Composites Communications最新文献

Silicone rubber features excellent thermal stability, outstanding low-temperature performance, and superb processability, however, the poor damping property and low mechanical strength limit its applications. In this work, we synthesized two types of phenyl MQ resins as molecular fillers and incorporated them into phenyl silicone rubber to prepare damping composites. The effects of both the phenyl MQ resin structures and contents on the damping properties and mechanical performances were investigated. The results showed that phenyl MQ resins exhibit multiscale damping effects with temperature rising, and phenyl MQ resins are distributed in silicone rubber with a "sea-island" structure, which strengthen the mechanical property. The composite exhibit optimal performance with incorporating 30phr diphenyl MQ resins: the damping factor at 150 °C increased from 0.1 to 0.18, the temperature range for tan δ > 0.3 expanded by 115.2 %, and maintained a tensile strength of 6.3 MPa. This study paves a new path to design and prepare silicone rubber composite that balance high damping performances with better mechanical strength.

This research presents a comprehensive framework for predicting the damage in lightweight composite high-pressure hydrogen storage tanks and optimizes their design to prevent failure. By integrating advanced analytical methods, numerical analysis, and deep learning techniques, we introduced a novel approach to enhance design optimization and damage prediction. The framework employs the 3D elasticity anisotropy theory to predict mechanical performance, incorporating failure criteria for accurate damage analysis. A parametric study was conducted to examine the effects of thickness, pressure, and diameter on the behavior of the tank. The analytical results were compared against finite element analysis using the WoundSim software, underscoring the significance of the modeling assumptions. Furthermore, we developed a new framework that combines deep neural networks with differential evolution optimization (DNN-DEO) to predict stress and damage in composite pressure vessels while identifying the optimal design parameters (pressure, radius, and thickness) to minimize failure risks and maintain high performance. A graphical user interface (GUI) was also designed to automate calculations and predictions, providing an intuitive tool for users. This integrated approach offers a powerful solution for optimizing the design and operation of lightweight composite hydrogen storage tanks, ensuring the reliability and efficiency of hydrogen storage systems.

The integration of inorganic nanoparticles as reinforcement imparts epoxy resin with outstanding comprehensive performance, facilitating the utilization across diverse applications spanning coatings, adhesives, and composites. However, the persistent challenge lies in achieving optimal interfacial compatibility between the fillers and the resin matrix. This study focuses on the synthesis of tri-functionally modified spherical silica particles with an average size of 700 nm by empolying three distinct types of silane coupling agents in a synergistic manner to achieve desired functionalization. These agents endow the resulting particles with reactive groups, including epoxy and amine functionalities, which enhance interfacial compatibility and adhesion between the silica particle fillers and the epoxy resin matrix. Additionally, the incorporation of non-reactive phenyl groups serves to reduce the viscosity of resin composites. These modified silica particles are integrated into the sealant formulations, which exhibits commendable reliability and processability. It exhibits a 95 % reduction in moisture permeability, an increase in glass transition temperature by approximately 30 °C, and a decrease in coefficient of thermal expansion by 5∗10⁻⁶/k, compared to the sealant prepared with unmodified silica. The outcomes of this research present promising avenues for the advancement of cutting-edge display technologies.

The dispersibility of fillers and interfacial interaction are crucial for polymer nanocomposites. Developing a simple, efficient and green method to simultaneously reduce and functionalize graphene oxide (GO) for preparing graphene/rubber composites with excellent dispersibility and enhanced interfacial interactions remains one of the main trends in developing and expanding the application of graphene in the rubber industry. In this work, VOC (volatile organic compound) -free and environmentally friendly L-methionine (L-Met) was selected as the functional modifier of GO. Then, L-Met-modified GO (MGO) was blended with natural rubber (NR) in an aqueous phase to prepare MGO/NR composites. The amphiphilic property of L-Met not only reduced GO but also endowed it with less agglomeration and excellent water dispersibility, which was a prerequisite for achieving uniform dispersion of GO in the NR matrix. In addition, the amino acids on the surface of MGO enhanced the compatibility between GO and the protein-phospholipid layer on the outer layer of NR latex particles. Meanwhile, L-Met containing sulfur bonds promoted rubber vulcanization, resulting in the formation of a cross-linked network between the modified GO and NR molecular chains. Compared to unmodified GO/NR composites, MGO/NR composites exhibited excellent mechanical properties and low heat build-up. More importantly, the solid tire prepared by using L-Met as the interface modifier for GO filled rubber showed lower rolling resistance and temperature rise, and the energy saving efficiency was improved. This strategy is expected to provide a new insight into the green production of organically modified graphene and the preparation of low-energy-consumption green graphene tires.

A multifunctional elastomer-organohydrogel hybrid patch for skin surface strain sensors, denoted as MSR-PO, composed of a modified conductive silicone rubber (MSR) and PAA-based organohydrogel (PO), is fabricated by surface radical polymerization of the PO layer on MSR film, the two layers are linked together with the strong chemical bonding, thereby amalgamating their distinct functionalities. The MSR layer endows the patch with great wear-resisting, anti-freezing, and durable sensing properties. The gel layer coated on conductive silicone rubber imparts the patch with excellent adhesive capability to match the modulus of the skin, which is in favor of monitoring large and small movements of the human body, showing great potential in the field of wearable flexible electronic sensing. Importantly, the rubber layer and gel layer of MSR-PO patch exhibit outstanding synergistic antibacterial effects. This work will inspire the design and fabrication of novel rubber-gel asymmetric patch for multifunctional applications.

