, and the fully improved version combining chaos with the optimal sinusoidal dynamic penalty, sin(dy5)MRFO, achieved the best figure of 61,807.36 dollars per hour. All three outperformed the phasor particle swarm optimization benchmark, which came in at 61,895.57 dollars per hour. The stepwise improvement demonstrates that the two enhancements are not redundant but superimpose their benefits, and that the framework scales to systems far larger than the six-unit case.

The authors are careful to note where the work goes next. The current approach converts the multi-objective problem into a single objective through price penalty factors, but future versions could adopt Pareto-based multi-objective optimization to generate full sets of non-dominated trade-off solutions. The test cases also assume deterministic demand, so extending the algorithm to stochastic dispatch with wind and solar integration is another goal, as is combining the dynamic penalty with feasibility-first constraint-handling strategies. For now, the study offers a compelling demonstration that borrowing the foraging logic of a filter-feeding fish, perturbing it with chaos, and teaching it to calibrate its own punishments can squeeze meaningful savings and emission reductions out of one of power engineering’s hardest scheduling problems.

Subject of Research: An improved manta ray foraging optimization algorithm with chaotic switching factors and dynamic penalty functions for solving the combined economic emission dispatch problem in power systems.

Article Title: Manta ray foraging optimization algorithm with dynamic penalty function and chaos switching factor to solve combined economic emission dispatch problem

Article References: Sun, Y.-F., Wang, J.-S., Zhang, X.-Y., Li, Y.-X., Guan, X.-Y., & Liu, X. (2026). Manta ray foraging optimization algorithm with dynamic penalty function and chaos switching factor to solve combined economic emission dispatch problem. Results in Engineering, 32, Article 113383. https://doi.org/10.1016/j.rineng.2026.113383

Image Credits: AI Generated

DOI: Not provided

Keywords: manta ray foraging optimization, combined economic emission dispatch, metaheuristic algorithms, chaotic maps, dynamic penalty function, power system optimization, emission reduction, fuel cost minimization, CEC-2022 benchmarks, swarm intelligence, constraint handling, Results in Engineering

Buildings are responsible for an enormous share of global electricity demand, and the way their air conditioners, lights, and heating systems are scheduled can make the difference between a grid under strain and one running smoothly. For years, engineers have tried to optimize these schedules with evolutionary algorithms and reinforcement learning, but the problem becomes brutally hard when dozens of buildings share a single electrical network and must be coordinated simultaneously. A new study published in Discover Artificial Intelligence proposes a strikingly different approach: instead of searching step by step for the best schedule, a generative artificial intelligence model learns to paint one directly out of pure noise, guided by a map of how the buildings are physically wired together.

The framework, called DMCBO, short for Diffusion-driven Multi-objective Collaborative Optimization for Building Clusters, was developed by Yalan Zheng of Zhengzhou Technical College and Taixiang Yin of North China University of Water Resources and Electric Power. Their central insight is that the joint scheduling of equipment settings and load distribution across an entire cluster of buildings can be treated as a high-dimensional data sample, much like an image. Diffusion models, the same family of generative techniques behind modern text-to-image systems, work by progressively corrupting data with Gaussian noise and then learning to reverse that corruption. The researchers reformulated multi-objective building optimization as exactly this kind of conditional denoising process: starting from random noise, the model strips away noise step by step until a complete, Pareto-optimal collaborative schedule for every piece of equipment in every building emerges.

Technically, the forward diffusion process is a fixed Markov chain that adds tiny amounts of Gaussian noise to real scheduling data over one hundred steps, with the noise coefficient increasing linearly from 0.0001 to 0.02 until the original data becomes indistinguishable from standard Gaussian noise. A neural network is then trained to predict the noise that was added at each step, given the noisy schedule and a conditional encoding vector describing the current situation. Once trained, the model can generate fresh solutions at inference time by sampling pure noise and running the reverse process, refining it over one hundred denoising steps into a schedule tailored to the prevailing weather, occupancy, and grid conditions. Because this generation is a gradient-based learning process rather than an iterative search, it avoids the local optima traps that plague traditional multi-objective evolutionary algorithms and the reward oscillations that destabilize deep reinforcement learning.

What makes the system genuinely cluster-aware is its use of graph neural networks. The researchers model the building cluster as a graph mapped onto a radial distribution microgrid topology, where nodes represent buildings and edges encode physical constraints such as maximum line thermal capacities and transformer limits. A two-layer graph convolutional network passes messages between neighboring buildings, fusing spatial-topological dependencies and network constraints into a single graph embedding. This embedding is concatenated with an adjustable preference weight vector to form the condition that steers the denoising trajectory. In effect, when the model generates a control strategy for one building, it simultaneously perceives the state of its neighbors and the limits of the shared electrical infrastructure, suppressing systemic coordination errors from 0.18 down to 0.06 in the team’s experiments.

The multi-objective aspect is handled through an elegant scalarization trick. Each objective, such as energy consumption, operating cost, or thermal comfort, is normalized to a dimensionless value between zero and one using Min-Max normalization based on historical operational bounds. The normalized objectives are then blended into a single scalar condition via a weight vector whose components sum to one. During training, these weights are sampled from a uniform Dirichlet distribution, exposing the model to the entire space of possible preferences so it learns the distribution of the full Pareto front. At deployment, a decision maker can slide the weight vector like an adjustment knob, instantly generating schedules that emphasize energy saving, comfort, or a balanced compromise, all within the same trained model.

The denoising backbone itself is a U-Net adapted to process one- and two-dimensional building scheduling tensors. Diffusion time steps are embedded through sinusoidal positional encodings, while the graph-derived condition is injected into every residual block via Adaptive Layer Normalization. A bottleneck layer with multi-head self-attention captures long-range dependencies across the entire daily scheduling horizon, and symmetric skip connections preserve high-resolution information during reconstruction. The authors trained the model for 300 epochs on dual NVIDIA Tesla V100 GPUs, and the loss curves dropped from 0.75 to below 0.03 with training and validation curves closely coinciding, indicating no overfitting. As the reverse diffusion step count decreased from 90 to 10 during analysis, the noise-prediction error distribution became sharply concentrated at zero, demonstrating extremely high denoising precision at the end of generation.

Validation was carried out on the open-access Building Data Genome Project 2 dataset, which supplied real hourly weather records, operational schedules, and annual electricity, cooling, and heating load profiles across diverse climate zones. Because that dataset provides metered energy data rather than physical building models, the team used United States Department of Energy Commercial Reference Buildings as templates and calibrated them in EnergyPlus, a high-fidelity thermodynamic simulation platform, to create a digital twin consistent with the real data. All results were benchmarked against a rule-based control strategy with static thermostat setpoints of 25 degrees Celsius in summer and 22 degrees in winter, and every experiment was repeated ten independent times to confirm statistical significance.

The headline numbers are impressive. In high-demand summer conditions, DMCBO achieved an average energy-saving rate of 28.6 percent and cut peak load by 38.5 percent, shaving the afternoon peak from roughly 560 kilowatts down to about 440 kilowatts and saving approximately 142.80 US dollars per day. Cumulative daily power savings exceeded 1,000 kilowatt-hours, compared with 400 kilowatt-hours for a traditional collaborative strategy and just 120 kilowatt-hours without coordination. In winter, peak shaving and valley filling reached about 39 and 42 percent respectively, far above the 22 and 25 percent of conventional methods. When the solution sets were projected into objective space, DMCBO dominated the baselines: it reached a hypervolume indicator of 0.95 within 12,000 function evaluations, versus 0.85 for NSGA-II and 0.70 for MOPSO, while keeping operating costs between 140 and 160 dollars against more than 210 dollars for deep reinforcement learning and 260 to 300 dollars for a GAN-based generative approach.

Robustness and scalability proved equally compelling. Under injected stochastic disturbances mimicking sudden weather changes and occupancy swings, the framework confined power fluctuations to a narrow band of plus or minus 5 kilowatts, whereas the deep reinforcement learning baseline swung between minus 15 and plus 20 kilowatts. Scaling from 5 to 50 buildings, the global energy-saving rate actually rose from 28.5 to 31.2 percent, because larger clusters unlock more load-complementary opportunities that the graph encoder can safely exploit within encoded grid boundaries. Computation time grew to 9.8 seconds at the 50-building scale, but that remains comfortably within the 15-minute control horizon used by real building management systems, and the hypervolume index slipped only slightly to 0.88. Ablation experiments confirmed that both the graph neural network module and the choice of 100 diffusion steps, which yields a 50-millisecond inference latency, were essential to this balance of speed, accuracy, and stability.

The authors are candid about limitations. The framework has so far been validated only in simulation, and EnergyPlus simplifies sub-minute mechanical actuation latencies that real HVAC hardware would introduce; they propose cascading PID loops and predictive safety margins to absorb those delays in practice. Data quality, generalization to residential buildings and extreme climates, the computational cost of scaling to city-sized networks, and the black-box nature of the generative process all remain open challenges, with explainable AI and transfer learning flagged as future work. Still, the architecture is designed to slot into standard building management systems as a supervisory layer, transmitting setpoints over protocols such as BACnet or Modbus. If the simulation gains survive contact with physical buildings, the idea of generating optimal energy schedules from noise, guided by a graph of the grid itself, could become a powerful new tool in the push toward decarbonized cities.

Subject of Research: Conditional diffusion models with graph neural networks for multi-objective energy optimization in building clusters

Article Title: Diffusion model-driven multi-objective collaborative optimization for building energy management using graph neural networks

Article References: Zheng, Y., & Yin, T. (2026). Diffusion model-driven multi-objective collaborative optimization for building energy management using graph neural networks. Discover Artificial Intelligence, 6(1), Article 1383. https://doi.org/10.1007/s44163-026-02140-z

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02140-z

Keywords: diffusion models, graph neural networks, building energy management, multi-objective optimization, EnergyPlus, smart buildings, Pareto optimization, deep learning, energy efficiency, HVAC control, microgrids, generative AI

When a tunnel catches fire, temperatures inside the concrete lining can soar to 800 or 900 degrees Celsius, and the emergency response almost always involves dousing the structure with water. A new study suggests that this familiar firefighting image—flames followed by a torrent of cooling water—may be doing far more damage to the concrete than engineers have typically accounted for. Researchers investigating basalt fiber-reinforced concrete have found that how a fire-damaged structure is cooled can be as important as how hot it got in the first place, with water quenching consistently inflicting greater strength loss than slow, natural cooling across every temperature they tested.

The research, published in Case Studies in Construction Materials, systematically examined how heating temperature, cooling regime, fiber content, and impact loading combine to determine whether concrete survives a fire with its load-bearing capacity intact. The team, led by Yang Liu and colleagues including Delin Li, Jinyue Dong, Yingda Zhang, Xianliang Zhou, and Qiong Zhou, chose basalt fiber-reinforced concrete, or BFRC, as their test material because of the fiber’s unusual combination of properties. Basalt fibers, drawn from volcanic rock, offer high tensile strength, thermal stability up to melting points between 1350 and 1450 degrees Celsius, and a cost profile far more attractive than premium alternatives such as carbon fiber. This makes them an appealing candidate for tunnel linings, where fire resistance and economy must be balanced.

