Enhancing Mechanical and Biocorrosion Response of a MgZnCa Bulk Metallic Glass through Variation in Spark Plasma Sintering Time
Bibliographic record
Abstract
Development of metallic glasses is hindered by the difficulties in manufacturing bulk parts large enough for practical applications. Spark plasma sintering (SPS) has emerged as an effective consolidation technique in the formation of bulk metallic glasses (BMGs) from melt-spun ribbons. In this study, Mg65Zn30Ca5 melt-spun ribbons were sintered at prolonged sintering times (15 min to 180 min) via SPS under a pressure of 90 MPa and at a temperature of 150 °C (which is below the crystallization temperature), to provide an insight into the influence of sintering time on the consolidation, structural, and biodegradation behavior of Mg-BMGs. Scanning Electron Microscopy was used to characterize the microstructure of the surface, while the presence of the amorphous phase was characterized using X-ray diffraction and Electron Backscatter Diffraction. Pellets 10 mm in diameter and height with near-net amorphous structure were synthesized at 150 °C with a sintering time of 90 min, resulting in densification as high as 98.2% with minimal crystallization. Sintering at extended durations above 90 min achieved higher densification and resulted in a significant amount of local and partial devitrification. Mechanical properties were characterized via compression and microhardness testing. Compression results show that increased sintering time led to better structural integrity and mechanical properties. Notably, SPS150_90 displayed ultimate compressive strength (220 MPa) that matches that of the cortical bone (205 MPa). Corrosion properties were characterized via potentiodynamic polarization with Phosphate Buffered Solution (PBS). The results suggest that the sintered samples have significantly better corrosion resistance compared to the crystalline form. Overall, SPS150_90 was observed to have a good balance between corrosion properties (10× better corrosion resistance to as-cast alloy) and mechanical properties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".