Repair Coatings for TSA
Bibliographic record
Abstract
Abstract Thermally sprayed aluminium (TSA) has successfully been used for corrosion protection for several decades. Coating lifetimes of more than 30 years in corrosive marine atmosphere has been documented. However, it has also been found that coating TSA with thick, protective organic coatings may result in rapid corrosion of the TSA and very short lifetimes. A corrosion mechanism resembling crevice corrosion is causing the degradation. The problem is avoided by not covering TSA with thick organic coatings. However, in many situations it is difficult to avoid coating the TSA. E.g. when repairing damages in the TSA in the field, the repair coating often overlaps with the surrounding TSA. Hence, a repair coating that can protect steel, but not trigger the crevice corrosion mechanism when applied on the TSA is needed. In this investigation different repair coatings have been studied with respect to performance when applied on both TSA and bare steel. Coatings have been selected based on four different hypotheses: (i) coatings that can buffer the low pH developed in the corrosion of TSA, (ii) open coatings that can let the acidic environment be washed out and (iii) conductive coatings that can make electrochemical reactions take place outside the TSA/coating crevice and (iv) sacrificial coatings. A number of coatings have been selected for testing with this in mind and tested in the ISO 20340 cyclic ageing test.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".