Topological Programmability of Isomerizable Polymers.

Topology isomerizable networks (TINs) can be programmed into numerous polymers exhibiting unique and spatially defined (thermo-) mechanical properties. However, capturing the dynamics in topological transformations and revealing the intrinsic mechanisms of mechanical property modulation at the microscopic level is a significant challenge. Here, we use a combination of coarse-grained molecular dynamics simulations and reaction kinetic theory to reveal the impact of dynamic bond exchange reactions on the topology of branched chains. We find that, the grafted units follow a geometric distribution with a converged uniformity, which depends solely on the average grafted units of branched chains. Furthermore, we demonstrate that the topological structure can lead to spontaneous modulation of mechanical properties. The theoretical framework provides a research paradigm for studying the topology and mechanical properties of TINs.

Impact of Sacrificial Hydrogen Bonds on the Structure and Properties of Rubber Materials: Insights from All-Atom Molecular Dynamics Simulations.

The inclusion of sacrificial hydrogen bonds is crucial for advancing high-performance rubber materials. However, the molecular mechanisms governing the impact of these bonds on material properties remain unclear, hindering progress in advanced rubber material research. This study employed all-atom molecular dynamics simulations to thoroughly investigate how hydrogen bonds affect the structure, dynamics, mechanics, and linear viscoelasticity of rubber materials. As the modified repeating unit ratio (ß) increased, both interchain and intrachain hydrogen bond content rose, with interchain bonds playing a predominant role. This physical cross-linking network formed through interchain hydrogen bonds restricts molecular chain movement and relaxation and raises the glass transition temperature of rubber. Within a certain content of hydrogen bonds, the mechanical strength increases with increasing ß. However, further increasing ß leads to a subsequent decrease in the mechanical performance. Optimal mechanical properties were observed at ß = 6%. On the other hand, a higher ß value yields an elevated stress relaxation modulus and an extended stress relaxation plateau, signifying a more complex hydrogen-bond cross-linking network. Additionally, higher ß increases the storage modulus, loss modulus, and complex viscosity while reducing the loss factor. In summary, this study successfully established the relationship between the structure and properties of natural rubber containing hydrogen bonds, providing a scientific foundation for the design of high-performance rubber materials.

Remarkable Toughening of Plastic with Monodispersed Nano-CaCO3: From Theoretical Predictions to Experimental Validation.

The structure-property relationship of poly(vinyl chloride) (PVC)/CaCO3 nanocomposites is investigated by all-atom molecular dynamics (MD) simulations. MD simulation results indicate that the dispersity of nanofillers, interfacial bonding, and chain mobility are imperative factors to improve the mechanical performance of nanocomposites, especially toughness. The tensile behavior and dissipated work of the PVC/CaCO3 model demonstrate that 12 wt % CaCO3 modified with oleate anion and dodecylbenzenesulfonate can impart high toughness to PVC due to its good dispersion, favorable interface interaction, and weak migration of PVC chains. Under the guidance of MD simulation, we experimentally prepared a transparent PVC/CaCO3 nanocomposite with good mechanical properties by in situ polymerization of monodispersed CaCO3 in vinyl chloride monomers. Interestingly, experimental tests indicate that the optimum toughness of a nanocomposite (a 368% increase in the elongation at break and 204% improvement of the impact strength) can be indeed realized by adding 12 wt % CaCO3 modified with oleic acid and dodecylbenzenesulfonic acid, which is remarkably consistent with the MD simulation prediction. In short, this work provides a proof-of-concept of using MD simulation to guide the experimental synthesis of PVC/CaCO3 nanocomposites, which can be considered as an example to develop other functional nanocomposites.

Conformation and Dynamics along the Chain Contours of Polymer-Grafted Nanoparticles.

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.

Predicting Natural Rubber Crystallinity by a Novel Machine Learning Algorithm Based on Molecular Dynamics Simulation Data.

Natural rubber (NR) with excellent mechanical properties, mainly attributed to its strain-induced crystallization (SIC), has garnered significant scientific and technological interest. With the aid of molecular dynamics (MD) simulations, we can investigate the impacts of crucial structural elements on SIC on the molecular scale. Nonetheless, the computational complexity and time-consuming nature of this high-precision method constrain its widespread application. The integration of machine learning with MD represents a promising avenue for enhancing the speed of simulations while maintaining accuracy. Herein, we developed a crystallinity algorithm tailored to the SIC properties of natural rubber materials. With the data enhancement algorithm, the high evaluation value of the prediction model ensures the accuracy of the computational simulation results. In contrast to the direct utilization of small sample prediction algorithms, we propose a novel concept grounded in feature engineering. The proposed machine learning (ML) methodology consists of (1) An eXtreme Gradient Boosting (XGB) model to predict the crystallinity of NR; (2) a generative adversarial network (GAN) data augmentation algorithm to optimize the utilization of the limited training data, which is utilized to construct the XGB prediction model; (3) an elaboration of the effects induced by phospholipid and protein percentage (ω), hydrogen bond strength (εH), and non-hydrogen bond strength (εNH) of natural rubber materials with crystallinity prediction under dynamic conditions are analyzed by employing weight integration with feature importance analysis. Eventually, we succeeded in concluding that εH has the most significant effect on the strain-induced crystallinity, followed by ω and finally εNH.

