Improving the photochromism of MoO₃ by n-butylamine intercalation and tuning its discoloration using ethanol as a hole scavenger.

Inspired by the human skin that can intrinsically and automatically heal damages, and maintain the original functionalities and structure stability, a self-healing semiconductive touch panel is developed based on the TiO₂nanocomposite hydrogel.

Healing agent microcapsules have been used to realize self-healing for polymeric composites. In this work a novel kind of microcapsules encapsulating ethylenediamine (EDA) with epoxy resin as shell material were prepared by interfacial polymerization technology. The oil phase was epoxy resin prepolymer and carbon tetrachloride, and the water phase was EDA and deionized water. Under the action of emulsifier, a stable water-in-oil emulsion was formed. Then the emulsion was added to dimethyl silicone oil, stirred and dispersed, to prepare microcapsules. In addition, the factors affecting the preparation of microcapsules were studied. In this study, Fourier transform infrared(FTIR) was carried out to demonstrate the chemical structure of ethylenediamine microcapsules. Optical microscope(OM) and scanning electron microscope(SEM) were used to observe the morphology of microcapsules. Thermogravimetric analysis and differential scanning calorimetry were done to investigate the thermal properties of microcapsules. Permeability experiment and isothermal aging test were executed to verify the environment resistance of microcapsules. Results showed that EDA was successfully coated in epoxy resin and the microcapsule size was in the range of 50~630 μm. The synthesized microcapsules were thermally stable below 75 °C and perfect permeability resistance to ethanol solvent.

Owing to high specific strength and designability, unidirectional carbon fiber reinforced polymer (UD-CFRP) has been utilized in numerous fields to replace conventional metal materials. Post machining processes are always required for UD-CFRP to achieve dimensional tolerance and assembly specifications. Due to inhomogeneity and anisotropy, UD-CFRP differs greatly from metal materials in machining and failure mechanism. To improve the efficiency and avoid machining-induced damage, this paper undertook to study the correlations between cutting parameters, fiber orientation angle, cutting forces, and cutting-induced damage for UD-CFRP laminate. Scanning acoustic microscopy (SAM) was employed and one-/two-dimensional damage factors were then created to quantitatively characterize the damage of the laminate workpieces. According to the 3D Hashin’s criteria a numerical model was further proposed in terms of the finite element method (FEM). A good agreement between simulation and experimental results was validated for the prediction and structural optimization of the UD-CFRP.

To reduce the reliance on large training sets for representative volume element (RVE) generation using machine learning, this work presents a novel approach based on physics-informed neural network (PINN) to generate RVEs for unidirectional fiber-reinforced composites with non-uniform fiber distributions and high-volume fractions. The method embeds physical constraints including fiber non-overlap, volume fraction, and boundary conditions directly into the neural network’s loss function. This integration eliminates the need for large training datasets, which is typically required by traditional machine learning methods. Moreover, it achieves volume fractions exceeding 0.8, surpassing the jamming limit of conventional generation techniques. Exhaustive statistical measurements taken at different scales confirm that the proposed method could accurately reproduce local fiber distribution patterns in realistic microstructures while maintaining complete randomness at larger scales. Finite element analysis was employed on the generated RVEs to predict the elastic properties and damage behavior that taking into account the interfacial debonding and nonlinear damage in matrix. The predictions of both macroscopic mechanical properties (elastic properties and strength) and microscopic damage patterns show good agreement with experimental results. The proposed PINN-based framework provides an efficient and reliable tool for computational micromechanics of polymer matrix composites.