A novel β-nucleating agent for isotactic polypropylene

β-Nucleated Polypropylene: Preparation, Nucleating Efficiency, Composite, and Future Prospects

The β-crystals of polypropylene have a metastable crystal form. The formation of β-crystals can improve the toughness and heat resistance of a material. The introduction of a β-nucleating agent, over many other methods, is undoubtedly the most reliable method through which to obtain β-PP. Furthermore, in this study, certain newly developed β-nucleating agents and their compounds in recent years are listed in detail, including the less-mentioned polymer β-nucleating agents and their nucleation characteristics. In addition, the various influencing factors of β-nucleation efficiency, including the polymer matrix and processing conditions, are analyzed in detail and the corresponding improvement measures are summarized. Finally, the composites and synergistic toughening effects are discussed, and three potential future research directions are speculated upon based on previous research.

Effect of Shear Stress during Processing on Structure, Morphology, and Properties of Isotactic Polypropylene Nucleated with Silsesquioxane-Based β-Nucleating Agent

The study aimed to determine the influence of shear stress during real-life industrial processes such as compression molding and injection molding to different cavities on the crystallization of the isotactic polypropylene nucleated with a novel silsesquioxane-based β-nucleating agent. Octakis(N²,N⁶-dicyclohexyl-4-(3-(dimethylsiloxy)propyl)naphthalene-2,6-dicarboxamido)octasilsesquioxane (SF-B01) is a highly effective nucleating agent (NA) based on the hybrid organic-inorganic silsesquioxane cage. The samples containing various amounts of the silsesquioxane-based and commercial iPP β-nucleants (0.01-0.5 wt%) were prepared by compression molding and injection molding, including forming in the cavities with different thicknesses. The study of the thermal properties, morphology, and mechanical properties of iPP samples allows for obtaining comprehensive information about the efficiency of silsesquioxane-based NA in shearing conditions during the forming. As a reference sample, iPP nucleated by commercial β-NA (namely N²,N⁶-dicyclohexylnaphthalene-2,6-dicarboxamide, NU-100) was used. The static tensile test assessed the mechanical properties of pure and nucleated iPP samples formed in different shearing conditions. Variations of the β-nucleation efficiency of the silsesquioxane-based and commercial nucleating agents caused by shear forces accompanying the crystallization process during forming were evaluated by differential scanning calorimetry (DSC) and wide-angle X-ray scattering (WAXS). The investigations of changes in the mechanism of interactions between silsesquioxane and commercial nucleating agents were supplemented by rheological analysis of crystallization. It was found that despite the differences in the chemical structure and solubility of the two nucleating agents, they influence the formation of the hexagonal iPP phase in a similar way, taking into consideration the shearing and cooling conditions.

Machine learning-guided design and development of multifunctional flexible Ag/poly (amic acid) composites using the differential evolution algorithm

The development of flexible composites is of great significance in the flexible electronic field. In combination with machine learning technology, the introduction of artificial intelligence to flexible materials design, synthesis, characterization and application research will greatly promote the flexible materials research efficiency. In this study, the back propagation (BP) neural network based on the differential evolution (DE) algorithm was applied to determine the electrical properties of the flexible Ag/poly (amic acid) (PAA) composite structure and to develop flexible materials for its different applications. In the machine learning model, the concentration of PAA, the ion exchange time of AgNO₃, and the concentration and reduction time of NaBH₄ are set as input parameters, and the product of the sheet resistance of the Ag/PAA film and the processing time are set as output information. To overcome the situation whereby the BP neural network solution process could fall into the local optimum, the initial threshold and the weight of the BP neural network and the data import model are optimized by the DE algorithm. Utilizing 1077 learning samples and 49 predictive samples, a machine learning model with very high accuracy was established and relative errors of predictions less than 1.96% were achieved. In terms of this model, the optimized fabrication conditions of the Ag/PAA composites, which are suitable for strain sensors and electrodes, were predicted. To identify the availability and applicability of the proposed algorithm, a strain gauge sensor, a triboelectric nanogenerator (TENG) and a capacitive pressure sensor array were fabricated successfully using the optimized process parameters. This work shows that machine learning can be used to quickly optimize the process and provide guidance for material and process design, which is of significance for the development of flexible materials and devices.

Capacitive soft strain sensors via multicore-shell fiber printing

A new method for fabricating textile integrable capacitive soft strain sensors is reported, based on multicore-shell fiber printing. The fiber sensors consist of four concentric, alternating layers of conductor and dielectric, respectively. These wearable sensors provide accurate and hysteresis-free strain measurements under both static and dynamic conditions.