Molecular dynamics simulation of the impact of the surface topology of carbon black on the mechanical properties of elastomer nanocomposites.

Carbon black has always played a pivotal role in reinforcing elastomers because it remarkably improves the mechanical properties. The reinforcing effect of carbon black is influenced by its grades, which mainly depend on the difference in the structure of the carbon black particles. Despite many traditional experiments on the performance of carbon black composites, there has been less emphasis on reinforcement mechanisms due to the challenges associated with unraveling the intermolecular interactions. In this paper, a coarse grained molecular dynamics simulation was employed to examine the relationship between the morphology of the carbon black particles and the mechanical properties of the elastomer nanocomposites. Specifically, three different morphological carbon black nanoparticle models, including the smooth particle model, rough particle model, and the rough ellipsoid model, were constructed first. We then focused on investigating the changes of the mechanical properties by systematically varying the filling fraction of the carbon black particles, and the strength of the interfacial interaction between the filler and the rubber. The results indicated that the surface roughness and the filler's shape had a significant impact on the mechanical properties of the filled rubber models. The mechanical enhancement effect of the rough ellipsoidal carbon black is around 50-400% higher than that of the smooth carbon black, and the stronger the interfacial interactions, the more pronounced the enhancement. In addition, the rough ellipsoid filled system has low hysteresis, low permanent deformation, and high fatigue resistance. In general, this work explores the strengthening mechanism of carbon black on the elastomer at the molecular level and generates new insight into the design and fabrication of novel reinforcing fillers.

ReaxFF molecular dynamics simulation of the thermal decomposition reaction of bio-based polyester materials.

Bio-based polyester elastomers have been widely studied by researchers in recent years because of their comprehensive sources of monomers and environmentally friendly characteristics. However, compared with traditional petroleum-based elastomers, the thermal decomposition temperature of bio-based polyester elastomers is generally low, limiting the application of bio-based elastomers. An effective strategy to increase the intrinsic thermal decomposition temperature (Td) of bio-based elastomers is to increase the length of the monomer carbon chain in the bio-based elastomers. In this work, the content of dodecanedioic acid (DDA) in a bio-based polyester elastomer composed of butanediol (BDO) and succinic acid (SUA) was increased to improve the Td of the bio-based polyester elastomer through the reaction force-field molecular dynamics (ReaxFF-MD) simulations. And the thermal decomposition mechanism of the bio-based polyester was analyzed in detail. By calculating the change rate of the molecular chain mean square displacement (MSD), it was determined that when the content of DDA was 50%, the Td of the bio-based elastomer was up to 718 K. By calculating the activation energy of thermal decomposition and further analyzing the thermal decomposition process, it is found that the thermal decomposition of the bio-based polyester elastomer is mainly through breaking the C-O bond in the backbone. This work is expected to provide theoretical guidance for designing and fabricating highly heat-resistant bio-based elastomers by systematically exploring the thermal decomposition mechanism of bio-based polyester elastomers.

Advancing elastomer performance with dynamic bond networks in polymer-grafted single-chain nanoparticles: a molecular dynamics exploration.

This research introduces a method to enhance the mechanical properties of elastomers by grafting polymer chains onto single-chain flexible nanoparticles (SCNPs) and incorporating dynamic functional groups. Drawing on developments in grafting polymers onto hard nanoparticle fillers, this method employs the distinct flexibility of SCNPs to diminish heterogeneity and enhance core size control. We use molecular dynamics (MD) simulations for a mesoscale analysis of structural properties, particularly the effects of dynamic functional group quantities and their distribution. The findings demonstrate that increased quantities of functional groups are correlated with enhanced mechanical strength and toughness, showing improved stress-strain responses and energy dissipation capabilities. Moreover, the uniformity in the distribution of these functional groups is crucial, promoting a more cohesive and stable dynamic bonding network. The insights gained from MD simulations not only advance our understanding of the microstructural control necessary for optimizing macroscopic properties, but also provide valuable guidance for the design and engineering of advanced polymer nanocomposites, thereby enhancing the material performance through strategic molecular design.

Dispersion and Diffusion Mechanism of Nanofillers with Different Geometries in Bottlebrush Polymers: Insights from Molecular Dynamics Simulation.

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 (Rg) 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 Machine Learning Framework to Predict the Tensile Stress of Natural Rubber: Based on Molecular Dynamics Simulation Data.

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.