(509j) Monte Carlo Modeling Of Gradient Copolymer Composition Distributions | AIChE

(509j) Monte Carlo Modeling Of Gradient Copolymer Composition Distributions

Authors 

Cho, A. S. - Presenter, Northwestern University
Wang, L. - Presenter, Northwestern University


Recently there has been significant demand for copolymeric materials with highly defined architectures for use in specialized applications. In order to control the microstructure of these materials, control must be exerted over the chain length, composition, and sequencing. Although the effects of chain length and overall composition have been well documented in the literature, the role of sequencing along each chain has yet to be investigated in great detail. Because it is possible for materials to have identical chain sizes and overall compositions yet different chain sequencing, novel material properties may result. This concept is particularly applicable in the design of gradient copolymers, which are copolymers possessing a smooth composition gradient along the length of the chain. These materials have garnered much interest as blend compatibilizers as they have shown improved performance over their random and block copolymer counterparts.

Synthesis of these materials involves the use of a controlled method of polymerization in order to control length, composition, and sequencing when used in tandem with a semi-batch reactor. Nitroxide-mediated controlled radical polymerization (NM-CRP) has emerged as a robust synthesis route for the production of these materials due to its compatibility with a wide range of reacting monomers. An alkoxyamine, α-methyl-styryl-di-tert-butyl nitroxide (A-T), was utilized in previous homopolymerization studies and copolymerization studies by our collaborators in syntheses involving styrene and 4-acetoxystyrene. Building upon previous mechanistic modeling work involving NM-CRP copolymerization, a new methodology was implemented in order to provide a greater level of detail of NM-CRP gradient copolymerization. Kinetic Monte Carlo (KMC) models were developed in order to distinguish materials with similar compositions but different sequencing patterns. Due to the discrete nature of these models, they are able to keep track of information regarding molecular weight distributions (MWD) and chemical composition distributions (CCD) of gradient copolymers, which is not possible with moment-based continuum models. Simulated CCD results can then be directly compared to experimental data obtained from matrix-assisted laser desorption/ionization-time of flight mass spectroscopy (MALDI-ToF-MS) which is capable of visually depicting chain topology.