Multivariate analysis of TOF-SIMS spectra from self-assembled monolayers

Recently the concept of engineered biomaterial surfaces has started a revolution in the biomaterials community. These biomaterial surfaces are designed using knowledge from cell biology to produce a healing response that will integrate the biomaterials into the body. These surfaces will require specific, complex chemistries that will elicit the desired responses. Such complex surfaces will require an equally detailed surface characterization method. Due to its molecular specificity and high sensitivity, TOF-SIMS appears to be an ideal method for this challenge. Nevertheless TOF-SIMS spectra are complex and difficult to interpret. This complexity results from the shear number of peaks within the spectra, the inter-related nature of the peaks, and lack of fundamental understanding of TOF-SIMS fragmentation mechanisms. This work approaches addressing these problems through use of multivariate analysis. Multivariate analysis enables detailed spectral interpretation and provides insight into fragmentation mechanisms by extracting the salient information from within the complex spectral data set.; Multivariate spectral interpretation was explored using a series of self-assembled monolayers that varied in surface order, surface functionality, formation method, and chain length. A multivariate SAM ratio was developed that correlates with thermodynamic properties of the surface. This ratio is the first to demonstrate a direct relationship between TOF-SIMS data and surface thermodynamic parameters.; A model for TOF-SIMS fragmentation of SAMs was created and explored using multivariate analysis of a thiol containing a hydroxyl end group. This model explains the emission of fragments from the surface over a time course experiment. This is the first use of multivariate analysis with TOF-SIMS data to provide mechanistic information about the TOF-SIMS process. This methodology provides a technique for studying TOF-SIMS fragmentation using actual data without the need for molecular dynamic simulations.; This work proposes that the use of multivariate methods for the interpretation and analysis of TOF-SIMS data will unlock the information about surface structure, chemistry, and order from within the TOF-SIMS spectra. The information gained from multivariate analysis would not be readily attainable using standard univariate methods and will enable characterization of the complex surfaces of the future.