Attributed to its strain-induced crystallization (SIC), natural rubber (NR) exhibits more excellent mechanical properties compared to other elastomeric materials and has been attracting numerous scientific and technological attention. However, a systematical understanding of the structure–mechanics relation of NR is still lacking. Herein, for the first time, we employ molecular dynamics simulation to examine the effects of the key structural factors on the SIC and mechanical properties at the molecular level. We examine the effects of phospholipid and protein mass fraction (ω), the strength of hydrogen-bond interaction (εH), and the strength of non-hydrogen-bond interaction (εNH) on structural morphology, dynamic behavior, and mechanical properties. NR tends to form local clusters due to the hydrogen-bond interaction formed between phospholipids or proteins and chain ends, which is absent in the case of cis-1,4-polyisoprene (PIP). The polymer chain mobility of NR is retarded due to the formed clusters or even physical network at great εH and high ω. Interestingly, we find that the stress–strain behavior of NR is greatly manipulated by εH and ω, as evidenced by the increase of the chain orientation and the SIC, compared with the cases of PIP. This underlying mechanism results from the alignment of the molecular chains induced by the formed clusters along the deformed direction, and the clusters during the deformation become more stable, particularly at great εH. Lastly, we adopt a machine learning algorithm named extreme gradient boosting via data augmentation, finding that εH has the most significant influencing weight factor on the stress–strain behavior of NR. In general, this work demonstrates a detailed molecular-level structure–mechanics relation of NR and provides some rational guidelines for experimentally designing and synthesizing biomimetic NR.

The mechanical properties of polymer nanocomposites (PNCs) depend sensitively on the structure (e.g., orientation, dispersion, and so on) of the incorporated nanofillers. Many studies have shown that the alignment of anisotropic nanofillers can improve the mechanical properties of PNCs. However, achieving this alignment typically requires complex preparation processes. To address this challenge, researchers have introduced dynamic covalent bonds to form reversible cross-linked polymer systems, which would lead to unique properties, such as self-healing, recyclability, and reprocessibility. In addition, inspired by the above ideas, we introduce nanorods as fillers into a linear vitrimer system to form nanorod vitrimer composites (NVCs). In NVCs, we can easily manipulate the alignment of the nanorods due to bond exchange reactions (BERs) in the vitrimer matrices. By using coarse-grained molecular dynamics (CGMD) simulations, we systematically investigate the factors affecting the nanorod orientation in NVCs. We find that the main factor affecting the nanorod orientation is the network rearrangement caused by BERs. Specifically, the BER potential barrier (ΔE sw) directly determines the probability of BERs’ occurrence. At the same time, the increase of the interfacial interaction between polymer chains and nanorods (εnp) confines the motion of the active beads, which slows down the rate of BERs. Additionally, the impact of temperature (T) and aspect ratio (ld) on the orientation of nanorods during uniaxial stretching or stress relaxation are also discussed. Finally, by combining uniaxial stretching and stress relaxation processes, we elucidate the orientation retention mechanism of NVCs and demonstrate the mechanical property enhancement phenomenon of the pre-oriented NVC systems. This work provides a simple strategy for manipulating the nanorod alignment in vitrimer matrices and uncovers guidelines for designing new functional polymer vitrimer nanocomposites at the molecular level.

No abstract

Densely grafted polymer chains onto spherical nanoparticles produce a diverse range of conformations. At high grafting densities, the corona region near the nanoparticle surface undergoes intense confinement due to a high concentration of chains in the concentrated polymer brush (CPB) region, which results in strong stretching for portions of the chains located within. In contrast, a semi-dilute polymer brush (SDPB) forms farther away from the core and offers reduced confinement for the polymer and more ideal conformations. However, conventional experimental methods are limited in their ability to provide detailed information on individual segments of grafted polymers in these regions; hence, molecular dynamics (MD) simulations are essential for gaining comprehensive insights into the behavior of the grafted chains. This study aims to explore the variations in polymer structure and dynamics that occur along the contour of the grafted chains as influenced by spatial confinement. We focus on the motions and relative positions of each bead along grafted polymers. Our results show that only the initial few grafted beads near the nanoparticle surface exhibit the strong stretching attributed segments in the CPB region of the brush. Increased grafting density or decreased chain flexibility leads to more stretched grafted chains and more aligned bond vectors. As a result, the relaxation dynamics of local regions of the polymer are also strongly influenced by these parameters. Although the grafted beads in the interior of the CPB region are highly sensitive to these parameters, those farther from the nanoparticle core experience significantly diminished effects. In comparison to the Daoud–Cotton (DC) model’s predictions of CPB size, beads near the nanoparticle surface show slower dynamic decay, especially in high grafting densities, aligning with the DC model’s estimates. Finally, we compare our simulations to previous works for additional insight into polymer-grafted nanoparticles.

