The stability of aluminum‐manganese intermetallic phases under the microgalvanic coupling conditions anticipated in magnesium alloys
Bibliographic record
Abstract
The electrochemical behaviour of two Al‐Mn materials (Al‐ 5.5 at % Mn and Al‐ 13.5 at % Mn) has been studied in 0.275 M NaCl and 0.138 M MgCl 2 solutions to simulate the cathodic environment of Al‐Mn particles during the corrosion of a Mg alloy. Upon polarization in NaCl solution to a potential in the range expected on a corroding Mg alloy, the Al‐5.5 at % Mn alloy proved unstable undergoing de‐alloying (loss of Al) and delamination of layers of the Al(OH) 3 formed. This leads to a steady increase H 2 O reduction current. When polarized in MgCl 2 solution the surface was partially protected from de‐alloying and the current for H 2 O reduction suppressed by the deposition of Mg(OH) 2 . The Al‐13.5 at % Mn alloy was considerably more stable when cathodically polarized. This increased stability was attributed to the higher density of Mn‐enriched areas in the alloy surface. This simulation of the microgalvanic cathodic behaviour of Al‐Mn intermetallic particles confirms that the appearance of corrosion product domes on the Al‐Mn intermetallic particles during the corrosion of Mg alloys as an indication of their cathodic behaviour and that Al‐Mn intermetallic particles are efficient, yet unstable cathodes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".