Analysis of Porous Electrospun Fibers from Poly(<scp>l</scp>-lactic acid)/Poly(3-hydroxybutyrate-<i>co</i>-3-hydroxyvalerate) Blends
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Solution blends of poly( l -lactic acid) (PLLA) and poly(3-hyroxybutyrate- co -3-hydroxyvalerate) (PHBV) in chloroform/DMF were electrospun at room temperature on a stationary collection plate. Polymer blend ratio, PHBV hydroxyvalerate content, solvent ratio, polymer concentration, and electrospinning process parameters were varied to determine optimal electrospinning conditions. The success of each formulation at producing nonwoven mats of continuous submicron diameter fibers was evaluated by optical and scanning electron microscopy. The diameter of the blend fibers was larger than electrospun fibers of either neat electrospun polymer, with a higher PLLA ratio favoring a porous surface morphology and higher PHBV ratios favoring beaded fiber morphology. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were used to analyze the thermal properties of the fibrous mats. The glass transition temperatures of the fibers from blends decreased as the PHBV weight ratio increased. The crystallinity of the PHBV fraction decreased as the ratio of the polymer in the blend decreased, whereas the crystallinity of PLLA was unaffected by the blend ratio. Dynamic mechanical analysis (DMA) indicated that the tensile strength of electrospun PHBV was improved by blending. Porous PLLA/PHBV electrospun fibers have potential for applications that need a high surface to volume ratio such as filtration, biomedical, energy storage devices, etc.
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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 | 0.001 | 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".