Recently, the team led by Professor Xin Xu from the School of Atmospheric Sciences, Nanjing University published a short communication in Science Bulletin entitled “Complex Terrain Causes Global Model Prediction Biases of 21.7 Zhengzhou Extreme Precipitation”. The study reveals that the orographic gravity wave drag triggered by complex terrain can cause significant location and intensity biases of the “21.7” Zhengzhou extreme precipitation in global numerical weather prediction (NWP) models.

Nanofluidic devices are attracting more and more attention due to their unique features: ions as carriers, which makes them highly biocompatible. However, it is still unclear how the ion transport influences the devices’ performance and how the multimodal nanofluidic devices are fabricated within the same architecture. Here, an innovative approach by a team from Southern University of Science and Technology has demonstrated that it is the comparison between the interionic distance and the Bjerrum length which governs the ionic states and thus the electrical responses. These findings pave the way towards more complex nanofluidic devices and circuits.

ROS serve as crucial mediators of redox homeostasis, playing decisive roles in diverse pathological processes. While inorganic sonosensitizers for SDT represent significant advances in ROS-based therapeutics, developing US-responsive sonosensitizers with optimal biocompatibility, and initiating innovative treatment strategies remains a substantial translational challenge. In this work, researchers demonstrate that FeOOH nanorods function as exceptional tribocatalysts capable of efficiently converting vibrational mechanical energy into therapeutic effects through sono-tribocatalytic activation.

Achieving precise, light-triggered control over solid-state materials remains a longstanding challenge. This study reports a pair of structurally analogous copper-based nanoclusters whose composition enables controllable photo-conversion efficiency, but also allows precise spatiotemporal regulation of material composition at the micron scale. The study reveals the photo-conversion mechanism at the atomic level, offering a new pathway for developing solid-state photo-responsive materials

Recent research introduces a machine learning capable of predicting whether a driver is likely to be involved in an accident before they get behind the wheel. The significance of this work lies in its proactive and developmental focus. Instead of reacting to accidents after they occur, the proposed framework enables transportation companies to evaluate risk tendencies prior to deployment.