Flocculation, Retention and Drainage in Papermaking: A Comparative Study of Polymeric Additives
Bibliographic record
Abstract
Abstract Fibre and filler flocculation, filler retention and drainage, induced by several polymeric retention aids, were compared in laboratory experiments on mixtures of kraft fibres and two calcium carbonate fillers. Some experiments were also performed on thermomechanical pulp and de‐inked pulp fibres. Flocculation was measured by a focused beam reflectance measurement probe. It was found that flocs induced by polyethylene‐oxide (PEO) and cofactor broke up with time and shear and could not be reformed subsequently. Floc strength was the highest for PEO and the weakest for polyethylenimine and polyaluminium chloride. When comparing filler retention under optimal flocculation conditions, we found similar filler retention for all retention aids. Salt did not affect drainage for cationic retention aids, but reduced the drainage rate for PEO. Drainage with PEO was considerably slower than for other retention aids.
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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 | 0.000 |
| 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 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".