• Crewther, Sheila
• Alexander, David
• Halliday, Mark
• Gardner, Wil
• Chouinard, Philippe
• Scurr, David
• Pigram, Paul
• Muir, Benjamin Ward

Abstract

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is used across many fields for the atomic and molecular characterisation of surfaces, with both high sensitivity and high spatial resolution. When large analysis areas are required, standard ToF-SIMS instruments allow for the acquisition of adjoining tiles, each of which is acquired by rastering the primary ion beam. For such large area scans, tiling artefacts are a ubiquitous challenge, manifesting as intensity gradients across each tile and/or sudden changes in intensity between tiles. Such artefacts are thought to be related to a combination of sample charging, local detector sensitivity issues and misalignment of the primary ion gun, among other instrumental factors. In this work, we investigated 6 different computational tiling artefact removal methods: tensor decomposition, multiplicative linear correction, linear discriminant analysis, seamless stitching, simple averaging and simple interpolating. To ensure robustness in the study, we applied these methods to 3 hyperspectral ToF-SIMS datasets and one OrbiTrap™ SIMS dataset. Our study includes a carefully designed statistical analysis and a quantitiative survey that subjectively assessed the quality of the various methods employed. Our results demonstrate that while certain methods are useful and preferred more often, no one particular approach can be considered universally acceptable and that the effectiveness of the artefact removal method is strongly dependent on the particulars of the dataset analysed. As examples, the multiplicative linear correction and seamless stitching methods tended to score more highly on the subjective survey, however for some data sets led to the introduction of new artifacts. In contrast, simple averaging and interpolation methods scored subjectively poorly on the biological data set, but more highly on a the microarray data sets. We discuss and explore these findings in depth, and present general recommendations given our findings to conclude the work.

Topics

• impedance spectroscopy
• surface
• spectrometry
• decomposition
• selective ion monitoring
• secondary ion mass spectrometry