Squeeze Cast Mg-Zn Alloys for Bioapplications: Tensile Properties and Microstructure
Bibliographic record
Abstract
In the past, Mg-Zn alloys prepared by a two-step manufacturing process of casting plus extrusion have been demonstrated to be a good candidate for biodegradable applications. But, studies on fabricating of Mg-Zn alloys with a single step process of squeeze casting capable of producing porosity-free Mg alloys, which can reduce the cost, are limited. In the present work, Zinc (Zn) addition varying from 1.0 up to 10.0 wt. % was introduced into liquid magnesium. The alloyed liquid was squeeze cast under an applied pressure of 90 MPa. The results of mechanical testing on the squeeze cast Mg-Zn alloys shows that Zn is an effective additive for enhancing their mechanical properties, specifically, tensile and yield strengths at room temperature, but reducing the elongation. While the Zn addition rises from 1.0 to 10.0 wt.%, the ultimate tensile and yield strengths increases to 181.0 MPa and 105.0 MPa from 140.7 MPa and 39.3 MPa, while the elongation-to-failure (ef) decreases to 3.7% from 6.2%, respectively. The reveal of the as-cast grain structure by an optical microscope (OM) indicates that the high Zn content reduces grain sizes considerably. The microstructures analyzed by a scanning electron microscope (SEM) with the energy dispersive spectroscopy (EDS) show that the secondary MgZn phase forms once Zn is introduced in sufficient amount. The grain refinement and the massive presence of the secondary MgZn phase at the boundaries of the refined grains should be responsible to the enhancement of the strengths and the reduction in the elongation. The developed pressurized casting without employing secondary manufacturing processes such as extrusion or heat treatment exhibits its advantages to enhance the mechanical properties of the Mg alloys with high Zn content over conventional fabrication processes, since high Zn-containing Mg alloys have a long freezing range and tend to form microshrinkage porosity.
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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".