The preparation of micron- to nanometer-sized functional materials with well-defined shapes and packing is a key process to their applications. There are many ways to control the crystal growth of organic semiconductors. Adding polymer additives has been proven a robust strategy to optimize semiconductor crystal structure and the corresponding optoelectronic properties. We have found that poly(3-hexylthiophene) (P3HT) can effectively regulate the crystallization behavior of N,N′-dioctyl perylene diimide (C8PDI). In this study, we combined P3HT and polyethylene glycol (PEG) to amphiphilic block copolymers and studied the crystallization modification effect of these block copolymers. It is found that the crystallization modification effect of the block copolymers is retained and gradually enhanced with P3HT content. The length of C8PDI crystals were well controlled from 2 to 0.4 μm, and the width from 210 to 35 nm. On the other hand, due to the water solubility of PEG block, crystalline PEG-b-P3HT/C8PDI micelles in water were successfully prepared, and this water phase colloid could be stable for more than 2 weeks, which provides a new way to prepare pollution-free aqueous organic semiconductor inks for printing electronic devices.

Water leakage in subway tunnel linings poses significant risks to structural safety and long-term durability, making accurate and efficient leakage detection a critical task. Existing deep learning methods, such as UNet and its variants, often suffer from large parameter sizes and limited ability to capture multi-scale features, which restrict their applicability in real-world tunnel inspection. To address these issues, we propose an Efficient Multi-Scale U-shaped KAN-based Segmentation Network (EMS-UKAN) for detecting water leakage in subway tunnel linings. To reduce computational cost and enable edge-device deployment, the backbone replaces conventional convolutional layers with depthwise separable convolutions, and an Edge-Enhanced Depthwise Separable Convolution Module (EEDM) is incorporated in the decoder to strengthen boundary representation. The PKAN Block is introduced in the bottleneck to enhance nonlinear feature representation and improve the modeling of complex relationships among latent features. In addition, an Adaptive Multi-Scale Feature Extraction Block (AMS Block) is embedded within early skip connections to capture both fine-grained and large-scale leakage features. Extensive experiments on the newly collected Tunnel Water Leakage (TWL) dataset demonstrate that EMS-UKAN outperforms classical models, achieving competitive segmentation performance. In addition, it effectively reduces computational complexity, providing a practical solution for real-world tunnel inspection.

Accurate segmentation of tunnel lining joints is essential for intelligent tunnel inspection, yet it remains challenging because of texture interference, occlusion-induced discontinuity, and the slender topology of joints in complex in situ environments. This study proposes DASMambaNet, a dedicated segmentation network that integrates Dynamic Adaptive Inhibition Convolution for direction-aware feature extraction and background suppression, a Multi-scale Context-aware Mamba Fusion module for long-range continuity reasoning, and a Lightweight Alignment and Confidence-Modulated Directional Fusion module for boundary-sensitive decoding. Experiments were conducted on the self-constructed TJSD dataset containing 4997 annotated images collected from real tunnel sections. The proposed method achieved 94.12% Dice, 74.63% IoU, 98.86% pixel accuracy, and 88.39% precision, outperforming representative CNN- and Transformer-based methods. Qualitative and ablation results further showed that the network effectively suppresses background noise, restores the continuity of occluded joints, and improves boundary localization. These results indicate that DASMambaNet provides an effective and practical solution for automated tunnel lining joint segmentation and can support subsequent tunnel inspection and structural condition assessment.