The experimental design was exhaustive. The researchers prepared a C35-grade concrete mix—the grade commonly recommended for tunnel design—using ordinary Portland cement, medium sand, and continuously graded gravel between 5 and 10 millimeters. Into this base mix they incorporated chopped basalt fibers, 12 millimeters long with a monofilament diameter of 17 micrometers, at volume fractions of 0, 0.1, and 0.2 percent. The fibers were added in small batches during dry mixing to prevent agglomeration, and the fresh concrete was cast into cylindrical specimens measuring 50 millimeters in diameter and 25 millimeters in height, then cured under standard conditions at 20 degrees Celsius and 95 percent relative humidity.

The thermal assault began in a high-temperature electric oven capable of reaching 1200 degrees Celsius. Specimens were heated at a controlled rate of 5 degrees Celsius per minute to target temperatures of 200, 400, 600, and 800 degrees Celsius, then held at temperature for a full hour to ensure the heat penetrated the interior. Half the specimens were then removed and allowed to cool naturally in ambient air, mimicking a structure left to cool on its own after a fire. The other half were plunged immediately into a tank of tap water, replicating the thermal shock of firefighting operations. After cooling, the water-immersed specimens were conditioned in air for seven days to return them to a natural moisture state before testing.

The dynamic mechanical properties were then probed using a split Hopkinson pressure bar, the workhorse instrument for measuring how materials behave under the extreme strain rates produced by impacts and blasts. The apparatus used bars 75 millimeters in diameter, made of high-strength steel with an elastic modulus of 210 gigapascals, with a striker bar propelled by compressed gas striking an incident bar that transmitted stress waves through the specimen. Three impact velocities—3.5, 4.4, and 5.2 meters per second—were tested, achieved by varying the operating gas pressure, with three specimens per condition. A thin pulse shaper was pasted at the impact end of the incident bar to smooth out high-frequency oscillations, and stress equilibrium between the two faces of each specimen was verified from strain-gauge signals, confirming the reliability of the data. Fragments from each impact were sieved through meshes ranging from 4.75 millimeters down to 75 micrometers so that a fractal dimension could be calculated, providing a quantitative measure of how thoroughly each specimen shattered.

The visual evidence of thermal damage followed a familiar but telling progression. At 200 degrees Celsius, the concrete surfaces turned bluish-gray, with no cracking. By 400 degrees, the color shifted to yellowish-gray, still structurally sound. At 600 degrees, fine cracks appeared along with slight surface peeling, and by 800 degrees the specimens showed severe cracking, spalling, and edge breakage. Water-cooled specimens generally exhibited more pronounced surface damage than their naturally cooled counterparts, and at 800 degrees the concave shape at the start of the stress-strain curve—evidence of pre-existing microcracks being closed under compression—was especially marked in the water-quenched samples.

The strength data told a stark quantitative story. For naturally cooled specimens containing 0.2 percent basalt fiber, the dynamic peak stress at an impact velocity of 3.5 meters per second fell from 51.1 megapascals at room temperature to 18.6 megapascals at 800 degrees—a reduction of 63.6 percent. Water cooling made matters worse across the board: at the same impact velocity, the reductions reached 23.9 percent at just 200 degrees and climbed to 64.9 percent at 800 degrees. Averaged across the temperature range, each 100-degree increment cost naturally cooled specimens roughly 6.7 to 8.0 percent of their strength, while water-cooled specimens lost about 7.5 to 8.1 percent per 100 degrees. The gap between the two cooling regimes widened at higher impact velocities: at 5.2 meters per second and 800 degrees, the water-cooled specimens suffered 6.4 percent greater strength loss than the naturally cooled ones, whereas at the lowest velocity the difference was a mere 1.3 percent. The explanation lies in competing timescales—at slow loading rates, cracks have time to propagate and dissipate energy, blunting the effect of water-induced damage, but at high rates the loading outpaces the material’s energy dissipation, and the microcracks created by thermal shock propagate catastrophically.

Against this backdrop of degradation, the basalt fibers demonstrated genuine protective power. At every temperature from 200 to 800 degrees, specimens with 0.2 percent fiber content outperformed both plain concrete and the 0.1 percent mix. At 400 degrees and an impact velocity of 3.5 meters per second, the naturally cooled fiber-reinforced specimens reached peak stresses of 40.1 megapascals, some 16.9 percent above plain concrete, while at 200 degrees under water cooling the 0.2 percent mix achieved 38.8 megapascals—a remarkable 32.4 percent improvement over unreinforced concrete. The mechanism, the authors explain, is architectural: randomly distributed fibers form a three-dimensional network that binds tightly with the cementitious matrix, reduces internal pores and cracks, and bridges microcracks before they can coalesce into fracture surfaces. But this protection has a ceiling. Above roughly 600 degrees, the fibers begin to degrade and lose their reinforcing capability, and the fiber-matrix interface—already weakened by thermal mismatch between fiber and paste—becomes a liability during water quenching.

Fractal analysis of the impact debris added another layer of insight. The fractal dimension of the fragments rose steadily with temperature, from around 1.82 at 200 degrees to above 2.3 at 800 degrees under the highest loading rate, indicating progressively finer and more complex fragmentation. Water-cooled specimens consistently shattered into finer pieces than naturally cooled ones, particularly at 600 and 800 degrees, confirming that the steep thermal gradients during quenching seed dense populations of microcracks that later explode into fracture networks under impact.

Microstructural investigation grounded these observations in chemistry and physics. X-ray diffraction of specimens heated to 800 degrees showed the disappearance of calcium hydroxide peaks and weakened calcium carbonate signals, evidence that these phases had thermally decomposed—reactions that release vapor, increase porosity, and weaken the matrix. Scanning electron microscopy revealed the deterioration in vivid detail: an unheated specimen showed a dense microstructure with abundant calcium-silicate-hydrate gel and a compact interfacial transition zone around each aggregate, while specimens heated to 800 degrees contained no detectable C-S-H gel at all, their structure riddled with pores and cracks and hosting instead a dicalcium silicate-like phase. Comparing cooling regimes at 400 degrees, the naturally cooled sample remained relatively dense and rich in hydration products, whereas its water-quenched twin displayed obvious cracks at the interfacial transition zone and a loosened surrounding matrix—the fingerprint of thermal shock stress racing through the cooling material.

The practical implications reach well beyond the laboratory. Tunnel linings that survive a fire are routinely assessed and, if necessary, repaired, but this study shows that the assessment itself must account for the cooling history. A lining quenched by firefighting water may be significantly weaker than its peak temperature alone would suggest, particularly if it subsequently faces dynamic loads from traffic vibration, rockfall impacts, or secondary events. Conversely, the results give engineers a positive tool: specifying basalt fiber reinforcement at around 0.2 percent by volume can meaningfully bolster the impact resistance of fire-exposed concrete below 600 degrees, at a modest material cost. The authors frame their work as a theoretical foundation for post-fire safety assessment of tunnel linings and for the wider engineering adoption of basalt fiber-reinforced concrete—a reminder that in structural fire engineering, what happens in the minutes after the flames matter almost as much as the fire itself.

Scientists have unveiled a computational design for a terahertz metasurface biosensor that combines five distinct materials—graphene, MXene, tungsten disulfide, gold, and copper—into a single multilayer architecture capable of detecting refractive-index changes associated with chikungunya virus infection. The study, published in Results in Optics by G. Arunachalam, A.R. Kalaiarasi, Giri.G. Hallur, and V. Parthasarathy, reports a numerically predicted sensitivity of 907 GHz per refractive-index unit, among the highest values reported for hybrid-material terahertz biosensors, and couples the electromagnetic design to an XGBoost machine-learning surrogate model that predicts sensor behavior with accuracies exceeding 99.8 percent.

Chikungunya virus is an enveloped, positive-sense single-stranded RNA alphavirus transmitted primarily by Aedes aegypti and Aedes albopictus mosquitoes. While mortality remains low, generally below 0.1 percent, the broader public health burden arises from severe polyarthralgia that can persist for months or even years in a substantial fraction of patients across tropical and subtropical regions. With no approved antiviral therapy or licensed vaccine currently available, diagnostic accuracy sits at the center of clinical management and transmission control. Yet existing diagnostic tools face significant practical constraints, particularly in point-of-care and field settings.

Serological methods such as enzyme-linked immunosorbent assay suffer from cross-reactivity with related alphaviruses, including Ross River and O’nyong-nyong viruses, reducing diagnostic specificity. Real-time reverse transcription polymerase chain reaction offers high sensitivity during the acute phase but depends on laboratory infrastructure, trained personnel, and controlled sample handling. Virus isolation in cell culture provides confirmatory results but requires biosafety level-3 facilities and extended processing time. These limitations have motivated the search for rapid, label-free sensing approaches capable of operating at clinically relevant concentrations in decentralized environments.

The new work turns to the terahertz band, spanning roughly 0.1 to 10 THz, which lies between microwave and infrared regions of the electromagnetic spectrum. Terahertz radiation is non-ionising, with photon energies in the millielectronvolt range, allowing interaction with biological samples without causing ionising damage. Crucially, terahertz waves couple to low-frequency vibrational and rotational modes of biomolecules such as proteins, nucleic acids, and polysaccharides, producing measurable spectral features that support label-free detection. Because biological media including blood plasma and viral suspensions exhibit refractive-index variations in this domain, differences on the order of 0.01 to 0.02 refractive-index units can shift resonance frequency and field distribution when coupled to resonant sensing structures.

The proposed sensor is built around a periodically repeated square unit cell containing two identical square resonators, 2000 nanometers on a side, coated with tungsten disulfide—a two-dimensional transition metal dichalcogenide with strong light–matter interaction, high carrier mobility, and excellent dielectric confinement. This layer localizes the electric field in the sensing region and increases the interaction volume between the resonant field and the surrounding analyte. A pair of concentric circular rings, 3200 and 3800 nanometers in diameter and functionalized with MXene, surrounds the resonators, enhancing electromagnetic coupling, promoting efficient charge transport, and strengthening plasmonic interactions between resonator elements. An outer gold ring, 6000 nanometers in diameter, serves as the main plasmonic resonator supporting strong localized surface plasmon resonances with low optical loss. The entire multi-resonant assembly is embedded within a monolayer graphene sheet measuring 14,000 by 14,000 nanometers, which acts as a broadband absorber and a high-mobility charge transport medium whose electrically tunable chemical potential allows dynamic, active control of the resonance characteristics. Copper elements woven into the architecture provide supplementary conductive pathways that reinforce current distribution and coupling among the hybrid resonators. The structure rests on a silicon dioxide substrate, chosen for its low dielectric loss and mechanical robustness, which preserves the resonance quality factor.

The authors stress that the multilayer design is compatible with established microfabrication techniques. Fabrication would begin with a cleaned silicon dioxide substrate, followed by polymer-assisted transfer of monolayer graphene and thermal annealing. Gold and copper resonators can be deposited through electron-beam evaporation or magnetron sputtering with lift-off patterning, MXene rings through solution-based deposition, and the tungsten disulfide layer through chemical vapor deposition or thin-film transfer. With a unit-cell dimension of approximately 14 micrometers, the required feature sizes fall comfortably within conventional photolithography capabilities, and because fabrication imperfections remain far smaller than the operating terahertz wavelength, moderate tolerances are expected to have only limited influence on the electromagnetic response. For biosensing, the graphene surface could subsequently be functionalized with chikungunya-specific antibodies or aptamers using EDC/NHS or pyrene-based linker chemistries.

