Triticale starch based bioplastics
Bibliographic record
Abstract
Triticale is being developed and aimed as an industrial crop and biorefinery feedstock for the Canadian manufacturing industry within the CTBI network (The Canadian Triticale Biorefinery Initiative). The fundamental understanding of the triticale starch and TPS (thermoplastic starch) properties will greatly help the development of triticale starch-based materials with improved properties. In this paper the potential of triticale starch for the TPS/polymer blend fabrication was explored and compared with wheat starch in terms of starch morphology, crystallization structure, and TPS rheological properties. Very similar results were obtained for the triticale starch comparing with the wheat starch. This confirms that studies made with wheat starch should be directly applicable to triticale. The possibility of using triticale to make 100% biobased blown film was investigated using a small lab-scale blown film line. Film thickness of 25 μm was obtained successfully for the triticale starch based PLA/TPS blends, in which sorbitol/glycerol mixture was used as plasticizers for TPS and 2% CESA chain extender to chain extend PLA in the blends. The successful development of the triticale starch based bio-materials will rapidly and significantly increase the net usages of biomass resources based on triticale, promote valued products and increase the sustainable development in the agricultural sector in Canada.
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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".