Ultrasensitive triethylamine gas sensors with ZnSe nanospheres/nest-like Cr-doped MoO3.

Developing high-sensitivity TEA sensors has extremely important significance for human health. Design of three-dimensional (3D) nanostructures assembled from one-dimensional nanomaterials can effectively improve sensing performance. In this work, a nest-like structure assembled by Cr-doped MoO3 (Cr-MoO3) nanorods with relatively higher specific surface area was prepared. In order to improve the sensing performance, Cr-MoO3 skeleton was combined with ZnSe nanospheres of different mass ratios as sensing materials (ZnSe/Cr-MoO3), and the successful construction of the heterojunction structure was supported by various spectroscopies and charge density calculation. The prepared composite with an optimal moiety ratio showed very high response values of 371 and 1301 for 10 ppm and 50 ppm for TEA at 200 °C, respectively. Simultaneously, the composite sensor also exhibited a low detection limit (1.7 ppb). The improvement of the sensing performance of ZnSe/Cr-MoO3 was attributed to the formation of oxygen vacancies induced by Cr doping, the 3D nest-like structure provided an efficient network for charge transport/collection and the n-n heterojunctions between Cr-MoO3 nanorods and ZnSe nanospheres. The simulation analysis based on density functional theory (DFT) calculations indicated that the heterojunctions could effectively enhance the adsorption energy of TEA and the more charges transferring from TEA to the Cr-MoO3 nanorods.

A quantitative identification method based on CWT and CNN for external and inner broken wires of steel wire ropes.

The detection of broken wires in steel wire ropes is of great significance for the production safety. However, the existing identification techniques mainly focus on the external broken wires problem. Here, the artificial feature extraction is one of the most important method, while only the prior knowledge of the artificial feature extraction method is adequate, the identification precision can be satisfied. Therefore, it is still a challenge to realize intelligent diagnosis for the broken wires. Besides, the identification of internal broken wires problem is still not well solved. In this paper, a quantitative identification method based on continuous wavelet transform (CWT) and convolutional neural network (CNN) is proposed to solve the internal and external broken wires identification problem. The key technology of this research is that the fault information from the time-frequency images converted by the magnetic flux leakage (MFL) signals can be automatically extracted through a designed CNN. The main innovation is that the complex signal processing work can be eliminated and the internal and external broken wires can be accurately identified simultaneously by combining CWT and CNN. The experimental results of a steel wire rope test rig are compared with the traditional recognition method, which shows that the proposed method achieved significant improvement on detection accuracy and recognition performance.