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• The sensor can detect H 2 S gas with a concentration as low as 0.5 ppm. • The frequency shift of sensor is derived from the change of mass loading on films. • The responses became much faster and stronger with the increase of RH. Surface acoustic wave (SAW) sensors based on sol–gel processed porous and nanoparticulate CuO films were explored for H 2 S detection operated at room temperature. The SAW sensor showed a negative frequency shift when exposed to H 2 S gas, due to the mass loading effect on the sensitive CuO film. It has a high sensitivity of H 2 S, a detection limit of 500 ppb and a good selectivity to H 2 S gas compared with H 2 , C 2 H 5 OH, acetone, NH 3 , CO, NO, NO 2 gases. Relative humidity (RH) was identified to have a critical influence on the sensing performance of the sensor. The responses became much faster and stronger with the increase of RH. The significant enhancement in the sensing performance is attributed to that the chemical reaction between H 2 S and CuO is hindered and the physical adsorption of CuO to H 2 S is enhanced. [ABSTRACT FROM AUTHOR]

Recent advancements in computer vision and augmented reality (AR) technology have created a pathway for enhanced human–computer interaction. Despite the potential benefits, the integration of these technologies is not yet common in the industry. Object detection is the foundation of AR systems for inspection, and an efficient object detection model will enable accurate object localization in AR devices. However, the limited computing capabilities and memory usage of wearable AR devices pose a significant challenge to the deployment of state-of-the-art detectors on these devices in real work scenarios. Moreover, as the person wearing the AR device keeps moving, the constantly changing scale of the captured image often leads to significant information loss when feature maps of different scales are fused. To address these challenges, this paper proposes a new compatible detector YOLO-Master based on improved YOLOv5 through deep learning and AR. The paper introduces several improvements, such as using MobileNetv3 as the backbone to reduce memory overhead and computational costs and designing an attention module MCA based on coordinate to reduce information loss. Besides, 20 hyperparameters of YOLOv5 are optimized by the genetic algorithm(GA) before model training, which can help reduces the loss value generated. YOLO-Master was trained and tested on hydropower device images collected from a power plant in the China Southern Power Grid, and the experiments on a self-made dataset demonstrated that the mAP of the proposed method can reach 70.1%, which is 2.9% higher than the baseline of 67.2%. Additionally, the number of parameters of the model was reduced by 66.7%, enabling the model to ensure accuracy while being compatible with real-time performance, meeting the urgent needs of engineering deployment. • A Compatible detector is proposed for device detection in AR inspection system. • Computation cost is improved by a lightweight backbone network. • A coordinate-based attention module is designed for effective feature fusion. • Optimizing hyperparameters of the new architecture to make the detector robust. [ABSTRACT FROM AUTHOR]

Gas sensors detecting low HCl gas concentrations used for early detection and continuous monitoring of leakage is of great concern because HCl gas is extremely hazardous. In order to tackle the potential hazardous threats, surface acoustic wave HCl gas sensors based on hydroxyl-rich sol-gel AlO x OH y films are reported. The fabricated SAW sensors show negative frequency shifts with exposure to HCl gas. The negative frequency shift is found to be caused by the increasing weight of sensitive films induced by the adsorption of HCl assisted by the hydroxyl groups and H 2 O captured on the films. The sensing performance of sensors is highly dependent on the heat-treatment temperature and thickness of films, as well as the ambient humidity because these parameters are highly influential on the amount of hydroxyl groups, H 2 O on films and the pore volume of films. For a sensor with a 198-nm-thick film annealed at 300 °C, it shows a response of ~ -1.4 KHz to HCl at a low concentration level (0.05 ppm) in an environment with a temperature of 25 °C and a RH value of 40%. Image 10822 • The sensor can detect HCl gas with a concentration as low as 0.05 ppm. • The hydroxyl groups and H 2 O on the sol-gel AlO x (OH) y films assist the response. • The frequency shift of sensor is derived from the change of weight of the films. [ABSTRACT FROM AUTHOR]

• The sensor can detect HCl gas with a concentration as low as 1 ppm. • H 2 O on CNCs layers assists the response. • Changes of mass of the CNCs layers cause the response. We report that cellulose nano-crystals (CNCs) can be used as a sensitive and selective layer in surface acoustic wave (SAW) sensors for in-situ HCl gas detection. CNCs were prepared through directly hydrolysis of cotton fiber and were spin-coated on quartz SAW resonators to form the sensitive layer. The CNCs have been identified to have abundant hydroxyl groups on their surfaces, which can act as the perfect adsorption sites for H 2 O, which can further act as the active sites for HCl gas adsorption. The absorption of HCl on the CNCs layer, thus leads to an increase of its mass, causing negative responses of the SAW sensors. Ambient humidity and thickness of CNCs layer are found to have significant influences on the responses of the SAW sensor. With an 80-nm-thick CNCs layer, the sensor shows a response of −2 kHz to 1 ppm HCl at 25 °C and relative humidity of 50 % with an excellent selectivity and recovery characteristics. [ABSTRACT FROM AUTHOR]