Development of hydrophobic paper substrates using silane and sol–gel based processes and deriving the best coating technique using machine learning strategies

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 d...

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Autores principales:	Kapil Manoharan, Mohd. Tahir Anwar, Shantanu Bhattacharya
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b1ea8c4fa0b74479bc6543f8f02ab8a9
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spelling	oai:doaj.org-article:b1ea8c4fa0b74479bc6543f8f02ab8a92021-12-02T15:56:41ZDevelopment of hydrophobic paper substrates using silane and sol–gel based processes and deriving the best coating technique using machine learning strategies10.1038/s41598-021-90855-72045-2322https://doaj.org/article/b1ea8c4fa0b74479bc6543f8f02ab8a92021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90855-7https://doaj.org/toc/2045-2322Abstract 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.Kapil ManoharanMohd. Tahir AnwarShantanu BhattacharyaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kapil Manoharan Mohd. Tahir Anwar Shantanu Bhattacharya Development of hydrophobic paper substrates using silane and sol–gel based processes and deriving the best coating technique using machine learning strategies
description	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.
format	article
author	Kapil Manoharan Mohd. Tahir Anwar Shantanu Bhattacharya
author_facet	Kapil Manoharan Mohd. Tahir Anwar Shantanu Bhattacharya
author_sort	Kapil Manoharan
title	Development of hydrophobic paper substrates using silane and sol–gel based processes and deriving the best coating technique using machine learning strategies
title_short	Development of hydrophobic paper substrates using silane and sol–gel based processes and deriving the best coating technique using machine learning strategies
title_full	Development of hydrophobic paper substrates using silane and sol–gel based processes and deriving the best coating technique using machine learning strategies
title_fullStr	Development of hydrophobic paper substrates using silane and sol–gel based processes and deriving the best coating technique using machine learning strategies
title_full_unstemmed	Development of hydrophobic paper substrates using silane and sol–gel based processes and deriving the best coating technique using machine learning strategies
title_sort	development of hydrophobic paper substrates using silane and sol–gel based processes and deriving the best coating technique using machine learning strategies
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b1ea8c4fa0b74479bc6543f8f02ab8a9
work_keys_str_mv	AT kapilmanoharan developmentofhydrophobicpapersubstratesusingsilaneandsolgelbasedprocessesandderivingthebestcoatingtechniqueusingmachinelearningstrategies AT mohdtahiranwar developmentofhydrophobicpapersubstratesusingsilaneandsolgelbasedprocessesandderivingthebestcoatingtechniqueusingmachinelearningstrategies AT shantanubhattacharya developmentofhydrophobicpapersubstratesusingsilaneandsolgelbasedprocessesandderivingthebestcoatingtechniqueusingmachinelearningstrategies
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Development of hydrophobic paper substrates using silane and sol–gel based processes and deriving the best coating technique using machine learning strategies

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