Porous Bioactive Nanofibers via Cryogenic Solution Blow Spinning and Their Formation into 3D Macroporous Scaffolds
Bibliographic record
Abstract
There is increasing focus on the development of bioactive scaffolds for tissue engineering and regenerative medicine that mimic the native nanofibrillar extracellular matrix. Solution blow spinning (SBS) is a rapid, simple technique that produces nanofibers with open fiber networks for enhanced cell infiltration. In this work, highly porous bioactive fibers were produced by combining SBS with thermally induced phase separation. Fibers composed of poly( d, l -lactide) (PLA) and dimethyl carbonate were sprayed directly into a cryogenic environment and subsequently lyophilized, rendering them highly porous. The surface areas of the porous fibers were an order of magnitude higher in comparison with smooth control fibers of the same diameter (43.5 m 2 ·g –1 for porous fibers produced from 15% w/v PLA in dimethyl carbonate) and exhibited elongated surface pores. Macroporous scaffolds were produced by spraying water droplets simultaneously with fiber formation, creating a network of fibers and ice microspheres, which act as in situ macroporosifiers. Subsequent lyophilization resulted in three-dimensional (3D) scaffolds formed of porous nanofibers with interconnected macropores due to the presence of the ice spheres. Nanobioactive glass was incorporated for the production of 3D macroporous, bioactive, therapeutic-ion-releasing scaffolds with potential applications in non-load-bearing bone tissue engineering. The bioactive characteristics of the fibers were assessed in vitro through immersion in simulated body fluid. The release of soluble silica ions was faster for the porous fibers within the first 24 h, with confirmation of hydroxyapatite on the fiber surface within 84 h.
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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".