Professor Chuan He's research group at Southern University of Science and Technology reported an example of asymmetric Si–H/O–H coupling between racemic monohydrosilanes and alcohols in the same catalytic system, simultaneously achieving enantiomeric construction of the silicon chiral center and precise control of the Z/E configuration of the alkene. Through mechanistic studies combined with DFT calculations, the stereopolymerization of silicon chirality and the cis-trans isomerization process of the alkene were elucidated in detail. This reaction exhibits excellent yields and good to excellent enantiomeric selectivity, providing a new scheme for the efficient synthesis of four stereoisomers [( R,Z), (R,E), (S,Z), (S,E)]. The article was published as an open access Research Article in CCS Chemistry, the flagship journal of the Chinese Chemical Society.

An international team has obtained the first-ever experimental evidence of electrical behavior that mimics a Josephson junction with two superconductors even though only one was present.

Researchers at Shanghai University have developed a physics-constrained, data-efficient artificial intelligence framework that enables accurate thermal field inversion in chiplet-based packaging systems using only limited temperature measurements. The approach addresses a critical challenge in advanced heterogeneous integration, where increasing power density and material heterogeneity complicate thermal monitoring and reliability assessment.

Chiplet-based packaging integrates multiple heterogeneous chiplets into a single system, offering improved flexibility, yield, and cost efficiency compared to traditional system-on-chip designs. However, the resulting increase in power density and deterioration of heat dissipation conditions can lead to localized overheating, threatening system stability and long-term reliability. Accurately reconstructing the temperature field of such systems from sparse sensor data is therefore essential, yet remains a difficult inverse problem in practical engineering applications.

A joint research team from NIMS, Tokyo University of Science, and Kobe University has developed a new artificial intelligence (AI) device that exploits ion behavior to perform information processing. The team succeeded in reducing the computational load to about 1/100 of that required for conventional deep learning. The technology is expected to contribute to enhancing the information processing performance of "edge AI" operating directly on terminal equipment (an edge device). This research was published in ACS Nano on October 14, 2025.