In-Situ Nanoreinforced Green Bionanomaterials from Natural Keratin and Montmorillonite (MMT)/Cellulose Nanocrystals (CNC)
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it