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Published May 18, 2005 | Supplemental Material
Journal Article Open

Simulations on the Thermal Decomposition of a Poly(dimethylsiloxane) Polymer Using the ReaxFF Reactive Force Field

Abstract

To investigate the failure of the poly(dimethylsiloxane) polymer (PDMS) at high temperatures and pressures and in the presence of various additives, we have expanded the ReaxFF reactive force field to describe carbon−silicon systems. From molecular dynamics (MD) simulations using ReaxFF we find initial thermal decomposition products of PDMS to be CH_3 radical and the associated polymer radical, indicating that decomposition and subsequent cross-linking of the polymer is initiated by Si−C bond cleavage, in agreement with experimental observations. Secondary reactions involving these CH_3 radicals lead primarily to formation of methane. We studied temperature and pressure dependence of PDMS decomposition by following the rate of production of methane in the ReaxFF MD simulations. We tracked the temperature dependency of the methane production to extract Arrhenius parameters for the failure modes of PDMS. Furthermore, we found that at increased pressures the rate of PDMS decomposition drops considerably, leading to the formation of fewer CH_3 radicals and methane molecules. Finally, we studied the influence of various additives on PDMS stability. We found that the addition of water or a SiO_2 slab has no direct effect on the short-term stability of PDMS, but addition of reactive species such as ozone leads to significantly lower PDMS decomposition temperature. The addition of nitrogen monoxide does not significantly alter the degradation temperature but does retard the initial production of methane and C_2 hydrocarbons until the nitrogen monoxide is depleted. These results, and their good agreement with available experimental data, demonstrate that ReaxFF provides a useful computational tool for studying the chemical stability of polymers.

Additional Information

© 2005 American Chemical Society. Received 15 February 2005. Published online 20 April 2005. Published in print 1 May 2005. We thank Los Alamos National Laboratory (LA-UR-04-8444) for providing partial funding for this project. The computational facilities of the MSC are also supported by grants from ARO-DURIP, ONR-DURIP, NSF-MRI, and the Beckman Institute. This work was also partially supported by DOE-ASC-Caltech.

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