AVS 59th Annual International Symposium and Exhibition
    Applied Surface Science Tuesday Sessions
       Session AS-TuP

Paper AS-TuP4
Multivariate Analysis Models to Predict Surface Chemistry or Performance using ToF-SIMS Mass Spectra Datasets

Tuesday, October 30, 2012, 6:00 pm, Room Central Hall

Session: Applied Surface Science Poster Session
Presenter: N. Sano, University of Surrey, UK
Authors: N. Sano, University of Surrey, UK
M.-L. Abel, University of Surrey, UK
J.F. Watts, University of Surrey, UK
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Latent (that is unintentional) adhesion between organics and inorganic surfaces is a well known phenomenon in many areas of materials science, e.g. the moulding of polymeric components and the storage of coated metal products where a polymeric surface is in intimate contact with the back of another sheet. A complete understanding of the adhesion and adhesion processes that occur at this interface may provide a key to obtaining optimum performance for a particular application.

In this work, we are considered with the effects of migration of organics to the surface of the polymeric host and their role at the polymer/inorganic interface. We have focused on three characteristic organics widely used as additives in a wide range of polymer formulations. In the samples we have studied, characteristic peaks from these additives dominate the ToF-SIMS analysis of the inorganic surface. In addition, surface chemistry of the inorganic surface induces different mechanical performances of the products. The storage period has the potential to play a significant role in the migration of minor components towards the interface under investigation, and data will be presented at two different periods (early and late stages).

In this study we show models using multivariate analysis describing how ToF-SIMS analysis can be applied to understand the surface chemistry of industrial materials. The behaviour of migration from the polymer to the inorganic side of the polymeric assembly induces three characteristic surface chemistries which influence mechanical performance. Our models show good predictions for a validation sample of materials.