Additive manufacturing of biodegradable magnesium-based materials: Design strategies, properties, and biomedical applications
Bibliographic record
Abstract
Magnesium (Mg)-based materials are a new generation of alloys with the exclusive ability to be biodegradable within the human/animal body. In addition to biodegradability, their inherent biocompatibility and similar-to-bone density make Mg-based alloys good candidates for fabricating surgical bioimplants for use in orthopedic and traumatology treatments. To this end, nowadays additive manufacturing (AM) along with three-dimensional (3D) printing represents a promising manufacturing technique as it allows for the integration of bioimplant design and manufacturing processes specific to given applications. Meanwhile, this technique also faces many new challenges associated with the properties of Mg-based alloys, including high chemical reactivity, potential for combustion, and low vaporization temperature. In this review article, various AM processes to fabricate biomedical implants from Mg-based alloys, along with their metallic microstructure, mechanical properties, biodegradability, biocompatibility, and antibacterial properties, as well as various post-AM treatments were critically reviewed. Also, the challenges and issues involved in AM processes from the perspectives of bioimplant design, properties, and applications were identified; the possibilities and potential scope of the Mg-based scaffolds/implants are discussed and highlighted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".