Two-Month In Vitro Degradation of 3D-Printed Biodegradable FeMnC Alloys for Biomedical Applications
Bibliographic record
Abstract
Abstract Over the last decade, Fe-Mn-based bioresorbable implants have attracted significant interest due to their outstanding mechanical properties, including ductility and strength, and their ability to degrade over medium-to-long healing periods, eliminating the need for secondary surgeries for implant removal. However, their slow degradation under physiological conditions limits their practical use, especially for short-term degradable implants. Additive manufacturing facilitates rapid production with tailored compositions, offering advantages over traditional casting methods. Yet, the structure, the microstructure, the degradation behavior, and the mechanical properties are known to be impacted by the fabrication methods. In this context, this study investigates the degradation behavior of 3D-printed FeMnC alloys produced via laser powder bed fusion using volumetric energy densities from 75 J/mm 3 to 87 J/mm 3 . Microstructure and degradation rate relationships were explored through microstructural characterization (SEM, XRD, EBSD) and static immersion tests in modified Hanks' solution over 60 days. XRD confirmed a fully austenitic microstructure in all conditions, while SEM and EBSD revealed refined structures along the building direction. The alloy printed at 87 J/mm 3 exhibited the lowest degradation rate for both immersion periods, with values near 0.04 mm/year after 14 days and 0.03 mm/year after 60 days.
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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".