Nitrogen-rich porous networks with additional polarity and basicity may serve as effective adsorbents for the Lewis electron pairing of iodine molecules. Herein a carbazole-functionalized porous aromatic framework (PAF) was synthesized through a Sonogashira–Hagihara cross-coupling polymerization of 1,3,5-triethynylbenzene and 2,7-dibromocarbazole building monomers. The resulting solid with a high nitrogen content incorporated the Lewis electron pairing effect into a π-conjugated nano-cavity, leading to an ultrahigh binding capability for iodine molecules. The iodine uptake per specific surface area was ~8 mg m−2 which achieved the highest level among all reported I2 adsorbents, surpassing that of the pure biphenyl-based PAF sample by ca. 30 times. Our study illustrated a new possibility for introducing electron-rich building units into the design and synthesis of porous adsorbents for effective capture and removal of volatile iodine from nuclear waste and leakage.

A series of soft porous aromatic frameworks (PAFs) with additional π-conjugated fragments provides sufficient space for the binding sites which serve as physicochemical stable mediums for radioiodine.

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Because the traditional oil level gauge transformer relies on manual regular patrol detection, it is not easy to know the state change of the oil level in real time. In recent years, the rapid development of AR/VR technology, the positioning, and the construction of maps of robots have also received widespread attention, and the oil level inspection task has gradually evolved in the direction of intelligent machine inspection. Based on this background, this paper studies the Cartographer algorithm, according to the mapping results for path planning, to achieve robot autonomous navigation of the designated location. After arriving at the destination, the user obtains the oil level by the depth camera, and uses the YOLOv4 to design an intelligent detection algorithm for oil level detection, using neural networks to learn the oil level state in various weather, and train a dedicated model. Through experimental verification, the inspection robot can complete the construction of the map and realize autonomous path navigation according to the target point, and the oil level detection system based on the YOLOv4 algorithm can achieve an accuracy of more than 90% for the recognition of oil level information, which has good applicability.

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