In this work a new approach to detect and to evaluate defects typical of carbon fiber parts manufactured via resin transfer molding (RTM) is presented. The approach is based on a robotic contact measurement system, as an alternative or combined solution to vision methods. The research is meant to provide more accurate local geometry information to the global information acquired using vision systems. The system consists of a collaborative robot equipped with a force-torque sensor that scans the surface of the workpiece with a defined motion strategy. To evaluate the system, tests were conducted on a 3D printed part presenting a feature representative of the defects found on carbon fiber components. The results are compared with the CAD model and data acquired using a structured light scanner, showing that robotic contact measurement can be a viable method to enhance the reconstruction of a part with a higher accuracy. The system is then applied and validated on a real part made with RTM.

With the increasing prevalence of electronic devices, there is a pressing need for composite films that offer both efficient thermal management and superior electromagnetic interference (EMI) shielding. To address this, we developed a novel composite film featuring a controllable conductive-magnetic dual-gradient structure, composed of cellulose nanofibers (CNF), MXene, silver nanowires (AgNWs), and hollow ferric oxide (Fe₃O₄). This film was fabricated using a straightforward, cost-effective layer-by-layer vacuum filtration method. Leveraging CNF as the matrix material endows the film with remarkable flexibility. The integration of 2D MXene, 1D AgNWs, and 0D hollow Fe₃O₄ significantly enhances the film's thermal conductivity through multidimensional particle interactions, achieving a maximum value of 2.92 W/mK. Additionally, the dual-gradient structure of the film-comprising a transition layer and a reflection layer-improves EMI shielding efficiency by balancing high EMI shielding effectiveness with low electromagnetic wave reflection. Specifically, for the dual-gradient configuration (MAF)-25-CNF, the absorption coefficient (A) of electromagnetic waves incident on the low conductivity side reaches 0.23, and the shielding effectiveness reaches 45.8 dB. These findings highlight the potential of MXene/AgNWs/Fe₃O₄/CNF composite films with a controllable conductive-magnetic dual-gradient structure for applications in electronics, electrical engineering, and wearable technologies.

Liquid crystal epoxy resin, as an ideal toughening agent, combines the features of liquid crystal ordering and network cross-linking, which can effectively optimize the comprehensive performance of blended epoxy resins and achieve an ideal balance of rigidity and toughness. Here we successfully synthesized rigid and flexible bio-based liquid crystal epoxy resin (THBR-EP) by finely tuning the length of alkyl side chains. Using aromatic diamine as curing agent, a homogeneous network without phase separation was constructed by taking advantage of the different activity but good compatibility with ordinary epoxy resins, which significantly improved the toughness of the blended system. Specifically, only a small amount of THBR-EP(2.5 wt%) was required to exhibit a maximum impact strength of 48.8 kJ/m². Moreover, the increase in system toughness was accompanied by a benign improvement in flexural strength, modulus and thermal stability. The ordered structure of the liquid crystals and their good compatibility with the resin matrix enhanced the ability to inhibit crack extension and energy dissipation. The feasibility to simultaneously achieve effective toughening and other property improvements of common resins broadens the application of resins under various demanding scenarios.

Flexible wearable sensors have attracted widespread attention in health monitoring, human-machine interaction, and biomedical applications. However, developing a flexible sensor that possesses high sensitivity, wide detection range, and matches the conductive material with the modulus of elasticity remains challenging. Here, we developed a coaxial wet spinning process to fabricate conductive fibers with a core-multi-hollow-shell structure, termed LHPTF. The shell comprises a hollow porous structure of TPU, while the core consists of gallium-based LM with excellent electrical conductivity. LHPTF fibers exhibit electrical conductivity of 8690 S cm⁻¹, high flexibility, appropriate strength, and high elongation at break. The hollow porous structure of TPU fibers can be adjusted with various hollow diameters, thereby enabling the switching between stable conduction and strain sensing of conductive fibers. Due to the protection provided by TPU, LHPTF fibers exhibit good environmental durability and stability. We also demonstrate the application of these fibers in wearable sensors and stable conductor.

For polymers and their composites, processing conditions and the resultant microstructures are crucial in determining their properties. Traditional machine learning (ML) methods typically focus on establishing direct relationships between processing parameters and material properties, often overlooking the critical intermediate step of how processing influences microstructure, limiting the predictive accuracy. In this study, we introduce an approach that first establishes a detailed relationship between processing parameters and the resultant microstructure, and then uses transfer learning and feature fusion to integrate this relationship into the prediction of material properties. Using carbon black-reinforced rubber composites (CRC) as an example, we compared ML models in predicting mechanical properties from processing data. A multi-task deep neural network performed best achieving an $R^2$ of 0.763 with only processing data as input. When incorporating transfer learning and feature fusion, the $R^2$ improved to 0.852 and 0.878, respectively. Shapley explanation analysis validated our approach, highlighting the importance of integrating processing, microstructure, and properties in ML models. This method emphasizes the critical effect of comprehensively considering processing–(micro)structure–property (P–S–P) relationships, leading to more accurate predictions in polymer composite research.