The molecular factors that govern interfacial interactions between a polymer melt and a solid surface remain largely unclear despite significant progress made in the last years. Simulations are increasingly employed to elucidate these features, however, equilibration and sampling with models of long macromolecules in such heterogeneous systems present significant challenges. In this study, we couple the application of preferential sampling techniques with connectivity-altering Monte Carlo algorithms to explore the configurational characteristics of a polyethylene melt in proximity to a surface and a highly curved nanoparticle. Designed algorithms allow efficient sampling at all length scales of large systems required to avoid finite-size effects. Using detailed atomistic models for the polymer and realistic structures for a silica surface and a fullerene, we find that at the extreme limit where particles are comparable to the polymer Kuhn segment length, curvature penalizes the formation of long train segments. As a result, an increased number of shorter contacts belonging to different chains are made competing with the anticipated decrease of the bound layer thickness with particle size if polymer adsorbed per unit area remained constant. For very small nanoparticles, formation of new train segments cannot compete with the overall reduction of adsorbance which is present irrespective of the enthalpic interactions; a result that demonstrates the need for an accurate description of polymer rigidity at these length scales.

The local dynamics and the conformational properties of polyisoprene next to a smooth graphite surface constructed by graphene layers are studied by a multiscale methodology. First, fully atomistic molecular dynamics simulations of oligomers next to the surface are performed. Subsequently, Monte Carlo simulations of a systematically derived coarse-grained model generate numerous uncorrelated structures for polymer systems. A new reverse backmapping strategy is presented that reintroduces atomistic detail. Finally, multiple extensive fully atomistic simulations with large systems of long macromolecules are employed to examine local dynamics in proximity to graphite. Polyisoprene repeat units arrange close to a parallel configuration with chains exhibiting a distribution of contact lengths. Efficient Monte Carlo algorithms with the coarse-grain model are capable of sampling these distributions for any molecular weight in quantitative agreement with predictions from atomistic models. Furthermore, molecular dynamics simulations with well-equilibrated systems at all length-scales support an increased dynamic heterogeneity that is emerging from both intermolecular interactions with the flat surface and intramolecular cooperativity. This study provides a detailed comprehensive picture of polyisoprene on a flat surface and consists of an effort to characterize such systems in atomistic detail.

Materials created by dispersing nanoparticles in a polymer matrix strive to meet the promise of enhanced and often unique properties at a reduced cost. The availability of structure− property relationships and predictive modeling are deemed necessary to tailor materials according to our needs. However, the road from detailed information at the atomistic level to macroscopic properties has been severely segmented due to diverse experimental, theoretical, and modeling methods employed to study polymer−particle mixtures, each with their own advantages and limitations. In this Perspective, we focus on seemingly simple polymer−nanoparticle mixtures where nanoparticles are bare or grafted with chains of the same chemical constitution as the matrix. We present a number of studies that attempt to quantitatively identify where complete miscibility is achieved. As we discuss, features pertaining to the nanoscale dimensions of particles continue to challenge our fundamental understanding on polymer−particle interactions. However, through a concerted approach of theory, experiments, and simulations, recent studies significantly expand our knowledge on the morphological behavior of these systems. Most importantly, our discussion demonstrates how new developments bridge knowledge of microscopic interactions with thermodynamic behavior, an achievement that has far more reaching implications in the area of polymer−particle mixtures.

A quantitative description of kinetics in acid-catalyzed polymer deprotection reactions requires proper identification of the controlling mechanisms. We examined the acid-catalyzed deprotection of a glassy poly(4-hydroxystyrene-co-tert-butyl acrylate) resin using infrared absorbance spectroscopy and stochastic simulations. We interpret experimental data with a model that explicitly accounts for acid transport, where heterogeneities at local length scales are introduced through a nonexponential distribution of waiting times between successive hopping events. A subdiffusive behavior with long-tail kinetics predicts key attributes of the observed deprotection rates, such as a fast initial deprotection, slow conversion at long times, and a nonlinear dependence on acid loading. Most importantly, only two parameters are introduced to offer a near-quantitative description of deprotection levels at low acid loadings and short times. The model is extended to high acid loadings and long times by incorporating a simple acid depletion model based on mutual encounters. Our study suggests that macroscopic deprotection rates are controlled by acid transport in the glassy deprotected polymer, which presents with a strongly non-Fickian behavior.

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