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Article Dans Une Revue Polymer Degradation and Stability Année : 2023

Robust water diffusion modeling in a structural polymer joint based on experimental X-ray tomographic data at the micrometer scale

Résumé

Structural bonding is a technique increasingly used in the industrial field. For applications in aggressive environments such as seawater, predicting the effect of moisture on the mechanical behavior of bonded assemblies is of paramount importance. The objective of this work is to analyze water diffusion in an epoxy adhesive material and, more specifically, to propose a robust method for choosing the most appropriate diffusion model. Experimental studies of the water absorption in a two-component epoxy structural adhesive, using gravimetry and X-ray tomography, were first performed. The presence of a population of pore-type defects in the polymeric joint helped to characterize the evolution of water diffusion kinetics. Thus, two diffusion mechanisms were identified: a first one related to the migration of water molecules within the adhesive matrix, and a second one related to the penetration of water into the pores. Then, Dual-Fick and Langmuir models were retained, as the two diffusion models most likely to capture the above mechanisms. Although it was shown that both models could give similar results in terms of global diffusion behavior, the results arising from these two models differ at the local scale, especially for extended periods of time. Therefore, special attention was paid to the second absorption mechanism, and a comparison of waterfronts between theoretical predictions and experimental tomographic data was achieved, leading to the final choice of a Dual-Fick diffusion model.
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Dates et versions

hal-04335846 , version 1 (11-12-2023)

Identifiants

Citer

A. Tintatu, Pierre Bidaud, C. Badulescu, Philippe Le Grognec, Jérôme Adrien, et al.. Robust water diffusion modeling in a structural polymer joint based on experimental X-ray tomographic data at the micrometer scale. Polymer Degradation and Stability, 2023, 218, pp.110594. ⟨10.1016/j.polymdegradstab.2023.110594⟩. ⟨hal-04335846⟩
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