Effects of Lanthanum and Zirconium on Cast Structure and Room Temperature Mechanical Properties of Mg-La-Zr Alloys
Bibliographic record
Abstract
Cast Mg-La and Mg-La-Zr alloy ingots were prepared from 99.96% magnesium and 99.9% lanthanum with a zirconium addition made using a Mg-33Zr master alloy. The microstructure was examined and tensile tests performed for the cast alloys. Lanthanum showed a mild grain refinement effect on magnesium, generating coarse equiaxed grains in the casting. The microstructure within the equiaxed grain contained the primary Mg dendrites and degenerated lamellar eutectic in the interdendritic regions. An addition of zirconium to the Mg-La alloys transformed coarse primary α-Mg dendrites into fine globular grains surrounded by eutectic regions. With this change tensile properties improved significantly in comparison with the binary Mg-La alloys of comparative lanthanum content. The hardness value increased linearly with lanthanum content due to an increase in the eutectic Mg12La phase. Fracture occurred owing to the decohesion between the primary Mg grains and eutectic Mg12La phases. An increase in the eutectic regions thus leads to a reduction in ductility by means of crack propagation through the regions.
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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) | 0.002 | 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".