Hybrid Bionanocomposites from Spent Hen Proteins
Bibliographic record
Abstract
Spent hens, a poultry by-product, have little economic value for processing and mostly end up in landfills. However, there are concerns over disposal of spent hens; therefore, it is pertinent to find out alternative uses that are environmentally sound. On the other hand, single-use plastic packaging is leading to a global environmental crisis. In this study, proteins were extracted from spent hen, plasticized, and processed into films by compression molding. The hybrid bionanocomposite films were successfully prepared using glycerol as a plasticizer, chitosan as a cross-linker, and varying concentrations of nanoclay as a nanoreinforcement. The effects of nanoreinforcements, plasticization, and cross-linking were then evaluated on thermal, mechanical, and barrier properties of the prepared bionanocomposite films. Various concentrations of nanoclay and chitosan were dispersed in the protein matrix. However, with the same plasticizer loading, the optimum addition of chitosan and nanoclay led to almost twofold increase in the mechanical strength, compared to neat protein films. The results indicated that at optimal conditions, a good intercalation and/or exfoliation of the protein biopolymers into clay interlayer galleries was observed leading to improved thermal, thermomechanical, and barrier properties. These hybrid bionanocomposite films have great future potential to be used in packaging and other applications.
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) | 0.001 | 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".