Molecular simulation of miscibility of poly(2,6‐dimethyl‐1,4‐phenylene ether)/poly(styrene‐co‐acrylonitrile) blend with the compatibilizer triblock terpolymer SBM

Abstract

Polymer blend of poly(2,6‐dimethyl‐1,4‐phenylene ether) (PPE) and poly(styrene‐co‐acrylonitrile) (SAN), which has broad commercial interest, has limited miscibility. A triblock terpolymer, polystyrene‐block‐polybutadiene‐block‐poly(methyl methacrylate) (SBM), is often used as compatibilizer to improve the miscibility of PPE/SAN. In this work, dissipative particle dynamics and molecular dynamics of Material Studio were used to study the essentials that influence miscibility of the blend systems, and then Flory–Huggins parameter χ, radial distribution function (RDF) and morphologies are analyzed. It shows that the blends with more content of styrene in SAN (above 90 wt%), whose mass percentage is 60%, are best miscible. For the systems of PPE/SAN added with SBM, the miscibility increases and then decreases with the increase of SBM content. A longer chain of styrene (S) in SBM leads to wrapped structure of PMMA by PB, wrapped by PS, resulting in decrease of the miscibility. From studies and simulation of χ and RDF, the best blend system for commercial and industrial use is the one with mass ratio of PPE/SAN/SBM 36/54/10, in which S content in SAN is above 90 wt%. For SBM, the ratio of chain length styrene (S)/butadiene (B) is lessthan 1, while B and M are the same in chain length. POLYM. COMPOS., 2011. © 2011 Society of Plastics Engineers

Fully Atomistic Molecular Dynamics Simulation of the Structure and Morphology of Small‐Molecular Additives in Rubber Matrices

“…Also, it is widely believed that the smaller the difference between two solubility parameters is and the smaller the Flory-Huggins parameter is, the better the compatibility of the two components will be. [14][15][16][17] From the perspective of energy, binding energy (E binding = E 1 + E 2 − E total ), interaction energy (ΔE = −E binding ), and interfacial interaction energy per unit area (𝛾 = −ΔE/S) are regarded as the parameters of compatibility judgment. And the smaller the absolute values of ΔE and 𝛾 are (the bigger the values of E binding is), the more compatibility there is between the components.…”

Molecular dynamics simulations of copolymer compatibilizers for polylactide/poly(butylene succinate) blends

“…Among many simulation methods, molecular dynamics (MD) simulations have successfully been used to obtain detailed atomistic level information on the structural, dynamic, and thermomechanical properties of polymer blends. [23][24][25][26][27][28][29] Arenaza et al 24 employed MD simulations to study the miscibility poly(Llactide) (PLLA) and poly(DL-lactide) (PDLLA) with poly (styrene) (PS) and poly(vinyl phenol) (PVPh). Through analyzing the Flory-Huggins interaction parameter, they found that polylactide/PVPh blends are miscible while the polylactide/PS blends are immiscible, and both results agree with experimental reports.…”

“…For simulation studies of polymer blends with compatibilizers, Zhuang et al 27 performed MD simulations to study the miscibility of poly(2,6-dimethyl-1,4-phenylene ether) (PPE)/poly(styrene- co -acrylonitrile) (SAN) blends using polystyrene- block -polybutadieneblock-poly(methyl methacrylate) (SBM) as a compatibilizer. By analyzing the Flory–Huggins parameter, radial distribution function and morphologies, they found that the miscibility of the blends increases and then decreases with the increase of the amount of SBM.…”

Preparation of modified sisal microcrystalline cellulose PHS‐g‐MCC and its application in NR/SiO2 composites

“…With the rapid development of computer science, MD simulation technology has become known as “the third research method” in material design, alongside experimental methods and theoretical research 22 . The traditional experimental testing takes a long time and is expensive and many traditional experiments only describe and compare the experimental results, failing to explain some empirical phenomena in depth 23,24 . MD simulations can provide insightful information on the atomic scale, making it difficult to provide experimental methods 25 .…”

“…22 The traditional experimental testing takes a long time and is expensive and many traditional experiments only describe and compare the experimental results, failing to explain some empirical phenomena in depth. 23,24 MD simulations can provide insightful information on the atomic scale, making it difficult to provide experimental methods. 25 On the other hand, MD refers to the study of microscopic phenomena from a molecular or atomic point of view, so as to predict or explain macroscopic phenomena from a microscopic point of view and we can predict whether a molecule is suitable for improving a particular performance.…”