Characterization and application of methylcellulose and potato starch blended films in controlled release of urea
Bibliographic record
Abstract
Abstract Biodegradable blended films from methylcellulose (MC) and potato starch (PST) have been developed by the casting process. In the present work the influences of concentrations of MC and PST on rheological properties, swelling, mechanical properties such as tensile strength, percentage elongation at break and water vapor transmission rate (WVTR) of the prepared blended films have been studied. Fourier transform infrared (FTIR) analysis of pure MC, PST, their mixture and the mixture with glutaraldehyde and urea was performed to investigate the interactions in blended films. The blended films of MC and PST showed an increase in tensile strength due to the cross linking reactions of the amylopectin molecule of PST in the physical gel state. The change of percentage elongation at break increased with MC concentration and the opposite trend was found in the case of the WVTR due to the network structure of the blended films. The blended films showed a large improvement in the abovementioned properties compared with each single component, due to the interaction formed between hydroxyl groups of PST and the methoxy groups of MC. Experiments were also conducted to investigate the controlled urea release through blended films and the kinetics of the process. Interesting results were found with the prepared MC and PST blended 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) | 0.000 | 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".