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

Using the human body to transmit signals, Georgia Tech engineers have created a wireless networking system that allows tiny implantable sensors and actuators to communicate with each other and with wearable devices. Their system means devices can work together like never before, sensing in one part of the body and triggering a therapeutic response elsewhere—perhaps releasing medicine or stimulating a nerve.

Imagine if your car weren't powered by a conventional battery under the hood but by energy stored in its walls or even its roof? This is a future that Dr. Bhagya Dharmasiri and her colleagues at the Deakin Institute for Frontier Materials have been exploring, bringing load-bearing structures and rechargeable energy storage together to create structural battery composites.

Reinforced concrete floor slabs account for about 40% of buildings' embodied carbon emissions. Until now, however, there has been no practical alternative for complex, multistory buildings.

Mechanical and aerospace engineering professor Anupam Pandey's research team has found that microscopic folds in soft materials can act like tiny, programmable traffic gates for liquid droplets. The folds can stop droplets, let them pass or merge them together without surface coatings or additional hardware. Remarkably, the droplets never touch the fold. They sense it from a distance and slow down, stop short or change course in response. The study is published in the Proceedings of the National Academy of Sciences.

Queensland researchers have reached a milestone in powering Australia's critical minerals refining industry, successfully delivering the nation's first grid-connected sodium-sulfur (NaS) battery energy storage system enabled by Queensland-made DC-DC converters.

Researchers at the Department of Energy's (DOE) Oak Ridge National Laboratory (ORNL) have developed a computational method that could help electric grid operators and data center developers manage power more efficiently as artificial intelligence drives unprecedented growth in electricity demand.

The animals may help reveal the causes of brain disease in people and point the way to treatments. But researchers already are calling for new ethical guidelines.

Deicing technologies often rely on heat to melt ice. But University of Texas at Dallas researchers have found that, under extreme cold, making ice even colder can weaken its grip on surfaces.

Storing energy as heat is a proven pathway to address the challenge solar power faces when it's dark or cloudy outside by enabling the capture, storage and on-demand release of thermal energy. The process, called high-temperature thermal energy storage (TES), however, can be prohibitively expensive.

A Maine lobster can end up in a roll, a bisque or a butter bath. At the University of Maine, what's left of it can now end up under a golf ball. Researchers at UMaine are making golf tees from discarded lobster shells. The project builds on more than a decade of UMaine research into new uses for an underutilized byproduct of Maine's lobster industry.

The Smart Materials research unit, coordinated by Athanassia Athanassiou, deputy scientific director of the Italian Institute of Technology (IIT-Istituto Italiano di Tecnologia) in Genoa, Italy, has developed a new method to make wood flexible in a controlled way and therefore easier to shape. Unlike current approaches, which are more energy-intensive or less sustainable, IIT's treatment could offer a green alternative.

Researchers at The University of Manchester, Shandong Jiaotong University and Harbin Engineering University have developed a machine learning framework that enables rapid predictions of how protected steel beams respond during a fire, offering engineers a faster way to assess fire safety performance in industrial structures.

Western University engineers have taken a major step toward solving a long-standing challenge in nuclear power. Their study, published in Nature Communications, describes for the first time how hydrogen can combine with zirconium to form tiny, brittle structures that create intense stresses in CANDU reactor pressure tubes. These stresses may cause the surrounding metal to deform and even crack.

A satellite phased-array transceiver that folds up for launch and unfolds in space has been shown to work successfully in orbit, as reported by researchers from Science Tokyo. By using an innovative approach to correct nonplanar deformations in the deployable membrane antenna, this work lays the foundation for satellite constellation-based wireless communications in the Beyond 5G and 6G era.

A new stretchable quantum-dot light-emitting diode (QLED) delivers incredibly high resolution while producing record brightness that can be clearly viewed even outside in direct sunlight.

In a recent paper published in Nature Nanotechnology, Korean researchers unveiled a new QLED material with applications far beyond televisions, stretching into many future daily-use technologies.

This new QLED can stretch in every direction, just like organic skin, making it extremely useful in future wearable applications and as a covering for soft robots. Unlike earlier attempts to make a stretchable QLED, this one doesn’t lose picture quality when it stretches, delivering high resolution and extreme brightness under tension.

QLED Explained

For most of us, QLEDs are familiar as one of the technologies available in modern televisions. Conventional QLED TVs use quantum dots—nanoscale semiconductor crystals—to help produce exceptionally bright and vivid colors. OLED displays, by comparison, use self-emissive pixels that can switch off individually, allowing them to produce deeper blacks. While OLEDs perform particularly well in dark viewing environments, the high brightness achievable with quantum-dot displays can make them especially well-suited for brightly lit environments.

Despite the technology’s suitability for the bright world around us rather than just darkened environments, major challenges remained in producing a stretchable version. Early attempts addressed the problem by allowing the wiring to stretch while the light-emitting pixels remained unchanged in size. This meant that as the material expanded, its effective resolution dropped because the same number of pixels became spread across a wider area, much like enlarging a low-resolution digital image.

Building on these efforts, some researchers attempted to replace conventional materials with more stretchable alternatives, but these tended to make achieving high resolutions and efficiently moving electrical charge through the material difficult. As a result, these systems produced dimmer, lower-resolution images, limiting their utility.

A New Process

The researchers developed a new fabrication approach to address some of the major problems facing stretchable QLED technology. Central to their innovation is the use of an elastic polymer incorporating MPA as a ligand, or bonding molecule. This approach improves charge injection efficiency while helping eliminate the image stretching and smearing that have affected previous stretchable displays.

Using this new process, the researchers produced a display with a resolution of about 16,000 pixels per inch, with features spaced at roughly 700 nanometers. Red, green, and blue emissive layers can also be precisely arranged, allowing the stretchable material to produce full-color images.

Testing a New QLED

In tests, the team’s new QLED proved to be a major advance. They successfully stretched the material to 1.65 times its original size with no damage to its light-emitting performance. Additionally, their stretchable QLEDs were the brightest ever tested at 53,300 nits, more than triple the brilliance of prior attempts.

Based on these tests, the researchers see numerous future applications for their material in extended reality devices, soft robots, electronic skin, wearable technologies, health care, and automotive displays. 

“We resolved both the drop in resolution and the limits on light-emitting performance, which had been the biggest challenges for stretchable displays, and secured core technology for commercialization,” concluded co-author Professor Moon Kee Choi of Ulsan National Institute of Science and Technology in Korea. “It will be used widely as a manufacturing platform for various next-generation soft devices, including wearables and electronic skin.”

The paper, “High-Resolution Intrinsically Stretchable Quantum-Dot Displays Through Thermally Assisted Intaglio Transfer Printing,” appeared in Nature Nanotechnology on September 14, 2026.

Ryan Whalen covers science and technology for The Debrief. He holds an MA in History and a Master of Library and Information Science with a certificate in Data Science. He can be contacted at ryan@thedebrief.org, and follow him on Twitter @mdntwvlf.

Scientists have developed a low-cost device that can continuously check air ducts at nuclear facilities for traces of radioactive material. The detector, created at the U.S. Department of Energy’s Oak Ridge National Laboratory, or ORNL, can take measurements once a minute when running on a battery. When connected through an Ethernet cable, it can collect […]

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Scientists have developed a new way to make an extremely strong metal-based material much less likely to break when it is bent or squeezed. The discovery could eventually lead to tougher materials for aircraft engines, turbines, energy systems and other machines that operate under extreme conditions. The research was led by engineers at Purdue University […]

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