Flexible wearable strain sensors based on laser-induced graphene (LIG) have attracted significant interest due to their simple preparation process, three-dimensional porous structure, excellent electromechanical characteristics, and remarkable mechanical robustness. In this study, we demonstrated that LIG with various defects could be prepared on the surface of polyimide (PI) film, patterned in a single step by adjusting the scanning speed while maintaining a constant laser power of 12.4 W, and subjected to two repeated scans under ambient air conditions. The results indicated that LIG produced at a scanning speed of 70 mm/s exhibited an obvious stacked honeycomb micropore structure, and the flexible strain sensor fabricated with this material demonstrated stable resistance. The sensor exhibited high sensitivity within a low strain range of 0.4–8.0%, with the gauge factor (GF) reaching 107.8. The sensor demonstrated excellent stability and repeatable response at a strain of 2% after approximately 1000 repetitions. The flexible wearable LIG-based sensor with a serpentine bending structure could be used to detect various physiological signals, including pulse, finger bending, back of the hand relaxation and gripping, blinking eyes, smiling, drinking water, and speaking. The results of this study may serve as a reference for future applications in health monitoring, medical rehabilitation, and human–computer interactions.

As a novel method to prepare graphene, the laser-induced graphene (LIG) technology has numerous outstanding properties and has been widely applied in various fields. Nevertheless, the challenge remains to easily and efficiently prepare multifunctional surfaces of graphene through laser microregulation and fine structure design. Here, we successfully fabricated a micron-structure gully graphene surface with hydrophobicity and electrothermal functionality under atmospheric conditions using a 10.6 μm CO₂ laser to directly write on the surface of a polyimide film (PI). The impact of the laser scanning speed on the surface morphology and chemical composition of the product was investigated by analyzing the SEM (scanning electron microscope) observations and Raman spectra, respectively. The mechanical stability of the surface was studied by analyzing the contact angle of water droplets on the surface after mechanical circulation and the delayed icing effect after repeated icing. The deicing and anti-icing performance of the surface were analyzed based on its resistance to surface icing and electric deicing time. According to the experimental results, we first observed a linear negative correlation between the generated structure linewidth and the laser scanning speed. Additionally, we successfully achieved one-step preparation of primitive continuous graphene structures with a superhydrophobic capability (151°). Furthermore, our findings indicate that micron-structured graphene surfaces exhibit excellent mechanical stability, effectively delay icing formation, and demonstrate efficient electric deicing effects. These results demonstrate the potential application of CO₂ laser-induced graphene technology in the field of surface preparation for deicing and anti-icing. This work offers a novel one-step approach for the fabrication of micron-structured heatable graphene surfaces with simultaneous superhydrophobicity, deicing, and anti-icing functionalities on polymer substrates.

Image dehazing is critical for visual enhancement and a wide range of computer vision applications. Despite significant advancements, challenges remain in preserving fine details and adapting to diverse, non-uniformly degraded scenes. To address these issues, we propose a novel image dehazing method that introduces a contrastive learning framework, enhanced by the InfoNCE loss, to improve model robustness. In this framework, hazy images are treated as negative samples and their clear counterparts as positive samples. By optimizing the InfoNCE loss, the model is trained to maximize the similarity between positive pairs and minimize that between negative pairs, thereby improving its ability to distinguish haze artifacts from intrinsic scene features and better preserving the structural integrity of images. In addition to contrastive learning, our method integrates a multi-scale dynamic feature fusion with a hybrid attention mechanism. Specifically, we introduce dynamically adjustable frequency band filters and refine the hybrid attention module to more effectively capture fine-grained, cross-scale image details. Extensive experiments on the RESIDE-6K and RS-Haze datasets demonstrate that our approach outperforms most existing methods, offering a promising solution for practical image dehazing applications.

