Electrospinning Process and Structure Relationship of Biobased Poly(butylene succinate) for Nanoporous Fibers
Bibliographic record
Abstract
-dimethylformamide (DMF), or chloroform/dimethyl sulfoxide (DMSO). These solutions were then electrospun at room temperature to produce nanoporous micron-sized fibers. The variables investigated were the solvent system used, grade of BioPBS, concentration of BioPBS, applied voltage, and the distance between the electrodes. In determining the optimal solution and electrospinning conditions, it was found that solution properties such as the solvent system, the grade of BioPBS, and the concentration of BioPBS had a significant effect on the fiber morphology. A chloroform/DMSO cosolvent system resulted in less bead defects among fibers compared to those produced from chloroform/DMF systems, regardless of the BioPBS grade. An increase in BioPBS concentration resulted in the reduction of bead defects, which at 15 (% w/v) resulted in bead-free uniform fibers. Increasing BioPBS concentration also increased the porosity of the fibers while reducing the pore size. Dynamic mechanical analysis showed that the reduction of bead defects resulted in increased tensile strength and Young's modulus of the electrospun fibrous nonwoven mat. The results of this study show that electrospun BioPBS fibers have high porosity at the micro- and nanoscale, resulting in a hierarchical structure that has sufficient mechanical properties for potential applications in wound healing and soft tissue engineering.
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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".