Long-term performance of epoxy-based coatings: Hydrothermal exposure
Bibliographic record
Abstract
We examined the effects of prolonged exposure to high temperature water on epoxy-based powder barrier coatings applied to steel panels, which are commonly used in many industrial applications including oil & gas pipelines. The coatings' performance was evaluated over 85 weeks at 65 °C using deionized water. We also compared the mass transport properties of free-standing coating films with the barrier performance of the coated steel panels. This research lays the groundwork for predicting cumulative damage and time-dependent barrier performance of a priori pristine coating systems. Although these coating systems are intended for decades of in-service use, we found that some degradation effects caused by permeant sorption within the coatings can be detected as early as 8 weeks in the ageing process. Despite the coatings' barrier performance, substrate oxidation was readily observed after 182 days, as shown by cross-sectional and focused ion beam milling analysis. Colorimetry measurements of the underlying substrate, conducted after coating removal, also revealed that the first 200 days of exposure were critical for the development of underlying corrosion reactions. This period is characterized by the likely degradation of the epoxy network and the onset of a steady state in mass transport mechanisms. We also analyzed the adhesion strength of the coated panels over time. The epoxy-based coating's pull-off strength declines rapidly due to water-induced plasticization, but the adhesion properties of the epoxy network show a slight recovery due to secondary cross-linking by Type II bound water. This study underscores the complexity associated with predicting the time-to-failure for epoxy coating systems. However, the data and analyses provided herein offer valuable insights into the implications of extreme exposure conditions, aiding in the construction of lifetime predictions using a stochastic process. In real-world scenarios, pipelines undergo various fluctuations in parameters like temperature and humidity, potentially leading to failure. A deterministic physical/chemical model under simplified conditions can serve as input for the probability distribution function of future failure events. • The effects of long-term (500+ days) hydrothermal exposure on epoxy-coated steel are evaluated using a range of techniques. • Under-coating corrosion was detected after only 200 days of exposure. • EIS measurements revealed significant declines in low-frequency impedance with notable fluctuations over time. • Coating adhesive strength markedly declined (by up to 80 %) with exposure time. • FIB-SEM of aged samples revealed increased corrosion penetration and coating disbondment over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".