Cationic Alkoxylated Amine Surfactant as a Debonding Agent for Papers Made of Sulfite-Bleached Fibers
Bibliographic record
Abstract
Today, there is a steadily increasing demand for the application of surfactants as debonding agents in tissue manufacturing. The work presented herein focused on evaluating the debonding ability of cationic alkoxylated amine surfactant on unrefined and refined fibers. The results showed that, as the dosage of the surfactant was increased to 10 mg/g on unrefined pulps, the adsorption of the surfactant on the fibers increased to 5 mg/g, and the tensile and burst indices of the papers made of the modified fibers decreased by as much as 12.6% and 14.9%, respectively. Also, the roughness, strain, and moisture content of the papers were enhanced, whereas the apparent density was reduced. Furthermore, the tear index of the papers increased upon the application of surfactant (10 mg/g) at the expense of reductions in tensile and burst indices, as well as the apparent density at any pressure applied in wet pressing. Also, as the pressure was increased, the surfactant impacted the fiber bonding more significantly. On the other hand, the application of surfactant (10 mg/g) somewhat increased the light scattering coefficient of the papers, regardless of the refining load. Furthermore, the adsorption of the surfactant on refined fibers increased with increasing refining load. However, the influence of the surfactant on the tear, tensile, and burst indices and the apparent density was impaired with increasing refining load. Also, the zero-span tensile index and brightness of the papers varied negligibly upon surfactant application.
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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 | 0.001 | 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 | 0.000 |
| 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".