Molecular Dynamics Simulations of Self-Healing Topological Copolymers with a Comblike Structure

Accelerated Prediction of Phase Behavior for Block Copolymer Libraries Using a Molecularly Informed Field Theory

Solution formulations involving polymers are the basis for a wide range of products spanning consumer care, therapeutics, lubricants, adhesives, and coatings. These multicomponent systems typically show rich self-assembly and phase behavior that are sensitive to even small changes in chemistry and composition. Longstanding computational efforts have sought techniques for predictive modeling of formulation structure and thermodynamics without experimental guidance, but the challenges of addressing the long time scales and large length scales of self-assembly while maintaining chemical specificity have thwarted the emergence of general approaches. As a consequence, current formulation design remains largely Edisonian. Here, we present a multiscale modeling approach that accurately predicts, without any experimental input, the complete temperature-concentration phase diagram of model diblock polymers in solution, as established postprediction through small-angle X-ray scattering. The methodology employs a strategy whereby atomistic molecular dynamics simulations is used to parametrize coarse-grained field-theoretic models; simulations of the latter then easily surmount long equilibration time scales and enable rigorous determination of solution structures and phase behavior. This systematic and predictive approach, accelerated by access to well-defined block copolymers, has the potential to expedite in silico screening of novel formulations to significantly reduce trial-and-error experimental design and to guide selection of components and compositions across a vast range of applications.

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.

Understanding the application of covalent adaptable networks in self-repair materials based on molecular simulation

Covalent adaptable networks (CANs) are widely used in the field of self-repair materials. They are a group of covalently cross-linked associative polymers that undergo reversible chemical reactions, and can be further divided into dissociative CANs (Diss-CANs) and associative CANs (Asso-CANs). Self-repair refers to the ability of a material to repair itself without external intervention, and can be classified into self-adhesion and self-healing according to the utilization of open stickers. Unlike conventional materials, the viscoelastic properties of CANs are influenced by both the molecular structure and reaction kinetics, ultimately affecting their repair performance. To gain deeper insight into the repair mechanism of CANs, we conducted simulations by using the hybrid MC/MD algorithm, as previously proposed in our research. Interestingly, we observed a significant correlation between reaction kinetics and repair behavior. Asso-CANs exhibited strong mechanical strength and high creep resistance, rendering them suitable as self-adhesion materials. On the other hand, Diss-CANs formed open stickers that facilitated local relaxation, aligning perfectly with self-healing processes. Moreover, the introduction of crosslinkers in the form of small molecules enhanced the repair efficiency. Theoretically, it was found that the repair timescale of Asso-CANs is slower than that of Diss-CANs with identical molecular structures. Our study not only clarifies the similarities and differences between Diss-CANs and Asso-CANs in terms of their self-repairing capabilities, but more importantly, it provides valuable insights guiding the effective utilization of CANs in the development of self-repair materials.

Combing experimental methods and molecular simulations to study self-healing behaviors of polyurethane elastomers containing multiple hydrogen bond networks and flexible blocks

The preparation of polymers with high self-healing ability is conducive to environmental protection and resource conservation. In the present work, two kinds of polyurethane (PU) elastomers were prepared: the one containing flexible end blocks (polypropylene glycol) and the other containing flexible end blocks and 2-ureido-4[1H]-pyrimidinone (UPy) groups that can form reversible quadruple hydrogen bonds. Both of the two PU elastomers have self-healing ability. At low temperatures the PU without UPy groups exhibits stronger self-healing ability, while at high temperatures the PU with UPy groups has better self-healing function. The difference can be attributed to the combined effect of segmental mobility and reversible network strength. Based on molecular simulations, we further observed that the self-healing behaviors are affected by four factors: healing temperature, reversible interaction strength, reversible interaction site density and segment diffusion ability.

Self-healing bottlebrush polymer networks enabled via a side-chain interlocking design

Exploring novel healing mechanisms is a constant impetus for the development of self-healing materials. Herein, we find that side-chain interlocking of bottlebrush polymers can form a dynamic network and thereby serve as a driving force for the self-healing process of the materials. Molecular dynamics simulation indicates that the interlocking is formed by the interpenetration between the long side chains of adjacent molecules and stabilized by van der Waals interactions and molecular entanglements of side chains. The interlocking can be tailored by changing the length and density of the side chains through atom transfer radical polymerization. As a result, the optimized bottlebrush polymer shows a healing efficiency of up to 100%. Unlike chemical interactions, side-chain interlocking eliminates the introduction of specific chemical groups. Therefore, bottlebrush polymers can even self-heal under harsh aqueous conditions, including acid and alkali solutions. Moreover, the highly dynamic side-chain interlocking enables bottlebrush polymers to efficiently dissipate vibration energy, and thus they can be used as damping materials. Collectively, side-chain interlocking expands the scope of physical interactions in self-healing materials and hews out a versatile way for polymers to accomplish self-healing capability in various environments.

