Assessing the reinforcement effect by response surface methodology of holocellulose from spent coffee grounds on biopolymeric films as food packaging materials
Bibliographic record
Abstract
The pollution caused by petroleum-derived plastic materials has become a major environmental problem that has encouraged the development of new compostable and environmentally friendly materials for food packaging based on biomodified polymers with household residues. This study aims to design, synthesize, and characterize a biobased polymeric microstructure film from polyvinyl alcohol and chitosan reinforced with holocellulose from spent coffee grounds for food-sustainable packaging. Chemical isolation with a chlorite-based solution was performed to obtain the reinforced holocellulose from the spent coffee ground, and the solvent casting method was used to obtain the films to study. Physicochemical and microscopic characterizations were conducted to identify and select the best formulations using a simplex-centroid design analysis. The response surface methodology results indicate that the new packaging material obtained with equal amounts of polymers and reinforced material (1:1:1) possesses the appropriate barrier properties and microstructural character to prevent water attack and hydrophobic behavior and thus could be used as an alternative for food packaging materials.
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".