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  5. FEM modelling to predict spatiotemporally resolved water uptake in organic coatings: Experimental validation by odd random phase electrochemical impedance spectroscopy measurements

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Article
English
2023

FEM modelling to predict spatiotemporally resolved water uptake in organic coatings: Experimental validation by odd random phase electrochemical impedance spectroscopy measurements

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English
2023
Progress in Organic Coatings
Vol 182
DOI: 10.1016/j.porgcoat.2023.107710

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Herman Terryn
Herman Terryn

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Mats Meeusen
J.P.B. van Dam
Negin Madelat
+7 more

Abstract

In this work, a new finite element modelling (FEM) approach is followed to model spatiotemporally resolved 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 ORP-EIS experiments and the discrepancies between them are critically discussed in order to carry out a meaningful model validation, eventually leading to model improvements.

How to cite this publication

Mats Meeusen, J.P.B. van Dam, Negin Madelat, Ehsan Jalilian, Benny Wouters, Tom Hauffman, Guy Van Assche, J.M.C. Mol, Annick Hubin, Herman Terryn (2023). FEM modelling to predict spatiotemporally resolved water uptake in organic coatings: Experimental validation by odd random phase electrochemical impedance spectroscopy measurements. Progress in Organic Coatings, 182, pp. 107710-107710, DOI: 10.1016/j.porgcoat.2023.107710.

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Publication Details

Type

Article

Year

2023

Authors

10

Datasets

0

Total Files

0

Language

English

Journal

Progress in Organic Coatings

DOI

10.1016/j.porgcoat.2023.107710

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