Chemically cross-linked thermoset shape memory polymers (TSMPs) are an important branch of smart materials due to their potentially wide applications in deplorable structures, soft robots, damage self-healing, and 4D printing. Further development and design of TSMP structures call for constitutive models. Although the Arruda–Boyce eight-chain model has been very successful and widely used for entropy-driven TSMPs, recent studies found that some new TSMPs, such as those using enthalpy as the primary driving force, show unit cells different from the eight-chain model. Considering that these new epoxy-based TSMP networks consist of a plenty of four-chain features, this study proposes a four-chain tetrahedron structure as the unit cell of the network to construct the constitutive model. In this model, Gibbs free energy is used to formulate the thermodynamic driving force. Then, by introducing a transition of the molecule deformation mechanism from that dominated by bond stretch to that dominated by bond angle opening, the traditional Langevin chain model is modified. It is found that this model can well capture the dramatic modulus change for the new TSMP in the thermomechanical experiments. Moreover, it shows that the original Treloar four-chain model and Arruda–Boyce eight-chain model underestimate the driving force for the enthalpy-driven TSMPs, and thus cannot well capture the thermomechanical behaviors. It is also found that under certain conditions, our four-chain model produces the same Cauchy stress as the eight-chain model does. This study may help researchers understand the thermomechanical response and design a special category of TSMPs with high recovery stress.

Shape memory polymers (SMPs) are one of the intriguing functional materials and have been widely and intensively studied. In order to apply these new polymers to load bearing engineering structures and devices, developing physics-based thermomechanical constitutive models is mandatory. The aim of this Tutorial is to demonstrate how to establish a thermomechanical constitutive model for SMPs. It begins with classifications of SMPs, followed by a discussion on the underlying physics for different SMPs. After that, three classical SMP thermomechanical modeling frameworks are introduced, which include the visco-elasto-plastic based rheological framework, the storage strain-based phase transition framework, and the representative unit cell based multi-branch framework. Next, three commonly adopted new model establishment methods are presented within these frameworks with detailed examples. Finally, future perspectives on this research direction are discussed. We hope that this Tutorial will help readers understand the roadmap from physics to mathematical modeling of SMPs.

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.

To meet the growing needs of public safety and sustainable development, it is highly desirable to develop flame-retardant polymer materials using a facile and low-cost method. Although conventional solution chemical synthesis has proven to be an efficient way of developing flame retardants, it often requires organic solvents and a complicated separation process. In this review, we summarize the progress made in utilizing simple ball milling (an important type of mechanochemical approach) to fabricate flame retardants and flame-retardant polymer composites. To elaborate, we first present a basic introduction to ball milling, and its crushing, exfoliating, modifying, and reacting actions, as used in the development of high-performance flame retardants. Then, we report the mixing action of ball milling, as used in the preparation of flame-retardant polymer composites, especially in the formation of multifunctional segregated structures. Hopefully, this review will provide a reference for the study of developing flame-retardant polymer materials in a facile and feasible way.