Simulation of End-Coupling Reactions at a Polymer−Polymer Interface: The Mechanism of Interfacial Roughness Development

Abstract

End-coupling between immiscible melts of two monofunctionalized polymers of a same length was modeled by dissipative particle dynamics starting from a flat interface and up to the formation of a mature lamellar microstructure. Influence of the reaction rate, chain length, and incompatibility of components on the kinetics of copolymer formation and morphology development was investigated. Regimes of linear and logarithmic growth of the conversion with time were observed before the flat interface became unstable. The conditions and mechanism of interfacial roughness development were studied in detail. It was demonstrated that overcrowding the interface with the copolymer product causing its phase separation plays the main role in spontaneous interface distortion. The instability leads to autocatalytic interface growth with exponential kinetics, when each new portion of the product creates more area for further reactions. It was followed by a slower terminal regime including formation and ripening of the lamellar microstructure. The late stage kinetics of end-coupling was strongly influenced by depletion of reactants and formation of ordered product layers. At certain conditions, it became asymptotically diffusion controlled in agreement with published experimental data.

A kinetic chain growth algorithm in coarse-grained simulations

“…[4,6,8,12,13,16] With such a protocol for bond formation in a CG model, it is possible to consider different processes of bonding from a CG point of view. Bond formation is instantaneous and it is impossible to observe any intermediate state of bonding.…”

“…Last but not least of this part, for describing a radical polymerization, it is necessary to make a comparison of the polymerization kinetics of the present algorithm with our previous model [21,22] or other similar models in which a constant reaction probability was adopted. [4][5][6][7][8][9][10][11][12][13][14][15][16] To avoid ambiguities, we define the constant reaction probability in the latter as P c r here. In our previous model, for any specific short period t 0 during the chain propagation, the change of monomer concentration is…”

“…Genzer et al, [4,5] He and the coworkers, [6,7] Berezkin et al, [8][9][10][11] Lu et al, [12] Yong et al, [13,14] Jang, [15] and Mukherji et al, [16] including Rabbel et al [17] in their recent works, all adopted similar reaction probability-based protocols to establish their respective reaction models. The simulation studies based on this idea can be identified as the second class, that is, "probability-based" protocols.…”

“…Within the capture radius, the bond formation in the second class in each step is not a certain event, instead it is probability-based. Genzer et al, [4,5] He and the coworkers, [6,7] Berezkin et al, [8][9][10][11] Lu et al, [12] Yong et al, [13,14] Jang, [15] and Mukherji et al, [16] including Rabbel et al [17] in their recent works, all adopted similar reaction probability-based protocols to establish their respective reaction models. However notably, few of these models could rationally relate the true reaction rate to their local reaction probability parameters.…”

Forging of Hierarchical Multiscale Capabilities for Simulation of Energetic Materials

“…These DPD formulations treat reactivity implicitly, and while explicit reactivity methods also exist, they are restricted to isothermal conditions. Explicit simulation within the DPD framework is akin to atomisticlevel reactive force-fields (e. g., ReaxFF [11]), which involves explicit bond-breaking and formation, either in a stochastic [16][17][18][19][20] or deterministic fashion [16,19]. An alternative particle-based CG methodology for EM simulation, termed dynamics with implicit degrees of freedom (DID), treats chemical reactivity through a reactive potential [21][22][23][24][25].…”