Dr. Stuart G. Croll Profile

Dr. Stuart G. Croll

at North Dakota State Univ

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Proceedings Article | 22 July 2003 Paper

Monte Carlo simulations of environmental degradation on polymer coatings

Brian Hinderliter, Stuart Croll

Proceedings Volume 5045, (2003) https://doi.org/10.1117/12.483814

KEYWORDS: Monte Carlo methods, Polymers, Reflectivity, Photon transport, Chemistry, Solar radiation models, Optical spheres, Data modeling, Solids, Quantum efficiency

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The degradation of a polymer coating and predicting the coating lifetime, based on physica properties and distribution within the coating of the polymer binder, pigments, and fillers, are economically very important. As technologies advance, allowing control of coatings at the nanoscale level, methods such as Monte Carlo can be used not only to predict the behavior of a nanodesigned coating with time but also to design coatings, such as optimizing pigment particle distributions or optimum hard and soft phase distributions of the binders in multiphase systems for maintaining the desired property with time. Erosion of the coating surface was simulated using Monte Carlo techniques where terrestrial solar flux is the initiator for polymer segment cleavage and removal. The impact on the sensitivity of the polymer adjacent to the detached polymer segment can be increased or decreased in the model based on the chemistry and surface energy of the remaining polymer matrix. Multiple phases with varying sensitivity to degradation can be modeled. The Monte Carlo generates a statistically similar surface topography and chemistry of the coating. The results of the Monte Carlo model are compared to measurable properties; such as gloss, fracture toughness, and wetting contact angle, using various published correlations of the property to the surface topology. The simulated properties change through the lifetime of the coating in ways that are consistent with observed behavior. Apparently, complicated changes in many properties can be described by the repeated application of simple, random processes.

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