(597f) Understanding Morphological Changes in Semi-Crystalline Polymers during Depolymerization Using Kinetic Monte Carlo | AIChE

(597f) Understanding Morphological Changes in Semi-Crystalline Polymers during Depolymerization Using Kinetic Monte Carlo


Gorugantu, S. - Presenter, Northwestern University
Adhikari, S., Columbia University
Rorrer, N. A., National Renewable Energy Laboratory
Buss, B. L., National Renewable Energy Laboratory
Beckham, G. T., National Renewable Energy Laboratory
Kumar, S. K., Columbia University
Allen, R. D., UOP LLC
Broadbelt, L. J., Northwestern University
Morais, A. R. C., University of Kansas
The chemical recycling of plastics towards monomer recovery requires efficient depolymerization mechanisms, which depend on the catalyst selection. While continuum models track the overall decomposition of polymeric species and the evolution of products, they do not allow tracking of individual chain sequences and lengths. Existing continuum models for PET solvolysis implement ‘lumping’ methods to track polymeric species as a function of temperature or time. These models provide little information on the influence of the structure and morphological properties, such as crystallinity, on the depolymerization mechanisms. Tracking specific chain sequences and changes in the morphological aspects becomes particularly important when studying condensation polymers like polyethylene terephthalate (PET), where the polymer chains are arranged in a random semi-crystalline fashion. This study aims to develop a Kinetic Monte Carlo (kMC) model to track specific chain sequences and morphological aspects to unravel the depolymerization pathways of PET solvolysis.

The kMC framework reconstructs polymer chain sequences, defines reaction channels, and simulates chain distributions and low molecular weight product yields. The Schulz-Flory distribution function is used to generate initial PET chain distributions using the polydispersity index and degree of polymerization. PET spherulitic morphology is defined by the distribution of the lengths of tails, crystals, ties, and loops. The kMC model then simulates chain scission reactions of these amorphous and crystalline segments as single events occurring at discrete time steps. Validation against uncatalyzed and catalyzed glycolysis experiments showed that random scission reactions are predominant under uncatalyzed solvolysis conditions, generating significant amounts of free amorphous chains with an average chain length of 5 or below. Under amine-catalyzed conditions, competition exists between random and end-chain scissions PET and repolymerization of monomers. Additionally, changes in the distributions of ties and loops suggest a disruption of the spherulitic morphology due to solvolysis. The explicit tracking of individual chain sequences and morphological properties enabled an understanding of the decrease in crystallinity during solvolysis with greater detail. The kMC framework provides detailed insights into the depolymerization chemistry for monomer recovery and can be applied to reactions responsible for microplastic formation at lower temperatures.