Microstructural Evolution and Thermal Stability of Long Period Stacking Ordered Phases in Mg97Er2Ni1 and Mg97Er2Zn1 Alloys
Bibliographic record
Abstract
The influence of transition metals (Ni and Zn) on the formation, morphology, and thermal stability of long-period stacking ordered (LPSO) phases in Mg97Er2Ni1 and Mg97Er2Zn1 alloys was investigated. In the as-cast state, both alloys consist of α-Mg and LPSO phases. Heat treatment at 540 °C for 20 h dissolves block-like and lamellar LPSO phases into the α-Mg matrix in the Mg97Er2Zn1 alloy, with lamellar LPSO phases reprecipitating during subsequent cooling from 540 °C to 400 °C. Comparative analysis shows that Ni significantly enhances the thermal stability of the LPSO phase compared to Zn. Ni favors the formation of block-shaped LPSO phases, while Zn facilitates lamellar LPSO precipitation within the α-Mg matrix. The LPSO phase in the Mg97Er2Ni1 alloy exhibits an exceptionally high melting temperature of 605 °C, the highest reported for an LPSO phase. Additionally, heat treatment at 500 °C for 100 h preserves the area fraction of the LPSO phase in the Mg97Er2Ni1 alloy, and at 540 °C for 100 h, the LPSO grains grow without phase dissolution or structural transformation of their 18R-type configuration. These findings provide valuable insights into the role of alloying transition metal elements in controlling the stability and morphology of LPSO phases, offering pathways for tailoring the morphology of the LPSO phase in the Mg-based alloys.
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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) | 0.001 | 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".