MARTINI Coarse-Grained Force Field for Thermoplastic Starch Nanocomposites.

Abstract

Thermoplastic starch (TPS) is an excellent film-forming material, and the addition of fillers, such as tetramethylammonium-montmorillonite (TMA-MMT) clay, has significantly expanded its use in packaging applications. We first used an all-atom (AA) simulation to predict several macroscopic (Young's modulus, glass transition temperature, density) and microscopic (conformation along 1-4 and 1-6 glycosidic linkages, composite morphology) properties of TPS melt and TPS-TMA-MMT composite. The interplay of polymer-surface (weakly repulsive), plasticizer-surface (attractive), and polymer-plasticizer (weakly attractive) interactions leads to conformational and dynamics properties distinct from those in systems with either attractive or repulsive polymer-surface interactions. A subset of AA properties was used to parametrize the MARTINI-2 coarse-grained (CG) force field (FF) for the melt and composite systems. The missing bonded parameters of amylose and amylopectin and the bead types for 1-4 and 1-6 linked α-D glucose were determined using two-body excess entropy, density, and bond and angle distributions in the AA TPS melt. This new MARTINI-2 CG model was also compared with the MARTINI-3 model for the TPS melt. However, the requirement of a polarizable water model necessitates the use of MARTINI-2 FF for the composite system. This liquid-liquid partitioning-based FF shows freezing and compaction of polymer chains near the clay surface, further accentuated by lowering of dispersive interactions between pairs of high-covalent-coordination ring units of TPS polymers and the montmorillonite sheet. A rescaling of the effective dispersive component of TPS-MMT cross interactions was used to optimize the MARTINI-2 FF for the composite system with structural (chain size distribution), thermodynamic (chain conformational entropy and density), and dynamic (self-diffusion coefficient) properties obtained from long AA simulations forming the constraints for optimization. The obtained CG FF parameters provided excellent estimates for several other properties of the melt and composite systems not used in parameter estimation, thus establishing the robustness of the developed model.