Over the years, researchers have been working to mimic the nature by inducing superhydrophobic properties into a variety of material surfaces so that they exhibit non-wetting properties. Many diverse applications have been found in the fields, such as space and aerospace, defence, automotive, biomedical applications and engineering, sensors, apparels, and so on. Superhydrophobic surfaces repel water generally due to their surface texture or chemical properties. In this article, we focus on the functional applications of the superhydrophobic surfaces, and state-of-the-art fabrication technologies and processes, and the limitations of these processes to generate the superhydrophobic surfaces have been developed over the years.

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Low energy surface coatings have found wide range of applications for generating hydrophobic and superhydrophobic surfaces. Most of the studies have been related to use of a single coating material over a single substrate or using a single technique. The degree of hydrophobicity is highly dependent on fabrication processes as well as materials being coated and as such warrants a high-level study using experimental optimization leading to the evaluation of the parametric behavior of coatings and their application techniques. Also, a single platform or system which can predict the required set of parameters for generating hydrophobic surface of required nature for given substrate is of requirement. This work applies the powerful machine learning algorithms (Levenberg Marquardt using Gauss Newton and Gradient methods) to evaluate the various processes affecting the anti-wetting behavior of coated printable paper substrates with the capability to predict the most optimized method of coating and materials that may lead to a desirable surface contact angle. The major application techniques used for this study pertain to dip coating, spray coating, spin coating and inkjet printing and silane and sol–gel base coating materials.