The electromagnetic modeling, performed in COMSOL Multiphysics, reveals a pronounced resonance at 0.355 THz, where electric-field simulations show the strongest and most spatially extensive enhancement. Varying the graphene chemical potential from 0.1 to 0.9 electronvolts produced dramatic modulation of the transmission contrast, which increased roughly 36-fold at the highest bias, from less than one percentage point to more than 27 percentage points, while peak transmission remained essentially unchanged above 98.6 percent. This asymmetric response—stable maxima paired with strongly modulated minima—arises because increasing chemical potential enhances graphene’s intraband conductivity, deepening resonant absorption through stronger light–matter interaction. The sensor also demonstrated remarkable angular robustness, maintaining peak transmittance above 97.8 percent across incident angles from 0 to 80 degrees, an important tolerance for realistic deployment where normal incidence cannot always be guaranteed.

Geometric optimization identified an outer circular resonator radius of 2500 nanometers and a square resonator side length of 2500 nanometers as the dimensions that maximize resonance strength, driving minimum transmittance down to approximately 65 to 67 percent and signifying optimal electromagnetic confinement within the active sensing region. When evaluated across refractive indices from 1.33 to 1.39, a range encompassing reported values for healthy plasma (approximately 1.35 RIU), healthy platelets (1.39 RIU), chikungunya-infected plasma (1.33 RIU), and infected platelets (1.38 RIU), the optimized configuration achieved a maximum sensitivity of 907 GHz/RIU, a figure of merit of 18.519 RIU⁻¹, a minimum detection limit of 0.061 RIU, a sensor resolution of 55.178, and a refractive-index resolution of 0.001 RIU. In comparative benchmarks against recently reported graphene- and hybrid-material-based terahertz biosensors—including graphene/copper/aluminum, graphene/silicon dioxide, MXene–graphene–black phosphorus, and graphene-integrated copper–silver designs—the proposed architecture’s sensitivity exceeded most published values, reflecting the synergistic field confinement produced by the five-material stack.

The machine-learning component addresses one of the central bottlenecks in metasurface development: the computational cost of repeated full-wave finite-element simulations during structural optimization. The team trained an XGBoost regressor, an ensemble method based on gradient-boosted decision trees, on simulation data covering incidence-angle variation and circular-ring dimension variation, using stratified sampling with 80 percent of data for training and 20 percent held out for independent testing, alongside five-fold cross-validation and grid-search hyperparameter optimization. The model captured more than 99.7 percent of response variance on unseen data, with mean absolute percentage errors below 0.162 percent and prediction accuracies from 99.838 to 99.959 percent. Acting as a computational surrogate, the trained model enables rapid interpolation across the design space without sacrificing the fidelity of the underlying electromagnetic model.

The authors are careful to frame the work as a computational proof-of-concept rather than an experimentally validated diagnostic. The sensor detects changes in the local dielectric environment surrounding the resonators, not viral particles or nucleic acids directly, and refractive-index sensing alone cannot intrinsically distinguish chikungunya from other viruses with similar dielectric properties. Analytical specificity in practice will depend on surface biofunctionalization with chikungunya-specific recognition molecules immobilized on the graphene or gold sensing surface. Future work will focus on device fabrication, biofunctionalization, calibration with reference refractive-index standards, experimental validation with clinically relevant plasma and platelet samples, and systematic evaluation of selectivity against related arboviruses—steps that would transform this numerically validated platform into a practical tool for rapid, label-free infectious disease diagnostics in the field.

The ocean has quietly absorbed billions of tons of humanity’s carbon emissions over the past two centuries, but scientists and companies racing to supercharge that natural service have long been hampered by a stubborn and costly engineering problem. Now researchers at Stony Brook University, working with the Research Foundation for the State University of New York, have unveiled a method for electrochemical ocean alkalinity enhancement that they say can dramatically lower the cost of ocean-based carbon dioxide removal by eliminating one of the most expensive steps in the entire process: the pretreatment of seawater before it enters an electrochemical reactor. The innovation, now available for licensing through SUNY TechConnect, promises high-quality carbon offsets at a substantially reduced cost while simultaneously helping to counteract the acidification that threatens marine ecosystems worldwide.

At the heart of the challenge is a delicate chemical balancing act. As atmospheric carbon dioxide dissolves into seawater, it forms carbonic acid, which lowers the ocean’s pH and erodes the carbonate chemistry that corals, shellfish and other calcifying organisms depend upon. Ocean carbon dioxide capture exploits a simple principle: if the ocean becomes more alkaline, it can absorb more CO2 from the air and lock it away safely as bicarbonate, one of the most stable and abundant forms of inorganic carbon on Earth. This process, known as ocean alkalinity enhancement, effectively turns the surface ocean into a vast, distributed carbon sink. But adding alkalinity to seawater is not trivial, and doing it in a way that is energy-efficient, scalable and safe has become one of the central engineering puzzles of the emerging carbon removal industry.

One of the most promising approaches relies on electrochemistry, specifically a technique called bipolar membrane electrodialysis. In this system, ion-selective membranes are stacked between two end electrodes, and the arrangement allows operators to split ordinary saltwater into its constituent acid and base. When brine containing sodium chloride flows through the stack, the process generates hydrochloric acid on one side and sodium hydroxide, a strong base, on the other. The elegance of the scheme lies in its product handling: if the hydrochloric acid is kept on land, where it can be stored, neutralized or put to industrial use, and only the sodium hydroxide along with the treated seawater is returned to the ocean, the receiving waters gain a net infusion of alkalinity. That added alkalinity shifts the carbonate equilibrium, allowing the ocean to draw down carbon dioxide from the atmosphere and convert it into dissolved bicarbonate that remains stable for millennia.

This electrochemical pathway has attracted serious commercial interest precisely because it produces what carbon markets call negative emissions, meaning carbon dioxide is durably removed from the atmosphere rather than merely avoided. Companies pursuing net-zero targets increasingly rely on such carbon removal credits to offset their residual emissions, and ocean alkalinity enhancement is widely regarded as one of the largest-capacity avenues available, given the sheer scale of the world’s oceans. Yet the technology has been held back by a deceptively mundane problem: what flows into the reactor matters just as much as what flows out.

Seawater, and many other brine streams that might feed these systems, contains divalent cations, most notably calcium and magnesium ions, alongside the sodium and chloride that the process is designed to split. When these divalent ions encounter the high-pH conditions created at the base-producing side of a bipolar membrane stack, they precipitate as solid calcium and magnesium compounds that coat the membranes in a crust known as scale. The consequences are cascading. Scaled membranes lose their ion-selective efficiency, forcing the system to push harder and consume more electricity to achieve the same output. Over time, the deposits shorten membrane lifetime, increasing maintenance frequency and replacement costs. The conventional answer has been water softening pretreatment, in which the incoming seawater is stripped of calcium and magnesium before it ever reaches the electrodialysis unit. While effective, such pretreatment adds significant capital and operating expense, undermining the economic case for the entire carbon removal operation and eroding the value proposition of the resulting carbon credits.

The Stony Brook researchers behind the new method have essentially designed around this bottleneck. Their technology enables electrochemical enhancement of ocean alkalinity without any pretreatment of the incoming seawater at all. By rethinking how the electrochemical system handles brine streams laden with divalent cations, the researchers have created a process that tolerates raw seawater directly, sidestepping the water softening infrastructure that previous designs demanded. According to the announcement from the Research Foundation for SUNY, the result is a system that produces negative emission carbon offsets of very high quality at a substantially reduced cost, simply because the most expensive upstream step has been engineered out of existence. The advantages claimed for the approach are straightforward and commercially significant: it is cheaper, more energy efficient, and requires no expensive water softening pretreatment.

The implications ripple across both the carbon markets and the broader climate technology landscape. Carbon offset buyers, including corporations striving to meet net-zero commitments, place a premium on removal credits that are durable, verifiable and cost-competitive. Ocean alkalinity enhancement already offers exceptional permanence, since bicarbonate dissolved in seawater represents one of the most long-lived forms of carbon storage known. By cutting costs at the front end, the Stony Brook method could help push the price of these high-quality ocean-based offsets toward levels that make them viable at gigatonne scale, a threshold that analysts consider essential if carbon removal is to play a meaningful role in stabilizing the global climate. Cheaper alkalinity generation also strengthens the second major application of the technology: mitigating ocean acidification directly, without necessarily framing it as a carbon market product at all.

Ocean acidification itself is a mounting crisis. Since the industrial revolution, the average surface ocean pH has dropped measurably as the sea has absorbed roughly a quarter to a third of anthropogenic CO2 emissions. The consequences have been documented across coral reefs, oyster hatcheries, pteropods and other calcifying organisms whose shells and skeletons become harder to build in increasingly acidic waters. A technology that adds alkalinity to coastal waters and open ocean regions simultaneously addresses this chemical stress and harvests the carbon removal benefit, a dual function that distinguishes ocean alkalinity enhancement from many other carbon removal approaches. In regions where fisheries, aquaculture and reef ecosystems are economically vital, localized deployment of alkalinity-enhancing electrochemical systems could offer both environmental resilience and revenue through carbon credit sales.

The scientific pedigree of the work reflects the growing maturity of the carbon removal field. Stony Brook University, a flagship research institution within the SUNY system, has become an active hub for climate and energy innovation, and the new method is protected under intellectual property identified in the licensing announcement. The Research Foundation for the State University of New York, the nation’s largest research foundation supporting the nation’s largest public university system, is actively seeking development partners, commercial partners and licensees to translate the laboratory advance into deployed systems. The foundation describes a broad portfolio of SUNY-led research spanning artificial intelligence for the public good, quantum technologies, next-generation semiconductors, biotechnology and medicine, and energy and climate solutions, with system-wide research expenditures approaching 1.5 billion dollars in fiscal year 2025.

For the carbon removal industry, the timing of the announcement is notable. As voluntary and compliance carbon markets mature, buyers are demanding higher fidelity in the offsets they purchase, favoring durable removal over avoidance-based credits. Electrochemical ocean alkalinity enhancement, with its well-understood chemistry and measurable inputs and outputs, is well positioned to meet rigorous monitoring, reporting and verification standards. The obstacle has always been cost, and the pretreatment bottleneck has been a substantial contributor. If the Stony Brook design performs as described at industrial scale, it could remove one of the largest cost wedges standing between laboratory demonstration and commercial deployment, accelerating a technology class that many climate analysts view as indispensable to achieving deep decarbonization.

The broader lesson of the advance may be as important as the technology itself. In the race to scale carbon removal, progress often comes not from exotic new chemistry but from eliminating the hidden costs and fragilities that make promising concepts uneconomic in practice. Water softening pretreatment is exactly the kind of unglamorous engineering detail that determines whether a climate technology remains a paper exercise or becomes infrastructure. By designing a bipolar membrane electrodialysis system that thrives on untreated seawater, the Stony Brook researchers have addressed one of those decisive details. The ocean, meanwhile, stands ready. Every unit of alkalinity added is an invitation for the sea to take up more carbon dioxide and, in doing so, to heal a little of the acidification that a warming world has inflicted upon it. What remains now is the work of scaling, partnering and commercializing, and the SUNY licensing announcement signals that the researchers and their institution intend to see this chemistry leave the laboratory and reach the sea.

The Denisovans, the enigmatic archaic human relatives first identified from a finger bone and a handful of teeth in a Siberian cave, have long been known almost exclusively through their DNA. Now, a landmark study published in Nature has pulled back the curtain on how these ancient hominins actually lived — what they ate, how they hunted, what tools they made, and the environments they braved — through an extraordinary assemblage of fossils and artefacts from Bianfu Cave in Yunnan Province, southwestern China. The findings paint the most detailed portrait to date of Denisovan biology, behaviour and ecology in eastern Asia, and suggest that these vanished people left a deep genetic and cultural imprint on later populations across Southeast Asia and Oceania.

