The efficient separation of Zr(IV) ions from strong acidic and radioactive solutions is a significant challenge, especially in the context of the aqueous reprocessing of nuclear fuels. The complexity of such solutions, which are often characterized by high acidity and the presence of radioactive elements, poses formidable challenges for separation processes. Herein, several mesoporous silicas (HMS, MCM-41, KIT-6, and SiO₂-70 Å) with excellent acid and radiation resistance properties were employed as sorbents to remove Zr(IV) ions from simulated high-level liquid waste. The batch experiments were designed to investigate the influence of adsorption time, HNO₃ concentration, initial Zr(IV) concentration, adsorbent dosage, and temperature on the adsorption behavior of Zr(IV). The results indicate that the adsorption equilibrium time of mesoporous silica materials was approximately 8 h, and all the adsorption processes followed the pseudo-second-order kinetics equation. The isotherms of Zr(IV) adsorption by KIT-6 exhibited good agreement with the Langmuir model, while the Freundlich model could be utilized to fit the adsorption on HMS, MCM-41, and SiO₂-70 Å. The adsorption capacity of MCM-41 for Zr(IV) in 3 mol/L HNO₃ was 54.91 mg/g, which is three times the adsorption capacity reported for commercial silica gel (17.91 mg/g). The thermodynamic parameters indicate that the adsorption processes for zirconium are endothermic reactions. Furthermore, the mesoporous silicas exhibited a pronounced selectivity in the adsorption of Zr(IV) within a simulated high-level liquid waste containing 10 co-existing cations (3 mol/L HNO₃). This suggests that mesoporous silicas have great potential for Zr(IV) removal in actual radioactive liquids with high acidity during spent fuel reprocessing.

Airport recognition remains an important and challenging topic for research. In general, there are four main steps in the process of airport recognition: pre-processing of remote sensing images, features extraction, rough location and the recognition of airport. This paper puts forward an automatic airport recognition method which adopts improved methods in each step. In pre-processing, an edge-preserve image smoothing algorithm based on Convexity Model is developed. In features extraction, the Canny operator and chain codes are used. An improved Κ-Means lines segmentation and estimate rules are used to find candidate areas in rough location of the airport. And those candidate regions are binarized and prior knowledge is used in airport recognition. Experimental data and application results show that the above methods are efficient and enhance the accuracy in airport recognition.