Mussel-inspired polydopamine (PDA) can serve as building blocks and interfaces in designing functional materials. Here, the use of PDA as an interlayer between polyaniline (PANi) and multidimensional carbon materials, such as graphene quantum dots (GQD), multiwalled carbon nanotubes (MWCNT), and graphene nanosheets (GNS), to improve the thermoelectric performance of p-type polymer-based materials has been reported. The introduction of PDA promotes the carrier mobility of GQD/PDA/PANi, CNT/PDA/PANi, and GNS/PDA/PANi ternary composites due to the superior adhesive property of PDA. An optimal conductivity of 4.98 × 104 S m–1 and a power factor of 92.17 μW m–1 K–2 at 363 K are achieved in GNS/PDA/PANi, which are much higher than the values of GNS/PDA and GNS/PANi. More surprisingly, despite the fact that GQD/PDA, CNT/PDA, and GNS/PDA binary composites show p-type properties, the pyrolysis treatment of GQD/PDA, CNT/PDA, and GNS/PDA at 800 °C results in a gain in both the electrical conductivity and negative Seebeck coefficient of c-GQD/PDA, c-CNT/PDA, and c-GNS/PDA. The c-CNT/PDA composites possess the highest Seebeck value of −30.2 μV K–1 and a maximum power factor value of 35.57 μW m–1 K–2. Finally, a flexible thermoelectric generator with 24 thermoelectric units composed of GNS/PDA/PANi and c-CNT/PDA is demonstrated, which gives an output voltage of 52.8 mV at a temperature difference of 60 °C.

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At present, machine learning methods are widely used in various industries for their high adaptability, optimization function, and self-learning reserve function. Besides, the world-famous cities have almost built and formed subway networks that promote economic development. This paper presents the art states of Defect detection of Shield Tunnel lining based on Machine learning (DSTM). In addition, the processing method of image data from the shield tunnel is being explored to adapt to its complex environment. Comparison and analysis are used to show the performance of the algorithms in terms of the effects of data set establishment, algorithm selection, and detection devices. Based on the analysis results, Convolutional Neural Network methods show high recognition accuracy and better adaptability to the complexity of the environment in the shield tunnel compared to traditional machine learning methods. The Support Vector Machine algorithms show high recognition performance only for small data sets. To improve detection models and increase detection accuracy, measures such as optimizing features, fusing algorithms, creating a high-quality data set, increasing the sample size, and using devices with high detection accuracy can be recommended. Finally, we analyze the challenges in the field of coupling DSTM, meanwhile, the possible development direction of DSTM is prospected.

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