# Molecular Modeling of the Microstructure Evolution during the   Carbonization of PAN-Based Carbon Fibers

**Authors:** Saaketh Desai, Chunyu Li, Tongtong Shen, Alejandro Strachan

arXiv: 1703.09612 · 2017-03-29

## TL;DR

This paper presents a combined kinetic Monte Carlo and molecular dynamics model to predict the microstructure evolution during carbonization of PAN-based carbon fibers, providing insights into their properties and structural features.

## Contribution

The study introduces a novel molecular modeling approach integrating KMC and MD to accurately simulate microstructure development during carbon fiber carbonization.

## Key findings

- Model accurately predicts microstructure features like graphitic sheets and hairpin structures.
- Predicted transverse modulus is slightly lower than experimental values.
- Higher reaction rates lead to porous structures with reduced moduli.

## Abstract

Development of high strength carbon fibers (CFs) requires an understanding of the relationship between the processing conditions, microstructure and resulting properties. We developed a molecular model that combines kinetic Monte Carlo (KMC) and molecular dynamics (MD) techniques to predict the microstructure evolution during the carbonization process of carbon fiber manufacturing. The model accurately predicts the cross-sectional microstructure of carbon fibers, predicting features such as graphitic sheets and hairpin structures that have been observed experimentally. We predict the transverse modulus of the resulting fibers and find that the modulus is slightly lower than experimental values, but is up to an order of magnitude lower than ideal graphite. We attribute this to the perfect longitudinal texture of our simulated structures, as well as the chain sliding mechanism that governs the deformation of the fibers, rather than the van der Waals interaction that governs the modulus for graphite. We also observe that high reaction rates result in porous structures that have lower moduli.

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Source: https://tomesphere.com/paper/1703.09612