Principal Component Analysis to Determine the Surface Properties That Influence the Self-Cleaning Action of...

It is well established that many leaf surfaces display self-cleaning properties. However, an understanding of how the surface properties interact is still not achieved. Consequently, 12 different leaf types were selected for analysis due to their water repellency and self-cleaning properties. The most hydrophobic surfaces demonstrated splitting of the ν_s CH₂ and ν CH₂ bands, ordered platelet-like structures, crystalline waxes, high-surface-roughness values, high-total-surface-free energy and apolar components of surface energy, and low polar and Lewis base components of surface energy. The surfaces that exhibited the least roughness and high polar and Lewis base components of surface energy had intracuticular waxes, yet they still demonstrated the self-cleaning action. Principal component analysis demonstrated that the most hydrophobic species shared common surface chemistry traits with low intra-class variability, while the less hydrophobic leaves had highly variable surface-chemistry characteristics. Despite this, we have shown through partial least squares regression that the leaf water contact angle (i.e., hydrophobicity) can be predicted using attenuated total reflectance Fourier transform infrared spectroscopy surface chemistry data with excellent ability. This is the first time that such a statistical analysis has been performed on a complex biological system. This model could be utilized to investigate and predict the water contact angles of a range of biological surfaces. An understanding of the interplay of properties is extremely important to produce optimized biomimetic surfaces.