Role of cerium, lanthanum, and strontium additions in an Al – Si – Mg (A356) alloy
Bibliographic record
Abstract
Abstract The effects of individual and combined additions of cerium (Ce), lanthanum (La), and strontium (Sr) on the eutectic modification and solidification characteristics of an Al – Si – Mg (A356) aluminum alloy were investigated using optical microscopy and thermal analysis techniques. Addition of Ce, La, and Sr resulted in different depression levels of the eutectic nucleation temperature and eutectic growth undercooling, generating modified eutectic structures exhibiting different levels of modification. Microstructural results showed that the best modification levels using individual additions were achieved by Sr which produced a fine fibrous eutectic structure, followed by La, which produced a refined lamellar structure, with Ce providing the lowest level of modification. On the other hand, a combined addition of Ce and Sr provided the highest modification level, with the production of a very fine fibrous eutectic silicon structure. In general, the addition of Sr helped to further increase the refinement obtained in the alloys containing La or Ce + La additions. In the latter alloy, the main intermetallic phases observed were La(Al,Si)2 and Al20(La,Ce)Ti2Si. The improved modification levels were found to be proportional to the depression in the eutectic nucleation temperature and the eutectic growth undercooling. A high cooling rate also improved the modification level by at least one level.
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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 | 0.001 | 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".