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Nitrogen dioxide (NO2) gas sensors are pivotal in upholding environmental integrity and human health, necessitating heightened sensitivity and exceptional selectivity. Despite the prevalent use of metal oxide semiconductors (MOSs) for NO2 detection, extant solutions exhibit shortcomings in meeting practical application criteria, specifically in response, selectivity, and operational temperatures. Here, we successfully employed a facile microwave-solvothermal method to synthesize a mesoporous CeO2/CNCs nanocomposite. This methodology entails the rapid and comprehensive dispersion of CeO2 nanoparticles onto helical carbon nanocoils (CNCs), resulting in augmented electronic conductivity and an abundance of active sites within the composite. Consequently, the gas-sensing sensitivity of the nanocomposite at room temperature experienced a notable enhancement. Moreover, the presence of cerium oxide and the conversion of Ce3+ and Ce4+ ions facilitated the generation of oxygen vacancies in the composites, thereby further amplifying the sensing performance. Experimental outcomes demonstrate that the nanocomposite exhibited an approximate 9-fold increase in response to 50 ppm NO2 in comparison to pure CNCs at room temperature. Additionally, the CeO2/CNCs sensor displayed remarkable selectivity towards NO2 when exposed to gases such as NH3, CO, SO2, CO2, and C2H5OH. This straightforward microwave-solvothermal method presents an appealing strategy for the research and development of intelligent sensors based on CNCs nanomaterials.

In the production and manufacturing industry, factors such as rolling equipment and processes may cause various defects on the surface of the steel plate, which greatly affect the performance and subsequent machining accuracy. Therefore, it is essential to identify defects in time and improve the quality of production. An intelligent detection system was constructed, and some improved algorithms such as dataset enhancement, annotation and lightweight convolution neural network are proposed in this paper. (1) Compared with the original YOLOV5 (You Only Look Once), the precision is 0.924, and the inference time is 29.8 ms, which is 13.8 ms faster than the original model. Additionally, the parameters and calculations are also far less than YOLOV5. (2) Ablation experiments were designed to verify the effectiveness of the proposed algorithms. The overall accuracy was improved by 0.062; meanwhile, the inference time was reduced by 21.7 ms. (3) Compared with other detection models, although RetinaNet has the highest accuracy, it takes the longest time. The overall performance of the proposed method is better than other methods. This research can better meet the requirements of the industry for precision and real-time performance. It can also provide ideas for industrial detection and lay the foundation for industrial automation.