Researchers proposed an image-aided terminal guidance attitude control method for Explosively Formed Penetrator (EFP)-equipped multirotors to achieve long-range precision strikes, solving disturbance, EFP dispersion and visual-attitude coupling problems. Published in Defence Technology, the work built a multi-source EFP dispersion model, designed an NTSM-STO composite controller, and validated its fast response, strong robustness and high hit rate via Monte Carlo simulations and real flight tests.

An international research collaboration, from Sun Yat-sen University, South China University of Technology, University of Liverpool and JiangSu CheeShine Performance Materials Co., has developed a mechanically adaptive artificial interphase for ultrafast zinc metal batteries. By constructing a spider-web-like nitrile butadiene rubber (NBR) nanofiber interphase, the researchers achieved synergistic regulation of homogeneous Zn²⁺ transport, free-water blocking, and interfacial deformation buffering. This strategy transforms unstable zinc growth into a uniform and confined surface deposition process. Based on this interphase, Zn//Zn symmetric cells can operate stably for more than 6,000 hours, while Zn//V₂O₅ full cells deliver stable cycling for 20,000 cycles at an ultrahigh rate of 50 A g^-1, providing a new interfacial engineering strategy for safe and high-power aqueous zinc batteries.

A team of researchers from the University of Washington, Janelia Research Campus, and EMBL Heidelberg developed a new class of fluorescent imaging tags called NovoTags. This new tagging system is based on an AI model developed by Nobel Laureate David Baker, which allowed the scientists to create proteins from scratch that bind to exceptionally bright fluorescent dyes developed by Luke Lavis at the Janelia Research Campus. NovoTags will allow researchers to identify specific proteins inside cells and observe many at once, accelerating the study of protein-protein interactions. Future developments in this technology could also help researchers combine fluorescence microscopy with in situ cryo-electron tomography, providing deeper insights into structure-function relationships inside cells.

Polymer-based flexible wireless sensors have demonstrated significant potential in health monitoring. However, existing studies have predominantly focused on material design, energy harvesting, and wireless communication, while research addressing signal attenuation, noise accumulation, and data distortion within wireless links remains relatively limited. Physiological signals are highly susceptible to multi-source interference during acquisition and transmission; without effective data processing and intelligent analysis strategies, monitoring outcomes may fail to meet the accuracy and stability requirements of clinical applications. To address this research gap, this review constructs a comprehensive framework encompassing signal sensing, link transmission, and intelligent processing, building on advances in flexible materials and wireless energy supply modes. Special attention is given to the critical roles of channel compensation, noise suppression, feature extraction, lightweight artificial intelligence models, and edge computing in enhancing data quality and reducing energy consumption. Furthermore, the review systematically analyzes the impact of wireless link stability and energy management on monitoring performance. By reinforcing the synergistic mechanisms between data processing and communication, this work aims to provide design principles and development directions for the next generation of high-reliability, low-power, and intelligent wireless flexible health monitoring systems.

The Fe–N–C single-atom catalyst represents a promising candidate for promoting the oxygen reduction reaction (ORR), which is crucial for fuel cell applications; yet, identifying optimal modification strategies to enhance its activity and stability remains challenging. Herein, the modulation of Fe–N–C catalysts via in-plane heteroatom doping and axial coordination is systematically investigated using integrated density functional theory (DFT) and machine learning (ML) approaches. The analysis reveals that axial ligands have a more profound influence on ORR performance than in-plane dopants, primarily by modulating the Fe dz2 orbital and weakening *OH adsorption. Through interpretable descriptors extracted from ML models, the key electronic and geometric properties governing catalyst behavior are identified, and several novel dual-modified candidates with enhanced activity relative to pristine Fe–N–C are subsequently predicted and validated by DFT calculations. This work provides a unified mechanistic and data-driven framework for accelerating the design of high-performance Fe–N–C ORR electrocatalysts.