Improving X-ray imaging under photon-starved conditions could enable lower-dose and higher-resolution imaging for medical diagnosis, security inspection, and industrial testing. Researchers from Chongqing University, the 26th Institute of CETC, and Stevens Institute of Technology developed a scalable photonic crystal scintillator using Marangoni effect-driven interfacial self-assembly. The approach increased average light output by 4.64-fold and improved the X-ray imaging signal-to-noise ratio by 13 dB while maintaining spatial resolution better than 25 μm.

A new review published in PhotoniX Synergy examines how artificial intelligence is transforming the design of terahertz (THz) metamaterial devices (meta-devices). The authors survey conventional and AI-assisted approaches for THz meta-absorbers, modulators, filters, sensors, detectors, and multifunctional devices, and show how machine learning, evolutionary algorithms, deep learning, generative models, and large language models can establish data-driven links between structural geometry and electromagnetic response. The review also discusses challenges including computational cost, limited interpretability, data scarcity, and manufacturability, and outlines future directions such as physics-informed learning, multi-scale optimization, and AI-assisted optical computing hardware for 6G communication, imaging, biosensing, spectroscopy, and security inspection.

Professor Yoonsu Park’s team in the Department of Chemistry automated the entire process from mixing raw materials to synthesis and analysis. The platform continuously produces compounds needed for drug development, showing the potential to increase production volume. The technology is expected to reduce the burden of repetitive experiments and help secure a wider range of drug candidates.