Crystallization behavior and morphology of polylactic acid (PLA) with aromatic sulfonate derivative
Bibliographic record
Abstract
ABSTRACT This article provides a detailed investigation of crystallization behavior and morphology of polylactic acid (PLA) in the presence of a nucleating agent: potassium salt of 5‐dimethyl sulfoisothalate, an aromatic sulfonate derivative (Lak‐301). Isothermal crystallization kinetics of PLA melt mixed with Lak at concentrations of 0.25–1 wt % was investigated at a range of crystallization temperature, 140–150 °C. To gain further insight on the effect of Lak, nonisothermal differential scanning calorimetry (DSC), wide angle X‐ray diffraction (WAXD), polarized optical microscope (POM), heat deflection temperature (HDT), and rheology were also performed. At 0.25 wt % Lak, crystallinity of PLA increased from 10% to 45%, and in 1 wt % Lak, maximum crystallinity of 50% was achieved. With 1 wt % Lak, crystallization half time reduced to 1.8 min from 61 min for neat PLA at 140 °C. The isothermal crystallization kinetics was analyzed using Avrami model. Values of the Avrami exponent for PLA with Lak were mainly in the range of 3 indicating a three dimensional crystal growth is favored. Crystallization rate was found to increase with increase in Lak content. Observation from POM confirmed that the presence of Lak in the PLA matrix significantly increased the nucleation density. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016, 133, 43673.
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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".