Plasticization of Pea Starch Films with Monosaccharides and Polyols
Bibliographic record
Abstract
ABSTRACT: Monosaccharides have several hydroxyl groups and a compatible structure with starch polymers resulting in effective plasticization in starch films. Two groups of plasticizers (polyols and monosaccharides) were used to compare their plasticizing efficiency. Fructose, glucose, mannose, galactose, glycerol, sorbitol, ethylene glycol, and maltitol were selected at 13.031 mmol per 100 g of pea starch. Edible starch films were produced after heat gelatinization and dehydration of the 3% starch dispersion. The microstructure, attenuated total reflection foorier transform infrared (ATR‐FTIR) characteristics, thickness, moisture content, tensile strength, modulus of elasticity, elongation‐at‐break, water vapor permeability, and transparency of films were determined. Microstructure of the film solutions showed that some swollen starch granules and their remnants existed in the film. Compared to the FTIR spectra of pure starch films, the spectra of plasticized films showed that more hydrogens bound hydroxyl groups and more water molecules were attracted around starch polymer chains. Ethers were produced in glycerol‐plasticized films. Monosaccharide‐plasticized films were comparable to the polyol‐plasticized films in tensile test, but more resistant in moisture permeation than the polyol‐plasticized films. It was assumed that the structural compatibility of monosaccharides with starch might result in denser polymer‐plasticizer complex, smaller size of free volume, and less segmental motions of starch chains. In conclusion, monosaccharides were identified as effective plasticizers for starch film.
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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".