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.

Structural Investigation of Aaptourinamine by a Novel Module-Assembly-Based Calculation

Natural products have various and complicated structures, which is still a challenge for elucidating these compounds, especially for those lacking two-dimensional nuclear magnetic resonance (2D NMR) correlations mainly caused by high C/H ratios or proton-deficient and multiple heteroatoms through the conventional structural analytical methods. We reported a novel module-assembly calculation method named Dooerafa, which included constructing the meta-structures by a grafting method based on the crucial and the limited 2D NMR correlations, ring-contraction strategy based on mechanic force field and quantum chemical theory, and self-assemble calculation in Python programming for shaping up the structural candidates along with DFT-GIAO calculation. This new method, verified by a known alkaloid spiroreticulatine with the structure determined by X-ray diffraction, was performed for the structural elucidation of aaptourinamine isolated from marine sponge Aaptos suberitoides, showing us a brand new scaffold of imidazo [4,5,1-ij]pyrrolo [3,2-f]quinolin-7(8H)-one, which has a biosynthetic relationship with the bioactive and structurally unique aaptamine alkaloid.

Alternating Methyl Methacrylate/n-Butyl Acrylate Copolymer Prepared by Atom Transfer Radical Polymerization

Poly(methyl methacrylate/n-butyl acrylate) [P(MMA/BA)] copolymer with an alternating structure was synthesized via an activator regenerated by electron transfer (ARGET) atom transfer radical (co)polymerization (ATRP) of 2-ethylfenchyl methacrylate (EFMA) and n-butyl acrylate (BA) with subsequent postpolymerization modifications (PPM). Due to the steric hindrance of the bulky pendant group of EFMA, as well as the low reactivity ratio of BA in copolymerization with methacrylates, copolymerization of EFMA and BA generated a copolymer with a high content of alternating dyads. A subsequent PPM procedure of the alternating EFMA/BA copolymer was comprised of the hydrolysis of a tertiary ester by trifluoroacetic acid and methylation by (trimethylsilyl)diazomethane. After the modifications, the architecture of the obtained alternating MMA/BA copolymers was compared with gradient and statistical copolymers with overall similar compositions, molecular weights, and dispersities. ¹³C NMR indicated the absence of either MMA/MMA/MMA or BA/BA/BA sequences, in contrast to an abundance of homotriads in either the statistical or especially in the gradient copolymer. All three copolymers had similar glass transition temperatures, as measured by differential scanning calorimetry (DSC), but the alternating copolymer had the narrowest range of glass transition.

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 (ε_center end), and the PF weight fraction (w)─are systematically examined. First, attributed to the penetration of polymer chains into PFs at a strong ε_center 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, ε_center 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, ε_center 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.

Designing high performance polymer nanocomposites by incorporating robustness-controlled polymeric nanoparticles: insights from molecular dynamics

Introducing polymeric nanoparticles into polymer matrices is an interesting topic, and the robustness of the polymeric nanoparticles is crucial for the properties of the polymer nanocomposites (PNCs). In this study, by incorporating star-shaped polymeric nanoparticles (SSPNs) into the polymer, the effect of the sphericity (η) and arm length (L) of the SSPNs on the mechanical properties of PNCs is systematically investigated, using a coarse-grained molecular dynamics simulation. In addition, the linear and spherical nanoparticles (NPs) are compared with SSPNs by fixing the approximate diameter and mass fraction of the NPs. The radial distribution function, the second virial coefficient, mean-squared displacement, bond autocorrelation function, and primitive path analysis are employed to systematically characterize the structure and dynamics of these new PNCs. It is found that the dispersion of the NPs is enhanced with the increase of η, and the entanglement density reaches maximum, which both contribute to the greatest mechanical reinforcing effect. More significantly, it is found that the classical Payne effect, namely the storage as a function of the strain amplitude, decreases remarkably, and with a much smaller loss factor for these SSPN filled polymer nanocomposites, compared to conventional PNCs filled with rigid NPs. Furthermore, the change of the arm length of the SSPNs is found to exhibit the same effect on the mechanical and viscoelastic properties, as the variation of the number of the arms. In general, this work shows that these new SSPN filled polymer nanocomposites can exceed conventional PNCs, by manipulating the robustness of the SSPNs using, for example, the number and length of the arms. This research may provide guidelines for the investigation of the structure-property relationships of the topological structure of polymeric nanoparticles.