Cement production accounts for 5%–8% of global CO₂ emissions, and carbonation has long been seen as a natural way to offset these outputs. However, a new study in Engineering finds this approach is deeply flawed: slow, decades-long absorption delays benefits by 30%–60%, and more than half of related projects fail to deliver real emission cuts. The research urges shifting to proven, immediate decarbonization methods instead.

Looking for a practical low-carbon fuel for the global energy shift? A new study in Engineering shares the latest progress in ammonia-fueled power generation. Researchers have tested 100% ammonia gas turbines and 20% ammonia–coal co-firing in large-scale power plants, showing stable performance and controlled emissions. Ammonia proves to be an efficient hydrogen carrier, bringing reliable, decarbonized power solutions closer to real-world use.

Lanthanide-doped nanoparticles exhibit exceptional photon conversion capabilities and photostability, making them promising tools for biomedical imaging, sensing, and theranostics. A new review published in PhotoniX Life summarizes recent advances in their synthesis, optical engineering, and potential clinical applications, while addressing challenges in biosafety and regulatory approval.

Zhejiang University researchers introduce a universal 'best-matched absorption' criterion to select optimal near-infrared (NIR) imaging windows for fluorescence wide-field microscopy. This work solves the critical gap of scattered light background in high numerical aperture (NA) systems, clarifies how system NA, imaging depth and probe brightness shift the optimal window, and offers tailored strategies for complex in vivo imaging scenarios, greatly improving image contrast and penetration for biomedical research and clinical applications.

A review accepted by PhotoniX Life examines how deep learning is transforming cell segmentation in live-cell microscopy. The article traces the field from thresholding and watershed algorithms to U-Net, Mask R-CNN, StarDist, Cellpose, Mesmer, transformer-based models, and emerging generalist segmentation frameworks. It highlights why segmentation is central to quantitative cell biology, where current models succeed, and what remains difficult, including cross-modality generalization, 3D volumetric data, annotation scarcity, user-friendly deployment, and organelle-level analysis.