Elastomers inevitably suffer scratches and damage during the application; thus, the design and fabrication of self-healing elastomers with covalent adaptive networks is a meaningful strategy to extend the service life of materials. In this study, a facile two-step approach was proposed to synthesize self-healing elastomers based on the dynamic oxime–carbamate bonds. Hydroxyl-terminated polybutadiene was first reacted with isophorone diisocyanate to synthesize the prepolymer with isocyanate groups terminated, followed by further reaction with dimethylglyoxime as a chain extender to obtain self-healing elastomers. Specially, all-atom molecular dynamics simulations were used to construct the same model as the experiments. Together with the experimental characterization of FTIR and 1H NMR, all-atom molecular dynamics simulations can further verify the formation of hydrogen bonds and dynamic oxime–carbamate bonds. By fixing the ratio of hydroxyl to isocyanate constant, we found that the mechanical strength increased with the increase of hard segment content. At the same time, the loss factor decreased in the glass transition region and at room temperature. Finally, the self-healing behavior of the elastomer was verified at a certain temperature. The corresponding mechanism is explained by means of molecular dynamics simulations, where dynamic oxime–carbamate bonds play more important roles than hydrogen bonds. The combined simulation and experimental studies provided a reasonable approach for the subsequent self-healing system.

The dispersion and diffusion mechanism of nanofillers in polymer nanocomposites (PNCs) are crucial for understanding the properties of PNCs, which is of great significance for the design of novel materials. Herein, we investigate the dispersion and diffusion behavior of two geometries of nanofillers, namely, spherical nanoparticles (SNPs) and nanorods (NRs), in bottlebrush polymers by utilizing coarse-grained molecular dynamics simulations. With the increase of the interaction strength between the nanofiller and polymer (εnp), both the SNPs and NRs experience a typical “aggregated phase–dispersed phase–bridged phase” state transition in the bottlebrush polymer matrix. We evaluate the validity of the Stokes–Einstein (SE) equation for predicting the diffusion coefficient of nanofillers in bottlebrush polymers. The results demonstrate that the SE predictions are slightly larger than the simulated values for small SNP sizes because the local viscosity that is felt by small SNPs in the densely grafted bottlebrush polymer does not differ much from the macroscopic viscosity. The relative size of the length of the NRs (L) and the radius of gyration (R g) of the bottlebrush polymer play a key role in the diffusion of NRs. In addition, we characterize the anisotropic diffusion of NRs to analyze their translational and rotational diffusion. The motion of NRs in the direction perpendicular to the end-to-end vector is more hindered, indicating that there is a strong coupling between the rotation of NRs and the motion of the polymer. The NR motion shows stronger anisotropic diffusion at short time scales because of the steric effects generated by side chains of the bottlebrush polymer. In general, our results provide a fundamental understanding of the dispersion of nanofillers and the microscopic mechanism of nanofiller diffusion in bottlebrush polymers.

A coarse-grained molecular dynamics simulation was employed to examine the relationship between the morphology of carbon black particles and the mechanical properties of elastomer nanocomposites.

Natural rubber (NR), with its excellent mechanical properties, has been attracting considerable scientific and technological attention. Through molecular dynamics (MD) simulations, the effects of key structural factors on tensile stress at the molecular level can be examined. However, this high-precision method is computationally inefficient and time-consuming, which limits its application. The combination of machine learning and MD is one of the most promising directions to speed up simulations and ensure the accuracy of results. In this work, a surrogate machine learning method trained with MD data is developed to predict not only the tensile stress of NR but also other mechanical behaviors. We propose a novel idea based on feature processing by combining our previous experience in performing predictions of small samples. The proposed ML method consists of (i) an extreme gradient boosting (XGB) model to predict the tensile stress of NR, and (ii) a data augmentation algorithm based on nearest-neighbor interpolation (NNI) and the synthetic minority oversampling technique (SMOTE) to maximize the use of limited training data. Among the data enhancement algorithms that we design, the NNI algorithm finally achieves the effect of approaching the original data sample distribution by interpolating at the neighborhood of the original sample, and the SMOTE algorithm is used to solve the problem of sample imbalance by interpolating at the clustering boundaries of minority samples. The augmented samples are used to establish the XGB prediction model. Finally, the robustness of the proposed models and their predictive ability are guaranteed by high performance values, which indicate that the obtained regression models have good internal and external predictive capacities.