The effect of chemical treatment on the adhesion strength and structural integrity of the epoxy coatings
Bibliographic record
Abstract
Abstract This research aims to investigate the effect of chemical treatment on the integrity of the epoxy coating applied on mild steel substrates. Grit blasted steel samples were chemically treated in 10 vol.% NaOCl solution, 10 vol.% CrCl3 and 30 vol.% H3PO4–5 vol.% HNO3 solutions prior to coating application. Post-cleaning surface morphology and chemical composition revealed the formation of oxidation products on steel surface. Under optimized conditions, a dry film thickness of 135 ± 3 μm of epoxy coating was achieved. The CT2 sample (pre-treated with CrCl3) presented higher coating adhesion strength (∼4.12 MPa) and the lowest rust area of ∼0.03% compared to other chemically treated samples during 720 h of immersion in 5 wt.% NaCl solution. The coating degradation mechanism was evaluated by electrochemical impedance spectroscopy (EIS) after 24, 48, 72 and 120 h of immersion in 3.5 wt. % NaCl solution. EIS analysis of the coated samples pretreated with NaOCl and CrCl3 solutions exhibited low water uptake and limited corrosion due to hindrance in the diffusion of ionic species through the coating. However, coated steel samples pretreated in acidic solutions displayed appreciable corrosion damage as confirmed from salt spray and immersion tests. For instance, the delamination of the CT3 and CT4 (acid pre-treated) coatings was confirmed from the EIS analysis., which represented the formation of a double layer and occurrence of faradaic (corrosion) reactions as the coating-substrate interface, resulting in ∼15%–30% delamination in 120 h of exposure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".