A model that predicts the attachment behavior of Ulva linza zoospores on surface topography

Abstract

A predictive model for the attachment of spores of the green alga Ulva on patterned topographical surfaces was developed using a constant refinement approach. This ‘attachment model’ incorporated two historical data sets and a modified version of the previously-described Engineered Roughness Index. Two sets of newly-designed surfaces were used to evaluate the effect of two components of the model on spore settlement. Spores attached in fewer numbers when the area fraction of feature tops increased or when the number of distinct features in the design increased, as predicted by the model. The model correctly predicted the spore attachment density on three previously-untested surfaces relative to a smooth surface. The two historical data sets and two new data sets showed high correlation (R ² = 0.88) with the model. This model may be useful for designing new antifouling topographies.

Keywords:

• Engineered Roughness Index
• Sharklet AF™
• recessed Sharklet AF™
• Ulva
• attachment model

Acknowledgements

A.B.B. and J.A.C/M.E.C gratefully acknowledge the financial support of the Office of Naval Research (Contract #N00014-02-1-0325 and #N00014-08-1-0010, respectively). Special thanks to Sean Royston for his technical assistance in production and fabrication of the topographical surfaces.