The research team, led by Qingfeng Ruan, Hua Li and Song Xing and their colleagues, conducted a comprehensive analysis of hominin fossils and the associated archaeological remains recovered from Bianfu Cave. The hominin fossils themselves date to roughly 167,000 to 134,000 years ago, placing them firmly in the Middle Pleistocene. But the cultural sequence preserved in the cave’s sediments is far longer, stretching from approximately 190,000 years ago to about 70,000 years ago. That span — some 120,000 years of continuous or near-continuous occupation — makes Bianfu Cave one of the longest cultural records that can most parsimoniously be attributed to Denisovans anywhere in the world.

The Denisovan identity of the fossils was established through two independent and technically demanding lines of evidence. The first involved analysis of the enamel–dentine junction, the internal boundary between the enamel cap and the underlying dentine of the teeth. This three-dimensional morphological interface acts something like a fingerprint of evolutionary relationships, revealing diagnostic traits of the tooth’s internal architecture that are invisible from the outside but characteristic of particular hominin lineages. The second line of evidence came from palaeoproteomics, the recovery and identification of ancient proteins preserved within fossilized tissues. Because proteins are far more abundant and durable than DNA, they can survive in warm, humid environments like those of southern China, where genetic material degrades rapidly. Sequencing these ancient proteins allowed the researchers to place the Bianfu hominins on the Denisovan branch of the human family tree with confidence.

The fossil collection itself is remarkable. It includes four teeth that substantially expand the known range of dental variation within the Denisovan lineage — a critical contribution, since Denisovans were previously represented by a very small number of dental specimens, making it difficult to gauge how variable their teeth could be. The new fossils also include two cranial fragments and a radius, a forearm bone, providing rare glimpses of Denisovan skull and postcranial anatomy that were previously almost entirely unknown. Together, these remains transform Denisovans from a genome and a scatter of isolated teeth into a population with recognizable skeletal biology.

Beyond the fossils, the archaeological record from Bianfu Cave offers something equally precious: a window into Denisovan daily life. The researchers reconstructed the ancient environment using faunal remains and pollen preserved in the cave deposits. These records indicate that across Marine Isotope Stages 6 through 4 — a period encompassing major glacial–interglacial climatic swings between roughly 190,000 and 57,000 years ago — the landscape around the cave was dominated by coniferous forest or a mosaic of forest and steppe. This was a cool, variable environment that Denisovans exploited successfully across tens of thousands of years, demonstrating a resilience to climatic change that adds to growing evidence of their adaptability, from the high altitudes of the Tibetan Plateau to the subtropical latitudes of mainland Southeast Asia.

The subsistence evidence is particularly striking. Analysis of the animal bones from the cave shows that its inhabitants practised specialized hunting focused on medium- to large-bodied prey. This was not opportunistic scavenging but a sustained, targeted hunting strategy — one that would have required knowledge of animal behaviour, coordinated effort and effective weaponry. Specialized big-game hunting of this kind has often been associated with modern humans and late Neanderthals in western Eurasia, so finding it in a Denisovan context more than 150,000 years ago dramatically narrows the perceived behavioural gap between these archaic lineages and our own species.

Yet the technological strategy documented at Bianfu Cave is, at first glance, surprisingly simple. The stone tool assemblage is characterized by expedient core reduction and tool production — that is, the inhabitants struck flakes from cores as needed, used them, and discarded them, rather than investing in carefully shaped, curated tools that were carried, maintained and resharpened over time. Alongside this, the Denisovans of Bianfu Cave made pervasive use of unmodified bones, employing skeletal elements of their prey directly as tools without altering them beforehand.

Taken together, the researchers interpret this pattern as an adaptive system that prioritized the exploitation of what they call object affordance — the inherent functional possibilities already present in a raw material or object — over intensive tool manufacture. A suitably shaped bone fragment could serve as a hammer, a digging implement or a scraping tool with little or no modification; a sharp flake struck from a convenient core could cut and butcher without further refinement. In the rich, resource-diverse environments of subtropical southwestern China, where raw materials and animal resources were plentiful, such a flexible, low-investment strategy may have been not a sign of simplicity but a highly efficient way of life. It suggests that Denisovans measured success not by the sophistication of their toolkit but by how skilfully they read and exploited the opportunities their landscape provided.

This behavioural portrait gains added significance when set against the wider geography of Denisovan presence. Over the past decade and a half, Denisovans have been documented across a staggering range: from the original genomic discoveries in the Altai Mountains of Siberia, first reported in 2010, to the mandible from the Tibetan Plateau described in 2019, Denisovan DNA recovered from sediments at Baishiya Karst Cave in 2020, a molar from the Annamite Chain of northern Laos announced in 2022, a male Denisovan mandible from Pleistocene Taiwan reported in 2025, and proteomic identification of the 146,000-year-old Harbin cranium, also in 2025. Bianfu Cave now anchors this story firmly in southwestern China, a region long suspected to have been a Denisovan heartland and a critical corridor for hominin movements between mainland Asia and Southeast Asia.

The study’s authors argue that the region’s Denisovans point to a substantial genetic and cultural legacy in later human populations. Modern populations in Southeast Asia and Oceania carry the highest known proportions of Denisovan ancestry anywhere in the world — a legacy of interbreeding that occurred as modern humans moved through and beyond island Southeast Asia. The new evidence from Bianfu Cave suggests that the ways of life practised by Denisovans in this region — their hunting strategies, their flexible technological approaches, their hard-won knowledge of tropical and subtropical environments — may have been part of the inheritance, alongside their genes, that helped later human populations thrive as they spread across the diverse and challenging landscapes of Asia and the Pacific.

For palaeoanthropologists, the significance of the Bianfu Cave discoveries is difficult to overstate. Denisovans have been the least anatomically documented of all known hominin groups — a lineage defined first by DNA and only slowly fleshed out with fragmentary fossils. The new teeth, cranial fragments and radius from Yunnan, combined with an unmatched 120,000-year archaeological sequence, mean that researchers can finally study Denisovans not just as a genome or a set of molecular signatures but as a living, adapting population: hunters of large game, makers and users of expedient tools, and inhabitants of conifer forests that swung between glacial cold and interglacial warmth. As analysis of the Bianfu material continues, it promises to reshape our understanding of how archaic humans lived in eastern Asia and how deeply their story is woven into our own.

Optical frequency combs have transformed the way scientists measure light and time, earning the Nobel Prize in Physics in 2005 and underpinning technologies ranging from atomic clocks to exoplanet detection. Yet the miniature on-chip versions of these combs, known as soliton microcombs, have long suffered from a frustrating paradox: the most stable and useful operating regimes have also been the most fragile and difficult to sustain. A newly published study in Light: Science & Applications reports a significant advance toward resolving this tension. Researchers led by J. Tang, J. Yang and E. Yan describe a multimodal locking-enabled soliton microcomb that combines robust, self-starting operation with an exceptionally low repetition rate on the scale of a day of stable measurement time, opening a path toward practical high-precision metrology systems that can run unattended for extended periods.

At the heart of the advance is a technique the authors call multimodal locking, a scheme that stabilizes not just a single resonance of the optical microresonator but multiple modes simultaneously, locking the comb dynamics to a regime in which dissipative Kerr solitons can form and persist without delicate manual tuning. In conventional microcomb systems, soliton formation typically requires a precise and rapid traversal of a “chaotic” modulation instability regime, a step that is notoriously sensitive to thermal transients in the resonator chip. Laser frequency must be swept with exquisite control while the soliton’s own heat dissipation shifts the resonator’s resonances, creating a feedback loop that can destroy the soliton state within microseconds. The multimodal locking approach circumvents this fragility by engaging several mode families of the resonator at once, so that the interplay among them pins the comb to a stable soliton state even as environmental and thermal conditions fluctuate.

The significance of achieving a low repetition rate in the same device cannot be overstated. A frequency comb’s repetition rate is the spacing between its individual comb teeth, and for many applications, coarser spacing is a decisive advantage. In laser ranging and distance metrology, for example, the ambiguity range of an interferometric measurement is set by the inverse of the comb’s free spectral range. Microcombs, generated in whispering-gallery-mode or ring resonators only a few millimeters across, usually exhibit repetition rates of tens to hundreds of gigahertz, which severely limits the unambiguous measurement range available to a comb-tooth-resolved distance measurement. By engineering a low-repetition-rate soliton state, the research team extends this ambiguity range dramatically, making the microcomb directly useful for absolute distance measurement, multi-wavelength interferometry and coherent communications, where each comb line must be spectrally resolvable and addressable.

Achieving both low repetition rate and robust soliton generation in a single microresonator is a formidable engineering challenge. Low repetition rates demand large resonator diameters, which in turn produce dense spectra of resonances and increased susceptibility to mode crossings with higher-order transverse and polarization mode families. Such crossings historically destabilize soliton formation, causing comb states to collapse or preventing single-soliton operation altogether. The multimodal locking strategy turns this liability into an asset: rather than engineering mode crossings away, the researchers harness the coupling among mode families to lock the comb dynamics into a favorable basin of attraction. The result is a soliton microcomb whose low repetition rate is preserved over day-scale timeframes, a duration that places the device among the most operationally stable microcomb demonstrations reported to date.

Day-scale stability matters because it transforms the microcomb from a laboratory demonstration into an instrument. Many high-precision experiments, from astronomical spectrograph calibration to the accumulation of statistical confidence in atomic spectroscopy, require comb sources that remain locked and stable across many hours or even days of continuous operation. Conventional fiber-laser-based frequency combs achieve this, but at the cost of table-top size, high power consumption and substantial maintenance overhead. Microcombs promised to shrink the technology onto a photonic chip, yet until now their operational fragility has confined most demonstrations to short observation windows. The robust low-repetition-rate soliton microcomb described in the new work demonstrates that chip-scale frequency combs can, with appropriate locking architecture, sustain the kind of long-duration, hands-off operation that real metrology campaigns demand.

The physics underlying soliton microcombs rests on the Kerr nonlinearity of the resonator material, typically silicon nitride, magnesium fluoride or fused silica. When continuous-wave pump light resonantly builds up inside the ring, the Kerr effect produces a four-wave-mixing process that seeds sidebands spaced by the resonator’s free spectral range. Under the right conditions of pump detuning and intracavity power, these sidebands evolve into a coherent train of ultrashort optical pulses, dissipative Kerr solitons, that circulate around the resonator and emit a broadband comb spectrum with tooth spacing equal to the repetition rate. The comb’s output frequencies are determined by two radio-frequency quantities, the pump laser frequency and the repetition rate, which is why the combs can serve as rulers for measuring optical frequencies against a reference. Any drift in either quantity propagates directly into measurement error, and the multimodal locking mechanism suppresses precisely this drift by anchoring the comb’s underlying mode structure to the resonator’s intrinsic stability.

