Characterisation of pyrolysis kinetics and detailed gas species formations of engineering polymers via reactive molecular dynamics (ReaxFF)

TBY Chen and ACY Yuen and B Lin and L Liu and ALP Lo and QN Chan and J Zhang and SCP Cheung and GH Yeoh, JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS, 153, 104931 (2021).

DOI: 10.1016/j.jaap.2020.104931

One of the major limiting factors in fire modelling involving solid pyrolysis of polymer materials is the fundamental understanding of thermal degradation process from solid fuel to gas volatiles. This also brings along other challenges such as the characterisation of the parent combustion fuel in the gas-phase chemical reaction process. To bridge the knowledge gap, this article proposes a methodology applying molecular dynamics (MD) simulation as a tool to characterise the thermal degradation process of polymer composites, especially the emission of volatile and toxic gas species. The method was applied to three common engineering polymers: i) high-density polyethene (HDPE), ii) polymethyl methacrylate (PMMA), and iii) high-impact polystyrene (HIPS). Based on the modelling results, the chemical distribution of the fully-decomposed chemical compounds was realised for the selected polymers. The chemical composition and charring kinetics were validated against thermogravimetry data via experimental measurement. Numerical simulations demonstrated good agreement with the thermogravimetric analysis experiments. It was found that all HDPE, PMMA, and HIPS formed fuel gas with alkane group (i.e. mainly C1-C3) that acted as the combustible source. Furthermore, the composition of char formations for the selective polymers can be predicted by the MD simulation through analysing the accumulation of pure carbon chain compounds. In this study, MD simulation identified the detailed decomposition process from solid to gas phases, which could further act as the precursors of combustible fuel gases in combustion models, and significantly enhance the reliability of toxic gas, charring, and smoke particulate predictions.

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