Abstract
In this study, pyrolysis gas chromatography paired with mass spectrometry (Py-GC/MS) has been investigated as an analytical technique for the identification and discrimination of commercial silicone elastomer formulations. Multivariate statistical analysis, specifically principle component analysis (PCA), was utilized in order to provide a direct link between the fingerprint behavior and the starting network structure. This work utilizes PCA to “map” the pyrolysis analyses such that underlying chemistries, systematic similarities, fillers, and morphologies may be predicted. It has been demonstrated that silicone materials formulated via differing cure chemistries have distinct degradation fingerprints. The application of PCA statistical methodologies to Py-GC/MS data allows these unique signatures to be rapidly and reliably identified. Furthermore, PCA allows the chemical origins of the degradation fingerprints to be assessed with comparative ease.
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