Most of the industrially used polymers are immiscible and incompatible and do not form a homogeneous mixture. Stabilizing these immiscible mixed plastics could increase their lifespan and enable previously unrecoverable mixed plastic wastes to be reprocessed and reused. Here, we study how reversible dynamics covalent bonds can reactivate mixed plastic ``dead'' chains into compatibilized multiblock copolymers. We develop a phenomenological bead-spring model and carry out large-scale hybrid molecular dynamics (MD) - Monte Carlo (MC) simulations of an incompatible homopolymer blend. These simulations show a clear transition from an immiscible blend to a progressively more miscible one via dynamic crosslinking when thermally activated. The creation of a ``living'' gMBCPs, is found to be the underpinning driver for the increased miscibility. They control the local density of microphase-separated domains and compatibilize the interfaces of the blend. We analyze the Rose modes of relaxation and bond segregations in this class of materials. The work provides design rules of thermomechanical recycling of polymers. We further propose an AI pipeline for accelerating the design of reversibility crosslinked polymer networks that exhibit self-healing, recyclability and adaptability.

A self-consistent scheme of differential equations is developed for predicting the frequency of the two smallest loop defects within polymer model networks. Without any adjustable parameter, we obtain excellent agreement with Monte Carlo simulations that sample loop formation only up to the given maximum loop size. The formation of loops of second generation leads to correlations between connected junctions that cannot be treated exactly by considering statistical arguments alone, which is in contrast to reversible networks where equilibrium statistics are sufficient. These correlations and the statistics of the junctions are provided by our model. Comparison with more realistic simulation data in three dimensions indicates that composition fluctuations of cross-links and chains clearly impact network formation. The differences between the statistics of the network junctions and our mean field predictions provide insight into the size of the domains with a predominance of chains or junctions and thus, regarding the quality of the mixture. Our results are highly relevant for an accurate modeling of network structure, improved estimates of the elastic properties of polymer networks, and for advanced analysis techniques of the network structure like network disassembly spectrometry or multiple quantum nuclear magnetic resonance.

Polymers are essential in consumer goods packaging materials, yet their use raises concerns about environmental safety due to potential migration of harmful contaminants. [1] This study utilizes atomistic molecular dynamics simulations to analyze monomer migration from three common packaging polymers—polyamide-6, polycarbonate, and poly(methyl methacrylate)—into various food simulant solvents. By modeling both bulk diffusion and interfacial transport, we investigated how factors such as polymer-monomer interactions, solvent compatibility, and free volume within the polymer matrix affect contaminant migration.

Key findings include the identification of dual transport mechanisms—continuous and hopping diffusion [2,3] —within polymer bulk, influenced by the physical properties of both the monomer and the polymer. Interfacial studies revealed food simulant solvent type as a critical factor, with polar solvents enhancing migration by disrupting polymer cohesion. For instance, ethanol significantly facilitated migration compared to other solvents. Notably, larger molecules such as triglycerides exhibited unique interfacial behavior preventing contaminant migration, highlighting the complexity of solvent-polymer interactions.

Our results align well with experimental trends and underscore the power of molecular simulations in predicting leaching behavior, offering insights for designing safer packaging materials. This research advances understanding of molecular-scale migration processes and supports the development of regulatory frameworks and industrial innovations to mitigate contamination risks in consumer products.

Wettability is of great interest to various scientific and industrial applications, such as surface chemistry, coatings, or marine biofouling. Contact angle calculations provide significant insight on the surface wettability of natural surfaces and surface coatings.
In the present work, we propose a new dissipative particle dynamics coarse-grained model to probe the spatiotemporal conditions and the coexistence of different phases that this calculation requires. The polymer substrate and liquid droplet are parametrized by a well-established bottom-up parametrization technique, starting from quantum chemistry calculations1.
The solid-liquid interface is estimated from the polymer surface density profile whereas the liquid-vapor interface is determined from the droplet 3D density field. A sphere is subsequently fitted to the 3D density profile and the equation of this sphere is solved to obtain the contact angle2.
The simulation protocol has been validated against a series of polymeric surfaces and it adequately captures the experimental trends regarding the hydrophilic and hydrophobic propensity of the substrate. Taking also into account the calculation speed (less than 2h in 4 CPUs), this model can be readily incorporated as a screening tool for any surface.

Figure 1. Snapshot of a water droplet wetting a polymer surface.

We present an extensive analysis of the relaxation dynamics of entangled linear polymer melts via long-time molecular dynamics simulations of a generic bead-spring model. We study the mean-squared displacements, the autocorrelation function of the end-to-end vector, P(t), the single-chain dynamic structure factor, S(q, t), and the linear viscoelastic properties, especially the shear stress relaxation modulus, G(t). The simulation data are compared with the theoretically expected scaling laws, and with analytical expressions that account for different relaxation mechanisms in the tube model, namely, reptation, contour length fluctuation (CLF), and constraint release (CR). CLF involves a t¹/⁴ scaling regime in the time-dependence of (1−P (t)). With increasing chain length, a gradual development of this scaling regime is observed. In the absence of CR, the tube model further predicts that at long times, the chain dynamics is governed by one central quantity, the “surviving tube fraction” µ(t). As a result, one expects S(q, t) ∝ G(t) ∝ P (t) in that time regime. We test this prediction by comparing S(q, t) and G(t) with P (t). For both quantities, proportionality with P (t) is not observed, indicating that CR has an important effect on the relaxation of these two quantities. Instead, to a very good approximation, we find G(t) ∝ P (t)² at late times, which is consistent with the dynamic tube dilation or double reptation approximations for the CR process. We also calculate non-local mobility functions, which can be used in dynamic density functional theories for entangled inhomogeneous polymer blends.