With the increasingly prominent role of high-resolution remote sensing data in supporting the digital economy and intelligent industries, on-orbit assembly of large space optical systems has become a critical pathway to overcome launch vehicle constraints and achieve large-aperture detection. However, during on-orbit assembly, the manipulator is required to simultaneously satisfy multiple objectives, including replicating standardized paths, avoiding non-cooperative obstacles, and precisely passing through key waypoints. Existing global methods are limited by the real-time bottleneck of high-dimensional search, while local methods lack global consistency guarantees. Although Dynamic Movement Primitives (DMPs) possess parametric trajectory representation capabilities, they struggle to reconcile the inherent conflict between path shape accuracy and safe obstacle avoidance under multi-objective constraints. Therefore, how to synergistically optimize path passing accuracy and obstacle avoidance safety for manipulators in dynamic, unstructured space environments has become a core challenge constraining the effectiveness of on-orbit autonomous assembly for large-aperture optical systems.

Researchers built more energy-efficient and high-performance phase change random access memory by learning how to sculpt heat within a nanometer-thin sandwich of materials. They engineered a "double-confined" structure that stacks alternating slices of antimony telluride (Sb₂Te₃) with two distinct helper compounds: highly conductive nickel ditelluride (NiTe₂) and heat-blocking molybdenum ditelluride (MoTe₂).

Researchers at Nanjing University have experimentally demonstrated a quantum communication protocol that enables three users to establish a shared secure key with substantially improved performance. By combining more efficient key generation with operation without phase locking, the work advances a key building block for future multiuser quantum networks.

Graph neural networks have been used in various graph-related tasks, where they have achieved excellent performance. However, incomplete graph structures, commonly found in real-life scenarios, can severely degrade performance. In a new study, researchers develop a novel graph completion network that reconstructs missing features and structure simultaneously while also maintaining performance with extremely weak supervision, which is difficult to achieve with conventional models.

Researchers have demonstrated a scalable 3.2 Tb/s O-band silicon photonic interconnect architecture designed to address the growing bandwidth demands of artificial intelligence (AI) computing. Published in PhotoniX Synergy, the work combines hybrid wavelength-port multiplexing with adaptive wavelength stabilization to enable high-bandwidth and robust optical I/O, providing a scalable pathway toward terabit-scale silicon photonic interconnects for next-generation AI computing systems.

A research team led by Prof. ZHANG Hongwen at the Institute of Solid State Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, has developed a portable surface-enhanced Raman spectroscopy (SERS) sensor that can rapidly detect trace amounts of sarin and other organophosphorus (OP) nerve agents.

The new sensor uses a specially designed molecular probe to recognize the P–C bond found in OP nerve agents. It can detect sarin in both liquid and gas phases, making it suitable for rapid on-site screening.