Versatilely Manipulating the Mechanical Properties of Polymer Nanocomposites by Incorporating Porous Fillers: A Molecular Dynamics Simulation Study.

Polymer nanocomposites (PNCs) have been attracting myriad scientific and technological attention due to their promising mechanical and functional properties. However, there remains a need for an efficient method that can further strengthen the mechanical performance of PNCs. Here, we propose a strategy to design and fabricate novel PNCs by incorporating porous fillers (PFs) such as metal-organic frameworks with ultrahigh specific surface areas and tunable nanospaces to polymer matrices via coarse-grained molecular dynamics simulations. Three important parameters─the polymer chain stiffness (k), the interaction strength between the PF center and the end functional groups of polymer chains (εcenter end), and the PF weight fraction (w)─are systematically examined. First, attributed to the penetration of polymer chains into PFs at a strong εcenter end, the dimension of polymer chains such as the radius of gyration and the end-to-end distance increases greatly as a function of k compared to the case of the neat polymer system. The penetration of polymer chains is validated by characterizing the radial distribution function between end functional groups and filler centers, as well as the visualization of the snapshots. Also, the dispersion state of PFs tends to be good because of the chain penetration. Then, the glass transition temperature ratio of PNCs to that of the neat systems exhibits a maximum in the case of k = 5ε, indicating that the strongest interlocking between polymer chains and PFs occurs at intermediate chain stiffness. The polymer chain dynamics of PNCs decreases to a plateau at k = 5ε and then becomes stable, and the relative mobility to that of the neat system as well presents the same variation trend. Furthermore, the mechanical property under uniaxial deformation is thoroughly studied, and intermediates k, εcenter end, and w can bring about the best mechanical property. This is because of the robust penetration and interaction, which is confirmed by calculating the stress of every component of PNCs with and without end functional groups and PF centers as well as the nonbonded interaction energy change between different components. Finally, the optimal condition (k = 5.36ε, εcenter end = 5.29ε, and w = 6.54%) to design the PNC with superior mechanical behavior is predicted by Gaussian process regression, an active machine learning (ML) method. Overall, incorporating PFs greatly enhances the entanglements and interactions between polymer chains and nanofillers and brings effective mechanical reinforcements with lower filler weight fractions. We anticipate that this will provide new routes to the design of mechanically reinforced PNCs.

Heterogeneous Dynamics of Polymer Melts Exerted by Chain Loops Anchored on the Substrate: Insights from Molecular Dynamics Simulation.

Understanding polymer-substrate interfacial dynamics at the molecular level is crucial for tailoring the properties of polymer ultrathin films (PUFs). Herein, through coarse-grained molecular dynamics simulation, the effect of length (Nloop) and rigidity (Kloop) of loop chains on the dynamics of linear chains is systematically explored, in which the loop chains are adsorbed on a solid substrate and the linear chains are covered on the loop chains. It is found that there is an optimal Kloop, which strongly confines the motion of the linear chains. Meanwhile, compared to increasing the rigidity of the loop chains, increasing the length of the loop chains can more effectively confine the motion of the linear chains. More interestingly, we observe that the mismatch of the length (ΔN) and rigidity (ΔK) between the loop and linear chains leads to dynamic asymmetry (ΔDc). The relationship between the ΔN, ΔK, and ΔDc are found to follow the mathematical expression of ΔDc ∼ (ΔN)α(ΔK)ß, in which the values of α and ß are around 4.58 and 0.83, separately. Remarkably, using the Gaussian process regression model, we construct a master curve of diffusion coefficient on the segmental and chain length scales of the linear chains as a function of Nloop and Kloop, which is further validated by our simulated prediction. In general, this work provides a fundamental understanding of polymer interfacial dynamics at the molecular level, enlightening some rational principles for manipulating the physical properties of PUFs.

Mechanical and Self-Healing Behavior of Matrix-Free Polymer Nanocomposites Constructed via Grafted Graphene Nanosheets.

Through molecular dynamics (MD) simulation, the structure and mechanical properties of matrix-free polymer nanocomposites (PNCs) constructed via polymer-grafted graphene nanosheets are studied. The dispersion of graphene sheets is characterized by the radial distribution function (RDF) between graphene sheets. We observe that a longer polymer chain length Lg leads to a relatively better dispersion state attributed to the formation of a better brick-mud structure, effectively screening the van der Waals interactions between sheets. By tuning the interaction strength εend-end between end functional groups of grafted chains, we construct physical networks with various robustness characterized by the formation of the fractal clusters at high εend-end values. The effects of εend-end and Lg on the mechanical properties are examined, and the enhancement of the stress-strain behavior is observed with the increase of εend-end and Lg. Structural evolution during deformation is quantified by calculating the orientation of the graphene sheets and their distribution, the stress decomposition, and the size of the clusters formed between end groups and their distribution. Then, we briefly study the effects of time and temperature on the self-healing behavior of these unique PNCs in the rubbery state. Lastly, the self-healing kinetics is quantitatively analyzed. In general, this work can provide some rational guidelines to design and fabricate matrix-free PNCs with both excellent mechanical and self-healing properties.