The utilization of blue lasers to excite phosphor materials holds great potential for the development of high-brightness laser-driven light sources. However, phosphor materials that can simultaneously constrain light spot expansion and enhance maximum luminous flux have been elusive, thereby limiting output luminance. This study presents a significant strategy to address the inherent trade-off between light spot confinement and luminous flux maximization in light sources through the design of core-cladding-like phosphor ceramics (CCPC) wafers. The YAG:Ce@Al₂O₃ CCPC wafer design effectively confines the light spot to an area as small as 0.53 mm² while achieving an ultra-high luminance of 3900 lm·mm⁻². This research presents a pioneering approach to the design of phosphor materials, targeting the realization of light sources with unprecedented luminance for broad frontier applications.

Microwave dielectric ceramics, as core materials for passive electronic components, are widely used in filters, dielectric antennas, and microwave communication systems. In high-frequency applications, ceramics with a low dielectric constant (ε_r) are preferred for their ability to reduce signal latency and simplify passive device fabrication. Ideal microwave dielectric ceramics should exhibit a near-zero temperature coefficient of resonant frequency (τ_f) and a high quality factor (Q×f, i.e., low dielectric loss tanδ = 1/Q). However, achieving ultra-low dielectric loss, high Q×f, and near-zero τ_f simultaneously remains a significant challenge. Olivine-type A₂BO₄ ceramics, with low ε_r (<10) and high Q×f (>100,000 GHz), offer exceptional low loss and high efficiency in high-frequency signal transmission, presenting broad prospects for next-generation wireless communications. Nevertheless, achieving a near-zero τ_f remains a critical challenge in this field.

Osteogenesis imperfecta (OI) refers to a group of bone disorders in which certain genetic mutations affect the formation of healthy bones. In a new study, researchers have developed a novel mouse model bearing a substitution mutation at position 342 in the specificity protein 7 (Sp7) gene. Utilizing this model, they investigated the role of mature bone cells called osteocytes in OI and clarified the associations between impaired bone remodeling and osteocyte dendrite defects.

Wuhan, China – A landmark study published today in National Science Review introduces "Pore Science and Engineering" as a transformative paradigm for designing porous materials. Led by Prof. Bao-Lian Su (Wuhan University of Technology) and Prof. Ming-Yuan He (East China Normal University), the research systematically classifies two evolutionary stages of porous materials while proposing quantitative design principles for future applications in energy, catalysis, and environmental remediation.

Climate modeling leveraged by calibrating the so-called “soil color” parameter to enhance surface albedo simulation in the Weather Research and Forecasting model. This weakens land-atmosphere interactions with attenuated sensible heat and evapotranspiration, and suppresses water vapor flow into the Tibetan Plateau. Eventually, it reduces summertime precipitation wet bias by ~16% on the plateau. This study highlights the promise of enhancing climate modeling through the optimization of key land surface parameters, which is highly affordable through satellite remote sensing.

Researchers from Shenyang University of Chemical Technology explored the application potential of the Catal-GPT in catalyst design, which was built upon the qwen2 large language model, proposing a new paradigm for AI-driven catalyst development. The results showed that the qwen2 model could provide the complete preparation workflows and the detailed optimization suggestions through conversational interaction. Future work aims for cross-system adaptability to transform catalyst discovery from trial-and-error to precision targeting.

Here, researchers from Beijing Institute of Nanoenergy and Nanosystems (Chinese Academy of Sciences) and Yonsei University present the latest progress in neuromorphic computing by integrating various neural networks, including SVM, ANN, CNN, RNN, and RC. Starting from the structure of synapses and neurons, they explore how these networks can be combined with neuromorphic devices to replicate more complex brain-like computations. They also propose future development directions for neuromorphic devices, focusing on advancements in their structures, materials, and applications across diverse fields such as vision, touch, hearing, smell, pain and other senses.

A soft, LEGO-like hydrogel system enables reversible 3D information encoding through supramolecular assembly and orthogonal stimulus responses, allowing over 800 billion data configurations in a 5×5 array. This low-cost, reconfigurable platform supports dynamic data storage, masking, and rewriting.