Thermorheological properties of poly (ε‐caprolactone)/polylactide blends
Bibliographic record
Abstract
Abstract Blends of poly (ε‐caprolactone) (PCL)/polylactide (PLA) were prepared by solution‐casting method to study their thermal and rheological properties. Differential scanning calorimetry thermographs have shown two separate melting peaks in the blends, which are indicative of immiscible structure at all compositions. Scanning electron microscopy images show droplet morphology of PCL into PLA matrix up to 40 wt% of PCL. Above this concentration, the co‐continuous morphology starts to appear, which becomes again droplet morphology for blends with concentration of PCL higher than about 60 wt%. The viscoelastic properties of the various blends were investigated using rotational rheometry. The enhancement of the elastic modulus of blends at small frequencies at which terminal zone behavior is expected, is a signature behavior of immiscible systems due to the presence of interface and contribution to the stress from interfacial tension. Two emulsion models were used to predict the viscoelastic properties of the blends from the corresponding properties of their pure components that led to the determination of the interfacial tension of PCL/PLA in agreement with experimental findings. POLYM. ENG. SCI., 2012. © 2012 Society of Plastics Engineers
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".