We use large scale coarse‐grained molecular dynamics simulations to study the kinetics of polymer melt crystallization. For monodisperse polymer melts of several chain lengths under various cooling protocols, we show that short chains have a higher terminal crystallinity value compared to longer ones. They align at the early stages and then cease evolving. Long chains, however, align, fold into lamella structures and then slowly optimize their dangling ends for the remaining simulation time. We then identify the mechanism behind bidisperse blend crystallization. To this end, we introduce a new algorithm (called Individual Chain Crystallinity) that allows the calculation of the crystallinity separately for short and long chains in the blend. We find that, in general, bidispersity hinders crystallization significantly. At first the crystallinity of the long chain components exceeds that of the monodisperse melt, but subsequently falls below the corresponding monodisperse melt curve after a certain “crossover time.” The time of the crossover can be attributed to the time required for the full crystallization of the short chains. This indicates that at the early stages the short chains are helping long chains to crystallize. However, after all short chains have crystallized they start to hinder the crystallization of the long chains by obstructing their motion. Lastly, polymer crystallization upon various thermodynamic protocols is studied. Slower cooling is found to increase the crystallinity value. Upon an instantaneous deep quench and subsequent isothermal relaxation, the crystallinity grows rapidly with time at early stages and subsequently saturates. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2016, 54, 2318–2326
We present a molecular dynamics study of two polyelectrolyte gels with different degrees of ionization coupled in a slab geometry. Our simulations show that a pressure gradient emerges between the two gels that results in the buildup of a Nernst-Donnan potential. This methodology is reverse to experiments of the piezoionic or mechanoelectric effect, in which an electric potential gradient appears upon application of a pressure gradient to a hydrogel. The Nernst-Donnan potential at the interface is found to scale linearly with temperature with the coefficient of proportionality given by the fraction of concentrations of the uncondensed counterions. We show that the potential difference can also be expressed as a linear function of the lateral pressure, thus providing a molecular interpretation of the piezoionic effect.
Ionic driven devices have been increasingly investigated in the drive to develop flexible and biointegrable electronics. One such device is a polyelectrolyte gel diode capable of rectifying ionic current. However, the underlying mechanism behind the rectification of current in polyelectrolyte gel diodes is not fully understood. Based on experimental data, it has been proposed that the rectification is due to the asymmetric distribution of ions at the interface between two gels doped with a cationic polyelectrolyte on one side and an anionic polyelectrolyte on the other. Additionally, an electrochemical model has been proposed to explain the mechanism quantitatively. Here, we explore the mechanism proposed by the Yamamoto–Doi model and validate it by using experimental data. We show that the diode operates via a physical mechanism that involves the electrochemical generation of proton and hydroxyl ions at the electrodes to generate current. Exponential currents (J) in the forward bias were observed and J=A−V (with A inversely proportional to the gel ionization and V the potential) in the backward bias, which coincides with predictions of the electrochemical Yamamoto–Doi model. Additionally, we also confirm the dependence of the electrochemical model on the dopant concentration in the backward bias regime.
We compare MD simulations of a model polyelectrolyte gel diode (PGD) to a Poisson–Boltzmann (PB) model. We study the rectifying behaviour at different electrostatic coupling strengths and suggest an updated PB model for improved modelling of PGDs.
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