Predicting the wettability of patterned ITO surface using ToF-SIMS images

Partial least squares (PLS) regression has been increasingly used as a tool for modelling linear relations between multivariate surface analytical measurements and measures of performance. However, the use of PLS to obtain quantitative predictions has only been partially explored. In this study, we construct a PLS model using time-of-flight secondary ion mass spectrometry (ToF-SIMS) and water contact angle (WCA) data obtained from a series of differently treated indium tin oxide (ITO) surfaces. This model displays a reasonable correlation between the WCA values and the SIMS data. To validate the model, ITO surfaces patterned with different areas of wettability were examined using ToF-SIMS imaging to produce WCA maps. The PLS model was applied to predict the spatial variation in WCA across the sample. The direct measurement of WCA on the patterned ITO surface was achieved using a small-scale interval grid from picolitre volume water droplets. We present the correlations between the predicted WCA from SIMS and the measured WCA, and highlight the discrepancies that arises in the comparison of the two datasets. This work is of direct benefit to industries where wettability is crucial but direct contact angle measurements are acquired on a scale smaller than it is possible to produce liquid drops, such as the plastic electronics and microfluidics industries.