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Data-driven prediction and optimization of liquid wettability of an initiated chemical vapor deposition-produced fluoropolymer
Journal article

Data-driven prediction and optimization of liquid wettability of an initiated chemical vapor deposition-produced fluoropolymer

Daniel Schwartz, Tien Nguyen, Zhengtao Chen, Kenneth K. S. Lau, Michael C. Grady, Ali Shokoufandeh and Masoud Soroush
AIChE journal, v 68(6), pn/a
16 Mar 2022
DOI: https://doi.org/10.1002/aic.17674
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Engineering Engineering, Chemical Science & Technology Technology
Initiated chemical vapor deposition (iCVD) is a reactive process that creates polymeric materials on a surface from vapor-phase monomers and thermal initiators. Our iCVD synthesis of poly(perfluorodecyl acrylate) (PPFDA) resulted in the growth of micro- and nano-worms normal to the surface. The micro- and nanostructures of the worms directly depend on iCVD process conditions. They in turn influence bulk properties such as their liquid wettability. The current absence of a physiochemical model that can explain the relationships between iCVD process conditions and bulk properties of the polymers motivates the use of data-driven modeling to capture and describe the relationships. In this work, we report iCVD data (contact angles of heptane, octane, and water on PPFDA and process conditions) from 49 batches and use artificial neural networks to model the relationships. The models are then used to determine the optimal iCVD process conditions that maximize the contact angles on PPFDA.

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6 citations in Web of Science
7 citations in Scopus

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Web of Science research areas
Engineering, Chemical
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