Effect of Acid-Hydrolyzed Thermoplastic Starch on the Mechanical, Thermal and Morphological Properties of Polyethylene Based Composites
Bibliographic record
Abstract
The effect of the addition of hydrolyzed thermoplastic maize starch on the physico mechanical properties of low-density polyethylene (LDPE)-based composites was studied. Acid-hydrolyzed native starch was thermoplasticized using 15 and 30% glycerol in weight relative to starch, after which the LDPE/thermoplastic starch (TPS) composites were prepared at TPS concentrations of 10, 25, and 50%. According to the results of Raman spectroscopy, the appearance of a new band at 756 cm-1 was observed, and it was attributed to the hydrolysis process and associated with the C-C-O vibrational modes of the glycosidic bond. The addition of both native and polyethylene hydrolyzed TPS reduced the Young’s modulus of the composites; but the reduction was greater for those containing native starch. Both the maximum stress and deformation decreased to a greater degree for the composites with hydrolyzed TPS. The composites containing TPS prepared with 15% glycerol exhibited a higher Young’s modulus compared to those with LDPE, although they exhibited fragile behavior. The degree of matrix crystallinity increased with the addition of TPS and showed the largest increase when TPS 50% hydrolyzed by weight was added, showing an increase of 35%. It was observed that the size of the TPS particles, both native and hydrolyzed, increased in size as the concentration of TPS in the matrix increased. The size of the hydrolyzed TPS particles was greater than that of the native TPS particles, and in the case the of the hydrolyzed TPS particles, some exhibited an ellipsoidal and/or fibrillar morphology.
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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 | 0.000 | 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 | 0.001 |
| 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".