The Role of Lithium Salt Concentration in the Active Corrosion Protection of Aluminium Alloys
Bibliographic record
Abstract
It has been demonstrated previously that lithium salts containing organic coatings offer effective active corrosion protection on AA2024-T3 alloys when exposed to neutral salt spray test for 168 hours [1, 2]. In the presence of a coating defect the lithium salts leach from the organic coating to the exposed metal substrate increasing the local pH to moderate alkaline conditions followed by the formation of a protective layer within the defect site [2, 3]. The lithium accumulation at the defect site and hence the formation of the protective layer is influenced by the inhibitor loading and solubility in the coating as well as the coating defect size. Three different surface compositions of the lithium protective layer have been recently identified as lithium-based layered double hydroxide (Li-LDH), lithium mixed pseudo-boehmite (Li-PB) and pseudo-boehmite (PB) each depending on the lithium leaching rate and the coating defect size [4]. For small defect sizes a high lithium concentration is hypothesized to result in the generation of Li-LDH. Furthermore, Li-PB is identified within moderate defect sizes and moderate inhibitor concentration and PB is generated within large defect sizes where low lithium concentrations are expected. On this basis, this paper describes the lithium active corrosion protection on aerospace aluminium alloys as a function of the inhibitor concentration. In doing so, commercial AA2198-T8 and AA2024-T3 aluminium alloys are immersed for 24 hours in lithium salt containing aqueous solutions at systematically varied lithium salts concentration from 10 -6 M to 10 -1 M. The alloys are then transferred to 10 -1 M aqueous NaCl solution at near neutral pH conditions and either LPR measurements are performed as a function of time over 100 hours or anodic polarization is performed. The surface composition of the alloys after 24 hours immersion in lithium containing aqueous solution is also characterized. The surface composition and the electrochemical characteristics of each lithium passive layer generated is determined as a function of the lithium concentration. The surface composition of the passive layer ranges from PB to Li-PB and Li-LDH with increasing inhibitor concentration. The polarization resistance obtained as a function of time is correlated to the corrosion rate of each generated passive layer thus demonstrating the corrosion protection performance of Li-LDH, Li-PB and PB. The generation and corrosion protection performance of each passive layer is also demonstrated under relatively thin electrolyte layers mimicking the exposure of a coating defect and the leaching of lithium salt from the coating matrix under atmospheric conditions. Visser, P., et al., Study of the formation of a protective layer in a defect from lithium-leaching organic coatings. Progress in Organic Coatings, 2016. 99 : p. 80-90. Visser, P., et al., Electrochemical Evaluation of Corrosion Inhibiting Layers Formed in a Defect from Lithium-Leaching Organic Coatings. Journal of The Electrochemical Society, 2017. 164 (7): p. C396-C406. Visser, P., et al., Mechanism of Passive Layer Formation on AA2024-T3 from Alkaline Lithium Carbonate Solutions in the Presence of Sodium Chloride. Journal of The Electrochemical Society, 2018. 165 (2): p. C60-C70. Visser, P., et al., The chemical throwing power of lithium-based inhibitors from organic coatings on AA2024-T3. Corrosion Science, 2019. 150 : p. 194-206.
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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 | 0.001 | 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) | 0.000 | 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".