CAD-NeRF rebuilds 3D scenes from < 10 images via ShapeNet CAD retrieval + self-supervised pose-density joint optimization

BG²VN toolbox generates Gaussian-based benchmark graphs with ground-truth vital nodes, offering the first standard test-bed for comparing vital-node mining algorithms

A novel electrochemical sensor using cheap conductive carbon black and a specific electrolyte provides a highly sensitive, stable, and simple method for detecting the antibiotic cefadroxil (CFL) in environmental water, pharmaceutical, and biological serum samples.

Recently, a collaborative team from multiple institutions, including CIOMP, published a review article in Light: Science & Applications, systematically expounding on the cutting-edge developments in Surface-Enhanced Raman Scattering (SERS)-integrated optical waveguide technology. By reviewing two major technical pathways—remote sensing probes and microfluidic sensing platforms—the study thoroughly analyzes key innovations such as optical fiber structure design, SERS substrate modification, and the integration of emerging technologies. It clarifies the core advantages of this technology in improving detection sensitivity, simplifying operational procedures, and enabling miniaturization. The research not only summarizes the technological breakthroughs and application achievements in this field but also identifies future challenges such as large-scale fabrication and specificity optimization. It provides important academic references for the development of ultra-sensitive trace liquid detection technologies and is expected to drive innovations in detection technologies in fields such as biomedicine and environmental monitoring.

A new electrocatalyst combining iron phthalocyanine (FePc) with mesoporous Ti₃C₂ MXene significantly enhances oxygen reduction reaction (ORR) performance under alkaline conditions, achieving higher activity and stability than commercial platinum-based catalysts, and demonstrating great potential for application in zinc-air batteries.  

A research paper by scientists from Tianjin University proposed a novel solution for high-speed steady-state visually evoked potential (SSVEP)-based brain–computer interfaces (BCIs), featuring a neural principle-based data augmentation technique (BGMix) and a Transformer-based model (AETF) to enhance EEG decoding efficiency.