Abstract
water
uptake in organic coatings. To this aim, we start from a physical model, where not only Fickian diffusion of
water is taken into account but also the adsorption/desorption reaction of water on the polymer matrix. Starting
from a number of important coating properties and crucial model parameters, derived from gravimetric and
Fourier transform infrared (FTIR) measurements as the model input, the local water concentration over the coating
thickness as a function of time is modelled for a polyethylene glycol diacrylate (PEGDA) coating. The modelled
water concentration is then used to calculate virtual capacitance values which are evaluated against experimental
capacitance values extracted from impedance measurements. The constraints of the FEM model and ORPEIS
experiments and the discrepancies between them are critically discussed in order to carry out a meaningful
model validation, eventually leading to model improvements.
uptake in organic coatings. To this aim, we start from a physical model, where not only Fickian diffusion of
water is taken into account but also the adsorption/desorption reaction of water on the polymer matrix. Starting
from a number of important coating properties and crucial model parameters, derived from gravimetric and
Fourier transform infrared (FTIR) measurements as the model input, the local water concentration over the coating
thickness as a function of time is modelled for a polyethylene glycol diacrylate (PEGDA) coating. The modelled
water concentration is then used to calculate virtual capacitance values which are evaluated against experimental
capacitance values extracted from impedance measurements. The constraints of the FEM model and ORPEIS
experiments and the discrepancies between them are critically discussed in order to carry out a meaningful
model validation, eventually leading to model improvements.
Original language | English |
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Article number | 107710 |
Pages (from-to) | 1-11 |
Number of pages | 11 |
Journal | Progress in Organic Coatings |
Volume | 182 |
DOIs | |
Publication status | Published - Sep 2023 |
Bibliographical note
Funding Information:This research is funded by the SBO project PredictCor (project number: FWOSBO22 ) of the Research Foundation – Flanders (FWO).
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© 2023 Elsevier B.V.
Copyright:
Copyright 2023 Elsevier B.V., All rights reserved.