Study on the effect of co‐plasticization of post‐industrial starch for high‐impact thermoplastic starch development
Bibliographic record
Abstract
Abstract This study analyzed the effect of employing plasticized or co‐plasticized post‐industrial starch in developing PBAT‐based thermoplastic starch (TPS) blends. In this work, the post‐industrial wheat starch was co‐plasticized with glycerol‐urea, glycerol‐citric acid, and glycerol‐succinic anhydride, and then melt‐extruded with PBAT to develop the TPS blends. The effect of co‐plasticization was investigated by analyzing the TPS blends' mechanical, thermal, and rheological characteristics. The results showed that co‐plasticized starch was more effective than solely glycerol‐plasticized starch in enhancing the TPS blends' mechanical, thermal, and rheological characteristics. The TPS containing citric acid as a co‐plasticizer showed increases of 246, 35, and 46% in impact strength, elongation at break, and crystallinity, respectively, compared to glycerol‐plasticized starch‐based TPS. Morphological analysis further revealed that the citric acid co‐plasticized starch improved the dispersion and compatibility of the plasticized starch within the PBAT matrix. Overall, the study showed that adding citric acid during the co‐plasticization of starch resulted in high‐impact TPS blends with enhanced material properties compared to glycerol‐plasticized starch and urea or succinic anhydride co‐plasticized starch.
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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".