Synergistic Effects of Cerium and Magnesium on Optimizing Electrical Conductivity and Mechanical Properties of AlSi3 Cast Alloys
Bibliographic record
Abstract
To improve the strength and electrical conductivity (EC) of AlSi3 base alloys, the effect of Ce addition on the solidification, microstructure, and the mechanical and electrical properties of AlSi3 and AlSi3Mg0.5 is investigated. The results show that Ce addition increases the EC of the AlSi3Mg0.5 alloy by 1.3% International Annealed Copper Standard (IACS), owing to the reduction in Mg solutes in the Al matrix as confirmed by X‐ray analysis. Microstructural analyses have shown that incorporating 0.5 wt% Ce refines the eutectic Si structures in the AlSi3 alloy; this refinement effect is significantly enhanced in the presence of Mg. Specifically, the addition of Ce to the Mg‐containing (AlSi3Mg0.5) alloy induced a complete morphological transformation of the eutectic Si from plate‐shaped to entirely fibrous structures. Transmission electron microscopy analysis reveals nanosized precipitates composed of Ce and Mg within the eutectic Si. These precipitates are likely to inhibit the growth of Si particles, resulting in significant eutectic modifications. The incorporation of Ce into the AlSi3Mg0.5 alloy enhanced its yield strength, ultimate tensile strength, and elongation by 5, 11, and 59%, respectively. These improvements in the mechanical properties are primarily attributed to the solid solution strengthening effects of Mg and the modification of the eutectic structure.
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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) | 0.000 | 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".