Crystallization of triethyl‐citrate‐plasticized poly(lactic acid) induced by chitin nanocrystals
Bibliographic record
Abstract
ABSTRACT The aim of this study was to gain a better understanding of the crystallization behavior of triethyl‐citrate‐plasticized poly(lactic acid) (PLA–TEC) in the presence of chitin nanocrystals (ChNCs). The isothermal crystallization behavior of PLA–TEC was studied by polarized optical microscopy, scanning electron microscopy, differential scanning calorimetry, and X‐ray diffraction (XRD). Interestingly, the addition of just 1 wt % ChNCs in PLA–TEC increased the crystallization rate in the temperature range of 135–125 °C. The microscopy studies confirmed the presence of at least three distinct types of spherulites: negative, neutral, and ring banded. The ChNCs also increased the degree of crystallinity up to 32%, even at a fast cooling rate of 25 °C min−1. The XRD studies further revealed the nucleation effect induced by the addition of ChNCs and thus explained the faster crystallization rate. To conclude, the addition of a small amount (1 wt %) of ChNC to plasticized PLA significantly affected its nucleation, crystal size, and crystallization speed; therefore, the proposed route can be considered suitable for improving the crystallization behavior of PLA. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47936.
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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".