The swelling properties of single component (gelatin) and electrostatically assembled (gelatin–gum tragacanth) edible composite films exposed to water and salt solution
Bibliographic record
Abstract
The four-dimensional (4D) printing concept, defined as a targeted change in material properties under stimuli such as water, ultraviolet (UV) exposure, and heat, has been under the spotlight in recent years due to its promising functionalities and design freedom for food applications. However, there is little progress in food applicability and compatibility compared to materials science. The complexity of food, poor response, and lack of trigger mechanisms are the major problems for expanding the 4D printing concept with edible ingredients. Therefore, exploring the possible mechanisms using edible materials to implement engineering-driven predictive changes in food applications is of significant interest. This study investigated the use of model systems composed of food biopolymer solutions (gum tragacanth and gelatin at 0.5, 2.5, and 5% w/v) and their swelling behavior in distilled water and salt (0.5 M CaCl2) solution to control the water uptake rate. In this respect, the film-forming solutions were prepared, the films were cast in Petri dishes (5–20 ml), and the film thickness and moisture content values were recorded. Their swelling properties were determined in two different media (water immersion and salt solution immersion tests). The viscoelastic properties of selected film-forming solutions were analyzed. The thickness of the films increased with the increased poured volume of the film-forming solution (P < 0.05). The biopolymer type and concentration had a significant effect on the physical properties of the films. All samples exhibited shear-thinning behavior and frequency dependency with elastic or viscous dominant properties.
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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".