Production and characterization of fully biobased foamed films based on gelatin
Bibliographic record
Abstract
The objective of this study was to produce and characterize gelatin foamed films using extrusion. Three “optimum” formulations containing tannic acid, nanoclays (Cloisite Na+), glycerol, water, and gelatin, as well as three “controls” without tannic acid and nanoclays were prepared by calendering. Furthermore, the three “optimum” formulations were produced by extrusion film blowing only, since no stable processing conditions could be obtained for the controls. A complete set of sample characterization was performed, including morphological, mechanical, physical, and thermal properties. The results showed that besides the processing method, the thickness was also controlled by the glycerol and water content, leading to density slightly above unity, with higher values for the optimum materials. The calendered films from the optimum formulations showed overall a higher number of cells and cell density than the controls. Higher elastic moduli and tensile strengths were obtained for the films from the optimum formulations made by either method because of the reinforcing effect of the tannic acid and nanoclays, but this led to lower strain at break. The thermal profiles were similar for all films, with higher stability for the optimum formulations. The results were also explained via chemical interactions between the components as observed by Fourier transform infrared spectroscopy. Overall, the optimum formulations not only produced better foamed films in terms of general properties but were much easier to process by both methods (calendering and blowing).
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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".