Morphological and Rheological Properties of PLA, PBAT, and PLA/PBAT Blend Nanocomposites Containing CNCs
Bibliographic record
Abstract
Morphological and rheological properties of poly(lactic acid), PLA (semicrystalline and amorphous), and poly(butylene adipate-co-terephthalate), PBAT, and their blends (75 wt%/25 wt%; PLA/PBAT) were investigated in the presence of cellulose nanocrystals (CNCs) prepared from solution casting followed by melt mixing. For the solution casting step, the CNCs were either incorporated into the matrix, the dispersed phase, or both. The dispersion and distribution of the CNCs in the neat polymers and localization in their blends were analyzed via scanning electron microscopy (SEM) and atomic force microscopy (AFM). The highly dispersed CNCs in the solution cast nanocomposites were agglomerated after melt mixing. In the blends with 1 wt% CNCs, the nanoparticles were mostly localized on the surface of the PBAT droplets irrespective of their initial localization. The rheological behavior of the single polymer matrix nanocomposites and their blends was determined in dynamic and transient shear flow in the molten state. Upon melt mixing the complex viscosity and storage modulus of the solution cast nanocomposites decreased markedly due to re-agglomeration of the CNCs. Under shearing at 0.1 s−1, a significant droplet coalescence was observed in the neat blends, but was prevented by the presence of the CNCs at the interface in the blend nanocomposites.
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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".