Effects of coolant chemistry on corrosion of 3003 aluminum alloy in automotive cooling system
Bibliographic record
Abstract
Abstract In this work, effects of coolant chemistry, including concentrations of chloride ions and ethylene glycol and addition of various ions, on corrosion of 3003 Al alloy were investigated by electrochemical impedance spectroscopy measurements and scanning electron microscopy characterization. In chloride‐free, ethylene glycol–water solution, a layer of Al‐alcohol film is proposed to form on the electrode surface. With the increase of ethylene glycol concentration, more Al‐alcohol film is formed, resulting in the increase in film resistance and charge‐transfer resistance. In the presence of Cl− ions, they would be involved in the film formation, decreasing the stability of the film. In 50% ethylene glycol–water solution, the threshold value of Cl− concentration for pitting initiation is within the range of 100 ppm to 0.01 M. When the ethylene glycol concentration increases to 70%, the threshold Cl− concentration for pitting is from 0.01 to 0.1 M. In 100% ethylene glycol, there is no pitting of 3003 Al alloy even at 0.1 M of Cl−. Even a trace amount of impurity cation could affect significantly the corrosion behavior of 3003 Al alloy in ethylene glycol–water solution. Addition of Zn2+ is capable of increasing the corrosion resistance of Al alloy electrode, while Cu2+ ions containing in the solution would enhance corrosion, especially pitting corrosion, of Al alloy. The effect of Mg2+ on Al alloy corrosion is only slight.
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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".