The Korea Research Institute of Standards and Science (KRISS, President Lee Ho Seong) has developed a diagnostic platform that amplifies the unique optical signals of molecules by more than a hundred million times, enabling the precise detection and quantification of trace amounts of Alzheimer’s disease biomarkers in body fluids.

The 2025 MRS International Risk Conference, jointly organized by China Finance Review International (CFRI), Suffolk University’s Sawyer Business School, and the Modern Risk Society (MRS), successfully concluded in Boston from 24 to 26 July 2025. The three-day conference united leading scholars and industry experts from around the world, emphasizing the importance of cutting-edge research in risk and finance.

Solid-state sodium batteries (SSSBs) are emerging as a promising alternative to conventional lithium-ion batteries, owing to their enhanced safety, cost-effectiveness as well as the abundance of sodium resources. However, despite their conceptual advantages, significant performance degradation, mainly associated to the electrode-electrolyte interfaces, has hindered their widespread application. A recent study led by researchers from the Beijing Institute of Technology provides a comprehensive mechanistic understanding of interfacial degradation in NASICON-type electrolyte-based solid-state sodium metal batteries. Their work focuses on Na₃Zr₂Si₂PO₁₂ (NZSP), a widely studied ceramic electrolyte known for its robust thermal stability and competitive ionic conductivity, yet plagued by poor long-term interfacial performance.

A group of researchers from Northeastern University of China developed a novel FeCrVNiAl eutectic high-entropy alloys (EHEA) that exhibits a remarkable combination of mechanical strength and high corrosion resistance for marine environments. The alloy integrates hierarchical nanoscale precipitates of B2 (NiAl) and L2₁ (Fe₂CrV) phases within its matrix, which are precisely controlled through solid solution and aging treatments. These precipitates induce multistage strengthening mechanisms, including dislocation interactions, strain hardening, and the formation of misfit dislocations at coherent interfaces. The result is an alloy capable of bearing compressive stresses up to ~3.05 GPa, while simultaneously maintaining outstanding ductility and strain-hardening capabilities. Additionally, the microstructure promotes the formation of a stable passive film comprising chromium oxide, which reduces corrosion current density and enhances resistance in saline, marine-like environments. This dual achievement addresses a key challenge faced by traditional marine materials, which often fail in harsh conditions either by lacking sufficient strength or corrosion resistance. The study offers a new microstructural design that not only achieves these demanding properties but also provides insights into the underlying mechanisms contributing to this synergy.

Solid-state batteries are widely recognized as the next-generation energy storage devices with high specific energy, high safety, and high environmental adaptability. However, the research and development of solid-state batteries are resource-intensive and time-consuming due to their complex chemical environment, rendering performance prediction arduous and delaying large-scale industrialization. Artificial intelligence serves as an accelerator for solid-state battery development by enabling efficient material screening and performance prediction. This review will systematically examine how the latest progress in using machine learning (ML) algorithms can be used to mine extensive material databases and accelerate the discovery of high-performance cathode, anode, and electrolyte materials suitable for solid-state batteries. Furthermore, the use of ML technology to accurately estimate and predict key performance indicators in the solid-state battery management system will be discussed, among which are state of charge, state of health, remaining useful life, and battery capacity. Finally, we will summarize the main challenges encountered in the current research, such as data quality issues and poor code portability, and propose possible solutions and development paths. These will provide clear guidance for future research and technological reiteration.

The advancement of fibre electronics is crucial for developing wearable smart textiles. However, traditional single-function fibres are typically limited to basic sensing and data collection capabilities, lacking effective computational and multimodal signal processing abilities, thus significantly restricting their potential in human activity recognition. Recently, Gupta et al. introduced an innovative single-fibre computer embedding eight microelectronic devices, integrating sensing, communication, and computation into a single fibre. Establishing a distributed cooperative fibre network substantially enhanced human activity recognition accuracy from 67% (single-fibre scenario) to 95%. This novel approach effectively addresses the limitations of conventional smart fibres, paving the way for multi-point sensing, edge-based inference, and real-time human–computer interactions in future intelligent textiles.