Controlling thermal radiation simultaneously across distinct infrared bands is vital for modern stealth and security. Moving toward this goal, scientists from the Hebrew University of Jerusalem developed a production-ready, dual-band metasurface that enables pixel-level, independent grayscale emissivity control. This flat optical device allows users to fool multi-sensor detectors by displaying matched thermal camouflage or projecting entirely different encrypted images to mid- and long-wave infrared cameras simultaneously.

Deep learning is rapidly changing fluorescence microscopy by enabling researchers to extract more information from fewer, noisier and faster-acquired images. A new review in PhotoniX Life systematically summarizes recent advances across four major directions: imaging quality, imaging speed, imaging throughput, and intelligent analysis. This article systematically reviews the applications of deep learning across four key areas: imaging quality, imaging speed, imaging throughput, and intelligent analysis. Starting from mainstream neural network architectures, it presents their specific implementations in super-resolution reconstruction, image denoising, aberration correction, fast 3D imaging, cell segmentation, and virtual fluorescent labeling. It also highlights current critical challenges, including strong dependence on training data, limited cross-platform generalization, and the need for validation of biological plausibility in reconstructed results. The aim of this paper is to help researchers understand the limitations of algorithmic capabilities and guide them in making informed choices and evaluations when applying these methods in real experimental settings.

Researchers have discovered that precisely balancing sulfur and selenium in eco-friendly quantum dots can minimize performance-killing atomic defects, unlocking record-breaking efficiency for solar hydrogen production. By fine-tuning the chalcogenide composition in copper-indium-sulfur-selenium quantum dots, the team found that a 1:1 sulfur-to-selenium ratio minimizes crystal lattice distortion and reduces anion vacancies to their lowest level. This structural optimization dramatically improves charge carrier concentration and lifetime, enabling more efficient conversion of sunlight into hydrogen fuel without using toxic heavy metals.

New study demonstrates a superconducting memory bit that operates close to the minimum energy allowed by physics

Carbonate cerium and neodymium isotope records from South China show that low-oxygen waters invaded shallow habitats twice during the Late Ordovician Mass Extinction, with a brief recovery between the two pulses. Earth-system models suggest that rapid climate swings reorganized continental weathering and nutrient delivery, amplifying marine oxygen loss and ecological stress.

East Asian summers prior to the late 1990s were primarily affected by tropical oceans and their accompanying phenomenon, El Niño-Southern Oscillation (ENSO). However, in recent years this has appeared to hold less sway on East Asian (EA) summers, and primary influences on the prevalence of heatwaves in EA summers have shifted to the presence of newly formed ice (NFI) in the Arctic and the East Asian jet stream (EAJS). This is thanks to an anomalous anticyclone phenomenon, propelled by a reversal of circulation from westerly to easterly, bringing in colder winds from the Arctic Oscillation and cooling down the EA summer climate.

Recently, a research team led by associate professor LI Xiangyan from the Institute of Solid State Physics, Hefei Institutes of Physical Science (HFIPS), Chinese Academy of Sciences, has uncovered how nano-voids evolve under irradiation and proposed a strategy to reduce helium damage in fusion reactor materials.

The research team from National University of Defense Technology systematically described the memory efficiency issues in the training process of large language models (LLMs) with Transformer architecture for scientific fields. As the scale of models continues to expand, memory requirements have become a key bottleneck restricting their applications in scientific fields such as biology, medicine, chemistry, and meteorology. The article systematically reviewed memory-efficient pre-training technologies at the algorithm level, system level, and software-hardware collaborative optimization, and combined with the AlphaFold 2 case, demonstrated how customized memory optimization methods can reduce storage overhead while maintaining prediction accuracy.