Green Development of Biodegradable Films Based on Native Yam (Dioscoreaceae) Starch Mixtures
Bibliographic record
Abstract
Mixtures of starch comprising starches from different botanical origins and species can improve the mechanical properties of films and coatings. Here, the aim is to develop a sustainable process of starch modification to obtain enhanced starch films through mixing three Dioscoreaceae starches and to study the films resultant mechanical (tensile strength and elongation at break), thermal (glass transition and melting temperature), and physicochemical (moisture, solubility, thickness, color, transparency, light transmission, water vapor permeability, crystallinity, and surface uniformity) properties. The films obtained after the mixing process show low moisture content and high transparency, high solubility desirable for biodegradability, and significantly different thickness. An improved light barrier is achieved and water vapor permeability barrier properties are obtained. Using differential scanning calorimetry, it is observed that the glass transition temperature of the films decreased. The starch mixture improves the mechanical characteristics of the films by 200% for tensile strength and 232% for elongation at break. After mixing, the films show increased crystallinity and no crack or pinholes in their surface. These findings demonstrate that the yam‐starch mixtures form strong and flexible films suitable for various industrial products through a simple green process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".