Dissipative particle dynamics simulations of tri-block co-polymer and water: Phase diagram validation and microstructure identification

Abstract

In this study, the phase diagram of Pluronic L64 and water is simulated via dissipative particle dynamics (DPD). The peculiar structures that form when the concentration varies from dilute to dense (i.e., spherical and rod-like micelles, hexagonal and lamellar phases, as well as reverse micelles) are recognized, and predictions are found to be in good agreement with experiments. A novel clustering algorithm is used to identify the structures formed, characterize them in terms of radius of gyration and aggregation number and cluster mass distributions. Non-equilibrium simulations are also performed, in order to predict how structures are affected by shear, both via qualitative and quantitative analyses. Despite the well-known scaling problem that results in unrealistic shear rates in real units, results show that non-Newtonian behaviors can be predicted by DPD and associated with variations of the observed microstructures.

Simulation of Mixing in Structured Fluids with Dissipative Particle Dynamics and Validation with Experimental Data

“…A certain correspondence with the phase diagram can be observed, showing the capability of the methodology to predict the presence of different microstructures from a qualitative point of view. Moreover, it is important to remark that these simulations were carried out with DPD parameters adopted in our previous work and not ad‐hoc modified, demonstrating how these parameters are portable to systems constituted of different Pluronic molecules, provided that the adopted coarse‐graining factors for the PPO and PEO chains remain the same.…”

“…The objective of this work is to assess the capability of DPD models to simulate Pluronic‐water mixtures. This work follows a previous one in which mixtures of water with another Pluronic (i.e., L64) were simulated by using a DPD model with parameters taken from the literature , . The main novelty of this work lies in the demonstration of the portability of these parameters.…”

“…The mixtures of water and Pluronic P103 have been simulated by using the open‐source particle‐based code LAMMPS. As mentioned in the introduction, the parameters taken from the literature , were verified to reproduce accurately the phase diagram of mixtures of water and Pluronic L64 in a previous work and are here used for the first time to reproduce the phase diagrams of Pluronic P103. The coarse‐graining degree for the polymer molecule was approximately 4.3 for the PEO part and 3.3 for the PPO part, resulting in a representation of the polymer molecule as A 4 B 18 A 4 for Pluronic P103, where A stands for the beads used to represent the PEO chain and B for the beads used to represent the PPO chain.…”

“…Simulation trajectories were analyzed with a clustering algorithm written in python and based on DBSCAN . The algorithm and its implementation, together with a link to a github repository, is reported in our previous work . Essentially, the algorithm, based on the relative positions of the beads, is capable of identifying clusters of polymer molecules (e.g., micelles), counting the number of clusters per box, and of calculating the radius of gyration of each cluster, by using the following equation: …”

MARTINI coarse‐grained model for poly‐ε‐caprolactone in acetone‐water mixtures

“…Interesting applications of DPD on co-polymer systems have been recently done, by studying their rheological behaviour in water solutions. [29,30] Other CG methods can be built up as a combination of both top-down and bottom-up techniques. This is the case for the MARTINI force field, developed by Marrink et al [31] Optimized at the beginning only for lipids systems, [15] it has been extended throughout the years to other biological systems, such as proteins, [32] carbohydrates, [33] DNA, [34] and also employed to model polymer melts and solutions.…”