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Epoxy vitrimers based on transesterification reactions (TERs) is a kind of recyclable thermosets which have been developed prosperously in recent years. However, the good thermal performance and the quick network...

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Polyaniline (PANi) hydrogels often exhibit highly mechanical and electrochemical properties, which have received extensive attention in the fields of batteries, supercapacitors, and sensors. However, the shortcomings such as hydrophobicity and easy aggregation of PANi frequently result in deterioration of mechanical and electrochemical performance of PANi hydrogels. Here, a bifunctional natural product, glycyrrhizic acid (GL), is utilized to prepare the homogeneous conductive PANi hydrogel, because GL not only can assemble into supramolecular hydrogel as the biocompatible matrix but also can salinize aniline monomers to facilitate the polymerization in situ to form uniformly dispersed PANi within GL matrix. Accordingly, the resulting GL/PANi hydrogel shows the Tyndall effect caused by the nanoclusters entangled by nanofibers and exhibits an improved storage modulus G′ (3.2 kPa) and loss modulus G″ (0.9 kPa), as well as the expected conductivity (0.17 S·m–1). In addition, the GL/PANi hydrogel is further reinforced by blending poly(vinyl alcohol) (PVA) for the required strength and stretchability as a flexible strain sensor. The results reveal that the obtained PVA/GL/PANi hydrogel has a fracture stress of 693 kPa at an elongation of 329%, with a fracture toughness of 82 MJ·m–3 and Young’s modulus of 47.9 kPa. Its gauge factor (GF) is measured to be 2.5 at lower strain (<130%) and up to 4.3 at larger strain (>130%). This good sensitivity and sensing stability make the PVA/GL/PANi hydrogel effectively monitor relevant human motion detections. Our work provides an innovative strategy to manufacture the homogeneous conductive PANi hydrogel for high-performance soft electronic devices.

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With the rapid developments in metro systems worldwide, more research concerning optimization of maintenance actions is needed, because the availability and service state of a metro system directly influences the daily activity of a city and its people. In particular, the prediction of wear and maintenance optimization of wheels is significant. Maintenance costs for a rail track subsystem represent more than half the total maintenance costs for a metro line. A hard rail–soft wheel compromise extends the life of the rails and increases the wheel replacement frequency with economic benefits. An improved strategy for predicting and maintaining wheel wear will allow agencies to improve reliability, enhance safety, and maximize wheel life while minimizing relevant costs. In this study, historical data are used to analyze wheel wear curves, and the flange thickness and wheel diameter are identified as the most important profile parameters. A new data-driven model of wheel wear trends is given for variations in wheel diameter and flange thickness. An approach for optimizing the wheel reprofiling strategy is based on this model and determines the optimum reprofiling point that maximizes wheel life while minimizing relevant costs. An initial case study on the Shanghai, China, metro network shows that the proposed approach can provide a reasonable solution for optimization of the reprofiling strategy.