Bioscouring and bleaching of knitted cotton fabrics in one‐step process using enzymatically generated hydrogen peroxide
Bibliographic record
Abstract
Abstract This research involves the study of knitted cotton fabric bleaching using hydrogen peroxide generated enzymatically. Initially the bioscouring was performed using the enzymes cellulose (EC. 3.2.1.4), pectinase (EC. 4.2.2.2), and lipase (EC. 3.1.1.3) to remove impurities. Then bleaching was performed using hydrogen peroxide, which was enzymatically produced by glucose oxidase (EC. 1.1.3.4) during oxidation of glucose. The generation of hydrogen peroxide and bleaching were assessed using experimental designs, where it was observed that the most significant effect to generate peroxide was time and for bleaching was the concentration of hydrogen peroxide. The whiteness and hydrophilicity of the treated knitted fabric were evaluated. The whiteness index of the enzymatically bleached fabric was 52 ± 1 °Berger with good hydrophilicity, by capillarity method 5.2 cm in 10 min. The results from this work demonstrate that this process can be an alternative for the textile preparation industry. There is a large reduction in water consumption compared to the conventional process. The water consumption is four times lower than the water consumption of the conventional process.
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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) | 0.001 | 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".