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To enhance the flexural properties of glass fiber‐reinforced vinyl ester resin (GF‐VER) composites, this study focused on the influence of the duration of surface treatment on the flexural properties using polydopamine (PDA) and nanosilicon dioxide (nano‐SiO2). The GF‐VER composites were prepared via hot pressing. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were utilized to characterize the microstructural and chemical attributes of the GF and GF‐VER composites. The mechanical properties of the GF‐VER composites were characterized using the flexural strength test. Furthermore, thermogravimetry was employed to investigate the thermal stability of the GF and the content of PDA nanoparticles on the GF. The results demonstrated that upon extending the treatment period, a greater quantity of PDA‐SiO2 nanoparticles adhered to the surface of the GF. The analysis of these results established that the optimal treatment duration is 12 h. This duration facilitated an enhanced adhesion between GF and VER owing to PDA‐SiO2, resulting in an optimal flexural strength of 562.8 MPa and signifying a 8.86% increase compared with the flexural strength of the untreated GF‐VER composites. Overall, the findings indicate that PDA‐SiO2 treatment greatly improves the flexural strength of GF‐VER composites. Highlights: The method for modifying the GF surface was simple and mild.Silica nanoparticles were grafted onto GFs using dopamine as a reaction platform.The effect of the duration of PDA‐SiO2 treatment on the flexural strength of GF‐VER composites was examined.SEM revealed an improved GF‐VER interface.The flexural strength of the GF‐VER composites improved by 8.86%. [ABSTRACT FROM AUTHOR]

In the textile industry, distinguishing between wool and cashmere can be a challenging task. Extensive research based on the microscopic images of the two has achieved very good results. However, the microscopic slide preparation process required for this approach is time-consuming and labor-intensive, limiting its practical application. To address this challenge, this paper proposes a new method that integrates artificial neural networks and hyperspectral imaging technology. The novelty of this approach lies in the fact that it does not require sample preparation, and is more simple, fast, and nondestructive. Firstly, a total of 225 wool samples and 160 cashmere samples were selected from the acquired hyperspectral images. The spectral curves (range 900–2500 nm) of these samples were extracted using the Region of Interest (ROI) tool in the ENVI software, and their characteristics were analyzed. Subsequently, due to the similarities and strong correlation between their spectral curves, Principal Component Analysis (PCA) was employed to reduce the dimensionality of the data. A single-layer neural network and a multi-layer neural network were developed using the LR (Logistic Regression) and MLP (Multilayer Perceptron) models, respectively, with a training-to-validation set ratio of 7:3. The model trained with LR achieved an accuracy of 90.3% on the training set and 81.0% on the validation set, suggesting underfitting. The MLP model performed best with five principal components, attaining a training set accuracy of 94.1% and a validation set accuracy of 92.2%. Precision, recall, and F 1-score were used to evaluate the two models, and comparison of the classification performance of the two models revealed that the MLP significantly outperformed the LR model. Therefore, the application of hyperspectral imaging technology enables rapid and non-destructive identification of wool and cashmere. [ABSTRACT FROM AUTHOR]