In-Situ Nanoreinforced Green Bionanomaterials from Natural Keratin and Montmorillonite (MMT)/Cellulose Nanocrystals (CNC)
Bibliographic record
Abstract
Biodegradability and renewability has led renewed interest in protein based films reinforced with nanoparticles. Bionanocomposites have gained attention because of their enhanced material properties with the aid of nanoreinforcements. The effects of two different nanoparticles, montmorillonite (MMT) and cellulose nanocrystals (CNCs), at different loading contents (0%, 1%, 3%, 5%, and 10%) were studied as a reinforcement material in modified chicken feather keratin. Compression molding was employed to prepare bionanocomposites films thermoplastically. The effect of CNC and MMT addition, their disposition and impact on the final material properties, was investigated by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), tensile testing, and dynamic mechanical analysis (DMA). The morphology of in-situ-modified keratin-based nanocomposites and the extent of nanoparticle dispersion was observed through scanning electron microscopy (SEM), transmission electron microscopy (TEM) and wide-angle X-ray diffraction (WAXD) respectively. The molecular level interactions of CNC’s and MMT’s with keratin biopolymer were investigated by X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR) techniques. Results indicated improved thermal stability and shift in glass transition temperature for both nanoreinforced biocomposites. Tensile strength was enhanced significantly with the addition of MMT; however, increased percent elongation was observed in case of CNC-reinforced biomaterials. The changes in the chemical bonding of keratin biopolymer reinforced with MMT/CNC compared to neat keratin biopolymer were observed by XPS spectra. These results suggest that high performance bionanomaterials can be developed from feather keratin through in situ dispersion of MMT and CNC nanoparticles, followed by compression molding.
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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".