robots, [3] actuators, [4] etc. Among them, zwitterionic hydrogels have recently attracted tremendous attention due to their exceptional electrical properties and special biological functions, boosting progress in energy storage and biomedical applications. [5] In particular, polybetaines, bearing a pair of cationic and anionic groups in their repeating units, exhibit equal charge stoichiometry and uniform charge distribution at the molecular level. The homogenously distributed dynamic ionic bonds within the polybetaine hydrogels contribute to an efficient and rapid self-healing capability, which allows for significant deformations and autorecovery after injury during their service life. Such characteristics make polybetaine hydrogels uniquely competitive in a wide range of applications. [6] The humidity and temperature in living environment varies greatly, roughly 30-90% relative humidity (RH) and −20 to 40 °C, [7] therefore, wide-humidity range applicable and freeze-resistant hydrogels are very crucial in order to achieve reliable applications at ambient conditions. Hydrogels tend to dry out when exposed to dry environments, [8] freeze at low temperatures, [9] and swell due to water absorption in high humidity environments, [10] all of which severely affect their properties, thus limiting further applications of hydrogels in flexible electronic devices. Various methods have been reported to improve the water retention and freezing tolerance of hydrogels. Encapsulating the hydrogels can effectively prevent the Hydrogels have entered the spotlight for applications in soft electronics. It is essential and challenging to obtain hydrogels that can function properly under varying environmental circumstances, that is, 30-90% relative humidity (RH) and −20 to 40 °C due to their intrinsic nature to lose and absorb water upon variations in humidity and temperature. In this work, a green solvent, solketal, is introduced into poly 3-dimethyl-2-(2-methylprop-2-enoyloxy)ethyl azaniumyl propane-1-sulfonate (poly(DMAPS)) zwitterionic hydrogels. Compared to glycerol, solketal endows hydrogels with greater possibility for further modification as well as improved water content and mechanical performance consistency over 30-90% RH. Encouragingly, the optimized hydrogel demonstrates its unique merits as a dielectric layer in iontronic sensors, featuring non-leaky ions, high sensitivity (1100 kPa −1 ), wide humidity, and temperature range applicability. A wide-humidity range healable and stretchable electrode is attained by combining the hydrogel substrate with Ag paste. A full-device healable and highly-sensitive sensor is developed. This study is a pioneering work that tackles the broad humidity range applicability issue of hydrogels, and demonstrates the ion-leakagefree ionic skins with zwitterionic dielectrics. The outcomes of the study will considerably promote advancements in the fields of hydrogel electronics and iontronic sensors.
Hydroelectricity is a major source of renewable electricity originating from a turbine driven by dammed largevolume water via a penstock or a drop shaft. Shafts suffer from risks of collapse due to the pressure from exterior structures and the erosion from inner water flow. Vertical shafts are an important part of hydroelectric power generation systems, and detecting defects in shafts guarantees the stable operation of hydropower stations. However, shaft defect detection is a great challenge due to the poor conditions, large drop in height, limited entrance size, lack of light and damp air, where suitable technology and equipment are not available. Aiming at defect detection for vertical shafts, we have developed a defect-detecting system based on unmanned airships, integrated panoramic CCD cameras, 3D laser scanners, inertial measurement units, barometric altimeters, illumination sensors, and control modules. Shaft defect detection methods (SDDMs) are proposed by fusing the multi-modal image features to extract typical defects on concrete surfaces. Compared with machine learning methods, the proposed method achieves the highest overall accuracy of 90.90% for defect detection. Our system was validated by experiments in the shafts of the Nuozhadu hydropower station to be functional for defect detection, which demonstrates its capability of reducing the risk of collapse and improving safety.
Crack detection is essential for the safety maintenance of road infrastructure. However, there are two major limitations to detecting road cracks accurately: (1) tiny cracks usually possess less distinctive features and are more susceptible to noises, so they are apt to be ignored; (2) most existing methods extract cracks with coarse and thicker boundaries, which needs further improvement. To address the above limitations, we propose CTCD-Net: a Cross-layer Transmission network for tiny road Crack Detection. Firstly, we propose a cross-layer information transmission module based on an attention mechanism to compensate for the disadvantage of unobvious features of tiny cracks. With this module, the feature information from upper layers is transmitted to the next one, layer by layer, to achieve information enhancement and emphasize the feature representation of tiny crack regions. Secondly, we design a boundary refinement block to further improve the accuracy of crack boundary locations, which refines boundaries by learning the residuals between the label images and the interim coarse maps. Extensive experiments conducted on three crack datasets demonstrate the superiority and effectiveness of the proposed CTCD-Net. In particular, our method largely improves the accuracy and completeness of tiny crack detection.
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