Comparison of sorbitol and glycerol as plasticizers for thermoplastic starch in TPS/PLA blends
Bibliographic record
Abstract
Abstract This article investigates the structure and properties of thermoplastic starch/PLA blends where the TPS phase is plasticized by sorbitol, glycerol, and glycerol/sorbitol mixtures. The blends were prepared using a twin‐screw extruder where starch gelatinization, water removal, and dispersion of TPS into a PLA matrix were carried out sequentially. The plasticizers were added to starch in the first stage of the extruder to allow complete starch gelatinization. The PLA was added at mid‐extruder and thoroughly mixed with the TPS. The plasticizer concentration was varied from 30 to 42% and the TPS content was varied from 27 to 60% on a weight basis. In all investigated blends, the PLA formed the continuous phase and the TPS was the dispersed phase. The viscosity, blend morphology, tensile mechanical properties as well as the thermal properties of the materials were measured. It was found that the glycerol/sorbitol ratio has an important effect on the blend properties. Finer blend morphologies, higher tensile strength and modulus but lower crystallization rate were found for the sorbitol plasticized blends. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
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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) | 0.001 | 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".