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The utilization of multifunctional composite materials presents significant advantages in terms of system efficiency, cost-effectiveness, and miniaturization, making them highly valuable for a wide range of industrial applications. One approach to harness the multifunctionality of carbon fiber reinforced polymer (CFRP) is to integrate it with a secondary material to form a hybrid composite. In our previous research, we explored the use of carbonaceous material derived from coconut shells as a sustainable alternative to inorganic fillers, aiming to enhance the out-of-plane mechanical performance of CFRP. In this study, our focus is to investigate the influence of carbonized coconut shell particles on the non-structural properties of CFRP, specifically electromagnetic interference (EMI) shielding, thermal stability, and water absorption resistance. The carbonized material was prepared by thermal processing at 400 °C. Varying proportions of carbonized material, ranging from 1% to 5% by weight, were thoroughly mixed with epoxy resin to form the matrix used for impregnating woven carbon fabric with a volume fraction of 29%. Through measurements of scattering parameters, we found that the hybrid composites with particle loadings up to 3% exhibited EMI shielding effectiveness suitable for industrial applications. Also, incorporating low concentrations of carbonized particle to CFRP enhances the thermal stability of hybrid CFRP composites. However, the inclusion of carbonized particle to CFRP has a complex effect on the glass transition temperature. Even so, the hybrid composite with 2% particle loading exhibits the highest glass transition temperature and lowest damping factor among the tested variations. Furthermore, when subjected to a 7-day water immersion test, hybrid composites with 3% or less amount of carbonized particle showed the least water absorption. The favorable outcome can be attributed to good interfacial bonding at the matrix/fiber interface. Conversely, at higher particle concentrations, aggregation of particles and formation of interfacial and internal pores was observed, ultimately resulting in deteriorated measured properties. The improved non-structural functionalities observed in these biocomposites suggest the potential for a more sustainable and cost-effective alternative to their inorganic-based counterparts. This advancement in multifunctional composites could pave the way for enhanced applications of biocomposites in various industries.

A sophisticated machine learning framework was developed to design thermally robust shape memory vitrimers (TRSMVs) with superior recycling efficiency, an elevatedT_g, and outstanding shape memory properties, surpassing traditional limitations.

Cultivated natural fibers have a huge possibility for green and sustainable reinforcement for polymers, but their limited load-bearing ability and flammability prevent them from wide applications in composites. According to the beam theory, normal stress is the maximum at the outermost layers but zero at the mid-plane under bending (with (non)linear strain distribution). Shear stress is the maximum at the mid-plane but manageable for most polymers. Accordingly, a laminated composite made of hybrid fiber-reinforced shape memory photopolymer was developed, incorporating strong synthetic glass fibers over a weak core of natural hemp fibers. Even with a significant proportion of natural hemp fibers, the mechanical properties of the hybrid composites were close to those reinforced solely with glass fibers. The composites exhibited good shape memory properties, with at least 52% shape fixity ratio and 71% shape recovery ratio, and 24 MPa recovery stress. After 40 s burning, a hybrid composite still maintained 83.53% of its load carrying capacity. Therefore, in addition to largely maintaining the load carrying capacity through the hybrid reinforcement design, the use of shape memory photopolymer endowed a couple of new functionalities to the composites: the plastically deformed laminated composite beam can largely return to its original shape due to the shape memory effect of the polymer matrix, and the flame retardancy of the polymer matrix makes the flammable hemp fiber survive the fire hazard. The findings of this study present exciting prospects for utilizing low-strength and flammable natural fibers in multifunctional load-bearing composites that possess both flame retardancy and shape memory properties.

Herein, a new lightweight syntactic foam is reported with strong mechanical properties, unique multifunctionalities, and recyclability. Multifunctionality of materials and structures has gained ever‐increasing interest as an excellent approach to designing minimalistic systems. Inspired by nature, these materials can perform multiple functions besides bearing a load. Due to their shape‐changing and damage‐healing property, shape memory vitrimers (SMVs) are a great example of multifunctional materials readily exploited for many applications. Using nickel and silver‐plated hollow glass microbubbles (HGMs), an SMV‐based syntactic foam is introduced here that supplements the multifunctionality of SMVs with electrical conductivity and ferromagnetism, which enables a series of additional potentials such as strain sensing, damage monitoring, Joule heating, and electromagnetic interference shielding. Despite its low density and outstanding mechanical properties, this foam exhibits shape memory behavior, which can be triggered by an electrical current, and damage healing capability due to its reversible dynamic covalent bonds. Especially its recyclability makes recycling the expensive silver‐coated and nickel‐coated HGMs feasible, making this foam cost‐effective and environmentally sustainable. With its many features and economical manufacturability, this syntactic foam has a potential to be utilized in many applications, ranging from aerospace structures to biomedical devices to household items.

Damage healing in fiber reinforced thermoset polymer composites has been generally divided into intrinsic healing by the polymer itself and extrinsic healing by incorporation of external healing agent. In this study, we propose to use a hybrid extrinsic-intrinsic self-healing strategy to heal delamination in laminated composite induced by low velocity impact. Especially, we propose to use an intrinsic self-healing thermoset vitrimer as an external healing agent, to heal delamination in laminated thermoset polymer composites. To this purpose, we designed and synthesized a new vitrimer, machined it into powders, and strategically sprayed a layer of vitrimer powders at the interface between the laminas during manufacturing. Also, a thermoset shape memory polymer with fire-proof property was used as the matrix. As a result, incorporation of about 3% by volume of vitrimer powders made the laminate exhibit multifunctionalities such as repeated delamination healing, excellent shape memory effect, improved toughness and impact tolerance, and decent fire-proof properties. In particular, the novel vitrimer powder imparted the laminate with first cycle and second cycle delamination healing efficiencies of 98.06% and 85.93%, respectively. The laminate also exhibited high recovery stress of 65.6 MPa. This multifunctional composite laminate has a great potential in various engineering applications, for example, actuators, robotics, deployable structures, and smart fire-proof structures.

Improving the fireproof performance of polymers is crucial for ensuring human safety and enabling future space colonization. However, the complexity of the mechanisms for flame retardant and the need for customized material design pose significant challenges. To address these issues, we propose a machine learning (ML) framework based on substructure fingerprinting and self-enforcing deep neural networks (SDNN) to predict the fireproof performance of flame-retardant epoxy resins. Our model is based on a comprehensive understanding of the physical mechanisms of materials and can predict fireproof performance and eliminate the needs for properties descriptors, making it more convenient than previous ML models. With a dataset of only 163 samples, our SDNN models show an average prediction error of 3% for the limited oxygen index (LOI). They also provide satisfactory predictions for the peak of heat release rate PHR and total heat release (THR), with coefficient of determination (R2) values of 0.87 and 0.85, respectively, and average prediction errors less than 17%. Our model outperforms the support vector model SVM for all three indices, making it a state-of-the-art study in the field of flame retardancy. We believe that our framework will be a valuable tool for the design and virtual screening of flame retardants and will contribute to the development of safer and more efficient polymer materials.