3D printing of polymeric foams by direct-ink-write is a recent technological breakthrough that enables the creation of versatile compressible solids with programmable microstructure, customizable shapes, and tunable mechanical response including negative elastic modulus. However, in many applications the success of these 3D printed materials as a viable replacement for traditional stochastic foams critically depends on their mechanical performance and micro-architectural stability while deployed under long-term mechanical strain. To predict the long-term performance of the two types of foams we employed multi-year-long accelerated aging studies under compressive strain followed by a time-temperature-superposition analysis using a minimum-arc-length-based algorithm. The resulting master curves predict superior long-term performance of the 3D printed foam in terms of two different metrics, i.e., compression set and load retention. To gain deeper understanding, we imaged the microstructure of both foams using X-ray computed tomography, and performed finite-element analysis of the mechanical response within these microstructures. This indicates a wider stress variation in the stochastic foam with points of more extreme local stress as compared to the 3D printed material, which might explain the latter’s improved long-term stability and mechanical performance.

Elastomers are made of chain-like molecules to form networks that can sustain large deformation. Rubbers are thermosetting elastomers that are obtained from irreversible curing reactions. Curing reactions create permanent bonds between the molecular chains. On the other hand, thermoplastic elastomers do not need curing reactions. Incorporation of appropriated filler particles, as has been practiced for decades, can significantly enhance mechanical properties of elastomers. However, there are fundamental questions about polymer matrix composites (PMCs) that still elude complete understanding. This is because the macroscopic properties of PMCs depend not only on the overall volume fraction ($\varphi$) of the filler particles, but also on their spatial distribution (i.e., primary, secondary, and tertiary structure). This work aims at reviewing how the mechanical properties of PMCs are related to the microstructure of filler particles and to the interaction between filler particles and polymer matrices. Overall, soft rubbery matrices dictate the elasticity/hyperelasticity of the PMCs while the reinforcement involves polymer–particle interactions that can significantly influence the mechanical properties of the polymer matrix interface. For $\varphi$ values higher than a threshold, percolation of the filler particles can lead to significant reinforcement. While viscoelastic behavior may be attributed to the soft rubbery component, inelastic behaviors like the Mullins and Payne effects are highly correlated to the microstructures of the polymer matrix and the filler particles, as well as that of the polymer–particle interface. Additionally, the incorporation of specific filler particles within intelligently designed polymer systems has been shown to yield a variety of functional and responsive materials, commonly termed smart materials. We review three types of smart PMCs, i.e., magnetoelastic (M-), shape-memory (SM-), and self-healing (SH-) PMCs, and discuss the constitutive models for these smart materials.

Using the energetic material pentaerythritol tetranitrate as a specific example of molecular crystal, we describe the development of a simple coarse-graining procedure by grouping several atoms or whole functional groups into single charge-neutral beads. As compared to fully atomistic calculations the coarse-grained model speeds up simulations by more than two orders of magnitude. Yet, by adjusting only two parameters in the coarse-grained interaction, the model accurately predicts the lattice constants, sublimation energy, pressure-volume curve up to P=10GPa, and energetically the most stable facets. Computed surface and desorption energies, bulk modulus, and equilibrium morphology are reported as well.

A class II atomistic force field with Lennard-Jones 6–9 nonbond interactions is used to investigate equations of state (EOS) for important high explosive detonation products N2 and H2O in the temperature range of 700–2500K and pressure range of 0.1–10GPa. A standard sixth order parameter-mixing scheme is then employed to study a 2:1 (molar) H2O:N2 mixture, to investigate, in particular, the possibility of phase separation under detonation conditions. The simulations demonstrate several important results, including (i) the accuracy of computed EOS for both N2 and H2O over the entire range of temperature and pressure considered, (ii) accurate mixing-demixing phase boundary as compared to experimental data, and (iii) the departure of mixing free energy from that predicted by ideal mixing law. The results provide comparison and guidance to state-of-the-art chemical kinetic models.