Topology optimization serves as a critical method for promoting lightweight structural design. Traditional methods predominantly focus on mechanical performance evaluation, often neglecting the critical correlation between modal characteristics and structural stiffness. The Evolutionary Structural Optimization (ESO) method is extensively employed in topology optimization; however, iterative oscillations lead to issues such as grid divergence and diminished solution quality. To address issues such as iterative oscillations and mesh divergence in the traditional Evolutionary Structural Optimization (ESO) method, this study applies a Simp Evolutionary Structural Optimization (SI-ESO) methodology. This method integrates intermediate density parameters and penalty factors into the progressive structural optimization process, thereby significantly enhancing iterative convergence and model quality. This work applied the optimized SI-ESO method to the lightweight redesign of an aviation generator’s rear cover, with validation conducted through additive manufacturing. Subsequently, the back cover of an aviation generator was redesigned and fabricated utilizing additive manufacturing technology. Empirical results indicate that under maximum stress conditions and employing the same additive process, the maximum deformation of the SI-ESO-optimized model is reduced compared to that of the ESO-designed model. Compared with the original design, the SI-ESO-optimized model achieved a 31% weight reduction, while relative to the ESO-optimized model, it exhibited a 27% lower maximum stress and a 10.53% higher first-order frequency, demonstrating both lightweighting and enhanced structural stiffness.

Ice accumulation poses considerable challenges in transportation, notably in the domain of general aviation. The present study combines the strengths and limitations of conventional aircraft deicing techniques with the emerging trend toward all-electric aircraft. This study aims to utilize laser-induced graphene (LIG) technology to create a multifunctional surface, seamlessly integrating hydrophobic properties with efficient electrical heating to mitigate surface icing effectively. We investigated the utilization of a 10.6 μm CO₂ laser for direct writing on polyimide (PI), a widely used insulating encapsulation material. From the thermomechanical perspective, our initial analysis using COMSOL Multiphysics software (V5.6) revealed that when the laser power P exceeds 5 W, the PI substrate experiences ablative damage. The experimental results show that when P ≤ 5 W, an increase in power has a positive impact on the quality, surface porosity, roughness reduction, line-spacing reduction, and water contact-angle enhancement of the graphene. Conversely, when P > 5 W, higher power negatively affects both the substrate and the graphene structure by inducing excessive ablation. However, it influences the graphene line height positively and is consistent with overall experimental–simulation congruence. Furthermore, the incorporation of high-quality graphene resulted in a surface that exhibited higher contact angles (CA > 120°), lower energy consumption, and higher heating efficiency compared to the use of traditional electrically heated materials for anti-icing applications. The potential applications of this one-step fabrication method extend across various industries, particularly aviation, marine engineering, and other ice-prone domains. Moreover, the method has extensive prospects for addressing pivotal challenges associated with ice formation and serves as an innovative and efficient anti-icing technology.

To address the issue of low accuracy and stability in traditional Convolutional Neural Networks (CNN)-based defect depth prediction for civil aircraft composites, we propose an improved Feature Enhancement Network (FEN)-CNN-Bidirectional Long Short-Term Memory (BiLSTM) impact damage depth prediction method. By integrating terahertz (THz) time-domain, frequency-domain, and absorbance spectroscopy with Confocal Laser Scanning Microscopy (CLSM) depth measurements, the correlation between THz spectral features and impact damage defect depth is systematically elucidated, thereby constructing a “THz features-depth” dataset. Furthermore, by leveraging the FEN model’s feature enhancement and denoising capabilities, along with the BiLSTM model’s bidirectional sequence modeling capability, the underlying relationship between terahertz spectral features and defect depth is deeply learned. This approach improves the stability and accuracy of spectral feature extraction by the CNN model under complex conditions. Ablation experiments revealed the improved model, compared to traditional CNN, reduced Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) by 43.08%, 44.4%, 57.18%, and 34.56%, respectively. Additionally, it decreased the Relative Standard Deviation (RSD) by 32.14%, and increased the Coefficient of Determination (R²) by 6.8%. Comparative experiments demonstrated the proposed model achieved an MSE of 0.0075 and an R² of 0.9539, outperforming other models. This study provides a novel method for precise low-velocity impact damage assessment in carbon fiber reinforced composites, enhancing safety evaluation for civil aircraft composite structures and contributing to aviation safety.