Applications for such a device span an unusually broad swath of science and engineering. In high-precision laser ranging, low-repetition-rate combs with tooth spacings in the low-gigahertz or even megahertz regime allow interferometric distance measurements over ranges of meters to kilometers without the need for auxiliary synthetic-wavelength techniques. In astronomical spectroscopy, comb calibration sources with well-controlled line spacing enable the detection of Earth-like exoplanets through radial velocity shifts measured in centimeters per second. In telecommunications, low-repetition-rate combs align naturally with standard channel grids used in wavelength-division multiplexed fiber networks, making them attractive as multi-carrier sources. In spectroscopy, dual-comb techniques benefit from repetition rates that are low enough to permit resolution of closely spaced molecular absorption features. A microcomb that is simultaneously robust, low-repetition-rate and day-scale stable therefore functions as a versatile enabling component across these domains rather than a single-purpose device.

The demonstration also speaks to a broader trend in photonics: the migration from delicate, expert-operated laboratory setups to self-contained, self-stabilizing systems. The self-starting character implied by multimodal locking is particularly important for this transition. A comb that requires a skilled operator to initiate soliton formation, sweeping the laser with practiced timing past the chaotic regime, cannot be deployed in an observatory dome, on a satellite platform or inside an industrial instrument. A comb that engages its soliton state reliably on demand and then holds it through thermal cycling, laser drift and mechanical perturbation removes the last practical barrier to integration. The authors’ demonstration of day-scale continuous operation is, in effect, a stress test passed by a technology that researchers have been attempting to harden for more than a decade.

The implications extend to metrology standards and timing as well. Optical atomic clocks, which now achieve fractional frequency uncertainties below one part in 10^18, rely on frequency combs to divide down their optical oscillations to countable microwave rates. Miniaturizing the comb stage of such clocks is a prerequisite for portable optical timekeeping, which would benefit navigation in GPS-denied environments, geodesy that monitors sea level and crustal motion with millimeter precision, and fundamental physics tests that track the constancy of natural constants over months and years. A robust chip-scale soliton microcomb with day-scale stability contributes a key subsystem to this vision, and the multimodal locking principle may generalize to other resonator platforms, materials and spectral regions, including the mid-infrared where molecular fingerprinting awaits compact comb sources.

Looking forward, the study suggests several directions that the photonics community is likely to pursue. Combining multimodal locking with on-chip pump lasers and integrated electronics would complete the transformation toward fully self-contained comb modules with no free-space optics. Extending the approach to lower repetition rates still, into the megahertz regime where fiber-laser combs operate, would bring microcombs into direct competition with their bulkier predecessors for the most demanding ranging and spectroscopy tasks. Improvements in resonator fabrication, reducing mode-volume disorder and engineering dispersion with greater precision, will further expand the parameter space in which robust soliton states can be locked. The present work demonstrates that the fragility once considered intrinsic to soliton microcombs is not a fundamental limit but an engineering problem, and one that can be solved by embracing, rather than avoiding, the multimodal structure of real optical resonators.

The research, published in Light: Science & Applications, marks a milestone in the maturation of microcomb technology. By uniting three properties that had previously been achieved only separately, self-starting robustness, low repetition rate and day-scale operational stability, the work delivers a soliton microcomb genuinely suited to high-precision metrology. As the demand for compact frequency references accelerates across navigation, astronomy, communications and fundamental science, the multimodal locking strategy reported by Tang, Yang, Yan and colleagues offers a clear and technically grounded route from chip-scale optical chaos to dependable, precision measurement light.

Wildfire scientists have unveiled a new rapid risk-assessment tool that shows substantial wildfire danger to homes and buildings in three major Arctic communities, revealing that between 14% and 26% of structures in Anchorage, Fairbanks, and Whitehorse face high or very high risk from approaching flames. The study, led by Jennifer I. Schmidt of the University of Alaska Anchorage together with Robert H. Ziel, Monika P. Calef, Anna Varvak, and Julio C. Postigo, was published in the International Journal of Disaster Risk Science. Rather than relying on complex fire-behavior simulations that demand specialist analysts and expensive software, the approach combines datasets that most local governments already possess: building footprints, zoning maps, and readily available vegetation layers. The result is an updatable, affordable portrait of which buildings face the greatest threat and where mitigation dollars can do the most good.

The urgency behind the work is hard to overstate. Wildfire costs and damages in the United States alone are now estimated at between USD 394 billion and 893 billion annually, and the 2019 to 2020 Australian fire disasters cost nearly USD 100 billion. The Arctic, often imagined as too cold and wet to burn, is not immune: since 1980, wildfires have cost Alaska between USD 2 billion and 5 billion, with projections suggesting costs could rise 68% by the end of the century. Canadian suppression expenses are expected to climb by 60 to 110% relative to the 1980 to 2009 baseline. Meanwhile, structure loss from wildfire has doubled over the past two decades in the western United States, and western Canada has suffered major structure-loss events in four of the last eight fire seasons. Nearly half the global population now lives within the wildland-urban interface, the zone where people and forests meet or intermingle, making tools that translate fire science into actionable local maps increasingly vital.

The researchers’ central premise is conceptually simple but technically consequential: for a structure to be at high risk, a flammable hazard must exist nearby, within roughly 500 meters, and the surrounding landscape must exhibit high wildfire exposure. The method begins with a flammability hazard layer at 30-meter resolution, built from a modified version of NASA’s ABoVE Landcover dataset in which generic evergreen classes were refined to reflect locally flammable species such as black spruce, white spruce, lodgepole pine, and subalpine fir, each assigned hazard ratings from 0 to 100. Critically, the team treated structures themselves as fuel. Each building contributes three fuel units to the surrounding flammability score, a value chosen after systematic testing of one through ten fuel units; three proved optimal, nudging densely built environments up a hazard category without causing wholesale reclassification. Structure density was computed at 90-meter resolution, multiplied by three, and added to the vegetation-based hazard to produce an enhanced flammability hazard layer.

Wildfire exposure, the second pillar of the assessment, captures the potential for hazard to travel. It is calculated as the average flammability hazard within a 100-meter or 500-meter radius, representing the likelihood that fire can reach and burn a given location even if the local fuels themselves are sparse. Exposure values, also ranging from 0 to 100, are classified into five tiers from very low to extreme, and concentrated pockets of flammable vegetation drive scores upward. The third ingredient is structure vulnerability, produced by multiplying the enhanced flammability hazard by a structure importance factor derived from zoning data: residential buildings and critical infrastructure such as hospitals, schools, and nursing homes receive the highest weight, followed by commercial and other structures. Where zoning was unavailable, as on military installations like Joint Base Elmendorf-Richardson in Anchorage and Fort Wainwright and Eielson Air Force Base in Fairbanks, the team collaborated with base personnel or interpreted aerial imagery to classify neighborhoods. The final risk score synthesizes hazard, exposure, and vulnerability into six categories, from extremely low to very high.

The results paint a nuanced picture of Arctic fire danger. Fairbanks showed the greatest proportion of structures in the top two risk categories at 26%, followed by Whitehorse at 22% and Anchorage at 14%. Anchorage, paradoxically, had the highest share of buildings in the top vulnerability tiers, 42%, yet the lowest risk, a consequence of its large urban footprint where dense housing raises vulnerability but fragmented, less-flammable vegetation and abundant developed ground suppress exposure. Fairbanks and Whitehorse, with structures woven into spruce- and pine-dominated boreal landscapes, showed closer alignment between vulnerability and risk. Anchorage was also the only study area where more than 1% of cells jumped two or more flammability categories when buildings were counted as fuel, at 7%, reflecting its 27% share of structure area containing seven or more buildings per cell. The analysis also confirmed the primacy of exposure: when a structure’s wildfire exposure was extreme, at least 87% of such buildings landed in the highest risk category across all three communities.

A key innovation lies in how the team redrew the wildland-urban interface itself. Traditional WUI maps distinguish urban, interface, and intermix zones; the researchers added a fourth category, the transmission zone, marking areas roughly 2.4 kilometers beyond the WUI boundary where fire could move into a community and where fuel breaks might be sited. Within the 500-meter buffer around structures, urban areas were defined by exposure values of 20 or less, interface zones by 21 to 60, and intermix by values above 60, cross-referenced with Alaska and Yukon wildfire management designations that prioritize suppression from critical down to unplanned or wilderness levels. Fairbanks claimed the largest overall WUI footprint, and the transmission zone proved the largest category in every study area, spanning 23,347 square kilometers around Anchorage, 6,180 square kilometers around Fairbanks, and 665 square kilometers around Whitehorse. Across all sites, most structures fell within interface zones, 52 to 61%, followed by intermix at 20 to 35%. Thousands of buildings sit in the high-risk interface and intermix zones where mitigation should concentrate: 15,537 structures in Anchorage, 16,212 in Fairbanks, and 3,734 in Whitehorse.

The method faced its most demanding test against a real disaster. On 17 August 2019, during southcentral Alaska’s hottest and driest summer on record, the likely human-caused McKinley wildfire was driven by high winds across 13.3 square kilometers in three days, destroying 52 residential structures, 84 outbuildings, and three commercial buildings, affecting 1,028 properties and forcing roughly 350 to 400 people to evacuate. By comparing pre-fire aerial imagery from 2017 with post-fire imagery from 2020 at 0.3-meter resolution and linking vanished buildings to the Matanuska-Susitna Borough structure database, the team identified exactly which structures burned. The verdict was statistically compelling: burned structures had significantly higher wildfire exposure than those that survived, and the proportion of burned structures with exposure values above 60 was significantly larger than among unburned ones. Structure risk scores above 60 were also significantly associated with burning. Notably, the test held even under conditions so extreme that normally resistant, low-flammability fuels were igniting.

The study’s design was shaped by sustained community engagement. Through workshops in 2019, 2021, and 2023, along with 15 to 20 meetings organized by Anchorage community councils and landowner associations and public events ranging from wildfire awareness days to Arbor Day, residents and agency staff reshaped everything from color schemes to model parameters. One consequential example: Anchorage residents and fire managers insisted that the maps reflect the spruce beetle outbreak first detected in 2016 and peaking around 2020, which killed and removed many spruce trees. The team responded with rules derived from aerial detection surveys, reclassifying white spruce cells in areas of beetle intensity of 11% or greater to mixed forest, and shifting mixed forest cells to open shrub, while a succession model updated vegetation on areas burned within the last 30 years. This iterative dialogue ensured outputs that both wildfire practitioners and the general public can interpret, a prerequisite the authors see as central to building fire-adapted communities.

The practical implications for planning are direct. Because mitigation funding is often tied to officially designated WUI areas, the ability to rapidly recalculate boundaries from updated building layers expands funding opportunities and keeps pace with community growth. The framework also points to different interventions in different zones: structures in intermix areas, surrounded by vegetation, benefit most from fuel treatments, while those in interface zones, where flammable vegetation is scarcer, are better served by home hardening measures such as fire-resistant roofs, siding, decks, screens, and unburnable buffers. Given that wildfire activity tends to occur where exposure exceeds 60, the authors recommend concentrating resources on the top two risk categories. Where national products like wildfirerisk.org rely on generalized fuel layers that can overestimate unburnable area in WUI zones and are updated too slowly for annually burning landscapes, this approach trades complexity for transparency, using local data that governments already collect. The underlying wildfire exposure toolbox is publicly available on GitHub, and the team argues that empowered residents, equipped with accurate, current, neighborhood-scale information, are crucial to reducing wildfire risk, particularly where communities may prefer action without government intervention.

A new research project at the New Jersey Institute of Technology is taking a closer look at a strange but important behavior in rubber-like materials: their properties can depend on what happened to them before. With a $125,000 New Directions grant from the American Chemical Society Petroleum Research Fund, chemical and materials engineering professor Gennady Gor and his students will investigate how petroleum-derived polymers absorb fluids, swell, contract and change their elasticity over time. The work could improve predictions of how elastomers perform in everything from industrial seals to oil and gas equipment.

