Study and application of an enzymatic pool in bioscouring of cotton knit fabric
Bibliographic record
Abstract
Abstract Grey cotton contains between 4–12 % non‐cellulosic impurities. The removal of these impurities is generally carried out by alkaline washing at elevated temperatures which may cause damage to the cellulose fibre and generate an effluent with a high environmental impact. An alternative approach is the use of bioscouring, with enzymes specifically removing the impurities under mild conditions of pH and temperature. In this study, the effect of a commercial enzymatic pool (cellulase, lipase, and pectinase) on the bioscouring of 100 % cotton knit fabric was evaluated. The effect of each enzyme and the interaction between them were evaluated with the aid of an experimental design and the characterization of the treated fabric (weight loss, degree of whiteness, degree of pectin removal, and hydrophilicity) was performed. The combination of the three enzymes on bioscouring led to the best results in terms of degree of whiteness (25.0 °Berger), pectin removal (87 %), and hydrophilicity (14 s). A comparison between the enzymatic treatment and the scouring confirmed that bioscouring can be as effective as the conventional process, being more environmentally sustainable because it occurs at neutral pH and consumes less water and energy.
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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".