Extrusion of Thermoplastic Starch: Effect of “Green” and Common Polyethylene on the Hydrophobicity Characteristics
Bibliographic record
Abstract
Novel plastics that are biodegradable, environmentally benign, and made from renewable natural resources are currently being researched as alternatives to traditional petroleum-based plastics. One such plastic, thermoplastic starch (TPS) is produced from starch processed at high temperatures in the presence of plasticizers, such as water and glycerol. However, because of its hydrophilic nature, TPS exhibits poor mechanical properties when exposed to environmental conditions, such as rain or humidity. The overall objective of this research work was to produce a thermoplastic starch based material with low water absorption that may be used to replace petroleum-based plastics. With a recent emergence of “green” polyethylene (GPE), sourced from renewable feedstock, it has become possible to develop novel biodegradable polymers for various applications. In this work, GPE was melt blended with starch in three different ways; reactive extrusion of GPE and starch facilitated by maleic anhydride (MAH) and dicumyl peroxide (DCP), melt blending of GPE and starch by extrusion, and melt blending of maleated polyethylene and starch by extrusion. Comprehensive testing and analysis has shown that all methods reduced water absorption significantly with some variations across the board.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".