Using cationic polymers to improve alkenyl succinic anhydride (ASA) sizing efficiency in high-yield pulp containing furnish
Bibliographic record
Abstract
High-yield pulp (HYP) such as bleached chemi-thermo-mechanical pulp (BCTMP) from aspen wood, is now being used in the production of high-quality fine papers, and this is particularly true in China. In this study, the effect of using cationic polymers such as poly-aluminum chloride (PAC), polyethylenimine (PEI), cationic polyacrylamide (CPAM), and poly-(diallyldimethylammonium chloride) (PDADMAC) on Alkenyl Succinic Anhydride (ASA) sizing efficiency in HYP-containing pulp furnish was investigated. The results showed that the addition of only ASA emulsion to the HYP-containing furnish, without any cationic additives, did not yield an appreciable degree of sizing in the paper sheet. PAC (or alum) could improve the ASA sizing efficiency. PEI, PDADMAC, CPAM, and NaHCO3 all improved ASA sizing efficiency in the presence of PAC. The charge density of PEI was important for improving the ASA sizing performance. Also, the sequence of adding the cationic polymer affected the ASA sizing performance.
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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) | 0.001 | 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".