Effect of polymer-nanoparticle interaction on strain localization in polymer nanopillars

Identifying microscopic factors that influence ductility in disordered solids

There are empirical strategies for tuning the degree of strain localization in disordered solids, but they are system-specific and no theoretical framework explains their effectiveness or limitations. Here, we study three model disordered solids: a simulated atomic glass, an experimental granular packing, and a simulated polymer glass. We tune each system using a different strategy to exhibit two different degrees of strain localization. In tandem, we construct structuro-elastoplastic (StEP) models, which reduce descriptions of the systems to a few microscopic features that control strain localization, using a machine learning-based descriptor, softness, to represent the stability of the disordered local structure. The models are based on calculated correlations of softness and rearrangements. Without additional parameters, the models exhibit semiquantitative agreement with observed stress-strain curves and softness statistics for all systems studied. Moreover, the StEP models reveal that initial structure, the near-field effect of rearrangements on local structure, and rearrangement size, respectively, are responsible for the changes in ductility observed in the three systems. Thus, StEP models provide microscopic understanding of how strain localization depends on the interplay of structure, plasticity, and elasticity.

Understanding creep suppression mechanisms in polymer nanocomposites through machine learning

While recent efforts have shown how local structure plays an essential role in the dynamic heterogeneity of homogeneous glass-forming materials, systems containing interfaces such as thin films or composite materials remain poorly understood. It is known that interfaces perturb the molecular packing nearby, however, numerous studies show the dynamics are modified over a much larger range. Here, we examine the dynamics in polymer nanocomposites (PNCs) using a combination of simulations and experiments and quantitatively separate the role of polymer packing from other effects on the dynamics, as a function of distance from the nanoparticle surfaces. After showing good qualitative agreement between the simulations and experiments in glassy structure and creep compliance, we use a machine-learned structure indicator, softness, to decompose polymer dynamics in our simulated PNCs into structure-dependent and structure-independent processes. With this decomposition, the free energy barrier for polymer rearrangement can be described as a combination of packing-dependent and packing-independent barriers. We find both barriers are higher near nanoparticles and decrease with applied stress, quantitatively demonstrating that the slow interfacial dynamics is not solely due to polymer packing differences, but also the change of structure-dynamics relationships. Finally, we present how this decomposition can be used to accurately predict strain-time creep curves for PNCs from their static configuration, providing additional insights into the effects of polymer-nanoparticle interfaces on creep suppression in PNCs.

Extensive Stable Physical Contacts between a Nanoparticle and a Highly Repulsive Polymeric Layer

Interaction between nanoparticles (NPs) and a layer of grafted and solvated polymer molecules has been widely explored for a variety of applications ranging from fabrication of nanocomposites and sensors to developing nanocoating for virus deactivation. In all of these applications, the solvated polymer molecules are necessarily philic to the NPs, and consequently, driven by the favorable NP-polymer interactions, there is the formation of numerous stable direct (i.e., without any intervening solvent molecule) NP-monomer (monomer of the polymer) contact pairs. In this paper, we propose a paradigm shift in this problem: we employ molecular dynamics (MD) simulations and establish that under appropriate conditions, it is possible to develop numerous stable direct contacts between a NP and a solvated polymer layer even when the polymer molecules are extremely phobic to the NP. Here, by "stable" contacts, we refer to the NP-Polymer contacts that remain intact for a finite duration of time; of course, such contacts, after being intact for a finite time duration, might get broken and reformed. In terms of the mechanism of the process, the NP is driven inside a grafted layer of collapsed (in the absence of solvent) and phobic (to the NP) polymer molecules by a liquid drop (polymer is philic to the liquid). Subsequently, the liquid molecules imbibe and diffuse inside the polymer layer, but the NPs, due to the large steric effect imposed by the polymer molecules, remain localized within the polymer layer. This ensures the establishment of several stable direct contacts between the NP and the highly phobic polymer molecules. We quantify these contacts by their numbers, stability, and frequency of occurrences as well as their dependences on the NP-polymer interaction energies and NP sizes. We also quantify the associated NP dynamics inside the polymeric layer. Finally, we argue that our finding will open up avenues for leveraging NP-polymer interactions for a myriad of applications even for cases where the polymer molecules are phobic to the NPs.

Role of Local Structure in the Enhanced Dynamics of Deformed Glasses

External stress can accelerate molecular mobility of amorphous solids by several orders of magnitude. The changes in mobility are commonly interpreted through the Eyring model, which invokes an empirical activation volume. Here, we analyze constant-stress molecular dynamics simulations and propose a structure-dependent Eyring model, connecting activation volume to a machine-learned field, softness. We show that stress has a heterogeneous effect on the mobility that depends on local structure through softness. The barrier impeding relaxation reduces more for well-packed particles, which explains the narrower distribution of relaxation time observed under stress.