Paper AS-WeM5
Identification and Quantification of ToF-SIMS Images with Topography using Multivariate Analysis

Wednesday, October 22, 2008, 9:20 am, Room 207

Surface topography is a crucial issue for the analysis of many innovative devices such as microfluidic systems, fibres, composite materials, sensors, organic electronics and biomedical devices. The strength and durability of these components is critically dependent on their nanoscale surface chemistry and molecular interactions. However, quantitative characterisation of surfaces with topography remains a significant challenge due to the lack of systematic and validated measurement and data analysis methods. Previously, we presented a systematic study of the effects of surface topography on ToF-SIMS and provided guidance to practical analysts for identifying and reducing topographical effects.¹ Here, we investigate the robust use of multivariate methods for the identification and quantification of ToF-SIMS images with surface topography using principal component analysis (PCA) and multivariate curve resolution (MCR). Multivariate analysis simplifies the description of data and is powerful for identifying trends and highlighting chemically significant areas on images. However, many challenges remain with its application to complex images obtained in practical analysis, especially where sample topography or detector saturation² gives rise to large non-linear intensity variations in the data. In this study, we use several model samples, including polymer fibres with multi-organic coatings, natural starch grains and human hair, to investigate the merits of different multivariate analysis strategies for samples with topography. The emphasis is placed on the accurate identification and quantification of surface chemistry using careful application of multivariate methods, combined with suitable data selection and preprocessing and valid interpretation of the results. This study extends from our previous work on flat samples³ and provides helpful guidance in the rapid, unbiased analysis of high-resolution raw spectral data in ToF-SIMS images of increasingly complex multi-organic surfaces and biomaterials.

¹ J L S Lee, I S Gilmore and M P Seah, Appl. Surf. Sci. in press
² M R Keenan, V S Smentkowski, J A Ohlhausen and P G Kotula, Surf. Interface Anal. 40 (2008) 97-106
³ J L S Lee, I S Gilmore and M P Seah, Surf. Interface Anal. 40 (2008) 1-14.