A Comparison of Thermally Sprayed Aluminum (TSA) Degradation Behavior under Immersion and CUI Simulation Conditions
Bibliographic record
Abstract
Abstract Thermally Sprayed Aluminum (TSA) is used as a protective coating to protect against internal and external corrosion in different industrial applications. There has been not much work to understand the tribological and corrosion behavior of TSA from corrosion under insulation (CUI) at elevated temperatures. Most of the reported studies on TSA have either been focused either on immersion tests or ambient temperature CUI tests. This research work unveils comparative behavior of TSA at elevated temperatures when tested in CUI simulation cell (per ASTM G189-07) versus that in the immersion condition in an autoclave environment using thermal insulation's leachate extract. The corrosion tests were conducted using isothermal wet (IW) and cyclic wet (CW) conditions per ASTM G189-07 deploying linear polarization resistance (LPR) scanning, followed by detailed microstructural characterizations using confocal laser microscopy, surface topography, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS). TSA coating subjected to CUI environment manifested significant wear from the flashing moisture and active substrate corrosion unlike immersion conditions where there was merely dissolution of iron particles embedded in the TSA matrix.
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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) | 0.002 | 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".