Researchers at the College of Design and Engineering at the National University of Singapore have developed a universal co-assembly strategy to produce chiral soft materials that emit strong and stable full-colour circularly polarised luminescence (CPL) across the visible spectrum, including in red, which is a historically challenging target. The hierarchical structures, formed by combining star-shaped polymers with simple chiral additives, exhibit long-term optical stability and mechanical robustness, retaining chiroptical properties for over 100 days. The researchers’ strategy enables the creation of CPL-active materials with potential applications in 3D displays, quantum photonic circuits and anti-counterfeiting technologies.

Quasi-solid-state electrolytes, which integrate the safety characteristics of inorganic materials, the flexibility of polymers, and the high ionic conductivity of liquid electrolytes, represent a transitional solution for high-energy-density lithium batteries. However, the mechanisms by which inorganic fillers enhance multiphase interfacial conduction remain inadequately understood. In this work, we synthesized composite quasi-solid-state electrolytes with high inorganic content to investigate interfacial phenomena and achieve enhanced electrode interface stability. Li_1.3Al_0.3Ti_1.7(PO₄)₃ particles, through surface anion anchoring, improve Li⁺ transference numbers and facilitate partial dissociation of solvated Li⁺ structures, resulting in superior ion transport kinetics that achieve an ionic conductivity of 0.51 mS cm⁻¹ at room temperature. The high mass fraction of inorganic components additionally promotes the formation of more stable interfacial layers, enabling lithium-symmetric cells to operate without short-circuiting for 6000 h at 0.1 mA cm⁻². Furthermore, this system demonstrates exceptional stability in 5 V-class lithium metal full cells, maintaining 80.5% capacity retention over 200 cycles at 0.5C. These findings guide the role of inorganic interfaces in composite electrolytes and demonstrate their potential for advancing high-voltage lithium battery technology.

Multimodal sentiment analysis is an information processing technique that attempts to predict human emotional states from multiple modalities like text, audio, and video. Due to challenges in aligning multiple modalities, existing methods are limited to analysis at course or fine granularity, which risks missing nuances in human emotional expression. Researchers have now developed an innovative approach to MSA that reduces computational time required to sentiment prediction while offering improved performance.

Recently, a team led by Professor Weifeng Zhang and Peng Ning from the College of Resources and Environmental Sciences at China Agricultural University proposed a sustainable production pathway to achieve an annual yield of 22.5 t·ha^–1 in the winter wheat-summer maize rotation system on the North China Plain, providing a scientific reference for solving this problem. The related paper has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025618).