Preparation and properties of extruded thermoplastic starch/polymer blends
Bibliographic record
Abstract
Abstract This article examines the starch gelatinization and the blend morphology development in blends of thermoplastic starch (TPS) with high‐density polyethylene, polypropylene, polystyrene, poly(lactic acid), and polycaprolactone. The TPS gelatinization and mixing with the second polymer was carried out on a twin‐screw extrusion process, where the starch was sequentially gelatinized, devolatilized, and then mixed in the molten state with a synthetic polymer. The role of excess water and process temperature on starch gelatinization was assessed by measuring the X‐ray scattering. All prepared blends included 25 % TPS that was dispersed in the synthetic polymer matrix. Compatibilized versions of these same blends were obtained by partially substituting the polymer matrices with maleated analogs. The blend morphology was probed by scanning electron microscopy. Complete starch gelatinization was obtained when the gelatinization process was carried out over 100°C regardless of amount of water used as co‐plasticizer. The blend morphologies were greatly improved when a maleated compatibilizer was added. Only TPS/PCL blends exhibited a finely dispersed TPS phase without the use of a compatibilizer. In general, the addition of the TPS reduced slightly the tensile modulus and strength of the different polymers and more importantly the elongation at break. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".