Prof. Shiyuan Liu’s team from Huazhong University of Science and Technology has reported a full-chip EUV curvilinear mask optimization framework that integrates deep learning with gradient-based inverse optimization. This framework achieves an estimated speedup of four orders of magnitude over conventional FDTD methods while preserving accuracy, thereby enabling full-chip EUV mask optimization within a practical runtime. Doctoral student Pinxuan He is the first author of this paper, and Prof. Shiyuan Liu and Dr. Jiamin Liu are the co-corresponding authors. Prof. Honggang Gu, Dr. Song Zhang, Prof. Qi Xia, and Prof. Hao Jiang also contributed to this research.

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.