Green Biocomposites from Nanoengineered Hybrid Natural Fiber and Biopolymer
Bibliographic record
Abstract
The surface grafting of polyhedral oligomeric silsesquioxanes (POSS) nanocages onto keratin biofiber and development of hybrid keratin fiber by dissolution of feather keratin, exfoliation/intercalation of nanoclay in the keratin matrix and regeneration into fiber are reported, respectively. The graft polymerization of POSS on to the surface of keratin fibers was observed by scanning electron microscopy (SEM) and transmission electron microscopy (TEM), and confirmed with X-ray photoelectron spectroscopy (XPS). The presence and dispersion of nanoclay, in in situ reinforced and regenerated fiber, was investigated and confirmed by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and TEM. The nanomodifications resulted in substantial improvements in the properties of all modified fibers including enhanced thermal stability and reduced moisture uptake compared to unmodified native fibers. The native and modified fibers were further blended with copolymer matrix of 30% styrene with 2-(acryloyloxy) ethyl stearate to prepare the biocomposite films. The properties of the resultant biocomposites were investigated using dynamic mechanical analysis (DMA), flame tests, and SEM. The investigations demonstrated improvements in storage moduli, fiber–matrix adhesion, and reductions in flammability of modified fiber reinforced biocomposites as compared to the neat fiber reinforced biocomposites.
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".