Elongational rheology of biodegradable poly(lactic acid)/poly[(butylene succinate)‐<i>co</i>‐adipate] binary blends and poly(lactic acid)/poly[(butylene succinate)‐<i>co</i>‐adipate]/clay ternary nanocomposites
Bibliographic record
Abstract
Abstract A series of blends based on poly(lactic acid) (PLA) and poly[(butylene succinate)‐co‐adipate] (PBSA) as well as their nanocomposites with nanoclay (PLA/PBSA/Clay ternary nanocomposites) were prepared using the twin‐screw extruder. The blends were prepared for PBSA contents ranging from 25 to 75 wt % and their corresponding nanocomposites were prepared at a single‐clay concentration. The morphology and structure of the blends and the nanocomposites were examined using field emission scanning electron microscopy, transmission electron microscopy, and X‐ray diffraction. Rheological properties (dynamic oscillatory shear measurements and elongational viscosities) of the blends, nanocomposites, and pure components were studied in detail. The strain hardening intensity of different blends and nanocomposites was compared with the behavior of the pure components. Strong strain hardening behavior was observed for blends composed of 50 wt % and higher PBSA content. However, the effect of PBSA content on the elongational viscosity was less pronounced in PLA/PBSA/Clay ternary nanocomposites. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013
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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".