Production and Characterization of Gelatin Biomaterials Based on Agave Microfibers and Bentonite as Reinforcements
Bibliographic record
Abstract
The objective of this work was to obtain biomaterials as gelatin films or biofilms produced by casting, reinforced with a microfiber (MF) from Agave angustifolia Haw bagasse and bentonite (BN) nanoparticles and evaluate the effect of such reinforcements at different concentrations. Agave microfibers were obtained by a non-abrasive chemical method. Three formulations based on gelatin with glycerol were reinforced with microfiber, bentonite and both materials with 1.5, 3.5 and 5.5% w/w solids content. Physicochemical properties were determined using SEM and FTIR, thickness, soluble matter and moisture. The XRD, barrier, mechanical and thermal properties were measured. The films’ micrographs showed agglomerations on the surface. Interactions between its functional groups were found. The solubility increased when the MF concentration increased. The thickness of the films was between 60 and 110 μm. The crystallinity ranged from 23 to 86%. The films with both MF and BN and 3.5% w/w solids had the lowest barrier properties, while the film with 5.5% w/w solids showed the highest mechanical properties, being thermally resistant. Overall, Agave microfibers together with bentonite were able to improve some of the films’ properties, but optimized mixing conditions had to be used to achieve good particle dispersion within the gelatin matrix to improve its final properties. Such materials might have the potential to be used as food packaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".