Elastomers are polymers capable of undergoing large, reversible deformations. Silicone, neoprene and natural or synthetic rubber are familiar examples. Their molecular chains can stretch, rearrange and return toward their original configuration, giving these materials their characteristic flexibility. Yet exposure to liquids or vapors can dramatically alter that behavior. Hydrocarbon molecules may enter the polymer network, pushing molecular chains apart and causing the material to expand. At the same time, the absorbed fluid can change the polymer’s stiffness, strength and ability to recover its original shape.

Gor’s project focuses on a phenomenon known as hysteresis, in which a material’s current state depends not only on its present conditions but also on the path it took to reach them. For an elastomer, that means the material may respond differently when it is swelling than when it is shrinking, even if its size and surrounding environment are identical. The difference can arise from molecular rearrangements, delayed relaxation or changes in the interactions between polymer chains and absorbed molecules. Such path-dependent behavior is central to understanding why materials sometimes fail to return fully to their previous mechanical state.

The question is especially relevant for petroleum-related technologies. Elastomers are widely used as O-rings, gaskets and seals because they can deform to fill microscopic gaps and maintain a hermetic barrier. In oil and gas environments, however, these components may encounter complex mixtures of hydrocarbons under pressure and at elevated temperatures. If a seal swells excessively, softens or loses its ability to recover after repeated exposure, it can become unreliable. A better understanding of hysteresis could help engineers select materials and operating conditions that reduce leakage and extend component lifetimes.

The new study represents a shift for Gor’s Laboratory for Materials Interfaces. His previous research has largely examined porous adsorbent materials, which collect molecules on their internal surfaces. Elastomers are different: they do not need pores to take up substantial amounts of fluid. Instead, molecules diffuse into the polymer matrix and interact with its flexible chains. The resulting process is more like a sponge absorbing liquid, although the molecular mechanisms are governed by diffusion, thermodynamics and the elasticity of the polymer network.

Günel Nabiyeva, a fourth-year doctoral student, will lead much of the experimental effort with undergraduate chemical engineering student Elitsa Mileva and second-year doctoral student Supun Rangana. The team has already begun collecting data on how rubber deforms in the presence of liquids and vapors. For Mileva, who has spent considerable time reviewing the scientific literature, the grant will provide an opportunity to move deeper into laboratory work. Nabiyeva describes the project as a new system for the research group because the laboratory has not previously studied elastomers in this way.

The researchers will expose polymer samples to controlled concentrations of hydrocarbon vapors and track how the materials respond. Their measurements will combine swelling, fluid uptake and elasticity rather than treating these properties separately. This approach is essential because a polymer can gain mass and volume while simultaneously becoming softer or, under some conditions, displaying a more complicated mechanical response. Different hydrocarbons may produce different results because their molecular size, chemical structure and compatibility with the polymer influence how quickly they enter the material and how strongly they interact with its chains.

Rangana will contribute expertise in ultrasonic sensing, which can measure subtle changes without physically contacting or damaging the sample. Ultrasonic waves travel through a material in ways that depend on its density, structure and mechanical properties. By analyzing changes in wave transmission or reflection, scientists can detect variations in stiffness and internal condition with high precision. Coupling these measurements to vapor exposure could reveal how quickly elasticity changes as molecules enter the polymer and whether the material follows the same path during desorption as it did during absorption.

The team hopes its results will fill a gap in the fundamental understanding of polymer hysteresis. Although hysteresis is well known in many areas of physics and engineering, relatively few studies have examined how history-dependent elasticity develops in swollen elastomers. The new measurements may help distinguish reversible molecular motions from more persistent structural changes and improve models used to predict polymer performance. Ultimately, the researchers aim to make elastomer behavior less mysterious and more reliable, helping engineers design seals and flexible components that remain dependable across repeated cycles of chemical exposure, swelling and recovery.

Subject of Research: Elastomers, petroleum-derived polymers, polymer swelling, elasticity and hysteresis

Article Title: Ultrasonic Experiments Aim to Reveal Why Rubber Remembers Its History

Web References: Gennady Gor’s NJIT profile; NJIT Laboratory for Materials Interfaces

Image Credits: Courtesy of New Jersey Institute of Technology

Keywords

Elastomers, polymers, hysteresis, rubber, polymer swelling, ultrasonic sensors, materials science, chemical engineering, petroleum research, NJIT

A new study is challenging one of the most persistent assumptions in space manufacturing: that building advanced composite structures beyond Earth is simply a matter of moving terrestrial production methods into orbit. Research published in npj Advanced Manufacturing examines how two defining features of the space environment—reduced gravity and reduced atmospheric pressure—can alter the manufacture of carbon-fiber-reinforced structures, materials widely regarded as essential for future spacecraft, habitats and large orbital infrastructure.

Carbon-fiber-reinforced polymers, commonly known as CFRPs, combine strong, lightweight carbon fibers with a polymer resin that binds the fibers into a rigid composite. Their exceptional strength-to-weight ratio makes them attractive for launch vehicles and spacecraft, where every kilogram matters. Yet manufacturing these materials is a delicate process. Fibers must be positioned accurately, resin must penetrate the reinforcement evenly, and gases or trapped voids must be removed before the material cures. On Earth, gravity and atmospheric pressure quietly assist—or complicate—each of these steps.

In space, those familiar forces change dramatically. Reduced gravity can alter how liquid resin flows through a carbon-fiber structure, while low pressure can encourage dissolved gases and volatile compounds to expand or escape. Together, these effects may influence impregnation, consolidation, curing and the final mechanical performance of a composite part. The new work by R. Strecker, T. Rieger, A. Tabelander and colleagues focuses on understanding these interactions before manufacturers attempt to produce increasingly complex structures in orbit.

The central issue is void formation. A composite that appears solid can contain microscopic pockets of air or vapor between its fibers. These voids interrupt the transfer of stress through the material and can reduce strength, stiffness and fatigue resistance. During conventional manufacturing on Earth, pressure is often applied to compress the layers and drive out unwanted gases while resin fills the spaces between fibers. In a reduced-gravity environment, however, buoyancy-driven movement is weakened, and the pathways through which bubbles travel may change. A bubble that would rise and escape during terrestrial processing could remain trapped inside the structure.

Pressure reduction introduces another layer of complexity. Lower ambient pressure can help remove volatile substances and trapped gases, but it can also cause dissolved gases in the resin to expand. If the resin is not sufficiently mobile, expanding bubbles may become locked in place as the material cures. The balance between degassing and bubble growth therefore becomes a critical manufacturing parameter. The study’s importance lies in treating pressure and gravity not as isolated variables, but as environmental conditions that can interact throughout the production cycle.

Resin flow is equally important. Carbon-fiber reinforcements are made from tightly packed filaments, creating narrow channels through which resin must travel. On Earth, gravity can influence resin distribution, especially in vertically oriented or large components. In reduced gravity, capillary forces and surface tension become relatively more significant. These forces can help draw resin into small spaces, but they may also create uneven wetting patterns depending on the fiber architecture, resin viscosity and processing orientation. Such changes could affect whether a component cures uniformly from its surface to its interior.

The findings carry implications far beyond laboratory samples. Future space manufacturers may seek to fabricate beams, pressure-vessel elements, antenna supports, solar-array components and repair patches directly in orbit. Producing these parts where they are needed could reduce the need to launch bulky finished structures from Earth. It could also enable designs too large to fit inside a rocket fairing, including trusses and habitation modules assembled gradually in space. But the economic and engineering promise of this approach depends on reliable quality control. A component that looks flawless may still fail if its internal fiber alignment, resin distribution or void content is inconsistent.

The research also highlights why manufacturing hardware designed for Earth cannot automatically be considered space-ready. Pumps, heaters, vacuum systems, molds and curing chambers must all operate under conditions in which heat transfer, fluid movement and gas removal behave differently. Temperature control is particularly important because resin viscosity changes strongly with temperature. A resin that flows readily during terrestrial processing might move more slowly or unevenly in orbit, while excessive heating could accelerate curing before impregnation is complete. Space-based production systems will therefore need carefully coordinated control of pressure, temperature, resin chemistry and processing time.

For engineers, the study provides a foundation for developing manufacturing protocols tailored to reduced-gravity environments. Instead of copying Earth-based workflows, future systems may need staged pressure changes, specialized fiber preforms, modified resin formulations or active bubble-management techniques. Sensors could monitor pressure, temperature and resin flow in real time, allowing automated systems to detect defects before a part is finished. Non-destructive inspection, including imaging and ultrasonic testing, will also be essential for verifying the integrity of components that cannot easily be returned to Earth for analysis.

The broader message is that space manufacturing is not merely an extension of industrial production—it is a new manufacturing regime governed by different physics. Understanding how gravity and pressure reshape the behavior of fluids, gases and composite materials could determine whether orbital fabrication remains a promising experiment or becomes a practical industrial capability. By examining the production of carbon-fiber-reinforced structures under space-relevant conditions, Strecker, Rieger, Tabelander and their colleagues are helping identify the hidden variables that will decide how safely and efficiently humanity can build beyond Earth.

Subject of Research: Effects of reduced gravity and reduced atmospheric pressure on the in-space manufacturing of carbon-fiber-reinforced structures

Article Title: Effects of reduced gravity and reduced atmospheric pressure on in-space manufacturing of carbon-fiber-reinforced structures

Article References: Strecker, R., Rieger, T., Tabelander, A. et al. Effects of reduced gravity and reduced atmospheric pressure on in-space manufacturing of carbon-fiber-reinforced structures. npj Adv. Manuf. 3, 26 (2026). https://doi.org/10.1038/s44334-026-00104-w

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s44334-026-00104-w

Keywords: in-space manufacturing, carbon-fiber-reinforced polymers, reduced gravity, reduced atmospheric pressure, composite materials, resin flow, void formation, space technology

A quiet shift in silicon manufacturing could be taking shape inside a new study on porous reaction-bonded silicon nitride, a ceramic material valued for its strength, thermal stability and resistance to chemical attack. Researchers T.G. Aguirre, E. Cakmak, M.D. Richardson and colleagues are investigating how the size of additively manufactured silicon components affects their conversion into silicon nitride through a pressureless nitriding process. Published in npj Advanced Manufacturing, the work connects binder jet 3D printing with a manufacturing route that could make complex ceramic structures easier to produce.

The central idea is deceptively simple: print a component from silicon powder, then expose it to nitrogen so that the silicon reacts and forms silicon nitride. In practice, however, the transformation is governed by a network of interacting physical and chemical processes. Nitrogen must penetrate the part, reach reactive silicon surfaces and diffuse through newly formed ceramic layers. Heat must also move through the structure, while gases generated or trapped during processing must escape. As component dimensions change, these processes can behave very differently.

Binder jet additive manufacturing offers a distinctive route for creating such parts. Instead of melting powder with a laser or electron beam, the process selectively deposits a liquid binder to join powder particles layer by layer. The resulting “green” component is then typically dried and thermally treated to remove the binder, leaving a fragile, porous body. For silicon-based structures, that interconnected porosity can become a processing advantage, allowing nitrogen to travel through the material during the subsequent reaction.

