The corrosion protection of AA2024-T3 aluminium alloy by leaching of lithium-containing salts from organic coatings
Bibliographic record
Abstract
Lithium carbonate and lithium oxalate were incorporated as leachable corrosion inhibitors in model organic coatings for the protection of AA2024-T3. The coated samples were artificially damaged with a scribe. It was found that the lithium-salts are able to leach from the organic coating and form a protective layer in the scribe on AA2024-T3 under neutral salt spray conditions. The present paper shows the first observation and analysis of these corrosion protective layers, generated from lithium-salt loaded organic coatings. The scribed areas were examined by scanning and transmission electron microscopy before and after neutral salt spray exposure (ASTM-B117). The protective layers typically consist of three different layered regions, including a relatively dense layer near the alloy substrate, a porous middle layer and a flake-shaped outer layer, with lithium uniformly distributed throughout all three layers. Scanning electron microscopy and white light interferometry surface roughness measurements demonstrate that the formation of the layer occurs rapidly and, therefore provides an effective inhibition mechanism. Based on the observation of this work, a mechanism is proposed for the formation of these protective layers.
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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) | 0.001 | 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".