State-of-the-Art of Thermal Spray Coatings for Corrosion Protection
Bibliographic record
Abstract
ABSTRACT Thermal-spray coatings are widely used in marine structures including offshore pipelines without external cathodic protection (CP). Al, Zn and Zn-Al thermal-spray coatings protect steel by acting both as barrier coatings and as sacrificial anodes at local defects where corrosion would otherwise occur. Zn provides better galvanic protection whereas Al is better as a less-reactive barrier layer. Zn-Al alloys appear to combine the protective properties of both Zn and Al. Although further research is required in order to specify the optimal alloy compositions for specific applications, 85% Zn-15% Al alloy is widely used. The best long-term protection is provided by suitably primed, sealed, and painted thermal-spray coatings. Thermal-spray coatings of acceptable structures and properties can be produced by flame spraying (wire or powder), arc spraying or plasma processing. However, due to economical reasons low melting point metals and their alloys are sprayed either by arc or flame. Surface preparation is considered to be a key factor in the production of uniform high quality coatings with maximum bond strength. Also of equal importance are the control of process facilities, equipment selection, and quality of consumable material for applying thermal-spray coatings. Well-bonded, relatively dense, sealed coatings have the ability to provide effective long term corrosion protection (10-20 years), with minimum periodic maintenance. Standards for evaluating thermal spray coatings have recently been developed.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".