In a new development, researchers have successfully created an efficient two-line system for hybrid seed production in Brassica napus (rapeseed) using CRISPR/Cas9 gene editing combined with the RUBY visual reporter. 

Researchers at Westlake University have disclosed a two-dimensional (2D) mechanically interlocked polymer (MIP) that mimics medieval chainmail at the molecular scale. This micrometer-scale 2D material exhibits exceptional flexibility and stiffness, potentially revolutionizing next-generation lightweight protective gear and smart armor systems.

In a paper published in National Science Review, an international team of scientists reports dramatic, reversible p-n switching during the semiconductor-to-semiconductor phase transition of BiI₃ under pressure. They propose a novel method for determining carrier polarity based on the self-driven photocurrent dominated by the photothermoelectric effect. The study further reveals the correlation between the switching of positive and negative photoconductivity and the carrier type under high pressure.

Through satellite gravimetry analysis of Antarctic Ice Sheet (AIS) mass changes from 2002 to 2023, striking mass change rates have been identified. The study reveals the most significant mass loss occurred during 2011-2020, primarily driven by accelerated ice loss in Amundsen Sea Embayment, West Antarctica and four key glacier basins in Wilkes Land-Queen Mary Land, East Antarctica. Remarkably, an unprecedented reversal was observed during 2021-2023, with the AIS exhibiting anomalous mass gain - a phenomenon never before recorded in the satellite observation era.

The origin and the central engine of GRBs have long been pending for identification. Now a joint observation by LEIA and GECAM gives new clues.

Novel physical effects based on chiral structural materials or materials with chiral interactions are being discovered and are becoming the cornerstone for constructing new spintronic devices. A Recent collaborative study published in Science Bulletin discovered a new way to deliberately control spin orientation in a more sophisticated manner. The approach enables the engineered ‘smart’ material, an oxide heterostructure of SrRuO₃-SrTiO₃, to decide when to halt its spin rotation.

In a breakthrough that brings nanoscience one step closer to precise control of chemical reactions, a team of researchers from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences (CAS) has consecutively removed the innermost atom and the outermost electron of a gold nanoparticle—without disturbing its overall structure. This precise manipulation allowed them to probe how the magnetic spin of the material influences its catalytic activity.

In a major leap toward more comfortable and reliable health monitoring, scientists from Sun Yat-sen University have developed a cutting-edge glucose sensor that combines advanced nanomaterials with machine learning to detect glucose without the need for blood samples. The new technology is built around organic electrochemical transistors (OECTs), which are known for their ability to function safely and efficiently in watery environments, making them ideal for wearable and biocompatible devices.

The integration of the high conductivity of MoS₂ and porously structural MOF materials led to a distinct improvement in device transconductance (g_m) from 6.5 mS to 19.34 mS. This approach combined with machine leaning paves the way for advancements in OECT technology and broaden the potential of hybrid materials applied in organic biosensors.

In a new study, a series of zirconium-based metal-organic frameworks (Zr-MOFs) were successfully synthesized in aqueous environments by researchers, followed by precise enzyme immobilization achieved through ligand-exchange strategies. A fully green technological system spanning material preparation to biocatalyst construction was established, with the developed immobilized enzyme formulations demonstrating remarkable enhancements in both catalytic activity and operational stability.

Satellite data and artificial intelligence, researchers have mapped two crucial forest fire risk indicators—canopy base height (CBH) and canopy bulk density (CBD)—with an impressive resolution of 100 meters.