Understanding and controlling the interactions between nanoscale objects and living cells is of great importance for diagnostic imaging and therapeutic applications. Quantum dots (QDs) have remarkable optical characteristics, such as uniquely feature bright, photostable, tunable and narrow fluorescence emissions, as well as broad absorption spectra. Here we report a platform of using quantum dots to investigate the cell uptake and the interactions between nanoscale objects and cells. QDs are uptaken by BHK cells easily through endocytosis. We could clearly differentiate the QDs outside the cell or inside the cell by quenching the QDs with similar sized gold nanoparticles and reduce the noise of fluorescent image. Microscopic images show that QDs are homogeneously distributed within the whole cell except the nucleus. However, unmodified QDs could not penetrate the nuclear membrane and move into the nucleus. Coupling QDs with Nuclear Localization Signal (NLS, CGGGPKKKRKVGG) can significantly enhance the translocation amount of QDs into the cell and cell nucleus. This method combined with microscopy imaging system can visualize the particle delivery routes and provide valuable information in the drug/gene delivery and tumor diagnosis.

No abstract

No abstract

Based on the examination of the fundamental characteristics of structures, structural health monitoring (SHM) has received increased attention in recent years. Studies have shown that the SHM method using entropy analysis can precisely identify the damaged location of the structure, which is very helpful for the daily inspection or maintenance of civil structures. Although entropy analysis has shown excellent accuracy, it still consumes too much time and too many resources in terms of data processing. To improve the dilemma, in this study, modified multi-scale symbolic dynamic entropy (MMSDE) is adopted to identify the damaged location of the civil structure. A damage index (DI) based on the entropy diagram is also proposed to clearly indicate the damage location. A seven-story numerical model was created to verify the efficiency of the proposed SHM system. The results of the analysis of each case of damage show that the MMSDE curve for the damaged floor is lower than that for the healthy floor, and the structural damage can be correctly diagnosed by the damage index. Subsequently, a scaled-down steel benchmark experiment, including 15 damage cases, was conducted to verify the practical performance of the SHM system. The confusion matrix was used to further evaluate the SHM system. The results demonstrated that the MMSD-based system can quickly diagnose structural safety with reliability and accuracy. It can be used in the field of long-term structural health monitoring in the near future.