A Comparative Study on the Anti-Corrosive Performance of Zinc Phosphate in Powder Coatings
Bibliographic record
Abstract
Powder coatings are gaining popularity for their economic and environmental benefits. Additives (pigments) such as zinc phosphate enhance the anti-corrosive properties of coatings, but their behavior in powder coatings has not been extensively studied. In this study, zinc phosphate was incorporated into three powder coating systems: polyester clearcoat, polyester and epoxy coatings with filler BaSO4. Neutral salt spray and electrochemical tests (OCP, LPR, and EIS) confirmed that the anti-corrosive performance improved with the addition of zinc phosphate. The optimal additive dosage was determined to be 2% for all of the coating systems studied here, based on salt spray tests. Here, the time until failure increased by 1.5 to 2 times. Using electrochemical tests, an optimal additive dosage of 8% was found for the polyester clearcoat, while the other coating systems maintained an optimal additive dosage of 2%. Performance increased by as much as one order of magnitude based on resistance/impedance measurements. This suggested a synergistic effect between the additive and the filler. The passivation layer was confirmed by both X-ray diffraction and Raman spectroscopy. Based on the results and discussion presented in this article, the discrepancy was caused by different features of the two tests, such that the electrochemical tests probe the function of intact coatings, whereas salt spray measures only the corrosion spreading from the scribe. It is proposed that the two test methods characterize different aspects of the coatings, corresponding to their service conditions. This has theoretical and practical significance in the evaluation of anti-corrosive coatings. Other properties of the coatings, including adhesion, gloss, distinctness-of-image, and pencil hardness, were measured as per applicable standards and the conformance was verified.
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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 | 0.001 |
| 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".