Enhanced bone tissue regeneration using a 3D-printed poly(lactic acid)/Ti6Al4V composite scaffold with plasma treatment modification
Bibliographic record
Abstract
The mechanical and biological properties of polylactic acid (PLA) need to be further improved in order to be used for bone tissue engineering (BTE). Utilizing a material extrusion technique, three-dimensional (3D) PLA-Ti6Al4V (Ti64) scaffolds with open pores and interconnected channels were successfully fabricated. In spite of the fact that the glass transition temperature of PLA increased with the addition of Ti64, the melting and crystallization temperatures as well as the thermal stability of filaments decreased slightly. However, the addition of 3-6 wt% Ti64 enhanced the mechanical properties of PLA, increasing the ultimate compressive strength and compressive modulus of PLA-3Ti64 to 49.9 MPa and 1.9 GPa, respectively. Additionally, the flowability evaluations revealed that all composite filaments met the print requirements. During the plasma treatment of scaffolds, not only was the root-mean-square (Rq) of PLA (1.8 nm) increased to 60 nm, but also its contact angle (90.4°) significantly decreased to (46.9°). FTIR analysis confirmed the higher hydrophilicity as oxygen-containing groups became more intense. By virtue of the outstanding role of plasma treatment as well as Ti64 addition, a marked improvement was observed in Wharton's jelly mesenchymal stem cell attachment, proliferation (4',6-diamidino-2-phenylindole staining), and differentiation (Alkaline phosphatase and Alizarin Red S staining). Based on these results, it appears that the fabricated scaffolds have potential applications in BTE.
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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".