The variability in hydrocarbon ions (CnH−) of polymers detected by ToF-SIMS: principal component analysis on carbon density and cross-linking degree

Negative hydrocarbon ions, C n H − (n = 1–10), are ubiquitous in time-of-flight secondary ion mass spectrometry, but their utility may have been overlooked. Recently, however, it has been demonstrated that the ion intensity ratio between C 6 H − and C 4 H − , denoted as ρ, can differentiate the chemical structures of polymers such as polyethylene, polypropylene, polyisoprene and polystyrene, as well as depth profile the cross-linking degree of poly (methyl methacrylate). It was found that ρ increases with the carbon density of polymers. Principal component analysis (PCA), a dimensionality reduction technique, can reveal hidden data structures through exploring the relationships among the C n H − intensities for the four polymers. Assisted by the biplot approach, PCA is key to uncovering hidden data structures, from which characteristic ions may be identifiable and their relationships classifiable. The four polymers were classified by their carbon densities, which dictate the variability of C n H − intensities and are captured by the first principal component (PC1). It also became clear that PC1 is correlated with ρ. This data-driven analytical approach is imperative when differentiating chemicals with similar structures, especially when diagnostic ions are lacking. We demonstrate the usefulness of this approach by examining poly (methyl methacrylate) with different degrees of cross-linking.