Preparation, Characterization and Processing of PCL/PHO Blends by 3D Bioplotting
Bibliographic record
Abstract
Abstract Blends of polycaprolactone (PCL) and poly(3-hydroxyoctanoate) P(3HO) were prepared by melt compounding. These immiscible blends exhibited droplet-matrix morphology at compositions up to 30 wt% P(3HO). Even though the addition of amorphous P(3HO) decreased the crystallinity of PCL, the crystallization temperature of the blends increased by 6 to 7 8C. Blends containing up to 30 wt% P(3HO) had higher crystallization rates, and lower crystallization half-times compared to neat PCL. The viscosity of PCL decreased upon addition of P(3HO), making the blends suitable for processing using a 3D bioplotter. Compositions with 10 to 30 wt% P(3HO) were ideal for processing, because of their improved crystallization kinetics, reduced stickiness and good flow properties. Estimation of the interfacial tension by fitting the Palierne model to the linear viscoelastic properties of the blends revealed good compatibility, which gave rise to synergistic effects in the thermal and mechanical properties. The fibres prepared by 3D bioplotting maintained droplet matrix morphology, with finer particle size than the original compounded material. In addition to favourable viscosity and thermal properties, the extruded fibres containing 30 wt% P(3HO) had comparable modulus to the neat PCL, while exhibiting good ductility. These blends may be suitable alternatives to PCL for biomedical applications, because they provide a range of crystallinities, crystallization rates and viscosities.
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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".