Comparison of Precipitated Calcium Carbonate/Polylactic Acid and Halloysite/Polylactic Acid Nanocomposites
Bibliographic record
Abstract
PLA nanocomposites with stearate coated precipitated calcium carbonate (PCC) and halloysite natural nanotubes (HNT) were prepared by melt extrusion. The crystallization behavior, mechanical properties, thermal dynamical mechanical analysis (DMTA), and the morphology of the PCC/PLA, HNT/PLA, and HNT/PCC/PLA composites were discussed. Compared to halloysite nanotubes, PCC nanoparticles showed a better nucleating effect, which decreased both the glass transition and cold crystallization temperatures. The tensile performance of PLA composites showed that the addition of inorganic nanofillers increased Young’s modulus but decreased tensile strength. More interestingly, PLA composites with PCC particles exhibited an effectively increased elongation at break with respect to pure PLA, while HNT/PLA showed a decreased ultimate deformation of composites. DMTA results indicated that PLA composites had a similar storage modulus at temperatures below the glass transition and the addition of nanofillers into PLA caused Tg to shift to lower temperatures by about 3°C. The morphological analysis of fractures surface of PLA nanocomposites showed good dispersion of nanofillers, formation of microvoids, and larger plastic deformation of the PLA matrix when the PCC particles were added, while a strong aggregation was noticed in composites with HNT nanofillers, which has been attributed to a nonoptimal surface coating.
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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".