A Facile Approach for the Mass Production of Submicro/Micro Poly (Lactic Acid) Fibrous Mats and Their Cytotoxicity Test towards Neural Stem Cells
Bibliographic record
Abstract
Despite many of the studies being conducted, the electrospinning of poly (lactic acid) (PLA), dissolved in its common solvents, is difficult to be continuously processed for mass production. This is due to the polymer solution droplet drying. Besides, the poor stretching capability of the polymer solution limits the production of small diameter fibers. To address these issues, we have examined the two following objectives: first, using an appropriate solvent system for the mass production of fibrous mats with fine-tunable fiber diameters; second, nontoxicity of the mats towards Neural Stem Cell (NSC). To this aim, TFA (trifluoroacetic acid) was used as a cosolvent, in a mixture with DCM (dichloromethane), and the solution viscosity, surface tension, electrical conductivity, and the continuity of the electrospinning process were compared with the solutions prepared with common single solvents. The binary solvent facilitated PLA electrospinning, resulting in a long lasting, stable electrospinning condition, due to the low surface tension and high conductivity of the binary-solvent system. The fiber diameter was tailored from nano to micro by varying effective parameters and examined by scanning electron microscopy (SEM) and image-processing software. Laminin-coated electrospun mats supported NSC expansion and spreading, as examined using AlamarBlue assay and fluorescent microscopy, respectively.
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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".