The researchers’ focus on size effects addresses a major challenge in scaling ceramic additive manufacturing from laboratory specimens to useful components. A thin printed sample may nitridize relatively quickly because nitrogen has only a short distance to travel. A thicker or larger part presents a far more demanding environment. Its interior can receive nitrogen more slowly, and the reaction front may progress unevenly from the outer surface inward. Differences in heating rate, temperature distribution and gas transport can produce variations in conversion, density and final microstructure.

The term “pressureless nitriding” is crucial to the study’s technological appeal. Many advanced ceramic manufacturing methods depend on external pressure, specialized furnaces or high-energy consolidation steps to achieve the desired structure. A pressureless route instead relies on controlled heating and the chemical affinity between silicon and nitrogen. If the reaction can be managed uniformly, manufacturers could potentially create intricate silicon nitride components without applying mechanical pressure during conversion, simplifying equipment requirements and expanding the range of printable geometries.

Silicon nitride is an especially attractive target because it combines several properties that are difficult to obtain in a single engineering material. It can withstand high temperatures, resist wear and corrosion, and maintain useful mechanical performance under demanding conditions. Those characteristics have made silicon nitride relevant to applications ranging from bearings and cutting tools to heat-resistant components and advanced energy systems. A porous version offers additional possibilities, including lightweight structures, filtration, thermal management and engineered surfaces where controlled pathways through the material are valuable.

The study’s attention to porosity is equally important. In reaction-bonded ceramics, pores are not merely defects; they can serve as channels for reactant transport. At the same time, excessive or poorly distributed porosity can weaken the final component and interfere with complete conversion. The scientific challenge is therefore to balance openness and strength. The printed silicon body must remain permeable enough for nitrogen to reach its interior, while the reaction must generate a coherent silicon nitride framework rather than leaving unreacted silicon or creating damaging gradients.

By examining components of different sizes, the researchers are addressing a question with direct implications for industrial design: can processing rules developed for small printed samples be applied to larger parts? The answer depends on how reaction kinetics, diffusion and heat transfer scale with geometry. A process that appears reliable in a small coupon may produce a different result in a thick wall, enclosed channel or complex lattice. Understanding those differences can help engineers determine allowable dimensions, optimize furnace schedules and design structures around the chemistry of nitridation rather than treating manufacturing as a purely geometric exercise.

The broader significance of the work lies in the convergence of three fields: powder-based additive manufacturing, reaction-bonded ceramics and transport-driven materials processing. Instead of printing a finished ceramic directly, the approach uses additive manufacturing to create a carefully engineered precursor and then transforms it chemically. That strategy could unlock shapes that are difficult to machine from dense silicon nitride, while reducing the need for conventional molds and tooling. As manufacturers seek lighter, more complex and more resource-efficient components, controlling size-dependent reactions may become as important as improving printer resolution.

The research arrives at a moment when additive manufacturing is moving beyond demonstrations of intricate shapes toward repeatable production of functional materials. Its emphasis on pressureless nitriding of binder jet additively manufactured silicon highlights a practical bottleneck often hidden behind spectacular 3D-printing images: the printed object is only the beginning. The final performance depends on what happens during post-processing, when gases, heat and chemical reactions determine the internal structure. By clarifying how component size influences that transformation, Aguirre, Cakmak, Richardson and their collaborators are helping map a route from powder and binder to porous silicon nitride parts designed for the high-temperature technologies of the future.

Subject of Research: Porous reaction-bonded silicon nitride produced through pressureless nitriding of binder jet additively manufactured silicon, with a focus on size effects.

Article Title: Porous reaction-bonded silicon nitride: Size effects for pressureless nitriding of binder jet additively manufactured silicon

Article References: Aguirre, T.G., Cakmak, E., Richardson, M.D. et al. “Porous reaction-bonded silicon nitride: Size effects for pressureless nitriding of binder jet additively manufactured silicon.” npj Advanced Manufacturing (2026). https://doi.org/10.1038/s44334-026-00110-y

Image Credits: AI Generated

DOI: 10.1038/s44334-026-00110-y

Keywords: Silicon nitride, reaction-bonded ceramics, pressureless nitriding, binder jet additive manufacturing, silicon, porous materials, size effects.

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.

A common complication of early birth is being thrust into a medical debate that reaches far beyond the neonatal intensive-care unit: how low is too low when a late preterm baby’s blood glucose falls, and what—if anything—does that episode mean years later? A new article in Pediatric Research examines late preterm hypoglycemia through the lens of long-term evidence, highlighting why a seemingly brief metabolic disturbance has become one of neonatology’s most closely watched questions.

Late preterm infants are typically born between 34 weeks and 36 weeks plus six days of gestation. They often appear stronger and more mature than very preterm babies, but their brains, hormone systems and feeding abilities are still developing. After birth, glucose levels can drop as the infant transitions from a constant placental supply to intermittent feeding. Immature glycogen stores, limited fat reserves, difficulty coordinating sucking and swallowing, and illness-related stress can all increase the risk of hypoglycemia.

Glucose is the brain’s primary energy source during early life, making neonatal hypoglycemia biologically concerning. When glucose delivery falls, the brain may initially compensate by using alternative fuels, including lactate and ketone bodies. If the shortage is severe, prolonged or repeatedly missed, however, energy production can become insufficient. This may interfere with neuronal signaling and, in extreme cases, contribute to cellular injury. The challenge is that many affected newborns show no obvious symptoms, while symptomatic infants may display only subtle signs such as jitteriness, poor feeding, lethargy, temperature instability or apnea.

The article by U. Felderhoff-Müser focuses attention on a crucial gap between immediate treatment and long-term certainty. Hospitals commonly screen infants considered vulnerable to hypoglycemia, including late preterm newborns, and intervene with feeding, buccal dextrose gel or intravenous glucose when indicated. These measures are designed to restore glucose quickly and prevent neurological stress. Yet the threshold used to define hypoglycemia is not a universal biological line. It is a clinical value shaped by gestational age, postnatal age, symptoms, measurement method and the balance between avoiding harm and preventing unnecessary intervention.

That uncertainty has made long-term follow-up essential. Researchers have investigated whether neonatal hypoglycemia is associated with later problems involving cognition, executive function, attention, language, motor coordination and school performance. Some studies have reported higher risks after severe, recurrent or prolonged episodes, while others have found limited or inconsistent effects, particularly when low glucose was detected and treated promptly. The differences may reflect more than glucose itself: prematurity, fetal growth restriction, maternal diabetes, infection, respiratory illness and socioeconomic conditions can independently influence neurodevelopment.

This is one reason the evidence remains difficult to interpret. Most long-term studies are observational, meaning researchers cannot randomly assign infants to experience different glucose levels. Instead, they compare children who had hypoglycemia with those who did not, while attempting to account for other medical and social factors. Even sophisticated statistical adjustments may not eliminate hidden confounding. In addition, studies use different definitions of hypoglycemia, different monitoring schedules and different ages or tools for developmental testing, making direct comparisons challenging.

The timing and pattern of low glucose may be as important as the lowest recorded number. A single short episode may carry a different biological significance from repeated episodes or an extended period below a treatment threshold. Continuous glucose monitoring has raised further questions by revealing fluctuations that intermittent heel-stick testing can miss, although sensor accuracy in newborns and the clinical meaning of every detected dip are still being evaluated. The emerging picture is therefore more complex than a simple division between “normal” and “abnormal.”

For clinicians and families, the practical message is not to dismiss hypoglycemia or to assume that every low reading predicts disability. Instead, the long-term evidence supports careful surveillance of infants at risk, rapid confirmation of abnormal results, and treatment strategies that protect the brain without disrupting feeding or exposing babies to unnecessary interventions. Developmental follow-up may be particularly important for infants with severe or recurrent episodes, neurological symptoms, or multiple additional risk factors. Such monitoring can identify difficulties early, when speech, occupational or educational support may have the greatest effect.

The discussion also underscores the need for better research. Future studies will need standardized glucose definitions, precise records of duration and recurrence, improved measurement technologies and follow-up extending into school age and adolescence. Researchers must also distinguish the effects of hypoglycemia from the conditions that caused it and examine outcomes beyond a single global developmental score. Felderhoff-Müser’s review places late preterm hypoglycemia in that broader scientific context: a common neonatal event whose immediate management is well established, but whose long-term consequences still demand careful, nuanced investigation.

Subject of Research: Late preterm hypoglycemia and its long-term neurodevelopmental evidence

Article Title: Late preterm hypoglycemia through the lens of long-term evidence

Article References: Felderhoff-Müser, U. “Late preterm hypoglycemia through the lens of long-term evidence.” Pediatric Research (2026). https://doi.org/10.1038/s41390-026-05316-6

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41390-026-05316-6

Keywords: late preterm infants, neonatal hypoglycemia, blood glucose, neurodevelopment, brain health, newborn care, long-term outcomes

Neonatal necrotizing enterocolitis (NEC) remains one of pediatrics’ most devastating intestinal disorders, and safer, mechanism-driven therapies are urgently needed. New findings suggest that tiny vesicles naturally present in human breast milk—exosomes—may offer protection by delivering a specific regulatory microRNA.

Researchers report that exosomes enriched with miR-144-5p can help restore the intestinal tight junction barrier, a key defense line that collapses during NEC. By focusing on miR-144-5p, the team moves beyond earlier observations that breast milk exosomes can reduce NEC severity and instead pinpoints a molecular cargo likely responsible for part of that benefit.

The study centers on how NEC damages epithelial integrity and promotes inflammatory signaling. Tight junction disruption is associated with increased permeability, bacterial translocation, and worsening inflammation—conditions that accelerate disease progression in vulnerable newborns. The investigators therefore examined whether miR-144-5p could counter these barrier failures in NEC contexts.

Mechanistically, the work links miR-144-5p action to the TLR4/NF-κB pathway, a well-known inflammatory axis. Toll-like receptor 4 (TLR4) senses danger-associated cues and can trigger NF-κB activation, driving transcription of pro-inflammatory mediators. In NEC, exaggerated signaling through this route contributes to tissue injury and impaired repair.

By modulating this cascade, miR-144-5p appears to dampen downstream inflammatory responses while promoting recovery of junctional structure. The paper’s data indicate that exosomal delivery is central: miRNAs packaged within vesicles can be taken up by recipient cells more efficiently than naked RNA, enabling functional gene regulation in target intestinal compartments.

Together, the results support a model in which breast milk exosomal miR-144-5p alleviates NEC by suppressing TLR4-driven NF-κB activation, thereby reducing inflammatory injury and enabling tighter regulation of epithelial junctions. If translated, such a cargo-guided approach could complement existing supportive NEC treatments.

While additional work will be required to confirm efficacy across models and to evaluate safety, the study provides a clear mechanistic roadmap: therapeutic exosomes or miRNA mimics that bias TLR4/NF-κB activity may be a promising strategy for protecting the neonatal gut.

Subject of Research: Neonatal necrotizing enterocolitis (NEC), breast milk-derived exosomes, miR-144-5p, tight junction barrier repair, TLR4/NF-κB signaling.

Article Title: Human milk exosomal-miR-144-5p alleviates neonatal necrotizing enterocolitis by regulating the TLR4/NF-κB pathway.

