Additive Manufacturing via Rotational-Axis Fused Filament Fabrication of Poly L-Lactic Acid–Based Bioabsorbable Stent: Morphology, Mechanical, Degradation and Biocompatibility Properties
Bibliographic record
Abstract
Bioabsorbable stents offer a significant advantage over metallic stents by providing temporary mechanical support to blood vessels and gradually degrading, thereby reducing the risk of long-term complications and restenosis. Polymeric stents, particularly those made from poly(l-lactic acid), have attracted attention for below-the-knee artery interventions because of their biocompatibility and potential for customized geometries and mechanical properties. However, challenges remain in the development of efficient and scalable fabrication techniques for such devices. This study aimed to develop and evaluate a rotational-axis fused filament fabrication method for the fabrication of bioabsorbable polymeric stents with improved mechanical and biological performance. A rotating-mandrel-based multiaxis system was used to print stents with a conventional closed-cell design while exploring variations in seam line overlaps and ring joint formations. Box-Behnken method was employed to systematically study the influence of key printing parameters─extrusion temperature, printing speed, and extrusion flow rate─on critical stent performance metrics: strut width, radial force, and flexural force. Microscopic analysis, radial compression, and three-point bending tests were performed for performance evaluation. Regression models and analysis of variance (ANOVA) revealed that the extrusion temperature and flow rate significantly influenced the mechanical properties. A multiobjective optimization approach was used to minimize the strut width while maximizing the radial and flexural strengths, resulting in a strut width of 205 μm and radial and flexural forces of 2.5 and 0.19 N, respectively, with an extrusion temperature, speed, and value of 200 °C, 90 mm/min, and 120%, respectively. Further characterization using microtomography and surface profilometry confirmed the structural integrity, consistent strut morphology, and surface quality. Accelerated degradation tests and in vitro biocompatibility assessments demonstrated favorable degradation profiles and cytotoxicity. A 95% mass change was observed in the printed stent after 10 d of accelerated degradation. This study presents a robust rotational-axis FFF method for scalable, bioabsorbable, and patient-specific stent fabrication.
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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) | 0.000 | 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 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".