Structure evolution of pullulan–alginate edible films during drying studied by low‐field NMR
Bibliographic record
Abstract
Abstract The evolutions of spatial structure for pullulan, alginate, and pullulan–alginate blend aqueous solutions during drying were investigated by low‐field nuclear magnetic resonance. For pullulan solutions, during the first 1,320 min of drying, NMR signal intensity in profiles did not vary with position. Thereafter, the apparent shift of the air–pullulan interface began at 1,440 min, indicating the onset of progressive shrinkage of pullulan samples. Similar shrinkage phenomena were observed for alginate and pullulan–alginate blend solutions. In contrast, spatial structural heterogeneity was observed for alginate solution during drying time from 1,200 to 1,560 min, due to the formation of a skin layer near the air–alginate interface. Based on the change of polymer solutions during drying, two‐stage evaporation process was detected in the aqueous pullulan, alginate, and their blend solutions. Moreover, the evaporation rate of water during the second stage of drying was significantly lower than that of the first stage. Practical applications Edible films have emerged as an alternative to synthetic petroleum‐based polymers for food packaging. The evolutions of spatial structure of pullulan–alginate solutions during drying were studied by monitoring of their NMR profiles. Information shown in this study would provide some scientific basis for studying film‐forming mechanism of edible films and their applications in the food field.
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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 | 0.001 |
| 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".