Al-Mn alloys for electrical applications: A review
Bibliographic record
Abstract
Development of lightweight Al alloys with high electrical conductivities, tensile properties and low cost are highly needed for market expansion of battery-powered electric vehicles (BEVs) in the automotive industry and durable transmission lines in the electric power industry (EPI). As an alloying element, Mn has a low maximum solubility of 1.25 wt% in Al under equilibrium solidification and forms Al6Mn dispersoid as a strengthening phase. Manganese (Mn) is usually introduced into wrought Al alloys such as AA3xxx series, because of its capability of improving uniform deformation and strengths of pure aluminum. In this article, the influence of Mn on the electrical and mechanical properties of Al alloys is discussed. The microstructure features and relevant electrical conductivities of Mn-containing Al alloys are reviewed. The strengthening mechanisms of Al alloys are overviewed. The cooling rate-dependent mechanical properties of Al-Mn alloys are presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".