Coalescence in PLA-PBAT blends under shear flow: Effects of blend preparation and PLA molecular weight
Bibliographic record
Abstract
Blends containing 75 wt. % of an amorphous polylactide (PLA) with two different molecular weights and 25 wt. % of a poly[(butylene adipate)-co-terephthalate] (PBAT) were prepared using either a Brabender batch mixer or a twin-screw extruder. These compounds were selected because blending PLA with PBAT can overcome various drawbacks of PLA such as its brittleness and processability limitations. In this study, we investigated the effects of varying the molecular weight of the PLA matrix and of two different mixing processes on the blend morphology and, further, on droplet coalescence during shearing. The rheological properties of these blends were investigated and the interfacial properties were analyzed using the Palierne emulsion model. Droplet coalescence was investigated by applying shear flows of 0.05 and 0.20 s−1 at a fixed strain of 60. Subsequently, small amplitude oscillatory shear tests were conducted to investigate changes in the viscoelastic properties. The morphology of the blends was also examined using scanning electron microscope (SEM) micrographs. It was observed that the PBAT droplets were much smaller when twin-screw extrusion was used for the blend preparation. Shearing at 0.05 s−1 induced significant droplet coalescence in all blends, but coalescence and changes in the viscoelastic properties were much more pronounced for the PLA-PBAT blend based on a lower molecular weight PLA. The viscoelastic responses were also somehow affected by the thermal degradation of the PLA matrix during the experiments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".