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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
- Source :
- Scientific Reports, Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
- Publication Year :
- 2021
- Publisher :
- Nature Publishing Group UK, 2021.
-
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.
- Subjects :
- Materials science
Fabrication
Science
02 engineering and technology
Substrate (printing)
engineering.material
010402 general chemistry
Machine learning
computer.software_genre
01 natural sciences
Dip-coating
Characterization and analytical techniques
Article
Contact angle
chemistry.chemical_compound
Coating
Sol-gel
Spin coating
Multidisciplinary
business.industry
021001 nanoscience & nanotechnology
Silane
Surface chemistry
Mechanical engineering
0104 chemical sciences
Design, synthesis and processing
chemistry
engineering
Medicine
Artificial intelligence
0210 nano-technology
business
computer
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Scientific Reports
- Accession number :
- edsair.doi.dedup.....16fb410a4bbfa56a46230ba348c1a45a