Effect of Pulp Fines on the Dye−Fiber Interactions during the Color-Shading Process
Bibliographic record
Abstract
Fines play a very important role in the papermaking process and paper properties. High-yield pulp (HYP) contains a large amount of higher specific surface area fines, which may lead to the absorption of more dye at the wet end section. Better understanding of the dye−fines interaction will help improve the dye efficiency in HYP-containing furnish. This study was focused on the fines from high-yield pulp and hardwood bleached kraft pulp (HBKP) on optical properties, particularly on the CIE (Commission Internationale d’Eclairage) whiteness and b * (negative values indicate blue, and positive values indicate yellow). The characteristics of both HYP fines and HBKP fines were presented, and their effects on dyeing (color-shading) process were investigated. Fines have a higher specific surface area and more dissolved and colloidal substances (DCS) than do the fibers. It was found that for a system made of HYP fibers, HYP fines had a negative effect, while HBKP fines had a positive effect on the color shading process. For a system made of HBKP fibers, a low content (5%) of HYP fines can increase the dye effectiveness, although when the HYP fine content increased further, the dye performance showed a decrease; HBKP fines retarded the dye effectiveness for the HBKP fiber system. For the mixture of HYP fines and HBKP fines, the turbidity measurement was used to explain the interactions between the HYP fines and HBKP fines. The dyes and HYP fines can form complexes, which then retain in the fiber network, thus improving the dye effectiveness and resulting in a higher CIE whiteness and lower b * of the paper sheets.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".