Passive Layer Evolution of Anodized B206 Aluminum in Seawater for Tidal Energy Applications: An Electrochemical Approach
Bibliographic record
Abstract
Aluminum–copper casting alloys are potential candidate materials for use in marine applications where high mechanical strength and superior fatigue resistance are desired. The corrosion and protection of aluminum alloy B206 in seawater through surface passivation continues to pose challenges, hampering its widespread use in marine structures. In this study, the electrochemical behavior of B206 is investigated in artificial seawater at temperatures and dissolved oxygen (DO) concentrations anticipated during service in marine environments. In particular, the influence of anodizing B206 in deaerated seawater on the subsequent corrosion behavior of the alloy is studied using potentiodynamic and potentiostatic polarization, electrochemical impedance spectroscopy (EIS), and Mott–Schottky analysis. The results showed that the effect of DO on the corrosion of B206 is more significant than the effect of temperature. In the absence of DO, results of potentiostatic polarization, EIS, and Mott–Schottky analysis at anodic potentials all indicated the development of a thicker, more protective passive layer in colder seawater. Moreover, passive layer thickness modeled using Power-Law was found to range between 3 and 9 nm, whilst decreasing in thickness with temperature. Donor densities of the n-type passive layer are on the order of 1021 cm−3 and increase with temperature. The findings presented in this study support the feasibility of implementing anodizing for B206 in marine service environments.
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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) | 0.000 | 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 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".