Preparation and characterization of graphite oxide nano‐reinforced biocomposites from chicken feather keratin
Bibliographic record
Abstract
Abstract BACKGROUND Natural polymers have gained increased attention in reducing the dependence on petroleum‐based materials. Chicken feather proteins are an abundant industrial by‐product suitable for the fabrication of sustainable thermoplastics. However, protein‐based plastics generally exhibit poor physical and thermal properties which limit their application. In this research, the fabrication of feather keratin based nano‐reinforced biocomposites by the addition of graphite oxide ( GO ) in a reactive extrusion system were investigated . The effects of GO carbon/oxygen ratio (C/O, 2.48, 2.07, and 1.55) and concentration (0.5–2%) of the selected GO on the conformational, physical and thermal properties of thermoplastic films were investigated. RESULTS Chicken feather– GO nanocomposites were successfully prepared at 150 °C in the reactive extrusion system. Tensile strength and Young modulus of chicken feather plastic films were significantly increased without affecting their elongation using low GO concentrations (0.5 to 1.5% w/w of protein). Our results suggest that higher content of hydroxyl groups and increased graphene interlayer space in GO facilitated interactions with feather keratin and plasticizers. CONCLUSIONS Graphite oxide proved to be an inexpensive alternative to graphene for the reinforcement of protein based composites. Extrusion provided a cost‐effective and environment‐friendly method for the processing of sustainable composites. © 2017 Society of Chemical Industry
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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".