Laser-Based Additive Manufacturing of Magnesium Alloys for Bone Tissue Engineering Applications: From Chemistry to Clinic
Bibliographic record
Abstract
Metallic biomedical implants are made from materials such as stainless steel, titanium, magnesium, and cobalt-based alloys. As a degradable biometal, magnesium (Mg) and its alloys are becoming more popular for applications in bone tissue engineering. Mg-based alloys have been found to be biocompatible, bioabsorbable, and bioactive, allowing them to be used as orthopedic implants with a low Young’s modulus. Computer-aided design can be used to design scaffolds with intricate porous structures based on patient-specific anatomical data. These models can be materialized rapidly and with reasonably acceptable dimensional accuracy by additive manufacturing (AM) techniques. It is known that lasers are the most widely investigated energy source for AM’ed Mg, as they offer some distinct advantages over other forms of energy. Recent studies have focused on developing biodegradable Mg scaffolds by using laser-based AM techniques. In this paper, we aim to review the recent progress of laser-based AM for Mg alloys and survey challenges in the research and future development of AM’ed Mg scaffolds for clinical applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".