Nano Phase-containing Al-0.3Mn Alloy for Potential EV Applications: Microstructure, Tensile Behavior and Electrical Conductivity
Bibliographic record
Abstract
An Al alloy containing 0.3 wt% Mn (Al-0.3Mn)for potential applications in electric vehicles (EV) was prepared by permanent steel mold casting (PSMC) along with high purity (HP) Al (99.9%).The microstructure of the as-cast Al-0.3Mn alloy was analyzed by scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS).The microstructure analyses revealed that the Al-0.3Mnalloy consisted of primary Al phase, micron-sized Al-Fe-Mn intermetallic phase, and nano-sized Al-Mn intermetallic phase.The tensile properties including ultimate tensile strength (UTS), yield strength (YS) and elongation (ef) were evaluated by tensile testing.The phase sensitive eddy current method was employed to measure the electrical conductivity.The addition of 0.3 wt% Mn increased both the UTS and YS of the cast HP Al significantly to 72.3 and 20.4 MPa from 59.2 and 14.0 MPa.The evaluation of tensile behaviors indicated that the Mn addition significantly improved the resilience and strain hardening rate of the PSMC HP Al, although the toughness of the PSMC Al-0.3Mn was comparable to that of PSMC HP Al.However, the ef and electrical conductivity of the cast alloy decreased to 28.9% and 45.6 %IACS from 37.1% and 61.1 %IACS.The difference in tensile behaviors and electrical conductivities between the PSMC Al-0.3Mn alloy and the PSMC HP Al should be attribute to the emergence of a large amount (2.1%) of the micron Al-Fe-Mn and nano Al-Mn intermetallic phases in the PSMC Al-0.3Mn alloy, compared to only 0.4% of Al-Fe intermetallics in the PSMC HP Al.
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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".