Development and characterization of gelatin-starch bioplastics: A comparative study of cassava, corn, and rice-based alternatives
Bibliographic record
Abstract
The increasing global challenge of plastic pollution necessitates the exploration of sustainable alternatives to conventional plastics. This study investigates the development of bioplastics using pharmaceutical gelatin residue combined with starches derived from cassava, corn, and rice. The primary objective is to enhance bioplastic formulations by evaluating their mechanical, thermal, and biodegradability characteristics. The bioplastics were subjected to Fourier-transform infrared (FTIR) spectroscopy, Thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) to assess their properties. The results indicate that cassava-based bioplastics exhibit superior hardness, water absorption, and biodegradation rate performance compared to their corn and rice counterparts. Specifically, cassava bioplastics demonstrated a hardness of 57.2 N, a water absorption capacity of 139.8 %, and a biodegradation rate of 67.0 % after 7 days. These findings underscore the potential of cassava-based bioplastics as viable, sustainable alternatives derived from renewable resources, offering an environmentally friendly solution to reduce plastic waste across various sectors, including packaging and agriculture.
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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 | 0.001 | 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 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".