AVS 47th International Symposium
    Material Characterization Wednesday Sessions
       Session MC-WeM

Paper MC-WeM6
Multivariate Analysis of TOF-SIMS Data of Dodecanethiol SAMs: Detailed Spectral Analysis and Insight Into Fragmentation

Wednesday, October 4, 2000, 10:00 am, Room 207

Session: Methods of Data Analysis
Presenter: D.J. Graham, University of Washington
Authors: D.J. Graham, University of Washington
B.D. Ratner, University of Washington
Correspondent: Click to Email
The analysis of novel engineered surfaces will require detailed, molecular specific characterization methods. The power of multivariate analysis in extracting such detailed information from TOF-SIMS spectra of a time series assembly of dodecanethiol SAMs was studied. PCA analysis of the negative spectra showed a relative increase in the intensity of molecular ion clusters and low mass hydrocarbon fragments (C to C3) with increasing time. This trend was also reflected in the positive data where a relative increase of C to C4 hydrocarbon fragments was seen at longer assembly times. This increase was accompanied by a relative decrease in the intensity of C5 and above hydrocarbon fragments. To assure these trends were not just an artifact of the PCA analysis we plotted the original spectral data from the peaks involved in the above trends. These plots verified that the trends seen in the PCA analysis reflected actual trends in the TOF-SIMS data. This data suggests that as the SAM surface becomes more ordered and crystalline the emission of longer fragments from the thiol chains is reduced relative to the emission of short fragments. Thus PCA is extracting information about the interaction and energetics of the surface. Using the PCA trends, a multivariate ratio (SAMratio) was created. This ratio was applied to a completely different set of thiol SAMs of varying chain length and head group. A correlation was found between the SAMratio and the parachor of the surfaces. Therefore PCA analysis was able to determine real data trends that lead to insight into the TOF-SIMS fragmentation process and a direct correlation with a thermodynamic property of the surface. The ability to extract this type information has the potential to revolutionize TOF-SIMS analysis by unlocking the information within the TOF-SIMS fragmentation pattern that is not accessible from univariate analysis.