Drone technology is poised for remarkable progress across multiple domains, with next-generation systems set to transform disaster response, healthcare, agriculture, logistics, archaeology, environmental management, farming, and numerous other fields vital for human development. Next-generation drones are expected to have far greater endurance, including extended flight ranges, longer operational duty cycles, and enhanced resilience. These capabilities will enable sustained, long-duration missions, such as long-distance medical or commercial deliveries, as well as wide-area surveillance across densely populated urban environments and expansive forested regions.

Professor Ying-Wei Yang's research group at Jilin University successfully constructed an azobenzene-modified metal-organic framework (MOF)-based nano-impeller platform using a dimensional engineering strategy. They synthesized two-dimensional layered Zn-Azo-MOF-1 and three-dimensional network Zn-Azo-MOF-2 with the same composition but different dimensions. They found that the photoisomerization efficiency of the two-dimensional framework (cis isomer content 33%) was an order of magnitude higher than that of the three-dimensional framework (3%). Mechanical grinding triggered interlayer slip in the two-dimensional structure, breaking the spatial confinement and activating efficient photoisomerization. Analysis of rotational energy barriers and framework-ligand interactions revealed the mechanism by which dimensionality and mechanical activation synergistically regulate the photo-switching dynamics. The constructed two-dimensional Zn-Azo-MOF-1 achieved 99% cargo release within 50 minutes under alternating UV-Vis irradiation, successfully extending the nano-impeller function from an amorphous platform to a crystalline platform, providing a dimensional engineering design principle for the programmable switching behavior of stimulus-responsive materials. The article was published as an open access Research Article in CCS Chemistry, the flagship journal of the Chinese Chemical Society.

Scientists have developed a 3D, AI-based tool for viewing hearing cells. To understand hearing damage from noise and aging, and develop new treatments, scientists need detailed images of hair cells. The new VASCilia tool uses deep learning to accelerate sensory cell image processing and analysis.