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Surface modification of polycarbonate by radio‐frequency plasma and optimization of the process variables with response surface methodology
Author(s) -
Gomathi N.,
Eswaraiah C.,
Neogi Sudarsan
Publication year - 2009
Publication title -
journal of applied polymer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.575
H-Index - 166
eISSN - 1097-4628
pISSN - 0021-8995
DOI - 10.1002/app.30691
Subject(s) - polycarbonate , contact angle , surface energy , materials science , diiodomethane , wetting , attenuated total reflection , surface roughness , argon , fourier transform infrared spectroscopy , formamide , analytical chemistry (journal) , polymer chemistry , polymer , chemical engineering , composite material , chemistry , organic chemistry , engineering
This study deals with the radio‐frequency plasma treatment of polycarbonate surfaces with argon. The wettability of polycarbonate was examined by static contact angle measurements with polar solvents (deionized water and formamide) and a nonpolar solvent (diiodomethane). The surface free energy of the polycarbonate obtained from the measured contact angle demonstrated that exposure to argon plasma resulted in an increased surface energy and polarity compared to the untreated polycarbonate. Attenuated total reflection/Fourier transform infrared spectroscopy indicated that argon plasma treatment resulted in surface chemistry changes by hydrogen abstraction from the phenyl ring and methyl group and chain scission at the ether and carbonyl sites. These led to the formation of hydroxyl groups and double bonds. With scanning electron microscopy and atomic force microscopy analysis, changes in the surface morphology and roughness before and after plasma treatment were observed. We followed an experimental matrix with the identified process variables affecting the wettability of the polymer, and optimized the experiments with the response surface methodology of a central composite design. A quadratic model was developed to represent the surface energy in terms of process variables. Optimized process conditions were derived from the predicted model and were confirmed by the experimental data at the predicted optimum conditions. The prediction accuracy of the model was found to be very high. © 2009 Wiley Periodicals, Inc. J Appl Polym Sci, 2009

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