Novel use of waste keratin and cotton linter fibers for prototype tissue papers and their evaluation
Bibliographic record
Abstract
Corporate environmental sustainability calls for sustainable product manufacturing with less creation of waste material or increased reuse of waste materials. One example is the use of keratin fiber from the poultry industry and cotton linter from the textile industry for paper and tissue manufacturing. In this paper, the feasibility of using these waste fibers to make paper was demonstrated in handsheets. The properties of these handsheets were compared to the properties of handsheets made with standard bleached eucalyptus tropical hardwood fibers. A blend of cotton linter and keratin fibers at 80/20 and 60/40 ratios showed a 59% and 73% improvement in sheet bulk, respectively, compared to eucalyptus handsheets. Similarly, air permeability of the cotton / keratin fiber handsheets improved 414% and 336%, respectively, versus the eucalyptus. However, the tensile index of the cotton and keratin fiber blends was lower than the eucalyptus sheets. There was no remarkable difference in water absorbency up to 20% keratin fiber. Above 20% of keratin fibers the water absorbency started to decrease, which is likely attributable to the hydrophobic nature of the protein-based keratin fiber.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".