Article References: Chen, Z., Chen, C., Hu, X. et al. Human milk exosomal-miR-144-5p alleviates neonatal necrotizing enterocolitis by regulating the TLR4/NF-κB pathway. Pediatr Res (2026). https://doi.org/10.1038/s41390-026-05305-9

Image Credits: AI Generated

DOI: 10.1038/s41390-026-05305-9

Keywords:

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:

A new Bayesian system called ALADYNOULLI is designed to forecast how multiple diseases unfold over time from longitudinal electronic health record (EHR) histories. The work targets a central challenge in longitudinal modelling: evaluating prediction without “information leakage,” so that future diagnoses never influence training for past timepoints. By explicitly simulating real clinical follow-up, the researchers aim to make risk estimates more trustworthy for decision-making.

In the primary assessment, ALADYNOULLI produced dynamic 1-year forecasts at 10 fixed moments during follow-up, starting at enrollment and extending to years 1 through 9. At each timepoint, models were retrained using only data available up to that moment, then tested on 1-year outcomes. The key metric was the median area under the receiver operating characteristic curve (AUC) across these dynamic evaluations, summarizing how predictive accuracy changes as new diagnoses accumulate.

The study spans 28 diseases and reports robust discrimination for several clinically important endpoints. Performance was strong for atherosclerotic cardiovascular disease (ASCVD), breast cancer, atrial fibrillation, heart failure, and Parkinson’s disease, with AUC values of 0.879, 0.867, 0.801, 0.811, and 0.796, respectively. Importantly, the evaluation excluded people with prevalent disease at prediction time, aligning the comparisons with prospective risk.

ALADYNOULLI was also benchmarked against established clinical risk tools where feasible. At enrollment, static models trained only on baseline information were compared for both 1-year and 10-year horizons. While longer-term predictions naturally degraded, ALADYNOULLI retained meaningful power—for example, for ASCVD at 10 years—highlighting the advantage of learning from longitudinal EHR trajectories rather than relying solely on cross-sectional inputs.

Calibration analyses further supported reliability: predicted and observed event rates matched closely across millions of at-risk patient-time observations. The researchers report a mean squared error on calibration of 4.67 × 10⁻⁷, with mean predicted rates (5.55 × 10⁻⁴) closely tracking mean observed rates (5.45 × 10⁻⁴). This suggests not only ranking accuracy, but also quantitative risk realism.

To test whether results were driven by subtle temporal artifacts, the team ran sensitivity analyses that excluded events occurring shortly before prediction and varied washout windows. They also examined alternative horizons and a dynamic 10-year rolling interpolation strategy, which updates risk estimates as annual information becomes available. Together, these checks help confirm that temporal patterns are being learned in a causally aligned manner.

Overall, the findings position ALADYNOULLI as a clinically relevant framework for multi-disease risk forecasting, combining longitudinal EHR modelling with predictive discrimination and calibration. By rigorously preventing leakage and validating dynamic follow-up scenarios, the approach offers a pathway toward more actionable, time-aware risk prediction in real-world healthcare settings.

Subject of Research: Longitudinal EHR-based multi-disease risk prediction using Bayesian modelling and genetic/EHR integration
Article Title: A Bayesian framework for longitudinal EHR and genetic discovery.
Article References: Urbut, S.M., Ding, Y., Nakao, T. et al. A Bayesian framework for longitudinal EHR and genetic discovery. Nature (2026). https://doi.org/10.1038/s41586-026-10780-5
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s41586-026-10780-5
Keywords: longitudinal EHR, Bayesian framework, disease risk prediction, dynamic forecasting, temporal validation, calibration, AUC, ASCVD

Climate change is poised to reshuffle the reliability problem facing decarbonized power systems, according to a new study that links weather extremes, long-term climate projections, and real-world infrastructure constraints. As grids rely more heavily on wind and solar, they become sensitive not just to the amount of renewable capacity installed, but to where that capacity is sited relative to transmission lines, load centers, and the local meteorological patterns that govern output.

Using high-resolution climate projections at 12-km scale, the researchers examined how future conditions could alter both renewable generation and electricity demand across two distinct US regions: New England and Texas. They then coupled these climate inputs with an optimization model operating at the county level, allowing the team to test how system design choices—particularly spatial siting of wind and solar and the presence of transmission bottlenecks—affect long-run adequacy.

The results suggest a sharp increase in resource inadequacy risk by mid-century. In both regions, climate change can drive prolonged periods of low renewable generation, which occur when meteorological conditions reduce wind and solar output while demand simultaneously rises. For Texas, the study finds the frequency of inadequacy events could increase dramatically—up to fivefold—if planning proceeds using non-climate-informed assumptions.

Crucially, the work shows that these risks are not determined by aggregate capacity targets alone. Fine-scale siting decisions can lock the system into unfavorable “weather-resource–grid” pairings, amplifying periods when local supply falls short and transmission limits prevent imports from compensating.

Yet the study also offers a pathway to resilience. Climate-informed planning can mitigate the emerging adequacy threat at relatively modest cost. Texas, for example, can maintain near-zero marginal investment cost by shifting wind capacity westward, improving alignment with future wind patterns and reducing reliance on constrained transfers.

New England requires a different strategy. The region faces a higher investment need—about a 2.34% increase—achieved through targeted solar and transmission expansion closer to load centers. This approach helps buffer localized shortfalls during future, climate-driven demand and renewable output mismatches.

Overall, the study argues that achieving climate-resilient decarbonization means moving beyond simple build-out schedules. Instead, it calls for high-resolution, climate-aware spatial planning that reveals siting opportunities invisible to coarse models—turning reliability from a static design metric into a dynamic, region-specific forecast problem.

Subject of Research: Climate change and resource adequacy risks in wind/solar grids, including optimal renewable siting
Article Title: Climate change reshapes resource adequacy risks and optimal renewable energy siting in wind and solar energy systems
Article References: Qiu, L., Khorramfar, R., Wang, S. et al. Climate change reshapes resource adequacy risks and optimal renewable energy siting in wind and solar energy systems. Nat Energy (2026). https://doi.org/10.1038/s41560-026-02109-3
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s41560-026-02109-3
Keywords:

Naked mole-rats have long puzzled biologists with their striking eusocial structure, where most adults remain reproductively suppressed while a queen controls the colony’s future. Now, a new study identifies a chemical “queen odour” that links social status to endocrine control, revealing a mammalian analogue to insect queen pheromones.

The molecule, termed IPM, is enriched by the queen and acts as the key mediator of reproductive suppression in non-breeding females. When IPM is present, circulating prolactin rises while progesterone is suppressed, locking the colony into a stable reproductive hierarchy characterized by reduced conflict and maintained group cohesion.

Crucially, the team connects this behavioural outcome to the brain. Using multiple approaches—including non-invasive functional ultrasound imaging (fUSI)—they show that IPM provokes strong activation of olfactory neurons and central neural circuits. This provides a neural substrate for how a chemical cue can trigger colony-wide physiological change, rather than relying on social learning or passive association.

The experiments also clarify sensory routing. Ablating olfaction abolishes aggression initiation and lowers prolactin levels, directly tying IPM detection to reproductive suppression. While the findings are consistent with sensing through the main olfactory epithelium, the authors note that the vomeronasal system may still contribute.

Queen removal normally destabilizes colonies, leading to aggression, reproductive competition, prolactin decline, and, in extreme cases, mortality. Yet when IPM is added to queenless bedding, the colony remains stable for months. During this period, fecal progesterone metabolite levels stay low, indicating sustained ovarian quiescence alongside elevated prolactin.

The study also addresses a longstanding paradox: transferring soiled bedding and urine from parent colonies fails to maintain suppression in isolated individuals. The authors propose that IPM’s chemistry helps explain this discrepancy. As a long-chain fatty acid ester with low volatility, IPM may persist within the colony environment but fail to travel effectively over distance or survive transfer pathways that preferentially carry other chemicals.

Comparative work extends beyond naked mole-rats. IPM is detected in breeding females of four additional Fukomys species. Although reproductive suppression mechanisms may differ across species, the presence of a similar queen-linked odorant suggests convergent chemical strategies for managing reproductive skew in eusocial mammals.

Overall, the research reframes reproduction control as a chemosensory-to-endocrine pathway: IPM activates olfactory–hypothalamic signalling, elevating prolactin and inhibiting GnRH release. This “social hormone” model offers a new principle for social evolution—chemical cues can synchronize behaviour and physiology at the scale of an entire mammal colony.

Subject of Research: Reproductive suppression and social hierarchy in eusocial mammals (naked mole-rats and related Fukomys species)

Article Title: A queen odour mediates reproductive suppression in a eusocial mammal

Article References:

Khallaf, M.A., Hart, D.W., Luo, W. et al. A queen odour mediates reproductive suppression in a eusocial mammal.
Nature (2026). https://doi.org/10.1038/s41586-026-10772-5

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41586-026-10772-5

Keywords: IPM; queen odour; reproductive suppression; prolactin; GnRH; olfaction; eusociality; functional ultrasound imaging; Fukomys

Bacterial cellulose—nature’s own polymer scaffold—is gaining attention as a renewable feedstock for supercapacitor electrodes. With modern devices demanding fast charge, high power bursts, and long cycle life, researchers are searching for electrode materials that combine performance with sustainability.

A new systematic literature review led by Prof. Dahlang Tahir at Hasanuddin University, Indonesia, maps how bacterial cellulose-derived carbon (BCC) is turned into energy-storage electrodes—and why some fabrication paths outperform others. The study synthesizes evidence from the scientific record to clarify which processing choices control electrochemical behavior.

The review focuses on BCC as a precursor to porous carbon. Bacterial cellulose forms a naturally pure, interconnected network of nanoscale fibers, and heat treatment can convert that architecture into carbon structures with tunable porosity—critical for charge storage. The authors emphasize that electrical performance is not just a matter of “making carbon,” but of preserving and engineering the fiber network before carbonization.

Across 49 Scopus-indexed journal articles, the team compares major strategies including direct carbonization, chemical activation to enlarge pore systems, heteroatom doping to modify surface chemistry, and composite fabrication with materials that can add rapid redox (pseudocapacitive) contributions.

A recurring message is the importance of pre-carbonization drying. Freeze-drying appears as the most commonly used approach because it limits collapse of the wet nanofiber structure during water removal. Since pore architecture governs ion access and charge transport, maintaining nanoscale structure can translate into higher effective capacitance.

The review also distinguishes test formats. Three-electrode measurements are frequently reported, but two-electrode devices better represent real supercapacitor operation, where electrode–electrode interactions shape performance.

When processing is optimized, the results point toward a pathway for BCC-based electrodes to rival or surpass commercial activated carbon under comparable conditions. Activation and heteroatom doping generally increase accessible surface area and create additional active sites, while composites often achieve the strongest capacitance by combining electrical double-layer effects with fast surface reactions.

Yet the authors warn that progress is constrained by inconsistent experimental reporting, uneven protocols, and limited mechanistic understanding. To move beyond laboratory demonstrations, they call for predictive design, data-driven structure–performance models, scalable carbonization methods, and robust flexible devices resistant to deformation and humidity.

Subject of Research: Supercapacitor electrodes using bacterial cellulose-derived carbon
Article Title: Bacterial cellulose-derived carbon electrodes for supercapacitors: Fabrication strategies, electrochemical performance, and mechanical properties — A review
News Publication Date: 9 June 2026
Web References: https://doi.org/10.1016/j.est.2026.123044
References: 10.1016/j.est.2026.123044
Image Credits: Lightenoughtotravel from Wikimedia Commons

Keywords

bacterial cellulose; supercapacitors; porous carbon; freeze-drying; chemical activation; heteroatom doping; electrode materials; two-electrode testing; pseudocapacitance; sustainable energy storage