Optimum Processing of Absorbable Carbon Nanofiber Reinforced Mg–Zn Composites Based on Two-Level Factorial Design
Bibliographic record
Abstract
To prevent a premature failure, absorbable magnesium implants must possess an adequate mechanical stability. Among many ways to improve the mechanical properties of magnesium is by particle reinforcement, such as using carbon nanofiber (CNF). This work reports an experimental design for optimum materials and processing of CNF-reinforced Mg–Zn composites based on a two-level factorial design. Four factors were analyzed: percentage of CNF, compaction pressure, sintering temperature, and sintering time, for three recorded responses: elastic modulus, hardness, and weight loss. Based on the two-level factorial design, mechanical properties and degradation resistance of the composites reach its optimum at a composition of 2 wt % CNF, 400 MPa of compaction pressure, and 500 °C of sintering temperature. The analysis of variance reveals a significant effect of all variables (p < 0.0500) except for the sintering time (p > 0.0500). The elastic modulus and hardness reach their highest values at 4685 MPa and 60 Hv, respectively. The minimum and maximum weight loss after three days of immersion in PBS are recorded at 54% and 100%, respectively. This work concludes the percentage of CNF, compaction pressure, and sintering temperature as the main factors affecting the optimum elastic modulus, hardness, and degradation resistance of CNF-reinforced Mg–Zn composites.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".