Development of a new family of food packaging bioplastics from cross‐linked gelatin based films
Bibliographic record
Abstract
Abstract The purpose of this work was to develop green bioplastic preparation process from gelatin by using solvent‐free approach with efficient use of resources. Film plasticizing was achieved by adding polyethylene glycol (Mw = 200 g.mol−1). Different cross linking agents – glutaraldehyde (GTA), N‐hydroxysuccinimide (NHS), Bis(succinimidyl)nona(ethylene glycol) (BS(PEG)9) and ferulic acid (FA) were tested to obtain water resistant films suitable for gas permeability characterization. Functional properties (mechanical and gas transfer properties) of gelatin‐based films were determined. Gelatin films cross‐linked with FA presented very good gas barrier properties (48 and 175 cm3.µm/m2.day.kPa respectively for O2 and CO2), compared to synthetic polymers, equivalent to polar polymer like nylon 6 or polyethylene terephthalate and better than non‐polar polymers like high density or low density polyethylene. Swelling ratio measurements in water displayed gelatin/FA films resisted to water without breaking during 4 h compared to 1.5 h for pure gelatin films. Films could support saturated water vapour environment at 20 °C during 15 days without breaking. Gelatin/PEG200 films presented very good permselectivity towards CO2 and O2 (14.5). This value was reduced to 8 when cross‐linked with FA, and to 4 after PEG200 addition, presenting still interesting level. All these results confirmed that gelatin is a promising biobased polymer to prepare food packaging films.
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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".