Superhydrophobic Coatings and Artificial Neural Networks: Design, Development and Optimization

Abstract

Recently, considerable attention has been devoted to the development of superhydrophobic surfaces due to their advantageous antifouling and antimicrobial capacity. While significant effort has been devoted to fabricating such surfaces, very few polymeric superhydrophobic surfaces can be considered durable against various externally imposed stresses. Pyrogenic hydrophobic silica nanoparticles were used to confer superhydrophobic properties to the coatings. 450 samples were prepared using a layer-by-layer approach, deposing an epoxy resin or PDMS layer as adhesive on a substrate (PC/ABS), followed by one or more layers of silica nanoparticles, or silica-resin mixed layers. The best coating obtained shows a contact angle of 157° and a tape peeling grade resistance. The applied method involves the spray deposition of a multilayer coating composed of: silica layer/epoxy resin layer/silica layer, followed by partial curing of the coating (15 min, 70 °C); another silica layer is then sprayed on the surface and is cured for 10 min. Given the high number of parameters involved, process optimization is quite tricky. Artificial Neural Networks are the best tool to deal with multivariate analysis problems and for this reason, data from all the prepared samples were collected into a matrix and was used to train a neural network capable of predicting the degree of hydrophobicity of a silica nanoparticles-based coating.