Influence of Nanoparticle Pretreatment on the Thermal, Rheological and Mechanical Properties of PLA-PBSA Nanocomposites Incorporating Cellulose Nanocrystals or Montmorillonite
Bibliographic record
Abstract
Nanoparticles based on cellulose nanocrystals (CNC) and montmorillonite clay (MMT) were prepared using spray freeze-drying. The nanoparticles were then used as reinforcement to prepare nanocomposites with poly(lactic acid) (PLA) as the polymer matrix. The effect of spray freeze-dried CNC (SFD-CNC) and spray freeze-dried MMT (SFD-MMT) on the rheological and mechanical properties of PLA and its blends with poly[(butylene succinate)-co-adipate)] (PBSA) were investigated. An epoxy chain extender was used during preparation of the blends and nanocomposites to enhance the mechanical properties of the products. Different methods such as scanning electron microscopy, X-ray diffraction and adsorption/desorption analyses were used to characterize the prepared nanoparticles and their localization in the blends. Dynamic oscillatory shear behavior, elongational viscosity and mechanical characteristics of the nanocomposites of PLA and the blends were evaluated. The results obtained for nanocomposites filled with unmodified SFD-MMT were compared with those obtained when the filler was a commercial organically modified montmorillonite nanoclay (methyl-tallow-bis(2-hydroxyeethyl) quaternary ammonium chloride) (C30B), which was not spray freeze-dried.
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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".