Abstract
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.
Highlights
Low energy surface coatings have found wide range of applications for generating hydrophobic and superhydrophobic surfaces
We have shown process development schemes to produce hydrophobic surface over cellulose based printable papers using various processes and coating materials and use a comparative method to find out the most optimum combination (Fig. 1b)
The current experimentation primarily studies the use of Machine Learning (ML) for predicting the process parameters to develop hydrophobic surfaces on paper using different coating materials and coating techniques
Summary
Low energy surface coatings have found wide range of applications for generating hydrophobic and superhydrophobic surfaces. Surfaces with anti-wetting properties with a contact angle of 150° and above find a lot of application in multiple areas of engineering and technology such as automotive and aerospace1–3, microfluidics4, electronics[5,6], oil water separation[7,8] etc. It has wide applications in coating currency notes to prevent their wear and tear due to sweating etc.[9,10]. Once these coating materials and target surfaces are decided it is important to define various processes which include coating techniques (dip, spin, spray, inkjet printing etc.), number of coats to reach the optimum desired nature, desired temperature and pressure conditions where the surface may be evaluated and coated and process related parameters for the target process like coating time in processes like dip coating, Fromm number in Inkjet Printing, or spin speeds and surface pretreatment in spin coating, or spray time and viscosity of coating material in spray coating etc
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