A wearable and shape-memory strain sensor with a coaxial configuration is designed, comprising a thermoplastic polyurethane fiber as the core support, well-aligned and interconnected carbon nanotubes (CNTs) as conductive filaments, and polypyrrole (PPy) coating as the cladding layer. In this design, the stress relaxation between CNTs is well confined by the outer PPy cladding layer, which endows the fibriform sensor with good reliability and repeatability. The microcracks generated when the coaxial fiber is under strain guarantee the superior sensitivity of this fibriform sensor with a gauge factor of 12 at 0.1% strain, a wide detectable range (from 0.1% to 50% tensile strain), and the ability to detect multimodal deformation (tension, bending, and torsion) and human motions (finger bending, breathing, and phonation). In addition, due to its shape-memory characteristic, the sensing performance of the fibriform sensor is well retained after its shape recovers from 50% deformation and the fabric woven from the shape-memory coaxial fibers can be worn on the elbow joints in a reversible manner (original-enlarged-recovered) and fitted tightly. Thus, this sensor shows promising applications in wearable electronics.

Micro-supercapacitors exhibiting excellent AC line-filtering with oriented coordination polymer thin-film electrodes are fabricated based on a substrate-independent electrode fabrication strategy.

It remains a major issue to assess health condition and degree of vibration damage of flood discharge structure by working features in recent years. In the process of acquisition and transmission, because vibration signals are susceptible to interference from high-frequency white noise and low-frequency water flow noise, they are usually shown in the form of nonstationary random signals with low signal to noise ratio. Modal information is hard to be precisely recognized as the character of structural vibration is drowned into the strong noise. In order to remove the noise and preserve structural characteristic information, a new characteristic information extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) entropy (CEEMDAN-PE) is proposed. Firstly, the vibration signal is decomposed into a series of intrinsic mode functions (IMFs) by CEEMDAN, and then low-frequency water flow noise can be filtered out through spectrum analysis of each IMF component. Secondly, the noise degree of each IMF is determined by permutation entropy and high-frequency noise in IMFs is filtered out by singular value decomposition. Finally, the noise elimination IMFs are reconstructed to obtain the operating characteristic information of flood discharge structure. The effectiveness of the proposed method on characteristic information extraction is validated by a simulation experiment. Furthermore, the proposed method was applied to the 5th overflow section of Three Gorges Dam and the analysis results show that the CEEMDAN-PE method can effectively remove the noise and extract dominant frequencies of flood discharge structure, which provides foundation for health monitoring and damage identification of flood discharge structure with a strong engineering practicability.