Chemical Strippers and Surface Tolerant Coatings: a Tandem Approach for Steel, Concrete and Fibreglass Surfaces
Bibliographic record
Abstract
Abstract In recent years, with ever increasing environmental and economic constraints, a diverse range of alternative surface preparation methodologies to abrasive blasting has emerged. With this shift from traditional methods of surface preparation, the use of state of the art chemical strippers is gaining acceptance in industrial and marine applications. Generic types of chemical strippers are discussed, the two that have been classified by convention as bond breakers and caustics, and the third and newly classified generic type known as selective adhesion release agents. The latter is the focus of this paper and is represented by the recent invention of SARA (Selective Adhesion Release Agent) technology. A 3-phase coating removal mechanism for this type of stripper has been elucidated. Different chemical stripper types were screened for efficacy on steel, rust, concrete and gel coat. Top performers were further evaluated for stripping (a) several generic coating types from abrasive blasted steel, and (b) surface tolerant penetrant sealer coatings applied to rusted steel, and mature and green concrete. Candidate coatings for (b) were epoxy penetrant sealers, moisture cured urethanes, high-build epoxies, a polysiloxane and a modified methyl methacrylate. Their penetrant and adhesion characteristics on various substrates were investigated, and they were then removed by different SARA strippers. The coatings were subsequently reapplied to the stripped substrates and their performance compared. Scanning electron microscopy, optical microscopy, viscosity measurements and pull-off adhesion tests were employed in this research. Select case histories demonstrate where SARA chemical strippers were very effective on steel, concrete and gel coat substrates.
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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 | 0.000 |
| 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 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".