Development of Novel Wax-enabled Thermoplastic Starch Blends and Their Morphological, Thermal and Environmental Properties
Bibliographic record
Abstract
Novel thermoplastic starch (TPS) melt-blends were prepared while using glycerol as plasticizer. To impart hydrophobicity, beeswax (BW) and paraffin wax (PW) polymers were introduced in combination with commonly available interfacial coupling agent, maleic anhydride (MA). Representative melt-blends of TPS containing different concentration of BW, PW and MA were prepared through in-situ reactive extrusion in a custom designed twin screw extruder. The modified TPS was comprehensively evaluated for morphology, thermal stability and moisture resistance properties, while keeping both the temperature profile of extruder and glycerol concentration at constant level. The results showed that the plasticization becomes problematic at wax levels more than 10% at required extrusion temperatures due to lower melting points of these compounds, however FTIR studies exhibited an effective grafting of MAH compounds on wax polymers. Although phase separation was observed during morphological studies for BW-enabled TPS blends, but MA treated PW and their TPS blends showed homogenous structure of extruded samples. It was further observed that PW-enabled TPS blends had better thermal stability and enhanced hydrophobicity compared to BW-enabled formulations due to unique chemical structure of paraffin wax.
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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".