Nano-engineered natural fiber in biocomposites and bisorption
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
P processing plants generate billions of pounds of feathers each year. Feathers are light and tough with over 90% protein. At present, in addition to few applications in animal feed and other products, the majority of the poultry feathers are disposed in landfills. Recently, due to strong emphasis on environmental awareness worldwide, utilization of natural fibers in the development of recyclable and environmentally sustainable composites/materials has been growing. In addition to environmental factors, biofibers offer many advantages over synthetic fibers in terms of low density, biodegradability, reduced dermal and reduced respiratory irritation and low cost. However, these fibers have intrinsic weaknesses such as moisture sensitivity, low thermal stability and high flammability etc. These drawbacks should be collectively addressed for biofibers to be used in a wide range of applications. Exploitation of nanotechnology, incorporation of nanostructures into biofibers has great potential to address these challenges. This presentation will discuss the modifications of Keratin from feathers for biosorption and biocomposite applications. The surface and in situ modifications of feather keratin were carried out. The structural changes and properties of the modified keratin were compared with untreated keratin fiber and confirmed by various characterization techniques such as SEM, XPS, FTIR, XRD, DSC and TGA. The modified fibres were used as biosorbents and also blended with co-polymer matrix to prepare the hybrid biocomposites. The modifications led to improvements in biosorption, thermal stability, flammability and other physical properties compared to the neat one.
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.
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 | 0.001 | 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 | 0.001 |
| 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